Abstract:

Precision functional magnetic resonance imaging (pfMRI) refers to a data acquisition strategy that collects large amounts of fMRI data in single individuals. Compared with traditional fMRI research, which collects a small amount of data for each participant and then reveals the underlying brain mechanisms of cognitive process or the shared brain function features of a specific population through the group average, the advantage of this method is that it can reveal the individual brain features, so it has been increasingly recognized and applied by researchers.

Classical pfMRI datasets usually contain data from multiple modalities, such as functional MRI data(resting state, task state), structural MRI data(e.g., T1-weighted images, T2-weighted images), gene expression, etc. Currently, most studies only use resting-state data. There is no consensus on the scanning time required by the pfMRI. Based on the existing studies, it can be found that its requirements of data amount vary greatly with brain regions, metrics, and times of sessions/runs. Summarizing the existing studies, it can be found that the scan time of the resting-state data ranged from 0.4 hours to 32 hours, and the amount of data ranged from 565 volumes to 79360 volumes. In terms of data quality, the pfMRI puts forward higher requirements. For example, in classical studies, volumes with frame-wise displacement greater than 0.2mm are usually censored in preprocessing, which reflects a stricter control of head motion noise.

Since the pfMRI was proposed, numerous studies (33 studies since 2020 alone) have used this method to reveal the individualized functional network organization from different perspectives, mainly including the following six aspects: (1) In terms of individual differences in functional network organization, researchers have revealed interindividual variability in functional network organization from different data analysis perspectives (node-based functional connectivity and edge-based functional connectivity) and different brain regions (cerebral cortex and subcortical structures), and found that the interindividual variability in the association network was the largest. Moreover, the researchers further proposed the concept of network variation, which represents regions that differ in the individual from the group mean functional connectivity pattern. It was found that network variation existed in all individuals, but there was also interindividual variation in the location, size, and network assignment of network variation. In addition, these network variants showed hemispheric asymmetry and included two types of boundary expansion and ectopic invasion. (2) Based on the findings of individual differences, researchers have successfully identified individuals through the features of functional network organization in the resting state and task state. (3) In addition, some researchers have used pfMRI to conduct more fine functional localization of the default mode network and inferior frontal gyrus and found that there are sub-regions responsible for different functions. (4) In terms of network hubs, the pfMRI was used to identify network hubs in individuals, which excludes the alternative interpretation for network hubs in traditional fMRI studies and proves the interindividual variability of network hubs. (5) In terms of individual functional network development and plasticity, the researchers compared the functional network organization of participants aged 8~23 years and revealed the features of functional network organization among participants of different age groups. It was also found that the characteristics of network organization were affected by hormone levels and had plasticity. (6) In clinical application, pfMRI was found to be able to identify individual stimulation targets during transcranial magnetic stimulation (TMS) treatment, and targeted functional network stimulation (TANS) was proposed. In addition, pfMRI has been used to explore the functional network organization features of patients with posttraumatic stress disorder (PTSD) and perinatal stroke.

In summary, pfMRI has made a wealth of research findings in exploring individual differences and individual identification in functional network organization, functional localization of local brain regions, identification of individual network hubs, development and plasticity of individual functional networks, and clinical application. It exhibits unique advantages compared to group-average fMRI studies in Individualized brain function research. It is of great significance to reveal the basic principles of brain cognition and the diagnosis and treatment of major brain diseases. Future research should further explore the relationship between the features of individual functional networks and behavioral performance, and try to overcome the limitation of long scanning time by improving data analysis methods (such as introducing multi-session hierarchical Bayesian model (MS-HBM)) or fMRI imaging technologies (such as multi-echo fMRI and multi-band fMRI), and attempt to introduce this method into task-state fMRI and multimodal research.

Key words: precision functional magnetic resonance imaging, brain functional network, individual difference, functional connectivity