Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates (2110.03453v1)

Abstract: Learning how to estimate a connectional brain template(CBT) from a population of brain multigraphs, where each graph (e.g., functional) quantifies a particular relationship between pairs of brain regions of interest (ROIs), allows to pin down the unique connectivity patterns shared across individuals. Specifically, a CBT is viewed as an integral representation of a set of highly heterogeneous graphs and ideally meeting the centeredness (i.e., minimum distance to all graphs in the population) and discriminativeness (i.e., distinguishes the healthy from the disordered population) criteria. So far, existing works have been limited to only integrating and fusing a population of brain multigraphs acquired at a single timepoint. In this paper, we unprecedentedly tackle the question: Given a baseline multigraph population, can we learn how to integrate and forecast its CBT representations at follow-up timepoints? Addressing such question is of paramount in predicting common alternations across healthy and disordered populations. To fill this gap, we propose Recurrent Multigraph Integrator Network (ReMI-Net), the first graph recurrent neural network which infers the baseline CBT of an input population t1 and predicts its longitudinal evolution over time (ti > t1). Our ReMI-Net is composed of recurrent neural blocks with graph convolutional layers using a cross-node message passing to first learn hidden-states embeddings of each CBT node (i.e., brain region of interest) and then predict its evolution at the consecutive timepoint. Moreover, we design a novel time-dependent loss to regularize the CBT evolution trajectory over time and further introduce a cyclic recursion and learnable normalization layer to generate well-centered CBTs from time-dependent hidden-state embeddings. Finally, we derive the CBT adjacency matrix from the learned hidden state graph representation.

• The paper introduces ReMI-Net, a novel method that integrates RNNs and GCNs to forecast the evolution of brain connectivity templates.
• It employs view normalization, cyclic recursion, and a graph-based recurrent block to effectively process multigraph neuroimaging data.
• Results demonstrate ReMI-Net outperforms standard methods in predicting connectional brain templates, enhancing biomarker detection in Alzheimer’s research.

Analysis of Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates

The paper "Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates" presents a novel approach to neuroimaging data analysis, specifically aiming to forecast the evolution of brain connectivity patterns over time. This objective is achieved through a proposed method called the Recurrent Multigraph Integrator Network (ReMI-Net), which is introduced as a convergence of recurrent neural networks (RNNs) and graph convolutional networks (GCNs). The primary focus of the paper is to develop and validate ReMI-Net for predicting the change in connectional brain templates (CBTs) from baseline multigraph data, a task that has implications for understanding the progression of neurological disorders such as Alzheimer's disease (AD).

Methodology and Experiments

The ReMI-Net architecture integrates and predicts the evolution of CBTs over time by processing a baseline population of brain multigraphs. Each multigraph represents different views of brain connectivity, and the ReMI-Net architecture leverages these views to initially derive a CBT at the baseline timepoint and then predict its longitudinal evolution. The architecture incorporates several unique components:

1. View Normalization Layer: Adjusts the scale and distribution differences across the views to mitigate bias.
2. Cyclic Recursion: Ensures each new prediction benefits from all previous ones through recurrent message passing.
3. Graph-Based Recurrent Block: Uses a novel message passing network to perform graph convolutional operations.

In their experiments, the authors evaluate the ReMI-Net by comparing its performance to DGN, a current standard method for integrating a population of brain multigraphs. The experiments are conducted on both a real-world Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and simulated data, presenting strong results at the baseline and follow-up timepoints.

Results

The numerical results reflect that ReMI-Net outperformed competing methods in terms of CBT centeredness and representativeness across both simulated and real-world datasets. The authors highlight the mean absolute errors in predicted CBTs, demonstrating that ReMI-Net provides a superior representation of brain connectivity changes in pathologically significant regions. Furthermore, the paper presents analyses on the reproducibility of biomarkers detected by ReMI-Net, noting an improvement in identifying key regions known to be implicated in Alzheimer’s disease as opposed to other methods.

Implications and Future Directions

The development of ReMI-Net offers significant potential for advancing both theoretical and clinical neuroscience. The application of graph neural networks in this context introduces a powerful tool for understanding network dynamics over time. Practically, this can translate into more precise identification of biomarkers for neurodegenerative diseases, potentially assisting in early diagnosis and intervention planning.

Further developments and applications of ReMI-Net may encompass larger datasets and diverse populations, potentially across different imaging modalities. The extension to various neurological and psychiatric disorders could yield generalized insights into connectivity-based biomarkers. Another avenue for future research could involve integrating more sophisticated recurrent network architectures, such as long short-term memory (LSTM) cells, to address long-range dependencies in longitudinal brain graph modeling.

ReMI-Net contributes meaningful advancements in network neuroscience, offering a methodologically robust approach to addressing the challenges associated with longitudinal brain data. By advancing the capabilities of neural network architectures specific to the brain connectomics domain, this paper sets a foundation for future explorations in dynamic neuroscience modeling.