A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

International audienceDespite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection

Implications of Inconsistencies between fMRI and dMRI on Multimodal Connectivity Estimation

International audienceThere is a recent trend towards integrating resting state functional magnetic resonance imaging (RS-fMRI) and diffusion MRI (dMRI) for brain connectivity estimation, as motivated by how estimates from these modalities are presumably two views reflecting the same underlying brain circuitry. In this paper, we show on a cohort of 60 subjects that conventional functional connectivity (FC) estimates based on Pearson's correlation and anatomical connectivity (AC) estimates based on fiber counts are actually not that highly correlated for typical RS-fMRI (~7 min) and dMRI (~32 gradient directions) data. The FC-AC correlation can be significantly increased by considering sparse partial correlation and modeling fiber endpoint uncertainty, but the resulting FC-AC correlation is still rather low in absolute terms. We further exemplify the inconsistencies between FC and AC estimates by integrating them as priors into activation detection and demonstrating significant differences in their detection sensitivity. Importantly, we illustrate that these inconsistencies can be useful in fMRI-dMRI integration for improving brain connectivity estimation

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

Control-Group Feature Normalization for Multivariate Pattern Analysis Using the Support Vector Machine

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We also show that control-based normalization provides better interpretation with respect to the estimated multivariate disease pattern and improves the classifier performance in many cases

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease effects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples

From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.National Alliance for Medical Image Computing (U.S.) (Grant NIH NIBIB NAMIC U54-EB005149)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-EB015902)National Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (R01MH074794)National Institutes of Health (U.S.). Advanced Multimodal Neuroimaging Training Progra

Generative models of brain connectivity for population studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-139).Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.by Archana Venkataraman.Ph.D

Functional disconnection and social cognition in schizophrenia

Introduction Social and emotional functions play a key role in schizophrenia. Both positive symptoms, such as hallucinations and persecutory delusions, as well as negative symptoms such as social withdrawal, and flattened affect impact socioemotional function. These functions involve distributed brain networks. The ‘Disconnection Hypothesis’, a plausible unifying theory of schizophrenia, proposes connectivity within such networks as a core pathological feature of schizophrenia. Connectivity is also related to specific genetic risk factors. Therefore the present project addresses the hypothesis that individuals with schizophrenia might show disconnection within socio-emotional brain networks, and examines the effects of a functional polymorphism of the BDNF gene on connectivity within these networks. Methods Here I examined the brain activation and connectivity for implicit emotional reaction and social judgment in schizophrenia, as well as with variation in the val66met polymorphism of BDNF. Brain activation was examined with functional magnetic resonance imaging, and effective connectivity was estimated using psycho-physiological interactions, from the bilateral amygdala to the whole brain (using a facial image paradigm for explicit approachability judgement and implicit fear response respectively). Results Individuals with schizophrenia showed reduced activation in the right lingual gyrus, right superior temporal gyrus and left amygdala during fear processing, as well as reduced connectivity from the left amygdala to the right temporo-parietal junction and precuneus. During approachability judgments, patients overactivated the right inferior frontal gyrus and right precuneus and showed reduced connectivity from the bilateral amygdala to the right inferior frontal gyrus. Met allele carriers of the BDNF val66met polymorphism showed overactivation in the medial anterior cingulate cortex, and bilateral insula, as well as reduced connectivity between the anterior cingulate cortex and hippocampus. For approachability judgment, met carriers overactivated the middle occipital gyrus, and showed reduced connectivity from the left amygdala to the right parahippocampal gyrus and medial frontal gyrus, as well as the left posterior cingulate gyrus, pre and post central gyrus, middle temporal gyrus and cerebellum. Conclusion In conclusion, connectivity between the amygdala and brain regions associated with a range of socially relevant functions were found to be reduced in both patients, and met allele carriers of the BDNF val66met SNP. Given the key role of the amygdala in affective processing this diffuse disconnection in networks for socio-emotional functions might mediate the aberrant emotional and social behavior seen in individuals with schizophrenia

The bed nucleus of the Stria Terminalis:Connections, genetics, & trait associations

This thesis examines the functional and structural connections of the Bed Nucleus of the Stria Terminalis (BNST). The principal motivation in doing so stems from the documented gap in our knowledge between the prolific pre-clinical animal BNST research, and that of human BNST research (Lebow & Chen, 2016). Understanding the human BNST may prove to be clinically important, as animal models often implicate this structure as being key in processes underlying the stress-response, disorders of negative affect, and in substance misuse- particularly related to alcohol (Herman et al., 2020; Maita et al., 2021). Therefore I further set out to test BNST connectivity relationships with related psychological phenotypes and examine any genetic associations. Chapter 1 provides an overview of the relevant BNST literature and a brief summary of the methods used in this thesis. In Chapter 2 I use the Human Connectome Project young human adults sample (n = ~1000) to map the intrinsic connectivity network of the BNST. In addition, I compare this network to that of the central nucleus of the amygdala, an area anatomically and functionally associated with the BNST (Alheid, 2009). Next, I test for associations across this network with self-report traits relating to dispositional negativity and alcohol use. Finally, I examine the heritability of specific BNST- amygdala sub-region functional connectivity, and co-heritability with the selfreport traits. In Chapter 3 I use the large UK biobank sample (n = ~ 19,000) to run a genome-wide association analysis, aiming to uncover specific common genetic variants that may be linked with BNST – amygdala sub-region functional connectivity. In Chapter 4, I focus on structural connectivity and use a mixture of macaque tracttracing analysis, and human and macaque diffusion MRI probabilistic tractography to examine the evidence for a connection between the subiculum and the BNST. As well, I test for associations between measures of white-matter microstructure and self-report dispositional negativity and alcohol-use phenotypes. Finally, in the Discussion, I bring together the findings of the research, noting their implications within the wider BNST literature and making several suggestions for improving similar analysis in future