The secret life of predictive brains: what's spontaneous activity for?

Noise or signal? Spontaneous activity of dorsal horn neurons: patterns and function in health and disease

Spontaneous activity refers to the firing of action potentials by neurons in the absence of external stimulation. Initially considered an artifact or "noise" in the nervous system, it is now recognized as a potential feature of neural function. Spontaneous activity has been observed in various brain areas, in experimental preparations from different animal species, and in live animals and humans using non-invasive imaging techniques. In this review, we specifically focus on the spontaneous activity of dorsal horn neurons of the spinal cord. We use a historical perspective to set the basis for a novel classification of the different patterns of spontaneous activity exhibited by dorsal horn neurons. Then we examine the origins of this activity and propose a model circuit to explain how the activity is generated and transmitted to the dorsal horn. Finally, we discuss possible roles of this activity during development and during signal processing under physiological conditions and pain states. By analyzing recent studies on the spontaneous activity of dorsal horn neurons, we aim to shed light on its significance in sensory processing. Understanding the different patterns of activity, the origins of this activity, and the potential roles it may play, will contribute to our knowledge of sensory mechanisms, including pain, to facilitate the modeling of spinal circuits and hopefully to explore novel strategies for pain treatment.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Bayesian interpretation of the prefrontal P2 ERP component based on stimulus/response mapping uncertainty

The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework. To test which source of uncertainty could be associated with the pP2, uncertainty induced by target probability and stimulus-response (S/R) mapping were modulated in three visuomotor tasks. Results showed that the pP2 had the largest amplitude in the task with the largest uncertainty regarding the S/R mapping and degraded as the S/R mapping became more predictable. The P3 was maximal in the tasks with larger uncertainty regarding the target probability. While we confirmed the P3 association with target probability, we extended our knowledge on the pP2 associating it with S/R mapping uncertainty. This component, which has been previously localized within the anterior insular cortex, may minimize S/R mapping uncertainty allowing response-related evidence accumulation and comparing current events with internal representations to extract action-related probabilities.

Imagination vs. routines: festive time, weekly time, and the predictive brain

This paper examines the relationship between societal structures shaped by traditions, norms, laws, and customs, and creative expressions in arts and media through the lens of the predictive coding framework in cognitive science. The article proposes that both dimensions of culture can be viewed as adaptations designed to enhance and train the brain's predictive abilities in the social domain. Traditions, norms, laws, and customs foster shared predictions and expectations among individuals, thereby reducing uncertainty in social environments. On the other hand, arts and media expose us to simulated experiences that explore alternative social realities, allowing the predictive machinery of the brain to hone its skills through exposure to a wider array of potentially relevant social circumstances and scenarios. We first review key principles of predictive coding and active inference, and then explore the rationale of cultural traditions and artistic culture in this perspective. Finally, we draw parallels between institutionalized normative habits that stabilize social worlds and creative and imaginative acts that temporarily subvert established conventions to inject variability.

A neuro-cognitive model of comprehension based on prediction and unification

Most architectures and models of language processing have been built upon a restricted view of language, which is limited to sentence processing. These approaches fail to capture one primordial characteristic: efficiency. Many facilitation effects are known to be at play in natural situations such as conversation (shallow processing, no real access to the lexicon, etc.) without any impact on the comprehension. In this study, on the basis of a new model integrating into a unique architecture, we present these facilitation effects for accessing the meaning into the classical compositional architecture. This model relies on two mechanisms, prediction and unification, and provides a unique architecture for the description of language processing in its natural environment.

Weakened effective connectivity between salience network and default mode network during resting state in adolescent depression

Adolescent major depressive disorder (MDD) is associated with altered resting-state connectivity between the default mode network (DMN) and the salience network (SN), which are involved in self-referential processing and detecting and filtering salient stimuli, respectively. Using spectral dynamical causal modelling, we investigated the effective connectivity and input sensitivity between key nodes of these networks in 30 adolescents with MDD and 32 healthy controls while undergoing resting-state fMRI. We found that the DMN received weaker inhibition from the SN and that the medial prefrontal cortex and the anterior cingulate cortex showed reduced self-inhibition in MDD, making them more prone to external influences. Moreover, we found that selective serotonin reuptake inhibitor (SSRI) intake was associated with decreased and increased self-inhibition of the SN and DMN, respectively, in patients. Our findings suggest that adolescent MDD is characterized by a hierarchical imbalance between the DMN and the SN, which could affect the integration of emotional and self-related information. We propose that SSRIs may help restore network function by modulating excitatory/inhibitory balance in the DMN and the SN. Our study highlights the potential of prefrontal-amygdala interactions as a biomarker and a therapeutic target for adolescent depression.

Neural representation in active inference: Using generative models to interact with-and understand-the lived world

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people-to action-oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities-and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

A generative model of memory construction and consolidation

Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) to (re)create sensory experiences from latent variable representations in entorhinal, medial prefrontal and anterolateral temporal cortices via the hippocampal formation. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for semantic memory, imagination, episodic future thinking, relational inference and schema-based distortions including boundary extension. The model explains how unique sensory and predictable conceptual elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimizing the use of limited hippocampal storage for new and unusual information. Overall, we believe hippocampal replay training generative models provides a comprehensive account of memory construction, imagination and consolidation.

An active inference perspective for the amygdala complex

The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands.

Intracellular Lactate Dynamics Reveal the Metabolic Diversity of Drosophila Glutamatergic Neurons

Lactate, an intermediary between glycolysis and mitochondrial oxidative phosphorylation, reflects the metabolic state of neurons. Here, we utilized a genetically-encoded lactate FRET biosensor to uncover subpopulations of distinct metabolic states among Drosophila glutamatergic neurons. Neurons within specific subpopulations exhibited correlated lactate flux patterns that stemmed from inherent cellular properties rather than neuronal interconnectivity. Further, individual neurons exhibited consistent patterns of lactate flux over time such that stimulus-evoked changes in lactate were correlated with pre-treatment fluctuations. Leveraging these temporal autocorrelations, deep-learning models accurately predicted post-stimulus responses from pre-stimulus fluctuations. These findings point to the existence of distinct neuronal subpopulations, each characterized by unique lactate dynamics, and raise the possibility that neurons with correlated metabolic activities might synchronize across different neural circuits. Such synchronization, rooted in neuronal metabolic states, could influence information processing in the brain.

Active inference as a theory of sentient behavior

This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.

Generating meaning: active inference and the scope and limits of passive AI

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

Predictable navigation through spontaneous brain states with cognitive-map-like representations

Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.

Intrinsic timescales and predictive allostatic interoception in brain health and disease

The cognitive neuroscience of brain diseases faces challenges in understanding the complex relationship between brain structure and function, the heterogeneity of brain phenotypes, and the lack of dimensional and transnosological explanations. This perspective offers a framework combining the predictive coding theory of allostatic interoceptive overload (PAIO) and the intrinsic neural timescales (INT) theory to provide a more dynamic understanding of brain health in psychiatry and neurology. PAIO integrates allostasis and interoception to assess the interaction between internal patterns and environmental stressors, while INT shows that different brain regions operate on different intrinsic timescales. The allostatic overload can be understood as a failure of INT, which involves a breakdown of proper temporal integration and segregation. This can lead to dimensional disbalances between exteroceptive/interoceptive inputs across brain and whole-body levels (cardiometabolic, cardiovascular, inflammatory, immune). This approach offers new insights, presenting novel perspectives on brain spatiotemporal hierarchies and interactions. By integrating these theories, the paper opens innovative paths for studying brain health dynamics, which can inform future research in brain health and disease.

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, what would be the biological utility of such a redundant and metabolically costly encoding scheme and what is the appropriate resolution and scale of neural recording to understand brain function? Imaging the activity of one million neurons at cellular resolution and near-simultaneously across mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number. While half of the neural variance lies within sixteen behavior-related dimensions, we find this unbounded scaling of dimensionality to correspond to an ever-increasing number of internal variables without immediate behavioral correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale recording is required to uncover the full neural substrates of internal and potentially cognitive processes.

Resting state functional connectivity changes following mindfulness-based stress reduction predict improvements in disease control for patients with asthma

BACKGROUND

The staggering morbidity associated with chronic inflammatory diseases can be reduced by psychological interventions, including Mindfulness-Based Stress Reduction (MBSR). Proposed mechanisms for MBSR's beneficial effects include changes in salience network function. Salience network perturbations are also associated with chronic inflammation, including airway inflammation in asthma, a chronic inflammatory disease affecting approximately 10% of the population. However, no studies have examined whether MBSR-related improvements in disease control are related to changes in salience network function.

METHODS

Adults with asthma were randomized to 8 weeks of MBSR or a waitlist control group. Resting state functional connectivity was measured using fMRI before randomization, immediately post-intervention, and 4 months post-intervention. Using key salience network regions as seeds, we calculated group differences in change in functional connectivity over time and examined whether functional connectivity changes were associated with increased mindfulness, improved asthma control, and decreased inflammatory biomarkers.

RESULTS

The MBSR group showed greater increases in functional connectivity between salience network regions relative to the waitlist group. Improvements in asthma control correlated with increased functional connectivity between the salience network and regions important for attention control and emotion regulation. Improvements in inflammatory biomarkers were related to decreased functional connectivity between the salience network and other networks.

CONCLUSIONS

Increased resting salience network coherence and connectivity with networks that subserve attention and emotion regulation may contribute to the benefits of MBSR for patients with asthma. Understanding the neural underpinnings of MBSR-related benefits in patients is a critical step towards optimizing brain-targeted interventions for chronic inflammatory disease management.

The neural correlates of value hierarchies: a prospective typology based on personal value profiles of emerging adults

Introduction

Value hierarchies, as motivational goals anchored in the self-schema, may be correlated with spontaneous activity in the resting brain, especially those involving self-relevance. This study aims to investigate the neural correlates of value hierarchies from the perspective of typology.

Methods

A total of 610 Chinese college students (30.31% women), aged 18 to 23, completed the personal values questionnaire and underwent resting-state functional magnetic resonance imaging.

Results

The latent profile analysis revealed three personal value profiles: traditional social orientation, modernized orientation, and undifferentiated orientation. Neuroimaging results revealed that individuals with modernized orientation prioritized openness to change value, and this personal-focus is related to the higher low-frequency amplitude of the posterior insula; individuals with traditional social orientation prioritized self-transcendence and conservation values, and this social-focus is related to the stronger functional connectivity of the middle insula with the inferior temporal gyrus, temporal gyrus, posterior occipital cortex, and basal ganglia, as well as weaker functional connections within the right middle insula.

Discussion

Taken together, these findings potentially indicate the intra-generational differentiation of contemporary Chinese emerging adults' value hierarchies. At the neural level, these are correlated with brain activities involved in processing self- and other-relevance.

Mapping and comparing fMRI connectivity networks across species

Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Increased functional connectivity between the auditory cortex and the frontoparietal network compensates for impaired visuomotor transformation after early auditory deprivation

Early auditory deprivation leads to a reorganization of large-scale brain networks involving and extending beyond the auditory system. It has been documented that visuomotor transformation is impaired after early deafness, associated with a hyper-crosstalk between the task-critical frontoparietal network and the default-mode network. However, it remains unknown whether and how the reorganized large-scale brain networks involving the auditory cortex contribute to impaired visuomotor transformation after early deafness. Here, we asked deaf and early hard of hearing participants and normal hearing controls to judge the spatial location of a visual target. Compared with normal hearing controls, the superior temporal gyrus showed significantly increased functional connectivity with the frontoparietal network and the default-mode network in deaf and early hard of hearing participants, specifically during egocentric judgments. However, increased superior temporal gyrus-frontoparietal network and superior temporal gyrus-default-mode network coupling showed antagonistic effects on egocentric judgments. In deaf and early hard of hearing participants, increased superior temporal gyrus-frontoparietal network connectivity was associated with improved egocentric judgments, whereas increased superior temporal gyrus-default-mode network connectivity was associated with deteriorated performance in the egocentric task. Therefore, the data suggest that the auditory cortex exhibits compensatory neuroplasticity (i.e. increased functional connectivity with the task-critical frontoparietal network) to mitigate impaired visuomotor transformation after early auditory deprivation.

Mind your heart to bear the weight: Cardiac interoception predicts action-related visual perception when wearing a heavy backpack

Visual perception can be modulated by the physiological potential for action. For instance, it was famously shown that a geographical slant appears steeper when wearing a heavy backpack than not wearing any. However, those results are not always replicated. In the present exploratory study, we test the hypothesis that the backpack weight's effect on perception relies on the ability of the cognitive system to integrate the physiological constraint's change rather than the change itself. Young adults (n = 54) wore an electrocardiogram monitor and completed a computerised task in which photographs of real geographical slants were displayed on a screen while wearing a heavy versus light backpack. The activity of the vagus nerve, as an index of physiological adaptability, was recorded as a proxy of the physiological state during the task. The participants also completed an interoception task assessing one's ability to detect his or her own heartbeat as the index of integration ability of the cognitive system. While Bayesian analyses revealed no difference in angle estimation between carrying a heavy versus light backpack, the results indicated that interoception predicted less accurate angle estimation only when wearing a heavy backpack. In contrast, there was anecdotal evidence that vagal activity changes predicted visual perception. Interoception might thus play a crucial role in the interplay between the physiological potential for action and action-related visual perception.

An active inference model of hierarchical action understanding, learning and imitation

We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.

Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.

In Between the Psychological and Physiological Self - The Impact of Covid-19 Pandemic on the Neuro-Socio-Ecological and Inflammatory Mind-Body-Brain System

The COVID-19 pandemic has had a profound impact on individuals' sense of self perturbating the sense of connectedness with the others, touching upon deep existential fears and deep intersubjective and cultural layers, emphasizing the importance of a neuro-socio-ecological alignment for the sense of security of psychological self. We can still observe after years how social distancing measures, quarantines, and lockdowns have disrupted social connections and routines, leading to feelings of isolation, anxiety and depressive symptomatology. Furthermore, from a physiological perspective, some people continue to experience health problems long after having COVID-19, and these ongoing health problems are sometimes called post-COVID-19 syndrome or post-COVID conditions (PASC). In this complex scenario, through the operationalization of the sense of self and its psychological and physiological baseline, our aim is to try to shed some new light on elements of resilience vs. vulnerability. Here we intend the self and its baseline as the crossroads between psychology and physiology and we show how COVID-19 pandemic, especially in post-COVID-19 syndrome (PACS), left traces in the mind-body-brain system at a neuro-socio-ecological and inflammatory level.

Event-related variability is modulated by task and development

In carefully designed experimental paradigms, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants compared to adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Importantly, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which in infants, was modulated by the task difficulty and age. Our approaches to characterize Event Related Variability (ERV) expand on classic ERP analyses and provide the first evidence for the functional role of ongoing neural variability in human infants.

Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity

Within the context of the computer metaphor, evoked brain activity acts as a primary carrier for the brain mechanisms of mental processing. However, many studies have found that evoked brain activity is not the major part of brain activity. Instead, spontaneous brain activity exhibits greater intensity and coevolves with evoked brain activity through continuous interaction. Spontaneous and evoked brain activities are similar but not identical. They are not separate parts, but always dynamically interact with each other. Therefore, the enactive cognition theory further states that the brain is characterized by unified and active patterns of activity. The brain adjusts its activity pattern by minimizing the error between expectation and stimulation, adapting to the ever-changing environment. Therefore, the dynamic regulation of brain activity in response to task situations is the core brain mechanism of mental processing. Beyond the evoked brain activity and spontaneous brain activity, the enactive brain activity provides a novel framework to completely describe brain activities during mental processing. It is necessary for upcoming researchers to introduce innovative indicators and paradigms for investigating enactive brain activity during mental processing.

Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

The development of oscillatory and aperiodic resting state activity is linked to a sensitive period in humans

Congenital blindness leads to profound changes in electroencephalographic (EEG) resting state activity. A well-known consequence of congenital blindness in humans is the reduction of alpha activity which seems to go together with increased gamma activity during rest. These results have been interpreted as indicating a higher excitatory/inhibitory (E/I) ratio in visual cortex compared to normally sighted controls. Yet it is unknown whether the spectral profile of EEG during rest would recover if sight were restored. To test this question, the present study evaluated periodic and aperiodic components of the EEG resting state power spectrum. Previous research has linked the aperiodic components, which exhibit a power-law distribution and are operationalized as a linear fit of the spectrum in log-log space, to cortical E/I ratio. Moreover, by correcting for the aperiodic components from the power spectrum, a more valid estimate of the periodic activity is possible. Here we analyzed resting state EEG activity from two studies involving (1) 27 permanently congenitally blind adults (CB) and 27 age-matched normally sighted controls (MCB); (2) 38 individuals with reversed blindness due to bilateral, dense, congenital cataracts (CC) and 77 age-matched sighted controls (MCC). Based on a data driven approach, aperiodic components of the spectra were extracted for the low frequency (Lf-Slope 1.5 to 19.5 Hz) and high frequency (Hf-Slope 20 to 45 Hz) range. The Lf-Slope of the aperiodic component was significantly steeper (more negative slope), and the Hf-Slope of the aperiodic component was significantly flatter (less negative slope) in CB and CC participants compared to the typically sighted controls. Alpha power was significantly reduced, and gamma power was higher in the CB and the CC groups. These results suggest a sensitive period for the typical development of the spectral profile during rest and thus likely an irreversible change in the E/I ratio in visual cortex due to congenital blindness. We speculate that these changes are a consequence of impaired inhibitory circuits and imbalanced feedforward and feedback processing in early visual areas of individuals with a history of congenital blindness.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the "social brain", are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants ( N = 24 $$ N=24 $$ ). In IS-RSA, we proposed a novel pipeline, that is, computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, we found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.

Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus

Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function.

Spontaneous Activity Patterns in Human Attention Networks Code for Hand Movements

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements.SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors.

Stimulus-evoked and resting-state alpha oscillations show a linked dependence on patterned visual experience for development

Persistent visual impairments after congenital blindness due to dense bilateral cataracts have been attributed to altered visual cortex development within a sensitive period. Occipital alpha (8-14 Hz) oscillations were found to be reduced after congenital cataract reversal, while participants performed visual motion tasks. However, it has been unclear whether reduced alpha oscillations were task-specific, or linked to impaired visual behavior in cataract-reversed individuals. Here, we compared resting-state and stimulus-evoked alpha activity between individuals who had been treated for dense bilateral congenital cataracts (CC, n = 13, mean duration of blindness = 11.0 years) and age-matched, normally sighted individuals (SC, n = 13). We employed the visual impulse response function, adapted from VanRullen and MacDonald (2012), to test for the characteristic alpha response to visual white noise. Participants observed white noise stimuli changing in luminance with equal power at frequencies between 0 and 30 Hz. Compared to SC individuals, CC individuals demonstrated a reduced likelihood of exhibiting an evoked alpha response. Moreover, stimulus-evoked alpha power was reduced and correlated with a corresponding reduction of resting-state alpha power in the same CC individuals. Finally, CC individuals with an above-threshold evoked alpha peak had better visual acuity than CC individual without an evoked alpha peak. Since alpha oscillations have been linked to feedback communication, we suggest that the concurrent impairment in resting-state and stimulus-evoked alpha oscillations indicates an altered interaction of top-down and bottom-up processing in the visual hierarchy, which likely contributes to incomplete behavioral recovery in individuals who experienced transient congenital blindness.

Social navigation modulates the anterior and posterior hippocampal circuits in the resting brain

Social navigation is a dynamic and complex process that requires the collaboration of multiple brain regions. However, the neural networks for navigation in a social space remain largely unknown. This study aimed to investigate the role of hippocampal circuit in social navigation from a resting-state fMRI data. Here, resting-state fMRI data were acquired before and after participants performed a social navigation task. By taking the anterior and posterior hippocampus (HPC) as the seeds, we calculated their connectivity with the whole brain using the seed-based static functional connectivity (sFC) and dynamic FC (dFC) approaches. We found that the sFC and dFC between the anterior HPC and supramarginal gyrus, sFC or dFC between posterior HPC and middle cingulate cortex, inferior parietal gyrus, angular gyrus, posterior cerebellum, medial superior frontal gyrus were increased after the social navigation task. These alterations were related to social cognition of tracking location in the social navigation. Moreover, participants who had more social support or less neuroticism showed a greater increase in hippocampal connectivity. These findings may highlight a more important role of the posterior hippocampal circuit in the social navigation, which is crucial for social cognition.

Functional connectivity abnormalities of brain networks in obsessive–compulsive disorder: a systematic review

Obsessive-compulsive disorder (OCD) is characterized by cognitive abnormalities encompassing several executive processes. Neuroimaging studies highlight functional abnormalities of executive fronto-parietal network (FPN) and default-mode network (DMN) in OCD patients, as well as of the prefrontal cortex (PFC) more specifically. We aim at assessing the presence of functional connectivity (FC) abnormalities of intrinsic brain networks and PFC in OCD, possibly underlying specific computational impairments and clinical manifestations. A systematic review of resting-state fMRI studies investigating FC was conducted in unmedicated OCD patients by querying three scientific databases (PubMed, Scopus, PsycInfo) up to July 2022 (search terms: “obsessive–compulsive disorder” AND “resting state” AND “fMRI” AND “function* *connect*” AND “task-positive” OR “executive” OR “central executive” OR “executive control” OR “executive-control” OR “cognitive control” OR “attenti*” OR “dorsal attention” OR “ventral attention” OR “frontoparietal” OR “fronto-parietal” OR “default mode” AND “network*” OR “system*”). Collectively, 20 studies were included. A predominantly reduced FC of DMN – often related to increased symptom severity – emerged. Additionally, intra-network FC of FPN was predominantly increased and often positively related to clinical scores. Concerning PFC, a predominant hyper-connectivity of right-sided prefrontal links emerged. Finally, FC of lateral prefrontal areas correlated with specific symptom dimensions. Several sources of heterogeneity in methodology might have affected results in unpredictable ways and were discussed. Such findings might represent endophenotypes of OCD manifestations, possibly reflecting computational impairments and difficulties in engaging in self-referential processes or in disengaging from cognitive control and monitoring processes.

A low-dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated.

METHODS

We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses.

RESULTS

Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation).

CONCLUSIONS

A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.

A Developmental Approach for Training Deep Belief Networks

Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in triggering the deep learning revolution, demonstrating for the very first time the feasibility of unsupervised learning in networks with many layers of hidden neurons. These hierarchical architectures incorporate plausible biological and cognitive properties, making them particularly appealing as computational models of human perception and cognition. However, learning in DBNs is usually carried out in a greedy, layer-wise fashion, which does not allow to simulate the holistic maturation of cortical circuits and prevents from modeling cognitive development. Here we present iDBN, an iterative learning algorithm for DBNs that allows to jointly update the connection weights across all layers of the model. We evaluate the proposed iterative algorithm on two different sets of visual stimuli, measuring the generative capabilities of the learned model and its potential to support supervised downstream tasks. We also track network development in terms of graph theoretical properties and investigate the potential extension of iDBN to continual learning scenarios. DBNs trained using our iterative approach achieve a final performance comparable to that of the greedy counterparts, at the same time allowing to accurately analyze the gradual development of internal representations in the deep network and the progressive improvement in task performance. Our work paves the way to the use of iDBN for modeling neurocognitive development.

One-year-later spontaneous EEG features predict visual exploratory human phenotypes

During visual exploration, eye movements are controlled by multiple stimulus- and goal-driven factors. We recently showed that the dynamics of eye movements -how/when the eye move- during natural scenes' free viewing were similar across individuals and identified two viewing styles: static and dynamic, characterized respectively by longer or shorter fixations. Interestingly, these styles could be revealed at rest, in the absence of any visual stimulus. This result supports a role of intrinsic activity in eye movement dynamics. Here we hypothesize that these two viewing styles correspond to different spontaneous patterns of brain activity. One year after the behavioural experiments, static and dynamic viewers were called back to the lab to record high density EEG activity during eyes open and eyes closed. Static viewers show higher cortical inhibition, slower individual alpha frequency peak, and longer memory of alpha oscillations. The opposite holds for dynamic viewers. We conclude that some properties of spontaneous activity predict exploratory eye movement dynamics during free viewing.

Dynamic functional connectivity: causative or epiphenomenal?

Dynamic coupling of neural signals is a hallmark of brain networks, but its potential relevance is still debated. Does coupling play a causal role for network functions, or is it just a by-product of structural connectivity or other physiological processes? With intervention techniques that have become available, experiments seem within reach that may provide answers to this long-standing question.

Triple network activation causes tinnitus in patients with sudden sensorineural hearing loss: A model-based volume-entropy analysis

Tinnitus can be defined as the conscious perception of phantom sounds in the absence of corresponding external auditory signals. Tinnitus can develop in the setting of sudden sensorineural hearing loss (SSNHL), but the underlying mechanism is largely unknown. Using electroencephalography, we investigated differences in afferent node capacity between 15 SSNHL patients without tinnitus (NT) and 30 SSNHL patients with tinnitus (T). Where the T group showed increased afferent node capacity in regions constituting a "triple brain network" [default mode network (DMN), central executive network (CEN), and salience network (SN)], the NT group showed increased information flow in regions implicated in temporal auditory processing and noise-canceling pathways. Our results demonstrate that when all components of the triple network are activated due to sudden-onset auditory deprivation, tinnitus ensues. By contrast, auditory processing-associated and tinnitus-suppressing networks are highly activated in the NT group, to overcome the activation of the triple network and effectively suppress the generation of tinnitus.

Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness

Brain activity is intrinsically organised into spatiotemporal patterns, but it is still not clear whether these intrinsic patterns are functional or epiphenomenal. Using a simultaneous fMRI-EEG implementation of a well-known bistable visual task, we showed that the latent transient states in the intrinsic EEG oscillations can predict upcoming involuntarily perceptual transitions. The critical state predicting a dominant perceptual transition was characterised by the phase coupling between the precuneus (PCU), a key node of the Default Mode Network (DMN), and the primary visual cortex (V1). The interaction between the lifetime of this state and the PCU- > V1 Granger-causal effect is correlated with the perceptual fluctuation rate. Our study suggests that the brain's endogenous dynamics are phenomenologically relevant, as they can elicit a diversion between potential visual processing pathways, while external stimuli remain the same. In this sense, the intrinsic DMN dynamics pre-empt the content of consciousness.

How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.

Beyond the veil of duality-topographic reorganization model of meditation

Meditation can exert a profound impact on our mental life, with proficient practitioners often reporting an experience free of boundaries between a separate self and the environment, suggesting an explicit experience of "nondual awareness." What are the neural correlates of such experiences and how do they relate to the idea of nondual awareness itself? In order to unravel the effects that meditation has on the brain's spatial topography, we review functional magnetic resonance imaging brain findings from studies specific to an array of meditation types and meditator experience levels. We also review findings from studies that directly probe the interaction between meditation and the experience of the self. The main results are (i) decreased posterior default mode network (DMN) activity, (ii) increased central executive network (CEN) activity, (iii) decreased connectivity within posterior DMN as well as between posterior and anterior DMN, (iv) increased connectivity within the anterior DMN and CEN, and (v) significantly impacted connectivity between the DMN and CEN (likely a nonlinear phenomenon). Together, these suggest a profound organizational shift of the brain's spatial topography in advanced meditators-we therefore propose a topographic reorganization model of meditation (TRoM). One core component of the TRoM is that the topographic reorganization of DMN and CEN is related to a decrease in the mental-self-processing along with a synchronization with the more nondual layers of self-processing, notably interoceptive and exteroceptive-self-processing. This reorganization of the functionality of both brain and self-processing can result in the explicit experience of nondual awareness. In conclusion, this review provides insight into the profound neural effects of advanced meditation and proposes a result-driven unifying model (TRoM) aimed at identifying the inextricably tied objective (neural) and subjective (experiential) effects of meditation.

Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

Allostasis as a core feature of hierarchical gradients in the human brain

This paper integrates emerging evidence from two broad streams of scientific literature into one common framework: (a) hierarchical gradients of functional connectivity that reflect the brain's large-scale structural architecture (e.g., a lamination gradient in the cerebral cortex); and (b) approaches to predictive processing and one of its specific instantiations called allostasis (i.e., the predictive regulation of energetic resources in the service of coordinating the body's internal systems). This synthesis begins to sketch a coherent, neurobiologically inspired framework suggesting that predictive energy regulation is at the core of human brain function, and by extension, psychological and behavioral phenomena, providing a shared vocabulary for theory building and knowledge accumulation.

The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.