Martin M. Monti

Willful Modulation of Brain Activity in Disorders of Consciousness

BACKGROUND The differential diagnosis of disorders of consciousness is challenging. The rate of misdiagnosis is approximately 40%, and new methods are required to complement bedside testing, particularly if the patients capacity to show behavioral signs of awareness is diminished. METHODS At two major referral centers in Cambridge, United Kingdom, and Liege, Belgium, we performed a study involving 54 patients with disorders of consciousness. We used functional magnetic resonance imaging (MRI) to assess each patients ability to generate willful, neuroanatomically specific, blood-oxygenation-level-dependent responses during two established mental-imagery tasks. A technique was then developed to determine whether such tasks could be used to communicate yes-or-no answers to simple questions. RESULTS Of the 54 patients enrolled in the study, 5 were able to willfully modulate their brain activity. In three of these patients, additional bedside testing revealed some sign of awareness, but in the other two patients, no voluntary behavior could be detected by means of clinical assessment. One patient was able to use our technique to answer yes or no to questions during functional MRI; however, it remained impossible to establish any form of communication at the bedside. CONCLUSIONS These results show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. Careful clinical examination will result in reclassification of the state of consciousness in some of these patients. This technique may be useful in establishing basic communication with patients who appear to be unresponsive.

The role of the right inferior frontal gyrus: inhibition and attentional control

There is growing interest regarding the role of the right inferior frontal gyrus (RIFG) during a particular form of executive control referred to as response inhibition. However, tasks used to examine neural activity at the point of response inhibition have rarely controlled for the potentially confounding effects of attentional demand. In particular, it is unclear whether the RIFG is specifically involved in inhibitory control, or is involved more generally in the detection of salient or task relevant cues. The current fMRI study sought to clarify the role of the RIFG in executive control by holding the stimulus conditions of one of the most popular response inhibition tasks–the Stop Signal Task–constant, whilst varying the response that was required on reception of the stop signal cue. Our results reveal that the RIFG is recruited when important cues are detected, regardless of whether that detection is followed by the inhibition of a motor response, the generation of a motor response, or no external response at all.

Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state

The vegetative (VS) and minimally conscious (MCS) states are currently distinguished on the basis of exhibited behaviour rather than underlying pathology. Although previous histopathological studies have documented different degrees of diffuse axonal injury as well as damage to the thalami and brainstem regions in VS and MCS, these differences have not been assessed in vivo, and therefore, do not provide a measurable pathological marker to aid clinical diagnosis. Currently, the diagnostic decision-making process is highly subjective and prone to error. Indeed, previous work has suggested that up to 43% of patients in this group may be misdiagnosed. We used diffusion tensor imaging (DTI) to study the neuropathology of 25 vegetative and minimally conscious patients in vivo and to identify measures that could potentially distinguish the patients in these two groups. Mean diffusivity (MD) maps of the subcortical white matter, brainstem and thalami were generated. The MCS and VS patients differed significantly in subcortical white matter and thalamic regions, but appeared not to differ in the brainstem. Moreover, the DTI results predicted scores on the Coma Recovery Scale (p<0.001) and successfully classified the patients in to their appropriate diagnostic categories with an accuracy of 95%. The results suggest that this method may provide an objective and highly accurate method for classifying these challenging patient populations and may therefore complement the behavioural assessment to inform the diagnostic decision making process.

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

The boundaries of language and thought in deductive inference

Is human thought fully embedded in language, or do some forms of thought operate independently? To directly address this issue, we focus on inference-making, a central feature of human cognition. In a 3T fMRI study we compare logical inferences relying on sentential connectives (e.g., not, or, if … then) to linguistic inferences based on syntactic transformation of sentences involving ditransitive verbs (e.g., give, say, take). When contrasted with matched grammaticality judgments, logic inference alone recruited “core” regions of deduction [Brodmann area (BA) 10p and 8m], whereas linguistic inference alone recruited perisylvian regions of linguistic competence, among others (BA 21, 22, 37, 39, 44, and 45 and caudate). In addition, the two inferences commonly recruited a set of general “support” areas in frontoparietal cortex (BA 6, 7, 8, 40, and 47). The results indicate that logical inference is not embedded in natural language and confirm the relative modularity of linguistic processes.

Brain–computer interfaces for communication with nonresponsive patients

A substantial number of patients who survive severe brain injury progress to a nonresponsive state of wakeful unawareness, referred to as a vegetative state (VS). They appear to be awake, but show no signs of awareness of themselves, or of their environment in repeated clinical examinations. However, recent neuroimaging research demonstrates that some VS patients can respond to commands by willfully modulating their brain activity according to instruction. Brain–computer interfaces (BCIs) may allow such patients to circumvent the barriers imposed by their behavioral limitations and communicate with the outside world. However, although such devices would undoubtedly improve the quality of life for some patients and their families, developing BCI systems for behaviorally nonresponsive patients presents substantial technical and clinical challenges. Here we review the state of the art of BCI research across noninvasive neuroimaging technologies, and propose how such systems should be developed further to provide fully fledged communication systems for behaviorally nonresponsive populations. Ann Neurol 2012;72:312–323

Dynamic Change of Global and Local Information Processing in Propofol-Induced Loss and Recovery of Consciousness

Whether unique to humans or not, consciousness is a central aspect of our experience of the world. The neural fingerprint of this experience, however, remains one of the least understood aspects of the human brain. In this paper we employ graph-theoretic measures and support vector machine classification to assess, in 12 healthy volunteers, the dynamic reconfiguration of functional connectivity during wakefulness, propofol-induced sedation and loss of consciousness, and the recovery of wakefulness. Our main findings, based on resting-state fMRI, are three-fold. First, we find that propofol-induced anesthesia does not bear differently on long-range versus short-range connections. Second, our multi-stage design dissociated an initial phase of thalamo-cortical and cortico-cortical hyperconnectivity, present during sedation, from a phase of cortico-cortical hypoconnectivity, apparent during loss of consciousness. Finally, we show that while clustering is increased during loss of consciousness, as recently suggested, it also remains significantly elevated during wakefulness recovery. Conversely, the characteristic path length of brain networks (i.e., the average functional distance between any two regions of the brain) appears significantly increased only during loss of consciousness, marking a decrease of global information-processing efficiency uniquely associated with unconsciousness. These findings suggest that propofol-induced loss of consciousness is mainly tied to cortico-cortical and not thalamo-cortical mechanisms, and that decreased efficiency of information flow is the main feature differentiating the conscious from the unconscious brain.

Neuroimaging and the vegetative state: resolving the behavioral assessment dilemma?

The accurate assessment of patients with impaired consciousness following a brain injury often remains a challenge to the most experienced clinician. A diagnosis of vegetative or minimally conscious state is made on the basis of the patients clinical history and detailed behavioral examinations, which rely upon the patient being able to move or speak in order to demonstrate residual cognitive function. Recently, the development of noninvasive neuroimaging techniques has fostered a rapid increase in the exploration of residual cognitive abilities in these patient populations. However, while this body of literature is growing rapidly, at present the enterprise remains one of scientific endeavor with no inclusion in standard clinical practice. Correctly administered behavioral testing in survivors of brain injury may provide sufficient information to identify patients who are aware and are able to signal that this is the case via a recognized motor output. However, it remains possible that a subgroup of these patients may retain some level of awareness, but lack the ability to produce any motor output and are therefore mistakenly diagnosed as vegetative. It is in this latter situation that functional neuroimaging may prove to be most valuable, as a unique clinical tool for probing volition and residual cognition without necessarily assuming that the patient is able to produce any motor output.

Thought Beyond Language Neural Dissociation of Algebra and Natural Language

A central question in cognitive science is whether natural language provides combinatorial operations that are essential to diverse domains of thought. In the study reported here, we addressed this issue by examining the role of linguistic mechanisms in forging the hierarchical structures of algebra. In a 3-T functional MRI experiment, we showed that processing of the syntax-like operations of algebra does not rely on the neural mechanisms of natural language. Our findings indicate that processing the syntax of language elicits the known substrate of linguistic competence, whereas algebraic operations recruit bilateral parietal brain regions previously implicated in the representation of magnitude. This double dissociation argues against the view that language provides the structure of thought across all cognitive domains.

Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.