Clinical Fellow, Harvard Medical School/Clinical Data Animation Center
Functional assessment of networks responsible for coma recovery using EEG.
Families and health-care providers caring for patients in coma due to severe brain injury frequently are unsure if continuing aggressive medical treatments will help the patient improve or if that will prolong the inevitable process of dying. A diagnostic and brain-monitoring tool that can early and accurately identify which patients have potential to recover would facilitate patient selection to clinical trials focusing on treatments that enhance neurological recovery. This ability to assess neural networks function non-invasively can offer new insights into the biologic mechanisms underlying coma recovery and support the development of individualized interventions based on specific patient’s responses. We are specifically studying how machine learning methods can help us decode the evolution of quantitative EEG trends associated with consciousness emergence in comatose patients with acute severe brain injury. Previous studies suggest that functional connectivity measured by EEG is a surrogate marker of large-scale cerebral networks integrity, however current methods lack the high specificity required to provide accurate prognostication in real-time. A machine learning approach enables the integration of large amounts of cerebral physiology data to clinical and pharmacologic information simultaneously and continuously. This context-sensitive neuromonitoring platform has the potential to be translated to other neurological diseases such as traumatic brain injury, stroke, and epilepsy.
Abstract: How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are "progressive and earlier" or "abrupt but delayed" account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations.
Pub.: 18 Jul '17, Pinned: 29 Jul '17
Abstract: Forty to sixty-six percent of patients resuscitated from cardiac arrest remain comatose, and historic outcome predictors are unreliable. Quantitative spectral analysis of continuous electroencephalography (cEEG) may differ between patients with good and poor outcomes.Consecutive patients with post-cardiac arrest hypoxic-ischemic coma undergoing cEEG were enrolled. Spectral analysis was conducted on artifact-free contiguous 5-min cEEG epochs from each hour. Whole band (1-30 Hz), delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), α/δ power ratio, percent suppression, and variability were calculated and correlated with outcome. Graphical patterns of quantitative EEG (qEEG) were described and categorized as correlating with outcome. Clinical outcome was dichotomized, with good neurologic outcome being consciousness recovery.Ten subjects with a mean age = 50 yrs (range = 18-65) were analyzed. There were significant differences in total power (3.50 [3.30-4.06] vs. 0.68 [0.52-1.02], p = 0.01), alpha power (1.39 [0.66-1.79] vs 0.27 [0.17-0.48], p < 0.05), delta power (2.78 [2.21-3.01] vs 0.55 [0.38-0.83], p = 0.01), percent suppression (0.66 [0.02-2.42] vs 73.4 [48.0-97.5], p = 0.01), and multiple measures of variability between good and poor outcome patients (all values median [IQR], good vs. poor). qEEG patterns with high or increasing power or large power variability were associated with good outcome (n = 6). Patterns with consistently low or decreasing power or minimal power variability were associated with poor outcome (n = 4).These preliminary results suggest qEEG metrics correlate with outcome. In some patients, qEEG patterns change over the first three days post-arrest.
Pub.: 25 Jun '17, Pinned: 29 Jul '17
Abstract: The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral observation scale, is widely used in the clinical diagnosis/assessment of patients with disorders of consciousness (DOC). However, the JFK CRS-R is associated with a high rate of misdiagnosis (approximately 40%) because DOC patients cannot provide sufficient behavioral responses. A brain-computer interface (BCI) that detects command/intention-specific changes in electroencephalography (EEG) signals without the need for behavioral expression may provide an alternative method.In this paper, we proposed an audiovisual BCI communication system based on audiovisual "yes" and "no" stimuli to supplement the JFK CRS-R for assessing the communication ability of DOC patients. Specifically, patients were given situation-orientation questions as in the JFK CRS-R and instructed to select the answers using the BCI.Thirteen patients (eight vegetative state (VS) and five minimally conscious state (MCS)) participated in our experiments involving both the BCI- and JFK CRS-R-based assessments. One MCS patient who received a score of 1 in the JFK CRS-R achieved an accuracy of 86.5% in the BCI-based assessment. Seven patients (four VS and three MCS) obtained unresponsive results in the JFK CRS-R-based assessment but responsive results in the BCI-based assessment, and 4 of those later improved scores in the JFK CRS-R-based assessment. Five patients (four VS and one MCS) obtained unresponsive results in both assessments.The experimental results indicated that the audiovisual BCI could provide more sensitive results than the JFK CRS-R and therefore supplement the JFK CRS-R.
Pub.: 11 Apr '17, Pinned: 29 Jul '17
Abstract: Recognition of potential for neurological recovery in patients who remain comatose after cardiac arrest is challenging and strains clinical decision making. Here, we utilize an approach that is based on physiological principles underlying recovery of consciousness and show correlation with clinical recovery after acute anoxic brain injury.A cohort study of 54 patients admitted to an Intensive Care Unit after cardiac arrest who underwent standardized bedside behavioral testing (Coma Recovery Scale – Revised [CRS-R]) during EEG monitoring. Blinded to all clinical variables, artifact-free EEG segments were selected around maximally aroused states and analyzed using a multi-taper method to assess frequency spectral content. EEG spectral features were assessed based on pre-defined categories that are linked to anterior forebrain corticothalamic integrity. Clinical outcomes were determined at the time of hospital discharge, using Cerebral Performance Categories (CPC).Ten patients with ongoing seizures, myogenic artifacts or technical limitations obscuring recognition of underlying cortical dynamic activity were excluded from primary analysis. Of the 44 remaining patients with distinct EEG spectral features, 39 (88%) fit into our predefined categories. In these patients, spectral features corresponding to higher levels of anterior forebrain corticothalamic integrity correlated with higher levels of consciousness and favorable clinical outcome at the time of hospital discharge (P = 0.014).Predicted transitions of neocortical dynamics that indicate functional integrity of anterior forebrain corticothalamic circuitry correlate with clinical outcomes in postcardiac-arrest patients. Our results support a new biologically driven approach toward better understanding of neurological recovery after cardiac arrest.
Pub.: 06 Jan '17, Pinned: 29 Jul '17
Abstract: Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27.
Pub.: 07 Jan '16, Pinned: 29 Jul '17
Abstract: In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities.We used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest.The simulations agree well with six common categories of EEG rhythms in postanoxic encephalopathy, including typical transitions in time. Plausible results were only obtained if excitatory synapses were more severely affected by short-term synaptic depression than inhibitory synapses.In postanoxic encephalopathy, the evolution of EEG patterns presumably results from gradual improvement of complete synaptic failure, where excitatory synapses are more severely affected than inhibitory synapses. The range of EEG patterns depends on the excitation-inhibition imbalance, probably resulting from long-term potentiation of excitatory neurotransmission.Our study is the first to relate microscopic synaptic dynamics in anoxic brain injury to both typical EEG observations and their evolution in time.
Pub.: 29 Jul '17, Pinned: 29 Jul '17
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