A mega-analysis of low frequency resting-state measures in psychosis-spectrum and mood disorders.

Objective: Conduct a mega-analysis of two complementary measures of resting-state functional magnetic resonance imaging (rsfMRI) dynamics--amplitude of low-frequency fluctuation (ALFF) and low-frequency spectral entropy (lfSE)--in a transdiagnostic mood and psychosis-spectrum sample to evaluate group differences and clinical symptom associations.

Design: ALFF and lfSE were calculated at the node-level by filtering data from 0.01 Hz to 0.

Social and behavioral correlates of sleep health among adults receiving medication treatment for opioid use disorder.

Objectives: Opioid use disorder (OUD) and its treatment (MOUD) are associated with altered sleep health. The purposes are to (1) describe profiles of sleep health among adults using medication for opioid use disorder (MOUD) and (2) examine the associations between multi-level individual, family, neighborhood, and social characteristics and sleep profiles. We hypothesized that poor quality of life, adverse life experiences, addiction behavior, dysfunctional family and social interactions, and negative neighborhood characteristics are associated with negative profiles of sleep health.

A Transdiagnostic fMRI Dataset With 300+ Deeply Phenotyped Subjects Across Resting and Task States.

YaleNeuroConnect is a human functional MRI (fMRI) dataset collected at Yale University that includes functional MRI data (and the respective functional connectomes) obtained under resting-state and six task conditions. There are 302 diagnostically and demographically diverse subjects, each with extensive neuropsychological testing and symptom inventories obtained outside of the MRI. Prior studies have shown that stronger predictive models relating the brain to external measures can be built with connectivity data obtained during continuous performance tasks instead of the more common resting-state.

Neural Mechanisms of Feedback Processing and Regulation Recalibration During Neurofeedback Training.

The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity.

Functional Connectome Correlates of Laterality Preferences: Insights into Hand, Foot, and Eye Dominance across the Lifespan.

Humans exhibit laterality preferences, with handedness being the most extensively studied. Accordingly, brain-handedness associations are well documented. However, laterality preferences extend beyond handedness to include other limbs, such as footedness and eyedness.

Governance for anti-racist AI in healthcare: integrating racism-related stress in psychiatric algorithms for Black Americans.

While the world is aware of America's history of enslavement, the ongoing impact of anti-Black racism in the United States remains underemphasized in health intervention modeling. This Perspective argues that algorithmic bias-manifested in the worsened performance of clinical algorithms for Black vs. white patients-is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity.

Trends in self-citation rates in high-impact neurology, neuroscience, and psychiatry journals.

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates-or how much authors cite themselves-have not yet been comprehensively investigated in the Neurosciences.

Disrupted Resting-State Functional Connectivity in the Social Visual Pathway in Children With Autism Spectrum Disorder.

The social visual pathway, which diverges from the dorsal pathway at the visual motion area (MT/V5) and runs from the posterior down to anterior portions of the superior temporal sulcus (STS), specializes in processing dynamic social information. This study examined resting-state functional connectivity within this pathway in children with autism spectrum disorder (ASD) and typically developing (TD) children. Using data from the Autism Brain Imaging Data Exchange (ABIDE) repository, we found significant hypoconnectivity between the posterior and middle STS (pSTS-mSTS) in the right hemisphere in children with ASD compared to those in TD children.

The inflammatory and genetic mechanisms underlying the cumulative effect of co-occurring pain conditions on depression.

Chronic pain conditions frequently coexist and share common genetic vulnerabilities. Despite evidence showing associations between pain and depression, the additive effect of co-occurring pain conditions on depression risk and the underlying mechanisms remain unclear. Leveraging data from 431,038 UK Biobank participants with 14-year follow-up, we found a significantly increased risk of depression incidence in individuals reporting pain, irrespective of body site or duration (acute or chronic), compared with pain-free individuals.

Brain handedness associations depend on how and when handedness is measured.

Hand preference is ubiquitous, intuitive, and often simplified to right- or left-handed. Accordingly, differences between right- and left-handed individuals in the brain have been established. Nevertheless, considering handedness as a binarized construct fails to capture the variability of brain-handedness associations across different domains or activities.

Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.

Importance: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.

Objective: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.

Design, Setting, And Participants: This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors.

Functional connectivity in the social perception pathway at birth is linked with attention to faces at 4 months.

Background: Processing faces and speech is supported by the right-lateralized social visual perception pathway (social pathway) involving medial temporal/visual 5 area (MT/V5) and superior temporal sulcus (STS). Little is known about development of the social pathway and its links with later social outcomes. We examined intrinsic functional connectivity (iFC) in the right social pathway in neurotypical neonates, compared it with iFC in the left social and bilateral dorsal attention pathways, and interrogated prospective links between iFC and social attention in neurodiverse neonates.

White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood.

Unlabelled: Social engagement and language are connected through early development. Alterations in their development can have a prolonged impact on children's lives. However, the role of white matter at birth in this ongoing connection is less well-known.

Connectome-based predictive modeling of early and chronic psychosis symptoms.

Previous research indicates that early (EP) and chronic (CP) psychosis share brain correlates and symptoms. However, notable clinical differences, such as treatment responses and symptom severity, exist, suggesting the need for further investigation. For example, the brain networks underlying EP and CP symptoms may be distinct, driven by factors like symptom severity and disease-related burden.

Infant sleep state coded from respiration and its relationship to the developing functional connectome: A feasibility study.

Most infants are scanned during natural sleep to maximize successful data acquisition by minimizing head and body motion. However, our understanding of how different sleep states affect the infant's functional connectome remains to be determined. In this feasibility study, we develop a novel approach to quantify active and quiet sleep during fMRI using time-locked infant respiration in twenty infants scanned within 47 weeks postmenstrual age.

Modality-level obstacles and initiatives to improve representation in fetal, infant, and toddler neuroimaging research samples.

Fetal, infant, and toddler (FIT) neuroimaging researchers study early brain development to gain insights into neurodevelopmental processes and identify early markers of neurobiological vulnerabilities to target for intervention. However, the field has historically excluded people from global majority countries and from marginalized communities in FIT neuroimaging research. Inclusive and representative samples are essential for generalizing findings across neuroimaging modalities, such as magnetic resonance imaging, magnetoencephalography, electroencephalography, functional near-infrared spectroscopy, and cranial ultrasonography.

What is the best brain state to predict autistic traits?

Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Nevertheless, little work has focused on the optimal brain states to reveal brain-phenotype relationships. In addition, there is a need to better understand the relevance of attentional abilities in mediating autistic features.

Neural Variability and Cognitive Control in Individuals With Opioid Use Disorder.

Importance: Opioid use disorder (OUD) impacts millions of people worldwide. Prior studies investigating its underpinning neural mechanisms have not often considered how brain signals evolve over time, so it remains unclear whether brain dynamics are altered in OUD and have subsequent behavioral implications.

Objective: To characterize brain dynamic alterations and their association with cognitive control in individuals with OUD.

Pain can't be carved at the joints: defining function-based pain profiles and their relevance to chronic disease management in healthcare delivery design.

Background: Pain is a complex problem that is triaged, diagnosed, treated, and billed based on which body part is painful, almost without exception. While the "body part framework" guides the organization and treatment of individual patients' pain conditions, it remains unclear how to best conceptualize, study, and treat pain conditions at the population level. Here, we investigate (1) how the body part framework agrees with population-level, biologically derived pain profiles; (2) how do data-derived pain profiles interface with other symptom domains from a whole-body perspective; and (3) whether biologically derived pain profiles capture clinically salient differences in medical history.

Brain age prediction and deviations from normative trajectories in the neonatal connectome.

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data.

Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations.

Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.

Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods.

Craving is a central feature of substance use disorders and disorders due to addictive behaviors. Considerable research has investigated neural mechanisms involved in the development and processing of craving. Recently, connectome-based predictive modeling, a data-driven method, has been used in four studies aiming to predict craving related to substance use, addictive behaviors, and food.