(JHBI TS/第6回) 2021年7月12日(月)16:30



16:30-16:35 Opening Remarks and announcements 

Talk 1: Akiko Uematsu

(Chair:Ryuta Aoki )

16:50 - 17:05

Talk 2: Daisuke Koshiyama

 (Chair:Ryuta Aoki )

17:05 - 17:50

Lecture: Tobias Hauser

(Chair:Sho K. Sugawara ) 

17:50 -

Free discussion between speakers and attendees

Open Discussion (to 18:50 at latest) 



Talk 1:  Akiko Uematsu
UTIDAHM, University of Tokyo; RIKEN BDR

Structural brain deterioration in the course of Schizophrenia: Multi-MRI contrast study

Understanding the pattern of progressive deterioration in the course of schizophrenia would shed light on their pathogenesis. In this talk, we would like to introduce the structural brain features of patients at risk of mental state (ARMS), first episode schizophrenia (FEP), chronic schizophrenia (SCZ), comparing to those of healthy controls (HC). We used multi-contrast MRI data, including T1-, T2-, diffusion-weighted images. We partially adapted HCP pipelines, described in Glasser et al. 2013, for image preprocessing. This pipeline allows us to estimate not only regional thickness, area, volumes but also T1w/T2w ratio cortical Myeline. This pipeline also allows to analyze vertex level (2D), which computationally less cost than voxel level (3D).  Integrating those data and diffusion tensor metrics derived from diffusion weighted images gives deeper interpretation on the findings from structural MRI data at neural level. The findings of current our study showed that there were various cortical regions that significantly different from HC group in all the stages. Especially superior temporal gyrus and superior longitudinal fasciculus, which stem from or pass superior temporal gyrus, showed linear deterioration as the stage got worse. It suggested superior temporal gyrus should be the key to understand the onset mechanism of Schizophrenia.


Talk 2: Daisuke Koshiyama
Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo

Mismatch negativity in schizophrenia

Patients with schizophrenia show various symptoms such as positive symptoms (e.g., hallucination, delusion), negative symptoms (e.g., affective flattening), and cognitive dysfunction. The auditory mismatch negativity (MMN) is automatically evoked in response to an unattended sound that is a rare deviant stimulus after repetitive standard stimuli. MMN is a translatable electroencephalographic biomarker, and the amplitude is reduced in patients with schizophrenia. Our group showed that MMN reduction is robustly associated with cognitive and psychosocial disability in patients with schizophrenia. Furthermore, we identified estimated source locations of MMN and their connectivity among the source locations in the temporo-frontal brain regions in patients with schizophrenia. Additionally, we applied a novel paradigm to deconstruct the subcomponent adaptation and deviance detection processes that are presumed to underlie the MMN. Patients with schizophrenia showed selective impairments in deviance detection but no impairment in adaptation to repeated tones. These findings accelerate the investigation of the pathophysiology and development of new treatments in schizophrenia. Future studies are needed to show more precise source locations of MMN and investigate the background of the altered deviance detection process in patients with schizophrenia.


Lecture: Tobias Hauser
Max Planck UCL Centre for Computational Psychiatry & Ageing Research; Welcome Trust Center, University College London

Do we need a developmental computational psychiatry?

Many psychiatric disorders arise during adolescence, a time when the brain undergoes fundamental reor-ganisation. However, it is unclear whether and how the emergence of mental health problems is linked to aberrant neurocognitive development. In my talk, I will discuss why it is important to understand (aber-rant) cognitive and brain development if we want to better understand how mental health problems arise. I will present findings showing how psychiatric traits are linked to adolescent brain myelination, illustrate why computational neuroscience approaches could help facilitate our understanding of the underlying mechanisms, and show how crowd-sourcing can be used to address these challenges.