Progression of auditory discrimination based on neural decoding predicts awakening from coma

Cardiac signals inform auditory regularity processing in the absence of consciousness

In healthy awake individuals, the neural processing of bodily signals is not only essential for survival but can also influence perception and compete with external stimulus processing. Yet, the mechanism underlying this bidirectional processing of bodily and external stimuli, as well as its persistence or modulation in unconscious states, remains largely unknown. Here, we investigated the role of cardiac activity on auditory regularity processing in coma. We recorded continuous electroencephalography and electrocardiography in 48 comatose patients on the first day after cardiac arrest during a closed-loop auditory paradigm. We tested whether sounds presented in synchrony with the ongoing heartbeat and sounds presented with fixed, isochronous intervals, would facilitate auditory processing, compared to an asynchronous sequence with variable heartbeat-to-sound and sound-to-sound intervals and a baseline without auditory stimulation. To assess sound prediction based on sequence regularity, we introduced sound omissions within the sequences, violating expected auditory patterns. In coma survivors only, the neural omission response differed in the synchronous against both control conditions. These results were corroborated by a multivariate decoding analysis of the single-trial neural responses to the synchronous omissions and baseline wherein survivors exhibited a higher degree of cardio-audio regularity encoding compared to nonsurvivors. Furthermore, omissions within the synchronous sequence elicited a heart rate deceleration exclusively in coma survivors, which was predictive of patient outcome. We show that the unconscious human brain infers on the temporal relationship across cardiac and auditory inputs and that the neural and cardiac correlates of cardio-audio regularity encoding are predictive of patient outcome.

Opportunities and Challenges in the Diagnosis and Treatment of Disorders of Consciousness

Disorders of consciousness (DOCs) are a dynamic and challenging field, presenting significant difficulties for clinicians and neurorehabilitation specialists due to the lack of reliable assessment methods and effective intervention strategies [...].

Temporal dynamics of neural synchrony and complexity of auditory EEG responses in post-hypoxic ischemic coma

The capacity to integrate information across brain regions and sufficient diversity of neural activity is necessary for consciousness. In patients in a post-hypoxic ischemic coma, the integrity of the auditory processing network is indicative of chances of regaining consciousness. However, our understanding of how measures of integration and differentiation of auditory responses manifest across time of coma is limited. We investigated the temporal evolution of neural synchrony of auditory-evoked electroencephalographic (EEG) responses, measured via their phase-locking value (PLV), and of their neural complexity in unconscious post-hypoxic ischemic comatose patients. Our results show that the PLV was predictive of chances to regain consciousness within the first 40 h post-cardiac arrest, while its predictive value diminished over subsequent time after coma onset. This was due to changing trajectories of PLV over time of coma for non-survivors, while survivors had stable PLV. The complexity of EEG responses was not different between patients who regained consciousness and those who did not, but it significantly diminished over time of coma, irrespective of the patient's outcome. Our findings provide novel insights on the optimal temporal window for assessing auditory functions in post-hypoxic ischemic coma. They are of particular importance for guiding the implementation of quantitative techniques for prognostication and contribute to an evolving understanding of neural functions within the acute comatose state.

Decoding Analyses Show Dynamic Waxing and Waning of Event-Related Potentials in Coma Patients

Background/Objectives: Coma prognosis is challenging, as patient presentation can be misleading or uninformative when using behavioral assessments only. Event-related potentials have been shown to provide valuable information about a patient's chance of survival and emergence from coma. Our prior work revealed that the mismatch negativity (MMN) in particular waxes and wanes across 24 h in some coma patients. This "cycling" aspect of the presence/absence of neurophysiological responses may require fine-grained tools to increase the chances of detecting levels of neural processing in coma. This study implements multivariate pattern analysis (MVPA) to automatically quantify patterns of neural discrimination between duration deviant and standard tones over time at the single-subject level in seventeen healthy controls and in three comatose patients. Methods: One EEG recording, containing up to five blocks of an auditory oddball paradigm, was performed in controls over a 12 h period. For patients, two EEG sessions were conducted 3 days apart for up to 24 h, denoted as day 0 and day 3, respectively. MVPA was performed using a support-vector machine classifier. Results: Healthy controls exhibited reliable discrimination or classification performance during the latency intervals associated with MMN and P3a components. Two patients showed some intervals with significant discrimination around the second half of day 0, and all had significant results on day 3. Conclusions: These findings suggest that decoding analyses can accurately classify neural responses at a single-subject level in healthy controls and provide evidence of small but significant changes in auditory discrimination over time in coma patients. Further research is needed to confirm whether this approach represents an improved technology for assessing cognitive processing in coma.

Development of the relationship between visual selective attention and auditory change detection

Understanding the developmental trajectories of the auditory and visual systems is crucial to elucidate cognitive maturation and its associated relationships, which are essential for effectively navigating dynamic environments. Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. However, the evolution of these processes and their relationship over time remains unclear. In this study, we recorded electroencephalography signals from 118 participants (42 adults and 76 typically developing children) during separate visual localization and auditory-embedded fixation tasks. Further, we employed both ERP analysis and multivariate pattern machine learning to investigate developmental patterns. ERP amplitude and decoding accuracy provided convergent evidence underlying a linear developmental trajectory for visual selective attention and an inverted U-shaped trajectory for auditory change detection from childhood to adulthood. Importantly, our findings confirmed the established association of an N2 pc-MMN in adults using a larger sample size, and further identified a positive correlation between decoding accuracy for visual target location and decoding accuracy for auditory stimulus type specifically in adults. However, both visual-auditory correlation effects were absent in children. Our study provides neurophysiological insights into the distinct developmental trajectories of visual selective attention and auditory change detection. It highlights that the close relationship between individual differences in the two processes emerges alongside their respective maturation and does not become evident until adulthood.

Evoked potentials in patients with disorders of consciousness

Acute coma in the intensive care unit and persistent disorders of consciousness (DoC) in neuro-rehabilitation are frequent in patients with hypoxic-ischemic encephalopathy after cardiac arrest (CA), traumatic brain injury, intracranial hemorrhage, or ischemic stroke. Reliable prognostication of long-term neurologic outcomes cannot be made by clinical examination alone in the early phase for many patients, and thus, additional investigations are necessary. Evoked potentials provide inexpensive, real-time, high temporal resolution, bedside, quantifiable information on different sensory pathways into the brain including local and global cortical processing. Short-latency somatosensory evoked potentials can reliably predict poor neurologic long-term outcome in the early phase after CA and are recommended by guidelines as one investigation within an early multimodal assessment. Middle-latency and event-related or cognitive evoked potentials provide information on the integrity of more advanced cortical processing, some closely related to consciousness. This information can help to identify those comatose patients with a good prognosis in the acute phase and help to better understand their precise clinical state and the chances of further recovery in patients with persistent DoC in neuro-rehabilitation. Further studies are necessary to improve the applicability of research findings in the clinical sphere.

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Global Field Time-Frequency Representation-Based Discriminative Similarity Analysis of Passive Auditory ERPs for Diagnosis of Disorders of Consciousness

Behavioural diagnosis of patients with disorders of consciousness (DOC) is challenging and prone to inaccuracies. Consequently, there have been increased efforts to develop bedside assessment based on EEG and event-related potentials (ERPs) that are more sensitive to the neural factors supporting conscious awareness. However, individual detection of residual consciousness using these techniques is less established. Here, we hypothesize that the cross-state similarity (defined as the similarity between healthy and impaired conscious states) of passive brain responses to auditory stimuli can index the level of awareness in individual DOC patients. To this end, we introduce the global field time-frequency representation-based discriminative similarity analysis (GFTFR-DSA). This method quantifies the average cross-state similarity index between an individual patient and our constructed healthy templates using the GFTFR as an EEG feature. We demonstrate that the proposed GFTFR feature exhibits superior within-group consistency in 34 healthy controls over traditional EEG features such as temporal waveforms. Second, we observed the GFTFR-based similarity index was significantly higher in patients with a minimally conscious state (MCS, 40 patients) than those with unresponsive wakefulness syndrome (UWS, 54 patients), supporting our hypothesis. Finally, applying a linear support vector machine classifier for individual MCS/UWS classification, the model achieved a balanced accuracy and F1 score of 0.77. Overall, our findings indicate that combining discriminative and interpretable markers, along with automatic machine learning algorithms, is effective for the differential diagnosis in patients with DOC. Importantly, this approach can, in principle, be transferred into any ERP of interest to better inform DOC diagnoses.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

A Comprehensive Review and Practical Guide of the Applications of Evoked Potentials in Neuroprognostication After Cardiac Arrest

Cardiorespiratory arrest is a very common cause of morbidity and mortality nowadays, and many therapeutic strategies, such as induced coma or targeted temperature management, are used to reduce patient sequelae. However, these procedures can alter a patient's neurological status, making it difficult to obtain useful clinical information for the reliable estimation of neurological prognosis. Therefore, complementary investigations are conducted in the early stages after a cardiac arrest to clarify functional prognosis in comatose cardiac arrest survivors in the first few hours or days. Current practice relies on a multimodal approach, which shows its greatest potential in predicting poor functional prognosis, whereas the data and tools to identify patients with good functional prognosis remain relatively limited in comparison. Therefore, there is considerable interest in investigating alternative biological parameters and advanced imaging technique studies. Among these, somatosensory evoked potentials (SSEPs) remain one of the simplest and most reliable tools. In this article, we discuss the technical principles, advantages, limitations, and prognostic implications of SSEPs in detail. We will also review other types of evoked potentials that can provide useful information but are less commonly used in clinical practice (e.g., visual evoked potentials; short-, medium-, and long-latency auditory evoked potentials; and event-related evoked potentials, such as mismatch negativity or P300).

Cardio-audio synchronization elicits neural and cardiac surprise responses in human wakefulness and sleep

The human brain can encode auditory regularities with fixed sound-to-sound intervals and with sound onsets locked to cardiac inputs. Here, we investigated auditory and cardio-audio regularity encoding during sleep, when bodily and environmental stimulus processing may be altered. Using electroencephalography and electrocardiography in healthy volunteers (N = 26) during wakefulness and sleep, we measured the response to unexpected sound omissions within three regularity conditions: synchronous, where sound and heartbeat are temporally coupled, isochronous, with fixed sound-to-sound intervals, and a control condition without regularity. Cardio-audio regularity encoding manifested as a heartbeat deceleration upon omissions across vigilance states. The synchronous and isochronous sequences induced a modulation of the omission-evoked neural response in wakefulness and N2 sleep, the former accompanied by background oscillatory activity reorganization. The violation of cardio-audio and auditory regularity elicits cardiac and neural responses across vigilance states, laying the ground for similar investigations in altered consciousness states such as coma and anaesthesia.

A Novel Approach to Screen for Somatosensory Evoked Potentials in Critical Care

BACKGROUND

Somatosensory evoked potentials (SSEPs) help prognostication, particularly in patients with diffuse brain injury. However, use of SSEP is limited in critical care. We propose a novel, low-cost approach allowing acquisition of screening SSEP using widely available intensive care unit (ICU) equipment, specifically a peripheral "train-of-four" stimulator and standard electroencephalograph.

METHODS

The median nerve was stimulated using a train-of-four stimulator, and a standard 21-channel electroencephalograph was recorded to generate the screening SSEP. Generation of the SSEP was supported by visual inspection, univariate event-related potentials statistics, and a multivariate support vector machine (SVM) decoding algorithm. This approach was validated in 15 healthy volunteers and validated against standard SSEPs in 10 ICU patients. The ability of this approach to predict poor neurological outcome, defined as death, vegetative state, or severe disability at 6 months, was tested in an additional set of 39 ICU patients.

RESULTS

In each of the healthy volunteers, both the univariate and the SVM methods reliably detected SSEP responses. In patients, when compared against the standard SSEP method, the univariate event-related potentials method matched in nine of ten patients (sensitivity = 94%, specificity = 100%), and the SVM had 100% sensitivity and specificity when compared with the standard method. For the 49 ICU patients, we performed both the univariate and the SVM methods: a bilateral absence of short latency responses (n = 8) predicted poor neurological outcome with 0% FPR (sensitivity = 21%, specificity = 100%).

CONCLUSIONS

Somatosensory evoked potentials can reliably be recorded using the proposed approach. Given the very good but slightly lower sensitivity of absent SSEPs in the proposed screening approach, confirmation of absent SSEP responses using standard SSEP recordings is advised.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest

BACKGROUND AND OBJECTIVES

Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest.

METHODS

Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months.

RESULTS

One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery.

DISCUSSION

Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Tracking auditory mismatch negativity responses during full conscious state and coma

The mismatch negativity (MMN) is considered the electrophysiological change-detection response of the brain, and therefore a valuable clinical tool for monitoring functional changes associated with return to consciousness after severe brain injury. Using an auditory multi-deviant oddball paradigm, we tracked auditory MMN responses in seventeen healthy controls over a 12-h period, and in three comatose patients assessed over 24 h at two time points. We investigated whether the MMN responses show fluctuations in detectability over time in full conscious awareness, or whether such fluctuations are rather a feature of coma. Three methods of analysis were utilized to determine whether the MMN and subsequent event-related potential (ERP) components could be identified: traditional visual analysis, permutation t-test, and Bayesian analysis. The results showed that the MMN responses elicited to the duration deviant-stimuli are elicited and reliably detected over the course of several hours in healthy controls, at both group and single-subject levels. Preliminary findings in three comatose patients provide further evidence that the MMN is often present in coma, varying within a single patient from easily detectable to undetectable at different times. This highlights the fact that regular and repeated assessments are extremely important when using MMN as a neurophysiological predictor of coma emergence.

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Background

Despite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.

Methods

We present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.

Results

Analysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).

Conclusion

statistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge

About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.

Psychomotor Speed Predicts Outcome in Patients with Acute Meningitis and Encephalitis: A Prospective Observational Study

Background and Purpose. Although acute meningitis and encephalitis are known to cause cognitive dysfunction, the prognostic values of neuropsychological and neurophysiological tests in predicting clinical outcomes are seldom studied. We investigated specific neurocognitive dysfunction and event-related potentials (ERPs), which can predict functional outcomes in patients with acute meningitis and encephalitis. Methods. We enrolled consecutive adult patients with acute meningitis and encephalitis and performed neuropsychological tests and ERP studies using a passive auditory oddball paradigm at enrollment. Patient functional outcomes were assessed using the Glasgow Outcome Scale at 6 (GOS6) months after discharge. Results. Twenty-two patients were included in the study. Among 21 patients who performed neuropsychological tests, Korean-Trail Making Test-Elderly's version, Part A time (TMT-A time) correlated with GOS6, which remained significant even after controlling for age. We identified a significant association between TMT-A time and P3a latency. Post-hoc analysis showed that patients with longer TMT-A time (≥23 s) tended to have longer P3a latency than those with shorter TMT-A time. Conclusions. Decreased psychomotor speed predicted poor clinical outcomes. Because TMT-A time can be performed at the bedside in a relatively short time, this might be a useful neuropsychological biomarker to predict or monitor clinical outcomes. Furthermore, passive oddball P3a may be useful in patients with more severe disease who are unable to perform the TMT task.

Complementary roles of neural synchrony and complexity for indexing consciousness and chances of surviving in acute coma

An open challenge in consciousness research is understanding how neural functions are altered by pathological loss of consciousness. To maintain consciousness, the brain needs synchronized communication of information across brain regions, and sufficient complexity in neural activity. Coordination of brain activity, typically indexed through measures of neural synchrony, has been shown to decrease when consciousness is lost and to reflect the clinical state of patients with disorders of consciousness. Moreover, when consciousness is lost, neural activity loses complexity, while the levels of neural noise, indexed by the slope of the electroencephalography (EEG) spectral exponent decrease. Although these properties have been well investigated in resting state activity, it remains unknown whether the sensory processing network, which has been shown to be preserved in coma, suffers from a loss of synchronization or information content. Here, we focused on acute coma and hypothesized that neural synchrony in response to auditory stimuli would reflect coma severity, while complexity, or neural noise, would reflect the presence or loss of consciousness. Results showed that neural synchrony of EEG signals was stronger for survivors than non-survivors and predictive of patients' outcome, but indistinguishable between survivors and healthy controls. Measures of neural complexity and neural noise were not informative of patients' outcome and had high or low values for patients compared to controls. Our results suggest different roles for neural synchrony and complexity in acute coma. Synchrony represents a precondition for consciousness, while complexity needs an equilibrium between high or low values to support conscious cognition.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

Individual EEG profiling of attention deficits in left spatial neglect: A pilot study

Electrophysiological group studies in brain-damaged patients can be run to capture the EEG correlates of specific cognitive impairments. Nonetheless, this procedure is not adequate to characterize the inter-individual variability present in major neuropsychological syndromes. We tested the possibility of getting a reliable individual EEG characterization of deficits of endogenous orienting of spatial attention in right-brain damaged (RBD) patients with left spatial neglect (N+). We used a single-trial topographical analysis (STTA; [39] of individual scalp EEG topographies recorded during leftward and rightward orienting of attention with central cues in RBD patients with and without (N-) neglect and in healthy controls (HC). We found that the STTA successfully decoded EEG signals related to leftward and rightward orienting in five out of the six N+, five out of the six N- patients and in all the six HC. In agreement with findings from conventional average-group studies, successful classifications of EEG signals in N+ were observed during the 400-800 ms period post-cue-onset, which reflects preserved voluntary engagement of attention resources (ADAN component). These results suggest the possibility of acquiring reliable individual EEG profiles of neglect patients.

Pre-attentive Visual Processing in Alzheimer's Disease: An Event-related Potential Study

INTRODUCTION

While identifying Alzheimer's Disease (AD) in its early stages is crucial, traditional neuropsychological tests tend to lack sensitivity and specificity for its diagnosis. Neuropsychological studies have reported visual processing deficits of AD, and event-related potentials (ERPs) are suitable to investigate pre-attentive processing with superior temporal resolution.

OBJECTIVE

This study aimed to investigate visual attentional characteristics of adults with AD, from pre-attentive to attentive processing, using a visual oddball task and ERPs.

METHODS

Cognitively normal elderly controls (CN) and patients with probable AD (AD) were recruited. Participants performed a three-stimulus visual oddball task and were asked to press a designated button in response to the target stimuli. The amplitudes of 4 ERPs were analyzed. Mismatchnegativity (vMMN) was analyzed around the parieto-occipital and temporo-occipital regions. P3a was analyzed around the fronto-central regions, whereas P3b was analyzed around the centro-parietal regions.

RESULTS

Late vMMN amplitudes of the AD group were significantly smaller than those of the CN group, while early vMMN amplitudes were comparable. Compared to the CN group, P3a amplitudes of the AD group were significantly smaller for the infrequent deviant stimuli, but the amplitudes for the standard stimuli were comparable. Lastly, the AD group had significantly smaller P3b amplitudes for the target stimuli compared to the CN group.

CONCLUSION

Our findings imply that AD patients exhibit pre-attentive visual processing deficits, known to affect later higher-order brain functions. In a clinical setting, the visual oddball paradigm could be used to provide helpful diagnostic information since pre-attentive ERPs can be induced by passive exposure to infrequent stimuli.

Applied potential of task-free event-related paradigms for assessing neurocognitive functions in disorders of consciousness

Diagnosing patients with disorders of consciousness is immensely difficult and often results in misdiagnoses, which can have fatal consequences. Despite the severity of this well-known issue, a reliable assessment tool has not yet been developed and implemented in the clinic. The main aim of this focused review is to evaluate the various event-related potential paradigms, recorded using EEG, which may be used to improve the assessment of patients with disorders of consciousness; we also provide a brief comparison of these paradigms with other measures. Notably, most event-related potential studies on the topic have focused on testing a small set of components, or even just a single component. However, to be of practical use, we argue that an assessment should probe a range of cognitive and linguistic functions at once. We suggest a novel approach that combines a set of well-tested auditory event-related potential components: N100, mismatch negativity, P3a, N400, early left anterior negativity and lexical response enhancement. Combining these components in a single, task-free design will provide a multidimensional assessment of cognitive and linguistic processes, which may help physicians make a more precise diagnosis.

Brain functional connectivity during the first day of coma reflects long-term outcome

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Coma patients show different connectivity patterns depending on long-term outcome.

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Time-variance of functional connectivity is an early prognostic marker for coma patients.

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Connectivity patterns observed in chronic patients may develop early after coma onset.

Objective

In patients with disorders of consciousness (DOC), properties of functional brain networks at rest are informative of the degree of consciousness impairment and of long-term outcome. Here we investigate whether connectivity differences between patients with favorable and unfavorable outcome are already present within 24 h of coma onset.

Methods

We prospectively recorded 63-channel electroencephalography (EEG) at rest during the first day of coma after cardiac arrest. We analyzed 98 adults, of whom 57 survived beyond unresponsive wakefulness. Functional connectivity was estimated by computing the ‘debiased weighted phase lag index’ over epochs of five seconds duration. We evaluated the network’s topological features, including clustering coefficient, path length, modularity and participation coefficient and computed their variance over time. Finally, we estimated the predictive value of these topological features for patients’ outcomes by splitting the patient sample in training and test datasets.

Results

Group-level analysis revealed lower clustering coefficient, higher modularity and path length variance in patients with favorable compared to those with unfavorable outcomes (p < 0.01). Within all features, the path length variance in the network provided the best positive predictive value (PPV) for favorable outcome and specificity for unfavorable outcome in the test dataset (PPV: 0.83, p < 0.01; specificity: 0.86, p < 0.01) with above-chance negative predictive value and accuracy. Of note, the exclusion of patients with epileptiform activity (20 in total) eliminates all false positive predictions (n = 6) for path length variance.

Interpretation

Topological features of functional connectivity differ as a function of long-term outcome in patients on the first day of coma. These differences are not interpretable in terms of consciousness levels as all patients were in a deep unconscious state. The time variance of path length is informative of comatose patients’ outcome, as patients with favorable outcome exhibit a richer repertoire of path length than those with unfavorable outcomes.

Combined Behavioral and Mismatch Negativity Evidence for the Effects of Long-Lasting High-Definition tDCS in Disorders of Consciousness: A Pilot Study

Objective

To evaluate the effects of long-term High-definition transcranial direct current stimulation (HD-tDCS) over precuneus on the level of consciousness (LOC) and the relationship between Mismatch negativity (MMN) and the LOC over the therapy period in patients with Disorders of consciousness (DOCs).

Methods

We employed a with-in group repeated measures design with an anode HD-tDCS protocol (2 mA, 20 min, the precuneus) on 11 (2 vegetative state and nine minimally conscious state) patients with DOCs. MMN and Coma Recovery Scale-Revised (CRS-R) scores were measured at four time points: before the treatment of HD-tDCS (T0), after a single session of HD-tDCS (T1), after the treatment of 7 days (T2) and 14 days (T3). A frequency-deviant oddball paradigm with two deviation magnitudes (standard stimulus: 1000 Hz, small deviant stimuli: 1050 Hz, large deviant stimuli: 1200 Hz) was adopted to elicit MMN.

Results

Significant improvements of CRS-R score were found after 7-day (T2) and 14-day (T3) treatment compared with baseline (T0). Regarding the MMN, significant improvements of MMN amplitudes were observed after a single session of stimulation (T1), 7-day (T2) and 14-day treatment (T3) compared with baseline (T0). Additionally, there were significant negative correlations between CRS-R scores and MMN amplitudes elicited by both large and small deviant stimuli.

Conclusion

Long-term HD-tDCS over precuneus might improve signs of consciousness in patients with DOCs as measured by CRS-R total scores, and MMN could be an assistant assessment in the course of tDCS treatment.

Comparison of Multi-class Machine Learning Methods for the Identification of Factors Most Predictive of Prognosis in Neurobehavioral assessment of Pediatric Severe Disorder of Consciousness through LOCFAS scale

Severe Disorders of Consciousness (DoC) are generally caused by brain trauma, anoxia or stroke, and result in conditions ranging from coma to the confused-agitated state. Prognostic decision is difficult to achieve during the first year after injury, especially in the pediatric cases. Nevertheless, prognosis crucially informs rehabilitation decision and family expectations. We compared four multi-class machine learning classification approaches for the prognostic decision in pediatric DoC. We identified domains of a neurobehavioral assessment tool, Level of Cognitive Functioning Assessment Scale, mostly contributing to decision in a cohort of 124 cases. We showed the possibility to generalize to new admitted pediatric cases, thus paving the way for real employment of machine learning classifiers as an assistive tool to prognostic decision in clinics.

Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment

Concussion has been shown to leave the afflicted with significant cognitive and neurobehavioural deficits. The persistence of these deficits and their link to neurophysiological indices of cognition, as measured by event-related potentials (ERP) using electroencephalography (EEG), remains restricted to population level analyses that limit their utility in the clinical setting. In the present paper, a convolutional neural network is extended to capitalize on characteristics specific to EEG/ERP data in order to assess for post-concussive effects. An aggregated measure of single-trial performance was able to classify accurately (85%) between 26 acutely to post-acutely concussed participants and 28 healthy controls in a stratified 10-fold cross-validation design. Additionally, the model was evaluated in a longitudinal subsample of the concussed group to indicate a dissociation between the progression of EEG/ERP and that of self-reported inventories. Concordant with a number of previous studies, symptomatology was found to be uncorrelated to EEG/ERP results as assessed with the proposed models. Our results form a first-step towards the clinical integration of neurophysiological results in concussion management and motivate a multi-site validation study for a concussion assessment tool in acute and post-acute cases.

EEG and ANS markers of attention response in vegetative state: Different responses to own vs. other names

Covert measures of information-processing are valuable tools to support assessment of patients' disorders of consciousness because of their potential in revealing what seem to be hidden. Those measures allow to overcome some limitations of traditional behavioural methods, which are often biased by difficulties in detecting reliable patients' responses. Therefore, we aimed at exploring patterns of psychophysiological responses (electroencephalography - EEG, skin conductance level - SCL, skin conductance response - SCR, heart rate - HR) marking potentially-preserved processing of personally-relevant stimuli in a sample of VS patients. In particular, we compared the processing of own vs. other names due to the intrinsic salience, relevance, and familiarity of such stimuli. Analysis of electroencephalography, skin conductance and heart rate modulations highlighted a consistent pattern of increased skin conductance and heart rate measures in response to patients' own name with respect to other names. Further, we observed increased delta and decreased alpha activity over frontal areas in response to their own name with respect to other names. Own-name stimuli might preserve their peculiar qualification even after severe brain damage and might call on residual attention orientation and preferred coding resources, suggesting the existence of partly preserved information-processing pathways that extends beyond basic auditory sensory processing.

EEG Reactivity Evaluation Practices for Adult and Pediatric Hypoxic-Ischemic Coma Prognostication in North America

PURPOSE

The aim of this study was to assess the variability in EEG reactivity evaluation practices during cardiac arrest prognostication.

METHODS

A survey of institutional representatives from North American academic hospitals participating in the Critical Care EEG Monitoring Research Consortium was conducted to assess practice patterns involving EEG reactivity evaluation. This 10-question multiple-choice survey evaluated metrics related to technical, interpretation, personnel, and procedural aspects of bedside EEG reactivity testing and interpretation specific to cardiac arrest prognostication. One response per hospital was obtained.

RESULTS

Responses were received from 25 hospitals, including 7 pediatric hospitals. A standardized EEG reactivity protocol was available in 44% of centers. Sixty percent of respondents believed that reactivity interpretation was subjective. Reactivity bedside testing always (100%) started during hypothermia and was performed daily during monitoring in the majority (71%) of hospitals. Stimulation was performed primarily by neurodiagnostic technologists (76%). The mean number of activation procedures modalities tested was 4.5 (SD 2.1). The most commonly used activation procedures were auditory (83.3%), nail bed pressure (63%), and light tactile stimuli (63%). Changes in EEG amplitude alone were not considered consistent with EEG reactivity in 21% of centers.

CONCLUSIONS

There is substantial variability in EEG reactivity evaluation practices during cardiac arrest prognostication among North American academic hospitals. Efforts are needed to standardize protocols and nomenclature according with national guidelines and promote best practices in EEG reactivity evaluation.

A Primer on Electroencephalography and Event-Related Potentials for Organizational Neuroscience

Electroencephalography (EEG) was the first of the noninvasive brain measures in neuroscience. Technical advances over the last 100 years or so have rendered EEG a true brain imaging technique. Here, we provide an accessible primer on the biophysics of EEG, on measurement aspects, and on the analysis of EEG data. We use the example of event-related potentials (ERPs), although the issues apply equally to other varieties of EEG signals, and provide an overview of analytic methods at the base of the so-called electrical neuroimaging framework. We detail the interpretational strengths of electrical neuroimaging for organizational researchers and describe some domains of ongoing technical developments. We likewise emphasize practical considerations with the use of EEG in more real-world settings. This primer is intended to provide organizational researchers specifically, and novices more generally, an access point to understanding how EEG may be applied in their research.

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma

Objective

Prominent research in patients with disorders of consciousness investigated the electrophysiological correlates of auditory deviance detection as a marker of consciousness recovery. Here, we extend previous studies by investigating whether somatosensory deviance detection provides an added value for outcome prediction in postanoxic comatose patients.

Methods

Electroencephalography responses to frequent and rare stimuli were obtained from 66 patients on the first and second day after coma onset.

Results

Multivariate decoding analysis revealed an above chance‐level auditory discrimination in 25 patients on the first day and in 31 patients on the second day. Tactile discrimination was significant in 16 patients on the first day and in 23 patients on the second day. Single‐day sensory discrimination was unrelated to patients’ outcome in both modalities. However, improvement of auditory discrimination from first to the second day was predictive of good outcome with a positive predictive power (PPV) of 0.73 (CI = 0.52–0.88). Analyses considering the improvement of tactile, auditory and tactile, or either auditory or tactile discrimination showed no significant prediction of good outcome (PPVs = 0.58–0.68).

Interpretation

Our results show that in the acute phase of coma deviance detection is largely preserved for both auditory and tactile modalities. However, we found no evidence for an added value of somatosensory to auditory deviance detection function for coma‐outcome prediction.

A framework for the extended monitoring of levels of cognitive function in unresponsive patients

Generally, prognostication of coma outcome currently combines behavioral, reflex, and possibly neuroimaging tests that are interpreted by an attending physician. Electroencephalography, particularly, event-related brain potentials (ERP) have received attention due to evidence demonstrating the positive predictive value of certain ERP including the mismatch negativity (MMN) and the P3a, for coma emergence. We describe a set of ERP paradigms designed to require and reflect increasing levels of cognitive processing with the added objective of determining the influence of each paradigm's context strength on its ability to elicit ERPs. These paradigms were then used without explicit instructions to participants to attend to the stimuli to determine which paradigms possessed sufficient context "strength" to elicit ERPs in the absence of active participation on the part of the subject; a circumstance often encountered in brain injury patients. These paradigms were then validated on two groups of adults-younger and older, and the difference due to active participation was validated on another group of younger adults. Results show that paradigms with stronger stimulus context features performed better than those with weaker contexts, and that older adults generally had significantly attenuated and delayed responses compared to younger adults. Based on these findings, it is recommended the use of the auditory oddball paradigm that includes novel stimuli to elicit the mismatch negativity and P300, and semantic violation sentences to elicit the N400. These findings also reinforce the procedure of instructing participants about the requirements of a protocol-regardless of the patient's diagnosis or apparent state-in order to help those who are able to attend to show the most robust responses possible.

Measuring Depth in Still Water: Electrophysiologic Indicators of Residual Consciousness in the Unresponsive Patient

Searching for evidence of consciousness in outwardly unresponsive patients presents significant clinical challenges as the spectrum of disorders of consciousness has become more clearly defined, with clinical examination, functional MRI, and electrophysiologic tests having complementary roles in the investigation of minimally conscious patients, those in a locked-in state, coma, or in a vegetative state. Serial bedside electrophysiologic testing can probe for higher order cortical responses temporally and spatially propagated through cortical networks, while long-latency event-related potentials may help differentiate patients with coma or vegetative state from a state of residual consciousness. Transcranial magnetic stimulation co-registered to high-density EEG may reveal widespread pulse-stimulated cortical activation of various brain regions. These emerging electrophysiologic techniques show promise as powerful diagnostic, prognostic, and therapeutic tools.

Cardio-audio synchronization drives neural surprise response

Successful prediction of future events depends on the brain’s capacity to extract temporal regularities from sensory inputs. Neuroimaging studies mainly investigated regularity processing for exteroceptive sensory inputs (i.e. from outside the body). Here we investigated whether interoceptive signals (i.e. from inside the body) can mediate auditory regularity processing. Human participants passively listened to sound sequences presented in synchrony or asynchrony to their heartbeat while concomitant electroencephalography was recorded. We hypothesized that the cardio-audio synchronicity would induce a brain expectation of future sounds. Electrical neuroimaging analysis revealed a surprise response at 158–270 ms upon omission of the expected sounds in the synchronous condition only. Control analyses ruled out that this effect was trivially based on expectation from the auditory temporal structure or on differences in heartbeat physiological signals. Implicit neural monitoring of temporal regularities across interoceptive and exteroceptive signals drives prediction of future events in auditory sequences.

The potential role of auditory evoked potentials to assess prognosis in comatose survivors from cardiac arrest

AIM

Few data are available on the use of brainstem auditory evoked potentials (BAEPs) in combination with other electrophysiological tools to assess prognosis of comatose survivors from cardiac arrest (CA).

METHODS

Retrospective analysis of data from all adult patients (>18years of age) admitted to our Dept of Intensive Care after CA over a 6-year period who were comatose (Glasgow Coma Scale <9) on admission, had been treated with targeted temperature management and had BAEP testing. We collected variables related to CA, as well as electroencephalography (EEG) findings, N20 somatosensory evoked potentials, and the presence of I, III and/or V waves on BAEP testing. Outcome was assessed at 3 months using the Cerebral Performance Categories (3-5=poor outcome).

RESULTS

We studied 65 patients; 48 (74%) had a poor neurological outcome. BAEP assessment was performed day 3 [3,4] after the CA. At least one of the three waves was absent bilaterally in 34 patients (52%); of these patients, 29 (85%) had a poor neurological outcome (sensitivity 60%, specificity 71%, positive predictive value [PPV] 85% and negative predictive value [NPV] 39%). Three patients (5%) had bilateral absence of all three waves, all of whom had a poor neurological outcome.

CONCLUSIONS

In this series of patients after CA, at least one of the BAEP waves was absent bilaterally in half the survivors; however, their use for prediction of poor neurological outcome remains limited.

Clinical neurophysiology for neurological prognostication of comatose patients after cardiac arrest

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A multimodal prognostic approach is recommended after cardiac arrest.

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EEG (background and, reactivity, repetitive epileptiform features) and SSEP are core assessments.

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Some outlook into long-latency evoked potentials is offered.

Early prognostication of outcome in comatose patients after cardiac arrest represents a daunting task for clinicians, also considering the nowadays commonly used targeted temperature management with sedation in the first 24–48 h. A multimodal approach is currently recommended, in order to minimize the risks of false-positive prediction of poor outcome, including clinical examination off sedation, EEG (background characterization and reactivity, occurrence of repetitive epileptiform features), and early-latency SSEP responses represent the core assessments in this setting; they may be complemented by biochemical markers and neuroimaging.

This paper, which relies on a recent comprehensive review, focuses on an updated review of EEG and SSEP, and also offers some outlook into long-latency evoked potentials, which seem promising in clinical use.

Cognitive Processing in Non-Communicative Patients: What Can Event-Related Potentials Tell Us?

Event-related potentials (ERP) have been proposed to improve the differential diagnosis of non-responsive patients. We investigated the potential of the P300 as a reliable marker of conscious processing in patients with locked-in syndrome (LIS). Eleven chronic LIS patients and 10 healthy subjects (HS) listened to a complex-tone auditory oddball paradigm, first in a passive condition (listen to the sounds) and then in an active condition (counting the deviant tones). Seven out of nine HS displayed a P300 waveform in the passive condition and all in the active condition. HS showed statistically significant changes in peak and area amplitude between conditions. Three out of seven LIS patients showed the P3 waveform in the passive condition and five of seven in the active condition. No changes in peak amplitude and only a significant difference at one electrode in area amplitude were observed in this group between conditions. We conclude that, in spite of keeping full consciousness and intact or nearly intact cortical functions, compared to HS, LIS patients present less reliable results when testing with ERP, specifically in the passive condition. We thus strongly recommend applying ERP paradigms in an active condition when evaluating consciousness in non-responsive patients.

Clinical Applications for EPs in the ICU

In critically ill patients, evoked potential (EP) testing is an important tool for measuring neurologic function, signal transmission, and secondary processing of sensory information in real time. Evoked potential measures conduction along the peripheral and central sensory pathways with longer-latency potentials representing more complex thalamocortical and intracortical processing. In critically ill patients with limited neurologic exams, EP provides a window into brain function and the potential for recovery of consciousness. The most common EP modalities in clinical use in the intensive care unit include somatosensory evoked potentials, brainstem auditory EPs, and cortical event-related potentials. The primary indications for EP in critically ill patients are prognostication in anoxic-ischemic or traumatic coma, monitoring for neurologic improvement or decline, and confirmation of brain death. Somatosensory evoked potentials had become an important prognostic tool for coma recovery, especially in comatose survivors of cardiac arrest. In this population, the bilateral absence of cortical somatosensory evoked potentials has nearly 100% specificity for death or persistent vegetative state. Historically, EP has been regarded as a negative prognostic test, that is, the absence of cortical potentials is associated with poor outcomes while the presence cortical potentials are prognostically indeterminate. In recent studies, the presence of middle-latency and long-latency potentials as well as the amplitude of cortical potentials is more specific for good outcomes. Event-related potentials, particularly mismatch negativity of complex auditory patterns, is emerging as an important positive prognostic test in patients under comatose. Multimodality predictive algorithms that combine somatosensory evoked potentials, event-related potentials, and clinical and radiographic factors are gaining favor for coma prognostication.

EEG as an Indicator of Cerebral Functioning in Postanoxic Coma

Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.

The mismatch negativity as an index of cognitive decline for the early detection of Alzheimer's disease

Evidence suggests that Alzheimer's disease (AD) is part of a continuum, characterized by long preclinical phases before the onset of clinical symptoms. In several cases, this continuum starts with a syndrome, defined as mild cognitive impairment (MCI), in which daily activities are preserved despite the presence of cognitive decline. The possibility of having a reliable and sensitive neurophysiological marker that can be used for early detection of AD is extremely valuable because of the incidence of this type of dementia. In this study, we aimed to investigate the reliability of auditory mismatch negativity (aMMN) as a marker of cognitive decline from normal ageing progressing from MCI to AD. We compared aMMN elicited in the frontal and temporal locations by duration deviant sounds in short (400 ms) and long (4000 ms) inter-trial intervals (ITI) in three groups. We found that at a short ITI, MCI showed only the temporal component of aMMN and AD the frontal component compared to healthy elderly who presented both. At a longer ITI, aMMN was elicited only in normal ageing subjects at the temporal locations. Our study provides empirical evidence for the possibility to adopt aMMN as an index for assessing cognitive decline in pathological ageing.