Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks

Title: Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks
ArXiv: 2510.03351v1
Version: v1
Authors: Song Wang, Zhenyu Lei, Zhen Tan, Jundong Li, Javier Rasero, Aiying Zhang, Chirag Agarwal, University of Central Florida, University of Virginia, Work done at the University of Virginia. Corresponding author:, ,, .
Sections: 16
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## Contents
- 1 Introduction
- 2 Related Work
- 3 ConceptNeuro : Our Framework
  - 3.1 Preliminaries
    - From fMRI to Graphs.
  - 3.2 Concept Generation using Large Language Models
  - 3.3 Structural Encoding for Concept Subgraphs
    - General message passing.
  - 3.4 Concept Bottleneck Classifier
- 4 Experiments
  - 4.1 Experimental Settings
  - 4.2 Experimental Results
- 5 Conclusion
- References
- Appendix A Disorder-related Terms
- Appendix B Generated Concepts (Unfiltered)

## 1 Introduction

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive tool that captures dynamic changes in neural activity through blood-oxygen-level-dependent (BOLD) responses [1]. Functional connectivity (FC), defined as the degree of similarity in BOLD activity across brain regions–particularly during resting-state–has emerged as an important tool to quantify individual differences in cognition [2] and behavior [3, 4], as well as for diagnosing and characterizing brain disorders [5, 6]. However, due to the high dimensionality of FC and the lack of domain-specific constraints, current predictive efforts based on this signal typically yield unreliable models that fail to translate into clinical practice and produce findings with limited interpretability.

To this end, there are two key obstacles in advancing FC-based neuropsychiatric diagnosis. First, existing models fail to integrate existing domain knowledge into the modeling framework. Neuroimaging and psychiatric research already provide rich knowledge about disorder-related regions and networks, yet most learning frameworks treat FC graphs as unstructured input, leaving this information unused and resulting in models that may overfit noise or miss clinically relevant signals. Second, current predictive models lack interpretability, i.e., black-box approaches such as standard graph neural networks (GNNs) may achieve competitive accuracy but provide little insight into disorder-specific connectivity patterns, limiting clinical trust and scientific discovery.

Present work.
To address these challenges, we propose ConceptNeuro, a novel framework that integrates large language models (LLMs) with graph-based concept modeling. Our framework is formulated using concept bottleneck models (CBMs) [7, 8] that implicitly introduce an intermediate layer of human-understandable concepts between raw data representations and downstream predictions.
Under the CBM framework, our approach tackles both obstacles in FC-based neuropsychiatric diagnosis with two novel designs:
(1) LLM-guided concept generation. To incorporate prior neurobiological knowledge, we collect disorder-related terms from established neuroimaging resources (e.g., NeuroQuery [9]), which serve as anchors for generating candidate concepts. Guided by these terms, LLMs automatically produce diverse and disorder-specific functional connectivity concepts that capture clinically meaningful interactions between brain regions.
(2) Connectivity-based concept modeling. To ensure interpretability, we introduce a concept bottleneck layer that mediates predictions through human-understandable connectivity concepts. Specifically, each concept is represented as a relationship between two groups of brain regions, where the connectivity strength is reflected by the edges in the induced subgraph. These subgraphs are then structurally encoded and passed into a concept classifier for disorder prediction.
With these designs, ConceptNeuro is, to our knowledge, the first framework to enable automated concept generation for fMRI-based connectivity analysis. By bridging raw FC data with clinically interpretable representations, our method delivers accurate classification of psychiatric disorders while providing transparent, disorder-specific explanations that align with neurobiological insights.

We validate ConceptNeuro on the task of disorder diagnosis, using fMRI datasets associated with multiple psychiatric disorders. Experimental results demonstrate that ConceptNeuro achieves improved prediction accuracy and inherent interpretability compared to standard black-box approaches. In particular, the selected concepts highlight brain region interactions that align with knowledge of domain experts. Our contributions can be summarized as: 1 we introduce an automated pipeline for generating and filtering clinically meaningful connectivity-based concepts from brain fMRI data; 2 we design a concept-based GNN that integrates these concepts into the classification of brain disorders; and 3 we conduct comprehensive experiments showing that our framework balances predictive performance with interpretability, offering insights into disorder-specific brain connectivity patterns.

## 2 Related Work

This work lies at the intersection of brain network analysis and concept-guided graph neural networks.

Brain Network Analysis. Analyzing brain networks focuses on uncovering the complex patterns of connectivity in the human brain [10, 11, 12]. This line of research has recently attracted significant attention due to its wide range of applications, such as detecting biomarkers for neurological disorders [13], providing insights into cognitive mechanisms [14, 15], and characterizing differences across various brain network types [16]. A central task in this domain is predicting brain-related attributes, including demographic information and cognitive or task-specific states [17, 18].
Graph neural networks (GNNs) have emerged as a dominant methodology for these prediction tasks [19, 10], owing to their strength in modeling structured relational data [20, 21, 22].

Concept-guided Graph Neural Networks.
Concept bottleneck models (CBMs) enhance interpretability by mediating predictions through human-understandable concepts [7, 8]. They have been applied in domains such as radiology and pathology to link predictions with clinically meaningful features [23, 24], though most approaches depend on manually defined concept sets, limiting scalability. Recent advances leverage LLMs to automatically propose candidate concepts [25, 26], reducing annotation costs while maintaining interpretability.
In graph learning, explainability has traditionally relied on post-hoc methods that highlight predictive subgraphs [27, 28], but these offer only local, instance-level insights. To move toward global interpretability, recent work explores embedding concepts into GNN architectures. For example, neuron-level analyses reveal that hidden units can act as detectors of interpretable graph motifs [21], while Graph Concept Bottleneck Models explicitly encode concept relationships as a graph structure to improve transparency [29]. Despite these advances, concept-guided GNNs remain underexplored, with most studies focusing on vision or tabular data rather than graph-structured domains. This motivates frameworks that can automatically generate and integrate domain-specific connectivity concepts for tasks such as neuroimaging-based diagnosis.

## 3 ConceptNeuro : Our Framework

Here, we describe our framework that aims to perform neuropscychiatric diagnosis using interpretable connectivity concepts. To achieve this goal, ConceptNeuro leverages concept generation using LLMs (Sec. [3.2](https://arxiv.org/html/2510.03351v1#S3.SS2)), connectivity-based concept modeling (Sec. [3.3](https://arxiv.org/html/2510.03351v1#S3.SS3)), and training a concept classifier for neuropscychiatric diagnosis (Sec. [3.4](https://arxiv.org/html/2510.03351v1#S3.SS4)). In Fig. [1](https://arxiv.org/html/2510.03351v1#S3.F1), we present an overview of our end-to-end framework.

### 3.1 Preliminaries

Problem Formulation. Let $\mathcal{D}={(G_{i},y_{i})}_{i=1}^{M}$ be the dataset containing $M$ subject samples, where $G_{i}=(V,E,A_{i})$ is the fMRI graph for subject $i$ and $y_{i}\in\mathcal{Y}$ is the disorder label from $N=|\mathcal{Y}|$ classes. In our task, $N$ could be two for binary classification or greater than two for the identification of multiple disorders. Notably, we work on a specific parcellation $V$ (e.g., 100–400 ROIs, depending on the atlas) for each dataset. The subject-specific weighted adjacency $A_{i}\in\mathbb{R}^{|V|\times|V|}$ is derived from ROI time series (details in Sec. [3.1](https://arxiv.org/html/2510.03351v1#S3.SS1)) obtained based on Pearson’s correlation. Let $\mathcal{S}=\{c_{1},\dots,c_{N_{C}}\}$ denote the set of candidate concepts, where concept $c$ is defined as a pair of disjoint region sets $(V_{c}^{1},V_{c}^{2})$ with $(V_{c}^{1},V_{c}^{2})\subseteq V$, accompanied by a *direction prior* $\delta_{c}\in\{-1,+1\}$ indicating hypothesized (negative/positive) connectivity.

#### From fMRI to Graphs.

We follow the minimal preprocessing pipeline for fMRI data  [30, 31], which includes motion correction, B0 distortion correction, and gradient nonlinearities distortion correction. To further minimize motion-related confounds, we apply iterative spatial smoothing, regression of motion parameters, and frame censoring  [32, 33]. After preprocessing, ROI-level fMRI time-series were calculated as the mean of the values of the voxels within the ROI from the selected parcellation scheme. Formally, for each subject $i$, ROI $v$, we demote the extracted time series as $x_{i}(v)\in\mathbb{R}^{T}$, where $T$ is the number of time points.

Figure: Figure 1: Overview of the Proposed Framework. (Left) We first prompt LLMs to generate disorder-specific functional connectivity concepts, which are refined through filtering to remove irrelevant or redundant concepts, resulting in a compact set of $N_{c}$ concepts. (Center) For each subject, we extract subgraphs corresponding to these concepts and encode them together with the subject’s input functional connectivity graph. (Right) Finally, we compute concept scores, which are then passed through a concept bottleneck classifier to perform disorder prediction in an interpretable manner.
Refer to caption: x1.png

### 3.2 Concept Generation using Large Language Models

A key challenge in building interpretable concept-based models is generating a sufficiently rich yet clinically meaningful set of candidate concepts. To this end, we design structured prompts for LLMs that guide them to propose *connectivity concepts* directly tied to functional brain regions. We design the prompts to ask to elicit short, specific features of fMRI connectivity that distinguish a given disorder. Meanwhile, we explicitly instruct the model to avoid features that are not based on functional connectivity (e.g., fractional anisotropy measures) and to restrict outputs to pairs or sets of well-defined brain regions anchored in prior neuroimaging knowledge. Importantly, we collect disorder-related terms from established neuroimaging resources (e.g., NeuroQuery [9]) to provide additional guidance for the LLMs.
For instance, the related terms for anxiety may include amygdala, prefrontal, insula, and cingulate.

Each generated concept follows the pattern of concise phrases such as “reduced connectivity between amygdala and dorsolateral prefrontal cortex” or “hyperconnectivity between posterior cingulate cortex and medial prefrontal cortex.” These are then parsed into structured region sets $(V_{c}^{1},V_{c}^{2})$ using atlas-specific aliases and ontology lookups, ensuring compatibility with downstream graph-based analysis. The motivation behind this design is twofold: i) leveraging LLMs’ ability to synthesize domain knowledge into a broad and diverse candidate pool and ii) enforcing domain-informed constraints so that the resulting concepts remain interpretable, clinically relevant, and directly mappable onto fMRI connectivity data.

To refine the LLM outputs, we apply rule-based filters: we discard concepts that involve fewer than two regions in either set, overlap excessively with existing concepts, or cannot be reliably resolved to atlas-defined regions. The resulting set $\mathcal{S}$ forms a compact yet diverse collection of connectivity-based concepts, which balances coverage across disorders while avoiding redundancy.

Illustrative Example. A concept “hyperconnectivity between amygdala and prefrontal cortex
” maps to $V_{c}^{1}={\textit{`L/R amygdala'}}$ and $V_{c}^{2}={\textit{`L/R ofrontal cortex parcels'}}$. For each subject $i$, we will examine only the edges between these sets to compute a *concept score* and a *subgraph embedding*.

### 3.3 Structural Encoding for Concept Subgraphs

With the concepts obtained from the LLMs’ output for each disorder, we need to extract the subgraph of each concept to be integrated into our concept-guided GNN. Particularly, for a concept $c\in\mathcal{S}$ and subject $i$, the concept subgraph is given by:

$$ $G_{i}^{c}=\big(V_{c}^{1}\cup V_{c}^{2},\ E_{c},\ A_{i}^{c}\big),\quad E_{c}=\{(u,v)\mid u\in V_{c}^{1},\,v\in V_{c}^{2}\},$ (1) $$

with $A_{i}^{c}$ the $|V_{c}^{1}|\times|V_{c}^{2}|$ submatrix of $A_{i}$.
Based on the extracted concept subgraph, we select $N_{c}$ concepts based on their average adjacency strength across subjects.
Formally, for a concept $c\in\mathcal{S}$ with subgraph adjacency $A_{i}^{c}$ for subject $i$, we define its average connectivity score as

$$ $\bar{s}_{c}=\frac{1}{M}\sum_{i=1}^{M}\frac{1}{|E_{c}|}\sum_{(u,v)\in E_{c}}A_{i}^{c}(u,v),$ (2) $$

where $M$ is the number of subjects and $E_{c}$ is the edge set of the concept subgraph.
We then rank all candidate concepts $\{c\}$ by $\bar{s}_{c}$ and retain the top $N_{c}$ concepts to form the final concept set.

To capture higher-order structural patterns within each concept subgraph, we construct initial node features using two components: a one-hot vector indicating the ROI identity in the atlas, and the *average neighbor degree* of the node, computed with networkx, which summarizes the typical degree of its neighbors and thus provides a local connectivity descriptor. Given these initial features, we learn structural embeddings of nodes through an edge-weighted message-passing framework. Formally, the node representation at layer $t$ is updated as:

$$ $\mathbf{h}_{u}^{(0)}\;=\;\bigg[\,\mathbf{e}_{u}\;\bigg\|\;\frac{1}{\deg(u)}\sum_{v\in\mathcal{N}(u)}\deg(v)\,\bigg],$ (3) $$

where $\mathbf{e}_{u}\in\{0,1\}^{|V|}$ is the one-hot vector corresponding to ROI $u$,
$\deg(u)$ is the degree of node $u$,
$\mathcal{N}(u)$ is the neighbor set of $u$,
and $[\cdot\,\|\,\cdot]$ denotes concatenation of feature vectors.

#### General message passing.

For node $u$ at layer $t\!\to\!t{+}1$, the encoding process is described as follows:

$$ $\mathbf{h}_{u}^{(t+1)}=U^{(t)}\!\Big(\mathbf{h}_{u}^{(t)},\;\bigoplus_{v\in\mathcal{N}(u)}M^{(t)}\big(\mathbf{h}_{u}^{(t)},\mathbf{h}_{v}^{(t)},A_{i}(u,v)\big)\Big),$ (4) $$

where $\mathbf{h}_{u}^{(t)}\in\mathbb{R}^{d_{t}}$ is the node state,
$\mathcal{N}(u)$ is the neighborhood of $u$,
$\bigoplus\in\{\sum,\mathrm{mean},\max\}$ is a permutation-invariant aggregator,
$M^{(t)}$ is a learnable message function, $U^{(t)}$ is a learnable update function.
After $T$ layers, we obtain the final subgraph embedding via attention pooling:

$$ $\alpha_{u}=\mathrm{softmax}\!\left(w^{\top}x_{u}^{(T)}\right),\quad\mathbf{h}_{i}^{c}=\sum_{u\in V_{c}^{1}\cup V_{c}^{2}}\alpha_{u}\,\mathbf{h}_{u}^{(T)}.$ (5) $$

This design ensures that both ROI identity and local structural properties contribute to the learned representation, while message passing integrates higher-order connectivity patterns across regions. We denote the structural encoder by $g(\cdot)$, and obtain the concept-specific representation of subject $i$ as
$\mathbf{h}_{i}^{c}=g(G_{i}^{c}),$
where $G_{i}^{c}$ is the concept subgraph for subject $i$.

### 3.4 Concept Bottleneck Classifier

For each subject input $G_{i}$, we obtain its embedding $\mathbf{z}_{i}=g(G_{i})$ using the structural encoder described in Sec. [3.3](https://arxiv.org/html/2510.03351v1#S3.SS3).
We then compute its concept score $s_{i,c}$ for each concept $c$ via dot product:

$$ $s_{i,c}=\mathbf{z}_{i}^{\top}\mathbf{h}^{c}_{i},\quad\forall c\in\mathcal{S}.$ (6) $$

This yields a concept similarity vector $\mathbf{s}_{i}=[s_{i,1},s_{i,2},\ldots,s_{i,N_{c}}]\in\mathbb{R}^{N_{c}}$, where $N_{c}$ is the number of concepts. The vector $s_{i}$ serves as the concept-level representation of subject $i$, which is then passed through a multi-layer perceptron (MLP) classifier to produce the final class logits:

$$ $\mathbf{o}_{i}=\sigma\!\left(\mathbf{W}\cdot\mathbf{s}_{i}+\mathbf{W}_{z}\cdot\mathbf{z}_{i}+\mathbf{b}\right),$ (7) $$

where $\mathbf{s}_{i}\in\mathbb{R}^{N_{c}}$ is the concept score vector of subject $i$,
$\mathbf{W}\in\mathbb{R}^{N\times N_{c}}$ and $\mathbf{W}_{z}\in\mathbb{R}^{N\times d}$ are trainable weight matrices,
$\mathbf{b}\in\mathbb{R}^{N}$ is the bias term,
$\sigma(\cdot)$ is a nonlinear activation function (e.g., Sigmoid). $d$ is the dimension of embeddings.
The final predictive distribution is given by the softmax:

$$ $p(y\mid i)=\frac{\exp\!\left(o_{i,y}\right)}{\sum_{y^{\prime}\in\mathcal{Y}}\exp\!\left(o_{i,y^{\prime}}\right)},\qquad\forall y\in\mathcal{Y}.$ (8) $$

This design ensures that predictions are directly mediated through concept scores, while the MLP provides a flexible mapping from the concept space to the disorder label space.

Classification loss. The primary objective is the standard cross-entropy loss over the predicted class distribution: $\mathcal{L}_{\mathrm{cls}}=-\sum_{i=1}^{M}\log p(y_{i}\mid i),$
where $p(y\mid i)$ is defined in Eq. ([8](https://arxiv.org/html/2510.03351v1#S3.E8)) through the softmax over class logits.

Sparsity on concepts. To encourage the model to rely on a compact set of highly informative concepts, we add an $\ell_{1}$ penalty on the first-layer weights $\mathbf{W}$ of the MLP, which directly control the influence of each concept on the downstream prediction: $\mathcal{L}_{\mathrm{sp}}=\lambda_{\mathrm{sp}}\,\|\mathbf{W}\|_{1},$
where $\lambda_{\mathrm{sp}}$ is a tunable coefficient. This loss promotes sparsity across concepts, making it easier to identify a small subset of clinically meaningful connectivity patterns that drive classification decisions.

Direction-aware constraints. If $\delta_{c}\in\{\pm 1\}$ is provided for a concept $c$, we incorporate this prior into the concept-to-class mapping. Specifically, for concepts labeled with $\delta_{c}=+1$ (hyperconnectivity), we enforce that their learned contribution to the class logits is non-negative; for $\delta_{c}=-1$ (hypoconnectivity), the contribution is enforced to be non-positive.
Practically, we implement this by adding a hinge penalty that punishes sign violations.
This constraint serves as an inductive bias that aligns the learned concept-class associations with domain knowledge.

Overall objective. The final training loss combines all terms:

$$ $\displaystyle\mathcal{L}=\;{-\sum_{i=1}^{M}\log\frac{\exp(o_{i,y_{i}})}{\sum_{y^{\prime}\in\mathcal{Y}}\exp(o_{i,y^{\prime}})}}+\;\lambda_{\text{sp}}{\|\mathbf{W}\|_{1}}+\lambda_{\text{dir}}\sum_{c=1}^{N_{c}}\sum_{j=1}^{N}\Big[\max\!\big(0,\;-\delta_{c}\,\mathbf{W}_{j,c}\big)\Big]^{2},$ (9) $$

This formulation ensures that the learned model is both accurate and interpretable, and grounded in both clinical priors and the parsimony of concept usage.

Summary.
Our framework provides a clinically meaningful bridge between complex fMRI connectivity signals and psychiatric diagnosis by ensuring that predictions are mediated through interpretable connectivity concepts. Unlike black-box models, which often struggle to gain trust in clinical practice, ConceptNeuro produces explanations that are directly anchored in neurobiological constructs familiar to clinicians, such as amygdala–prefrontal connectivity or cingulate–insula interactions. This interpretability enables practitioners to not only verify model decisions but also use them as hypotheses for further investigation into disorder mechanisms.

## 4 Experiments

We organize our experiments around the following research questions: RQ1: Does ConceptNeuro consistently outperform vanilla GNN baselines across multiple architectures and disorder prediction tasks? RQ2: Do the concepts discovered by ConceptNeuro align with domain experts’ understanding of clinically meaningful brain connectivity? RQ3: What patterns emerge when analyzing the distribution of concept importance across subjects, and do these patterns reflect disorder-specific neural signatures? RQ4: How critical are the design choices of ConceptNeuro, such as learned concept weights and inclusion of diverse concept sets, for achieving strong predictive performance?

### 4.1 Experimental Settings

Datasets. We conduct experiments on two fMRI datasets. (1) Resting-state fMRI (rs-fMRI) data from the Adolescent Brain Cognitive Development (ABCD) Study(^1^11[https://abcdstudy.org/](https://abcdstudy.org/)) [34], the largest longitudinal neuroimaging study of brain development in youth in the United States. At baseline, 11,099 participants aged 9–11 years were enrolled. After excluding individuals without usable rs-fMRI scans or those who failed the ABCD quality control procedures, our final sample comprised 7,844 participants. All rs-fMRI data were preprocessed using the standard ABCD pipelines [31]. Cortical regions were parcellated using the Glasser atlas [35], and subcortical regions were defined using the Aseg atlas [36]. Functional connectivity was then estimated as the statistical association between ROI-level rs-fMRI time series.
(2) Human Connectome Project-Development (HCP-D) dataset, which contained data from a total of 1300 youth ranging in age from 5 to 21 years [37]. In the current release (version 2.0), the baseline data from $652$ participants were available. We excluded participants with excessive head motion during scanning, and the final sample included 528 participants [38]. Preprocessed rs-fMRI data using the HCP minimal preprocessing pipelines v3.22 [30] were available. We used the same Glasser atlas for cortical parcellation and applied the same procedure for FC estimation.

Diagnostic Task. In this study, we focus on five psychiatric disorders commonly examined in pediatric populations: obsessive-compulsive disorder (OCD), anxiety, attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder. Diagnostic information was obtained through the parent-report Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children, Computerized Version (K-SADS-COMP) or the Child Behavior Checklist (CBCL) subscales for dimensional assessment [39]. Particularly, for the ABCD dataset, the task is framed as binary classification, where the goal is to determine whether a subject is diagnosed with a given disorder. In contrast, for the HCP-D dataset, we use the raw symptom score as the label for a multi-class classification task. This symptom score ranges from 0 to 10, resulting in an 11-class setup. Importantly, the raw score is not a formal clinical diagnosis; rather, it represents the number of symptoms observed for a specific disorder. As such, it can be interpreted as a proxy for disease severity. For the HCP-D dataset, only four disorders are considered (as the symptom scores of OCD are unavailable).

Baselines. To benchmark our proposed framework, we compare it against several widely used models in graph learning and representation learning. Graph Convolutional Network (GCN) [40] leverages spectral graph convolutions to aggregate neighborhood information. Graph Isomorphism Network (GIN) [41] strengthens expressive power by using injective aggregation functions. Graph Attention Network (GAT) [42] incorporates attention mechanisms to assign learnable weights to neighboring nodes. GraphSAGE [43] introduces an inductive framework that learns aggregation functions to generalize to unseen nodes and graphs.

Implementation Details. The loss weights used in Eq. [9](https://arxiv.org/html/2510.03351v1#S3.E9) are set as one. We use a learning rate of $1\times 10^{-3}$ and weight decay of $10^{-4}$. We use GPT-4.1 as the LLM for generating concepts. We employ a 2-layer GNN with a hidden dimension of 64, where each layer is followed by BatchNorm and ReLU activation, and global mean pooling is used to obtain graph embeddings. Regularization is applied through node dropout with probability $0.5$ and weight decay of $10^{-4}$. The Adam optimizer is used with a learning rate of $1\times 10^{-3}$. Training is performed for $500$ epochs with $100$ balanced mini-batches per epoch, each containing up to 16 positive and 16 negative samples. Validation is conducted every 5 epochs, and early stopping patience is set to 20. All experiments are conducted on a NVIDIA A6000 GPU with 48GB of memory.

### 4.2 Experimental Results

Here, we discuss experimental results that answer key questions highlighted at the beginning of this section (RQ1-RQ4).

**Table 1: Mean accuracy (%) on disorder classification tasks on the ABCD dataset, with standard error across five random seeds. We observe that ConceptNeuro-augmented GNN architectures consistently outperform their vanilla counterparts across all five classes.**
| GNN Architecture | Method | Anxiety | OCD | ADHD | ODD | Conduct |
| --- | --- | --- | --- | --- | --- | --- |
| GCN | Vanilla | 61.2$\pm$1.7 | 70.1$\pm$1.1 | 57.8$\pm$1.8 | 65.5$\pm$1.3 | 69.3$\pm$1.5 |
| ConceptNeuro | 65.8$\pm$1.0 | 76.1$\pm$1.7 | 62.6$\pm$1.3 | 67.5$\pm$1.9 | 72.3$\pm$1.2 |  |
| GAT | Vanilla | 61.0$\pm$1.4 | 69.8$\pm$1.2 | 57.2$\pm$1.9 | 65.1$\pm$1.3 | 68.7$\pm$2.0 |
| ConceptNeuro | 64.5$\pm$1.1 | 74.9$\pm$1.5 | 62.0$\pm$1.7 | 66.9$\pm$1.3 | 71.4$\pm$1.6 |  |
| GraphSAGE | Vanilla | 61.5$\pm$1.8 | 70.5$\pm$1.3 | 58.1$\pm$1.7 | 66.2$\pm$2.0 | 69.0$\pm$1.4 |
| ConceptNeuro | 65.0$\pm$1.6 | 75.3$\pm$1.2 | 62.2$\pm$1.9 | 67.1$\pm$1.2 | 71.8$\pm$1.7 |  |
| GIN | Vanilla | 62.7$\pm$1.3 | 71.0$\pm$1.4 | 61.4$\pm$1.1 | 67.9$\pm$1.8 | 73.2$\pm$1.0 |
| ConceptNeuro | 64.9$\pm$0.9 | 72.5$\pm$1.0 | 63.8$\pm$1.4 | 69.7$\pm$1.2 | 75.9$\pm$0.9 |  |

RQ1) ConceptNeuro outperforms Vanilla Graph Neural Networks. Table [1](https://arxiv.org/html/2510.03351v1#S4.T1) reports the accuracy of different GNN architectures on five disorder classification tasks. We observe several consistent patterns emerge. First, across all four GNN architectures, ConceptNeuro substantially outperforms the corresponding vanilla baseline. For example, with GCN, the average improvement ranges from $+2.0\%$ (Conduct) to nearly $+5.0\%$ (Anxiety), with gains consistently larger than the reported standard errors. Similar margins are observed for GAT and GraphSAGE, where our approach improves performance by $3.4\%$ across five disorders. These results highlight the benefit of explicitly incorporating LLM-guided concepts into otherwise standard black-box GNN pipelines.

Second, the improvements are not limited to a single architecture, but hold across convolution-based (GCN), attention-based (GAT), sampling-based (GraphSAGE), and injective (GIN) encoders. This demonstrates that our framework is architecture-agnostic and provides complementary information to the underlying message-passing mechanism. Notably, even strong baselines such as GIN-Vanilla benefit from our design: for ADHD, accuracy increases from $61.4\%$ to $63.8\%$, and for Conduct class from $73.2\%$ to $75.9\%$.

Third, our framework demonstrates strong performance on the multi-class classification task using the HCP-D dataset, as reported in Table [2](https://arxiv.org/html/2510.03351v1#S4.T2). Unlike the binary classification setting, here the prediction problem is extended to 11 classes (ranging from 0 to 10), which substantially increases task difficulty and reduces the baseline accuracy across all architectures. Despite this, ConceptNeuro consistently outperforms the vanilla GNN counterparts across all four disorders.

Our results reveal that different disorders vary in difficulty. While tasks such as ADHD and Anxiety yield lower absolute accuracies across all methods, reflecting their more heterogeneous neural signatures, Conduct disorder consistently achieves higher accuracy. Importantly, in all cases, our method narrows this performance gap while retaining interpretability, suggesting that concept-based modeling helps extract clinically meaningful connectivity patterns that align with diagnostic labels.

Overall, our findings show that ConceptNeuro consistently improves predictive performance across diverse GNN backbones, while maintaining generalizability to multiple disorder types.

**Table 2: Mean accuracy (%) on disorder classification tasks on the HCP-D dataset, with standard error across five random seeds. We observe that ConceptNeuro-augmented GNN architectures consistently outperform their vanilla counterparts across all four classes. Note that the HCP-D dataset does not have labels for the OCD class.**
| GNN Architecture | Method | Anxiety | ADHD | ODD | Conduct |
| --- | --- | --- | --- | --- | --- |
| GCN | Vanilla | 29.9$\pm$2.1 | 45.8$\pm$1.9 | 34.1$\pm$1.6 | 19.8$\pm$1.5 |
| ConceptNeuro | 34.8$\pm$2.5 | 52.8$\pm$2.0 | 40.2$\pm$2.4 | 28.7$\pm$2.3 |  |
| GAT | Vanilla | 28.1$\pm$1.5 | 44.0$\pm$2.7 | 35.0$\pm$2.8 | 18.6$\pm$2.6 |
| ConceptNeuro | 34.8$\pm$2.3 | 53.2$\pm$2.4 | 44.8$\pm$2.5 | 31.0$\pm$1.5 |  |
| GraphSAGE | Vanilla | 29.4$\pm$1.6 | 45.8$\pm$1.8 | 37.6$\pm$2.9 | 22.7$\pm$2.4 |
| ConceptNeuro | 38.3$\pm$2.2 | 54.3$\pm$2.5 | 46.9$\pm$1.6 | 30.9$\pm$2.1 |  |
| GIN | Vanilla | 31.1$\pm$1.4 | 48.7$\pm$1.6 | 35.4$\pm$2.5 | 21.3$\pm$2.3 |
| ConceptNeuro | 38.6$\pm$2.4 | 56.4$\pm$1.8 | 47.1$\pm$2.4 | 30.3$\pm$2.1 |  |

**Table 3: Agreement between model-extracted concepts and expert-selected concepts. We report two metrics: (i) Concept Agreement, the fraction of shared concepts at top–$k$, and (ii) Ranking Agreement, the ratio of subjects for which the model-selected features matched expert-selected features. Both are shown at top–3, top–5, and top–10.**
|  | Concept Agreement | Ranking Agreement |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Disorder | Top–3 | Top–5 | Top–10 | Top–3 | Top–5 | Top–10 |
| Anxiety | 66.7% | 80.0% | 70.0% | 61.2% | 73.5% | 81.0% |
| OCD | 33.3% | 40.0% | 80.0% | 47.6% | 59.2% | 77.8% |

RQ2) Domain Expert Analysis/Verification.
To further validate the interpretability of our framework, we compared the model-extracted concepts (ranked by concept scores in Eq. [6](https://arxiv.org/html/2510.03351v1#S3.E6)) with those selected by clinical domain experts in neuroimaging and psychiatry.
Agreement was assessed using two complementary metrics: (i) *Concept Agreement*, which measures the proportion of shared concepts between the top-$k$ sets from the model and experts, and
(ii) *Ranking Agreement*, which quantifies the average per-subject alignment of selected features across the cohort.

As shown in Table [3](https://arxiv.org/html/2510.03351v1#S4.T3), concept agreement values are consistently strong at larger cutoffs
(e.g., 70–80% at top–10), suggesting that our framework captures many clinically meaningful connectivity
patterns. At stricter cutoffs, concept agreement is more variable across disorders (e.g., 66.7% for Anxiety vs. 33.3%
for OCD at top–3), reflecting differences in the prioritization of the most salient features.
The ranking agreement metric further supports these findings: for both Anxiety and OCD, agreement rates
increase steadily from top–3 ($\approx$47–61%) to top–10 ($\approx$78–81%), demonstrating that our framework
recovers clinically validated concepts consistently across subjects.

Overall, these results demonstrate that ConceptNeuro not only provides competitive predictive performance
but also yields interpretable concept-level explanations that align well with domain expertise, both
at the set-level (via ranking overlap) and subject-level (via ranking agreement).

Figure: Figure 2: The distribution of similarity scores.
Refer to caption: distribution.png

RQ3) Concept Analysis of Importance Distributions over Subjects.
To further investigate how individual concepts contribute to disorder prediction,
we focus on the case of *Anxiety* and select two representative concepts:
the most important concept (*Altered connectivity from orbitofrontal cortex (OFC) to amygdala*) and the least important
concept (*Increased connectivity between thalamus and prefrontal regions*). For each of these concepts, we compute the distribution of
cosine similarity scores across all subjects, as shown in
Fig. [2](https://arxiv.org/html/2510.03351v1#S4.F2).
The results reveal clear differences in their subject-level relevance. The most important concept consistently aligns with a large subset of subjects and represents a core connectivity pattern strongly associated with Anxiety. In contrast, the least important concept displays a distribution centered closer to zero, suggesting weak relevance across subjects. This comparison highlights how our framework quantifies the relevance of concepts across individuals. By examining these distributions, clinicians can distinguish between robust, disorder-specific connectivity markers and marginal features, thereby improving the interpretability and the reliability of model outputs.

Figure: (a) GCN ablation results across disorders.
Refer to caption: x2.png

RQ4) Ablation Study.
To disentangle the contribution of each design choice in ConceptNeuro, we perform two ablation variants: (1) replacing our achieved concepts with randomly sampled concepts, and (2) restricting the model to use only the top-5 most important concepts. The results are shown in Fig. [3](https://arxiv.org/html/2510.03351v1#S4.F3).
First, the random concepts setting results in a substantial drop in accuracy across all disorders and architectures. This degradation confirms that the functional connectivity concepts extracted by ConceptNeuro are not arbitrary; rather, they encode clinically meaningful neurobiological patterns. In particular, models trained with random concepts perform close to chance level, highlighting the necessity of guided concept generation.
Second, the top–5 concepts only setting performs better than random concepts and even approaches the full model in some cases, especially for disorders like Anxiety and OCD where a small subset of connectivity patterns are highly discriminative. However, across all tasks the top–5 setting still underperforms the full model, demonstrating that while a few high-importance concepts are valuable, broader concept coverage is essential for capturing the heterogeneity of psychiatric disorders.
Overall, these ablations show that both the diversity and quality of the extracted concepts are critical. The full ConceptNeuro framework thus provides the best balance between interpretability and predictive performance.

## 5 Conclusion

In this work, we introduced an interpretable framework for neuropsychiatric diagnosis that combines large language models (LLMs) and graph neural networks (GNNs) through the use of connectivity-based concepts. By leveraging LLMs to generate clinically grounded candidate concepts, our method enables predictions that are both accurate and interpretable. The concept bottleneck classifier enforces sparsity and direction-aware regularization, ensuring that model decisions align with clinical priors while relying on a compact set of meaningful connectivity patterns. Extensive experiments across multiple disorders demonstrated that our approach achieves strong predictive performance, while expert analysis verified that the identified concepts capture clinically relevant connectivity features.

Limitations. While our framework advances interpretable neuropsychiatric diagnosis by integrating LLM-generated connectivity concepts with concept-guided GNNs, several limitations remain. First, our evaluation is conducted on publicly available datasets (e.g., ABCD, HCP-D), which, although widely used, may contain biases in demographics, acquisition protocols, and diagnostic labels. These factors could limit generalizability to other populations or clinical settings. Second, the interpretability of our method is inherently constrained by the quality of the candidate concepts provided by LLMs; although we employ filtering and expert validation, spurious or incomplete concepts may still arise. Third, while our direction-aware regularization incorporates clinical priors, it assumes that such priors are correct and consistent across individuals, which may not always hold in heterogeneous disorders. Finally, our framework is intended as a research tool and has not been clinically validated. Translation to practice would require large-scale prospective studies, integration with multimodal assessments, and careful oversight to ensure safety, fairness, and reliability.

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## Appendix A Disorder-related Terms

To provide additional guidance for concept generation, we extract disorder-related terms from NeuroQuery [9]. These terms serve as anchors to help ensure that the generated concepts are clinically meaningful and aligned with prior neuroimaging knowledge.

- •
Anxiety Disorders: amygdala, prefrontal, insula, cingulate, thalamus, occipital, brainstem, somatization, dlpfc, orbitofrontal, left, acc, right, hippocampus, fa, fusiform gyrus, memory, precuneus, parahippocampal, motion, ofc, hope, frontal, task
- •
Oppositional Defiant Disorder (ODD): reward, fa, cerebellum, white matter, tensor, matter, occipital, precuneus, white, cingulate, prefrontal, insula, frontal, motion, striatum, superior, right, motor, inferior, left, orbitofrontal, task, striatal, somatization
- •
Attention-Deficit/Hyperactivity Disorder (ADHD): cerebellum, precuneus, cingulate, occipital, insula, prefrontal, frontal, motor, inferior, right, striatal, superior, default, left, orbitofrontal, motion, task, reward, acc, sensorimotor, caudate, lobe, working memory, thalamus
- •
Obsessive-Compulsive Disorder (OCD): thalamus, cingulate, frontal, ofc, fa, occipital, insula, striatal, white matter, right, left, cerebellum, acc, anterior, orbitofrontal, tensor, stg, nucleus, superior, task, lobe, matter, basal ganglia
- •
Conduct Disorder: insula, dmn, fusiform, matter, occipital, precuneus, fusiform gyrus, thalamus, prefrontal, cingulate, sma, orbitofrontal, motion, amygdala, ofc, frontal, anterior, brainstem, cerebellum, default, right, superior, motor, default mode

## Appendix B Generated Concepts (Unfiltered)

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: Please see weaknesses. In particular, I would be willing to raise my score if all my concerns in the weaknesses section are addressed.
soundness: 3
strengths: - I like the idea of LLM generated concepts and using these concepts to guide model training. LLM encompasses useful priors as it is trained extensively on medical context. Leveraging these priors provides a novel way to both improve the performance and provide more insights that are aligned with the clinical knowledge. 

- The method is model agnostic. The author tested the model against multiple GNN baselines and the model showed statistically significant improvements on all models.
summary: The author provides a framework for performing neuropscychiatric diagnosis using interpretable connectivity concepts. Specifically, the autfirst prompt LLMs to generate disorderspecific functional connectivity concepts. Then, for each subject, the framework encodes the concepts along with the subject’s input functional connectivity graph. The embeddings are then used to predict a concept score and predictions for downstream tasks. The authors ran extensive experiments to compare the model's performance with vanilla baselines. Ablations are also performed to compare the impact of concept on model's performance.
weaknesses: - While the author tested extensive baselines, all baselines are relatively old (before 2020). It would be nice if the authors can test some newer models, like https://www.sciencedirect.com/science/article/abs/pii/S1361841521002784, https://arxiv.org/abs/2204.07054 or https://arxiv.org/abs/2210.06681. The results would be more convincing if the method also performs well on these newer datasets. 

- With the LLM generated insights, it would be nice if the authors can identify and share the most useful concept for each disease / condition. These can be used as useful clinical insights that can be used to guide further research in the area. 

- Table 3 shows interesting numbers about concept agreements, but no baselines are compared. For GNN posthoc explanation methods (i.e. https://link.springer.com/chapter/10.1007/978-3-031-16452-1_36), the authors can also potentially identify the important connections among samples, and evaluate their agreement scores. It would be nice if some baseline comparisons are included.

---

confidence: 5
contribution: 2
initial_rating: 2
presentation: 2
questions: Please see Weakness.
soundness: 2
strengths: 1. Leverages prior knowledge from LLMs to generate clinically relevant candidate concepts.

2. The writing of the paper is clear, and the experimental results appear promising.
summary: This work presents an interpretable framework for neuropsychiatric diagnosis that integrates large language models (LLMs) and graph neural networks (GNNs) using connectivity-based concepts. The proposed method utilizes LLMs to generate clinically relevant candidate concepts, enabling interpretable predictions. The concept bottleneck classifier incorporates sparsity and direction-aware regularization, ensuring that model decisions align with clinical knowledge while focusing on a compact set of connectivity patterns.
weaknesses: 1. The proposed method heavily relies on the prior knowledge of LLMs in clinical medicine. If the LLM outputs contain subtle hallucinations or errors, it could significantly affect both the model's performance and its clinical applicability.

2. Pearson's correlation is used to calculate the relationship weights between ROIs. Can this linear approach truly capture the complex relationships between brain regions? Previous works have highlighted this issue and employed attention mechanisms to construct brain maps [1]. Could a deeper analysis and comparison be provided?

3. The comparison methods used are outdated, primarily from 2017–2019. Could experiments be added that compare with more recent methods from the past three years?

4. The paper claims that the method is interpretable. Can meaningful clinical biomarkers be provided through the method and experiments?

5. The proposed method mainly introduces concepts and improves upon the GNN architecture. Can the authors further elaborate on the technical innovations in their approach?

[1] Wang, Yibin, et al. "Residual graph transformer for autism spectrum disorder prediction." Computer Methods and Programs in Biomedicine 247 (2024): 108065.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: - Section 3.1 (around line 153): As I understand it, should $V_c^{1}$ and $V_c^{2}$ each be subsets of $V$, rather than $(V_c^{1}, V_c^{2})$ being a subset of $V$? Could you clarify the intended set relationship and revise the notation accordingly?
- Section 3.3 (around line 215): Could you provide a brief description of NetworkX usage, algorithm variants and hyperparameters, and a short rationale for how these choices affect concept subgraph construction and results?
- Around line 218: You state “Formally, the node representation at layer $t$ is updated as:”, but Eq. (3) uses $h_u^{(0)}$ with a superscript 0—does this refer to a layer-$t$ update or a layer-0 initialization? If it is a typographical error, could you correct the equation and adjust subsequent derivations/results as needed?
soundness: 2
strengths: ## Strengths
- Methodological novelty: introduces a concept‑bottleneck, aligning interpretable functional‑connectivity subgraph concepts with predictions. Compared to standard GNN pipelines, the decision pathway is clearer and more auditable, providing an interpretable rs‑fMRI diagnostic route that can facilitate clinical communication and hypothesis generation.
- Knowledge injection: leverages LLMs together with neuroimaging resources to automatically generate and filter concepts, yielding a systematic and extensible concept library.
- Strong interpretability: concepts are defined as structured subgraphs; the paper reports subject‑level Top‑K concepts and population‑level distributions, supported by expert assessment/selection.
- Broad empirical coverage: consistent gains over vanilla counterparts across multiple datasets and GNN backbones, with validation on multi‑class severity prediction tasks.
- Thorough ablations: replacing concepts with random ones or restricting to a very small set substantially degrades performance, underscoring the critical role of concept quality and supporting the methodological claim.

## Novelty and Significance
- The paper combines LLM‑driven knowledge injection with an explicit concept bottleneck in GNNs for rs‑fMRI diagnosis, yielding a structured and interpretable concept layer; robustness is demonstrated across multiple GNN backbones.
- Compared to CBM/prototype approaches and post‑hoc explainers, the work emphasizes a forward, prior‑to‑prediction interpretability pathway (“prior concepts → structured subgraph → prediction”).
- Significance: establishes a clinically communicable route for graph learning on rs‑fMRI, with potential for cross‑task transfer and hypothesis generation in neuroimaging.
summary: - Proposes a concept‑guided GNN framework with an explicit concept bottleneck that links interpretable functional‑connectivity subgraph concepts to diagnostic predictions on rs‑fMRI for neuropsychiatric disorders.
- Describes an LLM‑based pipeline to automatically generate, filter, and structurally encode disorder‑related functional‑connectivity concepts grounded in neuroimaging knowledge resources.
- Reports empirical results across multiple datasets and GNN backbones, showing consistent improvements over vanilla counterparts, including in multi‑class severity prediction settings.
- Provides concept‑level analyses (e.g., importance distributions and case visualizations) indicating alignment with established neurobiological priors to support interpretability claims.
weaknesses: ## Weaknesses
- Robustness and reproducibility of LLM‑driven concept generation are under‑validated (sensitivity to prompts, LLM variants, random seeds). The study appears to rely on GPT‑4.1; please add sensitivity analyses to alternative models/prompts.
- Limited comparisons against strong interpretability baselines (e.g., GNNExplainer, PGExplainer, prototype/CBM variants), which weakens the positioning of the method.
- Prompting is under‑specified: provide complete prompt templates and construction rationale, assess prompt robustness, and release prompts to improve reproducibility.
- Direction‑aware constraints are insufficiently described; include ablations isolating the effect/necessity of the direction‑aware loss term (with formulation and hyperparameters) and discuss observed behaviors.

## Clarity and Presentation
- The manuscript is well structured and reads clearly. To further improve clarity and reproducibility, I recommend:
  - Providing the complete LLM-based concept generation prompt templates, together with representative examples of filtered concepts and illustrative failure cases;
  - Completing the anonymized code repository and including scripts and documentation for one-click reproduction of experiments and figures.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 3
questions: 1. I am curious about the selection of the hyperparameter $N_c$ , i.e., the number of concepts. How was this value determined? It appears to play an important role, as suggested by the top-5 concept in the ablation studies.
2. The unfiltered concept pool contains around 20 concepts per disease. Given the concept filtering process and the sparse loss term, I tend to believe that the model may ultimately select fewer than five dominant concepts, even though it dynamically selects top-k concepts. Could the authors clarify how many concepts are effectively retained after training (sparsity)?
3. I assume that the authors split the ABCD dataset into five subsets for evaluation. Could the authors clarify the number of patients and healthy controls included in each subset?
soundness: 2
strengths: 1. The study employs LLM to generate cortical concepts of interest, which are then used to extract both whole-brain and subgraph functional connectivity (FC) features, achieving a local–global representation.
2. The proposed model is lightweight, consisting of only a two-layer GNN.
summary: This study proposes an interpretable GNN-based architecture that integrates domain concepts generated by GPT-4.1, mainly aimed at neurodevelopmental diagnosis. Compared to the vanilla GNN, the model demonstrates improved predictive performance. However, the interpretability evidence presented in the paper appears limited and somewhat insufficient to substantiate the claimed clinical insights.
weaknesses: 1. Although the paper emphasizes internal interpretability and clinical meaning, there are very few explicitly interpretable results presented. Apart from the example shown in line 448, the interpretability claims remain largely unsubstantiated. I am curious about whether the identified FCs truly correspond to disease-specific patterns.
2. Regarding the evaluation of explanation quality, it remains unclear how the “gold standard” concepts from domain experts were obtained. How reliable and consistent are these expert-provided references?
3. In my view, many of the unfiltered concepts listed in Appendix C could also be derived from meta-analyses or review articles. What advantages does the proposed LLM-based approach offer compared to such traditional methods? Additionally, have the authors considered employing RAG to better ground LLM outputs in neuroscience literature?
4. I was unable to find usable code or implementation details in the provided anonymous repository, despite the authors’ statement (line 914): 
>  “The entire codebase, including data preprocessing scripts, model implementation, and training procedures, is provided in our anonymous repository: https://anonymous.4open.science/status/ConceptNeuro ”