Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Paediatric Patients with Intractable Epilepsy

Fluoro-18-deoxyglucose positron emission tomography (FDG-PET) is an important tool for the pre-surgical assessment of children with drug-resistant epilepsy. Standard assessment is carried out visually and this is often subjective and highly user-dependent. Voxel-wise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo/hyper-metabolism, associated to the epileptogenic area. In the clinical settings, this analysis is carried out using commercially available software. These software packages suffer from two main limitations when applied to paediatric PET data: 1) paediatric scans are spatially normalised to an adult standard template and 2) statistical comparisons use an adult control dataset. The aim of this work is to provide a reliable observer-independent pipeline for the analysis of paediatric FDG-PET scans, as part of pre-surgical planning in epilepsy. METHODS: A pseudo-control dataset (n = 19 for 6-9y, n = 93 for 10-20y) was used to create two age-specific FDG-PET paediatric templates in standard paediatric space. The FDG-PET scans of 46 epilepsy patients (n = 16 for 6-9y, n = 30 for 10-17y) were retrospectively collated and analysed using voxel-wise statistics. This was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping v.8 (SPM8) pipeline (including age-specific paediatric templates and normal database). A kappa test was used to assess the level of agreement between findings of voxel-wise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using post-surgical seizure-free patients. RESULTS: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localisation, especially for the younger patient group: kScenium=0.489 versus kSPM=0.805. The proposed pipeline also showed a sensitivity of ~70% in both age ranges, for the localisation of hypo-metabolic areas on paediatric FDG-PET scans in post-surgical seizure-free patients. CONCLUSION: We show that by creating age-specific templates and using paediatric control databases, our pipeline provides an accurate and sensitive semi-quantitative method for assessing FDG-PET scans of patients under 18y

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes

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

Arterial Spin Labeling methods for quantitative brain perfusion mapping

Il flusso sanguigno cerebrale rappresenta uno dei piu' importanti parametri fisiologici, questo infatti riveste un ruolo cruciale per l'adeguato mantenimento dei normali livelli metabolici sia negli animali che nell'uomo e informa sull'omeostasi del cervello. Inoltre, essendo strettamente connesso con il metabolismo del glucosio, pu\uf2 essere considerato un marker essenziale per valutare i complessi meccanismi del cervello sia in condizioni normali che patologiche. Le innovazioni che ci sono state negli ultimi decenni nel campo della Risonanza Magnetica hanno permesso lo sviluppo di nuove metodiche di imaging per misurare in vivo la perfusione cerebrale senza ricorrere all'uso di radiazioni ionizzanti, come nel caso della Tomografia Assiale Computerizzata (TAC) o la Tomografia ad Emissioni di Positroni (PET). In particolare, tra queste nuove metodiche ricordiamo la tecnica denominata Arterial Spin Labeling (ASL): questa \ue8 stata creata per misurare in maniera non invasiva i parametri di perfusione usando le molecole di acqua contenute nel sangue, opportunamente marcate, come tracciante endogeno. Di conseguenza, tutti i potenziali rischi connessi all'uso di traccianti iniettati vengono eliminati con questa metodica, la quale permette di studiare longitudinalmente nel tempo i pazienti, anche ad intervalli ravvicinati, per poter monitore la progressione della malattia e al tempo stesso pu\uf2 essere facilmente applicata nei soggetti sani grazie alla sua non invasivit\ue0 per studiare i diversi meccanismi fisiologici. Lo sviluppo delle sequenze ASL \ue8 al momento attuale un campo in continua evoluzione e le applicazioni di questa tecnica sia nel campo clinico che delle neuroscienze stanno costantemente crescendo. Lo scopo di questa tesi \ue8 legato principalmente allo sviluppo e la valutazione della tecnica ASL sia per studiare le funzioni cerebrali in relazione a diversi tipi di task che per mappare in maniera quantitativa la perfusione in condizioni di riposo. Diversi aspetti di questa metodica, dall'acquisizione fino alle analisi di post-processing, saranno presi in considerazione in questa tesi cos\uec come alcune possibili soluzioni per risolvere alcuni dei principali problemi ancora aperti in questo campo. Nel Capitolo I, il contesto generale viene introdotto. In particolare, vengono brevemente introdotte le principali metodiche di imaging per lo studio della perfusione cerebrale, focalizzandosi nel dettaglio sui principi fisici della tecnica ASL. Nell'ultima parte del capitolo, i diversi modelli per la quantificazione del dato ASL sono descritti assieme alla pipeline generalmente adotta per l'analisi. I successivi due capitoli introducono alcune delle principali limitazioni che si incontrano quando le sequenze ASL vengono implementate nei diversi magneti di risonanza. Questa tecnica \ue8 infatti particolarmente complessa per quanto riguarda la parte di acquisizione e di settaggio dei vari parametri. Di consequenza, alcuni step preliminari sono stati fatti sulle sequenze ASL commerciali (in due diversi scanners) per migliorare la qualit\ue0 dei dati e avere delle stime di perfusione piu' affidabili (Capitolo II). Un nuovo framework generale per minimizzare gli artefatti che si osservano quando il dato ASL viene acquisito con un readout volumetrico (3D) \ue8 invece introdotto nel Capitolo III . Questo in particolare rappresenta un approccio innovativo e piu' completo rispetto alle soluzioni generalmente adottate per risolvere questo problema, in quanto combina sia la parte di acquisizione, con una nuova strategia per acquisire i dati ASL in 3D, che la successiva parte di analisi, con uno specifico algoritmo per ridurre questi artefatti che possono seriamente corrompere i risultati finali. Nel Capitolo IV le potenzialit\ue0 della tecnica ASL per studi di imaging funzionale sono valutate usando diversi tipi di task motori e confrontando i risultati con quelli ottenuti dalla tecnica di imaging funzionale denominata BOLD (Blood Oxygenation Level Dependent), che \ue8 largamente considerata il gold-standard per questo tipo di studi. In particolare, i risultati delle due tecniche sono confrontati in termini di attivazione, localizzazione, sensibilit\ue0 e accuratezza spaziale. Il protocollo di attivazione motorio include movimenti attivi e passivi della mano, allo scopo di valutare dal punto di vista fisiologico le differenze emodinamiche indotte dai due task, pensando a possibili applicazioni cliniche del movimento passivo in pazienti. Il Capitolo V introduce due problemi fondamentali che devono essere risolti per poter applicare con sicurezza le sequenze ASL nel contesto clinico, specialmente quando singoli pazienti devono essere valutati. Nella prima parte, un nuovo approccio per valutare l'affidabilit\ue0 delle stime di perfusione \ue8 introdotto, derivando in questo modo informazioni complementari che possono aiutare per una piu' precisa interpretazione dei risultati. Nella seconda parte, un criterio automatico per identificare a livello individuale le aree di alterata perfusione (ad esempio incremento o decremento del flusso) \ue8 descritto. Questo metodo in particolare verr\ue0 largamente utilizzato nel capitolo successivo. Nel Capitolo VI il disegno sperimentale e l'applicazione clinica in pazienti epilettici \ue8 descritta. Il capitolo si focalizza sulla valutazione dei risultati ASL in relazione a quelli derivanti da altre tecniche di imaging largamente adottate nel campo dell'epilessia, come la PET o la localizzazione della sorgente. Lo scopo principale \ue8 quello di applicare un approccio multimodale che combina diverse tecniche non invasive per la valutazione prechirurgica di pazienti con epilessia farmaco-resistente. Due studi successivi vengono descritti. Nel primo studio, l'obiettivo principale \ue8 legato alla valutazione delle potenzialit\ue0 dell'ASL per identificare cambiamenti di perfusioni legati al focus epilettico in confronto ai risultati dati dalla PET, la quale ha dimostrato di avere un'alta specificit\ue0 e sensibilit\ue0 per la localizzazione del focus. Nel secondo studio, analisi ASL a livello individuale con il metodo precedentemente descritto nel capitolo V vengono introdotte. In entrambi gli studi, i dati elettrofisiologici e la localizzazione della sorgente vengono usati come correlati e riferimenti per valutare criticamente i risultati ASL. In un sottogruppo di pazienti, le informazioni derivanti dall'operazione chirurgica sono state ulteriormente utilizzate per validare le potenzialit\ue0 della tecnica ASL in questo tipo di patologia. La combinazione multimodale di diverse tecniche e dei loro rispettivi metodi di analisi rappresenta uno strumento essenziale nel contesto prechirurgico di questa patologia, in grado di fornire informazioni complementari per un quadro piu' completo e una localizzazione piu' precisa del focus epilettico. Nel contesto dell'epilessia, la tecnica ASL rappresenta uno strumento innovativo, scarsamente utilizzato ma che potrebbe assumere un ruolo sempre piu' importante per la valutazione delle epilessie farmaco-resistenti.Cerebral blood flow (CBF) is one of the most important physiological parameters, in particular it is crucial for proper maintenance of normal metabolic rates in animals and humans and informs on the homeostastis of the brain. CBF, which is tightly coupled to glucose metabolism, is also a well-established correlate of brain function and therefore an essential marker for evaluating the complex brain mechanisms at both normal and diseased states. The scientific and technological revolution of Magnetic Resonance Imaging (MRI) in the last decades allowed the development of new imaging techniques for the in vivo measurement of brain perfusion without using ionizing radiation, unlike techniques as Computed Tomography (CT) or Positron Emission Tomography (PET). Among these different MRI sequences, Arterial Spin Labeling (ASL) was created for non-invasively measuring the perfusion parameters by using magnetically labeled arterial blood water as an endogenous tracer. Therefore, this method eliminates all the potential risks connected to external contrast agents, allowing repeated and longitudinal studies in patients for monitoring the disease progression as well as easily studying the physiological brain mechanisms in healthy subjects thanks to its non invasiveness. The development of ASL techniques is currently an active research area and applications of ASL both in clinics and neuroscience are steadily growing. The main focus of this thesis is on the development and evaluation of ASL methods ultimately with applications for functional brain imaging and quantitative mapping in resting-state conditions. Different aspects of ASL sequences, from acquisition to post-processing analysis, will be assessed in the context of this thesis as well as possible solutions for solving some open problems in this field. In Chapter I, the general context of our studies is introduced. In particular, it presents a brief introduction to perfusion imaging techniques and basic physical principles of the main ASL sequences. In the last part of the chapter, different models for perfusion quantification from ASL data are described along with the general pipeline employed for ASL data analysis. The next two chapters are devoted to introduce some of the main limitations encountered when ASL sequences are implemented and acquired in the MRI scanners. Indeed, a series of preliminary steps were performed on the product ASL sequences available in two different MRI scanners in order to increase the data quality and obtain more reliable perfusion estimates (Chapter II). An improved framework for minimising severe artefacts shown by the use of ASL with a 3D readout module is then introduced in Chapter III. This is a more complete approach which combines both the acquisition phase, involving the use of a new strategy for acquiring 3D ASL data, and post-processing analysis phase, with an ad-hoc algorithm for further reducing these artefacts which can seriously compromise the final results. In Chapter IV the ASL suitability for functional imaging studies is investigated by using different types of motor tasks and comparing the results to those obtained with the Blood Oxygenation Level Dependent (BOLD) functional MRI technique, which is still considered to be the gold-standard in this type of studies. In particular, the results from two techniques will be compared in terms of activation, localisation, sensitivity and spatial accuracy. The motor protocol includes active and passive hand movements, in order to evaluate from a more physiological point of view the different haemodynamic changes induced by these tasks. Chapter V introduces two main questions that need to be addressed in order to confidently apply ASL sequence in clinical settings, especially when single patients have to be evaluated. Therefore, patient-specific analyses are described. In the first part, a novel approach for assessing the reliability of perfusion estimates is proposed, in order to provide complementary information that can help for a more precise interpretation of the results. In the second part, an automatic criterion for identifying on single patients areas of altered (increased/decreased) perfusion is introduced. This approach in particular will be extensively used in the following chapter. In Chapter VI, the experimental design and clinical applications on a group of patients are described. In particular, the chapter focuses on the evaluation of ASL results in comparison to those derived from other techniques as electrical source localization and PET in a group of twelve epileptic patients. The scope is to apply a multimodal approach combining noninvasive techniques of acquisition and analysis for the presurgical evaluation of drug-resistant epilepsy. Two subsequent ASL studies are described. In the first study, the main focus was on the assessment of the ASL suitability for detecting perfusion changes correlated to the epileptic focus in comparison to the results given by PET, which has shown during the last decades to have a high sensitivity and specificity in localising the focus in epileptic patients. In the second study, we sought to perform the ASL analysis at patient level and automatically identify the altered areas that might be connected to the focus by using the proposed approach introduced in Chapter V. In both cases, electrophysiological data and electrical source imaging were used as correlates and references for critically evaluating the ASL findings. In a subgroup of patients, the post-operative MRI scans and the clinical outcome information were also available and used as ground truth for assessing ASL and source imaging pre-operative results. The combination of multimodal techniques and their respectively methods of analysis are useful tools in the presurgical workup of epilepsy providing different methods of localization of the same epileptic foci and complementary information for a more complete picture of the disease. Furthermore, ASL represents a novel tool to the study of epilepsy and may play an increasingly important role in the evaluation of patients with refractory focal epilepsy

Functional Sensitivity of Dual-Echo ASL in Localizing Active and Imagery Hand Movements

Dual-echo arterial spin labeling (DE-ASL) techniques have been recently proposed for thesimultaneous acquisition of ASL and blood-oxygenation-level-dependent (BOLD) functionalmagnetic resonance imaging (fMRI) data (Woolrich et al., 2006). The images acquired at the firstecho time are perfusion weighted (ASL), while the images from the second echo are primarilyT2* weighted, thus sensitive to the BOLD signal (Leontiev and Buxton, 2007). The sequence isuseful when the simultaneous estimation of blood flow and BOLD signal are targeted. Thepurpose of this study was to assess the sensitivity of the DE-ASL sequence in comparison to theconventional one (BOLD-fMRI) in detecting brain activations elicited by active and motor imageryhand movements

Epileptic brain networks as detected by time-variant effective connectivity and graph analysis of high-density EEG data

The surgical resection of the epileptogenic zone (EZ) may be the only therapeutic option for reducing seizures in focal drug-resistant epilepsy. Connectivity analysis using electroencephalographic (EEG) data is an influential methodology to centralize the epileptogenic zone (EZ) for the most correct possible surgical resection. The time evolution is one of the important factor to investigate the directional communication of network nodes. The directional influence between a given pair of signals can be detriment using Granger causality extensions. Direct Transfer function (DTF) and Adaptive Direct Transfer Function (ADTF) are two extensions, which can be used for time invariant and time variant signal flow analyses respectively (Wilke et al., 2011). In this study, we primarily aim to further investigate the brain connectivity in epileptic patients by evaluating the time-variant causal interaction patterns among hdEEG source time series using ADTF

Using Social Network Analysis to enhance the understanding of Brain Connectivity

Graph-based network modelling is becoming increasingly pervasive touching at very different fields, ranging from social networks to brain connectivity. This works is a first attempt to borrow the concept of \u201ctranstopic messaging\u201d from social network for its exploitation in the functional connectivity framework. Basically, different functional tasks are mapped to different \u201csemantic topics\u201d, and the overall relevance (according to given metrics) of the nodes of the network graph in ruling the spread of the different \u201ctopics\u201d is assessed. This rises the connectivity analysis of one level of abstraction allowing to assess the overall transtopical relevance of each node of the graph providing information on the higher-level structure of the network

Connectivity modulations induced by reaching and grasping movements

Functional neuroimaging enables the assessment of the brain function in both rest and active conditions. While traditional functional connectivity studies focus on determining distributed patterns of brain activity, the analysis of pair-wise correlations in the time series associated to brain regions allows a paradigm shift to graph theory making available a whole set of parameters for the analysis of the functional network. Then, the study of the properties of the networks as well as of their modulations can be performed in the space of the so-identified features potentially leading to the detection of condition-specific (static or dynamic) fingerprints. Following this guideline, this study is a first attempt to using graph-based measures for capturing task-specific signatures of a reach&amp;grasp movement. The weighted clustering coefficient (CW), characteristic path length (SW) and small-worldness (SW) were considered and performance was assessed against classical measures (eventrelated (de)synchronization). Neurophysiological data were collected through high-density EEG and a stereophotogrammetric system was used for capturing the onset and end of the movement. Though not reaching statistical significance, these preliminary results witness the modulation of the function network due to reach&amp;grasp and provide evidence in favour of the possibility of capturing such a modulation through graph-based properties. This would allow to shed light on the movement-induced reorganization of the network, which has a clear translational impact for the assessment of the recovery of patients after acute stroke