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Predictors of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis

Amelie Haugg, Fabian M. Renz, Andrew A. Nicholson, Cindy Lor, Sebastian J. Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener (+36 others)
2021 NeuroImage  
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  ...  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  ...  Here, for the first time, we employ exploratory machine learning methods to compute the influence of a wide range of different subjectand study-specific factors on real-time fMRI neurofeedback success.  ... 
doi:10.1016/j.neuroimage.2021.118207 pmid:34048901 fatcat:uwffxcna7fbqljsovyblmoa37e

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis [article]

Amelie Haugg, Fabian M Renz, Andrew A Nicholson, Cindy Lor, Sebastian J Goetzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener (+36 others)
2020 bioRxiv   pre-print
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  ...  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  ...  Here, for the first time, we employ machine learning methods to compute the influence of a wide range of different subject-and study-specific factors on real-time fMRI neurofeedback success.  ... 
doi:10.1101/2020.10.21.349118 fatcat:2brcvaet6jae7lvop2oaxoclaa

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

Amelie Haugg, Fabian M Renz, Andrew A Nicholson, Cindy Lor, Sebastian J Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener (+3 others)
2021
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  ...  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  ...  Here, for the first time, we employ exploratory machine learning methods to compute the influence of a wide range of different subjectand study-specific factors on real-time fMRI neurofeedback success.  ... 
doi:10.5167/uzh-208143 fatcat:57upschfszftbcstfwjym4wvki

Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation

Katya Rubia
2018 Frontiers in Human Neuroscience  
Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which  ...  such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation.  ...  NEUROFEEDBACK USING REAL-TIME fMRI AND NIRS NF is an operant conditioning procedure that, by trial and error, teaches participants to volitionally self-regulate specific regions or networks through real-time  ... 
doi:10.3389/fnhum.2018.00100 pmid:29651240 pmcid:PMC5884954 fatcat:lmr7qekeh5cnxa2jqpupgbfj7e

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Sook-Lei Liew, Artemis Zavaliangos-Petropulu, Neda Jahanshad, Catherine E Lang, Kathryn S Hayward, Keith R Lohse, Julia M Juliano, Francesca Assogna, Lee A Baugh, Anup K Bhattacharya, Bavrina Bigjahan, Michael R Borich (+55 others)
2020 Human Brain Mapping  
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic  ...  Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data  ...  Machine-learning algorithms to reliably perform quality control of the segmentations would remove an enormous barrier to performing this work.  ... 
doi:10.1002/hbm.25015 pmid:32310331 pmcid:PMC8675421 fatcat:lvelmlkkzrf2nemyl6jqbdqsqq

ACNP 57th Annual Meeting: Poster Session II

2018 Neuropsychopharmacology  
Following fear conditioning, mice were sacrificed for further brain and peripheral tissue analysis.  ...  A composite avoidance score (average of Z-scores across open field, light-dark box, and trauma reminder) demonstrated a main effect of increased avoidance behaviors by two-way ANOVA (Fstress = 9.10, p  ...  An improved MEGA-SPECIAL sequence was performed on the left DLPFC and bilateral thalami with voxel sizes of ~15cc and TE/TR = 80/2000ms, 15 min acquisition time.  ... 
doi:10.1038/s41386-018-0267-6 fatcat:febeq6uwefgdzm65ccmzrmjwjy

Final Program 2016 Mid-Year Meeting International Neuropsychological Society July 6–8, 2016 London, England

2016 Journal of the International Neuropsychological Society  
ranking and a better real performance (reduced RTs).  ...  Subsequently data were collected and preprocessed from the questionnaires and then introduced into the R (Programming Language and Machine Learning Platform) for analysis and extraction of useful knowledge  ...  Results: The statistical analysis showed significant main effects (α<.05) for all the variables and a significant interaction between lexicality, length and presentation time.  ... 
doi:10.1017/s1355617716001181 fatcat:7z6k6i6pazcufdcf6qtzphi6bq

Poster Session II

2012 Neuropsychopharmacology  
Neurofeedback was implemented using a custom real-time fMRI system utilizing AFNI real-time features and a custom GUI software.  ...  The availability of real-time functional magnetic resonance imaging (rtfMRI) and recent advances in rtfMRI neurofeedback (rtfMRI-nf) permit, for the first time, direct targeting of this region.  ...  the potential benefit of real-time fMRI feedback.  ... 
doi:10.1038/npp.2012.220 fatcat:coblfrtlujc6hdycjbnrdwzslm

ACNP 59th Annual Meeting: Poster Session I

2020 Neuropsychopharmacology  
Neurochemical analyses were performed on the brains of postnatal offspring using high-performance liquid chromatography and protein concentration analysis.  ...  We sacrificed subsets of offspring from each litter at postnatal days 7, 14, and 21, which are important time points during serotonin system development.  ...  Therefore, we designed and conducted a proof-of-concept feasibility study to test engagement of PCC with real-time fMRI neurofeedback (rtfMRI-nf) during mindfulness training in adolescents.  ... 
doi:10.1038/s41386-020-00890-7 pmid:33279934 pmcid:PMC7735198 fatcat:mqfz5f66vfbuvdbnrj7b2jt334

Symposium

2020 European psychiatry  
First, I will discuss how a complex interaction between early and late limbic-prefrontal ECT-induced FC changes have an impact on clinical improvement of patients with TRD.  ...  Although its mechanism of action remains poorly understood, multimodal neuroimaging data (i.e., resting-state functional connectivity (FC), brain morphometry and brain spectroscopy) could provide a better  ...  The real-time fMRI neurofeedback (NFB) is an innovative technique that allows to record the signal from a given brain region and to display it back in real-time to the participant.  ... 
doi:10.1192/j.eurpsy.2020.10 fatcat:gea3knhlorglbp2majz6smazki

ACNP 55th Annual Meeting: Panels, Mini-Panels and Study Groups

2016 Neuropsychopharmacology  
Mean dwell time for each state was evaluated as a percentage of the scanning time. Group differences of dwelling time were assessed using ANOVA and post hoc analysis.  ...  Total RNA was isolated, reverse transcribed, and underwent real-time Taqman qPCR amplification.  ...  This presentation will describe new machine learning and longitudinal findings showing that deficient activation of these circuits during the resolution of cognitive conflict can classify adolescents with  ... 
doi:10.1038/npp.2016.239 fatcat:wg7pyurlzvhp7bvlmlbxtkunl4

Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging

Philip A. Kragel, Leonie Koban, Lisa Feldman Barrett, Tor D. Wager
2018 Neuron  
By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.  ...  traditional approaches; and help explain how the brain represents mental constructs and processes.  ...  , R01 MH076136, and R01 MH116026; NIH National Institute on Drug Abuse T32 DA017637-14.  ... 
doi:10.1016/j.neuron.2018.06.009 pmid:30048614 pmcid:PMC6296466 fatcat:iiyf4yomlrerbeou754obutike

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Sook-Lei Liew, Artemis Zavaliangos-Petropulu, Neda Jahanshad, Catherine E Lang, Kathryn S Hayward, Keith R Lohse, Julia M Juliano, Francesca Assogna, Lee A Baugh, Anup K Bhattacharya, Bavrina Bigjahan, Michael R Borich (+55 others)
2020
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta-and mega-analytic  ...  ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke  ...  Machine-learning algorithms to reliably perform quality control of the segmentations would remove an enormous barrier to performing this work.  ... 
doi:10.7892/boris.147708 fatcat:w4fk6mmgazbahlneuqwol7ojlq

A conceptual framework for the neurobiological study of resilience

Raffael Kalisch, Marianne B. Müller, Oliver Tüscher
2014 Behavioral and Brain Sciences  
Drawing on concepts and findings from transdiagnostic psychiatry, emotion research, and behavioral and cognitive neuroscience, we propose a unified theoretical framework for the neuroscientific study of  ...  On this basis, it posits that a positive (non-negative) appraisal style is the key mechanism that protects against the detrimental effects of stress and mediates the effects of other known resilience factors  ...  advances in machine learning.  ... 
doi:10.1017/s0140525x1400082x pmid:25158686 fatcat:5jooovtaarbqzg25eml7jl7zji

The value of "negative" appraisals for resilience. Is positive (re)appraisal always good and negative always bad?

Alexandra M. Freund, Ursula M. Staudinger
2015 Behavioral and Brain Sciences  
AbstractIn contrast to the PASTOR model by Kalisch et al. we point to the potential negative long-term effects of positive (re)appraisals of events for resilience.  ...  This perspective posits that emotional reactions to events provide important guidelines as to which events, environments, or social relations should be sought out and which ones should be avoided in the  ...  advances in machine learning.  ... 
doi:10.1017/s0140525x14001526 pmid:26785853 fatcat:puf4hsseovgjpnnhnbmcu2tkci
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