Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users

Acute psychosocial stress modulates neural and behavioral substrates of cognitive control

Acute psychosocial stress affects learning, memory, and attention, but the evidence for the influence of stress on the neural processes supporting cognitive control remains mixed. We investigated how acute psychosocial stress influences performance and neural processing during the Go/NoGo task-an established cognitive control task. The experimental group underwent the Trier Social Stress Test (TSST) acute stress induction, whereas the control group completed personality questionnaires. Then, participants completed a functional magnetic resonance imaging (fMRI) Go/NoGo task, with self-report, blood pressure and salivary cortisol measurements of induced stress taken intermittently throughout the experimental session. The TSST was successful in eliciting a stress response, as indicated by significant Stress > Control between-group differences in subjective stress ratings and systolic blood pressure. We did not identify significant differences in cortisol levels, however. The stress induction also impacted subsequent Go/NoGo task performance, with participants who underwent the TSST making fewer commission errors on trials requiring the most inhibitory control (NoGo Green) relative to the control group, suggesting increased vigilance. Univariate analysis of fMRI task-evoked brain activity revealed no differences between stress and control groups for any region. However, using multivariate pattern analysis, stress and control groups were reliably differentiated by activation patterns contrasting the most demanding NoGo trials (i.e., NoGo Green trials) versus baseline in the medial intraparietal area (mIPA, affiliated with the dorsal attention network) and subregions of the cerebellum (affiliated with the default mode network). These results align with prior reports linking the mIPA and the cerebellum to visuomotor coordination, a function central to cognitive control processes underlying goal-directed behavior. This suggests that stressor-induced hypervigilance may produce a facilitative effect on response inhibition which is represented neurally by the activation patterns of cognitive control regions.

Neuroimaging of opioid effects in humans across conditions of acute administration, chronic pain therapy, and opioid use disorder

Evidence of central nervous system (CNS) exogenous opioid effects in humans has been primarily gained through neuroimaging of three participant populations: individuals after acute opioid administration, those with opioid use disorder (OUD), and those with chronic pain receiving opioid therapy. In both the brain and spinal cord, opioids alter processes of pain, cognition, and reward. Opioid-related CNS effects may persist and accumulate with longer opioid use duration. Meanwhile, opioid-induced benefits versus risks to brain health remain unclear. This review article highlights recent accumulating evidence for how exogenous opioids impact the CNS in humans. While investigation of CNS opioid effects has remained largely disparate across contexts of opioid acute administration, OUD, and chronic pain opioid therapy, integration across these contexts may enable advancement toward effective interventions.

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Updated Perspectives on the Neurobiology of Substance Use Disorders Using Neuroimaging

Substance use problems impair social functioning, academic achievement, and employability. Psychological, biological, social, and environmental factors can contribute to substance use disorders. In recent years, neuroimaging breakthroughs have helped elucidate the mechanisms of substance misuse and its effects on the brain. Functional magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance spectroscopy (MRS) are all examples. Neuroimaging studies suggest substance misuse affects executive function, reward, memory, and stress systems. Recent neuroimaging research attempts have provided clinicians with improved tools to diagnose patients who misuse substances, comprehend the complicated neuroanatomy and neurobiology involved, and devise individually tailored and monitorable treatment regimens for individuals with substance use disorders. This review describes the most recent developments in drug misuse neuroimaging, including the neurobiology of substance use disorders, neuroimaging, and substance use disorders, established neuroimaging techniques, recent developments with established neuroimaging techniques and substance use disorders, and emerging clinical neuroimaging technology.

Endogenous opiates and behavior: 2021

This paper is the forty-fourth consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2021 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonizts and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Brain Structure and Function Predict Adherence to an Exercise Intervention in Older Adults

INTRODUCTION

Individual differences in brain structure and function in older adults are potential proxies of brain reserve or maintenance and may provide mechanistic predictions of adherence to exercise. We hypothesized that multimodal neuroimaging features would predict adherence to a 6-month randomized controlled trial of exercise in 131 older adults (age, 65.79 ± 4.65 yr, 63% female), alone and in combination with psychosocial, cognitive, and health measures.

METHODS

Regularized elastic net regression within a nested cross-validation framework was applied to predict adherence to the intervention in three separate models (brain structure and function only; psychosocial, health, and demographic data only; and a multimodal model).

RESULTS

Higher cortical thickness in somatosensory and inferior frontal regions and less surface area in primary visual and inferior frontal regions predicted adherence. Higher nodal functional connectivity (degree count) in default, frontoparietal, and attentional networks and less nodal strength in primary visual and temporoparietal networks predicted exercise adherence ( r = 0.24, P = 0.004). Survey and clinical measures of gait and walking self-efficacy, biological sex, and perceived stress also predicted adherence ( r = 0.17, P = 0.056); however, this prediction was not significant when tested against a null test statistic. A combined multimodal model achieved the highest predictive strength ( r = 0.28, P = 0.001).

CONCLUSIONS

Our results suggest that there is a substantial utility of using brain-based measures in future research into precision and individualized exercise interventions older adults.

Neural substrates of substance use disorders

PURPOSE OF REVIEW

Substance use disorders account for a tremendous burden to society, yet despite substantial progress in basic studies, our understanding of the brain-basis of these disorders is still emerging. This review summarizes the recent findings of neuroimaging studies with substance use disorder individuals.

RECENT FINDINGS

Resting-state functional connectivity studies support for some but not all substances of abuse and disruption in executive control. Structural neuroimaging findings point towards reduced subcortical volumes, which may emerge as an interaction between preexisting factors and recent substance use. Longitudinal studies implicate some of the same core brain structures and their functional role that have also been identified via case-control studies. Finally, meta-analyses support the idea of dysregulation of cortical control over subcortical salience processing.

SUMMARY

Although progress has been made and there is both structural and functional imaging evidence of an imbalance between brain structures involved in executive control and salience processing, there is emerging evidence that brain-behaviour relationships, which are core to discovering the neural processes that lead to and maintain substance use, are small and require larger consortia that prospectively examine individuals with substance use disorder.

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

We provide evidence to support the contention that many aspects of Autistic Spectrum Disorder (ASD) are related to interregional brain functional disconnectivity associated with maturational delays in the development of brain networks. We think a delay in brain maturation in some networks may result in an increase in cortical maturation and development in other networks, leading to a developmental asynchrony and an unevenness of functional skills and symptoms. The paper supports the close relationship between retained primitive reflexes and cognitive and motor function in general and in ASD in particular provided to indicate that the inhibition of RPRs can effect positive change in ASD.

Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders

Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.

Ketamine use disorder: preclinical, clinical, and neuroimaging evidence to support proposed mechanisms of actions

Ketamine, a noncompetitive NMDA receptor antagonist, has been exclusively used as an anesthetic in medicine and has led to new insights into the pathophysiology of neuropsychiatric disorders. Clinical studies have shown that low subanesthetic doses of ketamine produce antidepressant effects for individuals with depression. However, its use as a treatment for psychiatric disorders has been limited due to its reinforcing effects and high potential for diversion and misuse. Preclinical studies have focused on understanding the molecular mechanisms underlying ketamine's antidepressant effects, but a precise mechanism had yet to be elucidated. Here we review different hypotheses for ketamine's mechanism of action including the direct inhibition and disinhibition of NMDA receptors, AMPAR activation, and heightened activation of monoaminergic systems. The proposed mechanisms are not mutually exclusive, and their combined influence may exert the observed structural and functional neural impairments. Long term use of ketamine induces brain structural, functional impairments, and neurodevelopmental effects in both rodents and humans. Its misuse has increased rapidly in the past 20 years and is one of the most common addictive drugs used in Asia. The proposed mechanisms of action and supporting neuroimaging data allow for the development of tools to identify 'biotypes' of ketamine use disorder (KUD) using machine learning approaches, which could inform intervention and treatment.