Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan

The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan.

A mechanism for the emergence of low-dimensional structures in brain dynamics

Recent neuroimaging advancements have led to datasets characterized by an overwhelming number of features. Different dimensionality reduction techniques have been employed to uncover low-dimensional manifold representations underlying cognitive functions, while maintaining the fundamental characteristics of the data. These range from linear algorithms to more intricate non-linear methods for manifold extraction. However, the mechanisms responsible for the emergence of these simplified architectures remain a topic of debate. Motivated by concepts from dynamical systems theory, such as averaging and time-scale separation, our study introduces a novel mechanism for the collapse of high dimension brain dynamics onto lower dimensional manifolds. In our framework, fast neuronal activity oscillations average out over time, leading to the resulting dynamics approximating task-related processes occurring at slower time scales. This leads to the emergence of low-dimensional solutions as complex dynamics collapse into slow invariant manifolds. We test this assumption via neural simulations using a simplified model and then enhance the complexity of our simulations by incorporating a large-scale brain network model to mimic realistic neuroimaging signals. We observe in the different cases the convergence of fast oscillatory fluctuations of neuronal activity across time scales that correspond to simulated behavioral configurations. Specifically, by employing various dimensionality reduction techniques and manifold extraction schemes, we observe the reduction of high-dimensional dynamics onto lower-dimensional spaces, revealing emergent low-dimensional solutions. Our findings shed light on the role of frequency and time-scale separation in neuronal activity, proposing and testing a novel theoretical framework for understanding the inner mechanisms governing low-dimensional pattern formation in brain dynamics.

Early identification of autism spectrum disorder in preschoolers by static and dynamic amplitude of low-frequency fluctuations features

Objectives

Early identification and timely intervention is critical for young children with autism spectrum disorder (ASD). The current study aims to explore potential disparities in static and dynamic intrinsic brain function in preschoolers with ASD, and uncover underlying neural underpinnings that can be used for facilitating the identification of ASD.

Materials and methods

Static and dynamic amplitude of low-frequency fluctuations (ALFF) of 73 ASD preschoolers and 43 age-matched typically developing individuals (TDs) were extracted and compared to identify differences in intrinsic brain local connectivity associated with ASD. The dynamic ALFF (dALFF) utilized a sliding window technique that integrates static ALFF (sALFF) to gauge the variance of local brain activity over time. A receiver operating characteristic (ROC) analysis was conducted to evaluate the potential diagnostic capability of the sALFF and dALFF metrics in identifying ASD.

Results

Compared with TDs, ASD preschoolers exhibited lower levels of sALFF in the left middle temporal gyrus, medial orbitofrontal cortex, precuneus and reduced dALFF values in the left inferior orbitofrontal cortex, middle temporal gyrus. ROC analysis indicated that sALFF and dALFF could distinguish preschoolers with ASD from TDs with the areas under the curve (AUC) of 0.848 and 0.744 (p < 0.001), and their combination showed an increased accuracy with the AUC of 0.866 (p < 0.001). Nevertheless, there were no linear correlation between the ALFF values in children with ASD and clinical scales.

Conclusion

The findings suggest an association of regional left brain dysfunction with ASD in preschoolers. The values of sALFF and dALFF, particularly in the middle temporal gyrus, could act as possible indicators for the early detection of ASD.

Context modulates brain state dynamics and behavioral responses during narrative comprehension

Narrative comprehension is inherently context-sensitive, yet the brain and cognitive mechanisms by which brief contextual priming shapes story interpretation remain unclear. Using hidden Markov modeling (HMM) of fMRI data, we identified dynamic brain states as participants listened to an ambiguous spoken story under two distinct narrative contexts (affair vs. paranoia). We uncovered both context-invariant states-engaging auditory, language, and default mode networks-and context-specific states characterized by differential recruitment of control, salience, and visual networks. Narrative context selectively modulated the influence of character speech and linguistic features on brain state expression, with the central character's speech enhancing activation in shared states but suppressing activation in context-specific ones. Independent behavioral analyses revealed parallel context-dependent effects, with character-driven features exerting strong, selectively modulated influences on participants' judgments of narrative evidence. These findings demonstrate that brief narrative priming actively reshapes brain state dynamics and feature sensitivity during story comprehension, revealing how context guides moment-by-moment interpretive processing in naturalistic settings.

A reduction in energy costs induces integrated states of brain dynamics

In the human brain, interactions between multiple regions organize stable dynamics that enable enhanced cognitive processes. However, it is unclear how collective activities in the brain network can generate stable states while preserving unity across the whole brain scale under successive environmental changes. Herein, a network model was introduced in which network connections were adjusted to reduce the energy consumption level by avoiding excess changes in the activated states of each region during successive interactions. For time series data obtained from fMRI images, a connection matrix was generated by a simulation, and the predictions made by this matrix yielded accurate results relative to the real data. In this simulation, the adjustment process was activity-dependent, in which the interregional connections between intense active regions were reinforced to prohibit free behaviours. This resulted in a reduced excess energy loss and the integration of multiple regional activities into integrated dynamic states under constraints imposed by other regions. It was suggested that the simple rule of saving excess energy costs plays an important role in the mechanism that regulates large-scale brain networks and dynamics.

Mapping the Psychosis Spectrum - Imaging Neurosubtypes from Multi-Scale Functional Network Connectivity

This study aims to identify Psychosis Imaging Neurosubtypes (PINs)-homogeneous subgroups of individuals with psychosis characterized by distinct neurobiology derived from imaging features. Specifically, we utilized resting-state fMRI data from 2103 B-SNIP 1&2 participants (1127 with psychosis, 350 relatives, 626 controls) to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity (LNC), which captured system-wide interconnected multiscale information across three components (cognitive-related, typical, psychosis-related). Projections of psychosis participants' msFNC onto this subspace revealed three PINs through unsupervised learning, each with distinct cognitive, clinical, and connectivity profiles, spanning all DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective). PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognitive function, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Notably, 55% of relatives aligned with the same neurosubtype as their affected family members-a significantly higher rate than random chance (p-valueRelatives-to-PIN-1 < 0.001, p-valueRelatives-to-PIN-2 < 0.05, p-valueRelatives-to-PIN-3 < 0.001) compared to a non-significant 37% DSM-based classification, supporting a biological basis of these neurosubtypes. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs differed from original B-SNIP Biotypes, which were determined from electrophysiological, cognitive, and oculomotor data. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

Complexity in speech and music listening via neural manifold flows

Understanding the complex neural mechanisms underlying speech and music perception remains a multifaceted challenge. In this study, we investigated neural dynamics using human intracranial recordings. Employing a novel approach based on low-dimensional reduction techniques, the Manifold Density Flow (MDF), we quantified the complexity of brain dynamics during naturalistic speech and music listening and during resting state. Our results reveal higher complexity in patterns of interdependence between different brain regions during speech and music listening compared with rest, suggesting that the cognitive demands of speech and music listening drive the brain dynamics toward states not observed during rest. Moreover, speech listening has more complexity than music, highlighting the nuanced differences in cognitive demands between these two auditory domains. Additionally, we validated the efficacy of the MDF method through experimentation on a toy model and compared its effectiveness in capturing the complexity of brain dynamics induced by cognitive tasks with another established technique in the literature. Overall, our findings provide a new method to quantify the complexity of brain activity by studying its temporal evolution on a low-dimensional manifold, suggesting insights that are invisible to traditional methodologies in the contexts of speech and music perception.

Analyzing the brain's dynamic response to targeted stimulation using generative modeling

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations

Dynamic reconfigurations of the functional connectome across different connectivity states are highly heritable, predictive of cognitive abilities, and linked to mental health. Despite their established heritability, the specific polymorphisms that shape connectome dynamics are largely unknown. Given the widespread regulatory impact of modulatory neurotransmitters on functional connectivity, we comprehensively investigated a large set of single nucleotide polymorphisms (SNPs) of their receptors, metabolic enzymes, and transporters in 674 healthy adult subjects (347 females) from the Human Connectome Project. Preregistered modulatory neurotransmitter SNPs and dynamic connectome features entered a Stability Selection procedure with resampling. We found that specific subsets of these SNPs explain individual differences in temporal phenotypes of fMRI-derived connectome dynamics for which we previously established heritability. Specifically, noradrenergic polymorphisms explained Fractional Occupancy, i.e., the proportion of time spent in each connectome state, and cholinergic polymorphisms explained Transition Probability, i.e., the probability to transition between state pairs, respectively. This work identifies specific genetic effects on connectome dynamics via the regulatory impact of modulatory neurotransmitter systems. Our observations highlight the potential of dynamic connectome features as endophenotypes for neurotransmitter-focused precision psychiatry.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are, nevertheless, scarce. Here, we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform the functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of AUD (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration and that it is related to AUD risk.

Understanding cognitive processes across spatial scales of the brain

Cognition arises from neural operations at multiple spatial scales, from individual neurons to large-scale networks. Despite extensive research on coding principles and emergent cognitive processes across brain areas, investigation across scales has been limited. Here, we propose ways to test the idea that different cognitive processes emerge from distinct information coding principles at various scales, which collectively give rise to complex behavior. This approach involves comparing brain-behavior associations and the underlying neural geometry across scales, alongside an investigation of global and local scale interactions. Bridging findings across species and techniques through open science and collaborations is essential to comprehensively understand the multiscale brain and its functions.

Benchmarking macaque brain gene expression for horizontal and vertical translation

The spatial patterning of gene expression shapes cortical organization and function. The macaque is a fundamental model organism in neuroscience, but the translational potential of macaque gene expression rests on the assumption that it is a good proxy for patterns of corresponding proteins (vertical translation) and for patterns of orthologous human genes (horizontal translation). Here, we systematically benchmark regional gene expression in macaque cortex against (i) macaque cortical receptor density and in vivo and ex vivo microstructure and (ii) human cortical gene expression. We find moderate cortex-wide correspondence between macaque gene expression and protein density, which improves by considering layer-specific gene expression. Half of the examined genes exhibit significant correlation between macaque and human across the cortex. Interspecies correspondence of gene expression is greater in unimodal than in transmodal cortex, recapitulating evolutionary cortical expansion and gene-protein correspondence in the macaque. These results showcase the potential and limitations of macaque cortical transcriptomics for translational discovery within and across species.

Metacognition in the listening brain

How do you know you have heard right? Metacognition, the ability to assess and monitor one's own cognitive state, is key to understanding human communication in complex environments. However, the foundational role of metacognition in hearing and communication is only beginning to be explored, and the neuroscience behind it is an emerging field: how does confidence express in neural dynamics of the listening brain? What is known about auditory metaperceptual alterations as a hallmark phenomenon in psychosis, dementia, or hearing loss? Building on Bayesian ideas of auditory perception and auditory neuroscience, 'meta-listening' offers a framework for more comprehensive research into how metacognition in humans and non-humans shapes the listening brain.

Altered controllability of functional brain networks in obsessive-compulsive disorder

Disruptions in the dynamic transitions between brain states have been implicated in cognitive, emotional, and behavioral dysregulations across various mental disorders. However, the irregularities in dynamic brain state transitions associated with obsessive-compulsive disorder (OCD) remain unclear. The present study included 99 patients with OCD and 104 matched healthy controls (HCs) to investigate alterations in dynamic brain state transitions by using network control theory. Functional controllability metrics were computed and compared between the OCD group and HCs. Additionally, abnormal functional connectivity (FC) between the brain regions with statistical differences in functional controllability and remaining brain regions were assessed. Patients with OCD exhibited significantly decreased average controllability (AC) and increased modal controllability (MC) in the right parahippocampal gyrus (PHG), compared to the HCs. Further analysis showed significantly decreased FC between the right PHG and bilateral superior temporal gyrus and occipital gyrus, left postcentral gyrus, and right cingulate gyrus in OCD patients. The results suggest aberrant brain state transitions in OCD patients, alongside widespread disruptions within the brain functional connectome. This study highlights the critical role of altered functional controllability within the right PHG in the neuropathological mechanisms of OCD, providing novel insights into the pathogenesis of OCD.

The metabolic costs of cognition

Cognition and behavior are emergent properties of brain systems that seek to maximize complex and adaptive behaviors while minimizing energy utilization. Different species reconcile this trade-off in different ways, but in humans the outcome is biased towards complex behaviors and hence relatively high energy use. However, even in energy-intensive brains, numerous parsimonious processes operate to optimize energy use. We review how this balance manifests in both homeostatic processes and task-associated cognition. We also consider the perturbations and disruptions of metabolism in neurocognitive diseases.

Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways. This network model proposes that neural dynamics are constrained by a two-layered structural network interconnecting both astrocytic and neuronal populations, allowing us to investigate astrocytes' modulatory influences on whole-brain activity and emerging functional connectivity patterns. By developing a simulation methodology, informed by bifurcation and multilayer network theories, we demonstrate that the dialogue between astrocytic and neuronal networks manifests over fast-slow fluctuation mechanisms as well as through phase-amplitude connectivity processes. The findings from our research represent a significant leap forward in the modeling of glial-neuronal collaboration, promising deeper insights into their collaborative roles across health and disease states.

Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning

Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support dynamic reallocation of neural resources and optimal adaptation for upcoming challenges, an active process analogous to allostasis. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in proactively modulating emotional and executive functions. Yet, the underlying mechanisms of such proactive effects remain elusive. By leveraging pharmacological neuroimaging and hidden Markov modeling of brain state dynamics, we show that the CAR proactively modulates rapid spatiotemporal reconfigurations (state) of large-scale brain networks involved in emotional and executive functions. Behaviorally, suppression of CAR proactively impaired performance of emotional discrimination but not working memory (WM), while individuals with higher CAR exhibited better performance for both emotional and WM tasks. Neuronally, suppression of CAR led to a decrease in fractional occupancy and mean lifetime of task-related brain states dominant to emotional and WM processing. Further information-theoretic analyses on sequence complexity of state transitions revealed that a suppressed or lower CAR led to higher transition complexity among states primarily anchored in visual-sensory and salience networks during emotional task. Conversely, an opposite pattern of transition complexity was observed among states anchored in executive control and visuospatial networks during WM, indicating that CAR distinctly modulates neural resources allocated to emotional and WM processing. Our findings establish a causal link of CAR with brain network dynamics across emotional and executive functions, suggesting a neuroendocrine account for CAR proactive effects on human emotion and cognition.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications

The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

Multiscale organization of neuronal activity unifies scale-dependent theories of brain function

Brain recordings collected at different resolutions support distinct signatures of neural coding, leading to scale-dependent theories of brain function. Here, we show that these disparate signatures emerge from a heavy-tailed, multiscale functional organization of neuronal activity observed across calcium-imaging recordings collected from the whole brains of zebrafish and C. elegans as well as from sensory regions in Drosophila, mice, and macaques. Network simulations demonstrate that this conserved hierarchical structure enhances information processing. Finally, we find that this organization is maintained despite significant cross-scale reconfiguration of cellular coordination during behavior. Our findings suggest that this nonlinear organization of neuronal activity is a universal principle conserved for its ability to adaptively link behavior to neural dynamics across multiple spatiotemporal scales while balancing functional resiliency and information processing efficiency.

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance.

Fast-slow dynamics in a memristive ion channel-based bionic circuit

Electrophysiological properties of ion channels can influence the transport process of ions and the generation of firing patterns in an excitable biological neuron when applying an external stimulus and exceeding the excitable threshold. In this paper, a current stimulus is employed to emulate the external stimulus, and a second-order locally active memristor (LAM) is deployed to characterize the properties of ion channels. Then, a simple bionic circuit possessing the LAM, a capacitor, a DC voltage, and the current stimulus is constructed. Fast-slow dynamical effects of the current stimulus with low- and high-frequency are respectively explored. Numerical simulations disclose that the bionic circuit can generate bursting behaviors for the low-frequency current stimulus and spiking behaviors for the high-frequency current stimulus. Besides, fold and Hopf bifurcation sets are deduced and the bifurcation mechanisms for bursting behaviors are elaborated. Furthermore, the numerically simulated bursting and spiking behaviors are verified by PCB-based hardware experiments. These results reflect the feasibility of the bionic circuit in generating the firing patterns of spiking and bursting behaviors and the external current can be employed to regulate these firing patterns.

Control energy detects discrepancies in good vs. poor readers' structural-functional coupling during a rhyming task

Neuroimaging studies have identified functional and structural brain circuits that support reading. However, much less is known about how reading-related functional dynamics are constrained by white matter structure. Network control theory proposes that cortical brain dynamics are linearly determined by the white matter connectome, using control energy to evaluate the difficulty of the transition from one cognitive state to another. Here we apply this approach to linking brain dynamics with reading ability and disability in school-age children. A total of 51 children ages 8.25 -14.6 years performed an in-scanner rhyming task in visual and auditory modalities, with orthographic (spelling) and phonological (rhyming) similarity manipulated across trials. White matter structure and fMRI activation were used conjointly to compute the control energy of the reading network in each condition relative to a null fixation state. We then tested differences in control energy across trial types, finding higher control energy during non-word trials than word trials, and during incongruent trials than congruent trials. ROI analyses further showed a dissociation between control energy of the left fusiform and superior temporal gyrus depending on stimulus modality, with higher control energy for visual modalities in fusiform and higher control energy for auditory modalities in STG. Together, this study highlights that control theory can explain variations on cognitive demands in higher-level abilities such as reading, beyond what can be inferred from either functional or structural MRI measures alone.

Compressing human brain activity for studying brain function

Due to its complexity and size, the optimal scale or level at which to describe the brain remains an open question in neuroscience. A new study published in PLOS Biology shows that simplifying complex brain recordings makes them more useful for studying brain function.

Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought

From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term "cognon". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'.

Integrating brainstem and cortical functional architectures

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior

Traditional models of human brain activity often represent it as a network of pairwise interactions between brain regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order interactions from temporal brain signals involving three or more regions. However, to this day it remains unclear whether methods based on inferred higher-order interactions outperform traditional pairwise ones for the analysis of fMRI data. To address this question, we conducted a comprehensive analysis using fMRI time series of 100 unrelated subjects from the Human Connectome Project. We show that higher-order approaches greatly enhance our ability to decode dynamically between various tasks, to improve the individual identification of unimodal and transmodal functional subsystems, and to strengthen significantly the associations between brain activity and behavior. Overall, our approach sheds new light on the higher-order organization of fMRI time series, improving the characterization of dynamic group dependencies in rest and tasks, and revealing a vast space of unexplored structures within human functional brain data, which may remain hidden when using traditional pairwise approaches.

Brainwide hemodynamics predict neural rhythms across sleep and wakefulness in humans

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the brainwide dynamics underlying these oscillations are unknown. Using simultaneous EEG and fast fMRI in humans drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI networks predict alpha (8-12 Hz) and delta (1-4 Hz) fluctuations. We predicted moment-to-moment EEG power variations from fMRI activity in held-out subjects, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify the large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal, brainwide dynamics.

Connectome-based prediction modeling of cognitive control using functional and structural connectivity

BACKGROUND

Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.

METHODS

The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.

RESULTS

Our results showed that both structural (r values 0.263-0.375) and functional (r values 0.336-0.503) connectomes can significantly predict individuals' cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.

CONCLUSIONS

The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.

Contrastive learning of shared spatiotemporal EEG representations across individuals for naturalistic neuroscience

Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stimuli processing hinges on the precise identification and extraction of the shared neural patterns that are consistently present across individuals. Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER). CL-SSTER utilizes contrastive learning to maximize the similarity of EEG representations across individuals for identical stimuli, contrasting with those for varied stimuli. The network employs spatial and temporal convolutions to simultaneously learn the spatial and temporal patterns inherent in EEG. The versatility of CL-SSTER was demonstrated on three EEG datasets, including a synthetic dataset, a natural speech comprehension EEG dataset, and an emotional video watching EEG dataset. CL-SSTER attained the highest inter-subject correlation (ISC) values compared to the state-of-the-art ISC methods. The latent representations generated by CL-SSTER exhibited reliable spatiotemporal EEG patterns, which can be explained by properties of the naturalistic stimuli. CL-SSTER serves as an interpretable and scalable framework for the identification of inter-subject shared neural representations in naturalistic neuroscience.

Tonic and burst-like locus coeruleus stimulation distinctly shift network activity across the cortical hierarchy

Noradrenaline (NA) release from the locus coeruleus (LC) changes activity and connectivity in neuronal networks across the brain, modulating multiple behavioral states. NA release is mediated by both tonic and burst-like LC activity. However, it is unknown whether the functional changes in target areas depend on these firing patterns. Using optogenetics, photometry, electrophysiology and functional magnetic resonance imaging in mice, we show that tonic and burst-like LC firing patterns elicit brain responses that hinge on their distinct NA release dynamics. During moderate tonic LC activation, NA release engages regions associated with associative processing, while burst-like stimulation biases the brain toward sensory processing. These activation patterns locally couple with increased astrocytic and inhibitory activity and change the brain's topological configuration in line with the hierarchical organization of the cerebral cortex. Together, these findings reveal how the LC-NA system achieves a nuanced regulation of global circuit operations.

Shaping dynamical neural computations using spatiotemporal constraints

Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.

EEG spectral attractors identify a geometric core of brain dynamics

Multidimensional reconstruction of brain attractors from electroencephalography (EEG) data enables the analysis of geometric complexity and interactions between signals in state space. Utilizing resting-state data from young and older adults, we characterize periodic (traditional frequency bands) and aperiodic (broadband exponent) attractors according to their geometric complexity and shared dynamical signatures, which we refer to as a geometric cross-parameter coupling. Alpha and aperiodic attractors are the least complex, and their global shapes are shared among all other frequency bands, affording alpha and aperiodic greater predictive power. Older adults show lower geometric complexity but greater coupling, resulting from dedifferentiation of gamma activity. The form and content of resting-state thoughts were further associated with the complexity of attractor dynamics. These findings support a process-developmental perspective on the brain's dynamic core, whereby more complex information differentiates out of an integrative and global geometric core.

Investigating the interplay between segregation and integration in developing cortical assemblies

Introduction

The human brain is an intricate structure composed of interconnected modular networks, whose organization is known to balance the principles of segregation and integration, enabling rapid information exchange and the generation of coherent brain states. Segregation involves the specialization of brain regions for specific tasks, while integration facilitates communication among these regions, allowing for efficient information flow. Several factors influence this balance, including maturation, aging, and the insurgence of neurological disorders like epilepsy, stroke, or cancer. To gain insights into information processing and connectivity recovery, we devised a controllable in vitro model to mimic and investigate the effects of different segregation and integration ratios over time.

Methods

We designed a cross-shaped polymeric mask to initially establish four independent sub-populations of cortical neurons and analyzed how the timing of its removal affected network development. We evaluated the morphological and functional features of the networks from 11 to 18 days in vitro (DIVs) with immunofluorescence techniques and micro-electrode arrays (MEAs).

Results

The removal of the mask at different developmental stages of the network lead to strong variations in the degree of intercommunication among the four assemblies (altering the segregation/integration balance), impacting firing and bursting parameters. Early removal (after 5 DIVs) resulted in networks with a level of integration similar to homogeneous controls (without physical constraints). In contrast, late removal (after 15 DIVs) hindered the formation of strong inter-compartment connectivity, leading to more clustered and segregated assemblies.

Discussion

A critical balance between segregation and integration was observed when the mask was removed at DIV 10, allowing for the formation of a strong connectivity among the still-separated compartments, thus demonstrating the existence of a time window in network development in which it is possible to achieve a balance between segregation and integration.

Human brain state dynamics are highly reproducible and associated with neural and behavioral features

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Multi-Task Collaborative Pre-Training and Adaptive Token Selection: A Unified Framework for Brain Representation Learning

Structural magnetic resonance imaging (sMRI) reveals the structural organization of the brain. Learning general brain representations from sMRI is an enduring topic in neuroscience. Previous deep learning models neglect that the brain, as the core of cognition, is distinct from other organs whose primary attribute is anatomy. Capturing the high-level representation associated with inter-individual cognitive variability is key to appropriately represent the brain. Given that this cognition-related information is subtle, mixed, and distributed in the brain structure, sMRI-based models need to both capture fine-grained details and understand how they relate to the overall global structure. Additionally, it is also necessary to explicitly express the cognitive information that implicitly embedded in local-global image features. Therefore, we propose MCPATS, a brain representation learning framework that combines Multi-task Collaborative Pre-training (MCP) and Adaptive Token Selection (ATS). First, we develop MCP, including mask-reconstruction to understand global context, distort-restoration to capture fine-grained local details, adversarial learning to integrate features at different granularities, and age-prediction, using age as a surrogate for cognition to explicitly encode cognition-related information from local-global image features. This co-training allows progressive learning of implicit and explicit cognition-related representations. Then, we develop ATS based on mutual attention for downstream use of the learned representation. During fine-tuning, the ATS highlights discriminative features and reduces the impact of irrelevant information. MCPATS was validated on three different public datasets for brain disease diagnosis, outperforming competing methods and achieving accurate diagnosis. Further, we performed detailed analysis to confirm that the MCPATS-learned representation captures cognition-related information.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms

To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.

Macroscale brain states support the control of semantic cognition

A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty - global association and feature matching judgments - which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain's dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

Unveiling a novel memory center in human brain: neurochemical identification of the nucleus incertus, a key pontine locus implicated in stress and neuropathology

BACKGROUND

The nucleus incertus (NI) was originally described by Streeter in 1903, as a midline region in the floor of the fourth ventricle of the human brain with an 'unknown' function. More than a century later, the neuroanatomy of the NI has been described in lower vertebrates, but not in humans. Therefore, we examined the neurochemical anatomy of the human NI using markers, including the neuropeptide, relaxin-3 (RLN3), and began to explore the distribution of the NI-related RLN3 innervation of the hippocampus.

METHODS

Histochemical staining of serial, coronal sections of control human postmortem pons was conducted to reveal the presence of the NI by detection of immunoreactivity (IR) for the neuronal markers, microtubule-associated protein-2 (MAP2), glutamic acid dehydrogenase (GAD)-65/67 and corticotrophin-releasing hormone receptor 1 (CRHR1), and RLN3, which is highly expressed in NI neurons in diverse species. RLN3 and vesicular GABA transporter 1 (vGAT1) mRNA were detected by fluorescent in situ hybridization. Pons sections containing the NI from an AD case were immunostained for phosphorylated-tau, to explore potential relevance to neurodegenerative diseases. Lastly, sections of the human hippocampus were stained to detect RLN3-IR and somatostatin (SST)-IR.

RESULTS

In the dorsal, anterior-medial region of the human pons, neurons containing RLN3- and MAP2-IR, and RLN3/vGAT1 mRNA-positive neurons were observed in an anatomical pattern consistent with that of the NI in other species. GAD65/67- and CRHR1-immunopositive neurons were also detected within this area. Furthermore, RLN3- and AT8-IR were co-localized within NI neurons of an AD subject. Lastly, RLN3-IR was detected in neurons within the CA1, CA2, CA3 and DG areas of the hippocampus, in the absence of RLN3 mRNA. In the DG, RLN3- and SST-IR were co-localized in a small population of neurons.

CONCLUSIONS

Aspects of the anatomy of the human NI are shared across species, including a population of stress-responsive, RLN3-expressing neurons and a RLN3 innervation of the hippocampus. Accumulation of phosphorylated-tau in the NI suggests its possible involvement in AD pathology. Further characterization of the neurochemistry of the human NI will increase our understanding of its functional role in health and disease.

Reliability of variability and complexity measures for task and task-free BOLD fMRI

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Aberrant functional hubs and related networks attributed to cognitive impairment in patients with anti‑N‑methyl‑D‑aspartate receptor encephalitis

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis results in severe neuropsychiatric symptoms and persistent cognitive impairment; however, the underlying mechanism is still not fully understood. The present study utilized the degree centrality (DC), functional connectivity (FC) and multivariate pattern analysis (MVPA) to further explore neurofunctional symptoms in patients with anti-NMDAR encephalitis. A total of 29 patients with anti-NMDAR encephalitis and 26 healthy controls (HCs) were enrolled for neuropsychological assessment and resting-state functional MRI (rs-fMRI) scans. DC, FC and MVPA were examined to investigate cerebral functional activity and distinguish neuroimaging characteristics between the patient and HC groups based on the rs-fMRI data. Compared with the HCs, the patients exhibited cognitive deficits, anxiety and depression. In the DC analysis, the patients exhibited significantly decreased DC strength in the left rectus gyrus, left caudate nucleus (LCN) and bilateral superior medial frontal gyrus, as well as increased DC strength in the cerebellar anterior lobe, compared with the HCs. In the subsequent FC analysis, the LCN showed decreased FC strength in the bilateral middle frontal gyrus and right precuneus. Furthermore, correlation analysis indicated that disrupted cerebral functional activity was significantly correlated with the alerting effect and Hamilton Depression Scale score. Using DC maps and receiver operating characteristic curve analysis, the MVPA classifier exhibited an area under curve of 0.79, and the accuracy classification rate was 76.36%, with a sensitivity of 79.31% and a specificity of 78.18%. The present study revealed that the disrupted functional activity of hub and related networks in the cerebellum, including the default mode network and executive control network, contributed to deficits in cognition and emotion in patients with anti-NMDAR encephalitis. In conclusion, the present study provided imaging evidence and primary diagnostic markers for pathological and compensatory mechanisms of anti-NMDAR encephalitis, with the aim of improving the understanding of this disease.

Reconfigurations of cortical manifold structure during reward-based motor learning

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

Causal evidence for cholinergic stabilization of attractor landscape dynamics

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge

Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.