Emergence from Disorders of Consciousness: Optimizing Self-Agency Through Communication

Abstract

Language and communication deficits are intrinsic to disorders of consciousness. This article will provide an overview of language and communication deficits that can significantly confound the accuracy of diagnostic assessment in these patients. Authors will also discuss interventions to promote early communication using assistive technology and augmentative communication rehabilitation strategies. Finally, this article will discuss the importance of family education as well as ethical considerations connected to the recovery of communication and adaptive strategies to support patient autonomy and enhance self-agency.

Keywords: Assistive devices; Brain injury; Communication disorders; Consciousness disorders.

Fig. 1.. Dual stream model of neural basis of language.

The neural basis of language has been evolving to a more complex model with involvement of many cortical areas and white matter tracts than the classic model simply consisting of the Broca and Wernicke centers and the arcuate fasciculus (AF) connecting the above two. Adding to the dual stream model, there is another system inside the frontal lobe for “driving of speech.” Recently named the frontal aslant tract (FAT) is associated with initiation and spontaneity of speech. The dorsal stream is associated with phonological processing via the superior longitudinal fasciculus (SLF) as a major trunk of the network. The SLF/AF consists of several subcomponents in 2 layers, the SLF II, the SLF III, and the SLF temporo-parietal (SLF TP) in the superficial layer and the classic AF in the deep layer. The ventral stream is associated with semantic processing. It is assumed that the network consists of intratemporal network, such as the middle longitudinal fasciculus (MLF) and the inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF) as an interlobe network. (From: Fujii M, Maesawa S, Ishiai S, Iwami K, Futamura M, Saito K. Neural Basis of Language: An Overview of An Evolving Model. Neurol Med Chir (Tokyo). 2016;56(7):379-386. https://doi.org/10.2176/nmc.ra.2016-0014. Open Access.)

Fig. 2.. CRS-R Score Sheet.

^aDenotes minimally conscious state minus (MCS−). ^bDenotes minimally conscious status plus (MCS+). ^cDenotes emergence from eMCS. TCC test completion code. (From: Kalmar K, Giacino J. The JFK coma recovery scale-revised. Neuropsychological Rehabilitation. 2005;15(3–4):454- 460. https://doi.org/10.1080/09602010443000425.)

Fig. 3.

CRS-R Test Completion Codes to be applied to subscale scores. (Adapted from: Kalmar K, Giacino J. The JFK coma recovery scale-revised. Neuropsychological Rehabilitation. 2005;15(3–4):454-460. https://doi.org/10.1080/09602010443000425. Update by: Yelena Bodien, PhD, Camille Chatelle, PhD, Joseph Giacino, Ph.D. (2020).)

Fig. 4.

Functional MRI imaging during mental imagery comparison. (Top) DoC patient and (Bottom) healthy volunteers. (From: Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Using Functional Magnetic Resonance Imaging to Detect Covert Awareness in the Vegetative State. Arch Neurol. 2007;64(8):1098. https://doi.org/10.1001/archneur.64.8.1098. With permission.)

Fig. 5.. Percentage of functional MRI and EEG responders in patients and healthy subjects when presented with language, music, and motor imagery stimulus.

Results for patients without behavioral evidence of language function (Language−; ie, CRS-R/CAP-based behavioral diagnosis indicates coma, vegetative state, or MCS) are represented as red bars. Results for patients with behavioral evidence of language function (Language +; ie, CRS-R/CAP-based behavioral diagnosis indicates MCS + or posttraumatic confusional state) are represented as blue bars. Results for healthy subjects (Control) are represented as purple bars. (From: Edlow BL, Chatelle C, Spencer CA, et al. Early detection of consciousness in patients with acute severe traumatic. Brain. 2017 Sep 1;140(9):2399-2414. https://doi.org/10.1093/brain/awx176.)

Fig. 6.

BCI technology. (A and B): EEG-Based BCI cap and electrodes example. (C): Example of patient utilizing EEG-Based BCI with computer feedback.