メランコリー型うつ病に関わる脳機能結合と抗うつ薬による変化

広島大学(Hiroshima University)博士(医学)Doctor of Philosophy in Medical Sciencedoctora

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 recent approach for creating a theranostic biomarker, which consists mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (ii) 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.Comment: 46 pages, 5 figure

Effects of behavioural activation on the neural circuit related to intrinsic motivation

[Background] Behavioural activation is an efficient treatment for depression and can improve intrinsic motivation. Previous studies have revealed that the frontostriatal circuit is involved in intrinsic motivation; however, there are no data on how behavioural activation affects the frontostriatal circuit. [Aims] We aimed to investigate behavioural activation-related changes in the frontostriatal circuit. [Method] Fifty-nine individuals with subthreshold depression were randomly assigned to either the intervention or non-intervention group. The intervention group received five weekly behavioural activation sessions. The participants underwent functional magnetic resonance imaging scanning on two separate occasions while performing a stopwatch task based on intrinsic motivation. We investigated changes in neural activity and functional connectivity after behavioural activation. [Results] After behavioural activation, the intervention group had increased activation and connectivity in the frontostriatal region compared with the non-intervention group. The increased activation in the right middle frontal gyrus was correlated with an improvement of subjective sensitivity to environmental rewards. [Conclusions] Behavioural activation-related changes to the frontostriatal circuit advance our understanding of psychotherapy-induced improvements in the neural basis of intrinsic motivation. [Declaration of interest] None.This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas from Japan Society for the Promotion of Science, JSPS (grants 16H06395 and 16H06399), and grant 23118004 from the Ministry of Education, Culture, Sports, Science and Technology, Japan. This work was partially supported by the programme for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) by Japan Agency for Medical Research and Development, AMED (grant 15dm0207012h0002) and Integrated Research on Depression, Dementia and Development Disorders by AMED (grant 16dm0107093h0001). The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation or review of the manuscript or decision to submit the manuscript for publication

Identification of antidepressant dose-related, resting-state functional connectivity as a novel therapeutic target in neurofeedback: a machine learning-based fMRI study

Recent studies have indicated that resting-state functional connectivity (rs-fc) holds great promise for effectively delineating the disruption in the neural circuits caused by mental disorders. By applying machine learning techniques to mass rs-fc MRI data, we have earlier identified a small number of functional connections (FCs) that reliably distinguished healthy controls from patients with mental disorders such as autism spectrum disorder (ASD) and major depressive disorder (MDD). However, the extent to which the identified FCs were influenced by the administration of psychotropic drugs in the patients (e.g. antidepressant to treat MDD patients) is uncertain, making it difficult to evaluate the net effect of mental disorders on a particular FC. To approach this question, here we conducted a machine learning study to identify FCs associated with the administration of selective serotonin reuptake inhibitor (SSRI), a first-line antidepressant to treat MDD patients. We then compared the results with the independent set of FCs that reliably predicted the diagnostic status (MDD or healthy) of each individual [3]. A machine learning algorithm, developed previously to construct an FC-based classifier for ASD, was applied to a data set consisting of MDD patients (N=82 with SSRI and N=22 without SSRI) and healthy controls (N=143) in order to extract FCs specifically related to the status of SSRI administration. The algorithm identified a total of 23 SSRI dose-specific FCs distributed across the whole brain, by which the patients with and without SSRI treatment were successfully distinguished (area under the curve, AUC=0.80). The identified FCs did not overlap with the set of FCs that predicted the diagnostic status of an individual. Furthermore, this reliability of the classification was generalized to an independent cohort that consisted of individuals with ASD (N=29 with SSRI and N=45 without SSRI treatment) and typically-developed controls (N=107) (AUC=0.73). The present study suggests that the effects of MDD pathophysiology and SSRI treatment on FCs can be identified and evaluated separately. It is also suggested that the SSRI dose-related FCs may be a novel therapeutic target to treat MDD patients through neurofeedback, especially those who present poor drug compliance.Real-time Functional Imaging and Neurofeedback Conference 201

Functional Alterations of Postcentral Gyrus Modulated by Angry Facial Expressions during Intraoral Tactile Stimuli in Patients with Burning Mouth Syndrome: A Functional Magnetic Resonance Imaging Study

Previous findings suggest that negative emotions could influence abnormal sensory perception in burning mouth syndrome (BMS). However, few studies have investigated the underlying neural mechanisms associated with BMS. We examined activation of brain regions in response to intraoral tactile stimuli when modulated by angry facial expressions. We performed functional magnetic resonance imaging on a group of 27 BMS patients and 21 age-matched healthy controls. Tactile stimuli were presented during different emotional contexts, which were induced via the continuous presentation of angry or neutral pictures of human faces. BMS patients exhibited higher tactile ratings and greater activation in the postcentral gyrus during the presentation of tactile stimuli involving angry faces relative to controls. Significant positive correlations between changes in brain activation elicited by angry facial images in the postcentral gyrus and changes in tactile rating scores by angry facial images were found for both groups. For BMS patients, there was a significant positive correlation between changes in tactile-related activation of the postcentral gyrus elicited by angry facial expressions and pain intensity in daily life. Findings suggest that neural responses in the postcentral gyrus are more strongly affected by angry facial expressions in BMS patients, which may reflect one possible mechanism underlying impaired somatosensory system function in this disorder

Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI

esting-state fMRI has the potential to find abnormal behavior in brain activity and to diagnose patients with depression. However, resting-state fMRI has a bias depending on the scanner site, which makes it difficult to diagnose depression at a new site. In this paper, we propose methods to improve the performance of diagnosis of major depressive disorder (MDD) at an independent site by reducing the site bias effects using regression. For this, we used a subgroup of healthy subjects of the independent site to regress out site bias. We further improved the classification performance of patients with depression by focusing on melancholic depressive disorder. Our proposed methods would be useful to apply depression classifiers to subjects at completely new site

Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI

Resting-state fMRI has the potential to help doctors detect abnormal behavior in brain activity and to diagnose patients with depression. However, resting-state fMRI has a bias depending on the scanner site, which makes it difficult to diagnose depression at a new site. In this paper, we propose methods to improve the performance of the diagnosis of major depressive disorder (MDD) at an independent site by reducing the site bias effects using regression. For this, we used a subgroup of healthy subjects of the independent site to regress out site bias. We further improved the classification performance of patients with depression by focusing on melancholic depressive disorder. Our proposed methods would be useful to apply depression classifiers to subjects at completely new sites