Anterior insula regulates brain network transitions that gate conscious access

Amygdala-insula response to neutral stimuli and the prospective prediction of anxiety sensitivity

BACKGROUND

Anxiety sensitivity (AS) - the tendency to perceive benign physiological stimuli as harmful - is a risk factor for anxiety disorders. Cross-sectionally, AS has been linked to increased negative > neutral neural activity in key nodes implicated in threat detection and interoceptive - i.e., the amygdala (AMYG) and insula (INS). Examining the unique contributions of AMYG-INS blood‑oxygen-level-dependent (BOLD) response to neutral and negative stimuli to the prospective prediction of AS could increase understanding of the factors predisposing individuals to worsening AS over time.

METHODS

Forty-six participants, including 33 patients with mixed internalizing disorders, underwent fMRI scans to assess insula and amygdala activation in response to neutral and negative pictures. AS was measured via self-report at baseline (Time 1) and approximately 1.5 years later (Time 2). We assessed whether BOLD to negative and/or neutral pictures at baseline predicted changes in AS over time.

RESULTS

Greater insula and amygdala activation in response to neutral pictures at baseline predicted higher AS 1.5 years later, controlling for initial levels of AS and BOLD response to negative pictures. Cross-sectionally, greater insula and amygdala activation in response to negative pictures relative to neutral pictures was associated with higher Time 1 AS.

CONCLUSIONS

Heightened AMYG-INS BOLD to benign stimuli precedes and may place individuals at risk for increased AS. Once established, heightened AS appears to be characterized by a neural pattern that is more often-observed in the internalizing disorders - i.e., exaggerated response to negative (> neutral) stimuli.

Human high-order thalamic nuclei gate conscious perception through the thalamofrontal loop

Human high-order thalamic nuclei activity is known to closely correlate with conscious states. However, it is not clear how those thalamic nuclei and thalamocortical interactions directly contribute to the transient process of human conscious perception. We simultaneously recorded stereoelectroencephalography data from the thalamic nuclei and prefrontal cortex (PFC), while patients with implanted electrodes performed a visual consciousness task. Compared with the ventral nuclei and PFC, the intralaminar and medial nuclei presented earlier and stronger consciousness-related activity. Transient thalamofrontal neural synchrony and cross-frequency coupling were both driven by the θ phase of the intralaminar and medial nuclei during conscious perception. The intralaminar and medial thalamic nuclei thus play a gate role to drive the activity of the PFC during the emergence of conscious perception.

Modulation of thermal perception by VR-based visual stimulation to the embodied virtual body

Visual stimulation to the embodied virtual body could modulate human perception, however the associated neurophysiological mechanisms have not elucidated yet. The present study aimed to reveal the underlying neurophysiological mechanisms from a neurophysiological viewpoint. Fifteen healthy participants were subjected to three visual conditions (i.e., fire, water, and non-visual effect conditions) and psychological pain stimulation (thermal grill stimulation). Oscillatory neural activities during stimulation were measured with electroencephalogram. The association between accessory visual stimulation applied to the embodied virtual body, induced by virtual reality, and perception was examined through neuronal oscillatory analysis using electroencephalogram data. Regression analysis was performed to obtain data on brain regions contributing to sensory modulation with body illusion. The results of subjective measures under the fire and water conditions showed that thermal perception were modulated by a visual stimulus to the virtual hand. Furthermore, we found that the insula was commonly associated with thermal perception under the fire and water conditions. This result indicate that the insula may control sensory information as a gatekeeper as well as facilitate the access to human attention and cognition as a hub, suggesting the influence on perception and cognition.

Exploring the neural mechanisms underlying cooperation and competition behavior: Insights from stereo-electroencephalography hyperscanning

Cooperation and competition are essential social behaviors in human society. This study utilized hyperscanning and stereo-electroencephalography (SEEG) to investigate intra- and inter-brain neural dynamics underlying these behaviors within the insula and inferior frontal gyrus (IFG), regions critical for executive function and mentalizing. We found distinct high-gamma responses and connectivity patterns, with a stronger influence from IFG to insula during competition and more balanced interactions during cooperation. Inter-brain synchronization shows significantly higher insula gamma synchronization during competition and higher IFG gamma synchronization during cooperation. Cross-frequency coupling suggests that these gamma synchronizations result from intra- and inter-brain interactions. Competition stems from intra-brain alpha-gamma coupling from IFG to insula and inter-brain IFG alpha synchronization, while cooperation is driven by intra-brain beta-gamma coupling from insula to IFG and inter-brain insula beta synchronization. Our findings provide insights into the neural basis of cooperation and competition, highlighting the roles of both insula and IFG.

Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks - ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this 'tri-partite' view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low-dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes that interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans

Consciousness requires a dynamic balance of integration and segregation in brain networks. We report an fMRI-based metric, the integration-segregation difference (ISD), which captures two key network properties: network efficiency (integration) and clustering (segregation). With this metric, we quantify brain state transitions from conscious wakefulness to unresponsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift towards the segregation of brain networks during anesthesia. A common unimodal-transmodal sequence of disintegration and reintegration occurs in brain networks during, respectively, loss and return of responsiveness. Machine learning models using integration and segregation data accurately identify awake vs. unresponsive states and their transitions. Metastability (dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (diversity of neural activity) is more closely linked with segregation. A parallel analysis of sleep states produces similar findings. Our results demonstrate that the ISD reliably indexes states of consciousness.

Neural Correlates of Psychedelic, Sleep, and Sedated States Support Global Theories of Consciousness

Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.

When sensory input meets spontaneous brain activity

A recent study by Wu, Podvalny, and colleagues investigated how ongoing spontaneous brain activity interacts with sensory input and shapes conscious perception. It reports diverse effects of prestimulus activity in several key networks, revealing new roles of the prefrontal cortex and the default mode network in perception and consciousness.

Propofol disrupts the functional core-matrix architecture of the thalamus in humans

Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. Here we show a significant shift in this geometry during deep sedation, marked by a transmodal-deficient geometry. This alteration is closely linked to the spatial variations in the matrix cell composition within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis

Accurate and generalizable classification of brain states is essential for understanding their neural underpinnings and improving clinical diagnostics. Traditionally, functional connectivity patterns and graph-theoretic metrics have been utilized. However, cortical gradient features, which reflect global brain organization, offer a complementary approach. We hypothesized that a machine learning model integrating these three feature sets would effectively discriminate between baseline and atypical brain states across a wide spectrum of conditions, even though the underlying neural mechanisms vary. To test this, we extracted features from brain states associated with three meta-conditions including unconsciousness (NREM2 sleep, propofol deep sedation, and propofol general anesthesia), psychedelic states induced by hallucinogens (subanesthetic ketamine, lysergic acid diethylamide, and nitrous oxide), and neuropsychiatric disorders (attention-deficit hyperactivity disorder, bipolar disorder, and schizophrenia). We used support vector machine with nested cross-validation to construct our models. The soft voting ensemble model marked the average balanced accuracy (average of specificity and sensitivity) of 79% (62-98% across all conditions), outperforming individual base models (70-76%). Notably, our models exhibited varying degrees of transferability across different datasets, with performance being dependent on the specific brain states and feature sets used. Feature importance analysis across meta-conditions suggests that the underlying neural mechanisms vary significantly, necessitating tailored approaches for accurate classification of specific brain states. This finding underscores the value of our feature-integrated ensemble models, which leverage the strengths of multiple feature types to achieve robust performance across a broader range of brain states. While our approach offers valuable insights into the neural signatures of different brain states, future work is needed to develop and validate even more generalizable models that can accurately classify brain states across a wider array of conditions.

Network mechanisms of ongoing brain activity's influence on conscious visual perception

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

Non-additive effects of electrical stimulation of the dorsolateral prefrontal cortex and the vestibular system on muscle sympathetic nerve activity in humans

Sinusoidal galvanic vestibular stimulation (sGVS) induces robust modulation of muscle sympathetic nerve activity (MSNA) alongside perceptions of side-to-side movement, sometimes with an accompanying feeling of nausea. We recently showed that transcranial alternating current stimulation (tACS) of the dorsolateral prefrontal cortex (dlPFC) also modulates MSNA, but does not generate any perceptions. Here, we tested the hypothesis that when the two stimuli are given concurrently, the modulation of MSNA would be additive. MSNA was recorded from 11 awake participants via a tungsten microelectrode inserted percutaneously into the right common peroneal nerve at the fibular head. Sinusoidal stimuli (± 2 mA, 0.08 Hz, 100 cycles) were applied in randomised order as follows: (i) tACS of the dlPFC at electroencephalogram (EEG) site F4 and referenced to the nasion; (ii) bilateral sGVS applied to the vestibular apparatuses via the mastoid processes; and (iii) tACS and sGVS together. Previously obtained data from 12 participants supplemented the data for stimulation protocols (i) and (ii). Cross-correlation analysis revealed that each stimulation protocol caused significant modulation of MSNA (modulation index (paired data): 35.2 ± 19.4% for sGVS; 27.8 ± 15.2% for tACS), but there were no additive effects when tACS and sGVS were delivered concurrently (32.1 ± 18.5%). This implies that the vestibulosympathetic reflexes are attenuated with concurrent dlPFC stimulation. These results suggest that the dlPFC is capable of blocking the processing of vestibular inputs through the brainstem and, hence, the generation of vestibulosympathetic reflexes.

Stimulant medications in children with ADHD normalize the structure of brain regions associated with attention and reward

Children with ADHD show abnormal brain function and structure. Neuroimaging studies found that stimulant medications may improve brain structural abnormalities in children with ADHD. However, prior studies on this topic were conducted with relatively small sample sizes and wide age ranges and showed inconsistent results. In this cross-sectional study, we employed latent class analysis and linear mixed-effects models to estimate the impact of stimulant medications using demographic, clinical measures, and brain structure in a large and diverse sample of children aged 9-11 from the Adolescent Brain and Cognitive Development Study. We studied 273 children with low ADHD symptoms and received stimulant medication (Stim Low-ADHD), 1002 children with high ADHD symptoms and received no medications (No-Med ADHD), and 5378 typically developing controls (TDC). After controlling for the covariates, compared to Stim Low-ADHD and TDC, No-Med ADHD showed lower cortical thickness in the right insula (INS, d = 0.340, PFDR = 0.003) and subcortical volume in the left nucleus accumbens (NAc, d = 0.371, PFDR = 0.003), indicating that high ADHD symptoms were associated with structural abnormalities in these brain regions. In addition, there was no difference in brain structural measures between Stim Low-ADHD and TDC children, suggesting that the stimulant effects improved both ADHD symptoms and ADHD-associated brain structural abnormalities. These findings together suggested that children with ADHD appear to have structural abnormalities in brain regions associated with saliency and reward processing, and treatment with stimulant medications not only improve the ADHD symptoms but also normalized these brain structural abnormalities.

Anesthesia and the neurobiology of consciousness

In the 19th century, the discovery of general anesthesia revolutionized medical care. In the 21st century, anesthetics have become indispensable tools to study consciousness. Here, I review key aspects of the relationship between anesthesia and the neurobiology of consciousness, including interfaces of sleep and anesthetic mechanisms, anesthesia and primary sensory processing, the effects of anesthetics on large-scale functional brain networks, and mechanisms of arousal from anesthesia. I discuss the implications of the data derived from the anesthetized state for the science of consciousness and then conclude with outstanding questions, reflections, and future directions.

Evolutionarily conserved neural responses to affective touch in monkeys transcend consciousness and change with age

Affective touch-a slow, gentle, and pleasant form of touch-activates a different neural network than which is activated during discriminative touch in humans. Affective touch perception is enabled by specialized low-threshold mechanoreceptors in the skin with unmyelinated fibers called C tactile (CT) afferents. These CT afferents are conserved across mammalian species, including macaque monkeys. However, it is unknown whether the neural representation of affective touch is the same across species and whether affective touch's capacity to activate the hubs of the brain that compute socioaffective information requires conscious perception. Here, we used functional MRI to assess the preferential activation of neural hubs by slow (affective) vs. fast (discriminative) touch in anesthetized rhesus monkeys (Macaca mulatta). The insula, anterior cingulate cortex (ACC), amygdala, and secondary somatosensory cortex were all significantly more active during slow touch relative to fast touch, suggesting homologous activation of the interoceptive-allostatic network across primate species during affective touch. Further, we found that neural responses to affective vs. discriminative touch in the insula and ACC (the primary cortical hubs for interoceptive processing) changed significantly with age. Insula and ACC in younger animals differentiated between slow and fast touch, while activity was comparable between conditions for aged monkeys (equivalent to >70 y in humans). These results, together with prior studies establishing conserved peripheral nervous system mechanisms of affective touch transduction, suggest that neural responses to affective touch are evolutionarily conserved in monkeys, significantly impacted in old age, and do not necessitate conscious experience of touch.

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep

Consciousness requires a dynamic balance of integration and segregation in functional brain networks. An optimal integration-segregation balance depends on two key aspects of functional connectivity: global efficiency (i.e., integration) and clustering (i.e., segregation). We developed a new fMRI-based measure, termed the integration-segregation difference (ISD), which captures both aspects. We used this metric to quantify changes in brain state from conscious wakefulness to loss of responsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift to segregation in both whole brain and all brain subnetworks during anesthesia. Moreover, brain networks displayed similar sequences of disintegration and subsequent reintegration during, respectively, loss and return of responsiveness. Random forest machine learning models, trained with the integration and segregation of brain networks, identified the awake vs. unresponsive states and their transitions with accuracy up to 93%. We found that metastability (i.e., the dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (i.e., diversity and intricacy of neural activity) is more closely linked with segregation. The analysis of a sleep dataset revealed similar findings. Our results demonstrate that the integration-segregation balance is a useful index that can differentiate among various conscious and unconscious states.

The subcortical default mode network and Alzheimer's disease: a systematic review and meta-analysis

The default mode network is a central cortical brain network suggested to play a major role in several disorders and to be particularly vulnerable to the neuropathological hallmarks of Alzheimer's disease. Subcortical involvement in the default mode network and its alteration in Alzheimer's disease remains largely unknown. We performed a systematic review, meta-analysis and empirical validation of the subcortical default mode network in healthy adults, combined with a systematic review, meta-analysis and network analysis of the involvement of subcortical default mode areas in Alzheimer's disease. Our results show that, besides the well-known cortical default mode network brain regions, the default mode network consistently includes subcortical regions, namely the thalamus, lobule and vermis IX and right Crus I/II of the cerebellum and the amygdala. Network analysis also suggests the involvement of the caudate nucleus. In Alzheimer's disease, we observed a left-lateralized cluster of decrease in functional connectivity which covered the medial temporal lobe and amygdala and showed overlap with the default mode network in a portion covering parts of the left anterior hippocampus and left amygdala. We also found an increase in functional connectivity in the right anterior insula. These results confirm the consistency of subcortical contributions to the default mode network in healthy adults and highlight the relevance of the subcortical default mode network alteration in Alzheimer's disease.

Electroencephalographic signatures of consciousness: uncovering the fake news

Amongst electroencephalographic markers of anaesthetic-induced unresponsiveness, those that estimate loss of frontoparietal functional connectivity detect loss of sensory perceptual connection with the outside world, rather than full phenomenological unconsciousness. This transition to unconsciousness is manifest as further incremental changes in indices of electroencephalographic complexity.

Towards the neurobiology of insomnia: A systematic review of neuroimaging studies

Insomnia disorder signifies a major public health concern. The development of neuroimaging techniques has permitted to investigate brain mechanisms at a structural and functional level. The present systematic review aims at shedding light on functional, structural, and metabolic substrates of insomnia disorder by integrating the available published neuroimaging data. The databases PubMed, PsycARTICLES, PsycINFO, CINAHL and Web of Science were searched for case-control studies comparing neuroimaging data from insomnia patients and healthy controls. 85 articles were judged as eligible. For every observed finding of each study, the effect size was calculated from standardised mean differences, statistic parameters and figures, showing a marked heterogeneity that precluded a comprehensive quantitative analysis. From a qualitative point of view, considering the findings of significant group differences in the reported regions across the articles, this review highlights the major involvement of the anterior cingulate cortex, thalamus, insula, precuneus and middle frontal gyrus, thus supporting some central themes in the debate on the neurobiology of and offering interesting insights into the psychophysiology of sleep in this disorder.

Propofol Disrupts the Functional Core-Matrix Architecture of the Thalamus in Humans

Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. We observed a significant shift in this geometry during unconsciousness, marked by the dominance of unimodal over transmodal geometry. This alteration was closely linked to the spatial variations in the density of matrix cells within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Central autonomic network and early prognosis in patients with disorders of consciousness

The central autonomic network (CAN) plays a crucial role in modulating the autonomic nervous system. Heart rate variability (HRV) is a valuable marker for assessing CAN function in disorders of consciousness (DOC) patients. We used HRV analysis for early prognosis in 58 DOC patients enrolled within ten days of hospitalization. They underwent a five-minute electrocardiogram during baseline and acoustic/visual stimulation. The coma recovery scale-revised (CRS-R) was used to define the patient's consciousness level and categorize the good/bad outcome at three months. The high-frequency Power Spectrum Density and the standard deviation of normal-to-normal peaks in baseline, the sample entropy during the stimulation, and the time from injury features were used in the support vector machine analysis (SVM) for outcome prediction. The SVM predicted the patients' outcome with an accuracy of 96% in the training test and 100% in the validation test, underscoring its potential to provide crucial clinical information about prognosis.

Top-down control of vestibular inputs by the dorsolateral prefrontal cortex

The vestibular apparatus provides spatial information on the position of the head in space and with respect to gravity. Low-frequency sinusoidal galvanic vestibular stimulation (sGVS), a means of selectively changing the firing of vestibular afferents, induces a frequency-dependent perception of sway and, in some individuals, induces nausea. Given that vestibular afferents project to the insular cortex-which forms part of the vestibular cortex-and that the insula receives inputs from the dorsolateral prefrontal cortex (dlPFC), we tested the hypothesis that electrical stimulation of the dlPFC can modulate vestibular inputs. Sinusoidal electrical stimulation (± 2 mA, 0.08 Hz, 100 cycles) was delivered via surface electrodes over (1) the mastoid processes alone (sGVS), (2) electroencephalogram (EEG) site F4 (right dlPFC) and the nasion or (3) to each site concurrently (sGVS + dlPFC) in 23 participants. The same stimulation protocol was used in a separate study to investigate EEG site F3 (left dlPFC) instead of F4 in 13 participants. During sGVS, all participants reported perceptions of sway and 13 participants also reported nausea, neither sensation of which occurred as a result of dlPFC stimulation. Interestingly, when sGVS and dlPFC stimulations were delivered concurrently, vestibular perceptions and sensations of nausea were almost completely abolished. We conclude that the dlPFC provides top-down control of vestibular inputs and further suggests that dlPFC stimulation may provide a novel means of controlling nausea.

Large-scale momentary brain co-activation patterns are associated with hyperalgesia and mediate focal neurochemistry and cross-network functional connectivity in fibromyalgia

ABSTRACT

Fibromyalgia has been characterized by augmented cross-network functional communication between the brain's sensorimotor, default mode, and attentional (salience/ventral and dorsal) networks. However, the underlying mechanisms of these aberrant communication patterns are unknown. In this study, we sought to understand large-scale topographic patterns at instantaneous timepoints, known as co-activation patterns (CAPs). We found that a sustained pressure pain challenge temporally modulated the occurrence of CAPs. Using proton magnetic resonance spectroscopy, we found that greater basal excitatory over inhibitory neurotransmitter levels within the anterior insula orchestrated higher cross-network connectivity between the anterior insula and the default mode network through lower occurrence of a CAP encompassing the attentional networks during sustained pain. Moreover, we found that hyperalgesia in fibromyalgia was mediated through increased occurrence of a CAP encompassing the sensorimotor network during sustained pain. In conclusion, this study elucidates the role of momentary large-scale topographic brain patterns in shaping noxious information in patients with fibromyalgia, while laying the groundwork for using precise spatiotemporal dynamics of the brain for the potential development of therapeutics.

Theoretical Neurobiology of Consciousness Applied to Human Cerebral Organoids

Organoids and specifically human cerebral organoids (HCOs) are one of the most relevant novelties in the field of biomedical research. Grown either from embryonic or induced pluripotent stem cells, HCOs can be used as in vitro three-dimensional models, mimicking the developmental process and organization of the developing human brain. Based on that, and despite their current limitations, it cannot be assumed that they will never at any stage of development manifest some rudimentary form of consciousness. In the absence of behavioral indicators of consciousness, the theoretical neurobiology of consciousness being applied to unresponsive brain-injured patients can be considered with respect to HCOs. In clinical neurology, it is difficult to discern a capacity for consciousness in unresponsive brain-injured patients who provide no behavioral indicators of consciousness. In such scenarios, a validated neurobiological theory of consciousness, which tells us what the neural mechanisms of consciousness are, could be used to identify a capacity for consciousness. Like the unresponsive patients that provide a diagnostic difficulty for neurologists, HCOs provide no behavioral indicators of consciousness. Therefore, this article discusses how three prominent neurobiological theories of consciousness apply to human cerebral organoids. From the perspective of the Temporal Circuit Hypothesis, the Global Neuronal Workspace Theory, and the Integrated Information Theory, we discuss what neuronal structures and functions might indicate that cerebral organoids have a neurobiological capacity to be conscious.

Analogous cortical reorganization accompanies entry into states of reduced consciousness during anesthesia and sleep

Theories of consciousness suggest that brain mechanisms underlying transitions into and out of unconsciousness are conserved no matter the context or precipitating conditions. We compared signatures of these mechanisms using intracranial electroencephalography in neurosurgical patients during propofol anesthesia and overnight sleep and found strikingly similar reorganization of human cortical networks. We computed the "effective dimensionality" of the normalized resting state functional connectivity matrix to quantify network complexity. Effective dimensionality decreased during stages of reduced consciousness (anesthesia unresponsiveness, N2 and N3 sleep). These changes were not region-specific, suggesting global network reorganization. When connectivity data were embedded into a low-dimensional space in which proximity represents functional similarity, we observed greater distances between brain regions during stages of reduced consciousness, and individual recording sites became closer to their nearest neighbors. These changes corresponded to decreased differentiation and functional integration and correlated with decreases in effective dimensionality. This network reorganization constitutes a neural signature of states of reduced consciousness that is common to anesthesia and sleep. These results establish a framework for understanding the neural correlates of consciousness and for practical evaluation of loss and recovery of consciousness.

Temporospatial Nestedness in Consciousness: An Updated Perspective on the Temporospatial Theory of Consciousness

Time and space are fundamental elements that permeate the fabric of nature, and their significance in relation to neural activity and consciousness remains a compelling yet unexplored area of research. The Temporospatial Theory of Consciousness (TTC) provides a framework that links time, space, neural activity, and consciousness, shedding light on the intricate relationships among these dimensions. In this review, I revisit the fundamental concepts and mechanisms proposed by the TTC, with a particular focus on the central concept of temporospatial nestedness. I propose an extension of temporospatial nestedness by incorporating the nested relationship between the temporal circuit and functional geometry of the brain. To further unravel the complexities of temporospatial nestedness, future research directions should emphasize the characterization of functional geometry and the temporal circuit across multiple spatial and temporal scales. Investigating the links between these scales will yield a more comprehensive understanding of how spatial organization and temporal dynamics contribute to conscious states. This integrative approach holds the potential to uncover novel insights into the neural basis of consciousness and reshape our understanding of the world-brain dynamic.

Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia

Brain network models of cognitive control are central to advancing our understanding of psychopathology and cognitive dysfunction in schizophrenia. This review examines the role of large-scale brain organization in schizophrenia, with a particular focus on a triple-network model of cognitive control and its role in aberrant salience processing. First, we provide an overview of the triple network involving the salience, frontoparietal, and default mode networks and highlight the central role of the insula-anchored salience network in the aberrant mapping of salient external and internal events in schizophrenia. We summarize the extensive evidence that has emerged from structural, neurochemical, and functional brain imaging studies for aberrancies in these networks and their dynamic temporal interactions in schizophrenia. Next, we consider the hypothesis that atypical striatal dopamine release results in misattribution of salience to irrelevant external stimuli and self-referential mental events. We propose an integrated triple-network salience-based model incorporating striatal dysfunction and sensitivity to perceptual and cognitive prediction errors in the insula node of the salience network and postulate that dysregulated dopamine modulation of salience network-centered processes contributes to the core clinical phenotype of schizophrenia. Thus, a powerful paradigm to characterize the neurobiology of schizophrenia emerges when we combine conceptual models of salience with large-scale cognitive control networks in a unified manner. We conclude by discussing potential therapeutic leads on restoring brain network dysfunction in schizophrenia.

Post-injury pain and behaviour: a control theory perspective

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

Spatiotemporal neural dynamics of object recognition under uncertainty in humans

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Bistability of prefrontal states gates access to consciousness

Access of sensory information to consciousness has been linked to the ignition of content-specific representations in association cortices. How does ignition interact with intrinsic cortical state fluctuations to give rise to conscious perception? We addressed this question in the prefrontal cortex (PFC) by combining multi-electrode recordings with a binocular rivalry (BR) paradigm inducing spontaneously driven changes in the content of consciousness, inferred from the reflexive optokinetic nystagmus (OKN) pattern. We find that fluctuations between low-frequency (LF, 1-9 Hz) and beta (∼20-40 Hz) local field potentials (LFPs) reflect competition between spontaneous updates and stability of conscious contents, respectively. Both LF and beta events were locally modulated. The phase of the former locked differentially to the competing populations just before a spontaneous transition while the latter synchronized the neuronal ensemble coding the consciously perceived content. These results suggest that prefrontal state fluctuations gate conscious perception by mediating internal states that facilitate perceptual update and stability.

Observing cognitive processes in time through functional MRI model comparison

The temporal specificity of functional magnetic resonance imaging (fMRI) is limited by a sluggish and locally variable hemodynamic response trailing the neural activity by seconds. Here, we demonstrate for an attention capture paradigm that it is, never the less, possible to extract information about the relative timing of regional brain activity during cognitive processes on the scale of 100 ms by comparing alternative signal models representing early versus late activation. We demonstrate that model selection is not driven by confounding regional differences in hemodynamic delay. We show, including replication, that the activity in the dorsal anterior insula is an early signal predictive of behavioral performance, while amygdala and ventral anterior insula signals are not. This specific finding provides new insights into how the brain assigns salience to stimuli and emphasizes the role of the dorsal anterior insula in this context. The general analytic approach, named "Cognitive Timing through Model Comparison" (CTMC), offers an exciting and novel method to identify functional brain subunits and their causal interactions.

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

Functional geometry of the cortex encodes dimensions of consciousness

Consciousness is a multidimensional phenomenon, but key dimensions such as awareness and wakefulness have been described conceptually rather than neurobiologically. We hypothesize that dimensions of consciousness are encoded in multiple neurofunctional dimensions of the brain. We analyze cortical gradients, which are continua of the brain's overarching functional geometry, to characterize these neurofunctional dimensions. We demonstrate that disruptions of human consciousness - due to pharmacological, neuropathological, or psychiatric causes - are associated with a degradation of one or more of the major cortical gradients depending on the state. Network-specific reconfigurations within the multidimensional cortical gradient space are associated with behavioral unresponsiveness of various etiologies, and these spatial reconfigurations correlate with a temporal disruption of structured transitions of dynamic brain states. In this work, we therefore provide a unifying neurofunctional framework for multiple dimensions of human consciousness in both health and disease.

A role for the claustrum in cognitive control

Early hypotheses of claustrum function were fueled by neuroanatomical data and yielded suggestions that the claustrum is involved in processes ranging from salience detection to multisensory integration for perceptual binding. While these hypotheses spurred useful investigations, incompatibilities inherent in these views must be reconciled to further conceptualize claustrum function amid a wealth of new data. Here, we review the varied models of claustrum function and synthesize them with developments in the field to produce a novel functional model: network instantiation in cognitive control (NICC). This model proposes that frontal cortices direct the claustrum to flexibly instantiate cortical networks to subserve cognitive control. We present literature support for this model and provide testable predictions arising from this conceptual framework.

Prefrontal cortex as a key node in arousal circuitry

The role of the prefrontal cortex (PFC) in the mechanism of consciousness is a matter of active debate. Most theoretical and empirical investigations have focused on whether the PFC is critical for the content of consciousness (i.e., the qualitative aspects of conscious experience). However, there is emerging evidence that, in addition to its well-established roles in cognition, the PFC is a key regulator of the level of consciousness (i.e., the global state of arousal). In this opinion article we review recent data supporting the hypothesis that the medial PFC is a critical node in arousal-promoting networks.

Adolescents with a concussion have altered brain network functional connectivity one month following injury when compared to adolescents with orthopedic injuries

Concussion is a mild traumatic brain injury (mTBI) with increasing prevalence among children and adolescents. Functional connectivity (FC) within and between the default mode network (DMN), central executive network (CEN) and salience network (SN) has been shown to be altered post-concussion. Few studies have investigated connectivity within and between these 3 networks following a pediatric concussion. The present study explored whether within and between-network FC differs between a pediatric concussion and orthopedic injury (OI) group aged 10-18. Participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan at 4 weeks post-injury. One-way ANCOVA analyses were conducted between groups with the seed-based FC of the 3 networks. A total of 55 concussion and 27 OI participants were included in the analyses. Increased within-network FC of the CEN and decreased between-network FC of the DMN-CEN was found in the concussion group when compared to the OI group. Secondary analyses using spherical SN regions of interest revealed increased within-network FC of the SN and increased between-network FC of the DMN-SN and CEN-SN in the concussion group when compared to the OI group. This study identified differential connectivity patterns following a pediatric concussion as compared to an OI 4 weeks post-injury. These differences indicate potential adaptive brain mechanisms that may provide insight into recovery trajectories and appropriate timing of treatment within the first month following a concussion.

Increased insula activity precedes the formation of subjective illusory Gestalt

The constructive nature of human perception sometimes leads us to perceiving rather complex impressions from simple sensory input: for example, recognizing animal contours in cloud formations or seeing living creatures in shadows of objects. A special type of bistable stimuli gives us a rare opportunity to study the neural mechanisms behind this process. Such stimuli can be visually interpreted either as simple or as more complex illusory content on the basis of the same sensory input. Previous studies demonstrated increased activity in the superior parietal cortex during the perception of an illusory Gestalt impression compared to a simpler interpretation. Here, we examined the role of slow fluctuations of resting-state fMRI activity in shaping the subsequent illusory interpretation by investigating activity related to the illusory Gestalt not only during, but also prior to its perception. We presented 31 participants with a bistable motion stimulus, which can be perceived either as four moving dot pairs (local) or two moving illusory squares (global). fMRI was used to measure brain activity in a slow event-related design. We observed stronger IPS and putamen responses to the stimulus when participants perceived the global interpretation compared to the local, confirming the findings of previous studies. Most importantly, we also observed that the global stimulus interpretation was preceded by an increased activity of the bilateral dorsal insula, which is known to process saliency and gate information for conscious access. Our data suggest an important role of the dorsal insula in shaping complex illusory interpretations of the sensory input.

Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.

The altered state of consciousness induced by Δ9-THC

Altered states of consciousness (ASC) provide an opportunity for researchers to study the neurophysiological basis of changes in phenomenal experience. Δ9-tetrahydrocannabinol (THC) is the primary psychoactive constituent of cannabis, however whether the effects of THC include ASC features that are shared with other ASC induction mechanisms, such as classical psychedelics, has not been systematically addressed. We used survey (11D-ASC; State Mindfulness), self-report, and natural language processing (NLP) to assess 7.5 and 15 mg oral THC, relative to placebo, in 25 healthy, infrequent cannabis users. THC dose-dependently increased measures of ASC including Insightfulness, and increased ratings of mindfulness and mind-wandering. THC also increased language entropy as previously reported for LSD. Future studies may seek to determine whether reports of increased mindfulness or insight after THC are primarily representative of a psychotomimetic state (i.e., delusional thinking) or conversely, reflect an enhancement of conscious awareness that may be validated empirically.

Speech sound categorization: The contribution of non-auditory and auditory cortical regions

Which processes in the human brain lead to the categorical perception of speech sounds? Investigation of this question is hampered by the fact that categorical speech perception is normally confounded by acoustic differences in the stimulus. By using ambiguous sounds, however, it is possible to dissociate acoustic from perceptual stimulus representations. Twenty-seven normally hearing individuals took part in an fMRI study in which they were presented with an ambiguous syllable (intermediate between /da/ and /ga/) in one ear and with disambiguating acoustic feature (third formant, F3) in the other ear. Multi-voxel pattern searchlight analysis was used to identify brain areas that consistently differentiated between response patterns associated with different syllable reports. By comparing responses to different stimuli with identical syllable reports and identical stimuli with different syllable reports, we disambiguated whether these regions primarily differentiated the acoustics of the stimuli or the syllable report. We found that BOLD activity patterns in left perisylvian regions (STG, SMG), left inferior frontal regions (vMC, IFG, AI), left supplementary motor cortex (SMA/pre-SMA), and right motor and somatosensory regions (M1/S1) represent listeners' syllable report irrespective of stimulus acoustics. Most of these regions are outside of what is traditionally regarded as auditory or phonological processing areas. Our results indicate that the process of speech sound categorization implicates decision-making mechanisms and auditory-motor transformations.

Subcortical control of the default mode network: Role of the basal forebrain and implications for neuropsychiatric disorders

The precise interplay between large-scale functional neural systems throughout the brain is essential for performance of cognitive processes. In this review we focus on the default mode network (DMN), one such functional network that is active during periods of quiet wakefulness and believed to be involved in introspection and planning. Abnormalities in DMN functional connectivity and activation appear across many neuropsychiatric disorders, including schizophrenia. Recent evidence suggests subcortical regions including the basal forebrain are functionally and structurally important for regulation of DMN activity. Within the basal forebrain, subregions like the ventral pallidum may influence DMN activity and the nucleus basalis of Meynert can inhibit switching between brain networks. Interactions between DMN and other functional networks including the medial frontoparietal network (default), lateral frontoparietal network (control), midcingulo-insular network (salience), and dorsal frontoparietal network (attention) are also discussed in the context of neuropsychiatric disorders. Several subtypes of basal forebrain neurons have been identified including basal forebrain parvalbumin-containing or somatostatin-containing neurons which can regulate cortical gamma band oscillations and DMN-like behaviors, and basal forebrain cholinergic neurons which might gate access to sensory information during reinforcement learning. In this review, we explore this evidence, discuss the clinical implications on neuropsychiatric disorders, and compare neuroanatomy in the human vs rodent DMN. Finally, we address technological advancements which could help provide a more complete understanding of modulation of DMN function and describe newly identified BF therapeutic targets that could potentially help restore DMN-associated functional deficits in patients with a variety of neuropsychiatric disorders.

Altered centromedial amygdala functional connectivity in adults is associated with childhood emotional abuse and predicts levels of depression and anxiety

BACKGROUND

Childhood maltreatment is significantly associated with greater occurrence of mental disorders in adulthood such as depression and anxiety. As a key node of the limbic system, the amygdala is engaged in emotional processing and regulation and is dysfunctional in many psychiatric disorders. The present study aimed at exploring the association between childhood maltreatment and amygdala-based functional networks and their potential contributions to depression and anxiety.

METHODS

Totally 90 Chinese healthy volunteers participated in a resting-state fMRI experiment. Levels of childhood maltreatment experience were assessed using the Childhood Trauma Questionnaire (CTQ-SF) as well as levels of depression and anxiety. Associations between CTQ-SF scores and bilateral amygdala gray matter volume and resting-state functional connectivity (RSFC) of the amygdala and selected regions of interest were analyzed using multiple regression analyses with sex and age as covariates. A subsequent moderation analysis was performed to identify whether associations were predictive of depression and anxiety levels.

RESULTS

Childhood maltreatment was significantly negatively associated with RSFC between left amygdala and anterior insula. Further sub-region analyses revealed that this negative association only occurred for the left centromedial amygdala subregion, which subsequently moderated the relationship between levels of childhood emotional abuse and depression / anxiety.

LIMITATIONS

No psychiatric patients were involved and specific neural associations with different childhood maltreatment subtypes need to be examined in future studies.

CONCLUSION

The present findings provide evidence for altered RSFC of centromedial amygdala and the anterior insula associated with childhood maltreatment and which moderate levels of depression and anxiety in adulthood.

Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data

Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 - 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.

Interconnected sub-networks of the macaque monkey gustatory connectome

Macroscopic taste processing connectivity was investigated using functional magnetic resonance imaging during the presentation of sour, salty, and sweet tastants in anesthetized macaque monkeys. This examination of taste processing affords the opportunity to study the interactions between sensory regions, central integrators, and effector areas. Here, 58 brain regions associated with gustatory processing in primates were aggregated, collectively forming the gustatory connectome. Regional regression coefficients (or β-series) obtained during taste stimulation were correlated to infer functional connectivity. This connectivity was then evaluated by assessing its laterality, modularity and centrality. Our results indicate significant correlations between same region pairs across hemispheres in a bilaterally interconnected scheme for taste processing throughout the gustatory connectome. Using unbiased community detection, three bilateral sub-networks were detected within the graph of the connectome. This analysis revealed clustering of 16 medial cortical structures, 24 lateral structures, and 18 subcortical structures. Across the three sub-networks, a similar pattern was observed in the differential processing of taste qualities. In all cases, the amplitude of the response was greatest for sweet, but the network connectivity was strongest for sour and salty tastants. The importance of each region in taste processing was computed using node centrality measures within the connectome graph, showing centrality to be correlated across hemispheres and, to a smaller extent, region volume. Connectome hubs exhibited varying degrees of centrality with a prominent leftward increase in insular cortex centrality. Taken together, these criteria illustrate quantifiable characteristics of the macaque monkey gustatory connectome and its organization as a tri-modular network, which may reflect the general medial-lateral-subcortical organization of salience and interoception processing networks.

Hallucinations: A Functional Network Model of How Sensory Representations Become Selected for Conscious Awareness in Schizophrenia

Hallucinations are conscious perception-like experiences that are a common symptom of schizophrenia spectrum disorders (SSD). Current neuroscience evidence suggests several brain areas are involved in the generation of hallucinations including the sensory cortex, insula, putamen, and hippocampus. But how does activity in these regions give rise to aberrant conscious perceptions that seemingly invade ongoing conscious experience? Most existing models assume that sensory representations are sometimes spontaneously activated in the brain, and that these spontaneous activations somehow play a causal role in the generation of hallucinations. Yet, it remains unclear how these representations become selected for conscious processing. No existing theory of hallucinations has specified such a “selection mechanism.” Global Workspace (GW) theorists argue that the brain’s interconnected processors select relevant piece(s) of information for broadcasting to other brain processors, rendering the information accessible to consciousness; this process known as “ignition” is associated with synchronized activity across distributed cortical and subcortical brain regions. Yet, it remains unclear how certain information and representations become selected for conscious processing. While GW theorists maintain that attention plays an important role, they have not delineated a formal “selection mechanism.” This paper specifies a selection mechanism based upon two central hypotheses: (1) a functional network called the “salience network” plays a critical role in selecting sensory representations for conscious broadcast to the GW in normal (healthy) perception; (2) sensory representations become abnormally selected for conscious broadcast to the GW (instead of being filtered out of consciousness) in individuals with SSD that experience hallucinations.

Distinct neural-behavioral correspondence within face processing and attention networks for the composite face effect

The composite face effect (CFE) is recognized as a hallmark for holistic face processing, but our knowledge remains sparse about its cognitive and neural loci. Using functional magnetic resonance imaging with independent localizer and complete composite face task, we here investigated its neural-behavioral correspondence within face processing and attention networks. Complementing classical comparisons, we adopted a dimensional reduction approach to explore the core cognitive constructs of the behavioral CFE measurement. Our univariate analyses found an alignment effect in regions associated with both the extended face processing network and attention networks. Further representational similarity analyses based on the Euclidian distances among all experimental conditions were used to identify cortical regions with reliable neural-behavioral correspondences. Multidimensional scaling and hierarchical clustering analyses for neural-behavioral correspondence data revealed two principal components underlying the behavioral CFE effect, which fit best to the neural responses in the bilateral insula and medial frontal gyrus. These findings highlight the distinct neurocognitive contributions of both face processing and attentional networks to the behavioral CFE outcome, which bridge the gaps between face recognition and attentional control models.

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.

What emotions might be like in other animals

In this My word, Joseph LeDoux explores what the emotional lives of other mammals might be like. He proposes that better understanding of the brain mechanisms of emotional consciousness in humans might shed light on the kinds of conscious capacities that might be possible in non-human primates and non-primate mammals, given the kinds of brains they possess.