Search

The ability to make decisions based on external information, prior knowledge, and evidence is a crucial aspect of cognition and may determine the success and survival of an organism. Despite extensive work on decision-making mechanisms/models, understanding the effects of alertness on neural and cognitive processes remain limited. Here we use EEG and behavioral modeling to characterize cognitive and neural dynamics of perceptual decision-making in awake/low alertness periods in humans (14 male, 18 female) and characterize the compensatory mechanisms as alertness decreases. Well-rested human participants, changing between full-wakefulness and low alertness, performed an auditory tone-localization task, and its behavioral dynamics were quantified with psychophysics, signal detection theory, and drift-diffusion modeling, revealing slower reaction times, inattention to the left side of space, and a lower rate of evidence accumulation in periods of low alertness. Unconstrained multivariate pattern analysis (decoding) showed a ∼280 ms delayed onset driven by low alertness of the neural signatures differentiating between left and right decision, with a spatial reconfiguration from centroparietal to lateral frontal regions 150-360 ms. To understand the neural compensatory mechanisms with decreasing alertness, we connected the evidence-accumulation behavioral parameter to the neural activity, showing in the early periods (125-325 ms) a shift in the associated patterns from right parietal regions in awake, to right frontoparietal during low alertness. This change in the neurobehavioral dynamics for central accumulation-related cognitive processes defines a clear reconfiguration of the brain networks' regions and dynamics needed for the implementation of decision-making, revealing mechanisms of resilience of cognition when challenged by decreased alertness.SIGNIFICANCE STATEMENTMost living organisms make multiple daily decisions, and these require a degree of evidence from both the environment and the internal milieu. Such decisions are usually studied under sequential sampling models and involve making a behavioral choice based on sensory encoding, central accumulation, and motor implementation processes. Since there is little research on how decreasing alertness affects such cognitive processes, this study has looked at the cognitive and neural dynamics of perceptual decision-making in people while fully awake and in drowsy periods. Using computational modeling of behavior and neural dynamics on human participants performing an auditory tone-localization task, we reveal how low alertness modulates evidence accumulation-related processes and its corresponding compensatory neural signatures.

We are conscious beings: Somehow, the activity of our brains and nervous systems gives rise to states in which we have subjective experiences; and when we are in such states, we are aware of specific content. Researchers are only beginning to develop an understanding of the neural mechanisms underlying these phenomena. Much of the research in the last few decades has focussed on discerning the neural correlates of consciousness, using neuroimaging methods. Correlates, however, are not causes: to draw inferences about the processes that generate consciousness, rather than accompanying it, one must manipulate brain activity and examine the effects this has on conscious states and contents. One way to do so is to use brain stimulation techniques, such as transcranial magnetic stimulation (TMS). In this review, we survey the consciousness literature with a special emphasis on TMS studies. We begin by examining what is known about the neural substrates of states of consciousness���the kinds of brain activity that determine whether a person is awake, asleep, or suffering from a disorder of consciousness. We then delve into the contents of consciousness, by examining the literature on perceptual awareness. Throughout, we highlight current controversies and promising avenues for further research.

Highlights • pDOC patients are assessed for 2-years longitudinally with both the CRS-R and EEG. • Theta power and alpha clustering correlated strongest with changes in CRS-R scores. • EEG combined with CRS-R improved predictive power of future CRS-R scores. • Early changes in EEG outperformed early changes in CRS-R in terms of prognostic power. • Regular and repeated bedside EEG is feasible and has clinical utility for pDOC., Providing an accurate prognosis for prolonged disorder of consciousness (pDOC) patients remains a clinical challenge. Large cross-sectional studies have demonstrated the diagnostic and prognostic value of functional brain networks measured using high-density electroencephalography (hdEEG). Nonetheless, the prognostic value of these neural measures has yet to be assessed by longitudinal follow-up. We address this gap by assessing the utility of hdEEG to prognosticate long-term behavioural outcome, employing longitudinal data collected from a cohort of patients assessed systematically with resting hdEEG and the Coma Recovery Scale-Revised (CRS-R) at the bedside over a period of two years. We used canonical correlation analysis to relate clinical (including CRS-R scores combined with demographic variables) and hdEEG variables to each other. This analysis revealed that the patient’s age, and the hdEEG theta band power and alpha band connectivity, contributed most significantly to the relationship between hdEEG and clinical variables. Further, we found that hdEEG measures recorded at the time of assessment augmented clinical measures in predicting CRS-R scores at the next assessment. Moreover, the rate of hdEEG change not only predicted later changes in CRS-R scores, but also outperformed clinical measures in terms of prognostic power. Together, these findings suggest that improvements in functional brain networks precede changes in behavioural awareness in pDOC. We demonstrate here that bedside hdEEG assessments conducted at specialist nursing homes are feasible, have clinical utility, and can complement clinical knowledge and systematic behavioural assessments to inform prognosis and care.

The ability to make decisions based on external information, prior knowledge and evidence, is a crucial aspect of cognition and may determine the success and survival of an organism. Despite extensive work on decision-making mechanisms/models, understanding the effects of alertness on neural and cognitive processes remain limited. Here we use electroencephalography and behavioural modelling to characterise cognitive and neural dynamics of perceptual decision-making in awake/low alertness periods in humans (14 male, 18 female) and characterise the compensatory mechanisms as alertness decreases. Well-rested human participants, changing between full-wakefulness and low alertness, performed an auditory tone-localisation task and its behavioural dynamics was quantified with psychophysics, signal detection theory and drift-diffusion modelling, revealing slower reaction times, inattention to the left side of space, and a lower rate of evidence accumulation in periods of low alertness. Unconstrained multivariate pattern analysis (decoding) showed a ~280ms delayed onset driven by low alertness of the neural signatures differentiating between left and right decision, with a spatial reconfiguration from centro-parietal to lateral frontal regions 150-360ms. To understand the neural compensatory mechanisms with decreasing alertness, we connected the evidence-accumulation behavioural parameter to the neural activity, showing in the early periods (125-325ms) a shift in the associated patterns from right parietal regions in awake, to right fronto-parietal during low alertness. This change in the neurobehavioural dynamics for central accumulation related cognitive processes define a clear reconfiguration of the brain networks’ regions and dynamics needed for the implementation of decision-making, revealing mechanisms of resilience of cognition when challenged by decreased alertness.Significance statementMost living organisms make multiple daily decisions and these require a degree of evidence from both the environment and the internal milieu. Such decisions are usually studied under sequential sampling models and involve making a behavioural choice based on sensory encoding, central accumulation, and motor implementation processes. Since there is little research on how decreasing alertness affects such cognitive processes, this study has looked at the cognitive and neural dynamics of perceptual decision-making in people while fully awake and in drowsy periods. Using computational modelling of behaviour and neural dynamics on human participants performing an auditory tone-localisation task, we reveal how low alertness modulates evidence accumulation related processes and its corresponding compensatory neural signatures.

Selective attention is the process of directing limited capacity resources to behaviourally relevant stimuli while ignoring competing stimuli that are currently irrelevant. Studies in healthy human participants and in individuals with focal brain lesions have suggested that the right parietal cortex is crucial for resolving competition for attention. Following right-hemisphere damage, for example, patients may have difficulty reporting a brief, left-sided stimulus if it occurs with a competitor on the right, even though the same left stimulus is reported normally when it occurs alone. Such "extinction" of contralesional stimuli has been documented for all the major sense modalities, but it remains unclear whether its occurrence reflects involvement of one or more specific subregions of the temporo-parietal cortex. Here we employed repetitive transcranial magnetic stimulation (rTMS) over the right hemisphere to examine the effect of disruption of two candidate regions - the supramarginal gyrus (SMG) and the superior temporal gyrus (STG) - on auditory selective attention. Eighteen neurologically normal, right-handed participants performed an auditory task, in which they had to detect target digits presented within simultaneous dichotic streams of spoken distractor letters in the left and right channels, both before and after 20 min of 1 Hz rTMS over the SMG, STG or a somatosensory control site (S1). Across blocks, participants were asked to report on auditory streams in the left, right, or both channels, which yielded focused and divided attention conditions. Performance was unchanged for the two focused attention conditions, regardless of stimulation site, but was selectively impaired for contralateral left-sided targets in the divided attention condition following stimulation of the right SMG, but not the STG or S1. Our findings suggest a causal role for the right inferior parietal cortex in auditory selective attention.

Accurate diagnosis and prognosis of disorders of consciousness is complicated by the variability amongst patients’ trajectories. However, the majority of research and scientific knowledge in this field is based on cross-sectional studies. The translational gap in applying this knowledge to inform clinical management can only be bridged by research that systematically examines follow-up. In this study, we present findings from a novel longitudinal study of the long-term recovery trajectory of 39 patients, repeatedly assessed using the Coma Recovery Scale-Revised once every 3 months for 2 years, generating 185 assessments. Despite the expected inter-patient variability, there was a statistically significant improvement in behaviour over time. Further, improvements began approximately 22 months after injury. Individual variation in the trajectory of recovery was influenced by initial diagnosis. Patients with an initial diagnosis of unresponsive wakefulness state, who progressed to the minimally conscious state, did so at a median of 485 days following onset—later than 12-month period after which current guidelines propose permanence. Although current guidelines are based on the expectation that patients with traumatic brain injury show potential for recovery over longer periods than those with non-traumatic injury, we did not observe any differences between trajectories in these two subgroups. However, age was a significant predictor, with younger patients showing more promising recovery. Also, progressive increases in arousal contributed exponentially to improvements in behavioural awareness, especially in minimally conscious patients. These findings highlight the importance of indexing arousal when measuring awareness, and the potential for interventions to regulate arousal to aid long-term behavioural recovery in disorders of consciousness., Patients with disorder of consciousness showed longitudinal improvements in behaviour consistent with a pattern of late recovery. Variability in recovery was predicted by initial diagnosis and age. It was progressively influenced by arousal, highlighting the importance of measuring arousal and the potential for arousal interventions to aid recovery., Graphical Abstract Graphical Abstract

Clinicians are regularly faced with the difficult challenge of diagnosing consciousness after severe brain injury. As such, as many as 40% of minimally conscious patients who demonstrate fluctuations in arousal and awareness are known to be misdiagnosed as unresponsive/vegetative based on clinical consensus. Further, a significant minority of patients show evidence of hidden awareness not evident in their behaviour. Despite this, clinical assessments of behaviour are commonly used as bedside indicators of consciousness. Recent advances in functional high-density electroencephalography (hdEEG) have indicated that specific patterns of resting brain connectivity measured at the bedside are strongly correlated with the re-emergence of consciousness after brain injury. We report case studies of four patients with traumatic brain injury who underwent regular assessments of hdEEG connectivity and Coma Recovery Scale-Revised (CRS-R) at the bedside, as part of an ongoing longitudinal study. The first, a patient in an unresponsive wakefulness state (UWS), progressed to a minimally-conscious state several years after injury. HdEEG measures of alpha network centrality in this patient tracked this behavioural improvement. The second patient, contrasted with patient 1, presented with a persistent UWS diagnosis that paralleled with stability on the same alpha network centrality measure. Patient 3, diagnosed as minimally conscious minus (MCS-), demonstrated a significant late increase in behavioural awareness to minimally conscious plus (MCS+). This patient’s hdEEG connectivity across the previous 18 months showed a trajectory consistent with this increase alongside a decrease in delta power. Patient 4 contrasted with patient 3, with a persistent MCS- diagnosis that was similarly tracked by consistently high delta power over time. Across these contrasting cases, hdEEG connectivity captures both stability and recovery of behavioural trajectories both within and between patients. Our preliminary findings highlight the feasibility of bedside hdEEG assessments in the rehabilitation context and suggest that they can complement clinical evaluation with portable, accurate and timely generation of brain-based patient profiles. Further, such hdEEG assessments could be used to estimate the potential utility of complementary neuroimaging assessments, and to evaluate the efficacy of interventions.

We recently demonstrated that drowsiness, indexed using EEG, was associated with left-inattention in a group of 26 healthy right-handers. This has been linked to alertness-related modulation of spatial bias in left neglect patients and the greater persistence of left, compared with right, neglect following injury. Despite handedness being among the most overt aspects of human lateralization, studies of this healthy analogue of left neglect have only been conducted with predominantly or exclusively right-handed individuals. Here, with a group of 26 healthy non-right-handers we demonstrate that, unlike right-handers who showed a rightward shift in attention with drowsiness, non-right-handers showed the opposite pattern on an auditory spatial localization task. The current results are the first indication that factors linked to handedness can affect the development and extremity of spatial biases, potentially conferring resilience to clinical symptoms in non-right-handers and, given that 90% of us are right-handed, why left neglect is disproportionately persistent.

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method based on frequency and sleep graphoelements, which is capable of detecting micro variations in alertness. The validity of the automated method was verified by training and testing the algorithm using a dataset where participants are known to fall asleep. In addition, we tested generalizability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience.

Unilateral brain damage can lead to a striking deficit in awareness of stimuli on one side of space called Spatial Neglect. Patient studies show that neglect of the left is markedly more persistent than of the right and that its severity increases under states of low alertness. There have been suggestions that this alertness-spatial awareness link may be detectable in the general population. Here, healthy human volunteers performed an auditory spatial localisation task whilst transitioning in and out of sleep. We show, using independent electroencephalographic measures, that normal drowsiness is linked with a remarkable unidirectional tendency to mislocate left-sided stimuli to the right. The effect may form a useful healthy model of neglect and help in understanding why leftward inattention is disproportionately persistent after brain injury. The results also cast light on marked changes in conscious experience before full sleep onset.