Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations

A Comparative Assessment of Statistical Approaches for fMRI Data to Obtain Activation Maps

Functional Magnetic Resonance Imaging (fMRI) lets us peek into the human mind and try to identify which brain areas are associated with certain tasks without the need for an invasive procedure. However, the data collected during fMRI sessions is complex; this 4 dimensional sequence of 3 dimensional volumes as images of the brain does not allow for straightforward inference. Multiple models have been developed to analyze this data and each comes with its intricacies and problems. Two of the most common ones are 2-step General Linear Model (GLM) and Independent Component Analysis (ICA). We compare these approaches empirically by fitting the models to real fMRI data using packages developed and readily available in R. The real data, obtained from an open source database openneuro.org, is named BOLD5000. The task of interest for this thesis is image viewing versus fixation cross (resting state). We found that both the first-level GLM and ICA revealed significant activation located in the occipital lobe which is consistent with the literature on visual tasks. The second-level GLM results were consistent with the first level and found activation located in the occipital lobe as well. The Group ICA results however found activation located mainly in the temporal lobe.No embargoAcademic Major: Statistic

Detecting neuroimaging biomarkers for schizophrenia:a meta-analysis of multivariate pattern recognition studies

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7–83.5%) and a specificity of 80.3% (95% CI: 76.9–83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9–88.2%) and similar specificity (76.9%, 95% CI: 71.3–81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9–80.4%, specificity of 79.0%, 95% CI: 74.6–82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity

Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level

AbstractStandard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned

Thought Control Ability Moderates the Effect of Mind Wandering on Positive Affect via the Frontoparietal Control Network

Mind wandering is a phenomenon that involves thoughts shifting away from a primary task to the process of dealing with other personal goals. A large number of studies have found that mind wandering can predict negative emotions, but researchers have seldom focused on the positive role of mind wandering. The current study aimed to explore the relationships among mind wandering, emotions and thought control ability, which is the ability to inhibit one’s own unpleasant or unwanted intrusive thoughts. Here, we collected resting-state functional magnetic resonance imaging (rsfMRI) data from 368 participants who completed a set of questionnaires involving mind wandering, thought control ability and positive or negative emotions. The results revealed that (1) rsfMRI connectivity features related to thought control ability and mind wandering could divide individuals into two groups: HMW (high mind-wandering) group and LMW (low mind-wandering) group. The HMW group scored lower in thought control ability (TCA), higher in negative emotion (NE) and lower in positive emotion (PE) than the LMW group. (2) TCA moderated the association between MW and positive affect (PA). (3) Two groups exhibited different segregation within key nodes (SWKN) of the frontoparietal control network (FPCN), and the subsequent analysis showed that the SWKN of the FPCN was negatively correlated with PA. These findings indicate that TCA moderates the effect of mind wandering on affect via the FPCN, which may have important implications for our understanding of the positive role of mind wandering

Quantification of 18F-FDG PET kinetic parameters using an image-derived input function and multimodal integration with resting-state fMRI metrics

Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints.Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints