The ability to anticipate reward is essential for selecting adaptive behavior based on their personal value. Recent findings indicate dysfunctional reward processing in depression. In this paper, we will introduce our findings on this issue. Using functional magnetic resonance imaging, we first investigated neural response of major depressive disorder/subthreshold depression using the monetary incentive delay task. As a result, the subjects with major depressive disorder exhibited dysfunction in modulating striatal activity in accordance with the size of the reward. In addition, individuals with subthreshold depression showed neurofunctional changes in the fronto‐parietal region.
Besides, these changes were modulated after successful intervention by antidepressant or psychotherapy. Lastly, we proposed novel models to predict the activation of the striatum during reward anticipation by resting‐functional magnetic resonance imaging data.
In 2017, the therapeutic rTMS device for major depression has been finally approved in Japan. However, its efficacy varies among patients and also the optimized stimulation protocol has not been established yet. Brain biomarkers related to rTMS have been a recent focus of psychiatric research, including predictive markers, navigational markers, and monitoring makers. Resting‐state functional MRI (rs‐fMRI) and EEG rhythms are useful to estimate the neuronal circuitry dynamics. Thus, functional connectivity and biotype classification based on rs‐fMRI data and EEG power spectrum and theta‐gamma coupling (TGC) are good candidates for such rTMS biomarkers. Especially, neuronal circuitry associated with subgenual anterior cingulate cortex (sgACC) is so crucial and its associations with dorsolateral prefrontal cortex (DLPFC) and default mode network have been repeatedly reported to be important for rTMS biomarkers of major depression. It has also been reported that gamma power and TGC at awake EEG and delta power during slow wave sleep were locally enhanced around the stimulation site. It should be noted that the focus of rTMS is not limited to the stimulation cortical site of DLPFC but also its associated neuronal circuitries. Furthermore, such circuitry‐related brain biomarkers could optimize and personalize rTMS intervention. No potential conflicts of interest were disclosed.
Cross‐frequency interactions may coordinate neural circuits operating at different frequencies. Here we examined phase‐amplitude coupling (PAC) in the electroencephalograms of individuals with schizophrenia (SZ) and healthy control subjects (HCs) . We computed PAC during the baseline period of 40‐Hz auditory steady‐state stimulation and rest. We reanalyzed data from 18 subjects with SZ and 18 HCs. Overall, coupling of β and γ amplitude was higher during the auditory steady‐state response, while α/β PAC was higher during rest. θ/α PAC was higher in subjects with SZ than in HCs. θ/γ PAC was lateralized to the left hemisphere in HCs but was not lateralized in subjects with SZ. There are no potential conflicts of interest to disclose.
Human brain has developed the neural system of automatic sound change detection as reflected by the mismatch negativity (MMN) Many studies including “omission‐MMN” study have given evidence for “memory trace theory”. Actually, omission‐MMN was evoked only with SOAs shorter than 160 ms, suggesting the existence of temporal window of integration (TWI) mechanism. This TWI of 160‐170ms corresponds to the length of the sound trace encoded in neural sensory memory. MMN has recently become one of favorable neurophysiological biomarkers in schizophrenia. A definite reduction in early schizophrenia was evident in MMN to duration (dMMN) but not frequency deviants. The impaired dMMN might be caused by the dysfunction of TWI. Importantly, dMMN might provide the prediction of conversion to psychosis when dMMN was recorded in clinically at risk‐mental state (ARMS) individuals. The main MMN generator is located in the superior temporal gyrus (STG) . Several neuroimaging studies have revealed structural abnormalities of STG in schizophrenia. MMN also suggests the impaired function of NMDA receptors. In the postmortem study, DARPP‐32 and calcineurin (CaN) are closely associated with the abnormalities in the dopamine and glutamate systems in schizophrenia. The DARPP‐32 and CaN‐related pathogenesis in schizophrenia were more severe in the STG than previously found in the prefrontal cortex. A meta‐analysis of MMN abnormality in schizophrenia exhibited a large effect size of 0.95 (CI=0.85‐1.04) . Based on the above findings, dMMN impairment in the STG is expected to be a promissing biomarker in schizophrenia.
Epidemiological evidence such as twin studies have indicated high heritability for suicide. Since higher lethality of suicidal behavior is deemed to increase familial liability to suicidal behavior, we believe biological research of completed suicide is crucial to elucidate the genetic architecture underlying suicidal behavior. However, genetic research of complete suicide lags behind due to difficulty in obtaining samples from individuals who died by suicide. Under the difficult situation, we now retain one of the largest genomic samples of completed suicide thanks to bereaved family’s cooperation, and recently performed a first GWAS for completed suicide in the Japanese population. Here, we review the latest findings regarding the genetics of suicide mainly with introducing our own results.
It is widely acknowledged that over 90% of those who committed suicide had a psychiatric diagnosis at the time of death. Hence, suicide prevention is one of the top priorities for psychiatrists to address. However, the field of neuropsychiatry lacks biomarkers that could serve as objective indicators for diagnosis and severity assessment. Suicide is a complicated result caused by multiple factors and can be difficult to accurately predict. In recent years, research based on data analysis using artificial intelligence (AI) supported by machine learning has attracted attention in the medical field. In suicide prevention, an unprecedented amount of research using machine learning have been reported. In this paper, we will introduce new information about suicide prevention using machine learning as well as comment on the ethical, legal, and social implications (ELSI) that should be considered for implementation into society. There are no potential conflicts of interest to disclose.
The etiology of suicidal behavior is poorly understood while the pathology of various neuropsychiatric disorders has recently been reported to be closely associated with neuroinflammation. There may be subtypes or clinical stages in various neuropsychiatric disorders, on which the inflammatory processes have a profound influence. These processes may account for the underlying mechanism of clinical manifestations such as treatment‐ resistance, severe symptoms, and suicidal behavior observed in neuropsychiatric disorders. The relationship between suicidal behavior and neuroinflammation may shed a new light on the development of new diagnostic and therapeutic strategy for suicidal behavior.
Viral infection during pregnancy has been suggested to increase probability of autism spectrum disorder in offspring. This phenomenon has been modeled in rodents subjected to maternal immune activation (MIA) . Previous studies showed that maternal T helper 17 cells and the effector cytokine interleukin‐17A (IL‐17A) play a central role in MIA‐induced behavioral abnormalities and cortical dysgenesis called cortical patch in offspring. However, it is unclear how IL‐17A acts on fetal brain cells to cause ASD pathologies. To assess the effect of IL‐17A on cortical development, we performed direct administration of IL‐17A into lateral ventricles of fetal mouse brain. We analyzed injected brain focusing on microglia, which express IL‐17A receptors. We found that IL‐17A activated microglia and altered their localization in the cerebral cortex. Our data suggest that IL‐17A activates cortical microglia, which could lead to a series of ASD‐related brain pathology, including excessive phagocytosis of neural progenitor cells in the ventricular zone.