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±3.50
0.819±.058
0.796±.016
GAT
83.04±8.70
0.839±.147
0.816±.095
BrainNetCNN
78.44±7.57
0.794±.092
0.762±.075
BrainGNN
74.23±10.56
0.735±.122
0.594±.344
LUMINA(ours)
84.66±9.53
0.861±.097
0.849±.103
ABIDE
GCN
78.19±3.80
0.839±.040
0.775±.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±3.6
0.625±.054
0.618±.071
LUMINA w/o B/R
✓
✓
58.9±4.8
0.584±.053
0.585±.045
LUMINA w/o D/L
✓
✓
60.9±2.8
0.608±.040
0.619±.031
LUMINA w/o N/G
✓
✓
62.7±4.9
0.622±.048
0.602±.064
Table 2: Ablation study of LUMINA
Figure 2: Post-hoc analysis of ADHD and ASD via the LUMINA framework. Top 1% ( ≈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
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.
Rui Dong, Zitong Wang, Jiaxing Li +2
School of Computer Science and Engineering, Southeast University
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due to complex time-varying disruptions in brain connectivity. Functional MRI (fMRI) provides a powerful non-invasive modality for identifying functional alterations. Existing deep learning (DL) studies employ diverse neuroimaging features; however, static functional connectivity remains widely used, whereas dynamic connectivity modeling is comparatively underexplored. Moreover, many DL models lack interpretability. In this work, we propose D-GATNet, an interpretable temporal graph-based framework for automated ADHD classification using dynamic functional connectivity (dFC). Sliding-window Pearson correlation constructs sequences of functional brain graphs with regions of interest as nodes and connectivity strengths as edges. Spatial dependencies are learned via a multi-layer Graph Attention Network, while temporal dynamics are modeled using 1D convolution followed by temporal attention. Interpretability is achieved through graph attention weights revealing dominant ROI interactions, ROI importance scores identifying influential regions, and temporal attention emphasizing informative connectivity segments. Experiments on the Peking University site of the ADHD-200 dataset using stratified 10-fold cross-validation with a 5-seed ensemble achieved 85.18% +_5.64 balanced accuracy and 0.881 AUC, outperforming state-of-the-art methods. Attention analysis reveals cerebellar and default mode network disruptions, indicating potential neuroimaging biomarkers.
Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed
Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
Large Language Models have achieved remarkable success in language understanding and reasoning, and their multimodal extensions enable comprehension of images, video, and audio. Inspired by this, foundation models for brain functional connectivity networks derived from resting-state fMRI have shown promise in clinical tasks. However, existing methods do not align FCNs with the text modality, limiting the ability of LLMs to directly understand FCNs. To address this, we propose FCN-LLM, a framework that enables LLMs to understand FCNs through graph-level, multi-task instruction tuning. Our approach employs a multi-scale FCN encoder capturing brain-region, functional subnetwork, and whole-brain features, projecting them into the semantic space of LLM. We design multi-paradigm instruction tasks covering 19 subject-specific attributes across demographics, phenotypes, and psychiatric conditions. A multi-stage learning strategy first aligns FCN embeddings with the LLM and then jointly fine-tunes the entire model to capture high-level semantic information. Experiments on a large-scale, multi-site FCN database show that FCN-LLM achieves strong zero-shot generalization on unseen datasets, outperforming conventional supervised and foundation models. This work introduces a new paradigm for integrating brain functional networks with LLMs, offering a flexible and interpretable framework for neuroscience.