Bayesian Approach for dimensionality reduction of compartmental model parameters of the PET [18F]FDG tracer

openPositron Emission Tomography (PET) con [18F]FDG (Fluorodeossiglucosio) è una tecnica di imaging nucleare ampiamente utilizzata in oncologia. Ha un ruolo cruciale nell'esaminare i processi fisiologici e patofisiologici in vivo, consentendo la misurazione quantitativa dei cambiamenti biochimici nel corpo. Ciò facilita la rilevazione di anomalie nel metabolismo dei tessuti d'interesse. Per stabilire una connessione tra i segnali PET e i processi biochimici interni, è richiesto l'utilizzo di un modello compartimentale. La stima dei parametri può essere eseguita a livello di regione di interesse (ROI) o a livello di voxel, sfruttando appieno la risoluzione spaziale dello scanner PET. In situazioni specifiche, ad esempio nello studio di una lesione, le informazioni fornite dall'analisi a livello di ROI potrebbero non essere sufficienti, e sarebbe richiesta un'analisi a livello di voxel. La sfida risiede nel tipo di segnale ottenuto a livello di voxel, caratterizzato da un basso rapporto segnale-rumore (SNR), in contrasto al segnale ottenuto dall'analisi a livello di ROI. Attualmente, i metodi comunemente utilizzati per la stima dei microparametri a livello di ROI non sono altrettanto efficaci a livello di voxel, a causa del basso SNR. Approcci semplificati, come il metodo grafico di Patlak, sono applicabili, ma generano parametri con informazioni fisiologiche meno dettagliate. Pertanto, c'è la necessità di un approccio in grado di produrre risultati affidabili a livello di voxel. Una soluzione potrebbe essere negli stimatori Bayesiani, che sfruttano informazioni a priori per performare meglio in presenza di dati rumorosi. Tuttavia, la stima dei microparametri nel modello è basata sull'assunzione di disporre di una funzione di input priva di rumore. Questo richiede tipicamente campionamenti arteriali, che possono essere scomodi e gravosi per il paziente. Un'alternativa è la Image-derived Input Function (IDIF), che estrae la funzione di input direttamente dall'immagine PET, utilizzando le arterie carotidi interne (ICA) o le arterie carotidi comuni (CCA), con correzioni applicate per correggere i Partial Volume Effect (PVE). Questa tesi si concentra sull'esplorazione di un approccio bayesiano per consentire una stima adeguata dei microparametri a livello di voxel. In particolare, è stato utilizzato il metodo di stima Maximum A Posteriori (MAP). Nella nostra implementazione, le informazioni a priori derivano dalle stime a livello di ROI, con l'obiettivo finale di ottenere stime precise a livello di voxel. Inoltre, la stima MAP è stata testata con un vincolo su un parametro del modello, il k3, basato sulla stima voxel-wise di Ki ottenuta utilizzando l'analisi grafica di Patlak, per ridurre ulteriormente la complessità. Inoltre, è stata esaminata l'influenza dell'utilizzo di diverse funzioni di input estratte da diverse ROI vascolari in questo studio. L'analisi è stata eseguita su un dataset composto da 10 soggetti affetti da glioma sottoposti ad una acquisizione con tracciante [18F]FDG su un sistema PET/MR presso l'Ospedale Universitario di Padova. Per valutare l'affidabilità dei risultati ottenuti con la stima MAP, le mappe parametriche sono state confrontate con quelle ottenute utilizzando un altro approccio bayesiano, ovvero il Variational Bayes (VB). Le stime a livello di voxel ottenute dai due diversi metodi presentano una correlazione elevata. Per quanto riguarda la funzione di input, osserviamo che si ottengono stime simili utilizzando l'IDIF delle ICA con correzione del PVE o utilizzando l'IDIF delle CCA senza alcuna correzione. Questo approccio apre la strada a futuri studi sulla gestione dei dati rumorosi in relazione all'errore di misura e sull'ispezione dell'influenza dei vincoli sul calcolo a posteriori del parametro k3.Positron Emission Tomography (PET) with [18F]FDG (Fluorodeoxyglucose) is a nuclear imaging technique widely utilized in oncology. It plays a crucial role in examining physiological and pathophysiological processes in vivo, enabling the quantitative measurement of biochemical changes in the body. This facilitates the detection of anomalies in the metabolism of tissues of interest. To establish a connection between PET signals and internal biochemical processes, compartmental modeling is required. Parameter estimation can be performed either at region-of-interest (ROI) level or at the voxel level, fully exploiting the PET scanner spatial resolution. In specific situations, for example when studying a lesion, the information provided by ROI level analysis may not be enough, and a voxel level analysis would be required. The challenge lies in the type of signal obtained at the voxel level, characterized by a low Signal-to-Noise Ratio (SNR), in contrast to regional analysis. Currently, commonly employed methods for microparameter estimation at ROI level are not equally effective at the voxel level, due to the low SNR. Simplified approaches, such as the Patlak Graphical method, are available, but they generate parameters with less detailed physiological information. Therefore, there is a need for an approach able to produce reliable voxel-level results. A solution may be found in Bayesian approaches, which leverage a priori information to perform better in the presence of noisy data. Nevertheless, microparameter estimation in the model is based on the assumption of having a noise-free Input function. This typically requires arterial sampling, which can be uncomfortable and burdensome for the patient. An alternative is the Image-Derived Input Function (IDIF), which extracts the Input function directly from the PET image, using the Internal Carotid Arteries (ICA) or the Common Carotid Arteries (CCA), with corrections applied for Partial Volume Effect (PVE). This thesis focuses on exploring a Bayesian approach to enable adequate voxel-level microparameter estimation. Specifically, the Maximum a Posteriori (MAP) estimation method is employed. In our implementation, the prior information is derived from ROI-level estimates with the ultimate goal of obtaining precise voxel-level estimates. Additionally, the MAP estimation was tested with a constraint on a model parameter, k3, based on the voxel-wise Ki estimate obtained using Patlak's graphical analysis, to further reduce complexity. Furthermore, the impact of using different input functions extracted from different vascular ROIs was examined in this study. The analysis was performed on a dataset consisting of 10 subjects affected by glioma who underwent [18F]FDG acquisition on a PET/MR system at the University Hospital of Padova. To assess the reliability of the results obtained with the MAP, the parametric maps were compared to the ones obtained using another bayesian approach, i.e. the Variational Bayes (VB). Voxel-level esimates coming from the two different methods exhibit a high correlation. As for the input function, we observe that similar estimates are obtained when using ICA's IDIF with PVE correction or when using CCA's IDIF without any correction. This approach opens doors for future research in handling data fitting with respect to measurement error and examining the influence of constraints on the posterior calculation of the k3 parameter

Functional Connectivity Analysis of FMRI Time-Series Data

The term ``functional connectivity' is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. Yet these relationships have been quantified using very different measures and the extent to which they index the same constructs is unclear. We have implemented a variety of these functional connectivity measures in a new freely-available MATLAB toolbox. These measures are categorized into two groups: whole time-series and trial-based approaches. We evaluate these measures via simulations with different patterns of functional connectivity and provide recommendations for their use. We also apply these measures to a previously published fMRI dataset in which activity in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) was evaluated in 32 healthy subjects during a digit sorting task. Though all implemented measures demonstrate functional connectivity between dACC and DLPFC activity during event-related tasks, different participants appeared to display qualitatively different relationships.We also propose a new methodology for exploring functional connectivity in slow event-related designs, where stimuli are presented at a sufficient separation to examine the dynamic responses in brain regions. Our methodology simultaneously determines the level of smoothing to obtain the underlying noise-free BOLD response and the functional connectivity among several regions. Smoothing is accomplished through an empirical basis via functional principal components analysis. The coefficients of the basis are assumed to be correlated across regions, and the nature and strength of functional connectivity is derived from this correlation matrix. The model is implemented within a Bayesian framework by specifying priors on the parameters and using a Markov Chain Monte Carlo (MCMC) Gibbs sampling algorithm. We demonstrate this new approach on a sample of clinically depressed subjects and healthy controls in examining relationships among three brain regions implicated in depression and emotion during emotional information processing. The results show that depressed subjects display decreased coupling between left amygdala and DLPFC compared to healthy subjects and this may potentially be due to inefficient functioning in mediating connectivity from the rostral portion Brodmann's area24 (BA24)

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

Behavioural disinhibition in the syndromes associated with frontotemporal lobar degeneration

The different clinical syndromes caused by frontotemporal lobar degeneration (FTLD) have highly heterogenous and overlapping features which complicate clinical and research practice. Behavioural impairments are associated with all FTLD syndromes, cause high morbidity and lack proven symptomatic treatments. Treatments for cognitive and behavioural impairment in other neurodegenerative diseases include restoration of neurotransmitter deficits. Deficits in the neurotransmitters glutamate and GABA occur in FTLD syndromes and are associated with behavioural disinhibition in other diseases. I propose that these neurotransmitter deficits contribute to behavioural change in FTLD syndromes. This thesis has two main aims. First, to develop a transdiagnostic approach to FTLD syndromes to facilitate a better understanding of aetiology, pathophysiology and in due course their symptomatic treatment. Second, to use this approach to test the hypothesis that glutamate and GABA deficits are associated with behavioural disinhibition in FTLD syndromes. In a cross-sectional epidemiological study, I examined 310 of 365 regional patients with a FTLD-associated syndrome, including behavioural variant frontotemporal dementia, the nonfluent and semantic variants of primary progressive aphasia, progressive supranuclear palsy and corticobasal syndrome. Multivariate analyses of clinical features and brain morphometry identified components that showed considerable overlap across the diagnostic groups. The transdiagnostic components of clinical features predicted neuropathology better than the current FTLD diagnostic labels. Behavioural disturbance, including disinhibition, was associated with reduced functionally independent survival, irrespective of diagnosis. Next, I investigated the role of glutamate and GABA in behavioural disinhibition. Ultrahigh-field magnetic resonance spectroscopy was used to measure glutamate and GABA in the frontal cortex of 44 patients with a FTLD syndrome and 20 healthy controls. Bayesian modelling of a response inhibition task was used to quantify behavioural disinhibition. Both neurotransmitters were reduced in the frontal cortex, but not occipital cortex, of patients compared to controls. Glutamate and GABA concentrations in the frontal cortex were inversely associated with behavioural disinhibition. In summary, the transdiagnostic approach provided new insights into the phenotypic heterogeneity in FTLD syndromes. Behavioural disinhibition, which can occur to a variable degree in all FTLD syndromes, was associated with reduced functionally independent survival. GABA and glutamate deficits in the frontal cortex are associated with behavioural disinhibition and are a potential target for future treatments.Holt Fellowshi

Multivariate methods for interpretable analysis of magnetic resonance spectroscopy data in brain tumour diagnosis

Malignant tumours of the brain represent one of the most difficult to treat types of cancer due to the sensitive organ they affect. Clinical management of the pathology becomes even more intricate as the tumour mass increases due to proliferation, suggesting that an early and accurate diagnosis is vital for preventing it from its normal course of development. The standard clinical practise for diagnosis includes invasive techniques that might be harmful for the patient, a fact that has fostered intensive research towards the discovery of alternative non-invasive brain tissue measurement methods, such as nuclear magnetic resonance. One of its variants, magnetic resonance imaging, is already used in a regular basis to locate and bound the brain tumour; but a complementary variant, magnetic resonance spectroscopy, despite its higher spatial resolution and its capability to identify biochemical metabolites that might become biomarkers of tumour within a delimited area, lags behind in terms of clinical use, mainly due to its difficult interpretability. The interpretation of magnetic resonance spectra corresponding to brain tissue thus becomes an interesting field of research for automated methods of knowledge extraction such as machine learning, always understanding its secondary role behind human expert medical decision making. The current thesis aims at contributing to the state of the art in this domain by providing novel techniques for assistance of radiology experts, focusing on complex problems and delivering interpretable solutions. In this respect, an ensemble learning technique to accurately discriminate amongst the most aggressive brain tumours, namely glioblastomas and metastases, has been designed; moreover, a strategy to increase the stability of biomarker identification in the spectra by means of instance weighting is provided. From a different analytical perspective, a tool based on signal source separation, guided by tumour type-specific information has been developed to assess the existence of different tissues in the tumoural mass, quantifying their influence in the vicinity of tumoural areas. This development has led to the derivation of a probabilistic interpretation of some source separation techniques, which provide support for uncertainty handling and strategies for the estimation of the most accurate number of differentiated tissues within the analysed tumour volumes. The provided strategies should assist human experts through the use of automated decision support tools and by tackling interpretability and accuracy from different anglesEls tumors cerebrals malignes representen un dels tipus de càncer més difícils de tractar degut a la sensibilitat de l’òrgan que afecten. La gestió clínica de la patologia esdevé encara més complexa quan la massa tumoral s'incrementa degut a la proliferació incontrolada de cèl·lules; suggerint que una diagnosis precoç i acurada és vital per prevenir el curs natural de desenvolupament. La pràctica clínica estàndard per a la diagnosis inclou la utilització de tècniques invasives que poden arribar a ser molt perjudicials per al pacient, factor que ha fomentat la recerca intensiva cap al descobriment de mètodes alternatius de mesurament dels teixits del cervell, tals com la ressonància magnètica nuclear. Una de les seves variants, la imatge de ressonància magnètica, ja s'està actualment utilitzant de forma regular per localitzar i delimitar el tumor. Així mateix, una variant complementària, la espectroscòpia de ressonància magnètica, malgrat la seva alta resolució espacial i la seva capacitat d'identificar metabòlits bioquímics que poden esdevenir biomarcadors de tumor en una àrea delimitada, està molt per darrera en termes d'ús clínic, principalment per la seva difícil interpretació. Per aquest motiu, la interpretació dels espectres de ressonància magnètica corresponents a teixits del cervell esdevé un interessant camp de recerca en mètodes automàtics d'extracció de coneixement tals com l'aprenentatge automàtic, sempre entesos com a una eina d'ajuda per a la presa de decisions per part d'un metge expert humà. La tesis actual té com a propòsit la contribució a l'estat de l'art en aquest camp mitjançant l'aportació de noves tècniques per a l'assistència d'experts radiòlegs, centrades en problemes complexes i proporcionant solucions interpretables. En aquest sentit, s'ha dissenyat una tècnica basada en comitè d'experts per a una discriminació acurada dels diferents tipus de tumors cerebrals agressius, anomenats glioblastomes i metàstasis; a més, es proporciona una estratègia per a incrementar l'estabilitat en la identificació de biomarcadors presents en un espectre mitjançant una ponderació d'instàncies. Des d'una perspectiva analítica diferent, s'ha desenvolupat una eina basada en la separació de fonts, guiada per informació específica de tipus de tumor per a avaluar l'existència de diferents tipus de teixits existents en una massa tumoral, quantificant-ne la seva influència a les regions tumorals veïnes. Aquest desenvolupament ha portat cap a la derivació d'una interpretació probabilística d'algunes d'aquestes tècniques de separació de fonts, proporcionant suport per a la gestió de la incertesa i estratègies d'estimació del nombre més acurat de teixits diferenciats en cada un dels volums tumorals analitzats. Les estratègies proporcionades haurien d'assistir els experts humans en l'ús d'eines automatitzades de suport a la decisió, donada la interpretabilitat i precisió que presenten des de diferents angles

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds

Effects of Diversity and Neuropsychological Performance in an NFL Cohort

Objective: The aim of this study was to examine the effect of ethnicity on neuropsychological test performance by comparing scores of white and black former NFL athletes on each subtest of the WMS. Participants and Methods: Data was derived from a de-identified database in South Florida consisting of 63 former NFL white (n=28, 44.4%) and black (n=35, 55.6%) athletes (Mage= 50.38; SD= 11.57). Participants completed the following subtests of the WMS: Logical Memory I and II, Verbal Paired Associates I and II, and Visual Reproduction I and II. Results: A One-Way ANOVA yielded significant effect between ethnicity and performance on several subtests from the WMS-IV. Black athletes had significantly lower scores compared to white athletes on Logical Memory II: F(1,61) = 4.667, p= .035, Verbal Paired Associates I: F(1,61) = 4.536, p = .037, Verbal Paired Associates: II F(1,61) = 4.677, p = .034, and Visual Reproduction I: F(1,61) = 6.562, p = .013. Conclusions: Results suggest significant differences exist between white and black athletes on neuropsychological test performance, necessitating the need for proper normative samples for each ethnic group. It is possible the differences found can be explained by the psychometric properties of the assessment and possibility of a non-representative sample for minorities, or simply individual differences. Previous literature has found white individuals to outperform African-Americans on verbal and non-verbal cognitive tasks after controlling for socioeconomic and other demographic variables (Manly & Jacobs, 2002). This highlights the need for future investigators to identify cultural factors and evaluate how ethnicity specifically plays a role on neuropsychological test performance. Notably, differences between ethnic groups can have significant implications when evaluating a sample of former athletes for cognitive impairment, as these results suggest retired NFL minorities may be more impaired compared to retired NFL white athletes