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.

Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors.

A consistent limitation when designing event-related potential paradigms and interpreting results is a lack of consideration of the multivariate factors that affect their elicitation and detection in behaviorally unresponsive individuals. This paper provides a retrospective commentary on three factors that influence the presence and morphology of long-latency event-related potentials-the P3b and N400. We analyze event-related potentials derived from electroencephalographic (EEG) data collected from small groups of healthy youth and healthy elderly to illustrate the effect of paradigm strength and subject age; we analyze ERPs collected from an individual with severe traumatic brain injury to illustrate the effect of stimulus presentation speed. Based on these critical factors, we support that: (1) the strongest paradigms should be used to elicit event-related potentials in unresponsive populations; (2) interpretation of event-related potential results should account for participant age; and (3) speed of stimulus presentation should be slower in unresponsive individuals. The application of these practices when eliciting and recording event-related potentials in unresponsive individuals will help to minimize result interpretation ambiguity, increase confidence in conclusions, and advance the understanding of the relationship between long-latency event-related potentials and states of consciousness.

Localization and Identification of Brain Microstructural Abnormalities in Paediatric Concussion.

In the United States, approximately 2.53 million people sustain a concussion each year. Relative to adults, youth show greater cognitive deficits following concussion and a longer recovery. An accurate and reliable imaging method is needed to determine injury severity and symptom resolution. The primary objective of this study was to characterize concussions with diffusion tensor imaging (DTI). This was performed through a normative Z-scoring analysis of DTI metrics, fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD), to quantify patient-specific injuries and identify commonly damaged brain regions in paediatric concussion patients relative to healthy controls. It was hypothesized that personalizing the detection analysis through normative Z-scoring would provide an understanding of trauma-induced microstructural damage. Concussion patients were volunteers recruited from the Emergency Department of the McMaster Children's Hospital with a recent concussion (n = 26), 9 males and 17 females, mean age 14.22 ± 2.64, while healthy paediatric brain DTI datasets (25 males and 24 females, mean age 13.52 ± 1.03) were obtained from an MRI data repository. Significant abnormalities were commonly found in the longitudinal fasciculus, fronto-occipital fasciculus, and corticospinal tract, while unique abnormalities were localized in a number of other areas reflecting the individuality of each child's injury. Total injury burden, determined by the number of regions containing outliers per DTI metric per patient, was used as the metric to quantify the overall injury severity of each patient. The primary outcome of this analysis found that younger patients experienced a significantly greater injury burden when measured using fractional anisotropy (p < 0.001). These results show that DTI was able to detect microstructural changes caused by concussion, on a per-person basis, and has the potential to be a useful tool for improving diagnostic accuracy and prognosis of a concussion.

Evaluating Adherence to Return to School and Activity Protocols in Children After Concussion.

OBJECTIVE: The need to have a pediatric-specific concussion management protocol on Return to School (RTS) and Return to Activity (RTA) after concussion has been recognized internationally. The first step to evaluate the protocol effectiveness is to establish whether children and youth are adhering to these recommendations. The objective of this study was to explore the prevalence and predictors of adherence to RTS and RTA concussion management protocols for children/youth. DESIGN: A prospective cohort of children/youth with concussion. SETTING: Childhood Disability Research Centre. PARTICIPANTS: One hundred thirty-nine children/youth aged 5 to 18 years, diagnosed with concussion and symptomatic upon enrollment, were followed for up to 6 months. Primary recruitment occurred from a Children's Hospital Emergency Department. INTERVENTION: Provision of RTS/RTA guidelines. MAIN OUTCOME MEASURES: Measurement of adherence came from multiple sources, including the child's and parent's knowledge of protocols, research personnel evaluations, and self-reported stages of RTS/RTA and Post-Concussion Symptom Scale (PCSS) scores. RESULTS: Spearman correlations and logistic regression were used, investigating the relationship between PCSS and progression of protocols and determining predictors of adherence. Significant negative associations between total PCSS score and stage of RTS/RTA protocols were found. Fifty-three percent and 56% of the participants adhered to the RTS and RTA protocols, respectively. CONCLUSIONS: Children's knowledge of protocols and total PCSS scores significantly predicted adherence to RTS/RTA and may be the most important factors in predicting adherence during recovery from concussion.

Clinical neurophysiology: Research methods and event-related potential components as assessment tools.

The paramount importance of research design and research methodologies within the shared space of neurology, clinical neurophysiology, and cognitive neuroscience serves as the theme around which a range of topics is presented. After a tour of historical figures of human electrophysiology and electroencephalography (EEG), the discussion turns to event-related potential (ERP). Emphasizing the lengthy history of these manifestations of cognition, the chapter outlines the extensive research literature that has demonstrated the sensitivity of ERPs to a range cognitive functions, including attention, language processing, and memory. There follows a series of examples of ERP applications in the clinical domain, including disorders of consciousness, stroke, autism, coma, and concussion. These examples not merely demonstrate the general utility of these electrophysiological responses but stress that their independence from behavioral responses provides a much needed clinical method to assess individuals who are literally or virtually impossible to assess using traditional behaviorally based clinical tools. The chapter concludes with the suggestion that is time that the incontrovertible utility of ERPs be employed more fully within clinical contexts to assist the clinical community in providing objective assessments of a range of neurologic conditions.

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

Neurophysiological markers of cognitive deficits and recovery in concussed adolescents.

OBJECTIVE: The present study sought to determine: 1) whether concussed adolescents exhibited deficits in neurocognitive functioning as reflected by neurophysiological alterations; 2) if neurophysiological alterations could be linked to supplementary data such as the number of previous concussions and days since injury; and 3) if deficits in psychological health and behavioural tests increased during diagnosis duration. METHODS: Twenty-six concussed adolescents were compared to twenty-eight healthy controls with no prior concussions. Self-report inventories evaluated depressive and concussive symptomatology, while behavioral tests evaluated cognitive ability qualitatively. To assess neurophysiological markers of cognitive function, two separate auditory oddball tasks were employed: 1) an active oddball task measuring executive control and attention as reflected by the N2b and P300, respectively; and 2) a passive oddball task assessing the early, automatic pre-conscious awareness processes as reflected by the MMN. RESULTS: Concussed adolescents displayed delayed N2b and attenuated P300 responses relative to controls; showed elevated levels of depressive and concussive symptomatology; scored average-to- low-average in behavioral tests; and exhibited N2b response latencies that correlated with number of days since injury. CONCLUSION: These findings demonstrate that concussed adolescents exhibit clear deficiencies in neurocognitive function, and that N2b response latency may be a marker of concussion recovery.

N2b Reflects the Cognitive Changes in Executive Functioning After Concussion: A Scoping Review.

Objectives: The N2b is an event-related potential (ERP) component thought to index higher-order executive function. While the impact of concussion on executive functioning is frequently discussed in the literature, limited research has been done on the role of N2b in evaluating executive functioning in patients with concussion. The aims of this review are to consolidate an understanding of the cognitive functions reflected by the N2b and to account for discrepancies in literature findings regarding the N2b and concussion. Methods: A scoping review was conducted on studies that used the N2b to measure cognitive functioning in healthy control populations, as well as in people with concussions. Results: Sixty-six articles that met inclusion criteria demonstrated that the N2b effectively represents stimulus-response conflict management, response selection, and response inhibition. However, the 19 included articles investigating head injury (using terms such as concussion, mild head injury, and mild traumatic brain injury) found widely varied results: some studies found the amplitude of the N2b to be increased in the concussion group, while others found it to be decreased or unchanged. Conclusion: Based on the available evidence, differences in the amplitude of the N2b have been linked to response selection, conflict, and inhibition deficits in concussion. However, due to large variations in methodology across studies, findings about the directionality of this effect remain inconclusive. The results of this review suggest that future research should be conducted with greater standardization and consistency.

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.

Development of a point of care system for automated coma prognosis: a prospective cohort study protocol.

INTRODUCTION: Coma is a deep state of unconsciousness that can be caused by a variety of clinical conditions. Traditional tests for coma outcome prediction are based mainly on a set of clinical observations. Recently, certain event-related potentials (ERPs), which are transient electroencephalogram (EEG) responses to auditory, visual or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (ie, emergence). However, such tests require the skills of clinical neurophysiologists, who are not commonly available in many clinical settings. Additionally, none of the current standard clinical approaches have sufficient predictive accuracies to provide definitive prognoses. OBJECTIVE: The objective of this study is to develop improved machine learning procedures based on EEG/ERP for determining emergence from coma. METHODS AND ANALYSIS: Data will be collected from 50 participants in coma. EEG/ERP data will be recorded for 24 consecutive hours at a maximum of five time points spanning 30 days from the date of recruitment to track participants' progression. The study employs paradigms designed to elicit brainstem potentials, middle-latency responses, N100, mismatch negativity, P300 and N400. In the case of patient emergence, data are recorded on that occasion to form an additional basis for comparison. A relevant data set will be developed from the testing of 20 healthy controls, each spanning a 15-hour recording period in order to formulate a baseline. Collected data will be used to develop an automated procedure for analysis and detection of various ERP components that are salient to prognosis. Salient features extracted from the ERP and resting-state EEG will be identified and combined to give an accurate indicator of prognosis. ETHICS AND DISSEMINATION: This study is approved by the Hamilton Integrated Research Ethics Board (project number 4840). Results will be disseminated through peer-reviewed journal articles and presentations at scientific conferences. TRIAL REGISTRATION NUMBER: NCT03826407.

Electrophysiological evidence for the integral nature of tone in Mandarin spoken word recognition.

Current models of spoken word recognition have been predominantly based on studies of Indo-European languages. As a result, less is known about the recognition processes involved in the perception of tonal languages (e.g., Mandarin Chinese), and the role of lexical tone in speech perception. One view is that words in tonal languages are processed phonologically through individual segments, while another view is that they are processed lexically as a whole. Moreover, a recent study claimed to be the first to discover an early phonological processing stage in Mandarin (Huang et al., 2014). There seems to be a lack of investigations concerning tonal languages, as no clear conclusions have been reached about the nature of tonal processes, or a model of spoken word recognition that best incorporates lexical tone. The current study addressed these issues by presenting 18 native Mandarin speakers with aural sentences with medial target words. These either matched or mismatched the preceding visually presented sentences with medial target words (e.g, /jia1/home). Violation conditions involved target words that differed in the following ways: tone violation, where only the tone was different (e.g., /jia4/"price"), onset violation, where only the onset was different (e.g., /xia1/"shrimp"), and syllable violation, where both the tone and the onset were different (e.g., /tang2/"candy"). We did not find evidence for an early phonological processing stage in Mandarin. Instead, our findings indicate that Mandarin syllables are processed incrementally through phonological segments and that tone is strongly associated with lexical access. These results are discussed with respect to modifications for existing models in spoken word recognition to incorporate the processes involved with tonal language recognition.

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes.

There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions. However, as such evidence is limited to group-level analyzes, the extent to which they enable concussion detection at the individual-level is unclear. One promising avenue of research is the use of machine learning to create quantitative predictive models that can detect prior concussions in individuals. In this paper, we translate the recent group-level findings from ERP studies of concussed individuals into a machine learning framework for performing single-subject prediction of past concussion. We found that a combination of statistics of single-subject ERPs and wavelet features yielded a classification accuracy of 81% with a sensitivity of 82% and a specificity of 80%, improving on current practice. Notably, the model was able to detect concussion effects in individuals who sustained their last injury as much as 30 years earlier. However, failure to detect past concussions in a subset of individuals suggests that the clear effects found in group-level analyses may not provide us with a full picture of the neurophysiological effects of concussion.

Disruption of function: Neurophysiological markers of cognitive deficits in retired football players.

OBJECTIVE: Recent studies demonstrate that sports-related concussions can have negative consequences on long-term brain health. The goal of the present study was to determine whether retired Canadian Football League (CFL) athletes with a history of concussions exhibit alterations in neurocognitive functioning, along with changes in physical, social, and psychological health. METHODS: Our study compared nineteen retired CFL athletes' concussion histories to eighteen healthy age-matched controls with no history of concussion. Self-report inventories were used to assess depression, memory, attention, and general health. Neurophysiological markers of cognitive function were evaluated with event-related brain potentials (ERPs) as measured in two protocols: (1) A Mismatch Negativity (MMN) protocol for assessing the automatic early attentional brain mechanism; and, (2) a P300 auditory oddball task for assessing consciously controlled attention. RESULTS: Relative to controls, CFL players exhibited: response delays and reduced amplitudes in neurophysiological responses; overall decreases in cognitive function; and poorer scores on self-reports of physical, social, and psychological health; reflecting problems in all three categories. CONCLUSION: Our findings demonstrate that multiple concussions sustained over several years can lead to altered cognitive and psychosocial function. SIGNIFICANCE: Neurophysiological markers of conscious and pre-conscious attention provide an objective assessment for evaluating long-term cognitive consequences of concussion.

A Machine Learning Framework for Automatic and Continuous MMN Detection With Preliminary Results for Coma Outcome Prediction.

Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficult to detect. Previously, MMN detection was based on visual inspection of the averaged ERPs by a skilled clinician, a process that is expensive and not always feasible in practice. In this paper, we propose a practical machine learning (ML) based approach for detection of MMN component, thus, improving the accuracy of prediction of emergence from coma. Furthermore, the method can operate on an automatic and continuous basis thus alleviating the need for clinician involvement. The proposed method is capable of the MMN detection over intervals as short as two minutes. This finer time resolution enables identification of waxing and waning cycles of a conscious state. An auditory odd-ball paradigm was applied to 22 healthy subjects and 2 coma patients. A coma patient is tested by measuring the similarity of the patient's ERP responses with the aggregate healthy responses. Because the training process for measuring similarity requires only healthy subjects, the complexity and practicality of training procedure of the proposed method are greatly improved relative to training on coma patients directly. Since there are only two coma patients involved with this study, the results are reported on a very preliminary basis. Preliminary results indicate we can detect the MMN component with an accuracy of 92.7% on healthy subjects. The method successfully predicted emergence in both coma patients when conventional methods failed. The proposed method for collecting training data using exclusively healthy subjects is a novel approach that may prove useful in future, unrelated studies where ML methods are used.

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.

Fractal Analysis of Brain Blood Oxygenation Level Dependent (BOLD) Signals from Children with Mild Traumatic Brain Injury (mTBI).

BACKGROUND: Conventional imaging techniques are unable to detect abnormalities in the brain following mild traumatic brain injury (mTBI). Yet patients with mTBI typically show delayed response on neuropsychological evaluation. Because fractal geometry represents complexity, we explored its utility in measuring temporal fluctuations of brain resting state blood oxygen level dependent (rs-BOLD) signal. We hypothesized that there could be a detectable difference in rs-BOLD signal complexity between healthy subjects and mTBI patients based on previous studies that associated reduction in signal complexity with disease. METHODS: Fifteen subjects (13.4 ± 2.3 y/o) and 56 age-matched (13.5 ± 2.34 y/o) healthy controls were scanned using a GE Discovery MR750 3T MRI and 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe rs-BOLD (TE/TR = 35/2000ms), acquired over 6 minutes. Motion correction was performed and anatomical and functional images were aligned and spatially warped to the N27 standard atlas. Fractal analysis, performed on grey matter, was done by estimating the Hurst exponent using de-trended fluctuation analysis and signal summation conversion methods. RESULTS AND CONCLUSIONS: Voxel-wise fractal dimension (FD) was calculated for every subject in the control group to generate mean and standard deviation maps for regional Z-score analysis. Voxel-wise validation of FD normality across controls was confirmed, and non-Gaussian voxels (3.05% over the brain) were eliminated from subsequent analysis. For each mTBI patient, regions where Z-score values were at least 2 standard deviations away from the mean (i.e. where |Z| > 2.0) were identified. In individual patients the frequently affected regions were amygdala (p = 0.02), vermis(p = 0.03), caudate head (p = 0.04), hippocampus(p = 0.03), and hypothalamus(p = 0.04), all previously reported as dysfunctional after mTBI, but based on group analysis. It is well known that the brain is best modeled as a complex system. Therefore a measure of complexity using rs-BOLD signal FD could provide an additional method to grade and monitor mTBI. Furthermore, this approach can be personalized thus providing unique patient specific assessment.

EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect.

fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without reliance on behaviour. Because using fMRI in patients with DOC is difficult, studies increasingly employ EEG. However, there has been no verification that these modalities provide converging information at the individual subject level. The present study examined simultaneous EEG and fMRI in healthy volunteers during six mental imagery tasks to determine whether one mental imagery task generates more robust activation across subjects; whether activation can be predicted from familiarity with the imagined activity; and whether EEG and fMRI converge upon the same conclusions about individual imagery performance. Mental arithmetic generated the most robust activation in the majority of subjects for both EEG and fMRI, and level of activation could not be predicted from familiarity, with either modality. We conclude that overall, EEG and fMRI agree regarding individual mental imagery performance.

Normal Brain Response to Propofol in Advance of Recovery from Unresponsive Wakefulness Syndrome.

Up to 40% of individuals with unresponsive wakefulness syndrome (UWS) actually might be conscious. Recent attempts to detect covert consciousness in behaviorally unresponsive patients via neurophysiological patterns are limited by the need to compare data from brain-injured patients to healthy controls. In this report, we pilot an alternative within-subject approach by using propofol to perturb the brain state of a patient diagnosed with UWS. An auditory stimulation series was presented to the patient before, during, and after exposure to propofol while high-density electroencephalograph (EEG) was recorded. Baseline analysis revealed residual markers in the continuous EEG and event-related potentials (ERPs) that have been associated with conscious processing. However, these markers were significantly distorted by the patient's pathology, challenging the interpretation of their functional significance. Upon exposure to propofol, changes in EEG characteristics were similar to what is seen in healthy individuals and ERPs associated with conscious processing disappeared. At the 1-month follow up, the patient had regained consciousness. We offer three alternative explanations for these results: (1) the patient was covertly consciousness, and was anesthetized by propofol administration; (2) the patient was unconscious, and the observed EEG changes were a propofol-specific phenomenon; and (3) the patient was unconscious, but his brain networks responded normally in a way that heralded the possibility of recovery. These alternatives will be tested in a larger study, and raise the intriguing possibility of using a general anesthetic as a probe of brain states in behaviorally unresponsive patients.

Automatic and continuous assessment of ERPs for mismatch negativity detection.

Accurate and fast detection of event related potential (ERP) components is an unresolved issue in neuroscience and critical health care. Mismatch negativity (MMN) is a component of the ERP to an odd stimulus in a sequence of identical stimuli which has good correlation with coma awakening. All of the previous studies for MMN detection are based on visual inspection of the averaged ERPs (over a long recording time) by a skilled neurophysiologist. However, in practical situations, such an expert may not be available or familiar with all aspects of evoked potential methods. Further, we may miss important clinically essential events due to the implicit averaging process used to acquire the ERPs. In this paper we propose a practical machine learning approach for automatic and continuous assessment of the ERPs for detecting the presence of the MMN component. The proposed method is realized in a classification framework. Performance of the proposed method is demonstrated on 22 healthy subjects through a leave-one subject-out strategy where the MMN components are identified with about 93% accuracy.

Ballistocardiogram correction in simultaneous EEG/ fMRI recordings: a comparison of average artifact subtraction and optimal basis set methods using two popular software tools.

Electroencephalography data recorded during functional magnetic resonance imaging acquisition are subject to large cardiac-related artifacts that must be corrected during postprocessing. This study compared two widely used ballistocardiogram (BCG) correction algorithms as implemented in two software programs. Reduction of BCG amplitude, correlation of corrected data with electrocardiogram traces, correlation of independent components with electrocardiogram traces, and event-related potential signal-to-noise ratio from each algorithm were compared. Both algorithms effectively reduced the BCG artifact, with a slight advantage of average artifact subtraction over the optimal basis set method (0.1-2.2%) when the quality of the correction was examined at the individual subject level. This study provides users of these software tools with an important, practical, and previously unavailable comparison of the performance of these two methods.