Andrew M. Goldfine

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

OBJECTIVE To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury. METHODS We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range). RESULTS In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls. CONCLUSIONS EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury. SIGNIFICANCE EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate.

Reanalysis of “Bedside detection of awareness in the vegetative state: a cohort study.”

Cruse and colleagues reported1 that a new electroencephalography (EEG)-based tool was able to show that 3 out of 16 vegetative state (VS) patients performed a motor imagery task requiring language and short-term memory. This finding, if confirmed, has major implications for diagnosis and care of severely brain-injured patients. We were concerned about the method’s validity because of the difficulty of the task, and its critical reliance on certain statistical assumptions. To allow us to test the validity of the method, Cruse and colleagues graciously supplied their data and analysis software. Below we show that the patient data do not meet the statistical assumptions made in Cruse et al., likely because of the presence of various artifacts (Table). We then show that when the data are re-analyzed by methods that do not depend on these model assumptions, there is no evidence for task performance in the patients. Table Overview of analyses and findings. To begin, we examine the EEG data itself. The normals have findings typical of healthy adults (Figure 1A, left): rhythmicity in the alpha range (~10 Hz) with minimal eye-blink and muscle artifact. In contrast, the patients’ EEG (Figure 1A, right) is dominated by 1–4 Hz activity, as is typical of severe brain dysfunction, deep sleep or anesthesia2. Frequency-domain representation (Figure 1B) confirms these findings. It also reveals that the patient’s EEG has significant muscle artifact3 that fluctuates block-to-block. Figure 1 Time and frequency domain representations of the EEG of a typical normal (N2) and patient (P13) who had similar classification rates in Cruse et al. (75% and 78%, respectively; Webappendix for methods). A. Laplacian-montaged EEG of the first trial of ... To determine whether subjects performed motor imagery, Cruse and colleagues used a multivariate method (Support Vector Machine; SVM)4,5 to differentiate EEG signals recorded while subjects were asked to imagine moving their hand, vs. their toes. SVM is a powerful technique, but, without a gold-standard for task performance, the validity hinges on the appropriateness of the statistical model.6 As detailed below, the statistical model used in Cruse et al. did not account for relationships between adjacent blocks, or correlations between trials within a block. For calculation of accuracy (how often the SVM correctly classified trials as “hand” vs. “toe”), the Cruse et al. methods did not take into account the possibility of slow variations across blocks, as their approach always classified pairs of neighbouring blocks (e.g., hand and toe block 1, but never hand block 1 and toe block 4). We modified their analysis to use these alternative pairings for cross-validation6 (Webappendix). In two of the positive patients (Webappendix Figure 1), accuracy decreased to chance (P1), or worse-than-chance (P12) as the test-block-pairs were further apart. This drop in accuracy implies that idiosyncratic relationships between adjacent blocks contributed substantially to SVM performance in these subjects. For calculation of significance, Cruse and colleagues calculated p-values using a binomial distribution for the number of correct trials, an approach that assumes that each trial is an independent assay. We found that this assumption does not hold in the patients. First, frequency domain representation of the EEG (Figure 1B; Webappendix) reveals a lack of independence: data from individual trials are more nearly matched within a block than across blocks. Second, we applied the Cruse et al. analysis separately to all time points of the trials. For patients, we found that worse-than-chance classification occurred substantially more often than expected from binomial statistics. This excess of outliers implies that trials are correlated (Webappendix and Webappendix Figure 2). We next show that when the SVM results are re-analyzed with a statistical approach that takes into account the correlations mentioned above (Webappendix and Webappendix Table 1 for full details), there is no statistical evidence of a task-related signal. To take into account correlations between blocks, we defined accuracy using all block-pairs as test components6, rather than restricting consideration to adjacent block pairs. To account for dependence among trials, we determined significance via a permutation test that recognized the block design. With this approach, positive normals remained significant, but only one patient (P13) remained significant (p=0·0286; lowest possible p-value with 4 blocks). We further note that even for random data, a classifier would be expected to yield 1 in 20 positive subjects at p≤0.05. We therefore corrected for multiple comparisons via the False-Discovery Rate (FDR)7; normals remained significant but none of the patients were significant at p≤0·05. Finally, we applied an independent approach that asked whether there was a significant difference between task and rest periods, using univariate statistics (i.e., separate tests for each frequency and channel of the EEG; methods in Webappendix and8; Webappendix Figures 3 and 4). Normals showed the expected task-related changes in motor imagery tasks (decreases in EEG power from 7–30 Hz, especially over the motor cortices contralateral to the imagined limb movement; p≤0. 05 after FDR correction)9,10. None of the 16 patients had significant changes identified by this measure. This emphasizes that even if we were to accept the ‘positive’ patient classifications of Cruse et al. as different from chance, the EEG signals lack the expected physiological changes associated with motor imagery (in contrast to the suggestion made by Cruse and colleagues in connection with their Figure 2). In sum, we found that the method of Cruse et al. is not valid because the patient data do not meet the assumptions of their statistical model. Specifically, the model does not allow for correlations between nearby trials and blocks, which are likely induced by fluctuating artifact and arousal state; when these factors are taken into account, there is no statistical evidence for task performance in patients. Importantly, the model of Cruse et al. generally suffices for normals, where there is minimal artifact contamination. These findings cast doubt about conclusions drawn from this method, both in Cruse et al., and a more recent study11. SVM and related methods are useful tools, particularly in EEG analysis for Brain-Computer Interface (BCI)10,12. In BCI applications, subjects can confirm task performance and the consequences of classifier failure are limited to reduced device performance. But in the diagnostic setting (e.g., determination of consciousness, genomic diagnosis of cancer13,14), classifier failure can misinform clinical decision making, with major consequences for patients and families. Given this, and the ease of dissemination of EEG technology, standards of demonstration of validity need to be high. Our analysis suggests that the approach of Cruse et al. falls short of this standard. Finally, we wish to emphasize the importance of data sharing. This analysis would not have been possible without full access to the original data and code.15

Common resting brain dynamics indicate a possible mechanism underlying zolpidem response in severe brain injury

Zolpidem produces paradoxical recovery of speech, cognitive and motor functions in select subjects with severe brain injury but underlying mechanisms remain unknown. In three diverse patients with known zolpidem responses we identify a distinctive pattern of EEG dynamics that suggests a mechanistic model. In the absence of zolpidem, all subjects show a strong low frequency oscillatory peak ∼6–10 Hz in the EEG power spectrum most prominent over frontocentral regions and with high coherence (∼0.7–0.8) within and between hemispheres. Zolpidem administration sharply reduces EEG power and coherence at these low frequencies. The ∼6–10 Hz activity is proposed to arise from intrinsic membrane properties of pyramidal neurons that are passively entrained across the cortex by locally-generated spontaneous activity. Activation by zolpidem is proposed to arise from a combination of initial direct drug effects on cortical, striatal, and thalamic populations and further activation of underactive brain regions induced by restoration of cognitively-mediated behaviors. DOI: http://dx.doi.org/10.7554/eLife.01157.001

Consciousness: Its Neurobiology and the Major Classes of Impairment

Human consciousness requires brainstem, basal forebrain, and diencephalic areas to support generalized arousal, and functioning thalamocortical networks to respond to environmental and internal stimuli. Disconnection of these interconnected systems, typically from cardiac arrest and traumatic brain injury, can result in disorders of consciousness. Brain injuries can also result in loss of motor output out of proportion to consciousness, resulting in misdiagnoses. The authors review pathology and imaging studies and derive mechanistic models for each of these conditions. Such models may guide the development of target-based treatment algorithms to enhance recovery of consciousness in many of these patients.

What is the role of brain mechanisms underlying arousal in recovery of motor function after structural brain injuries

PURPOSE OF REVIEW Standard neurorehabilitation approaches have limited impact on motor recovery in patients with severe brain injuries. Consideration of the contributions of impaired arousal offers a novel approach to understand and enhance recovery. RECENT FINDINGS Animal and human neuroimaging studies are elucidating the neuroanatomical bases of arousal and of arousal regulation, the process by which the cerebrum mobilizes resources. Studies of patients with disorders of consciousness have revealed that recovery of these processes is associated with marked improvements in motor performance. Recent studies have also demonstrated that patients with less severe brain injuries also have impaired arousal, manifesting as diminished sustained attention, fatigue, and apathy. In these less severely injured patients, it is difficult to connect disorders of arousal with motor recovery because of a lack of measures of arousal that are independent of motor function. SUMMARY Arousal impairment is common after brain injury and likely plays a significant role in recovery of motor function. A more detailed understanding of this connection will help to develop new therapeutic strategies applicable for a wide range of patients. This requires new tools that continuously and objectively measure arousal in patients with brain injury, to correlate with detailed measures of motor performance and recovery.

Isolation Syndrome after Cardiac Arrest and Therapeutic Hypothermia

Here, we present the first description of an isolation syndrome in a patient who suffered prolonged cardiac arrest and underwent a standard therapeutic hypothermia protocol. Two years after the arrest, the patient demonstrated no motor responses to commands, communication capabilities, or visual tracking at the bedside. However, resting neuronal metabolism and electrical activity across the entire anterior forebrain was found to be normal despite severe structural injuries to primary motor, parietal, and occipital cortices. In addition, using quantitative electroencephalography, the patient showed evidence for willful modulation of brain activity in response to auditory commands revealing covert conscious awareness. A possible explanation for this striking dissociation in this patient is that altered neuronal recovery patterns following therapeutic hypothermia may lead to a disproportionate preservation of anterior forebrain cortico-thalamic circuits even in the setting of severe hypoxic injury to other brain areas. Compared to recent reports of other severely brain-injured subjects with such dissociation of clinically observable (overt) and covert behaviors, we propose that this case represents a potentially generalizable mechanism producing an isolation syndrome of blindness, motor paralysis, and retained cognition as a sequela of cardiac arrest and therapeutic hypothermia. Our findings further support that highly-preserved anterior cortico-thalamic integrity is associated with the presence of conscious awareness independent from the degree of injury to other brain areas.

Executive attention deficits after traumatic brain injury reflect impaired recruitment of resources

Deficits in attention are a common and devastating consequence of traumatic brain injury (TBI), leading to functional impairments, rehabilitation barriers, and long-term disability. While such deficits are well documented, little is known about their underlying pathophysiology hindering development of effective and targeted interventions. Here we evaluate the integrity of brain systems specific to attentional functions using quantitative assessments of electroencephalography recorded during performance of the Attention Network Test (ANT), a behavioral paradigm that separates alerting, orienting, and executive components of attention. We studied 13 patients, at least 6 months post-TBI with cognitive impairments, and 24 control subjects. Based on performance on the ANT, TBI subjects showed selective impairment in executive attention. In TBI subjects, principal component analysis combined with spectral analysis of the EEG after target appearance extracted a pattern of increased frontal midline theta power (2.5–7.5 Hz) and suppression of frontal beta power (12.5–22.5 Hz). Individual expression of this pattern correlated (r = − 0.67, p < 0.001) with executive attention impairment. The grading of this pattern of spatiotemporal dynamics with executive attention deficits reflects impaired recruitment of anterior forebrain resources following TBI; specifically, deafferentation and variable disfacilitation of medial frontal neuronal populations is proposed as the basis of our findings.

A Common Function of Basal Ganglia-Cortical Circuits Subserving Speed in Both Motor and Cognitive Domains

Abstract Distinct regions of the frontal cortex connect with their basal ganglia and thalamic counterparts, constituting largely segregated basal ganglia-thalamo-cortical (BTC) circuits. However, any common role of the BTC circuits in different behavioral domains remains unclear. Indeed, whether dysfunctional motor and cognitive BTC circuits are responsible for motor slowing and cognitive slowing, respectively, in Parkinson’s disease (PD) is a matter of debate. Here, we used an effortful behavioral paradigm in which the effects of task rate on accuracy were tested in movement, imagery, and calculation tasks in humans. Using nonlinear fitting, we separated baseline accuracy (Abase) and “agility” (ability to function quickly) components of performance in healthy participants and then confirmed reduced agility and preserved Abase for the three tasks in PD. Using functional magnetic resonance imaging (fMRI) and diffusion tractography, we explored the neural substrates underlying speeded performance of the three tasks in healthy participants, suggesting the involvement of distinct BTC circuits in cognitive and motor agility. Language and motor BTC circuits were specifically active during speeded performance of the calculation and movement tasks, respectively, whereas premotor BTC circuits revealed activity for speeded performance of all tasks. Finally, PD showed reduced task rate-correlated activity in the language BTC circuits for speeded calculation, in the premotor BTC circuit for speeded imagery, and in the motor BTC circuits for speeded movement, as compared with controls. The present study casts light on the anatomo-functional organization of the BTC circuits and their parallel roles in invigorating movement and cognition through a function of dopamine.