Informal Caregivers of Patients with Disorders of Consciousness: a Qualitative Study of Communication Experiences and Information Needs with Physicians

Due to improvements in medicine, the figures of patients with disorders of consciousness (DoC) are increasing. Diagnostics of DoC and prognostication of rehabilitation outcome is challenging but necessary to evaluate recovery potential and to decide on treatment options. Such decisions should be made by doctors and patients’ surrogates based on medico-ethical principles. Meeting information needs and communicating effectively with caregivers as the patients´ most common surrogate-decision makers is crucial, and challenging when novel tech-nologies are introduced. This qualitative study aims to explore information needs of informal DoC caregivers, how they manage the obtained information and their perceptions and experiences with caregiver-physician communication in facilities that implemented innovative neurodiagnostics studies. In 2021, we conducted semi-structured interviews with nine caregivers of clinically stable DoC patients in two rehabilitation centers in Italy and Germany. Participants were selected based on consecutive purposeful sampling. Caregivers were recruited at the facilities after written informed consent. All interviews were recorded, transcribed verbatim and translated. For analysis, we used reflexive thematic analysis according to Braun & Clarke (2006). Caregivers experienced the conversations emotionally, generally based on the value of the information provided. They reported to seek positive information, comfort and empathy with-in the communication of results of examinations. They needed detailed information to gain a deep understanding and a clear picture of their loved-one’s condition. The results suggest a mismatch between the perspectives of caregivers and the perspectives of medical profession-als, and stress the need for more elaborate approaches to the communication of results of neu-rodiagnostics studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12152-022-09503-0

Conscious processing of narrative stimuli synchronizes heart rate between individuals

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Content-State Dimensions Characterize Different Types of Neural Correlates of Consciousness

Identifying the neural correlates of consciousness (NCCs) is key to support the different scientific theories of consciousness. NCCs can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize NCCs according to their dynamics in both the 'state' and 'content' dimensions. The two-dimensional space is defined by the NCCs' capacity to distinguish the conscious states from non-conscious states, (x-axis) and the content (perceived versus unperceived, y-axis). According to the sign of the x and y-axis, NCCs are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of EEG NCCs: markers of connectivity, markers of complexity, and spectral summaries. The NCC-state is represented by the level of consciousness in 1) patients with disorders of consciousness; 2) healthy participants’ during a nap. On the other hand NCC-content by the conscious content in healthy participants' perception tasks: 1) auditory local-global paradigm and 2) visual awareness paradigm. In both cases, we see separate clusters of NCCs with correlated and anti-correlated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining NCC in a two-dimensional space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations

Content-State Dimensions Characterize Different Types of Neural Correlates of Consciousness

Identifying the neural correlates of consciousness (NCCs) is key to support the different scientific theories of consciousness. NCCs can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize NCCs according to their dynamics in both the 'state' and 'content' dimensions. The two-dimensional space is defined by the NCCs' capacity to distinguish the conscious states from non-conscious states, (x-axis) and the content (perceived versus unperceived, y-axis). According to the sign of the x and y-axis, NCCs are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of EEG NCCs: markers of connectivity, markers of complexity, and spectral summaries. The NCC-state is represented by the level of consciousness in 1) patients with disorders of consciousness; 2) healthy participants’ during a nap. On the other hand NCC-content by the conscious content in healthy participants' perception tasks: 1) auditory local-global paradigm and 2) visual awareness paradigm. In both cases, we see separate clusters of NCCs with correlated and anti-correlated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining NCC in a two-dimensional space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations

EEG brain states for real-time detection of covert cognition in disorders of consciousness

One of the biggest challenges in cognitive neuroscience is developing diagnostic tools for Disorders of Consciousness (DoC). Detecting dynamical connectivity brain states seems promising, specifically those linked to transient moments of enhanced cognitive states in patients. A growing body of evidence indicates that fMRI brain states properties are strongly modulated by the level of consciousness , as theoretically predicted by whole brain modeling. fMRI-based brain states, however, have very limited practical application due to methodological constraints. In this work we defined EEG-based brain states and explored their potential as a bedside, real-time tool to detect transient windows of enhanced brain states. We analysed data from 237 individual patients with chronic and acute DoCs -100 Unresponsive Wakefulness Syndrome (UWS), 96 Minimally Conscious State (MCS) and 41 acute- and 101 healthy controls obtained in three independent research centers (Fudan hospital in Shanghai, Pitié Salpêtrière in Paris and Purpan hospital in Toulouse). We determined five EEG functional connectivity brain states, and show that their probability of occurrence is strongly related to the level of consciousness. Distinctively, high entropy brain states are exclusively found in healthy subjects, while low-entropy brain states increase their probability with DoC’s severity, spanning from acute unarousable comatose state, to more chronic DoC’s patients, who are awake but show fluctuating (MCS) or absent awareness (VS). Furthermore, the brain state probability distribution of each individual subject —and even the presence of certain key brain states— significantly vary with the patients’ outcome. We also tested whether our procedure has an actual potential for real-time, bedside brain state detection, and proved that we can reliably estimate the concurrent brain state of a patient in real time, paving the way for a broad application of this tool for DoC patients’ diagnosis, follow-up, and neuroprognostication

Computational modelling in disorders of consciousness: closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state of the art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges