Controllability of structural brain networks

Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics

Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or non-linear models based on artificial neural networks (ANNs). Piecewise linear approximations of non-linear dynamics have proven useful in other technical applications. Moreover, such explicit models provide a clear advantage over ANN-based models when the dynamical system is not only supposed to be observed, but also controlled, in particular when a controller with guarantees is needed. Here we explore whether piecewise-linear dynamical system models (recurrent Switching Linear Dynamical System or rSLDS models) could be useful for modeling brain dynamics, in particular in the context of cognitive tasks. These models have the advantage that they can be estimated not only from continuous observations like field potentials or smoothed firing rates, but also from sparser single-unit spiking data. We first generate artificial neural data based on a non-linear computational model of perceptual decision-making and demonstrate that piecewise-linear dynamics can be successfully recovered from these observations. We then demonstrate that the piecewise-linear model outperforms a linear model in terms of predicting future states of the system and associated neural activity. Finally, we apply our approach to a publicly available dataset recorded from monkeys performing perceptual decisions. Much to our surprise, the piecewise-linear model did not provide a significant advantage over a linear model for these particular data, although linear models that were estimated from different trial epochs showed qualitatively different dynamics. In summary, we present a dynamical system modeling approach that could prove useful in situations, where the brain state needs to be controlled in a closed-loop fashion, for example, in new neuromodulation applications for treating cognitive deficits. Future work will have to show under what conditions the brain dynamics are sufficiently non-linear to warrant the use of a piecewise-linear model over a linear one.

Brain activity dynamics after traumatic brain injury indicate increased state transition energy and preference of lower order states

Traumatic Brain Injury (TBI) can cause structural damage to the neural tissue and white matter connections in the brain, disrupting its functional coactivation patterns. Although there are a wealth of studies investigating TBI-related changes in the brain's structural and functional connectomes, fewer studies have investigated TBI-related changes to the brain's dynamic landscape. Network control theory is a framework that integrates structural connectomes and functional time-series to quantify brain dynamics. Using this approach, we analyzed longitudinal trajectories of brain dynamics from acute to chronic injury phases in two cohorts of individuals with mild and moderate to severe TBI, and compared them to non-brain-injured, age- and sex-matched control individuals' trajectories. Our analyses suggest individuals with mild TBI initially have brain activity dynamics similar to controls but then shift in the subacute and chronic stages of the injury (1 month and 12 months post-injury) to favor lower-order visual-dominant states compared to higher-order default mode dominant states. We further find that, compared to controls, individuals with mild TBI have overall decreased entropy and increased transition energy demand in the sub-acute and chronic stages that correlates with poorer attention performance. Finally, we found that the asymmetry in top-down to bottom-up transition energies increased in subacute and chronic stages of mild TBI, possibly indicating decreased efficacy of top-down inhibition. We replicate most findings with the moderate to severe TBI dataset, indicating their robustness, with the notable exception of finding the opposite correlation between global transition energy and mean reaction time (MRT). We attribute differences to the cohorts' varied injury severity, with perhaps a stronger compensatory mechanism in moderate to severe TBI. Overall, our findings reveal shifting brain dynamics after mild to severe TBI that relate to behavioral measures of attention, shedding light on post-injury mechanisms of recovery.

Network control energy reductions under DMT relate to serotonin receptors, signal diversity, and subjective experience

Psychedelics offer a profound window into the human brain through their robust effects on perception, subjective experience, and brain activity patterns. The serotonergic psychedelic N,N-dimethyltryptamine (DMT) induces a profoundly immersive altered state of consciousness lasting under 20 min, allowing the entire experience to be captured during a single functional magnetic resonance imaging (fMRI) scan. Using network control theory, we map energy trajectories of 14 individuals undergoing fMRI during DMT and placebo. We find that global control energy is reduced after DMT injection compared to placebo. Longitudinal trajectories of global control energy correlate with longitudinal trajectories of electroencephalography (EEG) signal diversity (a measure of entropy) and subjective drug intensity ratings. At the regional level, spatial patterns of DMT's effects on these metrics correlate with serotonin 2a receptor density from positron emission tomography (PET) data. Using receptor distribution and pharmacokinetic information, we recapitulate DMT's effects on global control energy trajectories, demonstrating control models can predict pharmacological effects on brain dynamics.

Segregation and integration of resting-state brain networks in a longitudinal long COVID cohort

Long COVID is characterized by debilitating fatigue, likely stemming from abnormal interactions among brain regions, but the neural mechanisms remain unclear. Here, we utilized a nested-spectral partition (NSP) approach to study the segregation and integration of resting-state brain functional networks in 34 patients with long COVID from acute to chronic phase post infection. Compared to healthy controls, patients with long COVID exhibited significantly higher fatigue scores and shifted the brain into a less segregated state at both 1 month and 3 months post infection. During the recovery of fatigue severity, there was no significant difference of segregation/integration. A positive correlation between network integration and fatigue was observed at 1 month, shifting to a negative correlation by 3 months. Gene Ontology analysis revealed that both acute and long-term effects of fatigue were associated with abnormal social behavior. Our findings reveal the brain network reconfiguration trajectories during post-viral fatigue progression that serve as functional biomarkers for tracking neurocognitive sequelae.

A general framework for characterizing optimal communication in brain networks

Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain's most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

Development of brain metastable dynamics during the equivalent of the third gestational trimester

Metastability, a concept from dynamical systems theory, provides a framework for understanding how the brain shifts between various functional states and underpins essential cognitive, behavioural, and social function. While studied in adults, metastability in early brain development has only received recent attention. As the brain undergoes dramatic functional and structural changes over the third gestational trimester, here we review how these are reflected in changes in brain metastable dynamics in preterm, preterm at term-equivalent and full-term neonates. We synthesize findings from EEG, fMRI, fUS, and computational models, focusing on the spatial distribution and temporal dynamics of metastable states, which include functional integration and segregation, signal predictability and complexity. Despite fragmented evidence, studies suggest that neonatal metastability develops over the equivalent of the third gestational trimester, with increasing ability for integration-segregation, broader range of metastable states, faster metastable state transitions and greater signal complexity. Preterms at term-equivalent age exhibit immature metastability features compared to full-terms. We explain and interpret these changes in terms of maturation of the brain in a free energy landscape and establishment of cognitive functions.

Mapping the coupling between tract reachability and cortical geometry of the human brain

The study of cortical geometry and connectivity is prevalent in research on the human brain. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aimed to elucidate fundamental links between cortical geometry and white matter tract connectivity. We developed the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. Specifically, spectral analyses of the cortical surface yielded a set of geometrical eigenmodes, which were then used to explain the locations on the cortical surface reached by specific white matter tracts, referred to as tract reachability. In two independent datasets, we confirmed that tract reachability was well characterized by cortical geometry. We further observed that TGC had high test-retest ability and was specific to each individual. Interestingly, low-frequency TGC was found to be heritable and more informative than the high-frequency components in behavior prediction. Finally, we found that TGC could reproduce task-evoked cortical activation patterns. Collectively, our study provides a new approach to mapping coupling between cortical geometry and connectivity, highlighting how these two aspects jointly shape the connected brain.

The Neural Association Between Symptom and Cognition in Major Depressive Disorder: A Network Control Theory Study

Major depressive disorder (MDD) is characterized by intercorrelated clinical symptoms and cognitive deficits, whose neural mechanisms in relation to these disturbances remain unclear. To elucidate this, we applied the relatively new approach of Network Control Theory (NCT), which considers how network topology informs brain dynamics based on white matter connectivity data. We used the NCT parameter of average controllability (AC) to assess the potential control that brain network nodes have on brain-state transitions associated with clinical and cognitive symptoms in MDD. DTI and high-resolution T1-weighted anatomical imaging were performed on 170 MDD patients (mean age 31.6 years; 72 males, 98 females) and 137 healthy controls (HC; mean age 33.4 years; 64 males, 73 females). We used an NCT approach to compare AC between the groups. We then performed partial Spearman's rank correlation and moderation/mediation analyses for AC and cognition and clinical symptom scores. Compared with HC, MDD patients had lower AC in the left precuneus and superior parietal lobule and higher AC in the right precentral gyrus (preCG) and superior frontal gyrus (SFG), predominantly in the default-mode, somatomotor, and attention networks. In the HC group, AC of right preCG was positively associated with processing speed. While in the MDD group, AC of right SFG was negatively associated with memory function and also negatively moderated the association between memory and anxiety symptoms. The current study highlighted that the altered brain controllability may provide a novel understanding of the neural substrate underlying cognitive control in MDD. Disrupted control of right SFG during state transitions may partially explain the variable relationship between memory and anxiety symptoms in MDD.

The Computational Bottleneck of Basal Ganglia Output (and What to Do About it)

What the basal ganglia do is an oft-asked question; answers range from the selection of actions to the specification of movement to the estimation of time. Here, I argue that how the basal ganglia do what they do is a less-asked but equally important question. I show that the output regions of the basal ganglia create a stringent computational bottleneck, both structurally, because they have far fewer neurons than do their target regions, and dynamically, because of their tonic, inhibitory output. My proposed solution to this bottleneck is that the activity of an output neuron is setting the weight of a basis function, a function defined by that neuron's synaptic contacts. I illustrate how this may work in practice, allowing basal ganglia output to shift cortical dynamics and control eye movements via the superior colliculus. This solution can account for troubling issues in our understanding of the basal ganglia: why we see output neurons increasing their activity during behavior, rather than only decreasing as predicted by theories based on disinhibition, and why the output of the basal ganglia seems to have so many codes squashed into such a tiny region of the brain.

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury

The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models' dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections that may have an impact on functional outcomes after TBI.

Neurostructural, Neurofunctional, and Clinical Features of Chronic, Untreated Schizophrenia: A Narrative Review

Studies of individuals with chronic, untreated schizophrenia (CUS) can provide important insights into the natural course of schizophrenia and how antipsychotic pharmacotherapy affects neurobiological aspects of illness course and progression. We systematically review 17 studies on the neuroimaging, cognitive, and epidemiological aspects of CUS individuals. These studies were conducted at the Shanghai Mental Health Center, Institute of Mental Health at Peking University, and Huaxi MR Research Center between 2013 and 2021. CUS is associated with cognitive impairment, severe symptoms, and specific demographic characteristics and is different significantly from those observed in antipsychotic-treated individuals. Furthermore, CUS individuals have neurostructural and neurofunctional alterations in frontal and temporal regions, corpus callosum, subcortical, and visual processing areas, as well as default-mode and somatomotor networks. As the disease progresses, significant structural deteriorations occur, such as accelerated cortical thinning in frontal and temporal lobes, greater reduction in fractional anisotropy in the genu of corpus callosum, and decline in nodal metrics of gray mater network in thalamus, correlating with worsening cognitive deficits and clinical outcomes. In addition, striatal hypertrophy also occurs, independent of antipsychotic treatment. Contrasting with the negative neurostructural and neurofunctional effects of short-term antipsychotic treatment, long-term therapy frequently results in significant improvements. It notably enhances white matter integrity and the functions of key subcortical regions such as the amygdala, hippocampus, and striatum, potentially improving cognitive functions. This narrative review highlights the progressive neurobiological sequelae of CUS, the importance of early detection, and long-term treatment of schizophrenia, particularly because treatment may attenuate neurobiological deterioration and improve clinical outcomes.

Topography of putative bi-directional interaction between hippocampal sharp-wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using the network control theory, here, we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from the positron emission tomography, providing insight into the bioenergetic footing of resting-state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

When neuromodulation met control theory

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood

Social engagement and language are connected through early development. Alterations in their development can have a prolonged impact on children's lives. However, the role of white matter at birth in this ongoing connection is less well-known. Here, we investigate how white matter at birth jointly supports social engagement and language outcomes in 642 infants. We use edge-centric network control theory to quantify edge controllability, or the ability of white-matter connections to drive transitions between diverse brain states, at 1 month. Next, we used connectome-based predictive modeling (CPM) to predict the Quantitative Checklist for Autism in Toddlers (Q-CHAT) for social engagement risks and the Bayley Scales of Infant and Toddler Development (BSID-III) for language skills at 18 months from edge controllability. We created the social engagement network (SEN) to predict Q-CHAT scores and the language network (LAN) to predict BSID-III scores. The SEN and LAN were complex, spanning the whole brain. They also significantly overlapped in anatomy and generalized across measures. Controllability in the SEN at 1 month partially mediated associations between Q-CHAT and BSID-III language scores at 18 months. Further, controllability in the SEN significantly differed between term and preterm infants and predicted Q-CHAT scores in an external sample of preterm infants. Together, our results suggest that the intertwined nature of social engagement and language development is rooted in an infant's white-matter controllability.

Significance Statement

During infancy and toddlerhood, social engagement and language emerge together. Delays are often observed in both simultaneously. These interactions persist into later childhood, potentially affecting life quality. We reveal that the interplay between social engagement and language milestones in toddlerhood is rooted in the infant's structural connectivity, which may assist in early risk identification of developmental delays. Insights into the early brain foundations for emerging social engagement and language skills may open opportunities for individualized interventions to improve developmental outcomes.

Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout

Genetic information is involved in the gradual emergence of cortical areas since the neural tube begins to form, shaping the heterogeneous functions of neural circuits in the human brain. Informed by invasive tract-tracing measurements, the cortex exhibits marked interareal variation in connectivity profiles, revealing the heterogeneity across cortical areas. However, it remains unclear about the organizing principles possibly shared by genetics and cortical wiring to manifest the spatial heterogeneity across the cortex. Instead of considering a complex one-to-one mapping between genetic coding and interareal connectivity, we hypothesized the existence of a more efficient way that the organizing principles are embedded in genetic profiles to underpin the cortical wiring space. Leveraging vertex-wise tractography in diffusion-weighted MRI, we derived the global connectopies (GCs) in both female and male to reliably index the organizing principles of interareal connectivity variation in a low-dimensional space, which captured three dominant topographic patterns along the dorsoventral, rostrocaudal, and mediolateral axes of the cortex. More importantly, we demonstrated that the GCs converge with the gradients of a vertex-by-vertex genetic correlation matrix on the phenotype of cortical morphology and the cortex-wide spatiomolecular gradients. By diving into the genetic profiles, we found that the critical role of genes scaffolding the GCs was related to brain morphogenesis and enriched in radial glial cells before birth and excitatory neurons after birth. Taken together, our findings demonstrated the existence of a genetically determined space that encodes the interareal connectivity variation, which may give new insights into the links between cortical connections and arealization.

Visual hallucinations in Parkinson's disease: spotlight on central cholinergic dysfunction

Visual hallucinations are a common non-motor feature of Parkinson's disease and have been associated with accelerated cognitive decline, increased mortality and early institutionalization. Despite their prevalence and negative impact on patient outcomes, the repertoire of treatments aimed at addressing this troubling symptom is limited. Over the past two decades, significant contributions have been made in uncovering the pathological and functional mechanisms of visual hallucinations, bringing us closer to the development of a comprehensive neurobiological framework. Convergent evidence now suggests that degeneration within the central cholinergic system may play a significant role in the genesis and progression of visual hallucinations. Here, we outline how cholinergic dysfunction may serve as a potential unifying neurobiological substrate underlying the multifactorial and dynamic nature of visual hallucinations. Drawing upon previous theoretical models, we explore the impact that alterations in cholinergic neurotransmission has on the core cognitive processes pertinent to abnormal perceptual experiences. We conclude by highlighting that a deeper understanding of cholinergic neurobiology and individual pathophysiology may help to improve established and emerging treatment strategies for the management of visual hallucinations and psychotic symptoms in Parkinson's disease.

Driving brain state transitions via Adaptive Local Energy Control Model

The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.

Transdiagnostic white matter controllability deficits across patients with affective and anxiety spectrum disorders

BACKGROUND

Affective and anxiety disorders including major depression disorder (MDD), post-traumatic stress disorder (PTSD), and social anxiety disorder (SAD) are characterized by network dysconnectivity. Network controllability quantifies the capability of specific brain regions to impact functional dynamics based on the underlying structural connectome. This study aimed to investigate transdiagnostic and illness-specific network controllability alterations across these three disorders.

MATERIALS AND METHODS

The study enrolled 233 currently untreated and non-comorbid subjects, including 68 MDD patients, 51 PTSD patients, 46 SAD patients, and 68 healthy controls (HCs). White matter network controllability was compared among the four groups, and its associations with symptom severity and duration of untreated illness were evaluated.

RESULTS

Compared with HCs, patients with PTSD, MDD and SAD exhibited reduced average controllability in the somatomotor, subcortical, and default mode network, notably in brain regions such as the superior frontal gyrus, postcentral gyrus, paracentral gyrus, pallidum, posterior cingulate, and putamen. MDD and SAD patients exhibited reduced average controllability in the left lateral occipital gyrus and bilateral accumbens. SAD patients showed reduced average controllability in the dorsal attention network. These controllability changes did not correlate with illness duration or symptom severity.

LIMITATIONS

The cross-sectional design limits causal inference, and adjusting for age and sex differences may not completely eliminate their influence on the results.

CONCLUSION

The present study revealed shared and specific alterations of network controllability in MDD, PTSD, and SAD, suggesting reduced ability of specific brain regions/networks in driving the brain system into different functional states across these disorders.

Altered controllability of functional brain networks in obsessive-compulsive disorder

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

Double-blind, randomized, placebo-controlled pilot clinical trial with gamma-band transcranial alternating current stimulation for the treatment of schizophrenia refractory auditory hallucinations

Gamma oscillations are essential for brain communication. The 40 Hz neural oscillation deficits in schizophrenia impair left frontotemporal connectivity and information communication, causing auditory hallucinations. Transcranial alternating current stimulation is thought to enhance connectivity between different brain regions by modulating brain oscillations. In this work, we applied a frontal-temporal-parietal 40 Hz-tACS stimulation strategy for treating auditory hallucinations and further explored the effect of tACS on functional connectivity of brain networks. 32 schizophrenia patients with refractory auditory hallucinations received 20daily 20-min, 40 Hz, 1 mA sessions of active or sham tACS on weekdays for 4 consecutive weeks, followed by a 2-week follow-up period without stimulation. Auditory hallucination symptom scores and 64-channel electroencephalograms were measured at baseline, week2, week4 and follow-up. For clinical symptom score, we observed a significant interaction between group and time for auditory hallucinations symptoms (F(3,90) = 26.964, p < 0.001), and subsequent analysis showed that the 40Hz-tACS group had a higher symptom reduction rate than the sham group at week4 (p = 0.036) and follow-up (p = 0.047). Multiple comparisons of corrected EEG results showed that the 40Hz-tACS group had higher functional connectivity in the right frontal to parietal (F (1,30) = 7.24, p = 0.012) and right frontal to occipital (F (1,30) = 7.98, p = 0.008) than the sham group at week4. Further, functional brain network controllability outcomes showed that the 40Hz-tACS group had increased average controllability (F (1,30) = 6.26, p = 0.018) and decreased modality controllability (F (1,30) = 6.50, p = 0.016) in the right frontal lobe compared to the sham group. Our polit study indicates that 40Hz-tACS combined with medicine may be an effective treatment for targeting symptoms specific to auditory hallucinations and altering functional connectivity and controllability at the network level.

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Neuropsychiatric disorders are caused by many factors and produce a wide range of symptomatic maladaptive behaviors in patients. Despite this great variance in causes and resulting behavior, we believe the maladaptive behaviors that characterize neuropsychiatric disorders are most proximally determined by networks of neurons and that this forms a common conceptual link between these disorders. Operating from this premise, it follows that treating neuropsychiatric disorders to reduce maladaptive behavior can be accomplished by modifying the patient's network of neurons. In this proof-of-concept computational psychiatry study, we tested this approach in a simple model organism that is controlled by a neural network and that exhibits aversion-resistant alcohol drinking - a key maladaptive behavior associated with alcohol use disorder. We demonstrated that it was possible to predict personalized network modifications that substantially reduced maladaptive behavior without inducing side effects. Furthermore, we found that it was possible to predict effective treatments with limited knowledge of the model and that information about neural activity during certain types of trials was more helpful in predicting treatment than information about model parameters. We hypothesize that this is a general feature of developing effective treatment strategies for networks of neurons. This computational study lays the groundwork for future studies utilizing more biologically realistic network models in conjunction with in vivo data.

Noncanonical EEG-BOLD coupling by default and in schizophrenia

Neuroimaging methods rely on models of neurovascular coupling that assume hemodynamic responses evolve seconds after changes in neural activity. However, emerging evidence reveals noncanonical BOLD (blood oxygen level dependent) responses that are delayed under stress and aberrant in neuropsychiatric conditions. To investigate BOLD coupling to resting-state fluctuations in neural activity, we simultaneously recorded EEG and fMRI in people with schizophrenia and psychiatrically unaffected participants. We focus on alpha band power to examine voxelwise, time-lagged BOLD correlations. Principally, we find diversity in the temporal profile of alpha-BOLD coupling within regions of the default mode network (DMN). This includes early coupling (0-2 seconds BOLD lag) for more posterior regions, thalamus and brainstem. Anterior regions of the DMN show coupling at canonical lags (4-6 seconds), with greater lag scores associated with self-reported measures of stress and greater lag scores in participants with schizophrenia. Overall, noncanonical alpha-BOLD coupling is widespread across the DMN and other non-cortical regions, and is delayed in people with schizophrenia. These findings are consistent with a "hemo-neural" hypothesis, that blood flow and/or metabolism can regulate ongoing neural activity, and further, that the hemo-neural lag may be associated with subjective arousal or stress. Our work highlights the need for more studies of neurovascular coupling in psychiatric conditions.

Aberrant controllability of functional connectome during working memory tasks in patients with schizophrenia and unaffected siblings

Background

Working memory deficit, a key feature of schizophrenia, is a heritable trait shared with unaffected siblings. It can be attributed to dysregulation in transitions from one brain state to another.

Aims

Using network control theory, we evaluate if defective brain state transitions underlie working memory deficits in schizophrenia.

Method

We examined average and modal controllability of the brain's functional connectome in 161 patients with schizophrenia, 37 unaffected siblings and 96 healthy controls during a two-back task. We use one-way analysis of variance to detect the regions with group differences, and correlated aberrant controllability to task performance and clinical characteristics. Regions affected in both unaffected siblings and patients were selected for gene and functional annotation analysis.

Results

Both average and modal controllability during the two-back task are reduced in patients compared to healthy controls and siblings, indicating a disruption in both proximal and distal state transitions. Among patients, reduced average controllability was prominent in auditory, visual and sensorimotor networks. Reduced modal controllability was prominent in default mode, frontoparietal and salience networks. Lower modal controllability in the affected networks correlated with worse task performance and higher antipsychotic dose in schizophrenia (uncorrected). Both siblings and patients had reduced average controllability in the paracentral lobule and Rolandic operculum. Subsequent out-of-sample gene analysis revealed that these two regions had preferential expression of genes relevant to bioenergetic pathways (calmodulin binding and insulin secretion).

Conclusions

Aberrant control of brain state transitions during task execution marks working memory deficits in patients and their siblings.

Low-dimensional controllability of brain networks

Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.

Sleep Deprivation Effects on Brain State Dynamics Are Associated With Dopamine D2 Receptor Availability Via Network Control Theory

BACKGROUND

Sleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on static brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored.

METHODS

We used functional magnetic resonance imaging to examine resting-brain state dynamics after one night of SD compared with rested wakefulness (N = 41) and assessed the association of brain state dynamics with striatal brain dopamine D2 receptor availability measured by positron emission tomography [11C]raclopride using network control theory.

RESULTS

SD reduced dwell time and persistence probabilities, with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontoparietal network and low somatomotor network activity. Using network control theory, we showed that after SD, there was an overall increase in the control energy required for brain state transitions, with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and restful wakefulness and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2 receptor distribution.

CONCLUSIONS

These findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2 receptors.

Development of the brain network control theory and its implications

Brain network control theory (NCT) is a groundbreaking field in neuroscience that employs system engineering and cybernetics principles to elucidate and manipulate brain dynamics. This review examined the development and applications of NCT over the past decade. We highlighted how NCT has been effectively utilized to model brain dynamics, offering new insights into cognitive control, brain development, the pathophysiology of neurological and psychiatric disorders, and neuromodulation. Additionally, we summarized the practical implementation of NCT using the nctpy package. We also presented the doubts and challenges associated with NCT and efforts made to provide better empirical validations and biological underpinnings. Finally, we outlined future directions for NCT, covering its development and applications.

Amygdala self-neuromodulation capacity as a window for process-related network recruitment

Neurofeedback (NF) has emerged as a promising avenue for demonstrating process-related neuroplasticity, enabling self-regulation of brain function. NF targeting the amygdala has drawn attention to therapeutic potential in psychiatry, by potentially harnessing emotion-regulation processes. However, not all individuals respond equally to NF training, possibly owing to varying self-regulation abilities. This underscores the importance of understanding the mechanisms behind successful neuromodulation (i.e. capacity). This study aimed to investigate the establishment and neural correlates of neuromodulation capacity using data from repeated sessions of amygdala electrical fingerprint (Amyg-EFP)-NF and post-training functional magnetic resonance imaging (fMRI)-NF sessions. Results from 97 participants (healthy controls and post-traumatic stress disorder and fibromyalgia patients) revealed increased Amyg-EFP neuromodulation capacity over training, associated with post-training amygdala-fMRI modulation capacity and improvements in alexithymia. Individual differenaces in this capacity were associated with pre-training amygdala reactivity and initial neuromodulation success. Additionally, amygdala downregulation during fMRI-NF co-modulated with other regions such as the posterior insula and parahippocampal gyrus. This combined modulation better explained EFP-modulation capacity and improvement in alexithymia than the amygdala modulation alone, suggesting the relevance of this broader network to gained capacity. These findings support a network-based approach for NF and highlight the need to consider individual differences in brain function and modulation capacity to optimize NF interventions. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

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

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

Modulating Inter-Muscular Coordination Patterns in the Upper Extremity Induces Changes to Inter-Muscular, Cortico-Muscular, and Cortico-Cortical Connectivity

OBJECTIVE

The changes in neural drive to muscles associated with modulation of inter-muscular coordination in the upper extremity have not yet been investigated. Such information could help elucidate the neural mechanisms behind motor skill learning.

METHODS

Six young, neurologically healthy participants underwent a six-week training protocol to decouple two synergist elbow flexor muscles as a newly learned motor skill in the isometric force generation in upward and medial directions. Concurrent electroencephalography and surface electromyography from twelve upper extremity muscles were recorded in two conditions (As-Trained & Habitual) across two assessments (Week 0 vs. Week 6). Changes to inter-muscular connectivity (IMC), functional muscle networks, cortico-muscular connectivity (CMC), cortico-cortical connectivity (CCC) as well as functional brain network controllability (FBNC) associated with the modulation of inter-muscular coordination patterns were assessed to provide a perspective on the neural mechanisms for the newly learned motor skills.

RESULTS

Significant decreases in elbow flexor IMC, CMC, and increases in CCC were observed. No significant changes were observed for FBNC.

CONCLUSION

The results of this study suggest that modulating the inter-muscular coordination of the elbow flexor muscle synergy during isometric force generation is associated with multiple yet distinct changes in functional connectivity across the central and peripheral perspectives.

SIGNIFICANCE

Understanding the neural mechanisms of modulating inter-muscular coordination patterns can help inform motor rehabilitation regimens.

Causality-based Subject and Task Fingerprints using fMRI Time-series Data

Recently, there has been a revived interest in system neuroscience causation models due to their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, our goal is to verify the feasibility and effectiveness of using a causality-based approach for fMRI fingerprinting. Specifically, we propose an innovative method that utilizes the causal dynamics activities of the brain to identify the unique cognitive patterns of individuals (e.g., subject fingerprint) and fMRI tasks (e.g., task fingerprint). The key novelty of our approach stems from the development of a two-timescale linear state-space model to extract 'spatio-temporal' (aka causal) signatures from an individual's fMRI time series data. To the best of our knowledge, we pioneer and subsequently quantify, in this paper, the concept of 'causal fingerprint.' Our method is well-separated from other fingerprint studies as we quantify fingerprints from a cause-and-effect perspective, which are then incorporated with a modal decomposition and projection method to perform subject identification and a GNN-based (Graph Neural Network) model to perform task identification. Finally, we show that the experimental results and comparisons with non-causality-based methods demonstrate the effectiveness of the proposed methods. We visualize the obtained causal signatures and discuss their biological relevance in light of the existing understanding of brain functionalities. Collectively, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

A control theoretic approach to evaluate and inform ecological momentary interventions

OBJECTIVES

Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA.

METHODS

We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions.

RESULTS

We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings.

CONCLUSIONS

Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment

BACKGROUND AND OBJECTIVES

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs.

METHODS

Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory.

RESULTS

Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049).

DISCUSSION

This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

Functional brain controllability in Parkinson's disease and its association with motor outcomes after deep brain stimulation

Introduction

Considering the high economic burden and risks of deep brain stimulation (DBS) surgical failure, predicting the motor outcomes of DBS in Parkinson's disease (PD) is of significant importance in clinical decision-making. Functional controllability provides a rationale for combining the abnormal connections of the cortico-striato-thalamic-cortical (CSTC) motor loops and dynamic changes after medication in DBS outcome prediction.

Methods

In this study, we analyzed the association between preoperative delta functional controllability after medication within CSTC loops and motor outcomes of subthalamic nucleus DBS (STN-DBS) and globus pallidus interna DBS (GPi-DBS) and predicted motor outcomes in a Support Vector Regression (SVR) model using the delta controllability of focal regions.

Results

While the STN-DBS motor outcomes were associated with the delta functional controllability of the thalamus, the GPi-DBS motor outcomes were related to the delta functional controllability of the caudate nucleus and postcentral gyrus. In the SVR model, the predicted and actual motor outcomes were positively correlated, with p = 0.020 and R = 0.514 in the STN-DBS group, and p = 0.011 and R = 0.705 in the GPi- DBS group.

Discussion

Our findings indicate that different focal regions within the CSTC motor loops are involved in STN-DBS and GPi-DBS and support the feasibility of functional controllability in predicting DBS motor outcomes for PD in clinical decision-making.

Functional and connectivity correlates associated with Parkinson's disease psychosis: a systematic review

Neural underpinnings of Parkinson's disease psychosis remain unclear to this day with relatively few studies and reviews available. Using a systematic review approach, here, we aimed to qualitatively synthesize evidence from studies investigating Parkinson's psychosis-specific alterations in brain structure, function or chemistry using different neuroimaging modalities. PubMed, Web of Science and Embase databases were searched for functional MRI (task-based and resting state), diffusion tensor imaging, PET and single-photon emission computed tomography studies comparing Parkinson's disease psychosis patients with Parkinson's patients without psychosis. We report findings from 29 studies (514 Parkinson's psychosis patients, mean age ± SD = 67.92 ± 4.37 years; 51.36% males; 853 Parkinson's patients, mean age ± SD = 66.75 ± 4.19 years; 55.81% males). Qualitative synthesis revealed widespread patterns of altered brain function across task-based and resting-state functional MRI studies in Parkinson's psychosis patients compared with Parkinson's patients without psychosis. Similarly, white matter abnormalities were reported in parietal, temporal and occipital regions. Hypo-metabolism and reduced dopamine transporter binding were also reported whole brain and in sub-cortical areas. This suggests extensive alterations affecting regions involved in high-order visual processing and attentional networks.

Neuro-immune communication at the core of craving-associated brain structural network reconfiguration in methamphetamine users

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Topography of putative bidirectional interaction between hippocampal sharp wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across neocortex and the mechanisms enabling it remain unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with widefield imaging of dorsal neocortex, we found spatially and temporally precise bidirectional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability was correlated with UP/DOWN states in mouse default mode network, with highest modulation by RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN→UP transitions, while large-amplitude SWRs were often followed by DOWN states originating in RSC. We explain these electrophysiological results with a model in which HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

The topology and geometry of neural representations

A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functional regions with robustness to noise and idiosyncrasies of individual brains that do not correspond to computational differences. Previous studies have characterized brain representations by their representational geometry, which is defined by the representational dissimilarity matrix (RDM), a summary statistic that abstracts from the roles of individual neurons (or responses channels) and characterizes the discriminability of stimuli. Here, we explore a further step of abstraction: from the geometry to the topology of brain representations. We propose topological representational similarity analysis, an extension of representational similarity analysis that uses a family of geotopological summary statistics that generalizes the RDM to characterize the topology while de-emphasizing the geometry. We evaluate this family of statistics in terms of the sensitivity and specificity for model selection using both simulations and functional MRI (fMRI) data. In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds). In fMRI, the ground truth is a visual area and the models are the same and other areas measured in different subjects. Results show that topology-sensitive characterizations of population codes are robust to noise and interindividual variability and maintain excellent sensitivity to the unique representational signatures of different neural network layers and brain regions.

Release the Krakencoder: A unified brain connectome translation and fusion tool

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.

Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.

A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

Altered Structural Connectivity and Functional Brain Dynamics in Individuals With Heavy Alcohol Use Elucidated via Network Control Theory

BACKGROUND

Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework.

METHODS

Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances.

RESULTS

We found that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings.

CONCLUSIONS

This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.

Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation

The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.

Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.

Geometric Scaling Law in Real Neuronal Networks

We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling law in neuronal connection probability relative to spatial distance. This power-law behavior significantly differs from the exponential distance rule previously observed in coarse-grained brain networks. We demonstrate that the geometric scaling law carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Perturbing either the empirical probability model's parameters or its type results in the loss of these advantageous properties. Furthermore, we derive an explicit quantitative predictor for neuronal connectivity, incorporating only interneuronal distance and neurons' in and out degrees. Our findings establish a direct link between brain geometry and topology, shedding lights on the understanding of how the brain operates optimally within its confined space.

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion

Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.