Global waves synchronize the brain's functional systems with fluctuating arousal

Disentangling the Switching Behavior in Functional Connectivity Dynamics in Autism Spectrum Disorder: Insights from Developmental Cohort Analysis and Molecular-Cellular Associations

Characterizing the transition or switching behavior between multistable brain states in functional connectivity dynamics (FCD) holds promise for uncovering the underlying neuropathology of Autism Spectrum Disorder (ASD). However, whether and how switching behaviors in FCD change in patients with developmental ASD, as well as their cellular and molecular basis, remains unexplored. This study develops a region-wise FCD switching index (RFSI) to investigate the drivers of FCD. This work finds that brain regions within the salience, default mode, and frontoparietal networks serve as abnormal drivers of FCD in ASD across different developmental stages. Additionally, changes in RFSI at different developmental stages of ASD correlated with transcriptomic profiles and neurotransmitter density maps. Importantly, the abnormal RFSI identifies in humans has also been observed in genetically edited ASD monkeys. Finally, single-nucleus RNA sequencing data from patients with developmental ASD are analyzed and aberrant switching behaviors in FCD may be mediated by somatostatin-expressing interneurons and altered differentiation patterns in astrocyte State2. In conclusion, this study provides the first evidence of abnormal drivers of FCD across different stages of ASD and their associated cellular and molecular mechanisms. These findings deepen the understanding of ASD neuropathology and offer valuable insights into treatment strategies.

Autonomic physiological coupling of the global fMRI signal

The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic and peripheral physiological signals. Here we show that a major mode of these brain-body cofluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo functional magnetic resonance imaging (fMRI) data acquisition sequences, we identify widespread cofluctuations in the low-frequency range (0.01-0.1 Hz) between resting-state global fMRI signals, electroencephalogram (EEG) activity, and a host of peripheral autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body cofluctuations observed at rest are elicited by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Furthermore, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot fully explain the origin of these signals in the brain. These findings suggest that the global fMRI signal is a substantial component of the arousal response governed by the autonomic nervous system.

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals

Sustained attention (SA) is a critical cognitive ability that emerges in infancy and affects various aspects of development. Research on SA typically occurs in lab settings, which may not reflect infants' real-world experiences. Infant wearable technology can collect multimodal data in natural environments, including physiological signals for measuring SA. Here we introduce an automatic sustained attention prediction (ASAP) method that harnesses electrocardiogram (ECG) and accelerometer (Acc) signals. Data from 75 infants (6- to 36-months) were recorded during different activities, with some activities emulating those occurring in the natural environment (i.e., free play). Human coders annotated the ECG data for SA periods validated by fixation data. ASAP was trained on temporal and spectral features from the ECG and Acc signals to detect SA, performing consistently across age groups. To demonstrate ASAP's applicability, we investigated the relationship between SA and perceptual features-saliency and clutter-measured from egocentric free-play videos. Results showed that saliency in infants' and toddlers' views increased during attention periods and decreased with age for attention but not inattention. We observed no differences between ASAP attention detection and human-coded SA periods, demonstrating that ASAP effectively detects SA in infants during free play. Coupled with wearable sensors, ASAP provides unprecedented opportunities for studying infant development in real-world settings.

The human brainstem's red nucleus was upgraded to support goal-directed action

The red nucleus, a large brainstem structure, coordinates limb movement for locomotion in quadrupedal animals. In humans, its pattern of anatomical connectivity differs from that of quadrupeds, suggesting a different purpose. Here, we apply our most advanced resting-state functional connectivity based precision functional mapping in highly sampled individuals (n = 5), resting-state functional connectivity in large group-averaged datasets (combined n ~ 45,000), and task based analysis of reward, motor, and action related contrasts from group-averaged datasets (n > 1000) and meta-analyses (n > 14,000 studies) to precisely examine red nucleus function. Notably, red nucleus functional connectivity with motor-effector networks (somatomotor hand, foot, and mouth) is minimal. Instead, connectivity is strongest to the action-mode and salience networks, which are important for action/cognitive control and reward/motivated behavior. Consistent with this, the red nucleus responds to motor planning more than to actual movement, while also responding to rewards. Our results suggest the human red nucleus implements goal-directed behavior by integrating behavioral valence and action plans instead of serving a pure motor-effector function.

Increased Modulation of Low-Frequency Cardiac Rhythms on Resting-State Left Insula Alpha Oscillations in Major Depressive Disorder: Evidence from a Magnetoencephalography Study

A growing body of evidence suggests that the link between the cardiac autonomic nervous system (ANS) and the central nervous system (CNS) is crucial to the onset and development of major depressive disorder (MDD), affecting perception, cognition, and emotional processing. The bottom-up heart-brain communication pathway plays a significant role in this process. Previous studies have shown that slow-frequency oscillations of peripheral signals (e.g., respiration, stomach) can influence faster neural activities in the CNS via phase-amplitude coupling (PAC). However, the understanding of heart-brain coupling remains limited. Additionally, while MDD patients exhibit altered brain activity patterns, little is known about how heart rate variability (HRV) affects brain oscillations. Therefore, we used PAC to investigate heart-brain coupling and its association with depression. We recorded MEG and ECG data from 55 MDD patients (35 females) and 52 healthy subjects (28 females) at rest and evaluated heart-brain PAC at a broadband level. The results showed that the low-frequency component of HRV (HRV-LF) significantly modulated MEG alpha power (10 Hz) in humans. Compared with the healthy group, the MDD group exhibited more extensive heart-brain coupling cortical networks, including the pars triangularis. LF-alpha coupling was observed in the bilateral insula in both groups. Notably, results revealed a significantly increased sympathetic-dominated HRV-LF modulation effect on left insula alpha oscillations, along with increased depressive severity. These findings suggest that MDD patients may attempt to regulate their internal state through enhanced heart-brain modulation, striving to restore normal physiological and psychological balance.

Cognitive reserve linked to network-specific brain-ventricle coupling

Despite showing significant impact in cognitive preservation, the relationship between brain activity captured with functional Magnetic Resonance Imaging (fMRI) in gray matter and ventricular cerebrospinal fluid dynamics remains poorly understood. We analyzed 599 fMRI scans from 163 elderly participants at rest with varying degrees of cognitive impairment employing a unified phase coupling analysis that breaks from convention by incorporating both tissue and ventricular signal fluctuations. This whole-brain approach identified distinct brain-ventricle coupling modes that differentiate between cognitive status groups and correlate with specific cognitive abilities. Beyond the previously reported anti-phase coupling between global brain signals and ventricles-which we confirm occurs more frequently in cognitively normal controls-our analysis method uncovered additional coupling modes where signals in specific brain networks temporarily align with ventricle signals. At the cortical level, these modes reveal patterns corresponding to known resting-state networks: one overlapping with the Default Mode Network occurs significantly less frequently in Alzheimer's Disease patients, while another revealing the Frontoparietal Network correlates positively with memory scores. Our findings demonstrate that different brain-ventricle coupling modes correlate with specific cognitive domains, with particular modes predicting memory, executive function, and visuospatial abilities. The coupling between signals in brain ventricles and established resting-state networks challenges our current understanding of functional network formation, suggesting an integral link with brain fluid motion. This reconceptualization of brain dynamics through the lens of fluid-tissue interactions establishes a fundamental physical basis for cognitive preservation, suggesting that therapeutic interventions targeting these interactions may prove more effective than approaches focused solely on cellular or molecular mechanisms.

Sensory Encoding Alternates With Hippocampal Ripples across Cycles of Forebrain Spiking Cascades

The brain's response to external events depends on its internal arousal states, which are dynamically governed by neuromodulatory systems and have recently been linked to coordinated spike timing cascades in widespread brain networks. At rest, both arousal fluctuations and spiking cascades are evident throughout the forebrain and play out over multisecond time scales. Here, by analyzing large-scale neural recording data collected by the Allen Institute, it is demonstrated that these intrinsic processes persist across the mouse brain even during periods of continuous visual stimulation. In the stationary animal, each quasi-periodic cascade cycle systematically influenced 1) the efficacy of encoding in visually responsive brain areas and 2) the incidence of memory-related hippocampal ripples. During this cycle, the phase of high arousal is marked by high efficiency in visual encoding whereas the phase of low arousal is marked by the occurrence of hippocampal ripples. However, during bouts of active locomotion, this cycle is abolished and brain maintained a state of elevated visual coding efficiency, with ripples being nearly absent. It is hypothesized that the infra-slow cascade dynamics reflect an adaptive cycle of alternating exteroceptive sensory sampling and internal mnemonic function that persistently pervades the forebrain, only stopping during active exploration of the environment.

Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations

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

The brain's action-mode network

The brain is always intrinsically active, using energy at high rates while cycling through global functional modes. Awake brain modes are tied to corresponding behavioural states. During goal-directed behaviour, the brain enters an action-mode of function. In the action-mode, arousal is heightened, attention is focused externally and action plans are created, converted to goal-directed movements and continuously updated on the basis of relevant feedback, such as pain. Here, we synthesize classical and recent human and animal evidence that the action-mode of the brain is created and maintained by an action-mode network (AMN), which we had previously identified and named the cingulo-opercular network on the basis of its anatomy. We discuss how rather than continuing to name this network anatomically, annotating it functionally as controlling the action-mode of the brain increases its distinctiveness from spatially adjacent networks and accounts for the large variety of the associated functions of an AMN, such as increasing arousal, processing of instructional cues, task general initiation transients, sustained goal maintenance, action planning, sympathetic drive for controlling physiology and internal organs (connectivity to adrenal medulla), and action-relevant bottom-up signals such as physical pain, errors and viscerosensation. In the functional mode continuum of the awake brain, the AMN-generated action-mode sits opposite the default-mode for self-referential, emotional and memory processing, with the default-mode network and AMN counterbalancing each other as yin and yang.

Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity (FC) profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, this study uses a data-driven approach to model the association between FC variability and interindividual differences in intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and the relative functional distance of this area from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that, with decreasing functional affinity with high-cognition systems, vlPFC functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Evidence for domain-general arousal from semantic and neuroimaging meta-analyses reconciles opposing views on arousal

Arousal refers to changes in brain-body state underpinning motivated behavior but lacks a proper definition and taxonomy. Neuroscience and psychology textbooks offer surprisingly different views on what arousal is, from a global brain-wide modulation of neuronal activity to a multidimensional construct, with specific brain-body patterns tuned to a given situation. The huge number of scientific articles mentioning arousal (~50,000) highlights the importance of the concept but also explains why such a vast literature has never been systematically reviewed so far. Here, we leverage the tools of natural language processing to probe the nature of arousal in a data-driven, comprehensive manner. We show that arousal comes in seven varieties: cognitive, emotional, physiological, sexual, related to stress disorders, to sleep, or to sleep disorders. We then ask whether domain-general arousal exists at the cortical level, and run meta-analyses of the brain imaging literature to reveal that all varieties of arousal, except arousal in sleep disorders for lack of data, converge onto a cortical network composed of the presupplementary motor area and the left and right dorsal anterior insula. More precisely, we find that activity in dysgranular insular area 7 (Jülich atlas), the region with the highest convergence across varieties of arousal, is also specifically associated with arousal. The domain-general arousal network might trigger the reorganization of large-scale brain networks-a global mechanism-resulting in a context-specific configuration-in line with the multidimensional view. Future taxonomies of arousal refining the alignment between concepts and data should include domain-general arousal as a central component.

Developmental Variations in Recurrent Spatiotemporal Brain Propagations from Childhood to Adulthood

The brain undergoes profound structural and functional transformations from childhood to adolescence. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, characterized by heterogeneous maturation patterns across brain regions and networks. However, the maturation of the intrinsic spatiotemporal propagations of brain activity remains largely unexplored. This study aims to bridge this gap by delineating spatiotemporal propagations from childhood to early adulthood. By leveraging a recently developed approach that captures time-lag dynamic propagations, we characterized intrinsic dynamic propagations along three axes: sensory-association (S-A), 'task-positive' to default networks (TP-D), and somatomotor-visual (SM-V) networks, which progress towards adult-like brain dynamics from childhood to early adulthood. Importantly, we demonstrated that as participants mature, there is a prolonged occurrence of the S-A and TP-D propagation states, indicating that they spend more time in these states. Conversely, the prevalence of SM-V propagation states declines during development. Notably, top-down propagations along the S-A axis exhibited an age-dependent increase in occurrence, serving as a superior predictor of cognitive scores compared to bottom-up S-A propagation. These findings were replicated across two independent cohorts (N = 677 in total), emphasizing the robustness and generalizability of these findings. Our results provide new insights into the emergence of adult-like functional dynamics during youth and their role in supporting cognition.

Brain-body dysconnectivity: deficient autonomic regulation of cortical function in first-episode schizophrenia

BACKGROUND

An accumulating body of evidence indicates that peripheral physiological rhythms help regulate and organize large-scale brain activity. Given that schizophrenia (SZ) is characterized by marked abnormalities in oscillatory cortical activity as well as changes in autonomic function, the present study aimed to identify mechanisms by which central and autonomic nervous system deficits may be related. We evaluated phase-amplitude coupling (PAC) as a physiological mechanism through which autonomic nervous system (ANS) and central nervous system (CNS) activity are integrated and that may be disrupted in SZ.

METHODS

PAC was measured between high-frequency heart rate variability (HF-HRV) as an index of parasympathetic activity and electroencephalography (EEG) oscillations in 36 individuals with first-episode SZ and 38 healthy comparison participants at rest.

RESULTS

HRV-EEG coupling was lower in SZ in the alpha and theta bands, and HRV-EEG coupling uniquely predicted group membership, whereas HRV and EEG power alone did not. HRV-EEG coupling in the alpha band correlated with measures of sustained attention in SZ. Granger causality analyses indicated a stronger heart-to-brain effect than brain-to-heart effect, consistent across groups.

CONCLUSIONS

Lower HRV-EEG coupling provides evidence of deficient autonomic regulation of cortical activity in SZ, suggesting that patterns of dysconnectivity observed in brain networks extend to brain-body interactions. Deficient ANS-CNS integration in SZ may foster a breakdown in the spatiotemporal organization of cortical activity, which may contribute to core cognitive impairments in SZ such as dysregulated attention. These findings encourage pursuit of therapies targeting autonomic function for the treatment of SZ.

QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab , an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4-6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics.

Symptom-based depression subtypes: brain dynamic specificity and its association with gene expression profiles

At least 227 combinations of symptoms meet the criteria for Major Depressive Disorder (MDD). However, in clinical practice, patients consistently present symptoms in a regular rather than random manner, and the neural basis underlying the MDD subtypes remains unclear. To help clarify the neural basis, patients with MDD were clustered by symptom combinations to investigate the neural underpinning of each subtype using functional resonance imaging (fMRI). Four symptom-based subtypes of MDD were identified using latent profile analysis according to the clinical scales. Subsequently, brain dynamics were evaluated using fMRI, and the dysregulations in attention and limbic network were observed among the subtypes. Correlation between brain dynamics and symptom combinations was then assessed via canonical correlation analysis (CCA). The brain-symptom correlation was higher when evaluated in subtypes (r = 0.77 to 0.92) compared to the entire group (r = 0.5). The loading weight in CCA showed that dynamics in transmodal networks contributed the most to the correlation in the subtypes characterized by typical depression symptoms, whereas unimodal networks contributed the most to subtypes characterized by anxiety and insomnia. Finally, gene expression underlying the CCA model, along with its biological encoding process, performed using a postmortem gene expression atlas revealed distinct gene enrichments for different subtypes. These findings highlight that distinct symptom clusters in MDD have specific neural correlates, providing insights into depression's heterogeneous diagnosis and precision medicine opportunities.

The intrinsic propagation directionality of fMRI infra-slow activity during visual tasks

The temporal order of propagation in the blood-oxygen-level-dependent (BOLD) infra-slow activity (ISA, 0.01-0.1 Hz) of functional magnetic resonance imaging (fMRI) can indicate the functional organization of the brain. While prior studies have revealed the temporal order of propagation of BOLD ISA during rest, how it emerges during cognitive tasks remains unclear. Furthermore, its differences between the gray and white matters at the whole-brain scale are unexplored. In this study, we probed the propagation of BOLD ISA using a publicly available fMRI dataset from participants performing visual detection and discrimination tasks (N = 46, 29 females). We examined the temporal order of propagation based on ISA oscillatory phase differences among brain parcels. During visual task performance, ISA in both the gray and white matters propagated in a direction from the visual cortex to the association cortex, including the default mode network (DMN). This result differs from the previously reported propagation direction during rest that traveled from the visual and somatosensory cortices to the DMN, suggesting that the functional organization may change when performing cognitive tasks. In addition, the propagation in the white matter represented more complex patterns than that in the gray matter, exhibiting that the cingulum preceded DMN. Our results may help the understanding of how task performance alters the sensory-DMN propagation according of ISA.

Hemodynamic cortical ripples through cyclicity analysis

A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

Arousal effects on oscillatory dynamics in the non-human primate brain

Arousal states are thought to influence many aspects of cognition and behavior by broadly modulating neural activity. Many studies have observed arousal-related modulations of alpha (~8 to 15 Hz) and gamma (~30 to 50 Hz) power and coherence in local field potentials across relatively small groups of brain regions. However, the global pattern of arousal-related oscillatory modulation in local field potentials is yet to be fully elucidated. We simultaneously recorded local field potentials in numerous cortical and subcortical regions in the primate brain and assessed oscillatory activity and inter-regional coherence associated with arousal state. In high arousal states, we found a uniquely strong and coherent gamma oscillation between the amygdala and basal forebrain. In low arousal rest-like states, a relative increase in coherence at alpha frequencies was present across sampled brain regions, with the notable exception of the medial temporal lobe. We consider how these patterns of activity may index arousal-related brain states that support the processing of incoming sensory stimuli during high arousal states and memory-related functions during rest.

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

Temporal dysregulation of the somatomotor network in agitated depression

Agitated depression (A-MDD) is a severe subtype of major depressive disorder, with an increased risk of suicidality and the potential to evolve into bipolar disorder. Despite its clinical significance, the neural basis remains unclear. We hypothesize that psychomotor agitation, marked by pressured speech and racing thoughts, is linked to disruptions in brain dynamics. To test this hypothesis, we examined brain dynamics using time delay estimation and edge-centre time series, as well as dynamic connections between the somatomotor network (SMN) and the default mode network in 44 patients with A-MDD, 75 with non-agitated MDD (NA-MDD), and 94 healthy controls. Our results revealed that the neural co-activity duration was shorter in the A-MDD group compared with both the NA-MDD and controls (A-MDD versus NA-MDD: t = 2.295; A-MDD versus controls: t = 2.192, all P < 0.05). In addition, the dynamic of neural fluctuation in SMN altered in the A-MDD group than in the NA-MDD group (t = -2.616, P = 0.011) and was correlated with agitation severity (β = -0.228, P = 0.011). The inter-network connection was reduced in the A-MDD group compared with the control group (t = 2.102, P = 0.037), especially at low-amplitude time points (t = 2.139, P = 0.034). These findings indicate rapid neural fluctuations and disrupted dynamic coupling between the SMN and default mode network in A-MDD, potentially underlying the psychomotor agitation characteristic of this subtype. These insights contribute to a more nuanced understanding of the heterogeneity of depression and have implications for differential diagnosis and treatment strategies.

Brainwide hemodynamics predict neural rhythms across sleep and wakefulness in humans

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

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients.

METHODS

We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age.

RESULTS

SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001).

LIMITATIONS

Evaluation of brain dynamics was constrained by MRI temporal resolution.

CONCLUSIONS

Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

Sex-specific age-related differences in cerebrospinal fluid clearance assessed by resting-state functional magnetic resonance imaging

Cerebrospinal fluid (CSF) flow may assist the clearance of brain wastes, such as amyloid-β (Aβ) and tau, and thus play an important role in aging and dementias. However, a lack of non-invasive tools to assess the CSF dynamics-related clearance in humans hindered the understanding of the relevant changes in healthy aging. The global infra-slow (<0.1 Hz) brain activity measured by the global mean resting-state fMRI signal (gBOLD) was recently found to be coupled by large CSF movements. This coupling has been found to correlate with various pathologies of Alzheimer's disease (AD), particularly Aβ pathology, linking it to waste clearance. Using resting-state fMRI data from a group of 719 healthy aging participants, we examined the sex-specific differences of the gBOLD-CSF coupling over a wide age range between 36-100 years of age. We found that this coupling index remains stable before around age 55 and then starts to decline afterward, particularly in females. Menopause may contribute to the accelerated decline in females.

Multimodal state-dependent connectivity analysis of arousal and autonomic centers in the brainstem and basal forebrain

Vigilance is a continuously altering state of cortical activation that influences cognition and behavior and is disrupted in multiple brain pathologies. Neuromodulatory nuclei in the brainstem and basal forebrain are implicated in arousal regulation and are key drivers of widespread neuronal activity and communication. However, it is unclear how their large-scale brain network architecture changes across dynamic variations in vigilance state (i.e., alertness and drowsiness). In this study, we leverage simultaneous EEG and 3T multi-echo functional magnetic resonance imaging (fMRI) to elucidate the vigilance-dependent connectivity of arousal regulation centers in the brainstem and basal forebrain. During states of low vigilance, most of the neuromodulatory nuclei investigated here exhibit a stronger global correlation pattern and greater connectivity to the thalamus, precuneus, and sensory and motor cortices. In a more alert state, the nuclei exhibit the strongest connectivity to the salience, default mode, and auditory networks. These vigilance-dependent correlation patterns persist even after applying multiple preprocessing strategies to reduce systemic vascular effects. To validate our findings, we analyze two large 3T and 7T fMRI datasets from the Human Connectome Project and demonstrate that the static and vigilance-dependent connectivity profiles of the arousal nuclei are reproducible across 3T multi-echo, 3T single-echo, and 7T single-echo fMRI modalities. Overall, this work provides novel insights into the role of neuromodulatory systems in vigilance-related brain activity.

Psychedelic 5-HT2A receptor agonism: neuronal signatures and altered neurovascular coupling

Psychedelics hold therapeutic promise for mood disorders due to rapid, sustained results. Human neuroimaging studies have reported dramatic serotonin-2A receptor-(5-HT2AR)-dependent changes in functional brain reorganization that presumably reflect neuromodulation. However, the potent vasoactive effects of serotonin have been overlooked. We found psilocybin-mediated alterations to fMRI-HRFs in humans, suggesting potentially altered NVC. To assess the neuronal, hemodynamic, and neurovascular coupling (NVC) effects of the psychedelic 5-HT2AR agonist, 2,5-Dimethoxy-4-iodoamphetamine (DOI), wide-field optical imaging (WFOI) was used in awake Thy1-jRGECO1a mice during stimulus-evoked and resting-state conditions. While DOI partially altered tasked-based NVC, more pronounced NVC alterations occurred under resting-state conditions and were strongest in association regions. Further, calcium and hemodynamic activity reported different accounts of RSFC changes under DOI. Co-administration of DOI and the 5-HT2AR antagonist, MDL100907, reversed many of these effects. Dissociation between neuronal and hemodynamic signals emphasizes a need to consider neurovascular effects of psychedelics when interpreting blood-oxygenation-dependent neuroimaging measures.

Neurobehavioral meaning of pupil size

Pupil size is a widely used metric of brain state. It is one of the few signals originating from the brain that can be readily monitored with low-cost devices in basic science, clinical, and home settings. It is, therefore, important to investigate and generate well-defined theories related to specific interpretations of this metric. What exactly does it tell us about the brain? Pupils constrict in response to light and dilate during darkness, but the brain also controls pupil size irrespective of luminosity. Pupil size fluctuations resulting from ongoing "brain states" are used as a metric of arousal, but what is pupil-linked arousal and how should it be interpreted in neural, cognitive, and computational terms? Here, we discuss some recent findings related to these issues. We identify open questions and propose how to answer them through a combination of well-defined tasks, neurocomputational models, and neurophysiological probing of the interconnected loops of causes and consequences of pupil size.

Cardiovascular and vasomotor pulsations in the brain and periphery during awake and NREM sleep in a multimodal fMRI study

Introduction

The cerebrospinal fluid dynamics in the human brain are driven by physiological pulsations, including cardiovascular pulses and very low-frequency (< 0.1 Hz) vasomotor waves. Ultrafast functional magnetic resonance imaging (fMRI) facilitates the simultaneous measurement of these signals from venous and arterial compartments independently with both classical venous blood oxygenation level dependent (BOLD) and faster arterial spin-phase contrast.

Methods

In this study, we compared the interaction of these two pulsations in awake and sleep using fMRI and peripheral fingertip photoplethysmography in both arterial and venous signals in 10 healthy subjects (5 female).

Results

Sleep increased the power of brain cardiovascular pulsations, decreased peripheral pulsation, and desynchronized them. However, vasomotor waves increase power and synchronicity in both brain and peripheral signals during sleep. Peculiarly, lag between brain and peripheral vasomotor signals reversed in sleep within the default mode network. Finally, sleep synchronized cerebral arterial vasomotor waves with venous BOLD waves within distinct parasagittal brain tissue.

Discussion

These changes in power and pulsation synchrony may reflect systemic sleep-related changes in vascular control between the periphery and brain vasculature, while the increased synchrony of arterial and venous compartments may reflect increased convection of regional neurofluids in parasagittal areas in sleep.

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.

Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.

Enhanced Correlation between Arousal and Infra-Slow Brain Activity in Experienced Meditators

BACKGROUND/OBJECTIVES

Meditation induces changes in the nervous system, which presumably underpin positive psychological and physiological effects. Such neural changes include alterations in the arousal fluctuation, as well as in infraslow brain activity (ISA, <0.1 Hz). Furthermore, it is known that fluctuations of arousal over time correlate with the oscillatory phase of ISA. However, whether this arousal-ISA correlation changes after meditation practices remains unanswered.; Methods: The present study aims to address this question by analyzing a publicly available electroencephalogram (EEG) dataset recorded during meditation sessions in the groups of experienced meditators and novices. The arousal fluctuation is measured by galvanic skin responses (GSR), and arousal-ISA correlations are measured by phase synchronization between GSR and EEG ISAs.; Results: While both groups exhibit arousal-ISA correlations, experienced meditators display higher correlations than novices. These increased arousal-ISA correlations in experienced meditators manifest more clearly when oscillatory phase differences between GSR and EEG ISAs are either 0 or π radians. As such, we further investigate the characteristics of these phase differences with respect to spatial distribution over the brain. We found that brain regions with the phase difference of either 0 or π radians form distinct spatial clusters, and that these clusters are spatially correlated with functional organization estimated by the principal gradient, based on functional connectivity.; Conclusions: Since increased arousal-ISA correlations reflect enhanced global organization of the central and autonomic nervous systems, our findings imply that the positive effects of meditation might be mediated by enhanced global organization of the nervous system.

Spatiotemporal relationships between neuronal, metabolic, and hemodynamic signals in the awake and anesthetized mouse brain

Neurovascular coupling (NVC) and neurometabolic coupling (NMC) provide the basis for functional magnetic resonance imaging and positron emission tomography to map brain neurophysiology. While increases in neuronal activity are often accompanied by increases in blood oxygen delivery and oxidative metabolism, these observations are not the rule. This decoupling is important when interpreting brain network organization (e.g., resting-state functional connectivity [RSFC]) because it is unclear whether changes in NMC/NVC affect RSFC measures. We leverage wide-field optical imaging in Thy1-jRGECO1a mice to map cortical calcium activity in pyramidal neurons, flavoprotein autofluorescence (representing oxidative metabolism), and hemodynamic activity during wake and ketamine/xylazine anesthesia. Spontaneous dynamics of all contrasts exhibit patterns consistent with RSFC. NMC/NVC relative to excitatory activity varies over the cortex. Ketamine/xylazine profoundly alters NVC but not NMC. Compared to awake RSFC, ketamine/xylazine affects metabolic-based connectomes moreso than hemodynamic-based measures of RSFC. Anesthesia-related differences in NMC/NVC timing do not appreciably alter RSFC structure.

Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity

Intrinsic cortical activity forms traveling waves that modulate sensory-evoked responses and perceptual sensitivity. These intrinsic traveling waves (iTWs) may arise from the coordination of synaptic activity through long-range feature-dependent horizontal connectivity within cortical areas. In a spiking network model that incorporates feature-selective patchy connections, we observe iTW motifs that result from shifts in excitatory/inhibitory balance as action potentials traverse these patchy connections. To test whether feature-selective motifs occur in vivo, we examined data recorded in the middle temporal visual area (Area MT) of marmosets performing a visual detection task. We find that some iTWs form motifs that are feature selective, exhibiting direction-selective modulations in spiking activity. Further, motifs modulate the gain of target-evoked responses and perceptual sensitivity if the target matches the preference of the motif. These results suggest that iTWs are shaped by the patchy horizontal fiber projections in the cortex and can regulate neural and perceptual sensitivity in a feature-selective manner.

A computational mechanism of cue-stimulus integration for pain in the brain

The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.

The fMRI global signal and its association with the signal from cranial bone

The nature of the global signal, i.e. the average signal from sequential functional imaging scans of the brain or the cortex, is not well understood, but is thought to include vascular and neural components. Using resting state data, we report on the strong association between the global signal and the average signal from the part of the volume that includes the cranial bone and subdural vessels and venous collectors, separated from each other and the subdural space by multispectral segmentation procedures. While subdural vessels carried a signal with a phase delay relative to the cortex, the association with the cortical signal was strongest in the parts of the scan corresponding to the laminae of the cranial bone, reaching 80% shared variance in some individuals. These findings suggest that in resting state data vascular components may play a prominent role in the genesis of fluctuations of the global signal. Evidence from other studies on the existence of neural sources of the global signal suggests that it may reflect the action of multiple mechanisms (including cerebrovascular reactivity and autonomic control) concurrently acting to regulate global cerebral perfusion.

Widespread Autonomic Physiological Coupling Across the Brain-Body Axis

The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition

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

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Heritability of functional gradients in the human subcortico-cortical connectivity

The human subcortex plays a pivotal role in cognition and is widely implicated in the pathophysiology of many psychiatric disorders. However, the heritability of functional gradients based on subcortico-cortical functional connectivity remains elusive. Here, leveraging twin functional MRI (fMRI) data from both the Human Connectome Project (n = 1023) and the Adolescent Brain Cognitive Development study (n = 936) datasets, we construct large-scale subcortical functional gradients and delineate an increased principal functional gradient pattern from unimodal sensory/motor networks to transmodal association networks. We observed that this principal functional gradient is heritable, and the strength of heritability exhibits a heterogeneous pattern along a hierarchical unimodal-transmodal axis in subcortex for both young adults and children. Furthermore, employing a machine learning framework, we show that this heterogeneous pattern of the principal functional gradient in subcortex can accurately discern the relationship between monozygotic twin pairs and dizygotic twin pairs with an accuracy of 76.2% (P < 0.001). The heritability of functional gradients is associated with the anatomical myelin proxied by MRI-derived T1-weighted/T2-weighted (T1w/T2w) ratio mapping in subcortex. This study provides new insights into the biological basis of subcortical functional hierarchy by revealing the structural and genetic properties of the subcortical functional gradients.

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes

OBJECTIVE

Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

METHODS

Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns.

RESULTS

PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity.

CONCLUSIONS

Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology.

SIGNIFICANCE

Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.

Frequency-dependent alterations of global signal topography in patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear.

METHODS

We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed.

RESULTS

The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group.

CONCLUSION

The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Brain states as wave-like motifs

There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.

The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats

Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

The flattening of spacetime hierarchy of the N,N-dimethyltryptamine brain state is characterized by harmonic decomposition of spacetime (HADES) framework

The human brain is a complex system, whose activity exhibits flexible and continuous reorganization across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. Here, we investigate healthy participants taking the serotonergic psychedelic N,N-dimethyltryptamine (DMT) with the Harmonic Decomposition of Spacetime (HADES) framework that can characterize how different harmonic modes defined in space are expressed over time. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the leading hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES' dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.

Recent advances in understanding the neurobiology of pediatric functional neurological disorder

INTRODUCTION

Functional neurological disorder (FND) is a neuropsychiatric disorder that manifests in a broad array of functional motor, sensory, or cognitive symptoms, which arise from complex interactions between brain, mind, body, and context. Children with FND make up 10%-20% of presentations to neurology services in children's hospitals and up to 20% of adolescents admitted to hospital for the management of intractable seizures.

AREAS COVERED

The current review focuses on the neurobiology of pediatric FND. The authors present an overview of the small but growing body of research pertaining to the biological, emotion-processing, cognitive, mental health, physical health, and social system levels.

EXPERT OPINION

Emerging research suggests that pediatric FND is underpinned by aberrant changes within and between neuron-glial (brain) networks, with a variety of factors - on multiple system levels - contributing to brain network changes. In pediatric practice, adverse childhood experiences (ACEs) are commonly reported, and activation or dysregulation of stress-system components is a frequent finding. Our growing understanding of the neurobiology of pediatric FND has yielded important flow-on effects for assessing and diagnosing FND, for developing targeted treatment interventions, and for improving the treatment outcomes of children and adolescents with FND.