A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common hybrid connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5 figure

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA

Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Functional magnetic resonance imaging recordings in the resting-state (RS) from the human brain are characterized by spontaneous low-frequency fluctuations in the blood oxygenation level dependent signal that reveal functional connectivity (FC) via their spatial synchronicity. This RS study applied network analysis to compare FC between late-life depression (LLD) patients and control subjects. Raw cross-correlation matrices (CM) for LLD were characterized by higher FC. We analyzed the small-world (SW) and modular organization of these networks consisting of 110 nodes each as well as the connectivity patterns of individual nodes of the basal ganglia. Topological network measures showed no significant differences between groups. The composition of top hubs was similar between LLD and control subjects, however in the LLD group posterior medial-parietal regions were more highly connected compared to controls. In LLD, a number of brain regions showed connections with more distant neighbors leading to an increase of the average Euclidean distance between connected regions compared to controls. In addition, right caudate nucleus connectivity was more diffuse in LLD. In summary, LLD was associated with overall increased FC strength and changes in the average distance between connected nodes, but did not lead to global changes in SW or modular organization

Development of a pipeline for the study of resting-state fMRI abnormalities in neurological disorders

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, RoserThe study of brain functional connectivity abnormalities in neurological disorders is not straightforward. The absence of a standardized and well-defined pipeline and the lack of accepted imaging biomarkers give rise to the need to set certain guidelines and common measures to assess the presence of functional abnormalities in neurological disorders. To provide a solution to the current problem, this project studies the whole-brain network dynamics with resting-state functional magnetic resonance imaging (fMRI) data from 49 patients with Post-COVID-19 neurological syndrome, scanned twice, with a 6-month period between scans. These data are firstly preprocessed to further undergo a node-based (or data-driven) study, more specifically group Independent Component Analysis (ICA). Several decompositions of different dimensionalities are tested to find the optimal range number of independent components according to several levels of granularity (i.e., separation of the networks into subnetworks). The outcome is a set of spatial maps and timecourses, one for each independent component. Then, dual regression is needed to set the group-ICA maps to each individual subject, resulting in a collection of spatial maps and timecourses for each component and each subject. In parallel with dual regression, the independent components must be classified between noise and resting state networks (RSN) and subnetworks. Hierarchical maps are helpful to visualize this classification. Group data comparisons between two time points are carried out to finally identify biomarkers. Four biomarker candidates (i.e. quantitative individual measures obtained from the analyses) are studied: BOLD signal amplitude, full correlation, partial correlation, and covariance between brain regions. In addition, we implement different data-representation approaches that can help to understand the localization of the effects from the subnetwork to the network level. This representation might be helpful to interpret the findings from the point of view of cognitive and mental processes. According to the goal of the project, the resulting pipeline and the extracted biomarkers can be used for analyzing resting-state fMRI data from other neurological disorders

Analysis of structural and functional brain networks

The brain is a representative example of a network. It consists of numerous spatially distributed regions that continuously exchange information through structural connections. In the past decade, an increasing number of studies have explored the brain network in both structural and functional aspects; they have begun to decipher complex brain wirings, as well as elucidate how the rich functionality emerges from this architecture. Based upon previous studies, this thesis addresses three critical gaps in the field. (I) Although it is known that the community structures of brain network are spatially overlapping, conventional studies have focused on grouping brain regions into communities such that each region belongs to only one community. Therefore, a recent “link community” concept was employed to disentangle those overlapping architectures. (II) Spatial independent component analysis (sICA) and k-means clustering are two representative data-driven algorithms used to analyze functional networks. However, it is still unclear how these two methods compare to each other in terms of their theoretical basis and biological relevance. Hence, the relationship between these two methods were investigated. (III) Despite the multi-scale functional organization of the brain, previous studies have primarily examined the large-scale networks of the entire brain. Complex neural activity patterns in relatively smaller spatial scales have been poorly understood. Therefore, the fine-scale spatiotemporal patterns within visual cortex were explored. The distinguishing results obtained in this study may provide new insights regarding the brain\u27s organization, as well as a better understanding of mathematical and statistical tools for functional and structural network analysis