A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation

International audienceThe estimation of intra-subject functional connectivity is greatly complicated by the small sample size and complex noise structure in functional magnetic resonance imaging (fMRI) data. Pooling samples across subjects improves the conditioning of the estimation, but loses subject-specific connectivity information. In this paper, we propose a new sparse group Gaussian graphical model (SGGGM) that facilitates joint estimation of intra-subject and group-level connectivity. This is achieved by casting functional connectivity estimation as a regularized consensus optimization problem, in which information across subjects is aggregated in learning group-level connectivity and group information is propagated back in estimating intra-subject connectivity. On synthetic data, we show that incorporating group information using SGGGM significantly enhances intra-subject connectivity estimation over existing techniques. More accurate group-level connectivity is also obtained. On real data from a cohort of 60 subjects, we show that integrating intra-subject connectivity estimated with SGGGM significantly improves brain activation detection over connectivity priors derived from other graphical modeling approaches

Metabolic brain connectivity after acute unilateral vestibulopathy: Longitudinal analysis and single subject classification in the rat

Ziel Die Analyse der metabolischen Konnektivität des Gehirns basiert auf [18F]-Fluordesoxyglucose (FDG) Positronen-Emissions-Tomographie (PET). Die Ziele dieser Arbeit waren einerseits die Anwendung von Konnektivitätsanalysen auf einen präklinischen PET-Datensatz zur akuten unilateralen Vestibulopathie (AUV) und andererseits die Untersuchung der bildgestützten Klassifikation auf Basis von Konnektivitätsinformationen. Material und Methodik Der untersuchte präklinische AUV-Datensatz bestand aus 85 [18F]-FDG PET Bildern von Ratten, wobei je 17 Bilder an fünf Messtagen aufgenommen wurden. Ein Messtag war vor AUV und vier Messungen wurden an den Tagen 1, 3, 7 und 15 nach AUV durchgeführt. Parallel zur Bildgebung wurden klinische Verhaltensparameter der Tiere akquiriert. Die Bilder wurden nach der Rekonstruktion registriert, normalisiert und mittels eines Hirnatlas in 57 Hirnregionen segmentiert. Anschließend wurden die mittleren normalisierten Aktivitätswerte jeder Hirnregion und jedes Tieres extrahiert und für nachfolgende Analysen gespeichert. Durch die paarweise Korrelation der Aktivitätswerte aller Hirnregionen nach Pearson wurde für jeden Messtag das gruppenbasierte Hirnkonnektivitätsmuster bestimmt. Zur Analyse wurden diese Konnektivitätsmuster quantifiziert und zur Erstellung graphtheoretischer Strukturen verwendet. Zur Klassifikation wurden die einzelnen Messtage als individuelle Klassen betrachtet und alle enthaltenen Verbindungen mit linearen Funktionen genähert. Diese linearen Funktionen repräsentierten das Konnektivitätsmuster einer Gruppe und erlaubten den Vergleich mit den im PET bestimmten Aktivitätswerten des Einzeltieres. Mittels Abgleich der Kongruenz erfolgte die Klassifikation in die Klasse mit der höchsten Übereinstimmung. Diskussion Vestibuläre Kompensation nach AUV aktiviert zerebrale Anpassungsprozesse, welche zur Neustrukturierung funktioneller Netzwerke führen. Die longitudinale Quantifizierung der Konnektivitätsmuster ergab kurzfristige Änderungen nach AUV, die in ihrem Verlauf den klinischen Verhaltensparametern folgten. Außerdem zeigte die graphtheoretische Analyse einen Anstieg an Verbindungen während der vestibulären Kompensation insbesondere in zum vestibulären System gehörigen Hirnregionen. Die Analyse der Hirnkonnektivität erwies sich als geeignet, um Hirnplastizität in longitudinalen Experimenten sinnvoll abzubilden. Weiterhin wurde ein neuartiger Klassifikationsansatz auf Basis des mittels Pearsons Korrelation bestimmten Konnektivitätsmusters untersucht. Hierbei konnten höhere Klassifikationsgenauigkeiten als mit Methoden des maschinellen Lernens erreicht werden. Da neurodegenerative Erkrankungen immer häufiger als komplexe Netzwerkerkrankungen beschrieben werden, könnte diese Klassifikationstechnik möglicherweise die diagnostische Entscheidungsfindung in klinisch relevanten Krankheiten wie der Alzheimer Demenz unterstützen. Schlussfolgerung Die Analyse der metabolischen Hirnkonnektivität eignet sich zur Untersuchung neurologischer Fragestellungen und ergänzt die im PET gängigen Analysen im Bereich der Hirnbildgebung. Die hier beschriebenen präklinischen Ergebnisse müssen auf vergleichbaren klinischen Datensätzen bestätigt werden.Aim Metabolic brain connectivity analysis is based on [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). The objectives of this thesis were to apply these methods to a preclinical dataset of acute unilateral vestibulopathy (AUV) and to investigate the suitability of brain connectivity information for classification purposes. Material and methods The preclinical AUV dataset under investigation comprised 85 [18F]-FDG PET images from rats, specifically 17 images on five distinct measurement days. One measurement day was before AUV and four follow-up measurements were performed on days 1, 3, 7, and 15 after AUV. Additionally, clinical scoring parameters were recorded in parallel to PET imaging. After image reconstruction, images were registered, normalized, and segmented into 57 brain regions using an atlas-based method. Mean normalized activity values were extracted for every brain region in every subject and stored for further processing. Brain connectivity patterns were determined for every measurement day in a population-based approach by pairwise correlation of the activity values from all brain regions with Pearson’s correlation. These connectivity patterns were quantified and used to create graph theoretical structures for analysis. For classification purposes, each measurement day represented a class. The group-based and class-individual connectome was transferred to a single-subject level by fitting a linear function to each connection. This enabled the evaluation of the single subject connectome by comparing the image-derived activity values to the fitted functions. Classification was performed by testing the congruence between the single-subject connectome with the class connectomes and to assign the subject to the most matching class. Discussion Vestibular compensation after AUV activates various adaptive cerebral processes that result in functional network rearrangement. The longitudinal quantification of the connectivity patterns demonstrated short-term changes after AUV that follow the course of the clinical scoring parameters. Furthermore, during vestibular compensation graph theoretical analysis revealed an increase in connectivity especially in brain regions associated with the vestibular system. Brain connectivity methods prove the suitability to reasonably depict short-term changes of the metabolic connectome in longitudinal experimental setups. Moreover, classification based on Pearson’s correlation-derived connective information has not been investigated so far. The described approach using linear fitting was evaluated and reached higher classification accuracies compared to machine learning methods on the same dataset. As clinically relevant neurodegenerative disorders are increasingly considered as network disorders, this classification technique could potentially support diagnostic decisions in clinically relevant diseases such as Alzheimer’s disease. Conclusion Metabolic brain connectivity is suitable to investigate neurological questions and complements the toolkit of established cerebral image analysis in PET. The reported preclinical analysis results need to be validated on comparable clinical datasets

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification: Weighted Sparse Group Model for MCI Classification

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility

High-order resting-state functional connectivity network for MCI classification: High-Order Correlation and FC Network

Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis

Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity which may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.Comment: Accepted to Biometrics January 202

Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI

Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating healthy subjects from subjects with amnesic mild cognitive impairment and 97% accuracy disambiguating amnesic mild cognitive impairment subjects from those with Alzheimer's disease, accuracies are estimated using a held-out test set. Both results are significant at the 1% level