A secondary Outcome Analysis of a Randomized Trial Using a Mixed Lipid Emulsion Containing Fish Oil in Infants with Extremely Low Birth Weight: Cognitive and Behavioral Outcome at Preschool Age.

OBJECTIVE: To evaluate the impact of a parenteral lipid emulsion containing fish oil compared with a soybean oil based-lipid emulsion on the cognitive outcome and behavior of preschool children with extremely low birth weight. STUDY DESIGN: This was a retrospective secondary outcome analysis of a randomized controlled trial performed between June 2012 and June 2015. Infants with extremely low birth weight received either a mixed (soybean oil, medium chain triglycerides, olive oil, fish oil) or a soybean oil-based lipid emulsion for parenteral nutrition. Data from the Kaufman Assessment Battery for Children II, the Child Behavior Checklist 1.5-5, and anthropometry were collected from medical charts at 5.6 years of age. RESULTS: At discharge, 206 of the 230 study participants were eligible. At 5 years 6 months of age, data of 153 of 206 infants (74%) were available for analysis. There were no significant differences in Kaufman Assessment Battery for Children II scores for Sequential/Gsm, Simultaneous/Gv, Learning/Glr, and Mental Processing Index (mixed lipid: median, 97.5 [IQR, 23.5]; soybean oil: median, 96 [IQR, 19.5]; P = .43) or Child Behavior Checklist 1.5-5 scores for internalizing problems, externalizing problems, or total problems (mixed lipid: median, 37 [IQR, 12.3]; soybean oil: median, 37 [IQR, 13.5]; P = .54). CONCLUSIONS: A RandomForest machine learning regression analysis did not show an effect of type of lipid emulsion on cognitive and behavioral outcome. Parenteral nutrition using a mixed lipid emulsion containing fish oil did not affect neurodevelopment and had no impact on child behavior of infants with extremely low birth weights at preschool age. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01585935.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Emotion Regulation Modulates Dietary Decision-Making via Activity in the Prefrontal-Striatal Valuation System.

The consumption of indulgent, carbohydrate- and fat-rich foods is often used as a strategy to cope with negative affect because they provide immediate self-reward. Such dietary choices, however, can severely affect people's health. One countermeasure could be to improve one's emotion regulation ability. We used functional magnetic resonance imaging to examine the neural activity underlying the downregulation of incidental emotions and its effect on subsequent food choices. We investigated whether emotion regulation leads to healthier food choices and how emotion regulation interacts with the brain's valuation and decision-making circuitry. We found that 1) the downregulation of incidental negative emotions was associated with a subsequent selective increase in decisions for tasty but also for healthy foods, 2) food preferences were predicted by palatability but also by the current emotional state, and 3) emotion regulation modulated decision-related activation in the ventromedial prefrontal cortex and ventral striatum. These results indicate that emotional states are indeed important for food choice and that the process of emotion regulation might boost the subsequent processing of health attributes, possibly via neural reward circuits. In consequence, our findings suggest that increasing emotion regulation ability could effectively modulate food choices by stimulating an incidental upvaluation of health attributes.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

Action affordances and visuo-spatial complexity in motor imagery: An fMRI study.

Imagining a complex action requires not only motor-related processing but also visuo-spatial imagery. In the current study, we examined visuo-spatial complexity and action affordances in motor imagery (MI). Using functional magnetic resonance imaging, we investigated the neural activity in MI of reach-to-grasp movements of the right hand in five conditions. Thirty participants were scanned while imagining grasping an everyday object, grasping a geometrical shape, grasping next to an everyday object, grasping next to a geometrical shape, and grasping at nothing (no object involved). We found that MI of grasping next to an object recruited the visuo-spatial cognition network including posterior parietal and premotor regions more strongly than MI of grasping an object. This indicates that grasping next to an object requires additional processing resources rendering MI more complex. MI of a grasping movement involving a familiar everyday object compared to a geometrical shape yielded stronger activation in motor-related regions, including the bilateral supplementary motor area. This activation might be due to inhibitory processes preventing motor execution of motor scripts evoked by everyday objects (action affordances). Our results indicate that visuo-spatial cognition plays a significant role in MI.

Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline.

BACKGROUND: In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline "Brain-Computer Interface Race". A brain-computer interface (BCI) is a device facilitating control of applications via the user's thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game. METHODS: We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result. RESULTS: We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178 s to 143 s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120 s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196 s. DISCUSSION: We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed. CONCLUSIONS: The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.

Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study.

BACKGROUND: Atypical anticipation of social reward has been shown to lie at the core of the social challenges faced by individuals with autism spectrum disorder (ASD). However, previous research has yielded inconsistent results and has often overlooked crucial characteristics of stimuli. Here, we investigated ASD reward processing using social and nonsocial tangible stimuli, carefully matched on several key dimensions. METHODS: We examined the anticipation and consumption of social (interpersonal touch) and nonsocial (flavored milk) rewards in 25 high-functioning individuals with ASD and 25 neurotypical adult individuals. In addition to subjective ratings of wanting and liking, we measured physical energetic expenditure to obtain the rewards, brain activity with neuroimaging, and facial reactions through electromyography on a trial-by-trial basis. RESULTS: Participants with ASD did not exhibit reduced motivation for social or nonsocial rewards; their subjective ratings, motivated efforts, and facial reactions were comparable to those of neurotypical participants. However, anticipation of higher-value rewards increased neural activation in lateral parietal cortices, sensorimotor regions, and the orbitofrontal cortex. Moreover, participants with ASD exhibited hyperconnectivity between frontal medial regions and occipital regions and the thalamus. CONCLUSIONS: Individuals with ASD who experienced rewards with tangible characteristics, whether social or nonsocial, displayed typical subjective and objective motivational and hedonic responses. Notably, the observed hyperactivations in sensory and attentional nodes during anticipation suggest atypical sensory overprocessing of forthcoming rewards rather than decreased reward value. While these atypicalities may not have manifested in observable behavior here, they could impact real-life social interactions that require nuanced predictions, potentially leading to the misperception of reduced interest in rewarding social stimuli in ASD.

Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes.

Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction between attributes as inherent characteristics or features of the artwork and judgments as subjective evaluations remains an exciting topic. This paper reviews the literature of the last half century, to identify key attributes, and employs machine learning, specifically Gradient Boosted Decision Trees (GBDT), to predict 13 art judgments along 17 attributes. Ratings from 78 art novice participants were collected for 54 Western artworks. Our GBDT models successfully predicted 13 judgments significantly. Notably, judged creativity and disturbing/irritating judgments showed the highest predictability, with the models explaining 31% and 32% of the variance, respectively. The attributes emotional expressiveness, valence, symbolism, as well as complexity emerged as consistent and significant contributors to the models' performance. Content-representational attributes played a more prominent role than formal-perceptual attributes. Moreover, we found in some cases non-linear relationships between attributes and judgments with sudden inclines or declines around medium levels of the rating scales. By uncovering these underlying patterns and dynamics in art judgment behavior, our research provides valuable insights to advance the understanding of aesthetic experiences considering visual art, inform cultural practices, and inspire future research in the field of art appreciation.

Introduction of Solid Foods in Preterm Infants and Its Impact on Growth in the First Year of Life-A Prospective Observational Study.

The aim of this study was to investigate whether age at introduction of solid foods in preterm infants influences growth in the first year of life. This was a prospective observational study in very low birth weight infants stratified to an early (<17 weeks corrected age) or a late (≥17 weeks corrected age) feeding group according to the individual timing of weaning. In total, 115 infants were assigned to the early group, and 82 were assigned to the late group. Mean birth weight and gestational age were comparable between groups (early: 926 g, 26 + 6 weeks; late: 881 g, 26 + 5 weeks). Mean age at weaning was 13.2 weeks corrected age in the early group and 20.4 weeks corrected age in the late group. At 12 months corrected age, anthropometric parameters showed no significant differences between groups (early vs. late, mean length 75.0 vs. 74.1 cm, weight 9.2 vs. 8.9 kg, head circumference 45.5 vs. 45.0 cm). A machine learning model showed no effect of age at weaning on length and length z-scores at 12 months corrected age. Infants with comorbidities had significantly lower anthropometric z-scores compared to infants without comorbidities. Therefore, regardless of growth considerations, we recommend weaning preterm infants according to their neurological abilities.

Cross-cultural comparison of beauty judgments in visual art using machine learning analysis of art attribute predictors among Japanese and German speakers.

In empirical art research, understanding how viewers judge visual artworks as beautiful is often explored through the study of attributes-specific inherent characteristics or artwork features such as color, complexity, and emotional expressiveness. These attributes form the basis for subjective evaluations, including the judgment of beauty. Building on this conceptual framework, our study examines the beauty judgments of 54 Western artworks made by native Japanese and German speakers, utilizing an extreme randomized trees model-a data-driven machine learning approach-to investigate cross-cultural differences in evaluation behavior. Our analysis of 17 attributes revealed that visual harmony, color variety, valence, and complexity significantly influenced beauty judgments across both cultural cohorts. Notably, preferences for complexity diverged significantly: while the native Japanese speakers found simpler artworks as more beautiful, the native German speakers evaluated more complex artworks as more beautiful. Further cultural distinctions were observed: for the native German speakers, emotional expressiveness was a significant factor, whereas for the native Japanese speakers, attributes such as brushwork, color world, and saturation were more impactful. Our findings illuminate the nuanced role that cultural context plays in shaping aesthetic judgments and demonstrate the utility of machine learning in unravelling these complex dynamics. This research not only advances our understanding of how beauty is judged in visual art-considering self-evaluated attributes-across different cultures but also underscores the potential of machine learning to enhance our comprehension of the aesthetic evaluation of visual artworks.

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach.

INTRODUCTION: Sex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN). METHODS: Using an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns. RESULTS: Our findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns. CONCLUSION: This study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Pre- and post-task resting-state differs in clinical populations.

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings.

Creativity is a compelling yet elusive phenomenon, especially when manifested in visual art, where its evaluation is often a subjective and complex process. Understanding how individuals judge creativity in visual art is a particularly intriguing question. Conventional linear approaches often fail to capture the intricate nature of human behavior underlying such judgments. Therefore, in this study, we employed interpretable machine learning to probe complex associations between 17 subjective art-attributes and creativity judgments across a diverse range of artworks. A cohort of 78 non-art expert participants assessed 54 artworks varying in styles and motifs. The applied Random Forests regressor models accounted for 30% of the variability in creativity judgments given our set of art-attributes. Our analyses revealed symbolism, emotionality, and imaginativeness as the primary attributes influencing creativity judgments. Abstractness, valence, and complexity also had an impact, albeit to a lesser degree. Notably, we observed non-linearity in the relationship between art-attribute scores and creativity judgments, indicating that changes in art-attributes did not consistently correspond to changes in creativity judgments. Employing statistical learning, this investigation presents the first attribute-integrating quantitative model of factors that contribute to creativity judgments in visual art among novice raters. Our research represents a significant stride forward building the groundwork for first causal models for future investigations in art and creativity research and offering implications for diverse practical applications. Beyond enhancing comprehension of the intricate interplay and specificity of attributes used in evaluating creativity, this work introduces machine learning as an innovative approach in the field of subjective judgment.

A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study.

Introduction: Real-time fMRI-based neurofeedback (rt-fMRI-NFB) is a non-invasive technology that enables individuals to self-regulate brain activity linked to neuropsychiatric symptoms, including those associated with post-traumatic stress disorder (PTSD). Selecting the target brain region for neurofeedback-mediated regulation is primarily informed by the neurobiological characteristics of the participant population. There is a strong link between PTSD symptoms and multiple functional disruptions in the brain, including hyperactivity within both the amygdala and posterior cingulate cortex (PCC) during trauma-related processing. As such, previous rt-fMRI-NFB studies have focused on these two target regions when training individuals with PTSD to regulate neural activity. However, the differential effects of neurofeedback target selection on PTSD-related neural activity and clinical outcomes have not previously been investigated. Methods: Here, we compared whole-brain activation and changes in PTSD symptoms between PTSD participants (n = 28) that trained to downregulate activity within either the amygdala (n = 14) or the PCC (n = 14) while viewing personalized trauma words. Results: For the PCC as compared to the amygdala group, we observed decreased neural activity in several regions implicated in PTSD psychopathology - namely, the bilateral cuneus/precuneus/primary visual cortex, the left superior parietal lobule, the left occipital pole, and the right superior temporal gyrus/temporoparietal junction (TPJ) - during target region downregulation using rt-fMRI-NFB. Conversely, for the amygdala as compared to the PCC group, there were no unique (i.e., over and above that of the PCC group) decreases in neural activity. Importantly, amygdala downregulation was not associated with significantly improved PTSD symptoms, whereas PCC downregulation was associated with reduced reliving and distress symptoms over the course of this single training session. In this pilot analysis, we did not detect significant between-group differences in state PTSD symptoms during neurofeedback. As a critical control, the PCC and amygdala groups did not differ in their ability to downregulate activity within their respective target brain regions. This indicates that subsequent whole-brain neural activation results can be attributed to the effects of the neurofeedback target region selection in terms of neurophysiological function, rather than as a result of group differences in regulatory success. Conclusion: In this study, neurofeedback-mediated downregulation of the PCC was differentially associated with reduced state PTSD symptoms and simultaneous decreases in PTSD-associated brain activity during a single training session. This novel analysis may guide researchers in choosing a neurofeedback target region in future rt-fMRI-NFB studies and help to establish the clinical efficacy of specific neurofeedback targets for PTSD. A future multi-session clinical trial of rt-fMRI-NFB that directly compares between PCC and amygdala target regions is warranted.

Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD.

BACKGROUND: Alterations within large-scale brain networks-namely, the default mode (DMN) and salience networks (SN)-are present among individuals with posttraumatic stress disorder (PTSD). Previous real-time functional magnetic resonance imaging (fMRI) and electroencephalography neurofeedback studies suggest that regulating posterior cingulate cortex (PCC; the primary hub of the posterior DMN) activity may reduce PTSD symptoms and recalibrate altered network dynamics. However, PCC connectivity to the DMN and SN during PCC-targeted fMRI neurofeedback remains unexamined and may help to elucidate neurophysiological mechanisms through which these symptom improvements may occur. METHODS: Using a trauma/emotion provocation paradigm, we investigated psychophysiological interactions over a single session of neurofeedback among PTSD (n = 14) and healthy control (n = 15) participants. We compared PCC functional connectivity between regulate (in which participants downregulated PCC activity) and view (in which participants did not exert regulatory control) conditions across the whole-brain as well as in a priori specified regions-of-interest. RESULTS: During regulate as compared to view conditions, only the PTSD group showed significant PCC connectivity with anterior DMN (dmPFC, vmPFC) and SN (posterior insula) regions, whereas both groups displayed PCC connectivity with other posterior DMN areas (precuneus/cuneus). Additionally, as compared with controls, the PTSD group showed significantly greater PCC connectivity with the SN (amygdala) during regulate as compared to view conditions. Moreover, linear regression analyses revealed that during regulate as compared to view conditions, PCC connectivity to DMN and SN regions was positively correlated to psychiatric symptoms across all participants. CONCLUSION: In summary, observations of PCC connectivity to the DMN and SN provide emerging evidence of neural mechanisms underlying PCC-targeted fMRI neurofeedback among individuals with PTSD. This supports the use of PCC-targeted neurofeedback as a means by which to recalibrate PTSD-associated alterations in neural connectivity within the DMN and SN, which together, may help to facilitate improved emotion regulation abilities in PTSD.

Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles.

OBJECTIVE: Posttraumatic stress disorder (PTSD) is a debilitating psychiatric illness, experienced by approximately 10% of the population. Heterogeneous presentations that include heightened dissociation, comorbid anxiety and depression, and emotion dysregulation contribute to the severity of PTSD, in turn, creating barriers to recovery. There is an urgent need to use data-driven approaches to better characterize complex psychiatric presentations with the aim of improving treatment outcomes. We sought to determine if machine learning models could predict PTSD-related illness in a real-world treatment-seeking population using self-report clinical data. METHOD: Secondary clinical data from 2017 to 2019 included pretreatment measures such as trauma-related symptoms, other mental health symptoms, functional impairment, and demographic information from adults admitted to an inpatient unit for PTSD in Canada (n = 393). We trained two nonlinear machine learning models (extremely randomized trees) to identify predictors of (a) PTSD symptom severity and (b) functional impairment. We assessed model performance based on predictions in novel subsets of patients. RESULTS: Approximately 43% of the variance in PTSD symptom severity (R²avg = .43, R²median = .44, p = .001) was predicted by symptoms of anxiety, dissociation, depression, negative trauma-related beliefs about others, and emotion dysregulation. In addition, 32% of the variance in functional impairment scores (R²avg = .32, R²median = .33, p = .001) was predicted by anxiety, PTSD symptom severity, cognitive dysfunction, dissociation, and depressive symptoms. CONCLUSIONS: Our results reinforce that dissociation, cooccurring anxiety and depressive symptoms, maladaptive trauma appraisals, cognitive dysfunction, and emotion dysregulation are critical targets for trauma-related interventions. Machine learning models can inform personalized medicine approaches to maximize trauma recovery in real-world inpatient populations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Audio Feature Analysis for Acoustic Pain Detection in Term Newborns.

INTRODUCTION: Crying newborns signal a need or discomfort as part of the innate communication system. Exposure to pain is related to infants' unfavorable neurodevelopmental outcomes. There is a tremendous need for more objective methods to assess neonatal pain. An audio analysis of acoustic utterances could provide specific information on the patient's pain level. METHODS: We analyzed 67 videos of 33 term-born newborns recorded during a planned capillary blood sample, including the stimuli, non-noxious thermal stimulus, short noxious stimulus, and prolonged unpleasant stimulus, between December 2020 and March 2021. Two expert raters evaluated the infants' pain responses using the Neonatal Facial Coding System (NFCS). The mean values of 123 timbre features of the recorded audio data were analyzed by using specific toolboxes and libraries from the following programming environments: MIRtoolbox (MATLAB), MiningSuite (MATLAB), Essentia (Python), AudioCommons timbral models (Python), and Librosa (Python). RESULTS: The NFCS values were significantly higher during the short noxious stimulus (p < 0.001) and prolonged unpleasant stimulus (p < 0.001) than during the non-noxious thermal stimulus, whereas NFCS values during the short noxious stimulus and prolonged unpleasant stimulus were similar (p = 0.79). Brightness, roughness, percussive energy, and attack times were identified as the features having the highest impact on the NFCS. CONCLUSION: This hypothesis-generating study identified several salient acoustic features highly associated with pain responses in term newborns. Our analysis is an encouraging starting point for the targeted analysis of pain-specific acoustic features of neonatal cries and vocalizations from the perspective of real-time acoustic processing.

Neurodevelopmental Outcome of Extremely Low Birth Weight Infants with Cholestasis at 12 and 24 Months.

INTRODUCTION: The aims of the study were to describe the neurodevelopmental outcome of extremely low birth weight (ELBW) infants with parenteral nutrition-associated cholestasis (PNAC) and to assess whether PNAC is associated with adverse neurodevelopmental outcome. METHODS: The study is a secondary analysis of controlled trial (June 2012-October 2017) on PNAC incidence in ELBW infants receiving two different parenteral lipid emulsions (mixed lipid emulsion containing fish oil vs. soybean oil-based). Neurodevelopmental follow-up at 12- and 24-month corrected age was compared in infants with and without PNAC. A machine learning-based regression analysis was used to assess whether PNAC was associated with adverse neurodevelopmental outcome. RESULTS: For assessment of neurodevelopmental outcome (Bayley-III), 174 infants were available at 12-month (PNAC: n = 21; no PNAC: n = 153) and 164 infants at 24-month (PNAC: n = 20; no PNAC: n = 144) corrected age. The neurodevelopment of ELBW infants with PNAC was globally delayed, with significantly lower cognitive, language, and motor scores at both 12- and 24-month corrected age. Regression analyses revealed that PNAC was associated with an adverse motor outcome. CONCLUSION: ELBW infants with PNAC are at increased risk for adverse neurodevelopmental outcome.

Contributions of diagnostic, cognitive, and somatovisceral information to the prediction of fear ratings in spider phobic and non-spider-fearful individuals.

BACKGROUND: Physiological responding is a key characteristic of fear responses. Yet, it is unknown whether the time-consuming measurement of somatovisceral responses ameliorates the prediction of individual fear responses beyond the accuracy reached by the consideration of diagnostic (e.g., phobic vs. non phobic) and cognitive (e.g., risk estimation) factors, which can be more easily assessed. METHOD: We applied a machine learning approach to data of an experiment, in which spider phobic and non-spider fearful participants (diagnostic factor) faced pictures of spiders. For each experimental trial, participants specified their personal risk of encountering the spider (cognitive factor), as well as their subjective fear (outcome variable) on quasi-continuous scales, while diverse somatovisceral responses were registered (heart rate, electrodermal activity, respiration, facial muscle activity). RESULTS: The machine-learning analyses revealed that fear ratings were predominantly predictable by the diagnostic factor. Yet, when allowing for learning of individual patterns in the data, somatovisceral responses contributed additional information on the fear ratings, yielding a prediction accuracy of 81% explained variance. Moreover, heart rate prior to picture onset, but not heart rate reactivity increased predictive power. LIMITATIONS: Fear was solely assessed by verbal reports, only 27 females were considered, and no generalization to other anxiety disorders is possible. CONCLUSIONS: After training the algorithm to learn about individual-specific responding, somatovisceral patterns can be successfully exploited. Our findings further point to the possibility that the expectancy-related autonomic state throughout the experiment predisposes an individual to experience specific levels of fear, with less influence of the actual visual stimulations.

Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks.

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.