LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN

Minkyung Cha; Jooyoung Bae; Jaewon Jung; Ping Shu Ho; Ka Chun Cheung; Namjoon Kim

cs.LGMar 5, 2026

LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN

Authors: Minkyung Cha, Jooyoung Bae, Jaewon Jung, Ping Shu Ho, Ka Chun Cheung, Namjoon Kim

Organizations: Seoul National University, Seoul, Republic of Korea · NVIDIA AI Technology Center HK, Hong Kong

Abstract

Functional Magnetic Resonance Imaging(fMRI) has now become a classic way for measuring brain activity, and recent trend is shifting toward utilizing fMRI brain data for AI-driven diagnosis. Given that the brain functions as not a discrete but interconnected whole, Graph-based architectures represented by Graph Convolutional Network(GCN) has emerged as a dominant framework for such task, since they are capable of treating ROIs as dynamically interconnected nodes and extracting relational architecture between them. Ironically, however, it is the very nature of GCN's architecture that acts as an obstacle to its performance. The mathematical foundation of GCN, effective for capturing global regularities, acts as a tradeoff; by smoothing features across the connected nodes repeatedly, traditional GCN tend to blur out the contrastive dynamics that might be crucial in identifying certain neurological disorders. In order to break through this structural bottleneck, we propose LUMINA, a Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis. Our model is a Quad-Stream GCN that employs a bipolar RELU activation and a dual-spectrum graph Laplacian filtering mechanism, thereby capturing heterogeneous dynamics that were often blurred out in conventional GCN. By doing so, we can preserve the diverse range and characteristics of neural connections in each fMRI data. Through 5-fold cross validation on the ADHD200(N=144) and ABIDE(N=579) dataset, LUMINA demonstrates stable diagnostic performance in two of the most critical neurodevelopmental disorder in childhood, ADHD and ASD, outperforming existing models with an accuracy of 84.66% and 88.41% each.

Figures & tables

Figure 1: Overview of the LUMINA framework. Unlike conventional GCN(Top), our model(Bottom) incorporates a quad-stream input to capture diverse connectivity patterns. After processing by the Multi-Scale Regional Mixer and multiple GCN streams, they are passed through attention mechanism for final classification.

Dataset	Model	Accuracy (%)	AUC	F1-Score
ADHD-200	GCN	$81.71\pm 3.50$	$0.819\pm.058$	$0.796\pm.016$
	GAT	$83.04\pm 8.70$	$0.839\pm.147$	$0.816\pm.095$
	BrainNetCNN	$78.44\pm 7.57$	$0.794\pm.092$	$0.762\pm.075$
	BrainGNN	$74.23\pm 10.56$	$0.735\pm.122$	$0.594\pm.344$
	LUMINA(ours)	$\mathbf{84.66\pm 9.53}$	$\mathbf{0.861\pm.097}$	$\mathbf{0.849\pm.103}$
ABIDE	GCN	$78.19\pm 3.80$	$0.839\pm.040$	$0.775\pm.046$

Table 1: Classification performance of LUMINA and four baseline models. The best results are highlighted in bold.

Variant	Components			Metrics
	B/R	D/L	N/G	Acc (%)	AUC	F1
Dataset: ADHD-200
Baseline				$63.4\pm 3.6$	$0.625\pm.054$	$0.618\pm.071$
LUMINA w/o B/R		✓	✓	$58.9\pm 4.8$	$0.584\pm.053$	$0.585\pm.045$
LUMINA w/o D/L	✓		✓	$60.9\pm 2.8$	$0.608\pm.040$	$0.619\pm.031$
LUMINA w/o N/G	✓	✓		$62.7\pm 4.9$	$0.622\pm.048$	$0.602\pm.064$

Table 2: Ablation study of LUMINA

Figure 2: Post-hoc analysis of ADHD and ASD via the LUMINA framework. Top 1% ( $\approx 61$ ) connections with highest IGs are shown as lines, with top 5 highlighted in distinct colors. The rest are visualized on a colorscale from blue ( $-1$ ) to red ( $+1$ ).

Rank	R	IG	Node A (Network)	Node B (Network)
Top 5 ADHD Connections
1	0.67	0.0081	Right Parietal Operculum Cortex (SomatoMotor B)	Left Middle Temporal Gyrus; temporooccipital part (Control C)
2	0.63	0.0056	Right Middle Temporal Gyrus; temporooccipital part (Control C)	Left Middle Temporal Gyrus; temporooccipital part (Control C)
3	0.53	0.0051	Right Parietal Operculum Cortex (SomatoMotor B)	Right Middle Temporal Gyrus; temporooccipital part (Control C)
4	0.61	0.0051	Right Inferior Temporal Gyrus; posterior division (Control C)	Left Middle Temporal Gyrus; temporooccipital part (Control C)
5	0.75	0.0048	Right Postcentral Gyrus (SomatoMotor A)	Left Superior Temporal Gyrus; anterior division (TempPar)

Table 3: Top 5 Functional Connections for ADHD and ASD Groups

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BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis

Rui Dong, Zitong Wang, Jiaxing Li, Weihuang Zheng+1

School of Computer Science and Engineering, Southeast University

Brain Network Analysis Graph Neural Networks Multimodal Graph Learning LLMs for Graph Learning Text-Attributed Graph Learning

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Graph Neural Networks (GNNs) have been widely used in diverse brain network analysis tasks based on preprocessed functional magnetic resonance imaging (fMRI) data. However, their performances are constrained due to high feature sparsity and inherent limitations of domain knowledge within uni-modal neurographs. Meanwhile, large language models (LLMs) have demonstrated powerful representation capabilities. Combining LLMs with GNNs presents a promising direction for brain network analysis. While LLMs and MLLMs have emerged in neuroscience, integration of LLMs with graph-based data remains unexplored. In this work, we deal with these issues by incorporating LLM's powerful representation and generalization capabilities. Considering great cost for directly tuning LLMs, we instead function LLM as enhancer to boost GNN's performance on downstream tasks. Our method, namely BLEG, can be divided into three stages. We firstly prompt LLM to get augmented texts for fMRI graph data, then we design a LLM-LM instruction tuning method to get enhanced textual representations at a relatively lower cost. GNN is trained together for coarsened alignment. Finally we finetune an adapter after GNN for given downstream tasks. Alignment loss between LM and GNN logits is designed to further enhance GNN's representation. Extensive experiments on different datasets confirmed BLEG's superiority.Code can be available at https://github.com/KamonRiderDR/BLEG.

Abstract

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