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Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Complex cognitive abilities are thought to arise from the ability of the brain to adaptively reconfigure its internal network structure as a function of task demands. Recent work has suggested that this inherent flexibility may in part be conferred by the widespread projections of the ascending arousal systems. While the different components of the ascending arousal system are often studied in isolation, there are anatomical connections between neuromodulatory hubs that we hypothesise are crucial for mediating key features of adaptive network dynamics, such as the balance between integration and segregation. To test this hypothesis, we estimated the strength of structural connectivity between key hubs of the noradrenergic and cholinergic arousal systems (the locus coeruleus [LC] and nucleus basalis of Meynert [nbM], respectively). We then asked whether the strength of structural LC and nbM inter-connectivity was related to individual differences in the emergent, dynamical signatures of functional integration measured from resting state fMRI data, such as network and attractor topography. We observed a significant positive relationship between the strength of white-matter connections between the LC and nbM and the extent of network-level integration following BOLD signal peaks in LC relative to nbM activity. In addition, individuals with denser white-matter streamlines interconnecting neuromodulatory hubs also demonstrated a heightened ability to shift to novel brain states. These results suggest that individuals with stronger structural connectivity between the noradrenergic and cholinergic systems have a greater capacity to mediate the flexible network dynamics required to support complex, adaptive behaviour. Furthermore, our results highlight the underlying static features of the neuromodulatory hubs can impose some constraints on the dynamic features of the brain., Competing Interests: Declarations of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components. We then used Linear Discriminant Analysis to maximise the mean differences between the low-dimensional signatures of fast-and-slow reaction times and early-and-late learners, while also conserving variance present within these groups. The resultant basis set allowed us to describe meaningful differences between these groups and, importantly, to detail the patterns of brain activity which underpin these differences. Our results demonstrate non-linear interactions between three key brain activation maps with convergent trajectories observed at higher task repetitions consistent with optimization. Furthermore, we show subjects with the greatest reaction time improvements have delayed recruitment of left dorsal and lateral prefrontal cortex, as well as deactivation in parts of the occipital lobe and motor cortex, and that the slowest performers have weaker recruitment of somatosensory association cortex and left ventral visual stream, as well as weaker deactivation in the dorsal lateral prefrontal cortex. Overall our results highlight the utility of a kinematic description of brain states, whereby reformatting data into low-dimensional trajectories sensitive to the subtleties of a task can capture non-linear trends in a tractable manner and permit hypothesis generation at the level of brain states., (Copyright © 2021. Published by Elsevier Inc.)

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting 'Core' cells and supragranular-projecting 'Matrix' cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core-Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

Recent methodological advances have enabled researchers to track the network structure of the human brain over time. Together, these studies provide novel insights into effective brain function, highlighting the importance of the systems-level perspective in understanding the manner in which the human brain organizes its activity to facilitate behavior. Here, we review a range of recent fMRI and electrophysiological studies that have mapped the relationship between inter-regional communication and network structure across a diverse range of brain states. In doing so, we identify both behavioral and biological axes that may underlie the tendency for network reconfiguration. We conclude our review by providing suggestions for future research endeavors that may help to refine our understanding of the functioning of the human brain., (Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.)

Impairments in motor automaticity cause patients with Parkinson's disease to rely on attentional resources during gait, resulting in greater motor variability and a higher risk of falls. Although dopaminergic circuitry is known to play an important role in motor automaticity, little evidence exists on the neural mechanisms underlying the breakdown of locomotor automaticity in Parkinson's disease. This impedes clinical management and is in great part due to mobility restrictions that accompany the neuroimaging of gait. This study therefore utilized a virtual reality gait paradigm in conjunction with functional MRI to investigate the role of dopaminergic medication on lower limb motor automaticity in 23 patients with Parkinson's disease that were measured both on and off dopaminergic medication. Participants either operated foot pedals to navigate a corridor ('walk' condition) or watched the screen while a researcher operated the paradigm from outside the scanner ('watch' condition), a setting that controlled for the non-motor aspects of the task. Step time variability during walk was used as a surrogate measure for motor automaticity (where higher variability equates to reduced automaticity), and patients demonstrated a predicted increase in step time variability during the dopaminergic "off" state. During the "off" state, subjects showed an increased blood oxygen level-dependent response in the bilateral orbitofrontal cortices (walk>watch). To estimate step time variability, a parametric modulator was designed that allowed for the examination of brain regions associated with periods of decreased automaticity. This analysis showed that patients on dopaminergic medication recruited the cerebellum during periods of increasing variability, whereas patients off medication instead relied upon cortical regions implicated in cognitive control. Finally, a task-based functional connectivity analysis was conducted to examine the manner in which dopamine modulates large-scale network interactions during gait. A main effect of medication was found for functional connectivity within an attentional motor network and a significant condition by medication interaction for functional connectivity was found within the striatum. Furthermore, functional connectivity within the striatum correlated strongly with increasing step time variability during walk in the off state (r=0.616, p=0.002), but not in the on state (r=-0.233, p=0.284). Post-hoc analyses revealed that functional connectivity in the dopamine depleted state within an orbitofrontal-striatal limbic circuit was correlated with worse step time variability (r=0.653, p<0.001). Overall, this study demonstrates that dopamine ameliorates gait automaticity in Parkinson's disease by altering striatal, limbic and cerebellar processing, thereby informing future therapeutic avenues for gait and falls prevention., (Copyright © 2017 Elsevier Inc. All rights reserved.)

It is argued that during ongoing visual perception, the brain is generating top-down predictions to facilitate, guide and constrain the processing of incoming sensory input. Here we demonstrate that these predictions are drawn from a diverse range of cognitive processes, in order to generate the richest and most informative prediction signals. This is consistent with a central role for cognitive penetrability in visual perception. We review behavioural and mechanistic evidence that indicate a wide spectrum of domains-including object recognition, contextual associations, cognitive biases and affective state-that can directly influence visual perception. We combine these insights from the healthy brain with novel observations from neuropsychiatric disorders involving visual hallucinations, which highlight the consequences of imbalance between top-down signals and incoming sensory information. Together, these lines of evidence converge to indicate that predictive penetration, be it cognitive, social or emotional, should be considered a fundamental framework that supports visual perception., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Functional connectivity provides an informative and powerful framework for exploring brain organization. Despite this, few statistical methods are available for the accurate estimation of dynamic changes in functional network architecture. To date, the majority of existing statistical techniques have assumed that connectivity structure is stationary, which is in direct contrast to emerging data that suggests that the strength of connectivity between regions is variable over time. Therefore, the development of statistical methods that enable exploration of dynamic changes in functional connectivity is currently of great importance to the neuroscience community. In this paper, we introduce the 'Multiplication of Temporal Derivatives' (MTD) and then demonstrate the utility of this metric to: (i) detect dynamic changes in connectivity using data from a novel state-switching simulation; (ii) accurately estimate graph structure in a previously-described 'ground-truth' simulated dataset; and (iii) identify task-driven alterations in functional connectivity. We show that the MTD is more sensitive than existing sliding-window methods in detecting dynamic alterations in connectivity structure across a range of correlation strengths and window lengths in simulated data. In addition to the temporal precision offered by MTD, we demonstrate that the metric is also able to accurately estimate stationary network structure in both simulated and real task-based data, suggesting that the method may be used to identify dynamic changes in network structure as they evolve through time., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Self-generated cognition, or mind wandering, refers to the quintessentially human tendency to withdraw from the immediate external environment and engage in internally driven mentation. This thought activity is suggested to be underpinned by a distributed set of regions in the brain, referred to as the default network. To date, experimental assessment of mind wandering has typically taken place during performance of a concurrent attention-demanding task. The attentional demands of concurrent tasks can influence the emergence of mind wandering, and their application to clinical disorders with reduced cognitive resources is limited. Furthermore, few paradigms have investigated the phenomenological content of mind wandering episodes. Here, we present data from a novel thought sampling task that measures both the frequency and qualitative content of mind wandering, in the absence of a concurrent task to reduce cognitive demand. The task was validated in a non-pathological cohort of 31 older controls and resting-state functional connectivity analyses in a subset of participants (n=18) was conducted to explore the neural bases of mind wandering. Overall, instances of mind wandering were found to occur in 37% of experimental trials. Resting state functional connectivity analyses confirmed that mind wandering frequency was associated with regional patterns of both increased and decreased default network connectivity, namely in the temporal lobe, posterior cingulate cortex and dorsal medial prefrontal cortex. Our findings demonstrate that the novel task provides a context of low cognitive demand, which is conducive to mind wandering. Furthermore, performance on the task is associated with specific patterns of functional connectivity in the default network. Together, this new paradigm offers an important avenue to investigate the frequency and content of mind wandering in the context of low cognitive demands, and has significant potential to be applied in clinical conditions with reduced cognitive resources., (Copyright © 2014 Elsevier Inc. All rights reserved.)