Major depressive episodes are the largest cause of psychiatric disability, and can often resist treatment with medication and psychotherapy. Advances in the understanding of the neural circuit basis of depression, combined with the success of deep brain stimulation (DBS) in movement disorders, spurred several groups to test DBS for treatment-resistant depression. Multiple brain sites have now been stimulated in open-label and blinded studies. Initial open-label results were dramatic, but follow-on controlled/blinded clinical trials produced inconsistent results, with both successes and failures to meet endpoints. Data from follow-on studies suggest that this is because DBS in these trials was not targeted to achieve physiologic responses. We review these results within a technology-lifecycle framework, in which these early trial "failures" are a natural consequence of over-enthusiasm for an immature technology. That framework predicts that from this "valley of disillusionment," DBS may be nearing a "slope of enlightenment." Specifically, by combining recent mechanistic insights and the maturing technology of brain-computer interfaces (BCI), the next generation of trials will be better able to target pathophysiology. Key to that will be the development of closed-loop systems that semi-autonomously alter stimulation strategies based on a patient's individual phenotype. Such next-generation DBS approaches hold great promise for improving psychiatric care.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5871707PMC
http://dx.doi.org/10.3389/fnins.2018.00175DOI Listing

Publication Analysis

Top Keywords

deep brain
8
brain stimulation
8
treatment-resistant depression
8
dbs
5
closing loop
4
loop deep
4
stimulation treatment-resistant
4
depression major
4
major depressive
4
depressive episodes
4

Similar Publications

Deep cascaded registration and weakly-supervised segmentation of fetal brain MRI.

Heliyon

January 2025

BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.

View Article and Find Full Text PDF

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.

View Article and Find Full Text PDF

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data.

Cogn Neurodyn

December 2025

Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu India.

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment.

View Article and Find Full Text PDF

Obstructive sleep apnea (OSA) has been associated with structural and functional brain changes and cognitive impairment in sleep clinic samples. Persons with traumatic brain injury (TBI) are at increased risk of OSA compared to community samples, and many experience chronic cognitive disability. However, the impact of OSA on cognitive outcome after TBI is unknown.

View Article and Find Full Text PDF

High-Field-Blinded Assessment of Portable Ultra-Low-Field Brain MRI for Multiple Sclerosis.

J Neuroimaging

January 2025

Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.

Background And Purpose: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions.

Methods: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!