Influence of the segmentation on the characterization of cerebral networks of structural damage for patients with disorders of consciousness

Disorders of consciousness (DOC) are a consequence of a variety of severe brain injuries. DOC commonly results in anatomical brain modifications, which can affect cortical and sub-cortical brain structures. Postmortem studies suggest that severity of brain damage correlates with level of impairment in DOC. In-vivo studies in neuroimaging mainly focus in alterations on single structures. Recent evidence suggests that rather than one, multiple brain regions can be simultaneously affected by this condition. In other words, DOC may be linked to an underlying cerebral network of structural damage. Recently, geometrical spatial relationships among key sub-cortical brain regions, such as left and right thalamus and brain stem, have been used for the characterization of this network. This approach is strongly supported on automatic segmentation processes, which aim to extract regions of interests without human intervention. Nevertheless, patients with DOC usually present massive structural brain changes. Therefore, segmentation methods may highly influence the characterization of the underlying cerebral network structure. In this work, we evaluate the level of characterization obtained by using the spatial relationships as descriptor of a sub-cortical cerebral network (left and right thalamus) in patients with DOC, when different segmentation approaches are used (FSL, Free-surfer and manual segmentation). Our results suggest that segmentation process may play a critical role for the construction of robust and reliable structural characterization of DOC conditions

Modes and models in disorders of consciousness science

The clinical assessment of non-communicative brain damaged patients is extremely difficult and there is a need for paraclinical diagnostic markers of the level of consciousness. In the last few years, progress within neuroimaging has led to a growing body of studies investigating vegetative state and minimally conscious state patients, which can be classified in two main approaches. Active neuroimaging paradigms search for a response to command without requiring a motor response. Passive neuroimaging paradigms investigate spontaneous brain activity and brain responses to external stimuli and aim at identifying neural correlates of consciousness. Other passive paradigms eschew neuroimaging in favour of behavioural markers which reliably distinguish conscious and unconscious conditions in healthy controls. In order to furnish accurate diagnostic criteria, a mechanistic explanation of how the brain gives rise to consciousness seems desirable. Mechanistic and theoretical approaches could also ultimately lead to a unification of passive and active paradigms in a coherent diagnostic approach. In this paper, we survey current passive and active paradigms available for diagnosis of residual consciousness in vegetative state and minimally conscious patients. We then review the current main theories of consciousness and see how they can apply in this context. Finally, we discuss some avenues for future research in this domai

Funktionelle Bildgebungsverfahren in der Differenzierung zwischen dem Vegetativen Zustand und dem Minimalen Bewusstseinszustand: Eine systematische Analyse und Metaanalyse

Eine verlässliche Methode zur Unterscheidung und Diagnostik von Patienten in einem minimalen (MCS) oder vegetativen Zustand (VS; Synonyme: Wachkoma; Unresponsive Wakefulness Syndrom) steht bis heute nicht zur Verfügung. Das Hauptziel dieser Arbeit war es eine quantitative Darstellung der Sachlage auf dem Gebiet der Anwendung funktioneller bildgebender Verfahren zur Differenzierung der Bewusstseinszustände von Patienten, die nicht mehr in der Lage sind, in einer wie auch immer gearteten Weise mit ihrer Umgebung in Kontakt zu treten, aufzuzeigen. Hierzu wurden aus der Pubmed-Datenbank im Zeitraum von 01.10.2016 bis zum 15.6.2018 eine Gesamtanzahl von 25 relevanten Artikeln, welche die entsprechenden statistischen Daten zur Unterscheidung der VS- von MCS-Patienten anhand funktioneller bildgebender Verfahren aufwiesen, ermittelt. Die betrachteten Studien untersuchten im Mittel eine Anzahl von 31 Patienten und wiesen in Summe eine Gesamtanzahl an 796 Patienten auf. Für die an das systematische Review angeschlossene Metaanalyse ergab sich aus den 25 Studien eine gesamte Anzahl von 53 zu analysierenden Datensätzen. Vor der Kalkulation der Metaanalyse wurde eine ausführliche Qualitätsbeurteilung, nach den Richtlinien von QUADAS-2 erhoben. Hierbei zeigte sich allgemein eine Schwäche der Studienqualität in dem gesamten hier betrachteten Forschungsgebiet, sodass sich im Durchschnitt für die Biaswerte lediglich ein Wert von 3,2 ergab (der Wert 0 entspricht den stärksten Bias, der Wert 8 bedeutet eine biasfreie Studie). Die Gründe hierfür liegen hauptsächlich in der fehlenden Verblindung der Untersucher, in der Methode der Patientenauswahl und in der Darstellung der untersuchten abhängigen Variablen. In die angeschlossene Metaanalyse wurden nach der Einteilung der Studien entsprechend der Untersuchungsmethoden nach Aktiv-, Passiv- und Resting-State-Methode nur solche Studien mit einem Biaswert größer als oder gleich 3 aufgenommen. Hierbei konnte nachgewiesen werden, dass sich die Resting-State-Methode mit einer gepoolten Gesamteffektstärke von d = 0,78 (Konfidenzintervall von 0,54 bis 1,02) als die verlässlichste bei der Unterscheidung der Bewusstseinszustände des VS und MCS herausstellte. Im Gegenteil dazu ist die Aktive-State-Methode die am wenigsten aussagekräftige. Das abschließende Ergebnis der Methode der passiven Stimulation bleibt mit einem nur knapp an der Signifikanzgrenze liegenden Effekt unklar, sodass empfohlen werden kann weitere Studien auf diesem Gebiet abzuwarten. Die Ergänzung der bereits erprobten, standardisierten klinischen Untersuchungs-verfahren um solche zukunftweisenden bildgebenden Verfahren wird die Diagnostik im Bereich der Disorders of Consciousness (DoC) deutlich erleichtern und verbessern. Vor allem zur Sicherung der Diagnose in zweifelhaften Situationen sollte, wie die vorliegende Arbeit nahelegt, zukünftig stets eine zusätzliche funktionelle Bildgebung etabliert werden

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

DTI based structural damage characterization for Disorders of Consciousness

peer reviewe

Metabolic activity in external and internal awareness networks in severely brain-damaged patients.

OBJECTIVE: An extrinsic cerebral network (encompassing lateral frontoparietal cortices) related to external/sensory awareness and an intrinsic midline network related to internal/self-awareness have been identified recently. This study measured brain metabolism in both networks in patients with severe brain damage. DESIGN: Prospective [18F]-fluorodeoxyglucose-positron emission tomography and Coma Recovery Scale-Revised assessments in a university hospital setting. SUBJECTS: Healthy volunteers and patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS), minimally conscious state (MCS), emergence from MCS (EMCS), and locked-in syndrome (LIS). RESULTS: A total of 70 patients were included in the study: 24 VS/UWS, 28 MCS, 10 EMCS, 8 LIS and 39 age-matched controls. VS/UWS showed metabolic dysfunction in extrinsic and intrinsic networks and thalami. MCS showed dysfunction mostly in intrinsic network and thalami. EMCS showed impairment in posterior cingulate/retrosplenial cortices. LIS showed dysfunction only in infratentorial regions. Coma Recovery Scale-Revised total scores correlated with metabolic activity in both extrinsic and part of the intrinsic network and thalami. CONCLUSION: Progressive recovery of extrinsic and intrinsic awareness network activity was observed in severely brain-damaged patients, ranging from VS/UWS, MCS, EMCS to LIS. The predominance of intrinsic network impairment in MCS could reflect altered internal/self-awareness in these patients, which is difficult to quantify at the bedside

Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients

Patients surviving severe brain injury may regain consciousness without recovering their ability to understand, move and communicate. Recently, electrophysiological and neuroimaging approaches, employing simple sensory stimulations or verbal commands, have proven useful in detecting higher order processing and, in some cases, in establishing some degree of communication in brain-injured subjects with severe impairment of motor function. To complement these approaches, it would be useful to develop methods to detect recovery of consciousness in ways that do not depend on the integrity of sensory pathways or on the subject's ability to comprehend or carry out instructions. As suggested by theoretical and experimental work, a key requirement for consciousness is that multiple, specialized cortical areas can engage in rapid causal interactions (effective connectivity). Here, we employ transcranial magnetic stimulation together with high-density electroencephalography to evaluate effective connectivity at the bedside of severely brain injured, non-communicating subjects. In patients in a vegetative state, who were open-eyed, behaviourally awake but unresponsive, transcranial magnetic stimulation triggered a simple, local response indicating a breakdown of effective connectivity, similar to the one previously observed in unconscious sleeping or anaesthetized subjects. In contrast, in minimally conscious patients, who showed fluctuating signs of non-reflexive behaviour, transcranial magnetic stimulation invariably triggered complex activations that sequentially involved distant cortical areas ipsi- and contralateral to the site of stimulation, similar to activations we recorded in locked-in, conscious patients. Longitudinal measurements performed in patients who gradually recovered consciousness revealed that this clear-cut change in effective connectivity could occur at an early stage, before reliable communication was established with the subject and before the spontaneous electroencephalogram showed significant modifications. Measurements of effective connectivity by means of transcranial magnetic stimulation combined with electroencephalography can be performed at the bedside while by-passing subcortical afferent and efferent pathways, and without requiring active participation of subjects or language comprehension; hence, they offer an effective way to detect and track recovery of consciousness in brain-injured patients who are unable to exchange information with the external environment

Mapping topography and network of brain injury in patients with disorders of consciousness

BackgroundThere is a growing interest in the topography of brain regions associated with disorders of consciousness. This has caused increased research output, yielding many publications investigating the topic with varying methodologies. The objective of this study was to ascertain the topographical regions of the brain most frequently associated with disorders of consciousness.MethodsWe performed a cross-sectional text-mining analysis of disorders of consciousness studies. A text mining algorithm built in the Python programming language searched documents for anatomical brain terminology. We reviewed primary PubMed studies between January 1st 2000 to 8th February 2023 for the search query “Disorders of Consciousness.” The frequency of brain regions mentioned in these articles was recorded, ranked, then built into a graphical network. Subgroup analysis was performed by evaluating the impact on our results if analyses were based on abstracts, full-texts, or topic-modeled groups (non-negative matrix factorization was used to create subgroups of each collection based on their key topics). Brain terms were ranked by their frequency and concordance was measured between subgroups. Graphical analysis was performed to explore relationships between the anatomical regions mentioned. The PageRank algorithm (used by Google to list search results in order of relevance) was used to determine global importance of the regions.ResultsThe PubMed search yielded 24,944 abstracts and 3,780 full texts. The topic-modeled subgroups contained 2015 abstracts and 283 full texts. Text Mining across all document groups concordantly ranked the thalamus the highest (Savage score = 11.716), followed by the precuneus (Savage score = 4.983), hippocampus (Savage score = 4.483). Graphical analysis had 5 clusters with the thalamus once again having the highest PageRank score (PageRank = 0.0344).ConclusionThe thalamus, precuneus and cingulate cortex are strongly associated with disorders of consciousness, likely due to the roles they play in maintaining awareness and involvement in the default mode network, respectively. The findings also suggest that other areas of the brain like the cerebellum, cuneus, amygdala and hippocampus also share connections to consciousness should be further investigated

Global structural integrity and effective connectivity in patients with disorders of consciousness

Background: Previous studies have separately reported impaired functional, structural, and effective connectivity in patients with disorders of consciousness (DOC). The perturbational complexity index (PCI) is a transcranial magnetic stimulation (TMS) derived marker of effective connectivity. The global fractional anisotropy (FA) is a marker of structural integrity. Little is known about how these parameters are related to each other. Objective: We aimed at testing the relationship between structural integrity and effective connectivity. Methods: We assessed 23 patients with severe brain injury more than 4 weeks post-onset, leading to DOC or locked-in syndrome, and 14 healthy subjects. We calculated PCI using repeated single pulse TMS coupled with high-density electroencephalography, and used it as a surrogate of effective connectivity. Structural integrity was measured using the global FA, derived from diffusion weighted imaging. We used linear regression modelling to test our hypothesis, and computed the correlation between PCI and FA in different groups. Results: Global FA could predict 74% of PCI variance in the whole sample and 56% in the patients' group. No other predictors (age, gender, time since onset, behavioural score) improved the models. FA and PCI were correlated in the whole population (r = 0.86, p < 0.0001), the patients, and the healthy subjects subgroups. Conclusion: We here demonstrated that effective connectivity correlates with structural integrity in brain-injured patients. Increased structural damage level decreases effective connectivity, which could prevent the emergence of consciousness