You read my mind: fMRI markers of threatening appraisals in people with persistent psychotic experiences

Anomalous perceptual experiences are relatively common in the general population. Evidence indicates that the key to distinguishing individuals with persistent psychotic experiences (PEs) with a need for care from those without is how they appraise their anomalous experiences. Here, we aimed to characterise the neural circuits underlying threatening and non-threatening appraisals in people with and without a need for care for PEs, respectively. A total of 48 participants, consisting of patients with psychosis spectrum disorder (clinical group, n = 16), non-need-for-care participants with PEs (non-clinical group, n = 16), and no-PE healthy control participants (n = 16), underwent functional magnetic resonance imaging while completing the Telepath task, designed to induce an anomalous perceptual experience. Appraisals of the anomalous perceptual experiences were examined, as well as functional brain responses during this window, for significant group differences. We also examined whether activation co-varied with the subjective threat appraisals reported in-task by participants. The clinical group reported elevated subjective threat appraisals compared to both the non-clinical and no-PE control groups, with no differences between the two non-clinical groups. This pattern of results was accompanied by reduced activation in the superior and inferior frontal gyri in the clinical group as compared to the non-clinical and control groups. Precuneus activation scaled with threat appraisals reported in-task. Resilience in the context of persistent anomalous experiences may be explained by intact functioning of fronto-parietal regions, and may correspond to the ability to contextualise and flexibly evaluate psychotic experiences

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

You read my mind: fMRI markers of threatening appraisals in people with persistent psychotic experiences

Copyright © The Author(s) 2021. Anomalous perceptual experiences are relatively common in the general population. Evidence indicates that the key to distinguishing individuals with persistent psychotic experiences (PEs) with a need for care from those without is how they appraise their anomalous experiences. Here, we aimed to characterise the neural circuits underlying threatening and non-threatening appraisals in people with and without a need for care for PEs, respectively. A total of 48 participants, consisting of patients with psychosis spectrum disorder (clinical group, n = 16), non-need-for-care participants with PEs (non-clinical group, n = 16), and no-PE healthy control participants (n = 16), underwent functional magnetic resonance imaging while completing the Telepath task, designed to induce an anomalous perceptual experience. Appraisals of the anomalous perceptual experiences were examined, as well as functional brain responses during this window, for significant group differences. We also examined whether activation co-varied with the subjective threat appraisals reported in-task by participants. The clinical group reported elevated subjective threat appraisals compared to both the non-clinical and no-PE control groups, with no differences between the two non-clinical groups. This pattern of results was accompanied by reduced activation in the superior and inferior frontal gyri in the clinical group as compared to the non-clinical and control groups. Precuneus activation scaled with threat appraisals reported in-task. Resilience in the context of persistent anomalous experiences may be explained by intact functioning of fronto-parietal regions, and may correspond to the ability to contextualise and flexibly evaluate psychotic experiences.Medical Research Council; Institute of Psychiatry, Psychology, and Neuroscience, King’s College London; NIHR Maudsley Biomedical Research Centre

Lower cerebello-cortical functional connectivity in veterans with reactive aggression symptoms: A pilot study

A significant number of veterans experience irritability and aggression symptoms as a result of being exposed to extremely stressful and life-threatening situations. In addition to the well-established involvement of the brain's cortico-subcortical circuit in aggression-related behaviours, a role of the deep cerebellar nuclei (DCN) in reactive aggression has been suggested. In the present study, seed-based resting-state functional connectivity between the DCN and cortico-subcortical areas was explored in veterans with and without reactive aggression symptoms. Nineteen male veterans with reactive aggression symptoms and twenty-two control veterans without reactive aggression symptoms underwent 3T resting-state functional MRI scans. Region-of-interest (ROI) analyses that included the amygdala, hypothalamus and periaqueductal grey as ROIs did not yield significant group-related differences in resting-state functional connectivity with the DCN. However, exploratory whole-brain analysis showed that veterans with reactive aggression symptoms exhibited lower functional connectivity between the DCN and the orbitofrontal cortex compared to control veterans. Our findings provide preliminary evidence for the possible involvement of a cerebello-prefrontal pathway in reactive aggression in male veterans

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder, characterized by impairments in social communication and restricted, repetitive behaviors. Neuroimaging studies have shown complex patterns and functional connectivity (FC) in ASD, with no clear consensus on brain-behavior relationships or shared patterns of FC with typically developing controls. Here, we used a dimensional approach to characterize two distinct clusters of FC patterns across both ASD participants and controls using k-means clustering. Using multivariate statistical analyses, a categorical approach was taken to characterize differences in FC between subtypes and between diagnostic groups. One subtype was defined by increased FC within resting-state networks and decreased FC across networks compared with the other subtype. A separate FC pattern distinguished ASD from controls, particularly within default mode, cingulo-opercular, sensorimotor, and occipital networks. There was no significant interaction between subtypes and diagnostic groups. Finally, a dimensional analysis of FC patterns with behavioral measures of IQ, social responsiveness, and ASD severity showed unique brain-behavior relations in each subtype and a continuum of brain-behavior relations from ASD to controls within one subtype. These results demonstrate that distinct clusters of FC patterns exist across ASD and controls, and that FC subtypes can reveal unique information about brain-behavior relationships. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, with high variation in the types of severity of impairments in social communication and restricted, repetitive behaviors. Neuroimaging studies have shown complex patterns of communication between brain regions, or functional connectivity (FC), in ASD. Here, we defined two distinct FC patterns and relationships between FC and behavior in a group of participants consisting of individuals with and without ASD. One subtype was defined by increased FC within distinct networks of brain regions and decreased FC between networks compared with the other subtype. A separate FC pattern distinguished ASD from controls. The interaction between subtypes and diagnostic groups was not significant. Dimensional analyses of FC patterns with behavioral measures revealed unique information about brain-behavior relations in each subtype

Identifying and characterizing systematic temporally-lagged BOLD artifacts

Residual noise in the BOLD signal remains problematic for fMRI - particularly for techniques such as functional connectivity, where findings can be spuriously influenced by noise sources that can covary with individual differences. Many such potential noise sources - for instance, motion and respiration - can have a temporally lagged effect on the BOLD signal. Thus, here we present a tool for assessing residual lagged structure in the BOLD signal that is associated with nuisance signals, using a construction similar to a peri-event time histogram. Using this method, we find that framewise displacements - both large and very small - were followed by structured, prolonged, and global changes in the BOLD signal that depend on the magnitude of the preceding displacement and extend for tens of seconds. This residual lagged BOLD structure was consistent across datasets, and independently predicted considerable variance in the global cortical signal (as much as 30-40% in some subjects). Mean functional connectivity estimates varied similarly as a function of displacements occurring many seconds in the past, even after strict censoring. Similar patterns of residual lagged BOLD structure were apparent following respiratory fluctuations (which covaried with framewise displacements), implicating respiration as one likely mechanism underlying the displacement-linked structure observed. Global signal regression largely attenuates this artifactual structure. These findings suggest the need for caution in interpreting results of individual difference studies where noise sources might covary with the individual differences of interest, and highlight the need for further development of preprocessing techniques for mitigating such structure in a more nuanced and targeted manner

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods