Julia Sophia Crone

Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients

Despite advances in resting state functional magnetic resonance imaging investigations, clinicians remain with the challenge of how to implement this paradigm on an individualized basis. Here, we assessed the clinical relevance of resting state functional magnetic resonance imaging acquisitions in patients with disorders of consciousness by means of a systems-level approach. Three clinical centres collected data from 73 patients in minimally conscious state, vegetative state/unresponsive wakefulness syndrome and coma. The main analysis was performed on the data set coming from one centre (Liège) including 51 patients (26 minimally conscious state, 19 vegetative state/unresponsive wakefulness syndrome, six coma; 15 females; mean age 49 ± 18 years, range 11-87; 16 traumatic, 32 non-traumatic of which 13 anoxic, three mixed; 35 patients assessed >1 month post-insult) for whom the clinical diagnosis with the Coma Recovery Scale-Revised was congruent with positron emission tomography scanning. Group-level functional connectivity was investigated for the default mode, frontoparietal, salience, auditory, sensorimotor and visual networks using a multiple-seed correlation approach. Between-group inferential statistics and machine learning were used to identify each networks capacity to discriminate between patients in minimally conscious state and vegetative state/unresponsive wakefulness syndrome. Data collected from 22 patients scanned in two other centres (Salzburg: 10 minimally conscious state, five vegetative state/unresponsive wakefulness syndrome; New York: five minimally conscious state, one vegetative state/unresponsive wakefulness syndrome, one emerged from minimally conscious state) were used to validate the classification with the selected features. Coma Recovery Scale-Revised total scores correlated with key regions of each network reflecting their involvement in consciousness-related processes. All networks had a high discriminative capacity (>80%) for separating patients in a minimally conscious state and vegetative state/unresponsive wakefulness syndrome. Among them, the auditory network was ranked the most highly. The regions of the auditory network which were more functionally connected in patients in minimally conscious state compared to vegetative state/unresponsive wakefulness syndrome encompassed bilateral auditory and visual cortices. Connectivity values in these three regions discriminated congruently 20 of 22 independently assessed patients. Our findings point to the significance of preserved abilities for multisensory integration and top-down processing in minimal consciousness seemingly supported by auditory-visual crossmodal connectivity, and promote the clinical utility of the resting paradigm for single-patient diagnostics.

Deactivation of the Default Mode Network as a Marker of Impaired Consciousness: An fMRI Study

Diagnosis of patients with a disorder of consciousness is very challenging. Previous studies investigating resting state networks demonstrate that 2 main features of the so-called default mode network (DMN), metabolism and functional connectivity, are impaired in patients with a disorder of consciousness. However, task-induced deactivation – a third main feature of the DMN – has not been explored in a group of patients. Deactivation of the DMN is supposed to reflect interruptions of introspective processes. Seventeen patients with unresponsive wakefulness syndrome (UWS, former vegetative state), 8 patients in minimally conscious state (MCS), and 25 healthy controls were investigated with functional magnetic resonance imaging during a passive sentence listening task. Results show that deactivation in medial regions is reduced in MCS and absent in UWS patients compared to healthy controls. Moreover, behavioral scores assessing the level of consciousness correlate with deactivation in patients. On single-subject level, all control subjects but only 2 patients in MCS and 6 with UWS exposed deactivation. Interestingly, all patients who deactivated during speech processing (except for one) showed activation in left frontal regions which are associated with conscious processing. Our results indicate that deactivation of the DMN can be associated with the level of consciousness by selecting those who are able to interrupt ongoing introspective processes. In consequence, deactivation of the DMN may function as a marker of consciousness.

Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness☆

Recovery of consciousness has been associated with connectivity in the frontal cortex and parietal regions modulated by the thalamus. To examine this model and to relate alterations to deficits in cognitive functioning and conscious processing, we investigated topological network properties in patients with chronic disorders of consciousness recovered from coma. Resting state fMRI data of 34 patients with unresponsive wakefulness syndrome and 25 in minimally conscious state were compared to 28 healthy controls. We investigated global and local network characteristics. Additionally, behavioral measures were correlated with the local metrics of 28 regions within the fronto-parietal network and the thalamus. In chronic disorders of consciousness, modularity at the global level was reduced suggesting a disturbance in the optimal balance between segregation and integration. Moreover, network properties were altered in several regions which are associated with conscious processing (particularly, in medial parietal, and frontal regions, as well as in the thalamus). Between minimally conscious and unconscious patients the local efficiency of medial parietal regions differed. Alterations in the thalamus were particularly evident in non-conscious patients. Most of the regions affected in patients with impaired consciousness belong to the so-called ‘rich club’ of highly interconnected central nodes. Disturbances in their topological characteristics have severe impact on information integration and are reflected in deficits in cognitive functioning probably leading to a total breakdown of consciousness.

Impaired consciousness is linked to changes in effective connectivity of the posterior cingulate cortex within the default mode network

The intrinsic connectivity of the default mode network has been associated with the level of consciousness in patients with severe brain injury. Especially medial parietal regions are considered to be highly involved in impaired consciousness. To better understand what aspect of this intrinsic architecture is linked to consciousness, we applied spectral dynamic causal modeling to assess effective connectivity within the default mode network in patients with disorders of consciousness. We included 12 controls, 12 patients in minimally conscious state and 13 in vegetative state in this study. For each subject, we first defined the four key regions of the default mode network employing a subject-specific independent component analysis approach. The resulting regions were then included as nodes in a spectral dynamic causal modeling analysis in order to assess how the causal interactions across these regions as well as the characteristics of neuronal fluctuations change with the level of consciousness. The resulting pattern of interaction in controls identified the posterior cingulate cortex as the main driven hub with positive afferent but negative efferent connections. In patients, this pattern appears to be disrupted. Moreover, the vegetative state patients exhibit significantly reduced self-inhibition and increased oscillations in the posterior cingulate cortex compared to minimally conscious state and controls. Finally, the degree of self-inhibition and strength of oscillation in this region is correlated with the level of consciousness. These findings indicate that the equilibrium between excitatory connectivity towards posterior cingulate cortex and its feedback projections is a key aspect of the relationship between alterations in consciousness after severe brain injury and the intrinsic functional architecture of the default mode network. This impairment might be principally due to the disruption of the mechanisms underlying self-inhibition and neuronal oscillations in the posterior cingulate cortex.

Preserved oscillatory response but lack of mismatch negativity in patients with disorders of consciousness

OBJECTIVE The diagnostic validity of non-phase-locked oscillations (NPLOs) and mismatch negativity (MMN) in an oddball task for assessing attentional reactivity in patients with disordered consciousness was examined. METHODS Patients in a minimally conscious (MCS, n = 6) or vegetative (VS, n = 16) state and healthy controls (n = 15) were assessed. MMN and NPLOs were analyzed with single-subject, non-parametric statistics. RESULTS In 11 healthy controls and 2 VS patients, MMN was detected. More subjects showed NPLO differences in the alpha than in the theta or beta frequency ranges. In 14 healthy controls, 4 MCS patients, and 5 VS patients, lower amplitudes after deviants were found in the alpha frequency range. One healthy subject and one VS patient showed higher amplitudes after deviants. CONCLUSIONS Neither ERPs nor NPLOs could reliably distinguish MCS from VS patients. However, NPLOs were more sensitive than ERPs for detecting significantly different activity, and they possibly identified preserved processing better than ERPs. SIGNIFICANCE Intact neurophysiological attentional responses observed in the NPLOs of VS patients may indicate a need for other diagnostic techniques. Inter-individual differences in the direction of the effect should be considered as normal variance.

Abnormalities of functional brain networks in pathological gambling: a graph-theoretical approach

Functional neuroimaging studies of pathological gambling (PG) demonstrate alterations in frontal and subcortical regions of the mesolimbic reward system. However, most investigations were performed using tasks involving reward processing or executive functions. Little is known about brain network abnormalities during task-free resting state in PG. In the present study, graph-theoretical methods were used to investigate network properties of resting state functional magnetic resonance imaging data in PG. We compared 19 patients with PG to 19 healthy controls (HCs) using the Graph Analysis Toolbox (GAT). None of the examined global metrics differed between groups. At the nodal level, pathological gambler showed a reduced clustering coefficient in the left paracingulate cortex and the left juxtapositional lobe (supplementary motor area, SMA), reduced local efficiency in the left SMA, as well as an increased node betweenness for the left and right paracingulate cortex and the left SMA. At an uncorrected threshold level, the node betweenness in the left inferior frontal gyrus was decreased and increased in the caudate. Additionally, increased functional connectivity between fronto-striatal regions and within frontal regions has also been found for the gambling patients. These findings suggest that regions associated with the reward system demonstrate reduced segregation but enhanced integration while regions associated with executive functions demonstrate reduced integration. The present study makes evident that PG is also associated with abnormalities in the topological network structure of the brain during rest. Since alterations in PG cannot be explained by direct effects of abused substances on the brain, these findings will be of relevance for understanding functional connectivity in other addictive disorders.

EEG frequency analysis of responses to the own-name stimulus

OBJECTIVE The neuronal response to hearing a subjects own (SON) compared with other names has been examined in healthy subjects as well as in patients with disorders of consciousness. So far, on electroencephalographic data, only event-related potentials (ERPs) were considered. In this study, we examined the frequency properties of SON. METHODS Data of 17 healthy subjects were processed for equiprobable stimuli of SON, other- and own-name backwards by calculating ERPs, evoked and induced activity for a period of 2000 ms from stimulus onset in the delta, theta, lower and upper alpha bands and averaging for four consequent temporal segments of 500 ms each. RESULTS For SON, the N1 components amplitude was larger, while induced activity in the alpha band decreased in the second temporal segment (of 500-1000 ms). No differences between other- and own-name backwards were found. CONCLUSIONS The late reactivity may indicate responses to a stimulus after having recognised it. Alpha is known to play a role in attention and alertness. The results may reflect the fact that the SON stimulus enhances alertness. SIGNIFICANCE The findings correlate previous work about alertness and alpha activity with those about attention capturing of the SON stimulus. We suggest using frequency analysis in research on disorders of consciousness.

Neural repetition suppression: evidence for perceptual expectation in object-selective regions

It is an established finding that neuronal activity is decreased for repeated stimuli. Recent studies revealed that repetition suppression (RS) effects are altered by manipulating the probability with which stimuli are repeated. RS for faces is more pronounced when the probability of repetition is high than when it is low. This response pattern is interpreted with reference to the predictive coding (PC) account, which assumes that RS is influenced by top-down expectations. Recent findings challenge the generality of PC accounts of RS by showing repetition probability does not modulate RS for other visual stimuli than faces. However, a number of findings on visual processing are in line with PC. Thus, the influence of repetition probability on RS effects during object processing requires careful reinvestigations. In the present fMRI study, object pictures were presented in a high (75%) or low (25%) repetition probability context. We found increased RS in the high-probability context compared to the low-probability context in the left lateral occipital complex (LOC). The dorsal-caudal and the ventral-anterior subdivisions of the LOC revealed similar neuronal responses. These results indicate that repetition probability effects can be found for other visual objects than faces and provide evidence in favor of the PC account.

Top-down and bottom-up influences on the left ventral occipito-temporal cortex during visual word recognition: An analysis of effective connectivity

The functional role of the left ventral occipito‐temporal cortex (vOT) in visual word processing has been studied extensively. A prominent observation is higher activation for unfamiliar but pronounceable letter strings compared to regular words in this region. Some functional accounts have interpreted this finding as driven by top‐down influences (e.g., Dehaene and Cohen [ ]: Trends Cogn Sci 15:254–262; Price and Devlin [ ]: Trends Cogn Sci 15:246–253), while others have suggested a difference in bottom‐up processing (e.g., Glezer et al. [ ]: Neuron 62:199–204; Kronbichler et al. [ ]: J Cogn Neurosci 19:1584–1594). We used dynamic causal modeling for fMRI data to test bottom‐up and top‐down influences on the left vOT during visual processing of regular words and unfamiliar letter strings. Regular words (e.g., taxi) and unfamiliar letter strings of pseudohomophones (e.g., taksi) were presented in the context of a phonological lexical decision task (i.e., “Does the item sound like a word?”). We found no differences in top‐down signaling, but a strong increase in bottom‐up signaling from the occipital cortex to the left vOT for pseudohomophones compared to words. This finding can be linked to functional accounts which assume that the left vOT contains neurons tuned to complex orthographic features such as morphemes or words [e.g., Dehaene and Cohen [ ]: Trends Cogn Sci 15:254‐262; Kronbichler et al. [ ]: J Cogn Neurosci 19:1584–1594]: For words, bottom‐up signals converge onto a matching orthographic representation in the left vOT. For pseudohomophones, the propagated signals do not converge, but (partially) activate multiple orthographic word representations, reflected in increased effective connectivity. Hum Brain Mapp 35:1668–1680, 2014.

Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness.

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.