Neural Predictors Research Articles

Neural predictors of fear depend on the situation.

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n=21, 10 females and 11 males) viewed ∼20 second clips depicting spiders, heights, or social encounters, and rated fear after each video. Searchlight multivoxel pattern analysis (MVPA) was used to identify whether and which brain regions carry information that predicts fear experience, and the degree to which the fear-predictive neural codes in these areas depend upon or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern, but rather a collection of multiple heterogeneous brain states.Significance Statement Much of the debate on the nature of emotion concerns the uniformity or heterogeneity of representation for particular emotion categories. Here we provide evidence that widely distributed activation patterns characteristic of recent neural signatures of fear reflect an amalgam of functionally heterogeneous brain states. Participants completed a novel fMRI task that parametrically examined subjective fear within and across three content-rich and naturalistic situations: fear of heights, spiders, and social threats. Using searchlight analysis and machine learning methods, we show that the overwhelming majority of brain regions that predict fear only do so for certain situations. These findings carry implications for the generalization of findings on fear across species, translational models of fear and anxiety, and developing neural signatures of fear.

TMS-induced plasticity improving cognitive control in OCD II: Task-based neural predictors of treatment response

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) has the potential to increase the clinical effect of exposure with response prevention (ERP) psychotherapy for obsessive-compulsive disorder (OCD). We investigated the use of task-based functional MRI (tb-fMRI) for predicting clinical outcomes to different rTMS protocols combined with ERP in OCD. Method61 adults with OCD underwent rTMS and ERP and were randomized to different high frequency rTMS conditions: left dorsolateral prefrontal cortex (DLPFC; n=19), left pre-supplementary motor area (preSMA; n=23), and control stimulation at the vertex at low intensity (n=19). The Tower of London task and Stop-Signal Task were used to assess pretreatment activation during planning and inhibitory control, respectively. We adopted a Bayesian region-based approach to test whether clinical improvement can be predicted by tb-fMRI-derived measures of task-related brain activation or functional connectivity between task-relevant regions and the bilateral amygdala. ResultsFor the vertex group, but not the DLPFC/preSMA rTMS conditions, higher activation in several task-relevant regions during planning and response inhibition, and lower error-related activation, corresponded with better short-term clinical improvement. Lower precuneus activation with increased planning taskload correlated with symptom reduction in the DLPFC group. In the preSMA group, higher error-related activation and lower inhibition-related insular-amygdalar connectivity was associated with symptom reduction. ConclusionsPretreatment tb-fMRI-derived measures of activation and connectivity during planning and inhibition-related processes are associated with clinical response for specific rTMS conditions in OCD. Future placebo-controlled trials with larger sample sizes should combine clinical information and neural correlates to improve prediction of clinical outcome.

Disruption of neural periodicity predicts clinical response after deep brain stimulation for obsessive-compulsive disorder

Recent advances in surgical neuromodulation have enabled chronic and continuous intracranial monitoring during everyday life. We used this opportunity to identify neural predictors of clinical state in 12 individuals with treatment-resistant obsessive-compulsive disorder (OCD) receiving deep brain stimulation (DBS) therapy (NCT05915741). We developed our neurobehavioral models based on continuous neural recordings in the region of the ventral striatum in an initial cohort of five patients and tested and validated them in a held-out cohort of seven additional patients. Before DBS activation, in the most symptomatic state, theta/alpha (9 Hz) power evidenced a prominent circadian pattern and a high degree of predictability. In patients with persistent symptoms (non-responders), predictability of the neural data remained consistently high. On the other hand, in patients who improved symptomatically (responders), predictability of the neural data was significantly diminished. This neural feature accurately classified clinical status even in patients with limited duration recordings, indicating generalizability that could facilitate therapeutic decision-making.

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism.

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

Individual Behavioral and Neural Predictors of Future Alcohol Drinking Behavior

Individual Behavioral and Neural Predictors of Future Alcohol Drinking Behavior

Author Correction: Defining the r factor for post-trauma resilience and its neural predictors

Author Correction: Defining the r factor for post-trauma resilience and its neural predictors

Defining the r factor for post-trauma resilience and its neural predictors

Defining the r factor for post-trauma resilience and its neural predictors

DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning

Neural predictors have shown great potential in the evaluation process of neural architecture search (NAS). However, current predictor-based approaches overlook the fact that training a predictor necessitates a considerable number of trained neural networks as the labeled training set, which is costly to obtain. Therefore, the critical issue in utilizing predictors for NAS is to train a high-performance predictor using as few trained neural networks as possible. Although some methods attempt to address this problem through unsupervised learning, they often result in inaccurate predictions. We argue that the unsupervised tasks intended for the common graph data are too challenging for neural networks, causing unsupervised training to be susceptible to performance crashes in NAS. To address this issue, we propose a CurricuLum-guided Contrastive Learning framework for neural Predictor (DCLP). Our method simplifies the contrastive task by designing a novel curriculum to enhance the stability of unlabeled training data distribution during contrastive training. Specifically, we propose a scheduler that ranks the training data according to the contrastive difficulty of each data and then inputs them to the contrastive learner in order. This approach concentrates the training data distribution and makes contrastive training more efficient. By using our method, the contrastive learner incrementally learns feature representations via unsupervised data on a smooth learning curve, avoiding performance crashes that may occur with excessively variable training data distributions. We experimentally demonstrate that DCLP has high accuracy and efficiency compared with existing predictors, and shows promising potential to discover superior architectures in various search spaces when combined with search strategies. Our code is available at: https://github.com/Zhengsh123/DCLP.

Sex Differences in Response Inhibition–Related Neural Predictors of Posttraumatic Stress Disorder in Civilians With Recent Trauma

BackgroundFemales are more likely to develop posttraumatic stress disorder (PTSD) than males. Impaired inhibition has been identified as a mechanism for PTSD development, but studies on potential sex differences in this neurobiological mechanism and how it relates to PTSD severity and progression are relatively rare. Here, we examined sex differences in neural activation during response inhibition and PTSD following recent trauma. MethodsParticipants (n = 205, 138 female sex assigned at birth) were recruited from emergency departments within 72 hours of a traumatic event. PTSD symptoms were assessed 2 weeks and 6 months posttrauma. A Go/NoGo task was performed 2 weeks posttrauma in a 3T magnetic resonance imaging scanner to measure neural activity during response inhibition in the ventromedial prefrontal cortex, right inferior frontal gyrus, and bilateral hippocampus. General linear models were used to examine the interaction effect of sex on the relationship between our regions of interest and the whole brain, PTSD symptoms at 6 months, and symptom progression between 2 weeks and 6 months. ResultsLower response inhibition–related ventromedial prefrontal cortex activation 2 weeks posttrauma predicted more PTSD symptoms at 6 months in females but not in males, while greater response inhibition–related right inferior frontal gyrus activation predicted lower PTSD symptom progression in males but not females. Whole-brain interaction effects were observed in the medial temporal gyrus and left precentral gyrus. ConclusionsThere are sex differences in the relationship between inhibition-related brain activation and PTSD symptom severity and progression. These findings suggest that sex differences should be assessed in future PTSD studies and reveal potential targets for sex-specific interventions.

Gamma power and beta envelope correlation are potential neural predictors of deep hypnosis

Hypnosis is a psychological intervention that is commonly used to enhance the effectiveness of therapeutic suggestions. Despite extensive fascination and study, the neural mechanisms behind hypnosis remain elusive. In the current study, we undertook a systematic exploration of these neural correlates. We first extracted well-studied neurophysiological features from EEG sensors and source-localized data using spectral analysis and two measures of functional connectivity: weighted phase lag index (wPLI) and power envelope correlation (PEC). Next, we developed classification models that predicted self-rated hypnotic experience based on the extracted feature sets. Our findings reveal that gamma power computed on sensor-level data and beta PEC computed between source-localized brain networks are the top predictors of hypnosis depth. Further, a SHapley Additive exPlanations (SHAP) analysis suggested reduced gamma power in the midline frontal area and increased beta PEC between interhemispheric Dorsal Attention Networks (DAN) contribute to the hypnotic experience. These results broaden our understanding of the neural correlates of deep hypnosis, highlighting potential targets for future research. Moreover, this study demonstrates the potential of using predictive models in understanding the neural underpinnings of self-reported hypnotic depth, offering a template for future investigations.

Cross-sectional and longitudinal neural predictors of physical activity and sedentary behaviour from a 6-month randomized controlled trial

A sedentary lifestyle offers immediate gratification, but at the expense of long-term health. It is thus critical to understand how the brain evaluates immediate rewards and long-term health effects in the context of deciding whether to engage in moderate-to-vigorous physical activity (MVPA) or sedentary behaviour (SB). In this secondary analysis of a 6-month randomized controlled trial to increase MVPA and reduce SB among community-dwelling adults, we explored how neural activity during an executive control task was associated with MVPA and SB levels. At baseline, a subset of participants (n = 26/61) underwent task-based functional magnetic resonance imaging (fMRI) to examine neural activity underlying executive control using the Now/Later task. MVPA and SB were measured objectively using the Sensewear Mini at baseline, and 2, 4, and 6 months follow-up. We then examined the associations of baseline neural activation underlying executive control with: (1) baseline MVPA or SB; and (2) changes in MVPA and SB over 6 months. Our results determined that there is a complex neurocognitive system associated with MVPA levels, while SB appears to lack any neurocognitive control. In other words, MVPA appears to require neurocognitive effort, while SB may be the default behavioural pattern in adults.

Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression

Abstract Background: Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown. Methods: Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an a priori Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent a priori bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an a priori defined ventrolateral prefrontal cortex/insula region-of-interest. Findings: A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up (F[3,34] = .33, p = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes (F[4,33] = 3.86, p = .01). Conclusion: These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.

Individual cognitive therapy reduces frontal-thalamic resting-state functional connectivity in social anxiety disorder.

Previous neuroimaging studies in social anxiety disorders (SAD) have reported potential neural predictors of cognitive behavioral therapy (CBT)-related brain changes. However, several meta-analyses have demonstrated that cognitive therapy (CT) was superior to traditional exposure-based CBT for SAD. To explore resting-state functional connectivity (rsFC) to evaluate the response to individual CT for SAD patients. Twenty SAD patients who attended 16-week individual CT were scanned pre- and post-therapy along with twenty healthy controls (HCs). The severity of social anxiety was assessed with the Liebowitz Social Anxiety Scale (LSAS). Multi-voxel pattern analysis (MVPA) was performed on the pre-CT data to extract regions associated with a change in LSAS (∆LSAS). Group comparisons of the seed-based rsFC analysis were performed between the HCs and pre-CT patients and between the pre-and post-CT patients. MVPA-based regression analysis revealed that rsFC between the left thalamus and the frontal pole/inferior frontal gyrus was significantly correlated with ∆LSAS (adjusted R2 = 0.65; p = 0.00002). Compared with HCs, the pre-CT patients had higher rsFCs between the thalamus and temporal pole and between the thalamus and superior/middle temporal gyrus/planum temporale (p < 0.05). The rsFC between the thalamus and the frontal pole decreased post-CT (p < 0.05). SAD patients had significant rsFC between the thalamus and temporal pole, superior/middle temporal gyrus, and planum temporale, which may be indicators of extreme anxiety in social situations. In addition, rsFC between the thalamus and the frontal pole may be a neuromarker for the effectiveness of individual CT.

Cortical thickness of the inferior parietal lobule as a potential predictor of relapse in men with alcohol dependence.

Alcohol dependence is a disorder with a high recurrence rate that leads to a considerable public health burden. The risk of relapse appears to be related to a complex interplay of multiple factors. Herein, we aimed to explore the potential neural predictors of relapse in Chinese male patients with alcohol dependence.This study enrolled 58 male patients with alcohol dependence who had undergone acute detoxification. General demographic information and clinical features were collected. Magnetic resonance imaging data were used to measure cortical thickness across 34 regions of the brain. Patients were followed up at six months, and 51 patients completed the follow-up visit. These patients were divided into a relapser and an abstainer group. A binary logistic regression analysis was performed to investigate the potential risk factors of relapse.Compared to abstainers, relapsers showed higher inattention and non-planning impulsivity on the 11th version of the Barratt Impulsive Scale. The cortical thicknesses of the inferior-parietal lobules were significantly higher in abstainers compared with those in relapsers. Furthermore, binary logistic regression analysis showed that the thickness of the inferior parietal lobule predicted relapse, and lower non-planning impulse was a protective factor against relapse.Relapsers show poorer impulse control than abstainers, and structural magnetic resonance imaging revealed a decreased thickness of the inferior parietal lobule in relapsers. Our results indicate the thickness of the inferior parietal lobule as a potential relapse predictor in male patients with alcohol dependence.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions

Objective. Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving. Approach. Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC). Main results. We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration. Significance. We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search.

Neural architecture search (NAS) adopts a search strategy to explore the predefined search space to find superior architecture with the minimum searching costs. Bayesian optimization (BO) and evolutionary algorithms (EA) are two commonly used search strategies, but they suffer from being computationally expensive, challenging to implement, and exhibiting inefficient exploration ability. In this article, we propose a neural predictor guided EA to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is a BO acquisition function for which we design a graph-based uncertainty estimation network as the surrogate model. The second predictor is a graph-based neural network that directly predicts the performance of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP, respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Experimental results on five NAS search spaces indicate that NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-NP achieving state-of-the-art performance on four of the five search spaces.

An exploration of neural predictors of treatment compliance in cognitive-behavioral group therapy for hoarding disorder

A persistent and influential barrier to effective cognitive-behavioral therapy (CBT) for patients with hoarding disorder (HD) is treatment retention and compliance. Recent research has suggested that HD patients have abnormal brain activity identified by functional magnetic resonance (fMRI) in regions often engaged for executive functioning (e.g., right superior frontal gyrus, anterior insula, and anterior cingulate), which raises questions about whether these abnormalities could relate to patients' ability to attend, understand, and engage in HD treatment. We examined data from 74 HD-diagnosed adults who completed fMRI-measured brain activity during a discarding task designed to elicit symptom-related brain dysfunction, exploring which regions' activity might predict treatment compliance variables, including treatment engagement (within-session compliance), homework completion (between-session compliance), and treatment attendance. Brain activity that was significantly related to within- and between-session compliance was found largely in insula, parietal, and premotor areas. No brain regions were associated with treatment attendance. The results add to findings from prior research that have found prefrontal, cingulate, and insula activity abnormalities in HD by suggesting that some aspects of HD brain dysfunction might play a role in preventing the engagement needed for therapeutic benefit.

39.1 Temperament and Neural Predictors of Neurodevelopmental Disorder Risk in Infancy and Early Childhood

39.1 Temperament and Neural Predictors of Neurodevelopmental Disorder Risk in Infancy and Early Childhood

Neural predictors of surprise in controlled visual task and naturalistic viewing contexts

Neural predictors of surprise in controlled visual task and naturalistic viewing contexts