Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness

Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

Whole-brain dynamics of human sensorimotor adaptation

Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.

Clinical Decision on Disorders of Consciousness After Acquired Brain Injury: Stepping Forward

Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.

Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study

Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.

A more unstable resting-state functional network in cognitively declining multiple sclerosis

Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In a healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remain particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration: 15 years) and at 5-year follow-up. A sliding-window approach was used for functional MRI analyses, where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks were characterized using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥2/7 domains Z < −2) and 18% were mildly impaired (≥2/7 domains Z < −1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared with cognitively preserved patients and controls, i.e. showing higher promiscuity (P = 0.047), flexibility (P = 0.008) and cohesion (P = 0.008). Over time, cognitively declining patients showed a further increase in cohesion (P = 0.004), which was not seen in stable patients (P = 0.544). More cohesion was related to more severe structural damage (average r = 0.166, P = 0.015) and worse verbal memory (r = −0.156, P = 0.022), information processing speed (r = −0.202, P = 0.003) and working memory (r = −0.163, P = 0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that showed cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.

What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena

A central question in neuroscience concerns the relationship between consciousness and its physical substrate. Here, we argue that a richer characterization of consciousness can be obtained by viewing it as constituted of distinct information-theoretic elements. In other words, we propose a shift from quantification of consciousness-viewed as integrated information-to its decomposition. Through this approach, termed Integrated Information Decomposition (ΦID), we lay out a formal argument that whether the consciousness of a given system is an emergent phenomenon depends on its information-theoretic composition-providing a principled answer to the long-standing dispute on the relationship between consciousness and emergence. Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition. Building on ΦID's revised understanding of integrated information, termed ΦR, we also introduce the notion of ΦR-ing ratio to quantify how efficiently an entity uses information for conscious processing. A combination of ΦR and ΦR-ing ratio may provide an important way to compare the neural basis of different aspects of consciousness. Decomposition of consciousness enables us to identify qualitatively different 'modes of consciousness', establishing a common space for mapping the phenomenology of different conscious states. We outline both theoretical and empirical avenues to carry out such mapping between phenomenology and information-theoretic modes, starting from a central feature of everyday consciousness: selfhood. Overall, ΦID yields rich new ways to explore the relationship between information, consciousness, and its emergence from neural dynamics.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Using non-invasive neuroimaging to enhance the care, well-being and experimental outcomes of laboratory non-human primates (monkeys)

Over the past 10-20 years, neuroscience witnessed an explosion in the use of non-invasive imaging methods, particularly magnetic resonance imaging (MRI), to study brain structure and function. Simultaneously, with access to MRI in many research institutions, MRI has become an indispensable tool for researchers and veterinarians to guide improvements in surgical procedures and implants and thus, experimental as well as clinical outcomes, given that access to MRI also allows for improved diagnosis and monitoring for brain disease. As part of the PRIMEatE Data Exchange, we gathered expert scientists, veterinarians, and clinicians who treat humans, to provide an overview of the use of non-invasive imaging tools, primarily MRI, to enhance experimental and welfare outcomes for laboratory non-human primates engaged in neuroscientific experiments. We aimed to provide guidance for other researchers, scientists and veterinarians in the use of this powerful imaging technology as well as to foster a larger conversation and community of scientists and veterinarians with a shared goal of improving the well-being and experimental outcomes for laboratory animals.