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. 2024 Oct 25;15(1):e200390. doi: 10.1212/CPJ.0000000000200390

Clinical Implementation of fMRI and EEG to Detect Cognitive Motor Dissociation

Lessons Learned in an Acute Care Hospital

Yelena G Bodien 1,*, Matteo Fecchio 1,*, Holly J Freeman 1, William R Sanders 1, Anogue Meydan 1, Phoebe K Lawrence 1, John E Kirsch 1, David Fischer 1, Joseph Cohen 1, Emily Rubin 1, Julian H He 1, Pamela W Schaefer 1, Leigh R Hochberg 1, Otto Rapalino 1, Sydney S Cash 1, Michael J Young 1,†, Brian L Edlow 1,†,✉
PMCID: PMC11515113  PMID: 39473967

Abstract

Background

Cognitive motor dissociation (CMD) occurs when patients with severe brain injury follow commands on task-based functional MRI or EEG assessment despite demonstrating no behavioral evidence of language function. Recognizing the value of identifying patients with CMD, evidence-based guidelines published in the United States and Europe now recommend that these assessments are conducted as part of clinical care for select patients.

Recent Findings

We describe our institutionally supported approach for clinical assessment of CMD and report lessons learned so that other centers can more easily implement these evaluations. Among the key lessons are the need to consider ethical implications of CMD assessment; establish standardized local protocols for patient selection, data acquisition, analysis, and interpretation; and develop effective strategies for communication of test results.

Implications for Practice

Independent validation of methods to assess CMD is not available. Our approach for clinical CMD assessment is intended to be flexible, allowing for iterative improvements as the evidence base grows.

Introduction

For patients with severe brain injury, failing to detect signs of consciousness could lead to inaccurate diagnosis and prognosis, as well as premature withdrawal of life-sustaining treatment or limited access to rehabilitative care. Assessment of consciousness relies primarily on the behavioral examination, which results in approximately 40% of patients who retain consciousness being misdiagnosed as unconscious.1 Standardized behavioral measures such as the Coma Recovery Scale-Revised (CRS-R)2,3 decrease misdiagnosis rates but remain susceptible to confounding factors that may mask consciousness.

Over the past 2 decades, advanced diagnostic tools (e.g., task-based functional MRI [fMRI] and electroencephalography [EEG]) aimed at detecting consciousness have demonstrated that up to 25% of patients who do not follow commands at the bedside respond to commands covertly (e.g., “imagine playing tennis”).4-7 This phenomenon, known as cognitive motor dissociation (CMD),8 has been observed in patients with disorders of consciousness (DoC) across etiologies and recovery trajectories.9 Although task-based fMRI and EEG have methodological limitations,10,11 when combined with behavioral assessments, these approaches are poised to decrease DoC misdiagnosis and improve prognostic precision.

Before 2018, task-based fMRI and EEG assessment of consciousness were conceptualized as research tools used only at institutions with access to specialized infrastructure and personnel trained to acquire, analyze, and interpret the data. However, guidelines published by professional organizations in the United States3 and Europe12 now recommend that that these techniques be considered for clinical assessment of some patients (eTable 1), magnifying the need to address implementation challenges.

In response to receiving an increasing volume of requests from local clinicians to evaluate patients for CMD, we established the Massachusetts General Hospital Emerging Consciousness Program (MGH ECP) in January 2020. The goal of this program, which is composed of a multidisciplinary team of clinicians and investigators (eAppendix 1 in the supplementary materials), is to provide guideline-informed care to patients with DoC and to support their families with state-of-the-art diagnostic and prognostic information. In the ECP's efforts to integrate CMD detection into clinical assessment of patients with DoC, we learned valuable lessons that we present here for the clinical community.

Lesson 1: Ethical Considerations Warrant Explicit Deliberation When Using Task-Based fMRI and EEG to Detect Consciousness After Brain Injury

Essential ethical considerations emanate from decisions to conduct advanced fMRI and EEG assessments to detect and predict recovery of consciousness after severe brain injury.13 These considerations proceed from the fundamental principles of biomedical ethics, including respect for patient autonomy, beneficence, nonmaleficence, and justice. Accordingly, the provision of advanced assessments to behaviorally unresponsive patients after brain injury may be consistent with the framework that clarifying a patient's state of consciousness is intertwined with fundamental human and civil rights.14 When and how these assessments ought to be used; how to responsibly communicate results; approaches to equity and disparities in access; and how to optimally capture the benefits of these technologies while safeguarding against unintended harms are issues requiring proactive ethical consideration.15 Additional ethical considerations are provided in the supplementary materials (eAppendix 2, Lesson 1).

Lesson 2: Establish Institutional Consensus for Implementing Guideline Recommendations Related to the Use of Task-Based fMRI and EEG in DoC Assessment

We developed a proposal to apply published guidelines supporting the use of task-based fMRI and EEG for clinical assessment of CMD and sought support from leadership and core clinical teams within the Departments of Neurology and Radiology. The hospital's administrative leadership was then engaged to facilitate implementation. The implementation phase included hosting informational sessions for clinical teams to provide education about these techniques, their application to specific clinical cases, and operational procedures for ordering the tests. In planning, a concern among some colleagues was the lack of independent validation of these assessments and the nuance required to interpret findings. These concerns prompted adoption of commercially available data acquisition and analysis platforms when possible and development of standardized guidance for interpreting results and communicating findings.

Lesson 3: Understand Regulatory Considerations Related to Transitioning From Research to Clinical Implementation of Task-Based fMRI and EEG

Task-based fMRI and EEG have been used to detect CMD in the context of research for nearly 2 decades.4-6,16-18 Our local institutional policy limits clinical use of radiologic tools developed solely for research applications. However, use of FDA-approved/cleared tools that are indicated for other purposes (e.g., presurgical cortical mapping with BrainLab, Prism Clinical Imaging, and Syngo.Via) is permitted. We, therefore, use the commercially available BrainLab Elements blood-oxygen-level–dependent (BOLD) MRI mapping (iPlan Cranial 3.0.6.14, AG Munich Germany)19 platform for our initial evaluation of CMD and in parallel conduct a quality assurance analysis with FMRIB's Software Library (FSL) to quantitatively evaluate whether BOLD responses are localized to the expected brain regions.5 We acquire EEG data with FDA-approved clinical devices and continue to use previously published EEG acquisition and analytic pipelines5,6,17 for data analysis because FDA-approved approaches are not available. Studies fostering multisite standardization and validation of task-based fMRI and EEG for detecting CMD are needed to further support dissemination and clinical implementation.

Lesson 4: Develop Standard Operating Procedures for Patient Selection

Assessment of consciousness by task-based fMRI and EEG is not indicated for every patient with DoC, and clinical guidelines vary in recommending who should be assessed for CMD (e.g., patients with an ambiguous diagnosis after serial standardized behavioral assessments3 vs patients who do not follow commands on behavioral assessment12). Recently published decision trees providing guidance on when task-based fMRI20 and EEG21 are indicated recommend conducting standardized neurobehavioral assessment (e.g., with the CRS-R2) before pursuing task-based fMRI or EEG. Patients with a behavioral diagnosis of coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS), or low-level minimally conscious state (MCS) and with a broad spectrum of brain injury etiologies, may be candidates for task-based fMRI and EEG. Safety considerations and timing of assessments are detailed in the supplementary materials (eAppendix 2, Lesson 4, and eTable 2).

Lesson 5: Optimize Standardized Data Acquisition for the Clinical Setting

A barrier to translation of task-based fMRI and EEG is their reliance on technologies that have not been standardized, independently validated, or commercially produced for this indication. We simplified and standardized fMRI and EEG data acquisition research protocols for the clinical setting and describe these protocols and required equipment and resources in the supplementary materials (eAppendix 2, Lesson 5; eFigure 1, and eTables 3 and 4).

In brief, we administer the motor imagery command “imagine opening and closing your hand” in a blocked “ON/OFF” design. fMRI acquisition uses a 3T scanner and 12-channel or 32-channel head coil. The paradigm is approximately 5 minutes long. The EEG, which can be completed at the bedside or in an outpatient clinic room, uses a standard 19-electrode system; data are acquired at a sampling rate of at least 256 Hz. The EEG paradigm is 12 minutes long. For both fMRI and EEG, the paradigm is pre-recorded and played through an mp3 (fMRI) or mp4 (EEG) file.

Lesson 6: Optimize Data Analysis Pipelines for Rapid Result Reporting

There are no published guidelines for the optimal approach to analyzing fMRI and EEG data acquired to detect CMD. Multiple analytic strategies exist (e.g., region of interest4,5 vs multivariate pattern analysis16 for fMRI and support vector machine learning5,6,17 vs spectral analysis22 for EEG).7 Our primary fMRI analytic approach is the standard pipeline available in a commercially available fMRI software program. As a quality assurance step, we conduct a parallel, quantitative analysis with FSL following previously published procedures.5 CMD is confirmed when at least one suprathreshold cluster of voxels is present within the supplementary motor areas or premotor cortices (Figure 1). EEG data are analyzed using EEGLAB and customized MATLAB code (MathWorks, Natick, MA) providing 4 pieces of complementary information: (1) p value indicating the probability that the classifier distinguished task from rest conditions by chance, (2) accuracy of the classifier's performance, (3) channel-level power spectral density averaged across epochs, and (4) topological map of the contribution of each channel to classifying the conditions (Figure 1). These task-based fMRI and EEG analyses require expertise that is not uniformly available. Details of the data processing approach and considerations related to using local pipelines for clinical applications are provided in the supplementary materials (eAppendix 2, Lesson 6).

Figure 1. fMRI and EEG Results of a Motor Imagery Task in a Healthy Individual and a Patient With a Behavioral Diagnosis of Vegetative State.

Figure 1

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On the left, a healthy individual (top panel) and a patient with a diagnosis of vegetative state on the CRS-R (bottom panel) completed an fMRI motor imagery task consisting of the command to “imagine opening and closing your right hand.” fMRI data analyzed using a commercial platform (A, C) and research tools (B, D) show activations (red clusters, which are areas that exceed the statistical threshold of T = 3.1 [A, C] and Z = 3.1 [B, D]) within supplementary motor and premotor areas. When data are analyzed using research tools, a region of interest mask composed of the SMA and PMC (blue area in B, D) can be used to quantitatively confirm the location of the areas of activation. On the right, a healthy individual (top panel) and a patient with a diagnosis of vegetative state on the CRS-R (bottom panel) completed an EEG motor imagery task consisting of the command to “imagine opening and closing your right hand.” In both cases, the classifier discriminated between the “ON” condition (i.e., imagine) and “OFF” condition (i.e., stop imagining). In the topographic plots, hot colors (e.g., saturated red) are associated with electrodes that discriminate between the “ON” and “OFF” conditions. CRS-R = Coma Recovery Scale-Revised; EEG = electroencephalography; fMRI = functional MRI; PMC = premotor cortex; SMA = supplementary motor area.

Lesson 7: Interpreting fMRI and EEG Results

In the absence of published guidelines for interpreting task-based fMRI and EEG results, we developed a local CMD data interpretation approach that is quantitative, rigorous, and transparent (Figure 2). For fMRI, we aimed to achieve a balance between adhering to common neuroradiologic clinical practices (i.e., visual inspection of images, which can lead to variability in determining CMD) and leveraging a robust scientific literature that establishes the presence of CMD by identifying suprathreshold voxels in an ROI. For EEG, a visual inspection approach for detecting CMD does not exist, so we rely on quantitative methods (i.e., determining the statistical significance and accuracy of a classifier that discriminates command-following and “rest” conditions). An interpretation decision tree stratifies the determination of CMD as “probable,” “possible,” or “indeterminate” (Figure 2 and eAppendix 2, Lesson 7).

Figure 2. Task-Based fMRI and EEG Data Interpretation Decision Trees.

Figure 2

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(A) After fMRI data are acquired, data are visually inspected for evidence of artifact (e.g., from motion and metallic implants). Data are then analyzed using a commercially available fMRI software program and visualized with a statistical threshold (T = 3.1, p ∼ 0.001). If suprathreshold activations are observed, research software (e.g., FSL) is used as a quality assurance check to evaluate whether activations are within the expected regions associated with the task (e.g., SMA and PMC). If activations are not observed using commercial software or are not within the prespecified region of interest, the statistical threshold is decreased to T = 2.35 (p ∼ 0.01). The final interpretation of probable (green box) or possible (beige box) CMD is related to the threshold at which activations are observed, as well as their location. No activations in SMA or PMC are interpreted as an “indeterminate” result (red box). Note: the T-statistics and Z-statistics have slightly different meanings (e.g., only the Z-statistic is corrected at the cluster level), but the thresholds are the same to maintain consistency. (B) After EEG data acquisition, data are visually inspected for evidence of artifact. Data are then analyzed using an EEG support vector machine classifier. If the classifier significantly differentiates the task “ON” (“imagine”) from the task “OFF” (“stop imagining”) conditions (i.e., p < 0.05), the accuracy is evaluated to establish whether CMD is probable (green box) or possible (beige box). If the classifier does not differentiate the 2 conditions (p ≥ 0.05), the result is indeterminate (red box). In panels A and B, the number in the brackets of each colored box is linked to an interpretation of the result provided in Table 1. CMD = cognitive motor dissociation; fMRI = functional MRI; FSL = FMRIB Software Library; PMC = premotor cortex; ROI = region of interest; SMA = supplementary motor area.

The Challenge of Interpreting “Negative” fMRI and EEG Results

One of the paramount challenges in analyzing and interpreting task-based fMRI and EEG data is the high rate with which covert command-following is not detected in patients who follow commands at the bedside5,7,18 and even in healthy individuals.5,23 Factors such as motion degradation, arousal fluctuation, and even normal variability in brain function may contribute to an apparent false-negative result (eTable 5). Our approach to detecting CMD is unlikely to result in a false-positive result because of rigorous statistical thresholding used in data analysis, but as a result, the potential risk of false-negative results increases. Therefore, a positive result is considered meaningful while a negative result is considered “indeterminate.” If feasible, repeat assessment may be considered when results are indeterminate.

Lesson 8: Communicating Results to Families and Clinical Teams

Communicating task-based fMRI and EEG findings requires a balance between providing the results, explaining the nuanced interpretation of the findings, and describing the strengths and limitations of testing methods.24 To maximize clinical impact, our analyses are conducted within hours of testing, ensuring that results are shared in a timely manner. Until recently, scientific evidence supported assessment of CMD primarily for diagnostic purposes. However, CMD may also be associated with a greater likelihood of recovering at least partial independence by as early as 3 months after injury and a faster time to achieving that level of function.6,17 Thus, we communicate the diagnostic and prognostic relevance of CMD while providing context around the limitations of these approaches (Table 1 and eAppendix 2, Lesson 8).

Table 1.

Sharing fMRI and EEG Results With Families and Clinicians

Scenario fMRI or EEG CMD result Interpretation
1 Data quality unacceptable or irreparable analytic errors Factors such as excessive motion (fMRI or EEG), signal dropout from a ventricular peritoneal shunt (fMRI), or poor spatial registration (fMRI) prevent data analysis and interpretation
2 Indeterminate Negative results should be interpreted as indeterminate rather than an “inability to follow commands” because many factors can contribute to a negative response (e.g., fluctuating arousal, normal variability in brain responses, motion artifact, sedation, and task complexity)
3 Possible Despite the absence of evidence for language function on the behavioral examination, the patient may be able to understand language and follow commands
4 Probable Despite the absence of evidence for language function on the behavioral examination, the patient probably understands language and follows commands; in patients with acute disorders of consciousness, CMD may be associated with a greater likelihood of achieving at least partial independence
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Abbreviations: CMD = cognitive motor dissociation; fMRI = functional MRI.

The numbered scenarios in this table are intended to accompany Figure 2, where a decision tree based on fMRI and EEG findings leads to a determination regarding presence of CMD. Indeterminate, possible, and probable CMD are qualitative terms that represent increasing certainty based on the statistical threshold at which evidence of CMD is observed.

Lesson 9: Engaging Clinical Teams Through Education and Training

We have found that clinicians caring for patients with DoC are engaged and interested in using task-based fMRI and EEG to improve assessment of consciousness and are appreciative of an overview of these techniques and the evidence that supports their use. We provide frequent education in formal (e.g., through seminars) and informal (e.g., at the bedside or the scanner) settings on the advantages and limitations of using these approaches for detecting consciousness after a severe brain injury. MRI and EEG technicians are provided with basic training in the use of MRI compatible headphones, simultaneous initiation of the scanning protocol and paradigm, and the EEG tablet (eAppendix 2, Lesson 9).

Lesson 10: Understanding Payment and Reimbursement

Insurance companies are not uniformly accustomed to reimbursing hospitals for task-based fMRI and EEG for patients with DoC. However, multiple Current Procedural Terminology (CPT) codes may be applied for the technical and professional fees associated with task-based fMRI and EEG data acquisition and interpretation.25 Billing and reimbursement for these services can thus be achieved where appropriate (eAppendix 2, Lesson 10).

Conclusion

Clinical guidelines recommending task-based fMRI and EEG to detect consciousness were released more than 6 years ago.3,12 We describe our experience implementing these recommendations at an academic medical center. Challenges across sites may vary because of a lack of commercial tools that streamline data acquisition and analysis, inconsistent availability of technologies, and local policies. Important opportunities exist to harmonize approaches for detecting and monitoring CMD across sites, including the potential to establish a centralized registry of patients with CMD that ascertains the impact of CMD detection on health outcomes. The lessons we learned may be translatable to other health care systems, although we recognize that expanding access to these assessments requires coordinated validation and regulatory and implementation efforts, with continued embedded ethics expertise.26

TAKE-HOME POINTS

  • → Patients who seem unable to follow commands after brain injury may follow commands covertly when assessed with fMRI or EEG (i.e., cognitive motor dissociation [CMD]).

  • → Guidelines published by professional organizations in the United States and Europe now recommend that fMRI and EEG be considered for clinical assessment of CMD.

  • → We implemented clinical fMRI and EEG assessment for detection of CMD in a single academic medical center.

  • → In developing a program to provide these assessments clinically, we learned key lessons, such as the need to consider ethical implications of CMD assessment; establish standardized local protocols for patient selection, data acquisition, analysis, and interpretation; and develop effective strategies for communication of test results.

  • → The lessons we learned may be translatable to other health care systems, although expanding access to these assessments requires coordinated validation, implementation, and regulatory efforts.

Acknowledgment

The authors appreciate the support of the Massachusetts General Hospital (MGH) nurses, respiratory therapists, radiology technicians, and electrophysiology technicians who are instrumental in performing fMRI and EEG assessments for cognitive motor dissociation. The authors thank Drs. Merit Cudkowicz, Bruce Rosen, and Taylor Kimberly for providing institutional support for the MGH Emerging Consciousness Program and Breen Galperin and Sarah Duffy for providing administrative guidance in establishing the program. The authors are grateful to Dr. Andrew Cole for supporting our efforts on behalf of the MGH Division of Clinical Neurophysiology and Epilepsy. The authors also appreciate the support of Dr. Raj Gupta and the MGH Division of Neuroradiology.

Appendix. Authors

Name Location Contribution
Yelena G. Bodien, PhD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston; Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Matteo Fecchio, PhD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Holly J. Freeman, MS Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Analysis or interpretation of data
William R. Sanders, BSc Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston; Geisel School of Medicine at Dartmouth Medical School College, Hanover, NH Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Anogue Meydan, BS Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Major role in the acquisition of data
Phoebe K. Lawrence, BS Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Major role in the acquisition of data
John E. Kirsch, PhD Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown; Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston Major role in the acquisition of data; study concept or design; analysis or interpretation of data
David Fischer, MD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia Study concept or design
Joseph Cohen, BS Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston Major role in the acquisition of data; study concept or design
Emily Rubin, MD Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data
Julian H. He, MD Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; analysis or interpretation of data
Pamela W. Schaefer, MD Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston Major role in the acquisition of data; study concept or design; analysis or interpretation of data
Leigh R. Hochberg, MD, PhD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data
Otto Rapalino, MD Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Sydney S. Cash, MD, PhD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Michael J. Young, MD, MPhil Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data
Brian L. Edlow, MD Center for Neurotechnology and Neurorecovery, Department of Neurology and Harvard Medical School, Massachusetts General Hospital, Boston; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
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Study Funding

MGH Department of Neurology, NIH Director's Office (DP2 HD101400), and the Chen Institute MGH Research Scholar Award.

Disclosure

Y.G. Bodien, M. Fecchio, H.J. Freeman, W.R. Sanders, A. Meydan, P.K. Lawrence, M.J. Young, B.L. Edlow were funded by the MGH Department of Neurology and NIH Director's Office (DP2 HD101400). B.L. Edlow was funded by the Chen Institute MGH Research Scholar Award. J.E. Kirsch, D. Fischer, J. Cohen, E. Rubin, J.H. He, P.W. Schaefer, J.T. Giacino, L.R. Hochberg, O. Rapalino, S.S. Cash have no disclosures related to this work. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp.

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