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A substantial minority of patients who survive an acquired brain injury develop a state of sympathetic hyperactivity that can persist for weeks or months, consisting of periodic episodes of increased heart rate and blood pressure, sweating, hyperthermia, and motor posturing, often in response to external stimuli. The unifying term for the syndrome—paroxysmal sympathetic hyperactivity (PSH)—and clear diagnostic criteria defined by expert consensus were only recently established. PSH has predominantly been described after traumatic brain injury (TBI), in which it is associated with worse outcomes. The pathophysiology of the condition is not completely understood, although most researchers consider it to be a disconnection syndrome with paroxysms driven by a loss of inhibitory control over excitatory autonomic centres. Although therapeutic strategies to alleviate sympathetic outbursts have been proposed, their effects on PSH are inconsistent between patients and their influence on outcome is unknown. Combinations of drugs are frequently used and are chosen on the basis of local custom, rather than on objective evidence. New rigorous tools for diagnosis could allow better characterisation of PSH to enable stratification of patients for future therapeutic trials.

Concurrent with mental processes that require rigorous computation and control, a series of automated decisions and actions govern our daily lives, providing efficient and adaptive responses to environmental demands. Using a cognitive flexibility task, we show that a set of brain regions collectively known as the default mode network plays a crucial role in such “autopilot” behavior, i.e., when rapidly selecting appropriate responses under predictable behavioral contexts. While applying learned rules, the default mode network shows both greater activity and connectivity. Furthermore, functional interactions between this network and hippocampal and parahippocampal areas as well as primary visual cortex correlate with the speed of accurate responses. These findings indicate a memory-based “autopilot role” for the default mode network, which may have important implications for our current understanding of healthy and adaptive brain processing.

Traumatic brain injury (TBI) can have lifelong and dynamic effects on health and wellbeing. Research on the long-term consequences emphasises that, for many patients, TBI should be conceptualised as a chronic health condition. Evidence suggests that functional outcomes after TBI can show improvement or deterioration up to two decades after injury, and rates of all-cause mortality remain elevated for many years. Furthermore, TBI represents a risk factor for a variety of neurological illnesses, including epilepsy, stroke, and neurodegenerative disease. With respect to neurodegeneration after TBI, post-mortem studies on the long-term neuropathology after injury have identified complex persisting and evolving abnormalities best described as polypathology, which includes chronic traumatic encephalopathy. Despite growing awareness of the lifelong consequences of TBI, substantial gaps in research exist. Improvements are therefore needed in understanding chronic pathologies and their implications for survivors of TBI, which could inform long-term health management in this sizeable patient population.

Severe traumatic brain injury (TBI) is currently managed in the intensive care unit with a combined medical–surgical approach. Treatment aims to prevent additional brain damage and to optimise conditions for brain recovery. TBI is typically considered and treated as one pathological entity, although in fact it is a syndrome comprising a range of lesions that can require different therapies and physiological goals. Owing to advances in monitoring and imaging, there is now the potential to identify specific mechanisms of brain damage and to better target treatment to individuals or subsets of patients. Targeted treatment is especially relevant for elderly people—who now represent an increasing proportion of patients with TBI—as preinjury comorbidities and their therapies demand tailored management strategies. Progress in monitoring and in understanding pathophysiological mechanisms of TBI could change current management in the intensive care unit, enabling targeted interventions that could ultimately improve outcomes.

In this Perspectives article, Roozenbeek et al . discuss issues with epidemiological studies in traumatic brain injury (TBI) and variability in the definition of such injuries. They describe how changing epidemiological patterns have influenced mortality and outcomes following brain injury, and identify the need for standardized epidemiological monitoring in TBI. Traumatic brain injury (TBI) is a critical public health and socio-economic problem throughout the world. Reliable quantification of the burden caused by TBI is difficult owing to inadequate standardization and incomplete capture of data on the incidence and outcome of brain injury, with variability in the definition of TBI being partly to blame. Reports show changes in epidemiological patterns of TBI: the median age of individuals who experience TBI is increasing, and falls have now surpassed road traffic incidents as the leading cause of this injury. Despite claims to the contrary, no clear decrease in TBI-related mortality or improvement of overall outcome has been observed over the past two decades. In this Perspectives article, we discuss the strengths and limitations of epidemiological studies, address the variability in its definition, and highlight changing epidemiological patterns. Taken together, these analyses identify a great need for standardized epidemiological monitoring in TBI.

•LSD untethers functional connectivity from the constraint of structural connectivity.•Increased small-worldness of brain networks predicts LSD-induced ego-dissolution.•LSD has time-specific effects on brain network integration and segregation.•LSD increases the complexity of segregated brain states.•Results bridge pharmacodynamics, subjective experience and brain dynamics. Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, by connecting novel psychological phenomena with their neurobiological correlates. LSD is known to increase measures of brain complexity, potentially reflecting a neurobiological correlate of the especially rich phenomenological content of psychedelic-induced experiences. Yet although the subjective stream of consciousness is a constant ebb and flow, no studies to date have investigated how LSD influences the dynamics of functional connectivity in the human brain. Focusing on the two fundamental network properties of integration and segregation, here we combined graph theory and dynamic functional connectivity from resting-state functional MRI to examine time-resolved effects of LSD on brain networks properties and subjective experiences. Our main finding is that the effects of LSD on brain function and subjective experience are non-uniform in time: LSD makes globally segregated sub-states of dynamic functional connectivity more complex, and weakens the relationship between functional and anatomical connectivity. On a regional level, LSD reduces functional connectivity of the anterior medial prefrontal cortex, specifically during states of high segregation. Time-specific effects were correlated with different aspects of subjective experiences; in particular, ego dissolution was predicted by increased small-world organisation during a state of high global integration. These results reveal a more nuanced, temporally-specific picture of altered brain connectivity and complexity under psychedelics than has previously been reported.

Prominent theories of consciousness emphasise different aspects of neurobiology, such as the integration and diversity of information processing within the brain. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from awake volunteers, propofol-anaesthetised volunteers, and patients with disorders of consciousness, in order to identify consciousness-specific patterns of brain function. We demonstrate that cortical networks are especially affected by loss of consciousness during temporal states of high integration, exhibiting reduced functional diversity and compromised informational capacity, whereas thalamo-cortical functional disconnections emerge during states of higher segregation. Spatially, posterior regions of the brain’s default mode network exhibit reductions in both functional diversity and integration with the rest of the brain during unconsciousness. These results show that human consciousness relies on spatio-temporal interactions between brain integration and functional diversity, whose breakdown may represent a generalisable biomarker of loss of consciousness, with potential relevance for clinical practice. How do diversity (entropy) and integration of activity across brain regions interact to support consciousness? Here the authors show that anaesthetised individuals and patients with disorders of consciousness exhibit overlapping reductions in both diversity and integration in the brain’s default mode network.

Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain and that consciousness emerges in a critical zone between low and high-entropy states. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n = 15), patients in a minimally conscious state (n = 10), and patients in a vegetative state (n = 8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is associated with level of consciousness in a clinically relevant population, with higher fractal dimensions (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

Psychedelic drugs, such as psilocybin and LSD, represent unique tools for researchers investigating the neural origins of consciousness. Currently, the most compelling theories of how psychedelics exert their effects is by increasing the complexity of brain activity and moving the system towards a critical point between order and disorder, creating more dynamic and complex patterns of neural activity. While the concept of criticality is of central importance to this theory, few of the published studies on psychedelics investigate it directly, testing instead related measures such as algorithmic complexity or Shannon entropy. We propose using the fractal dimension of functional activity in the brain as a measure of complexity since findings from physics suggest that as a system organizes towards criticality, it tends to take on a fractal structure. We tested two different measures of fractal dimension, one spatial and one temporal, using fMRI data from volunteers under the influence of both LSD and psilocybin. The first was the fractal dimension of cortical functional connectivity networks and the second was the fractal dimension of BOLD time-series. In addition to the fractal measures, we used a well-established, non-fractal measure of signal complexity and show that they behave similarly. We were able to show that both psychedelic drugs significantly increased the fractal dimension of functional connectivity networks, and that LSD significantly increased the fractal dimension of BOLD signals, with psilocybin showing a non-significant trend in the same direction. With both LSD and psilocybin, we were able to localize changes in the fractal dimension of BOLD signals to brain areas assigned to the dorsal-attenion network. These results show that psychedelic drugs increase the fractal dimension of activity in the brain and we see this as an indicator that the changes in consciousness triggered by psychedelics are associated with evolution towards a critical zone.

The invasive nature of the current methods for monitoring of intracranial pressure (ICP) has prevented their use in many clinical situations. Several attempts have been made to develop methods to monitor ICP non-invasively. The aim of this study is to assess the relationship between ultrasound-based non-invasive ICP (nICP) and invasive ICP measurement in neurocritical care patients. This was a prospective, single-cohort observational study of patients admitted to a tertiary neurocritical care unit. Patients with brain injury requiring invasive ICP monitoring were considered for inclusion. nICP was assessed using optic nerve sheath diameter (ONSD), venous transcranial Doppler (vTCD) of straight sinus systolic flow velocity (FVsv), and methods derived from arterial transcranial Doppler (aTCD) on the middle cerebral artery (MCA): MCA pulsatility index (PIa) and an estimator based on diastolic flow velocity (FVd). A total of 445 ultrasound examinations from 64 patients performed from 1 January to 1 November 2016 were included. The median age of the patients was 53 years (range 37-64). Median Glasgow Coma Scale at admission was 7 (range 3-14), and median Glasgow Outcome Scale was 3 (range 1-5). The mortality rate was 20%. ONSD and FVsv demonstrated the strongest correlation with ICP (R = 0.76 for ONSD versus ICP; R = 0.72 for FVsv versus ICP), whereas PIa and the estimator based on FVd did not correlate with ICP significantly. Combining the 2 strongest nICP predictors (ONSD and FVsv) resulted in an even stronger correlation with ICP (R = 0.80). The ability to detect intracranial hypertension (ICP ≥ 20 mm Hg) was highest for ONSD (area under the curve [AUC] 0.91, 95% CI 0.88-0.95). The combination of ONSD and FVsv methods showed a statistically significant improvement of AUC values compared with the ONSD method alone (0.93, 95% CI 0.90-0.97, p = 0.01). Major limitations are the heterogeneity and small number of patients included in this study, the need for specialised training to perform and interpret the ultrasound tests, and the variability in performance among different ultrasound operators. Of the studied ultrasound nICP methods, ONSD is the best estimator of ICP. The novel combination of ONSD ultrasonography and vTCD of the straight sinus is a promising and easily available technique for identifying critically ill patients with intracranial hypertension.