Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

Leveraging edge-centric networks complements existing network-level inference for functional connectomes.

The human connectome is modular with distinct brain regions clustering together to form large-scale communities, or networks. This concept has recently been leveraged in novel inferencing procedures by averaging the edge-level statistics within networks to induce more powerful inferencing at the network level. However, these networks are constructed based on the similarity between pairs of nodes. Emerging work has described novel edge-centric networks, which instead use the similarity between pairs of edges to construct networks. In this work, we use these edge-centric networks in a network-level inferencing procedure and compare this novel method to traditional inferential procedures and the network-level procedure using node-centric networks. We use data from the Human Connectome Project, the Healthy Brain Network, and the Philadelphia Neurodevelopmental Cohort and use a resampling technique with various sample sizes (n=40, 80, 120) to probe the power and specificity of each method. Across datasets and sample sizes, using the edge-centric networks outperforms using node-centric networks for inference as well as edge-level FDR correction and NBS. Additionally, the edge-centric networks were found to be more consistent in clustering effect sizes of similar values as compared to node-centric networks, although node-centric networks often had a lower average within-network effect size variability. Together, these findings suggest that using edge-centric networks for network-level inference can procure relatively powerful results while remaining similarly accurate to the underlying edge-level effects across networks, complementing previous inferential methods.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences.

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.

Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States.

BACKGROUND: Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS: Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS: Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS: Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.

Connectome-based machine learning models are vulnerable to subtle data manipulations.

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

Elevated C-reactive protein mediates the liver-brain axis: a preliminary study.

BACKGROUND: Chronic liver diseases of all etiologies exist along a spectrum with varying degrees of hepatic fibrosis. Despite accumulating evidence implying associations between liver fibrosis and cognitive functioning, there is limited research exploring the underlying neurobiological factors and the possible mediating role of inflammation on the liver-brain axis. METHODS: Using data from the UK Biobank, we examined the cross-sectional association of liver fibrosis (as measured by Fibrosis-4 score) with cognitive functioning and regional grey matter volumes (GMVs) while adjusting for numerous covariates and multiple comparisons. We further performed post-hoc preliminary analysis to investigate the mediating effect of C-reactive protein (CRP) on the association between liver fibrosis and both cognitive functioning and GMVs. FINDINGS: We analysed behaviour from up to 447,626 participants (N ranged from 45,055 to 447,533 per specific cognitive metric) 37 years and older. 38,244 participants (age range 44-82 years) had GMV data collected at a median 9-year follow-up. Liver fibrosis showed significant associations with cognitive performance in reasoning, working memory, visual memory, prospective memory, executive function, and processing speed. Subgroup analysis indicated larger effects sizes for symbol digital substitution but smaller effect sizes for trail making in middle-aged people than their old counterparts. Neuroimaging analyses revealed significant associations between liver fibrosis and reduced regional GMVs, primarily in the hippocampus, thalamus, ventral striatum, parahippocampal gyrus, brain stem, and cerebellum. CRP levels were significantly higher in adults with advanced liver fibrosis than those without, indicating an elevated systemic inflammation. Moreover, the serum CRP significantly mediated the effect of liver fibrosis on most cognitive measures and regional GMVs in the hippocampus and brain stem. INTERPRETATION: This study provides a well-powered characterization of associations between liver fibrosis, cognitive impairment, and grey matter atrophy. It also highlights the possibly mediating role of systemic inflammation on the liver-brain axis. Early surveillance and prevention of liver diseases may reduce cognitive decline and brain GMV loss. FUNDING: National Science Foundation, and National Institutes of Health.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.