Identification and simulation of task-state brain functional activity linking to behavioral symptoms
Poster No:
439
Submission Type:
Abstract Submission
Authors:
Yunman Xia1, Songjun Peng2, Changsong Zhou2, Spase Petkoski3, Wenlian Lu4, JianFeng Feng1, Juergen Dukart5, Gunter Schumann1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Hong Kong Baptist University, Hong Kong, Hong Kong, 3Université Aix-Marseille, Marseille, France, 4Fudan University, Shanghai, China, 5Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7), Research Center Jülich, Jülich, Germany
First Author:
Yunman Xia
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Co-Author(s):
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Juergen Dukart
Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7), Research Center Jülich
Jülich, Germany
Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7), Research Center Jülich
Jülich, Germany
Gunter Schumann
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Extensive research has focused on understanding the pathological mechanisms underlying psychiatric symptoms and developing effective clinical treatments1,2. However, due in part to the complexity of behavioral symptoms and the limitations of neuroimaging techniques, there remains a significant gap between the neuroimage phenotypes of psychiatric disorders and clinical intervention strategies. Here, we leverage a large cohort of neuroimage and behavior data, along with a neuronal-level whole-brain computational modeling platform (Digital Twin Brain, DTB)3, to identify and simulate brain phenotypes linking to behavioral symptoms, as well as establish causal links between macroscale functional network and clinical treatment.
Methods:
Firstly, we utilized data from the IMAGEN cohort (N = 1,050, 19 years) to identify brain phenotypes linked to multiple behavioral symptoms. Task-specific functional connectomes were estimated using fMRI data from Monetary Incentive Delay (MID)4 and Stop signal task (SST)5. Behavioral symptoms were assessed using the Development and Well-Being Assessment6 and Strengths and Difficulties Questionnaire7. We then employed the connectome-based predictive model8 to predict six distinct symptoms based on task-state functional connectivity (FC). The summed strength of identified FCs was termed as NP factor. Secondly, we validated behavioral implications of NP factors in an independent clinical sample (STRATIFY/ESTRA dataset, N=513, 18–26 years, case/control=288/225). Thirdly, we developed task-state DTB models at 100 million neurons to simulate NP factor in both patients and healthy controls (Fig.1a and b). The simulation performance was evaluated by calculating Pearson's r between simulated and empirical BOLD signals or NP factors. Fourth, we investigated potential causal links between activation of glutamate receptors and NP factor in the DTB, then reverted NP factor from the diseased to healthy states. Lastly, we used an independent pharmacological dataset with repeated measures in healthy individuals to validate identified causal relationships.
Results:
We firstly identified two distinct NP factors associated with both externalizing and internalizing symptoms (Fig.1c). The positive factor was mainly located in regions including dorsal posterior cingulate cortex (PCC), inferior frontal gyrus (IFG), dorsolateral prefrontal cortex (DLPFC), and cerebellum. The negative factor was found in similar regions, including dorsal PCC, DLPFC, IFG, angular gyrus, and cerebellum. Only the negative NP factor exhibited a significant group difference in the independent validated sample (Fig.1d), thus it was targeted in further simulations. Based on individual T1, DTI and fMRI data, we developed task-state DTB models for two patients (depression and alcohol use disorder) and two healthy controls. For each individual, the DTB models successfully reproduced empirical BOLD signal of task-relevant regions for MID and SST (mean r = 0.9, Fig.1e). Simulated NP factors derived from simulated BOLD signals closely matched empirical values (r = 0.60-0.84), and also showed differences between patients and healthy controls (Fig.1e). Notably, the effects of manipulating AMPA in the DTB models differed between healthy and diseased models, that is increasing AMPA activity reduced NP factor in the healthy model but increased it in the diseased model (Fig.1f). The results from pharmacological data further supported our findings. Healthy individuals under ketamine condition exhibited similar reductions in the same FCs compared to placebo (Fig.1g), underscoring the role of glutamate system in driving neuroimage phenotypes linked to behaviors.
Conclusions:
Our findings demonstrate the utility of integrating macroscale brain functional connectome, computational modeling, and pharmacological studies. These findings offer new insights into the neurobiological mechanisms of psychiatry and suggest potential targets for clinical interventions.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Reward and Punishment
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Physiology, Metabolism and Neurotransmission:
Pharmacology and Neurotransmission
Keywords:
Computational Neuroscience
Glutamate
Modeling
Pharmacotherapy
Psychiatric
1|2Indicates the priority used for review
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Provide references using APA citation style.
Xie, C. (2023), 'A shared neural basis underlying psychiatric comorbidity', Nature Medicine, 29(5), Article 5.
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Bari, A. (2013), 'Inhibition and impulsivity: behavioral and neural basis of response control', Progress in Neurobiology,108, 44–79
Goodman, R. (2000), 'The Development and Well-being Assessment: description and initial validation of an integrated assessment of child and adolescentpsychopathology', Journal of Child Psychology and Psychiatry, 41, 645–655
Goodman, R. (1997), 'The Strengths and Diffi culties Questionnaire: a research note', Journal of Child Psychology and Psychiatry, 38, 581–586
Shen, X. (2017), 'Using connectome-based predictive modeling to predict individual behavior from brain connectivity', Nature Protocols, 12(3), Article 3