Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.

Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and Relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Rapid, reliable mobile assessment of affect-related motor processing.

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Impact of serotonergic medication on interoception in major depressive disorder.

Unmedicated individuals with major depressive disorder (MDD) show abnormal interoception, but it is unclear whether antidepressant treatment via serotonergic medication alters this relationship. The current cross-sectional study examined associations between neural and behavioral indices of interoceptive processing and chronic serotonergic medication administration in MDD. 47 selective serotonin reuptake inhibitor (SSRI)-medicated MDD (MDD-SSRI) individuals were propensity-matched with 48 unmedicated current MDD (MDD-UnMed) and 41 healthy comparison (HC) participants on demographics including age, sex, body mass index, education, as well as on dimensional scales of symptom severity including depression and anxiety. All participants completed an interoceptive attention task during functional magnetic resonance imaging, and a behavioral heartbeat tapping task under three conditions: Guessing, No Guessing, and Breath Hold. Relative to HC, both MDD groups: (1) exhibited lower mid-insula, amygdala, putamen, and caudate activation during interoceptive versus exteroceptive attention; and (2) showed poorer heartbeat tapping performance during the Breath Hold condition. However, the MDD-SSRI group reported higher intensity ratings of heartbeat and stomach sensations than MDD-UnMed and HC during the interoceptive attention task. These findings suggest that the attenuated patterns of neural activation observed in depressed individuals during interoceptive attention are not ameliorated by the chronic administration of serotonergic medications. However, amplified interoceptive sensation ratings suggest a potential impact of chronic serotonergic medication on conscious experiences of internal body states. Future investigations will need to determine the extent to which serotonergic medications acutely influence interoceptive processing, and whether such changes play a role in therapeutic responses during treatment initiation.

Association of Generalized Anxiety Disorder With Autonomic Hypersensitivity and Blunted Ventromedial Prefrontal Cortex Activity During Peripheral Adrenergic Stimulation: A Randomized Clinical Trial.

IMPORTANCE: ß-Adrenergic stimulation elicits heart palpitations and dyspnea, key features of acute anxiety and sympathetic arousal, yet no neuroimaging studies have examined how the pharmacologic modulation of interoceptive signals is associated with fear-related neurocircuitry in individuals with generalized anxiety disorder (GAD). OBJECTIVE: To examine the neural circuitry underlying autonomic arousal induced via isoproterenol, a rapidly acting, peripheral ß-adrenergic agonist akin to adrenaline. DESIGN, SETTING, AND PARTICIPANTS: This crossover randomized clinical trial of 58 women with artifact-free data was conducted from January 1, 2017, to November 31, 2019, at the Laureate Institute for Brain Research in Tulsa, Oklahoma. EXPOSURES: Functional magnetic resonance imaging was used to assess neural responses during randomized intravenous bolus infusions of isoproterenol (0.5 and 2.0 µg) and saline, each administered twice in a double-blind fashion. MAIN OUTCOMES AND MEASURES: Blood oxygen level-dependent responses across the whole brain during isoproterenol administration in patients with GAD vs healthy comparators. Cardiac and respiratory responses, as well as interoceptive awareness and anxiety, were also measured during the infusion protocol. RESULTS: Of the 58 female study participants, 29 had GAD (mean [SD] age, 26.9 [6.8] years) and 29 were matched healthy comparators (mean [SD] age, 24.4 [5.0] years). During the 0.5-µg dose of isoproterenol, the GAD group exhibited higher heart rate responses (b = 5.34; 95% CI, 2.06-8.61; P = .002), higher intensity ratings of cardiorespiratory sensations (b = 8.38; 95% CI, 2.05-14.71; P = .01), higher levels of self-reported anxiety (b = 1.04; 95% CI, 0.33-1.76; P = .005), and significant hypoactivation in the ventromedial prefrontal cortex (vmPFC) that was evident throughout peak response (Cohen d = 1.55; P < .001) and early recovery (Cohen d = 1.52; P < .001) periods. Correlational analysis of physiological and subjective indexes and percentage of signal change extracted during the 0.5-µg dose revealed that vmPFC hypoactivation was inversely correlated with heart rate (r56 = -0.51, adjusted P = .001) and retrospective intensity of both heartbeat (r56 = -0.50, adjusted P = .002) and breathing (r56 = -0.44, adjusted P = .01) sensations. Ventromedial prefrontal cortex hypoactivation correlated inversely with continuous dial ratings at a trend level (r56 = -0.38, adjusted P = .051), whereas anxiety (r56 = -0.28, adjusted P = .27) and chronotropic dose 25 (r56 = -0.14, adjusted P = .72) showed no such association. CONCLUSIONS AND RELEVANCE: In this crossover randomized clinical trial, women with GAD exhibited autonomic hypersensitivity during low levels of adrenergic stimulation characterized by elevated heart rate, heightened interoceptive awareness, increased anxiety, and a blunted neural response localized to the vmPFC. These findings support the notion that autonomic hyperarousal may be associated with regulatory dysfunctions in the vmPFC, which could serve as a treatment target to help patients with GAD more appropriately appraise and regulate signals of sympathetic arousal. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02615119.

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE.

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Predicting Age From Brain EEG Signals-A Machine Learning Approach.

Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction. Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced. Results: The stack-ensemble age prediction model achieved R2 = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds. Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.