Neural Predictors Research Articles

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

AIO-P: Expanding Neural Performance Predictors beyond Image Classification

Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimating neural network performance on image classification benchmarks. They are also search-space dependent; each predictor is designed to make predictions for a specific architecture search space with predefined topologies and set of operations. In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. We describe our proposed techniques for general graph representation, efficient predictor pretraining and knowledge infusion techniques, as well as methods to transfer to downstream tasks/spaces. Extensive experimental results show that AIO-P can achieve Mean Absolute Error (MAE) and Spearman’s Rank Correlation (SRCC) below 1p% and above 0.5, respectively, on a breadth of target downstream CV tasks with or without fine-tuning, outperforming a number of baselines. Moreover, AIO-P can directly transfer to new architectures not seen during training, accurately rank them and serve as an effective performance estimator when paired with an algorithm designed to preserve performance while reducing FLOPs.

Distinct neural networks predict cocaine versus cannabis treatment outcomes.

Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .

Emotion regulation flexibility: Neural predictors and consequences of switching between reappraisal and distraction strategies

Emotion regulation flexibility: Neural predictors and consequences of switching between reappraisal and distraction strategies

Neural Correlates of Individual Differences in Speech-in-Noise Performance in a Large Cohort of Cochlear Implant Users.

Understanding speech-in-noise (SiN) is a complex task that recruits multiple cortical subsystems. Individuals vary in their ability to understand SiN. This cannot be explained by simple peripheral hearing profiles, but recent work by our group ( Kim et al. 2021 , Neuroimage ) highlighted central neural factors underlying the variance in SiN ability in normal hearing (NH) subjects. The present study examined neural predictors of SiN ability in a large cohort of cochlear-implant (CI) users. We recorded electroencephalography in 114 postlingually deafened CI users while they completed the California consonant test: a word-in-noise task. In many subjects, data were also collected on two other commonly used clinical measures of speech perception: a word-in-quiet task (consonant-nucleus-consonant) word and a sentence-in-noise task (AzBio sentences). Neural activity was assessed at a vertex electrode (Cz), which could help maximize eventual generalizability to clinical situations. The N1-P2 complex of event-related potentials (ERPs) at this location were included in multiple linear regression analyses, along with several other demographic and hearing factors as predictors of SiN performance. In general, there was a good agreement between the scores on the three speech perception tasks. ERP amplitudes did not predict AzBio performance, which was predicted by the duration of device use, low-frequency hearing thresholds, and age. However, ERP amplitudes were strong predictors for performance for both word recognition tasks: the California consonant test (which was conducted simultaneously with electroencephalography recording) and the consonant-nucleus-consonant (conducted offline). These correlations held even after accounting for known predictors of performance including residual low-frequency hearing thresholds. In CI-users, better performance was predicted by an increased cortical response to the target word, in contrast to previous reports in normal-hearing subjects in whom speech perception ability was accounted for by the ability to suppress noise. These data indicate a neurophysiological correlate of SiN performance, thereby revealing a richer profile of an individual's hearing performance than shown by psychoacoustic measures alone. These results also highlight important differences between sentence and word recognition measures of performance and suggest that individual differences in these measures may be underwritten by different mechanisms. Finally, the contrast with prior reports of NH listeners in the same task suggests CI-users performance may be explained by a different weighting of neural processes than NH listeners.

Neural Predictors of Improvement With Cognitive Behavioral Therapy for Adolescents With Depression: An Examination of Reward Responsiveness and Emotion Regulation.

Earlier depression onsets are associated with more debilitating courses and poorer life quality, highlighting the importance of effective early intervention. Many youths fail to improve with evidence-based treatments for depression, likely due in part to heterogeneity within the disorder. Multi-method assessment of individual differences in positive and negative emotion processing could improve predictions of treatment outcomes. The current study examined self-report and neurophysiological measures of reward responsiveness and emotion regulation as predictors of response to cognitive-behavioral therapy (CBT). Adolescents (14-18years) with depression (N = 70) completed monetary reward and emotion regulation tasks while electroencephalogram (EEG) was recorded, and self-report measures of reward responsiveness, emotion regulation, and depressive symptoms at intake. Adolescents then completed a 16-session group CBT program, with depressive symptoms and clinician-rated improvement assessed across treatment. Lower reward positivity amplitudes, reflecting reduced neural reward responsiveness, predicted lower depressive symptoms with treatment. Larger late positive potential residuals during reappraisal, potentially reflecting difficulty with emotion regulation, predicted greater clinician-rated improvement. Self-report measures were not significant predictors. Results support the clinical utility of EEG measures, with impairments in positive and negative emotion processing predicting greater change with interventions that target these processes.

Adolescents' neural reactivity to parental criticism is associated with diminished happiness during daily interpersonal situations.

The goal of this study was to examine the relation between real-world socio-emotional measures and neural activation to parental criticism, a salient form of social threat for adolescents. This work could help us understand why heightened neural reactivity to social threat consistently emerges as a risk factor for internalizing psychopathology in youth. We predicted that youth with higher reactivity to parental criticism (vs neutral comments) in the subgenual anterior cingulate cortex (sgACC), amygdala and anterior insula would experience (i) less happiness in daily positive interpersonal situations and (ii) more sadness and anger in daily negative interpersonal situations. Participants (44 youth aged 11-16 years with a history of anxiety) completed a 10-day ecological momentary assessment protocol and a neuroimaging task in which they listened to audio clips of their parents' criticism and neutral comments. Mixed-effects models tested associations between neural activation to critical (vs neutral) feedback and emotions in interpersonal situations. Youth who exhibited higher activation in the sgACC to parental criticism reported less happiness during daily positive interpersonal situations. No significant neural predictors of negative emotions (e.g. sadness and anger) emerged. These findings provide evidence of real-world correlates of neural reactivity to social threat that may have important clinical implications.

Neural predictors of late-life suicide risk

Neural predictors of late-life suicide risk

Vibration analyses of railway systems using proposed neural predictors

Due to travelling on railway systems; there are many gaps and problems in cross areas. Therefore; it is necessary and very important to establish intelligent crossing systems in such areas. On the other hand, it is not possible for trains to stop or brake immediately against an obstacle due to their high speed and inertia. For this reason, it is necessary to work on the safety/warning of the other main factors and necessities (pedestrians and vehicles) in level crossings. This experimental investigation is carried out by using an experimental real-time train and crossing systems. The main vibration parameters are analysed by using neural networks. First, the dynamics of the train-rail system related to level crossings are examined, and the vibrations created by the train on rails are measured at different speeds. Then three types of proposed neural networks predictors, Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGB) and BFGS quasi-Newton backpropagation (BFGS) are used to predict the vibration of the train-rail system. From the results, it is seen that the proposed LMBP is more suitable for analysing and predicting the vibration of the train-rail system. It is clear that the speeds of the trains approaching the level crossing can be estimated from the vibration of the trains on the rails.

Explaining neural activity in human listeners with deep learning via natural language processing of narrative text

Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction.

Neuroeconomic predictors of smoking cessation outcomes: A preliminary study of delay discounting in treatment-seeking adult smokers

Large proportions of smokers are unsuccessful in evidence-based smoking cessation treatment and identifying prognostic predictors may inform improvements in treatment. Steep discounting of delayed rewards (delay discounting) is a robust predictor of poor smoking cessation outcome, but the underlying neural predictors have not been investigated. Forty-one treatment-seeking adult smokers completed a functional magnetic resonance imaging (fMRI) delay discounting paradigm prior to initiating a 9-week smoking cessation treatment protocol. Behavioral performance significantly predicted treatment outcomes (verified 7-day abstinence, n = 18; relapse, n = 23). Participants in the relapse group exhibited smaller area under the curve (d = 1.10) and smaller AUC was correlated with fewer days to smoking relapse (r = 0.56, p < 0.001) Neural correlates of discounting included medial and dorsolateral prefrontal cortex, posterior cingulate, precuneus and anterior insula, and interactions between choice type and relapse status were present for the dorsolateral prefrontal cortex, precuneus and the striatum. This initial investigation implicates differential neural activity in regions associated with frontal executive and default mode activity, as well as motivational circuits. Larger samples are needed to improve the resolution in identifying the neural underpinnings linking steep delay discounting to smoking cessation.

25.4 41032: Neural Predictors of Mood Dysregulation in Youth at Familial and Symptomatic Risk of Bipolar Disorder

Biomarkers of bipolar disorder are needed to aid in early diagnosis, and identifying neural markers of known prodromal behaviors is a step toward that goal. Mood dysregulation factors within affective lability scales are recognized as prodromal to the development of bipolar disorder. We identified neural and behavioral markers of future affective lability in at-risk youth from the Pittsburgh Bipolar Offspring Study (BIOS) and validated these in an independent sample of youth from the Longitudinal Assessment of Manic Symptoms (LAMS) study.

On the reliability of individual economic rationality measurements

A contemporary research agenda in behavioral economics and neuroeconomics aims to identify individual differences and (neuro)psychological correlates of rationality. This research has been widely received in important interdisciplinary and field outlets. However, the psychometric reliability of such measurements of rationality has been presumed without enough methodological scrutiny. Drawing from multiple original and published datasets (in total over 1,600 participants), we unequivocally show that contemporary measurements of rationality have moderate to poor reliability according to common standards. Further analyses of the variance components, as well as a allowing participants to revise previous choices, suggest that this is driven by low between-subject variance rather than high measurement error. As has been argued previously for other behavioral measurements, this poses a challenge to the predominant correlational research designs and the search for sociodemographic or neural predictors. While our results draw a sobering picture of the prospects of contemporary measurements of rationality, they are not necessarily surprising from a theoretical perspective, which we outline in our discussion.

Neural Marker of Habituation at 5 Months of Age Associated with Deferred Imitation Performance at 12 Months: A Longitudinal Study in the UK and The Gambia.

Across cultures, imitation provides a crucial route to learning during infancy. However, neural predictors which would enable early identification of infants at risk of suboptimal developmental outcomes are still rare. In this paper, we examine associations between ERP markers of habituation and novelty detection measured at 1 and 5 months of infant age in the UK (n = 61) and rural Gambia (n = 214) and infants’ responses on a deferred imitation task at 8 and 12 months. In both cohorts, habituation responses at 5 months significantly predicted deferred imitation responses at 12 months of age in both cohorts. Furthermore, ERP habituation responses explained a unique proportion of variance in deferred imitation scores which could not be accounted for by a neurobehavioural measure (Mullen Scales of Early Learning) conducted at 5 months of age. Our findings highlight the potential for ERP markers of habituation and novelty detection measured before 6 months of age to provide insight into later imitation abilities and memory development across diverse settings.

Neural Correlates of Cue Reactivity and the Regulation of Craving in Substance Use Disorders

Abstract. Theoretical background: Considerable progress has been made in illuminating the neural basis of the compulsive use patterns characterizing substance use disorders. It has been suggested to utilize these findings to alleviate the health burden associated with substance use. Objective: We address how neuroimaging research can provide these benefits. Methods: Based on neurobiological models of addiction, we highlight neuroimaging research elucidating neural predictors of relapse and how treatments modify these markers. Results: With the focus on cue reactivity, brain activity related to the motivational salience of drugs and automatized use behaviors can predict relapse. Cue reactivity changes with abstinence, and it remains to be determined whether such changes confer periods of critical relapse susceptibility. Conclusions: Several established and emerging interventions modulate brain activity associated with drug value. However, executive deficits in addiction may compromise interventions targeting control-related prefrontal brain areas. Lastly, it remains more difficult to change the brain responses mediating habitual behaviors.

Structural and Functional Neural Correlates of Treatment Response for Interpersonal Psychotherapy for Depressed Adolescents.

Precision medicine approaches hold tremendous promise to advance current clinical practice by providing information about which individuals will benefit from which treatments. This pilot study evaluated if baseline structure and function of the salience and emotion brain regions implicated in adolescent depression, specifically the amygdala and anterior cingulate cortex (ACC), predict response to Interpersonal Psychotherapy for Depressed Adolescents (IPT-A). Adolescents (n = 15; mean age = 14.5 (1.6); 80.0% female) diagnosed with a depressive disorder completed brain scans before the start of a 16 week trial of IPT-A. Clinical measures assessing depressive symptoms were completed before, during, and after a trial of therapy. Results show that at baseline, greater ACC activation in the context of an emotion-matching task and greater amygdala-ACC resting-state functional connectivity was related to greater improvement in depression symptoms. There was minimal evidence that brain structure predicted changes in depressive symptoms. The present study is the first to evaluate neural predictors of IPT-A response. While the results are preliminary, these findings suggest some avenues for future research to pursue in the hopes that more will benefit from treatment.

White matter predictors of worsening of subthreshold hypomania severity in non-bipolar young adults parallel abnormalities in individuals with bipolar disorder

BackgroundIdentifying neural predictors of worsening subthreshold hypomania severity can help identify risk of progression to BD. While diffusion Magnetic Resonance Imaging (dMRI) studies reported white matter microstructural abnormalities in tracts supporting emotional regulation in individuals with BD, it remains unknown whether similar patterns of white matter microstructure predict worsening of subthreshold hypomania severity in non-BD individuals. MethodsdMRI data were collected in: 81 non-BD individuals recruited across a range of subthreshold depression and hypomania, and followed for six months; and independent samples of 75 BD and 58 healthy individuals. All individuals were assessed using standardized diagnostic assessments, mood and anxiety symptom rating scales. Global probabilistic tractography and a tract-profile approach examined fractional anisotropy (FA), a measure of fiber collinearity, in tracts supporting emotional regulation shown to have abnormalities in BD: forceps minor (FMIN), anterior thalamic radiation (ATR), cingulum bundle (CB), and uncinate fasciculus (UF). ResultsLower FA in left CB (middle, β = −0.22, P = 0.022; posterior, β = −0.32, P < 0.001), right CB (anterior, β = −0.30, P = 0.003; posterior, β = −0.27, P = 0.005), and right UF (frontal, β = −0.29, P = 0.002; temporal, β = −0.40, P < 0.001) predicted worsening of subthreshold hypomania severity in non-BD individuals. BD versus healthy individuals showed lower FA in several of these segments: middle left CB (F = 8.7, P = 0.004), anterior right CB (F = 9.8, P = 0.002), and frontal right UF (F = 7.0, P = 0.009). Non-BD individuals with worsening 6-month hypomania had lower FA in these three segments versus HC and non-BD individuals without worsening hypomania, but similar FA to BD individuals. LimitationsRelatively short follow-up. ConclusionsWhite matter predictors of worsening subthreshold hypomania in non-BD individuals parallel abnormalities in BD individuals, and can guide early risk identification and interventions.

Design of neural predictors for predicting and analysing COVID-19 cases in different regions

Nowadays, some unexpected viruses are affecting people with many troubles. COVID-19 virus is spread in the world very rapidly. However, it seems that predicting cases and death fatalities is not easy. Artificial neural networks are employed in many areas for predicting the system’s parameters in simulation or real-time approaches. This paper presents the design of neural predictors for analysing the cases of COVID-19 in three countries. Three countries were selected because of their different regions. Especially, these major countries’ cases were selected for predicting future effects. Furthermore, three types of neural network predictors were employed to analyse COVID-19 cases. NAR-NN is one of the proposed neural networks that have three layers with one input layer neurons, hidden layer neurons and an output layer with fifteen neurons. Each neuron consisted of the activation functions of the tan-sigmoid. The other proposed neural network, ANFIS, consists of five layers with two inputs and one output and ARIMA uses four iterative steps to predict. The proposed neural network types have been selected from many other types of neural network types. These neural network structures are feed-forward types rather than recurrent neural networks. Learning time is better and faster than other types of networks. Finally, three types of neural predictors were used to predict the cases. The R2 and MSE results improved that three types of neural networks have good performance to predict and analyse three region cases of countries.

Distributed Path Following of Multiple Under-Actuated Autonomous Surface Vehicles Based on Data-Driven Neural Predictors via Integral Concurrent Learning

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models.