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.

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.

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.

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.

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.

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.

Short time sports exercise boosts motor imagery patterns: implications of mental practice in rehabilitation programs.

Motor imagery (MI) is a commonly used paradigm for the study of motor learning or cognitive aspects of action control. The rationale for using MI training to promote the relearning of motor function arises from research on the functional correlates that MI shares with the execution of physical movements. While most of the previous studies investigating MI were based on simple movements in the present study a more attractive mental practice was used to investigate cortical activation during MI. We measured cerebral responses with functional magnetic resonance imaging (fMRI) in twenty three healthy volunteers as they imagined playing soccer or tennis before and after a short physical sports exercise. Our results demonstrated that only 10 min of training are enough to boost MI patterns in motor related brain regions including premotor cortex and supplementary motor area (SMA) but also fronto-parietal and subcortical structures. This supports previous findings that MI has beneficial effects especially in combination with motor execution when used in motor rehabilitation or motor learning processes. We conclude that sports MI combined with an interactive game environment could be a promising additional tool in future rehabilitation programs aiming to improve upper or lower limb functions or support neuroplasticity.

Adaptive hybrid brain-computer interaction: ask a trainer for assistance!

In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.