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Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called “Decentralized ComBat” which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets. 

The functional connectivity within a specific set of brain networks (or domain) can assume different configurations known as domain states that change with time. Recently, we proposed an information theoretical framework that models the finite set of domain states as elements of an alphabet. Significant bits of information have been found to be shared among domains, but specific domain codification was not explored. This work describes a method to identify code words used to transmit and receive information between the cerebrum and the cerebellum based on dynamic domain connectivity estimated from functional magnetic resonance imaging (fMRI). Following the theory of jointly typical sets, the developed method identifies the codeword length and the specific combination of domain states on each codeword. Resting state fMRI data was taken from 121 subjects with no significant age difference between males and females. Group independent component analysis was utilized to identify important brain networks and group them in a cerebellum and six other domains representing the cerebrum. The amount of information between the cerebellum, the executive control and sensorimotor domains showed a statistically significant number of bits. The proposed method quantified specific temporal sequences of domain states encoded within bits shared between cerebellum and cerebrum.