psychiatric disorder
Research Papers
Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder
An important challenge in mental health research is to translate findings from cognitive neuroscience and neuroimaging research into effective treatments that target the neurobiological alterations involved in psychiatric symptoms. To address this challenge, in this review we propose a heuristic neurocircuit-based taxonomy to guide the treatment of obsessive-compulsive disorder (OCD). We do this by integrating information from several sources. First, we provide case vignettes in which patients with OCD describe their symptoms and discuss different clinical profiles in the phenotypic expression of the condition. Second, we link variations in these clinical profiles to underlying neurocircuit dysfunctions, drawing on findings from neuropsychological and neuroimaging studies in OCD. Third, we consider behavioral, pharmacological, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions. Finally, we suggest methods of testing this neurocircuit-based taxonomy as well as important limitations to this approach that should be considered in future research.
View Full Paper →Current practices in clinical neurofeedback with functional MRI-Analysis of a survey using the TIDieR checklist
BACKGROUND: A core principle of creating a scientific evidence base is that results can be replicated in independent experiments and in health intervention research. The TIDieR (Template for Intervention Description and Replication) checklist has been developed to aid in summarising key items needed when reporting clinical trials and other well designed evaluations of complex interventions in order that findings can be replicated or built on reliably. Neurofeedback (NF) using functional MRI (fMRI) is a multicomponent intervention that should be considered a complex intervention. The TIDieR checklist (with minor modification to increase applicability in this context) was distributed to NF researchers as a survey of current practice in the design and conduct of clinical studies. The aim was to document practice and convergence between research groups, highlighting areas for discussion and providing a basis for recommendations for harmonisation and standardisation. METHODS: The TIDieR checklist was interpreted and expanded (21 questions) to make it applicable to neurofeedback research studies. Using the web-based Bristol Online Survey (BOS) tool, the revised checklist was disseminated to researchers in the BRAINTRAIN European research collaborative network (supported by the European Commission) and others in the fMRI-neurofeedback community. RESULTS: There were 16 responses to the survey. Responses were reported under eight main headings which covered the six domains of the TIDieR checklist: What, Why, When, How, Where and Who. CONCLUSIONS: This piece of work provides encouraging insight into the ability to be able to map neuroimaging interventions to a structured framework for reporting purposes. Regardless of the considerable variability of design components, all studies could be described in standard terms of diagnostic groups, dose/duration, targeted areas/signals, and psychological strategies and learning models. Recommendations are made which include providing detailed rationale of intervention design in study protocols.
View Full Paper →e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors
Background: New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping, a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development. Objective: These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine. Method: Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders. Results: We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality. Conclusion: This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.
View Full Paper →Neurofeedback Training for Psychiatric Disorders Associated with Criminal Offending: A Review
Background: Effective treatment interventions for criminal offenders are necessary to reduce risk of criminal recidivism. Evidence about deviant electroencephalographic (EEG)-frequencies underlying disorders found in criminal offenders is accumulating. Yet, treatment modalities, such as neurofeedback, are rarely applied in the forensic psychiatric domain. Since offenders usually have multiple disorders, difficulties adhering to long-term treatment modalities, and are highly vulnerable for psychiatric decompensation, more information about neurofeedback training protocols, number of sessions, and expected symptom reduction is necessary before it can be successfully used in offender populations. Method: Studies were analyzed that used neurofeedback in adult criminal offenders, and in disorders these patients present with. Specifically aggression, violence, recidivism, offending, psychopathy, schizophrenia, attention-deficit hyperactivity disorder (ADHD), substance-use disorder (SUD), and cluster B personality disorders were included. Only studies that reported changes in EEG-frequencies posttreatment (increase/decrease/no change in EEG amplitude/power) were included. Results: Databases Psychinfo and Pubmed were searched in the period 1990-2017 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, resulting in a total of 10 studies. Studies in which neurofeedback was applied in ADHD (N = 3), SUD (N = 3), schizophrenia (N = 3), and psychopathy (N = 1) could be identified. No studies could be identified for neurofeedback applied in cluster B personality disorders, aggression, violence, or recidivism in criminal offenders. For all treatment populations and neurofeedback protocols, number of sessions varied greatly. Changes in behavioral levels ranged from no improvements to significant symptom reduction after neurofeedback training. The results are also mixed concerning posttreatment changes in targeted EEG-frequency bands. Only three studies established criteria for EEG-learning. Conclusion: Implications of the results for the applicability of neurofeedback training in criminal offender populations are discussed. More research focusing on neurofeedback and learning of cortical activity regulation is needed in populations with externalizing behaviors associated with violence and criminal behavior, as well as multiple comorbidities. At this point, it is unclear whether standard neurofeedback training protocols can be applied in offender populations, or whether QEEG-guided neurofeedback is a better choice. Given the special context in which the studies are executed, clinical trials, as well as single-case experimental designs, might be more feasible than large double-blind randomized controls.
View Full Paper →Neurofeedback: One of today's techniques in psychiatry?
OBJECTIVES: Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry. METHOD: Literature on neurofeedback and mental disorders but also on brain computer interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the Neurofeedback evaluation & training (NExT) section of the French Association of biological psychiatry and neuropsychopharmacology (AFPBN). RESULTS: Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics) and techniques (EEG, fMRI, signal processing). CONCLUSION: The field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain computer interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice.
View Full Paper →Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers
Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.
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