Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on bloodoxygenation- level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a “network disease” as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions

Multi-parametric Imaging Using Hybrid PET/MR to Investigate the Epileptogenic Brain

Neuroimaging analysis has led to fundamental discoveries about the healthy and pathological human brain. Different imaging modalities allow garnering complementary information about brain metabolism, structure and function. To ensure that the integration of imaging data from these modalities is robust and reliable, it is fundamental to attain deep knowledge of each modality individually. Epilepsy, a neurological condition characterised by recurrent spontaneous seizures, represents a field in which applications of neuroimaging and multi-parametric imaging are particularly promising to guide diagnosis and treatment. In this PhD thesis, I focused on different imaging modalities and investigated advanced denoising and analysis strategies to improve their application to epilepsy. The first project focused on fluorodeoxyglucose (FDG) positron emission tomography (PET), a well-established imaging modality assessing brain metabolism, and aimed to develop a novel, semi-quantitative pipeline to analyse data in children with epilepsy, thus aiding presurgical planning. As pipelines for FDG-PET analysis in children are currently lacking, I developed age-appropriate templates to provide statistical parametric maps identifying epileptogenic areas on patient scans. The second and third projects focused on two magnetic resonance imaging (MRI) modalities: resting-state functional MRI (rs-fMRI) and arterial spin labelling (ASL), respectively. The aim was to i) probe the efficacy of different fMRI denoising pipelines, and ii) formally compare different ASL data acquisition strategies. In the former case, I compared different pre-processing methods and assessed their impact on fMRI signal quality and related functional connectivity analyses. In the latter case, I compared two ASL sequences to investigate their ability to quantify cerebral blood flow and interregional brain connectivity. The final project addressed the combination of rs-fMRI and ASL, and leveraged graph-theoretical analysis tools to i) compare metrics estimated via these two imaging modalities in healthy subjects and ii) assess topological changes captured by these modalities in a sample of temporal lobe epilepsy patients

A Hybrid PET/MRI Brain Connectivity Approach for Improving Epilepsy Surgical Evaluation

Hybrid PET/MRI can non-invasively improve epileptic focus (EF) localization prior to surgical resection in drug-resistant epilepsy (DRE), especially when MRI is negative. In this thesis, we developed an 18F-fluorodeoxyglucose (FDG) PET-guided diffusion tractography (PET/DTI) approach to assess white matter (WM) integrity in MRI-negative DRE and evaluated its potential impact on epilepsy surgical planning. To validate the potential of PET/MRI, we first evaluated the diagnostic competence of PET/MRI in DRE and found that PET/MRI provides similar diagnostic information as PET/CT (current clinical standard). For the PET/DTI approach, we used asymmetry index (AI) mapping of FDG-PET to guide WM fiber tractography around glucose hypometabolic brain regions (potential EF). Fiber tractography was repeated in the contralateral brain region (opposite to EF), which served as a control for this study. WM fibers were quantified by calculating the fiber count, mean fractional anisotropy (FA), mean fiber length, and mean cross-section of each fiber bundle. WM integrity was assessed through fiber visualization and by normalizing ipsilateral fiber measurements to contralateral fiber measurements. The added value of PET/DTI in clinical decision-making was assessed by an experienced epileptologist. In over 60% of the patient cohort (n = 14), AI mapping findings were concordant with clinical reports on seizure-onset zone. Mean FA, fiber count, and mean fiber length were decreased in 14/14 (100%), 13/14 (93%), and 12/14 (86%) patients, respectively. PET/DTI improved diagnostic confidence in 10/14 (71%) patients and indicated surgical candidacy be reassessed in 3/6 (50%) patients who had not undergone surgery. FDG-PET coupled with diffusion tractography can be a powerful tool for detecting EF and assessing WM integrity around EF in MRI-negative epilepsy. PET/DTI could further enhance clinical decision-making in epilepsy surgery

Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy

ObjectiveTo investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes.Methods18F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using 18F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model.ResultsThe results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (PFDR = 0.0230, PFDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (PFDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (PFDR = 0.0152; PFDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914.ConclusionThese findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation

Neuroimaging of Sudden Unexpected Death in Epilepsy (SUDEP)

BACKGROUND: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of premature death among people with epilepsy. The precise mechanisms underlying SUDEP remain elusive, though work so far demonstrates a potential centrally mediated event in which autonomic, respiratory and/or arousal processes fail to recover following a significant seizure. Neuroimaging enables non-invasive assessment of the structural and functional architecture among sites and networks involved in regulating such processes; damage or alterations may indicate a central predisposition in those at high-risk and who suffer SUDEP, and provide non-invasive biomarkers. // METHODS: In this thesis, structural and functional imaging techniques were employed to address this possibility. Both retrospective investigations of those who succumbed to SUDEP, and prospective studies of those at high-risk, were performed. Voxel-based morphometry, volumetry and resting-state functional magnetic resonance imaging (RS-fMRI) network analysis techniques were utilised to identify and characterise brain structural and functional alterations relative to low-risk subjects and controls. // RESULTS: Brain morphometric and volumetric alterations among sites involved in cardiorespiratory regulation and recovery were found in those who later suffered SUDEP and in matched, living individuals at high risk. Prospective work revealed similar, and additional, structural alterations in those at high-risk which were associated with the extent of seizure-related hypoxemia; notably among the thalamus, periaqueductal grey (PAG), medulla, vermis and hippocampus. Network analysis of functional imaging data revealed disturbed patterns of connectivity in high-risk temporal lobe epilepsy (TLE) patients, and altered functional organisation in confirmed cases of SUDEP, among regulatory brain sites as well as the whole brain. // CONCLUSIONS: Structural and resting state functional connectivity disturbances were found in patients who suffered SUDEP, and those at elevated risk. Injury and connectivity disturbances may indicate damage or dysfunction within sites and networks involved central regulatory processes, which could facilitate SUDEP. However, further work is required to elucidate the precise mechanisms of volume and functional connectivity alterations, and to provide firm links between centrally mediated autonomic and respiratory dysfunction, SUDEP and related imaging findings. A more immediate use for the imaging outcomes revealed here may rest with the development of non-invasive biomarkers, which may one day assist in identifying those at risk and evaluating individual risk for SUDEP based on injury to brain sites or altered functional networks

Development of Pharmacological Magnetic Resonance Imaging Methods and their Application to the Investigation of Antipsychotic Drugs: a Dissertation

Pharmacological magnetic resonance imaging (phMRI) is the use of functional MRI techniques to elucidate the effects that psychotropic drugs have on neural activity within the brain; it is an emerging field of research that holds great potential for the investigation of drugs that act on the central nervous system by revealing the changes in neural activity that mediate observable changes in behavior, cognition, and perception. However, the realization of this potential is hampered by several unanswered questions: Are the MRI measurements reliable surrogates of changing neural activity in the presence of pharmacological agents? Is it relevant to investigate psychiatric phenomena such as reward or anxiolysis in anesthetized, rather than conscious animals? What are the methods that yield reproducible and meaningful results from phMRI experiments, and are they consistent in the investigations of different drugs? The research presented herein addresses many of these questions with the specific aims of 1) Developing pharmacological MRI methodologies that can be used in the conscious animal, 2) Validating these methodologies with the investigation of a non-stimulant, psychoactive compound, and 3) Applying these methodologies to the investigation of typical and atypical antipsychotic drugs, classes of compounds with unknown mechanisms of therapeutic action Building on recent developments in the field of functional MRI research, we developed new techniques that enable the investigator to measure localized changes in metabolism commensurate with changing neural activity. We tested the hypothesis that metabolic changes are a more reliable surrogate of changes in neural activity in response to a cocaine challenge, than changes observed in the blood-oxygen-level-dependent (BOLD) signal alone. We developed a system capable of multi-modal imaging in the conscious rat, and we tested the hypothesis that the conscious brain exhibits a markedly different response to systemic morphine challenge than the anesthetized brain. We identified and elucidated several fundamental limitations of the imaging and analysis protocols used in phMRI investigations, and developed new tools that enable the investigator to avoid common pitfalls. Finally, we applied these phMRI techniques to the investigation of neuroleptic compounds by asking the question: does treatment with typical or atypical antipsychotic drugs modulate the systems in the brain which are direct or indirect (i.e. downstream) substrates for a dopaminergic agonist? The execution of this research has generated several new tools for the neuroscience and drug discovery communities that can be used in neuropsychiatric investigations into the action of psychotropic drugs, while the results of this research provide evidence that supports several answers to the questions that currently limit the utility of phMRI investigations. Specifically, we observed that metabolic change can be measured to resolve discrepancies between anomalous BOLD signal changes and underlying changes in neural activity in the case of systemically administered cocaine. We found clear differences in the response to systemically administered morphine between conscious and anesthetized rats, and observed that only conscious animals exhibit a phMRI response that can be explained by the pharmacodynamics of morphine and corroborated by behavioral observations. We identified fundamental and drug-dependent limitations in the protocols used to perform phMRI investigations, and designed tools and alternate methods to facilitate protocol development. By applying these techniques to the investigation of neuroleptic compounds, we have gained a new perspective of the alterations in dopaminergic signaling induced by treatment with antipsychotic medications, and have found effects in many nuclei outside of the pathways that act as direct substrates for dopamine. A clearer picture of how neuroleptics alter the intercommunication of brain nuclei would be an invaluable resource for the classification of investigational antipsychotic drugs, and would provide the basis for future studies that examine the neuroplastic changes that confer therapeutic efficacy following chronic treatment with antipsychotic medications

Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability

Factors associated with clinical progression to severe COVID-19 in people with cystic fibrosis: A global observational study

BACKGROUND This international study aimed to characterise the impact of acute SARS-CoV-2 infection in people with cystic fibrosis and investigate factors associated with severe outcomes. Methods Data from 22 countries prior to 13$^{th}$ December 2020 and the introduction of vaccines were included. It was de-identified and included patient demographics, clinical characteristics, treatments, outcomes and sequalae following SARS-CoV-2 infection. Multivariable logistic regression was used to investigate factors associated with clinical progression to severe COVID-19, using the primary outcome of hospitalisation with supplemental oxygen. RESULTS SARS-CoV-2 was reported in 1555 people with CF, 1452 were included in the analysis. One third were aged 70%: a 17-fold increase in odds. Worse outcomes were independently associated with older age, non-white race, underweight body mass index, and CF-related diabetes. Prescription of highly effective CFTR modulator therapies was associated with a significantly reduced odds of being hospitalised with oxygen (AOR 0.43 95%CI 0.31-0.60 p<0.001). Transplanted patients were hospitalised with supplemental oxygen therapy (21.9%) more often than non-transplanted (8.8%) and was independently associated with the primary outcome (Adjusted OR 2.45 95%CI 1.27-4.71 p=0.007). CONCLUSIONS This is the first study to show that there is a protective effect from the use of CFTR modulator therapy and that people with CF from an ethnic minority are at more risk of severe infection with SARS-CoV-2

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective