An ICA with reference approach in identification of genetic variation and associated brain networks

To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p < 4 × 10−17), and discovered a brain functional network that was significantly associated with this genetic component (p < 1 × 10−4). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain

Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data

Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns, i.e., biomarkers. In this paper, EEG, fMRI, and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along the subject mode, and jointly analyzed using structure-revealing CMTF methods [also known as advanced CMTF (ACMTF)] focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role

Individual Differences and Episodic Memory: Examining Behaviour, Genetics, and Brain Activity.

Dual-process models propose that two processes support recognition memory; familiarity, a general sense that something has been previously encountered; and recollection, the retrieval of details concerning the context in which a previous encounter occurred. Event-related potential (ERP) studies of recognition memory have identified a set of old/new effects that are thought to reflect these processes: the 300-500ms bilateral-frontal effect, thought to reflect familiarity and the 500-800ms left-parietal effect, thought to reflect recollection. Whilst the exact functional role of these effects remains unclear, they are widely viewed as reliable indices of retrieval. The ERP literature reviewed in this thesis suggests that the characteristics of these recognition effects vary with task specific details and individual participant differences, suggesting that the recognition effects purported to index retrieval may be conditional on both task and participant. This thesis examined the influence of individual differences on behavioural measures of recognition and the neural correlates of recognition memory, focusing on factors of stimulus material, task performance and participant genotype. Clear evidence of stimulus differences were found, with pictures eliciting more anteriorly distributed effects than words, and a late onsetting frontopolar old/new effect that was unique for voices. Furthermore, the pattern of ERP activity associated with successful recognition of faces appeared to vary as a function of general face recognition ability, with participants poorer at remembering faces exhibiting a 300-500ms old/new effect not present for those good at remembering faces. The data also suggested that activity over right-frontal electrodes, evident in some previous studies, may be participant specific and could reflect additional retrieval support processes. Contrary to expectations, behavioural task performance was not found to significantly modulate the ‘typical’ recognition memory effects. However, a number of genetic polymorphisms were found to significantly influence both behavioural scores and the pattern of ERP activity associated with recognition memory. These results therefore suggest that inherent participant differences influence the neural correlates of recognition memory, in a way that variations in task performance do not. Overall, the results from this thesis therefore suggest that the ‘typical’ bilateral-frontal and left-parietal effects thought to index retrieval are not universal. Furthermore the results suggest that the specific processes engaged during retrieval (as indexed by variations in ERP activity) may be dependent on specific task requirements, stimulus material and the genetic makeup of the individual

Arousal, exploration and the locus coeruleus-norepinephrine system

The studies described in this thesis address a range of topics related to arousal, exploration, temporal attention, and the locus coeruleus-norepinephrine (LC-NE) system. Chapters 2 and 3 report two studies that investigated a recent theory about the role of the LC-NE system in the regulation of the exploration-exploitation trade-off. Chapter 4 reports a study on neurocognitive function in patients with dopamine-β-hydroxylase (DβH) deficiency. Chapter 5 reports an fMRI study on the neural correlates of perceptual curiosity. Chapter 6 and 7 reported several experiments investigating the effects of ‘accessory stimuli’ and temporal certainty on information processing, using scalp electrophysiology and sequential-sampling models of decision making. Taken together, the studies reported in this thesis suggest that arousal, exploration and temporal attention are closely related, which is likely due to a shared neural basis.LEI Universiteit LeidenFSW - Action Control - Ou