Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions.The authors received funding from the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Evelyn Trust (Cambridge, UK), the UK National Institute for Health Research (NIHR) as part of the Acute Brain Injury and Repair Theme of the Cambridge Biomedical Research Centre, the NIHR Brain Injury Healthcare Technology Cooperative, the NIHR Senior Investigator award, the James S. McDonnell Foundation, the Belgian National Fund for Scientific Research (FNRS), the European Commission, the Human Brain Project (EUH2020-fetflagship-hbp-sga1-ga720270), the Luminous project (EU-H2020-fetopen-ga686764), the French Speaking Community Concerted Research Action, the Belgian American Educational Foundation, the WallonieBruxelles Federation, the European Space Agency, the University and University Hospital of Liege (Belgium)

Critical care management of traumatic brain injury.

Traumatic brain injury (TBI) is a growing global problem, which is responsible for a substantial burden of disability and death, and which generates substantial healthcare costs. High-quality intensive care can save lives and improve the quality of outcome. TBI is extremely heterogeneous in terms of clinical presentation, pathophysiology, and outcome. Current approaches to the critical care management of TBI are not underpinned by high-quality evidence, and many of the current therapies in use have not shown benefit in randomized control trials. However, observational studies have informed the development of authoritative international guidelines, and the use of multimodality monitoring may facilitate rational approaches to optimizing acute physiology, allowing clinicians to optimize the balance between benefit and risk from these interventions in individual patients. Such approaches, along with the emerging impact of advanced neuroimaging, genomics, and protein biomarkers, could lead to the development of precision medicine approaches to the intensive care management of TBI

Moving to human trials for argon neuroprotection in neurological injury: a narrative review.

Despite the global burden of brain injury, neuroprotective agents remain elusive. There are no clinically effective therapies which reduce mortality or improve long-term cognitive outcome. Ventilation could be an easily modifiable variable in resuscitation; gases are relatively simple to administer. Xenon is the prototypic agent of a new generation of experimental treatments which show promise. However, use is hindered by its prohibitive cost and anaesthetic properties. Argon is an attractive option, being cheaper, easy to transport, non-sedating, and mechanistically distinct from xenon. In vitro and in vivo models provide evidence of argon reducing brain injury, with improvements in neurocognitive, histological, and biomarker metrics, as well as improved survival. Current data suggest that the effect of argon is mediated via the toll-like receptors 2 and 4, the extracellular signal-regulated kinase 1/2, and phosphatidylinositol 3 kinase (PI-3K)-AKT pathways. Ventilation with argon appears to be safe in pigs and preliminary human trials. Given recent evidence that arterial hyperoxia may be harmful, the supplementation of high-concentration argon may not necessitate changes to clinical practice. Given the logistic benefits, and the evidence for argon neuroprotection summarized in this manuscript, we believe that the time has come to consider developing Phase II clinical trials to assess its benefit in acute neurological injury

Default mode network connectivity during task execution.

Initially described as task-induced deactivations during goal-directed paradigms of high attentional load, the unresolved functionality of default mode regions has long been assumed to interfere with task performance. However, recent evidence suggests a potential default mode network involvement in fulfilling cognitive demands. We tested this hypothesis in a finger opposition paradigm with task and fixation periods which we compared with an independent resting state scan using functional magnetic resonance imaging and a comprehensive analysis pipeline including activation, functional connectivity, behavioural and graph theoretical assessments. The results indicate task specific changes in the default mode network topography. Behaviourally, we show that increased connectivity of the posterior cingulate cortex with the left superior frontal gyrus predicts faster reaction times. Moreover, interactive and dynamic reconfiguration of the default mode network regions' functional connections illustrates their involvement with the task at hand with higher-level global parallel processing power, yet preserved small-world architecture in comparison with rest. These findings demonstrate that the default mode network does not disengage during this paradigm, but instead may be involved in task relevant processing.The Evelyn Trust (RUAG/018) provided the required funding for this research. Additionally, D Vatansever is funded by the Yousef Jameel Academic Program administered via the Cambridge Commonwealth, European and International Trust; DK Menon is supported by funding from the NIHR Cambridge Biomedical Centre (RCZB/004), and an NIHRSenior Investigator Award (RCZB/014), and EA Stamatakis is funded by the Stephen Erskine Fellowship Queens' College Cambridge. We would also like to thank Sanja Abbott for programming the stimulus delivery, Dr. Guy Williams and Victoria Lupson and the rest of the staff in the Wolfson Brain Imaging Centre (WBIC) at Addenbrooke's Hospital for their assistance in scanning. Last but not least, we thank all the participants for their contribution to this study.This is the final version of the article. It was first available from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2015.07.05

ICP versus Laser Doppler Cerebrovascular Reactivity Indices to Assess Brain Autoregulatory Capacity

Objective: To explore the relationship between various autoregulatory indices in order to determine which approximate small-vessel/microvascular autoregulatory capacity most accurately. Methods: Utilizing a retrospective cohort of traumatic brain injury (TBI) patients (N=41) with: transcranial Doppler (TCD), intracranial pressure (ICP) and cortical laser Doppler flowmetry (LDF), we calculated various continuous indices of autoregulation and cerebrovascular responsiveness: A. ICP derived (pressure reactivity index (PRx) – correlation between ICP and mean arterial pressure (MAP), PAx – correlation between pulse amplitude of ICP (AMP) and MAP, RAC – correlation between AMP and cerebral perfusion pressure (CPP)), B. TCD derived (Mx – correlation between mean flow velocity (FVm) and CPP, Mx_a – correlation betrween FVm and MAP, Sx – correlation between systolic flow velocity (FVs) and CPP, Sx_a – correlation between FVs and MAP, Dx – correlation between diastolic flow index (FVd) and CPP, Dx_a – correlation between FVd and MAP), and LDF derived (Lx – correlation between LDF cerebral blood flow (CBF) and CPP, Lx_a – correlation between LDF-CBF and MAP). We assessed the relationship between these indices via Pearson correlation, Friedman test, principal component analysis (PCA), agglomerative hierarchal clustering (AHC) and k-means cluster analysis (KMCA). Results: LDF based autoregulatory index (Lx) was most associated with TCD based Mx/Mx_a and Dx/Dx_a across Pearson correlation, PCA, AHC and KMCA. Lx was only remotely associated with ICP based indices (PRx, PAx, RAC). TCD based Sx/Sx_a were more closely associated with ICP derived PRx, PAx and RAC. This indicates that vascular derived indices of autoregulatory capacity (ie. TCD and LDF based) co-vary, with Sx/Sx_a being the exception. Whereas, indices of cerebrovascular reactivity derived from pulsatile CBV (ie. ICP indices) appear to not be closely related to those of vascular origin. Conclusions: Transcranial Doppler Mx is the most closely associated with LDF based Lx/Lx_a. Both Sx/Sx-a and the ICP derived indices appear to be dissociated with LDF based cerebrovascular reactivity, leaving Mx/Mx-a as a better surrogate for the assessment of cortical small vessel/microvascular cerebrovascular reactivity. Sx/Sx_a co-cluster/co-vary with ICP derived indices, as seen in our previous work.This work was made possible through salary support through the Cambridge Commonwealth Trust Scholarship, the Royal College of Surgeons of Canada – Harry S. Morton Travelling Fellowship in Surgery, the University of Manitoba Clinician Investigator Program, R. Samuel McLaughlin Research and Education Award, the Manitoba Medical Service Foundation, and the University of Manitoba Faculty of Medicine Dean’s Fellowship Fund. These studies were supported by National Institute for Healthcare Research (NIHR, UK) through the Acute Brain Injury and Repair theme of the Cambridge NIHR Biomedical Research Centre, an NIHR Senior Investigator Award to DKM. Authors were also supported by a European Union Framework Program 7 grant (CENTER-TBI; Grant Agreement No. 602150) MC is supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1790). JD is supported by a Woolf Fisher Scholarship (NZ)

Default Mode Contributions to Automated Information Processing

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 play 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, 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.We thank Mrs. Victoria Lupson, Ms. Karen Welsh, Dr. Marius Mada, and the rest of the staff in the Wolfson Brain Imaging Centre (WBIC) at Addenbrooke’s Hospital for their assistance in MRI scanning. In addition, we thank all the participants for their contribution to this study. This study was funded by the Yousef Jameel Academic Program grant (awarded to D.V.). Additionally, D.K.M. is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Centre (RCZB/004) and an NIHR Senior Investigator Award (RCZB/014), and E.A.S. is funded by the Stephen Erskine Fellowship Queens’ College, Cambridge

P7C3-A20 neuroprotection is independent of Wallerian degeneration in primary neuronal culture

The antiapoptotic, neuroprotective compound P7C3-A20 reduces neurological deficits when administered to murine in-vivo models of traumatic brain injury. P7C3-A20 is thought to exert its activity through small-molecule activation of the enzyme nicotinamide phosphoribosyltransferase. This enzyme converts nicotinamide to nicotinamide mononucleotide, the precursor to nicotinamide adenine dinucleotide synthesis. Alterations to this bioenergetic pathway have been shown to induce Wallerian degeneration (WD) of the distal neurite following injury. This study aimed to establish whether P7C3-A20, through induction of nicotinamide phosphoribosyltransferase activity, would affect the rate of WD. The model systems used were dissociated primary cortical neurons, dissociated superior cervical ganglion neurons and superior cervical ganglion explants. P7C3-A20 failed to show any protection against WD induced by neurite transection or vincristine administration. Furthermore, there was a concentration-dependent neurotoxicity. These findings are important in understanding the mechanism by which P7C3-A20 mediates its effects - a key step before moving to human clinical trials

Paroxysmal sympathetic hyperactivity: the storm after acute brain injury

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.GM is funded by the Research Foundation, Flanders as senior clinical investigator. DKM is supported by the National Institute for Healthcare Research (NIHR), UK, through the Acute Brain Injury and Repair theme of the Cambridge NIHR Biomedical Research Centre and a NIHR Senior Investigator Award, and is supported by a European Union Framework Program 7 grant (CENTER-TBI; grant agreement no. 602150)