Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study

Abstract

Behavioral assessments could not suffice to provide accurate diagnostic information in individuals with disorders of consciousness (DoC). Multimodal neuroimaging markers have been developed to support clinical assessments of these patients. Here we present findings obtained by hybrid fludeoxyglucose (FDG-)PET/MR imaging in three severely brain-injured patients, one in an unresponsive wakefulness syndrome (UWS), one in a minimally conscious state (MCS), and one patient emerged from MCS (EMCS). Repeated behavioral assessment by means of Coma Recovery Scale-Revised and neurophysiological evaluation were performed in the two weeks before and after neuroimaging acquisition, to ascertain that clinical diagnosis was stable. The three patients underwent one imaging session, during which two resting-state fMRI (rs-fMRI) blocks were run with a temporal gap of about 30 min. rs-fMRI data were analyzed with a graph theory approach applied to nine independent networks. We also analyzed the benefits of concatenating the two acquisitions for each patient or to select for each network the graph strength map with a higher ratio of fitness. Finally, as for clinical assessment, we considered the best functional connectivity pattern for each network and correlated graph strength maps to FDG uptake. Functional connectivity analysis showed several differences between the two rs-fMRI acquisitions, affecting in a different way each network and with a different variability for the three patients, as assessed by ratio of fitness. Moreover, combined PET/fMRI analysis demonstrated a higher functional/metabolic correlation for patients in EMCS and MCS compared to UWS. In conclusion, we observed for the first time, through a test-retest approach, a variability in the appearance and temporal/spatial patterns of resting-state networks in severely brain-injured patients, proposing a new method to select the most informative connectivity pattern.

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model

“…If the pathological activity observed through fMRI in patients with DoC was purely the result of damaged connections, then there would be little concern that the model may not generalize to patient groups. However, we know that there are a number of issues that can cause pathological brain function that do not rely on the connections in the brain being damaged, such as metabolic processes through astrocytes or gliosis, neuronal damage, and thalamic damage [47,48]. While we observed differences in the model parameters between healthy controls and patients, the model was successfully able to improve prediction of brain function beyond what structure can offer alone.…”

Challenges and Perspectives of the Hybridization of PET with Functional MRI or Ultrasound for Neuroimaging

“…Most current PET-fMRI studies in the literature compared the fMRI-derived "restingstate" brain connectivity with the PET-derived neuroreceptor binding or metabolism in brain regions in the absence of any intervention in healthy and pathophysiological conditions (Muzic et al, 1998;Riedl et al, 2014Riedl et al, , 2016Aiello et al, 2015;Marchitelli et al, 2018;Scherr et al, 2019;Ripp et al, 2020). This paradigm offers the possibility to address neuroreceptor or biochemical correlates underlying disease-or state-induced change in functional connectivity in the same individual, at the same time (Cavaliere et al, 2018).…”

Gliosis and Neurodegenerative Diseases: The Role of PET and MR Imaging

“…Nowadays, integrated PET/MRI represents the most advanced tool for molecular imaging, opening new insights for the characterization of neurological and psychiatric disorders, and possibly for a multifaceted patient management (Aiello et al, 2015(Aiello et al, , 2016Cavaliere et al, 2018a). Indeed, this innovative clinical diagnostic scanner allows to combine the functional specificity of PET radiotracers (e.g., targeting metabolism, hypoxia, inflammation, specific ligands, or receptors) to both high-resolution and multiparametric information derived by MR in a single imaging acquisition session (Herzog et al, 2010;Aiello et al, 2019).…”