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ABSTRACT Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome‐wide exposures and brain subcortical shape mediators due to several challenges including (i) large‐scale genetic exposures, that is, millions of single‐nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome‐wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome‐wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square‐root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape‐on‐scalar model and a scalar‐on‐shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative. 

The transformation and transmission of brain stimuli reflect the dynamical brain activity in space and time. Compared with functional magnetic resonance imaging (fMRI), magneto- or electroencephalography (M/EEG) fast couples to the neural activity through generated magnetic fields. However, the MEG signal is inhomogeneous throughout the whole brain, which is affected by the signal-to-noise ratio, the sensors’ location and distance. Current non-invasive neuroimaging modalities such as fMRI and M/EEG excel high resolution in space or time but not in both. To solve the main limitations of current technique for brain activity recording, we propose a novel recurrent memory optimization approach to predict the internal behavioral states in space and time. The proposed method uses Optimal Polynomial Projections to capture the long temporal history with robust online compression. The training process takes the pairs of fMRI and MEG data as inputs and predicts the recurrent brain states through the Siamese network. In the testing process, the framework only uses fMRI data to generate the corresponding neural response in space and time. The experimental results with Human connectome project (HCP) show that the predicted signal could reflect the neural activity with high spatial resolution as fMRI and high temporal resolution as MEG signal. The experimental results demonstrate for the first time that the proposed method is able to predict the brain response in both milliseconds and millimeters using only fMRI signal. 

Understanding the intrinsic patterns of human brain is important to make inferences about the mind and brain-behavior association. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity without the effect of vascular confounds. The blood oxygenated level-dependent (BOLD) signal of functional MRI (fMRI) reveals the spatial and temporal brain activity across different brain regions. However, it is unclear how to associate the high temporal resolution Electrophysiological measures with high spatial resolution fMRI signals. Here, we present a novel interpretable model for coupling the structure and function activity of brain based on heterogeneous contrastive graph representation. The proposed method is able to link manifest variables of the brain (i.e. MEG, MRI, fMRI and behavior performance) and quantify the intrinsic coupling strength of different modal signals. The proposed method learns the heterogeneous node and graph representations by contrasting the structural and temporal views through the mind to multimodal brain data. The first experiment with 1200 subjects from Human connectome Project (HCP) shows that the proposed method outperforms the existing approaches in predicting individual gender and enabling the location of the importance of brain regions with sex difference. The second experiment associates the structure and temporal views between the low-level sensory regions and high-level cognitive ones. The experimental results demonstrate that the dependence of structural and temporal views varied spatially through different modal variants. The proposed method enables the heterogeneous biomarkers explanation for different brain measurements.