Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics.
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ABSTRACT:
SUBMITTER: Song M
PROVIDER: S-EPMC6145856 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Publications
Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics.
Song Ming M Yang Yi Y He Jianghong J Yang Zhengyi Z Yu Shan S Xie Qiuyou Q Xia Xiaoyu X Dang Yuanyuan Y Zhang Qiang Q Wu Xinhuai X Cui Yue Y Hou Bing B Yu Ronghao R Xu Ruxiang R Jiang Tianzi T
eLife 20180814
Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year-outcomes at the single-subject level. The model discriminated between patients who would later recove ...[more]
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Project description:The generation and maintenance of consciousness are fundamental but difficult subjects in the fields of psychology, philosophy, neuroscience, and medicine. However, recent developments in neuro-imaging techniques coupled with network analysis have greatly advanced our understanding of consciousness. The present review focuses on large-scale functional brain networks based on neuro-imaging data to explain the awareness (contents) and wakefulness of consciousness. Despite limitations, neuroimaging data suggests brain maps for important psychological and cognitive processes such as attention, language, self-referential, emotion, motivation, social behavior, and wakefulness. We considered a review of these advancements would provide new insights into research on the neural correlates of consciousness.
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Project description:Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.
| S-EPMC4199497 | biostudies-literature
Project description:Disorders of consciousness are impaired states of consciousness caused by severe brain injuries. Previous resting-state functional magnetic resonance imaging studies have reported abnormal brain network properties at different topological scales in patients with disorders of consciousness by using graph theoretical analysis. However, it is still unclear how inter-regional directed propagation activities affect the topological organization of functional brain networks in patients with disorders of consciousness. To reveal the altered topological organization in patients with disorders of consciousness, we constructed whole-brain directed functional networks by combining functional connectivity analysis and time delay estimation. Then we performed graph theoretical analysis based on the directed functional brain networks at three topological scales, from the nodal scale, the resting-state network scale to the global scale. Finally, the canonical correlation analysis was used to determine the correlations between altered topological properties and clinical scores in patients with disorders of consciousness. At the nodal scale, we observed decreased in-degree and increased out-degree in the precuneus in patients with disorders of consciousness. At the resting-state network scale, the patients with disorders of consciousness showed reorganized motif patterns within the default mode network and between the default mode network and other resting-state networks. At the global scale, we found a lower global clustering coefficient in the patients with disorders of consciousness than in the controls. The results of the canonical correlation analysis showed that the abnormal degree and the disrupted motif were significantly correlated with the clinical scores of the patients with disorders of consciousness. Our findings showed that consciousness impairment can be revealed by abnormal directed connection patterns at multiple topological scales in the whole brain, and the disrupted directed connection patterns may serve as clinical biomarkers to assess the dysfunction of patients with disorders of consciousness.
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Project description:Over the last 20 years, studies have provided greater insight into disorders of consciousness (DOC), also known as altered state of consciousness. Increased brain residual functions have been identified in patients with DOC due to the successful application of novel next-generation imaging technologies. Many unconscious patients have now been confirmed to retain considerable cognitive functions. It is hoped that greater insight regarding the psychological state of patients may be achieved through the use of functional magnetic resonance imaging and brain-computer interfaces. However, issues surrounding the research and treatment of DOC remain problematic. These include differing opinions on the definition of consciousness, difficulties in diagnosis, assessment, prognosis and/or treatment, and newly emerging ethical, legal and social issues. To overcome these, appropriate care must be offered to patients with DOC by clinicians and families, as DOC patients may now be considered to live in more than just a vegetative state. The present article reviews the controversy surrounding the definition of consciousness and the reliability of novel technologies, prognostic prediction, communication with DOC patients and treatment methods. The ethical and social issues surrounding the treatment of DOC and future perspectives are also considered.
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Project description:How are the myriad stimuli arriving at our senses transformed into conscious thought? To address this question, in a series of studies, we asked whether a common mechanism underlies loss of information processing in unconscious states across different conditions, which could shed light on the brain mechanisms of conscious cognition. With a novel approach, we brought together for the first time, data from the same paradigm-a highly engaging auditory-only narrative-in three independent domains: anesthesia-induced unconsciousness, unconsciousness after brain injury, and individual differences in intellectual abilities during conscious cognition. During external stimulation in the unconscious state, the functional differentiation between the auditory and fronto-parietal systems decreased significantly relatively to the conscious state. Conversely, we found that stronger functional differentiation between these systems in response to external stimulation predicted higher intellectual abilities during conscious cognition, in particular higher verbal acuity scores in independent cognitive testing battery. These convergent findings suggest that the responsivity of sensory and higher-order brain systems to external stimulation, especially through the diversification of their functional responses is an essential feature of conscious cognition and verbal intelligence.
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Project description: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.
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Project description:Chronic disorders of consciousness (DOC) encompass unresponsive wakefulness syndrome and minimally conscious state. Their anatomo-functional correlates are not clearly defined yet, although impairments of functional cortical networks have been reported, as well as the implication of the thalamus and deep brain structures. However, the pallidal functional connectivity with the thalamus and the cortical networks has not been studied so far. Using resting-state functional MRI, we conducted a functional connectivity study between the pallidum, the thalamus and the cortical networks in 13 patients with chronic DOC and 19 healthy subjects. We observed in chronic DOC patients that the thalami were no longer connected to the cortical networks, nor to the pallidums. Concerning the functional connectivity of pallidums, we reported an abolition of the negative correlation with the default mode network, and of the positive correlation with the salience network. The disrupted functional connectivity observed in chronic DOC patients between subcortical structures and cortical networks could be related to the mesocircuit model. A better understanding of the DOC underlying physiopathology could provide food for thought for future therapeutic proposals.
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Project description:IntroductionGiven that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness.MethodsResting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames.ResultsCompared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc.
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Project description:IntroductionFunctional brain-imaging techniques have revealed that clinical examination of disorders of consciousness (DoC) can underestimate the conscious level of patients. FDG-PET metabolic index of the best preserved hemisphere (MIBH) has been reported as a promising measure of consciousness but has never been externally validated and compared with other brain-imaging diagnostic procedures such as quantitative EEG.MethodsFDG-PET, quantitative EEG and cognitive evoked potential using an auditory oddball paradigm were performed in minimally conscious state (MCS) and vegetative state (VS) patient. We compared out-sample diagnostic and prognostic performances of PET-MIBH and EEG-based classification of conscious state to the current behavioral gold-standard, the Coma Recovery Scale - revised (CRS-R).ResultsBetween January 2016 and October 2019, 52 patients were included: 21 VS and 31 MCS. PET-MIBH had an AUC of 0.821 [0.694-0.930], sensitivity of 79% [62-91] and specificity of 78% [56-93], not significantly different from EEG (p = 0.628). Their combination accurately identified almost all MCS patients with a sensitivity of 94% [79-99%] and specificity of 67% [43-85]. Multimodal assessment also identified VS patients with neural correlate of consciousness (4/7 (57%) vs. 1/14 (7%), p = 0.025) and patients with 6-month recovery of command-following (9/24 (38%) vs. 0/16 (0%), p = 0.006), outperforming each technique taken in isolation.ConclusionFDG-PET MIBH is an accurate and robust procedure across sites to diagnose MCS. Its combination with EEG-based classification of conscious state not only optimizes diagnostic performances but also allows to detect covert cognition and to predict 6-month command-following recovery demonstrating the added value of multimodal assessment of DoC.
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Project description:Integrity of the default mode network (DMN) is believed to be essential for human consciousness. However, the effects of acute severe traumatic brain injury (TBI) on DMN functional connectivity are poorly understood. Furthermore, the temporal dynamics of DMN reemergence during recovery of consciousness have not been studied longitudinally in patients with acute severe TBI. We performed resting-state functional magnetic resonance imaging (rs-fMRI) to measure DMN connectivity in 17 patients admitted to the intensive care unit (ICU) with acute severe TBI and in 16 healthy control subjects. Eight patients returned for follow-up rs-fMRI and behavioral assessment six months post-injury. At each time point, we analyzed DMN connectivity by measuring intra-network correlations (i.e. positive correlations between DMN nodes) and inter-network anticorrelations (i.e. negative correlations between the DMN and other resting-state networks). All patients were comatose upon arrival to the ICU and had a disorder of consciousness (DoC) at the time of acute rs-fMRI (9.2 ± 4.6 days post-injury): 2 coma, 4 unresponsive wakefulness syndrome, 7 minimally conscious state, and 4 post-traumatic confusional state. We found that, while DMN anticorrelations were absent in patients with acute DoC, patients who recovered from coma to a minimally conscious or confusional state while in the ICU showed partially preserved DMN correlations. Patients who remained in coma or unresponsive wakefulness syndrome in the ICU showed no DMN correlations. All eight patients assessed longitudinally recovered beyond the confusional state by 6 months post-injury and showed normal DMN correlations and anticorrelations, indistinguishable from those of healthy subjects. Collectively, these findings suggest that recovery of consciousness after acute severe TBI is associated with partial preservation of DMN correlations in the ICU, followed by long-term normalization of DMN correlations and anticorrelations. Both intra-network DMN correlations and inter-network DMN anticorrelations may be necessary for full recovery of consciousness after acute severe TBI.
| S-EPMC6120794 | biostudies-literature