Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Feb. 16, 2022

Abstract Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the circuit relevant an individual patient’s subset of might more precisely and thus effectively aid in alleviation their specific symptoms. We tested this hypothesis a proof-of-concept study using fMRI functional connectivity neurofeedback. targeted between left dorsolateral prefrontal cortex/middle frontal gyrus precuneus/posterior cingulate cortex, connection has been well-established as relating depressive Specifically, shown data-driven manner be less anticorrelated melancholic depression than healthy controls. Furthermore, posterior dominant state—which results loss anticorrelation—is expected specifically relate increase rumination such brooding. In line predictions, we found that, neurofeedback training, participant normalized (restored anticorrelation), related (depressive brooding symptoms), but not unrelated (trait anxiety), were reduced. Because these look promising, paradigm next needs examined greater sample size better Nonetheless, here provide preliminary evidence for correlation normalization network reduction Showing reproducibility, two experiments took place several years apart by different experimenters. Indicative its potential clinical utility, effects remained one-two months later. Clinical trial registration : Both reported registered trials (UMIN000015249, jRCTs052180169).

Language: Английский

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Predicting the future of neuroimaging predictive models in mental health

Molecular Psychiatry, Journal Year: 2022, Volume and Issue: 27(8), P. 3129 - 3137

Published: June 13, 2022

Abstract Predictive modeling using neuroimaging data has the potential to improve our understanding of neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora literature reviewing published studies, mathematics machine learning, best practices for these approaches. As knowledge mental health learning continue evolve, we instead aim look forward “predict” topics that believe will be important in current future studies. Some most discussed such as bias fairness, handling dirty data, interpretable models, may less familiar broader community neuroimaging-based predictive psychiatry. In similar vein, transdiagnostic research targeting brain-based features intervention are modern psychiatry models well-suited tackle. this work, target an audience who researcher with fundamental procedures wishes increase their ongoing field. We accelerate utility applications by highlighting considering topics. Furthermore, though not focus, ideas generalize other clinical neurosciences different types (e.g., digital data).

Language: Английский

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Green concept of neuromarketing based on a systematic review using the bibliometric method

Green Finance, Journal Year: 2023, Volume and Issue: 5(3), P. 392 - 430

Published: Jan. 1, 2023

<abstract> <p>Unlike traditional marketing methods, neuromarketing has shown new insights and higher prediction accuracy. This research uses the bibliometric method to analyze objectives like analysis integration of green concept neuromarketing, recognition useful authors, years publication documents, authoritative journals that publish articles in this field keywords around neuromarketing. The tools presented expand improve perception enthusiasts researchers field, it compares results obtained from different approaches. From methodological point view, is qualitative based on <xref ref-type="bibr" rid="b41">Iden et al.'s (2017)</xref> model, consisting four steps planning, selecting, extracting implementing combining with setting rid="b92">Silva's (2015)</xref> form a review. A system implemented, VOS viewer software was used results.</p> <p>The findings are two phases. In first phase, performance analysis, share annual production percentage quarterly output related subject areas, countries' published documents by productive authors were identified studied. Also, knowledge maps drawn second 17 clusters found, including 109 items 131 keywords. theoretical contribution article consists which categorized into themes sustainability consumption. study framework theory, context, method, antecedents, decisions, outcomes. All previous its features studied proposed model.</p> </abstract>

Language: Английский

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Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis

IEEE Transactions on Biomedical Engineering, Journal Year: 2019, Volume and Issue: 66(10), P. 2768 - 2779

Published: Jan. 29, 2019

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality life for patients and potentially supports development new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data specific biomarkers disorders. These encountered following dilemma: A direct classification overfits to small number high-dimensional samples but unsupervised feature-extraction has risk extracting signal no interest. In addition, such often provided only diagnoses without presenting reasons these diagnoses. This study proposed deep neural generative model resting-state functional magnetic resonance (fMRI) data. The is conditioned by assumption subject's state estimates posterior probability given data, using Bayes' rule. applied diagnose schizophrenia bipolar Diagnostic accuracy was improved large margin over competitive approaches, namely classifications connectivity, discriminative/generative models regionwise signals, those with feature-extractors. visualizes regions largely related disorders, thus motivating further biological investigation.

Language: Английский

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Enhancing creative cognition with a rapid right-parietal neurofeedback procedure

Neuropsychologia, Journal Year: 2018, Volume and Issue: 118, P. 99 - 106

Published: Feb. 14, 2018

Language: Английский

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