(JHBI TS/Final Day1) 2023年11月8日(水)16:00


お知らせ:Dr.Konrad Wagstyl のトークが中止となりました。時間を繰り上げて開催いたします。

Chair:Akiko Uematsu and Akitoshi Ogawa

16:00-16:05 Opening Remarks and announcements 

Talk 1: Masaaki Shimizu (Tokyo Medical and Dental University)  


Talk 2:Yinghan Zhu (The University of Tokyo)


=中止= Lecture 1: Konrad Wagstyl (University College London)

16:35-17:20 Lecture 2:Mona Garvert (Julius-Maximilians-University Würzburg)
17:20- Free discussion between speakers and attendees
Open Discussion 



Talk 1:  Masaaki Shimizu
Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University

Effects of Methylphenidate on the intrinsic neural timescale in ADHD

In humans, it has been reported that different brain regions exhibit distinct timescales for information processing. These timescales are longer as information processing progresses from lower regions, known as unimodal regions, to higher-order regions, referred to as transmodal regions. Recent research has shed light on a potential connection between intrinsic neural timescales and psychiatric disorders.One such disorder is Attention-Deficit Hyperactivity Disorder (ADHD), characterized by core symptoms of inattention, hyperactivity, and impulsivity, which has been linked to abnormalities in information processing, including sensory inputs. This study utilizes intrinsic neural timescales to estimate the processing timescale of input information in each brain region before and after administering methylphenidate, a standard ADHD pharmacotherapy. The investigation aimed to discern whether methylphenidate influences information processing timescales and whether such changes contribute to improvements in symptoms, such as attention and cognitive functions. Our findings revealed significant reductions in timescales during methylphenidate administration in unimodal areas compared to the non-medicated state. These results suggest that alterations in information processing may play a role in the mechanism of methylphenidate's effects in treating ADHD, providing evidence for a potential relationship between changes in intrinsic neural timescales and ADHD symptoms.


Talk 2:  Yinghan Zhu
Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo 

Using Brain Structural Neuroimaging Measures to Predict Psychosis Onset for Individuals at Clinical High-Risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). We built a classifier using T1-weighted structural brain MRI scans from 1,165 individuals at CHR (CHR-PS+, n=144; CHR-PS-, n=793; and CHR-UNK, n=228), and 1,029 HCs obtained from 21 sites. The accuracy on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis.


Lecture 1:  Konrad Wagstyl
the Wellcome Centre for Human Neuroimaging, University College London
Linking cortical microstructure to in vivo measures of cortical structure in health and disease
The cerebral cortex exhibits a regionally varying microstructure which cannot be readily resolved using in vivo MRI. Nevertheless, cortical microstructure underpins many of the regional, developmental and pathological changes we can measure in vivo. I will first present work done to create an atlas of cortical gene expression bridging the gap between micro and macro scales of cortical structure. I will then demonstrate the application and clinical translation of analyses of cortical microstructure to the detection of subtle cortical lesions causing epilepsy in the Multicentre Epilepsy Lesion Detection project.
Lecture 2:  Mona Garvert
Julius-Maximilians-University Würzburg
Neural representations underlying flexible behaviour
The world is replete with statistical structure and often similar cause-effect relations hold across related experiences. Representing knowledge about relationships between events therefore facilitates goal-directed behaviour, because it enables the generalisation of information across related states. However, if generalisation depends on distances between states in a representational space, then it is critical that the exact metric underlying this representation is appropriate for the task at hand. Here, I will present data from experiments that combine virtual reality with computational modeling and functional magnetic resonance imaging (fMRI) to investigate how humans build and then use cognitive maps to infer reward values that were never directly experienced. Computational modelling suggests that humans form spatial maps of stimuli in the virtual environment and can use this representation for generalisation in a subsequent choice task. Importantly, choice is best predicted by generalisation over a map which combines objective Euclidean distances between stimuli with subjective distances experienced during exploration. Both the objective and the subjective maps as well as their conjunction are represented in the hippocampal-entorhinal formation and the orbitofrontal cortex flexibly adjusts the relevant task dimension. Together, this demonstrates that humans extract relational knowledge from their experiences and store it in hippocampal-entorhinal maps, which are then used to generalise across related states for novel inference.