Short association fibres of the insula-temporoparietal junction in early psychosis: a diffusion tensor imaging study

Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.

Asymmetric Insular Connectomics Revealed by Diffusion Magnetic Resonance Imaging Analysis of Healthy Brain Development

The insula has been implicated in playing important roles in various brain functions including consciousness, homeostasis, perception, self-awareness, language processing, and interpersonal experience. Abnormalities of the insula have been observed in patients suffering from addiction, deteriorating language function, anorexia, and emotional dysregulation. We analyzed typical development of insular connections in a large-scale pediatric population using 642 magnetic resonance imaging examinations. Interpreting large quantities of acquired data is one of the major challenges in connectomics. This article focuses its analysis on the connectivity observed between the insula and many other regions throughout the brain and performs a hemispheric asymmetry analysis comparing localized connectome measurements. Results demonstrate asymmetries in the pathways connecting the insula to the superior temporal region, pars opercularis, etc. that may be representative of language lateralization in the brain. Results also demonstrate multiple fiber pathways that exhibit hemispheric dominance in tract length and an inverted hemispheric dominance in tract counts, implying the presence of asymmetric lateralization of some of the brain's insular pathways. This study illustrates the investigative potential of performing connectomics-style analyses in a clinical context across a large population of children as part of routine imaging, demonstrating the feasibility of using current technologies to perform regionally focused clinical connectivity studies.

A data-driven transdiagnostic analysis of white matter integrity in young adults with major psychiatric disorders

Diffusion tensor imaging (DTI) has been utilized to index white matter (WM) integrity in the major psychiatric disorders. However, the findings within and across such disorders have been mixed. Given this, transdiagnostic sampling with data-driven statistical approaches may lead to new and better insights about the clinical and functional factors associated with WM abnormalities. Thus, we undertook a cross-sectional DTI study of 401 young adult (18-30 years old) outpatients with a major psychiatric (depressive, bipolar, psychotic, or anxiety) disorder and 61 healthy controls. Participants also completed self-report questionnaires and underwent neuropsychological assessment. Fractional anisotropy (FA) as well as axial (AD) and radial (RD) diffusivity was determined via a whole brain voxel-wise approach (tract-based spatial statistics). Hierarchical cluster analysis was performed on FA scores in patients only, obtained from 20 major WM tracts (that is, association, projection and commissural fibers). The three cluster groups derived were distinguished by having consistently increased or decreased FA scores across all tracts. Compared to controls, the largest cluster (N = 177) showed significantly increased FA in 55% of tracts, the second cluster (N = 169) demonstrated decreased FA (in 90% of tracts) and the final cluster (N = 55) exhibited the most increased FA (in 95% of tracts). Importantly, the distribution of primary diagnosis did not significantly differ among the three clusters. Furthermore, the clusters showed comparable functional, clinical and neuropsychological measures, with the exception of alcohol use, medication status and verbal fluency. Overall, this study provides evidence that among young adults with a major psychiatric disorder there are subgroups with either abnormally high or low FA and that either pattern is associated with suboptimal functioning. Importantly, these neuroimaging-based subgroups appear despite diagnostic and clinical factors, suggesting differential treatment strategies are warranted.

Autonomic Regulation and Auditory Hallucinations in Individuals With Schizophrenia: An Experience Sampling Study

Auditory Hallucinations (AH) cause substantial suffering and dysfunction, yet remain poorly understood and modeled. Previous reports have linked AH to increases in negative emotions, suggesting a role for the autonomic nervous system (ANS) in underlying this link. Employing an Experience Sampling Method (ESM) approach, 40 individuals with schizophrenia completed a 36-hour ambulatory assessment of AH and cardiac autonomic regulation. Participants carried mobile electronic devices that prompted them to report 10 times/d the severity of their momentary AH, along with a Holter monitor that continuously recorded their cardiac autonomic regulation. The clocks of the devices and monitors were synchronized, allowing for high time-resolution temporal linking of the AH and concurrent autonomic data. Power spectral analysis was used to determine the relative vagal (parasympathetic) contribution to autonomic regulation during 5 minutes prior to each experience sample. The participants also completed interview-based measures of AH (SAPS; PSYRATS). The ESM-measured severity of AH was significantly correlated with the overall SAPS-indexed AH severity, along with the PSYRATS-indexed AH frequency, duration, loudness, degree of negative content, and associated distress. A mixed-effect regression model indicated that momentary increases in autonomic arousal, characterized by decreases in vagal input, significantly predicted increases in ESM-measured AH severity. Vagal input averaged over the 36-hour assessment displayed a small but significant inverse correlation with the SAPS-indexed AH. The results provide preliminary support for a link between ANS regulation and AH. The findings also underscore the highly dynamic nature of AH and the need to utilize high time-resolution methodologies to investigate AH.