Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review

Introduction

Disorders of consciousness (DOC) has been an object of numbers of research regarding the diagnosis, treatment and prognosis in last few decades. We believe that the DOC could be considered as a disconnection syndrome, although the exact mechanisms are not entirely understood. Moreover, different conceptual frameworks highly influence results interpretation. The aim of this systematic review is to assess the current knowledge regarding neurophysiological mechanisms of DOC and to establish possible influence on future clinical implications and usage.

Methods

We have conducted a systematic review according to PRISMA guidelines through PubMed and Cochrane databases, with studies being selected for inclusion via a set inclusion and exclusion criteria.

Results

Eighty-nine studies were included in this systematic review according to the selected criteria. This includes case studies, randomized controlled trials, controlled clinical trials, and observational studies with no control arms. The total number of DOC patients encompassed in the studies cited in this review is 1,533.

Conclusion

Connectomics and network neuroscience offer quantitative frameworks for analysing dynamic brain connectivity. Functional MRI studies show evidence of abnormal connectivity patterns and whole-brain topological reorganization, primarily affecting sensory-related resting state networks (RSNs), confirmed by EEG studies. As previously described, DOC patients are identified by diminished global information processing, i.e., network integration and increased local information processing, i.e., network segregation. Further studies using effective connectivity measurement tools instead of functional connectivity as well as the standardization of the study process are needed.

Prediction of voluntary movements of the upper extremities by resting state-brain regional glucose metabolism in patients with chronic severe brain injury: A pilot study

Confirmation of the exact voluntary movements of patients with disorder of consciousness following severe traumatic brain injury (TBI) is difficult because of the associated communication disturbances. In this pilot study, we investigated whether regional brain glucose metabolism assessed by ¹⁸ F-fluorodeoxyglucose positron emission tomography (FDG-PET) at rest could predict voluntary movement in severe TBI patients, particularly those with sufficient upper limb capacity to use communication devices. We visually and verbally instructed patients to clasp or open their hands. After video capture, three independent rehabilitation therapists determined whether the patients' movements were voluntary or involuntary. The results were compared with the standardized uptake value in the primary motor cortex, referring to the Penfield's homunculus, by resting state by FDG-PET imaged 1 year prior. Results showed that glucose uptake in the left (p = 0.0015) and right (p = 0.0121) proximal limb of the primary motor cortex, based on Penfield's homunculus on cerebral cartography, may reflect contralateral voluntary movement. Receiver operating characteristic curve analysis showed that a mean cutoff standardized uptake value of 5.47 ± 0.08 provided the best sensitivity and specificity for differentiating between voluntary and involuntary movements in each area. FDG-PET may be a useful and robust biomarker for predicting long-term recovery of motor function in severe TBI patients with disorders of consciousness.

Brain laterality evaluated by F-18 fluorodeoxyglucose positron emission computed tomography in autism spectrum disorders

Background and rationale

Autism spectrum disorder (ASD) is a neuropsychiatric disorder that has no curative treatment. Little is known about the brain laterality in patients with ASD. F-18 fluorodeoxyglucose positron emission computed tomography (F-18 FDG PET/CT) is a neuroimaging technique that is suitable for ASD owing to its ability to detect whole brain functional abnormalities in a short time and is feasible in ASD patients. The purpose of this study was to evaluate brain laterality using F-18 FDG PET/CT in patients with high-functioning ASD.

Materials and methods

This case-control study recruited eight ASD patients who met the DSM-5 criteria, the recorded data of eight controls matched for age, sex, and handedness were also enrolled. The resting state of brain glucose metabolism in the regions of interest (ROIs) was analyzed using the Q.Brain software. Brain glucose metabolism and laterality index in each ROI of ASD patients were compared with those of the controls. The pattern of brain metabolism was analyzed using visual analysis and is reported in the data description.

Results

The ASD group’s overall brain glucose metabolism was lower than that of the control group in both the left and right hemispheres, with mean differences of 1.54 and 1.21, respectively. We found statistically lower mean glucose metabolism for ASD patients than controls in the left prefrontal lateral (Z = 1.96, p = 0.049). The left laterality index was found in nine ROIs for ASD and 11 ROIs for the control. The left laterality index in the ASD group was significantly lower than that in the control group in the prefrontal lateral (Z = 2.52, p = 0.012), precuneus (Z = 2.10, p = 0.036), and parietal inferior (Z = 1.96, p = 0.049) regions.

Conclusion

Individuals with ASD have lower brain glucose metabolism than control. In addition, the number of ROIs for left laterality index in the ASD group was lower than control. Left laterality defects may be one of the causes of ASD. This knowledge can be useful in the treatment of ASD by increasing the left-brain metabolism. This trial was registered in the Thai Clinical Trials Registry (TCTR20210705005).

Resting-State fMRI in Chronic Patients with Disorders of Consciousness: The Role of Lower-Order Networks for Clinical Assessment

Resting-state fMRI (rs-fMRI) is a widely used technique to investigate the residual brain functions of patients with Disorders of Consciousness (DoC). Nonetheless, it is unclear how the networks that are more associated with primary functions, such as the sensory–motor, medial/lateral visual and auditory networks, contribute to clinical assessment. In this study, we examined the rs-fMRI lower-order networks alongside their structural MRI data to clarify the corresponding association with clinical assessment. We studied 109 chronic patients with DoC and emerged from DoC with structural MRI and rs-fMRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS) and 10 with severe disability. rs-fMRI data were analyzed with independent component analyses and seed-based analyses, in relation to structural MRI and clinical data. The results showed that VS/UWS had fewer networks than MCS patients and the rs-fMRI activity in each network was decreased. Visual networks were correlated to the clinical status, and in cases where no clinical response occurred, rs-fMRI indicated distinctive networks conveying information in a similar way to other techniques. The information provided by single networks was limited, whereas the four networks together yielded better classification results, particularly when the model included rs-fMRI and structural MRI data (AUC = 0.80). Both quantitative and qualitative rs-fMRI analyses yielded converging results; vascular etiology might confound the results, and disease duration generally reduced the number of networks observed. The lower-order rs-fMRI networks could be used clinically to support and corroborate visual function assessments in DoC.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and ¹⁸F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

Altered Effective Connectivity Measured by Resting-State Functional Magnetic Resonance Imaging in Posterior Parietal-Frontal-Striatum Circuit in Patients With Disorder of Consciousness

Objective

Disorder of consciousness (DoC) resulting from severe brain injury is characterized by cortical and subcortical dysconnectivity. However, research on seed-based effective connectivity (EC) of DoC might be questioned as to the heterogeneity of prior assumptions.

Methods

Functional MRI data of 16 DoC patients and 16 demographically matched healthy individuals were analyzed. Revised coma recovery scale (CRS-R) scores of patients were acquired. Seed-based d mapping permutation of subject images (SDM-PSI) of meta-analysis was performed to quantitatively synthesize results from neuroimaging studies that evaluated resting-state functional activity in DoC patients. Spectral dynamic causal modeling (spDCM) was used to assess how EC altered between brain regions in DoC patients compared to healthy individuals.

Results

We found increased effective connectivity in left striatum and decreased effective connectivity in bilateral precuneus (preCUN)/posterior cingulate cortex (PCC), bilateral midcingulate cortex and left middle frontal gyrus in DoC compared with the healthy controls. The resulting pattern of interaction in DoC indicated disrupted connection and disturbance of posterior parietal-frontal-striatum, and reduced self-inhibition of preCUN/PCC. The strength of self-inhibition of preCUN/PCC was negatively correlated with the total score of CRS-R.

Conclusion

This impaired EC in DoC may underlie disruption in the posterior parietal-frontal-striatum circuit, particularly damage to the cortico-striatal connection and possible loss of preCUN/PCC function as the main regulatory hub.

Characterization of brain functional connectivity in treatment-resistant depression

OBJECTIVE

To characterize the functional connectivity (FC) of target brain regions for deep brain stimulation (DBS) in patients with treatment-resistant depression (TRD), and to evaluate its gender and brain lateralization dependence.

METHODS

Thirty-one TRD patients and twenty-nine healthy control (HC) subjects participated. FC of subcallosal cingulate gyrus (SCG), ventral caudate (VCa), nucleus accumbens (NAc), lateral habenula (LHb), and inferior thalamic peduncle (ITP) were evaluated using resting-state fMRI. FC was characterized by calculating the nodal 'degree', a major feature of the graph theory.

RESULTS

The degree measures of the left and right VCa, the left LHb, and the left ITP were significantly greater in the TRD than in the HC group. The degree was greater in females with TRD in all these regions except the right LHb. Finally, the left hemisphere was generally more affected by depression and presented significant degrees in LHb and ITP regions of the patients.

CONCLUSION

Our findings demonstrate the ability of degree to characterize brain FC and identify the regions with abnormal activities in TRD patients. This implies that the degree may have the potential to be used as an important graph-theoretical feature to further investigate the mechanisms underlying TRD, and consequently along with other diagnostic markers, to assist in the determination of the appropriate target region for DBS treatment in TRD patients.

How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis

Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).

Pain Perception in Disorder of Consciousness: A Scoping Review on Current Knowledge, Clinical Applications, and Future Perspective

Pain perception in individuals with prolonged disorders of consciousness (PDOC) is still a matter of debate. Advanced neuroimaging studies suggest some cortical activations even in patients with unresponsive wakefulness syndrome (UWS) compared to those with a minimally conscious state (MCS). Therefore, pain perception has to be considered even in individuals with UWS. However, advanced neuroimaging assessment can be challenging to conduct, and its findings are sometimes difficult to be interpreted. Conversely, multichannel electroencephalography (EEG) and laser-evoked potentials (LEPs) can be carried out quickly and are more adaptable to the clinical needs. In this scoping review, we dealt with the neurophysiological basis underpinning pain in PDOC, pointing out how pain perception assessment in these individuals might help in reducing the misdiagnosis rate. The available literature data suggest that patients with UWS show a more severe functional connectivity breakdown among the pain-related brain areas compared to individuals in MCS, pointing out that pain perception increases with the level of consciousness. However, there are noteworthy exceptions, because some UWS patients show pain-related cortical activations that partially overlap those observed in MCS individuals. This suggests that some patients with UWS may have residual brain functional connectivity supporting the somatosensory, affective, and cognitive aspects of pain processing (i.e., a conscious experience of the unpleasantness of pain), rather than only being able to show autonomic responses to potentially harmful stimuli. Therefore, the significance of the neurophysiological approach to pain perception in PDOC seems to be clear, and despite some methodological caveats (including intensity of stimulation, multimodal paradigms, and active vs. passive stimulation protocols), remain to be solved. To summarize, an accurate clinical and neurophysiological assessment should always be performed for a better understanding of pain perception neurophysiological underpinnings, a more precise differential diagnosis at the level of individual cases as well as group comparisons, and patient-tailored management.

Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness

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FDG-PET metabolic index of the best hemisphere is robust to diagnose MCS.

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FDG-PET slightly outperforms EEG-based automatic classification of conscious state.

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Optimal diagnostic performances are obtained by combining PET and EEG.

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PET and EEG combination identifies cortical activation suggestive of residual consciousness.

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PET and EEG combination also predict patients 6-month command-following.

Introduction

Functional brain-imaging techniques have revealed that clinical examination of disorders of consciousness (DoC) can underestimate the conscious level of patients. FDG-PET metabolic index of the best preserved hemisphere (MIBH) has been reported as a promising measure of consciousness but has never been externally validated and compared with other brain-imaging diagnostic procedures such as quantitative EEG.

Methods

FDG-PET, quantitative EEG and cognitive evoked potential using an auditory oddball paradigm were performed in minimally conscious state (MCS) and vegetative state (VS) patient. We compared out-sample diagnostic and prognostic performances of PET-MIBH and EEG-based classification of conscious state to the current behavioral gold-standard, the Coma Recovery Scale – revised (CRS-R).

Results

Between January 2016 and October 2019, 52 patients were included: 21 VS and 31 MCS. PET-MIBH had an AUC of 0.821 [0.694–0.930], sensitivity of 79% [62–91] and specificity of 78% [56–93], not significantly different from EEG (p = 0.628). Their combination accurately identified almost all MCS patients with a sensitivity of 94% [79–99%] and specificity of 67% [43–85]. Multimodal assessment also identified VS patients with neural correlate of consciousness (4/7 (57%) vs. 1/14 (7%), p = 0.025) and patients with 6-month recovery of command-following (9/24 (38%) vs. 0/16 (0%), p = 0.006), outperforming each technique taken in isolation.

Conclusion

FDG-PET MIBH is an accurate and robust procedure across sites to diagnose MCS. Its combination with EEG-based classification of conscious state not only optimizes diagnostic performances but also allows to detect covert cognition and to predict 6-month command-following recovery demonstrating the added value of multimodal assessment of DoC.

The Relationship Among Glucose Metabolism, Cerebral Blood Flow, and Functional Activity: a Hybrid PET/fMRI Study

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) estimate brain activities from different aspects, including regional glucose uptake (rGU) by ¹⁸FDG-PET, regional cerebral blood flow (rCBF) by arterial spin labeling, and dynamic changes of deoxyhemoglobin by blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). However, the relationships between them remain incompletely understood. In the current study, twenty-four subjects (14 males, 10 females) were recruited and investigated the correlation among rGU, rCBF, and BOLD fMRI-derived metrics reflecting the neural activity, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) by hybrid PET/fMRI. Correlation analyses were performed across subject and across space at both voxel level and region level, considering partial volume effects by adjusting for gray matter volume. Each pair of metrics showed significant across-space correlations. rGU against ReHo showed the highest mean correlation coefficients. rGU had higher correlations with three resting-state (RS) fMRI metrics than did ASL-rCBF. However, the across-subject correlations were not significant among functional modalities (rGU, rCBF, and RS-fMRI BOLD data) at either voxel level or region level even with a liberal threshold, except for significant across-subject correlation between RS-fMRI metrics (ALFF, ReHo, and DC). These comprehensive findings from hybrid PET/MR might provide complementary information to reveal the underlying mechanisms of the brain activity and open new perspective to interpret pathologic conditions.

Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory

OBJECTIVE

To determine brain functional connectivity (FC), based on the graph theory, in individuals with psychogenic nonepileptic seizures (PNES), in order to better understand the mechanisms underlying this disease.

METHODS

Twenty-three patients with PNES and twenty-five healthy control subjects were examined. Alterations in FC within the whole brain were examined using resting-state functional magnetic resonance imaging (MRI). We calculated measures of the nodal degree, a major feature of the graph theory, for all the cortical and subcortical regions in the brain. Pearson correlation was performed to determine the relationship between nodal degree in abnormal brain regions and patient characteristics.

RESULTS

The nodal degrees in the right caudate (CAU), left orbital part of the left inferior frontal gyrus (ORBinf), and right paracentral lobule (PCL) were significantly greater (i.e. hyper-connectivity) in individuals with PNES than in healthy control subjects. On the other hand, a lesser nodal degree (i.e. hypo-connectivity) was detected in several other brain regions including the left and right insula (INS), as well as the right putamen (PUT), and right middle occipital gyrus (MOG).

CONCLUSION

Our findings suggest that the FC of several major brain regions can be altered in individuals with PNES. Areas with hypo-connectivity may be involved in emotion processing (e.g., INS) and movement regulation (e.g., PUT), whereas areas with hyper-connectivity may play a role in the inhibition of unwanted movements and cognitive processes (e.g., CAU).

Preservation of Language Processing and Auditory Performance in Patients With Disorders of Consciousness: A Multimodal Assessment

The impact of language impairment on the clinical assessment of patients suffering from disorders of consciousness (DOC) is unknown or underestimated and may mask the presence of conscious behavior. In a group of DOC patients (n = 11; time post-injury range: 5–252 months), we investigated the main neural functional and structural underpinnings of linguistic processing, and their relationship with the behavioral measures of the auditory function using the Coma Recovery Scale-Revised (CRS-R). We assessed the integrity of the brainstem auditory pathways, of the left superior temporal gyrus and arcuate fasciculus, the neural activity elicited by passive listening of an auditory language task, and the mean hemispheric glucose metabolism. Our results support the hypothesis of a relationship between the level of preservation of the investigated structures/functions and the CRS-R auditory subscale scores. Moreover, our findings indicate that patients in minimally conscious state minus (MCS−): (1) when presenting the auditory startle (at the CRS-R auditory subscale) might be aphasic in the receptive domain, being severely impaired in the core language structures/functions; (2) when presenting the localization to sound might retain language processing, being almost intact or intact in the core language structures/functions. Despite the small group of investigated patients, our findings provide a grounding of the clinical measures of the CRS-R auditory subscale in the integrity of the underlying auditory structures/functions. Future studies are needed to confirm our results that might have important consequences for the clinical practice.

Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy

OBJECTIVE

To investigate the application of graph theory with functional connectivity to distinguish left from right temporal lobe epilepsy (TLE).

METHODS

Alterations in functional connectivity within several brain networks - default mode (DMN), attention (AN), limbic (LN), sensorimotor (SMN) and visual (VN) - were examined using resting-state functional MRI (rs-fMRI). The study accrued 21 left and 14 right TLE as well as 17 nonepileptic control subjects. The local nodal degree, a feature of graph theory, was calculated foreach of the brain networks. Multivariate logistic regression analysis was performed to determine the accuracy of identifying seizure laterality based on significant differences in local nodal degree in the selected networks.

RESULTS

Left and right TLE patients showed dissimilar patterns of alteration in functional connectivity when compared to control subjects. Compared with right TLE, patients with left TLE exhibited greater nodal degree' (i.e. hyperconnectivity) with right superomedial frontal gyrus (in DMN), inferior frontal gyrus pars triangularis (in AN), right caudate and left superior temporal gyrus (in LN) and left paracentral lobule (in SMN), while showing lesser nodal degree (i.e. hypoconnectivity) with left temporal pole (in DMN), right insula (in LN), left supplementary motor area (in SMN), and left fusiform gyrus (in VN). The LN showed the highest accuracy of 82.9% among all considered networks in determining laterality of the TLE. By combinations of local degree attributes in the DMN, AN, LN, and VN, logistic regression analysis demonstrated an accuracy of 94.3% by comparison.

CONCLUSION

Our study demonstrates the utility of graph theory application to brain network analysis as a potential biomarker to assist in the determination of TLE laterality and improve the confidence in presurgical decision-making in cases of TLE.

Positron Emission Tomography After Ischemic Brain Injury: Current Challenges and Future Developments

Positron emission tomography (PET) is widely used in clinical and animal studies, along with the development of diverse tracers. The biochemical characteristics of PET tracers may help uncover the pathophysiological consequences of cardiac arrest (CA) and ischemic stroke, which include cerebral ischemia and reperfusion, depletion of oxygen and glucose, and neuroinflammation. PubMed was searched for studies of the application of PET for "cardiac arrest," "ischemic stroke," and "targeted temperature management." Available studies were included and classified according to the biochemical properties involved and metabolic processes of PET tracers, and were summarized. The mechanisms of ischemic brain injuries were investigated by PET with various tracers to elucidate the pathological process from the initial decrease of cerebral blood flow (CBF) to the subsequent abnormalities in energy and oxygen metabolism, to the monitoring of inflammation. In general, the trends of cerebral blood flow and oxygen metabolism after ischemic attack are not unidirectional but closely related to the time point of injury and recovery. Glucose metabolism after injury showed significant differences in different brain regions whereas global cerebral metabolic rate of glucose (CMRglc) declined. PET monitoring of neuroinflammation shows comparable efficacy to immunostaining. The technology of PET targeting in brain metabolism and the development of tracers provide new tools to track and evaluate the brain's pathological changes after ischemic brain injury. Despite no existing evidence for an available PET-based prediction method, discoveries of new tracers are expected to provide more possibilities for the whole field.

Network Mapping of Connectivity Alterations in Disorder of Consciousness: Towards Targeted Neuromodulation

Disorder of consciousness (DoC) refers to a group of clinical conditions that may emerge after brain injury, characterized by a varying decrease in the level of consciousness that can last from days to years. An understanding of its neural correlates is crucial for the conceptualization and application of effective therapeutic interventions. Here we propose a quantitative meta-analysis of the neural substrate of DoC emerging from functional magnetic resonance (fMRI) and positron emission tomography (PET) studies. We also map the relevant networks of resulting areas to highlight similarities with Resting State Networks (RSNs) and hypothesize potential therapeutic solutions leveraging network-targeted noninvasive brain stimulation. Available literature was reviewed and analyzed through the activation likelihood estimate (ALE) statistical framework to describe resting-state or task-dependent brain activation patterns in DoC patients. Results show that task-related activity is limited to temporal regions resembling the auditory cortex, whereas resting-state fMRI data reveal a diffuse decreased activation affecting two subgroups of cortical (angular gyrus, middle frontal gyrus) and subcortical (thalamus, cingulate cortex, caudate nucleus) regions. Clustering of their cortical functional connectivity projections identify two main altered functional networks, related to decreased activity of (i) the default mode and frontoparietal networks, as well as (ii) the anterior salience and visual/auditory networks. Based on the strength and topography of their connectivity profile, biophysical modeling of potential brain stimulation solutions suggests the first network as the most feasible target for tES, tDCS neuromodulation in DoC patients.

Measures of CNS-Autonomic Interaction and Responsiveness in Disorder of Consciousness

Neuroimaging studies have demonstrated functional interactions between autonomic (ANS) and brain (CNS) structures involved in higher brain functions, including attention and conscious processes. These interactions have been described by the Central Autonomic Network (CAN), a concept model based on the brain-heart two-way integrated interaction. Heart rate variability (HRV) measures proved reliable as non-invasive descriptors of the ANS-CNS function setup and are thought to reflect higher brain functions. Autonomic function, ANS-mediated responsiveness and the ANS-CNS interaction qualify as possible independent indicators for clinical functional assessment and prognosis in Disorders of Consciousness (DoC). HRV has proved helpful to investigate residual responsiveness in DoC and predict clinical recovery. Variability due to internal (e.g., homeostatic and circadian processes) and environmental factors remains a key independent variable and systematic research with this regard is warranted. The interest in bidirectional ANS-CNS interactions in a variety of physiopathological conditions is growing, however, these interactions have not been extensively investigated in DoC. In this brief review we illustrate the potentiality of brain-heart investigation by means of HRV analysis in assessing patients with DoC. The authors' opinion is that this easy, inexpensive and non-invasive approach may provide useful information in the clinical assessment of this challenging patient population.

Making Sense of Connectivity

In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.

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

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.

Evaluation of drug effects on cerebral blood flow and glucose uptake in un-anesthetized and un-stimulated rats: application of free-moving apparatus enabling to keep rats free during PET/SPECT tracer injection and uptake

Objectives

The purpose of this study is the development of novel fluorine-18-fluorodeoxyglucose (¹⁸F-FDG)-PET and ^99mTc-hexamethylpropylene amine oxime (HMPAO) SPECT methods with free-moving apparatus on conscious rats to investigate brain activity without the effects of anesthesia and tactual stimulation. We also assessed the sensitivity of the experimental system by an intervention study using fluoxetine as a reference drug.

Materials and methods

A catheter was inserted into the femoral vein and connected to a free-moving cannula system. After fluoxetine administration, the rats were given an injection of ¹⁸F-FDG or ^99mTc-HMPAO via the intravenous cannula and released into a free-moving cage. After the tracer was trapped in the brain, the rats were anesthetized and scanned with PET or SPECT scanners. Then a volume of interest analysis and statistical parametric mapping were performed.

Results

We could inject the tracer without touching the rats, while keeping them conscious until the tracers were distributed and trapped in the brain using the developed system. The effects of fluoxetine on glucose uptake and cerebral blood flow were perceptively detected by volume of interest and statistical parametric mapping analysis.

Conclusion

We successfully developed free-moving ¹⁸F-FDG-PET and ^99mTc-HMPAO-SPECT imaging systems and detected detailed glucose uptake and cerebral blood flow changes in the conscious rat brain with fluoxetine administration. This system is expected to be useful to assess brain activity without the effects of anesthesia and tactual stimulation to evaluate drug effect or animal brain function.

Locked-in Syndrome and 18F-fluorodeoxyglucose-positron Emission Tomography/Computed Tomography: Observations from a Case of Basilar Artery Thrombosis

We report the case of a 59-year-old male patient suffering from locked-in syndrome (LIS) following basilar artery thrombosis despite an attempt of thrombolysis. Neurological examination showed quadriplegia and aphonia and a state of coma requiring mechanical ventilation was diagnosed. The use of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-positron emission tomography (PET) allowed to detect a normal ¹⁸F-FDG uptake in the main cerebral cortical areas and a significant reduction of ¹⁸F-FDG uptake in both cerebellar hemispheres, compatible with a functional deafferentation, helping confirming the clinical suspicion of LIS. The diagnosis of LIS, according to literature, is based on the clinical assessment and the utilization of scores as the Coma Recovery Scale-Revised. The standard neuroimaging techniques, although recognize the site of injury, are not able to differentiate the different conditions affecting a state of altered consciousness. Performing ¹⁸F-FDG-PET in patients with LIS might help addressing the correct diagnosis and prompting subsequent appropriate treatment, and therefore, ultimately improving the patient outcome. Therefore, ¹⁸F-FDG-PET should be taken into account in the early clinical assessment of doubtful cases.

In human non-REM sleep, more slow-wave activity leads to less blood flow in the prefrontal cortex

Cerebral blood flow (CBF) is related to integrated neuronal activity of the brain whereas EEG provides a more direct measurement of transient neuronal activity. Therefore, we addressed what happens in the brain during sleep, combining CBF and EEG recordings. The dynamic relationship of CBF with slow-wave activity (SWA; EEG sleep intensity marker) corroborated vigilance state specific (i.e., wake, non-rapid eye movement (NREM) sleep stages N1-N3, wake after sleep) differences of CBF e.g. in the posterior cingulate, basal ganglia, and thalamus, indicating their role in sleep-wake regulation and/or sleep processes. These newly observed dynamic correlations of CBF with SWA - namely a temporal relationship during continuous NREM sleep in individuals - additionally implicate an impact of sleep intensity on the brain's metabolism. Furthermore, we propose that some of the aforementioned brain areas that also have been shown to be affected in disorders of consciousness might therefore contribute to the emergence of consciousness.

Functional Networks in Disorders of Consciousness

Severe brain injury may cause disruption of neural networks that sustain arousal and awareness, the two essential components of consciousness. Despite the potentially devastating immediate and long-term consequences, disorders of consciousness (DoC) are poorly understood in terms of their underlying neurobiology, the relationship between pathophysiology and recovery, and the predictors of treatment efficacy. Recent advances in neuroimaging techniques have enabled the study of network connectivity, providing great potential to improve the clinical care of patients with DoC. Initial discoveries in this field were made using positron emission tomography (PET). More recently, functional magnetic resonance (fMRI) techniques have added to our understanding of functional network dynamics in this population. Both methods have shown that whether at rest or performing a goal-oriented task, functional networks essential for processing intrinsic thoughts and extrinsic stimuli are disrupted in patients with DoC compared with healthy subjects. Atypical connectivity has been well established in the default mode network as well as in other cortical and subcortical networks that may be required for consciousness. Moreover, the degree of altered connectivity may be related to the severity of impaired consciousness, and recovery of consciousness has been shown to be associated with restoration of connectivity. In this review, we discuss PET and fMRI studies of functional and effective connectivity in patients with DoC and suggest how this field can move toward clinical application of functional network mapping in the future.

Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity

A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.

Functional Evaluation of Awareness in Vegetative and Minimally Conscious State

Objective:

The aim of this study was to assess differences in brain activation in a large sample of Vegetative State (VS) and Minimally Conscious State (MCS) patients, using functional magnetic resonance imaging (fMRI).

Methods:

We studied 50 patients four to seven months after brain injury. By using international clinical criteria and validated behavioural scales such as the Glasgow Coma Scale and the Clinical Unawareness Assessment Scale, the patients were grouped into VS (n=23) and MCS (n=27). All patients underwent to fMRI examination. After 6 months, the patients were reassessed using Glasgow Outcome Scale and Revised Coma Recovery Scale.

Results:

fMRI showed significant (p<0.01, cluster-corrected) brain activation in the primary auditory cortex bilaterally during the acoustic stimuli in patients with both VS and MCS. However, ten patients clinically classified as VS, showed a pattern of brain activation very similar to that of MCS patients. Six months later, these ten VS patients had significant clinical improvement, evolving into MCS, whereas the other VS patients and patients with MCS remained clinically stable.

Conclusion:

Brain activity could help in discerning whether the status of wakefulness in VS is also accompanied by partial awareness, as occurs in MCS. This may have very important prognostic implications.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Hybrid PET/MR Imaging and Brain Connectivity

In recent years, brain connectivity is gaining ever-increasing interest from the interdisciplinary research community. The study of brain connectivity is characterized by a multifaceted approach providing both structural and functional evidence of the relationship between cerebral regions at different scales. Although magnetic resonance (MR) is the most established imaging modality for investigating connectivity in vivo, the recent advent of hybrid positron emission tomography (PET)/MR scanners paved the way for more comprehensive investigation of brain organization and physiology. Due to the high sensitivity and biochemical specificity of radiotracers, combining MR with PET imaging may enrich our ability to investigate connectivity by introducing the concept of metabolic connectivity and cometomics and promoting new insights on the physiological and molecular bases underlying high-level neural organization. This review aims to describe and summarize the main methods of analysis of brain connectivity employed in MR imaging and nuclear medicine. Moreover, it will discuss practical aspects and state-of-the-art techniques for exploiting hybrid PET/MR imaging to investigate the relationship of physiological processes and brain connectivity.

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

INTRODUCTION

The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.

OBJECTIVE

We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.

METHODS

We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients.

RESULTS

The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls.

CONCLUSIONS

The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.