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Register a new userAlternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction
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To explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper. We propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals. The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal $n$-back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and $n$-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods. To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ.
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Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for studying biological mechanisms. In this work, we develop an interpretable multimodal fusion model, namely gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. The gCAM-CCL model can generate interpretable activation maps, which quantify pixel-level contributions of the input features. This is achieved by combining intermediate feature maps using gradient-based weights. Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCLs performed well for both classification and mechanism analysis. Mechanism analysis suggests that during task-fMRI scans, several object recognition related regions of interests (ROIs) are first activated and then several downstream encoding ROIs get involved. Results also suggest that the higher cognition performing group may have stronger neurotransmission signaling while the lower cognition performing group may have problem in brain/neuron development, resulting from genetic variations.
Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity, related to the more elusive question Which areas cause the present activity of which others?. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that a dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal [...] Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer [...] Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this communication-through-coherence, making thus possible a fast on-demand reconfiguration of global information routing modalities.
Predicting brain maturity using noninvasive magnetic resonance images (MRI) can distinguish different age groups and help to assess neurodevelopmental disorders. However, group-wise differences are often less informative for assessing features of individuals. Here, we propose a simple method to predict the age of an individual subject solely based on structural connectivity data from diffusion tensor imaging (DTI). Our simple predictor computed a weighted sum of the strength of all connections of an individual. The weight consists of the fiber strength, given by the number of streamlines following tract tracing, multiplied by the importance of that connection for an observed feature--age in this case. We tested this approach using DTI data from 121 healthy subjects aged 4 to 85 years. After determining importance in a training dataset, our predicted ages in the test dataset showed a strong correlation (rho = 0.77) with real age deviating by, on average, only 10 years.
A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (i) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (ii) cluster the distance matrix for each node; (iii) build the consensus network from the corresponding partitions; (iv) extract groups of subjects by finding the communities of the consensus network thus obtained. Differently from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pre-training step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real data sets, show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix.
This paper proposes a novel topological learning framework that can integrate brain networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations with ground truth to assess the effectiveness of the topological loss in discriminating networks with different topology. The method is further applied to a twin brain imaging study in determining if the brain network is genetically heritable. The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional MRI (fMRI) onto the template structural brain network obtained through the diffusion MRI (dMRI).
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