NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy  for Brain Disease Diagnosis
Authors: Shengrong Li
Qi Zhu
Chunwei Tian
Xinyang Zhang
WEI SHAO
Jie Wen
Daoqiang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: dynamic brain networks
evolutionary mechanisms
spatio-temporal heterogeneity
graph convolution network
brain disease diagnosis
Primary area: machine_learning_for_sciences

Abstract: Dynamic functional brain networks (DFBNs) are powerful tools in neuroscience research. Recent studies reveal that DFBNs contain heterogeneous neural nodes with more extensive connections and more drastic temporal changes, which play pivotal roles in coordinating the reorganization of the brain. Moreover, the spatio-temporal patterns of these nodes are modulated by the brain's historical states. However, existing methods not only ignore the spatio-temporal heterogeneity of neural nodes, but also fail to effectively encode the temporal propagation mechanism of heterogeneous activities. These limitations hinder the deep exploration of spatio-temporal relationships within DFBNs, preventing the capture of abnormal neural heterogeneity caused by brain diseases. To address these challenges, this paper propose a neuro-heterogeneity guided temporal graph learning strategy (NeuroH-TGL).  Specifically, we first develop a spatio-temporal pattern decoupling module to disentangle DFBNs into topological consistency networks and temporal trend networks that align with the brain's operational mechanisms. Then, we introduce a heterogeneity mining module to identify pivotal heterogeneity nodes that drive brain reorganization from the two decoupled networks. Finally, we design temporal propagation graph convolution to simulate the influence of the historical states of heterogeneity nodes on the current topology, thereby flexibly extracting heterogeneous spatio-temporal information from the brain. Experiments show that our method surpasses several state-of-the-art methods, and can identify abnormal heterogeneous nodes caused by brain diseases.

NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
Shengrong Li", Qi Zhu't Chunwei Tian', Xinyang Zhang', Wei Shao',
Jie Wen Daoqiang Zhang'
Nanjjng University of Aeronautics and Astronautics
Harbin Institute of Technology
Lisrongenuaa.co edu. en, echuqinnual edu. en, chunweitianchit edu. en, xinyangenuaa, edu. cn, shaoxe12002205emuaa. edu. cn, wenjie@hie eed.. cn, dqzhang@nuaa edu.cn
Abstract
Dynamic functional brain networks (DFBNs) are powerful tools in neuroscience research. Recent studies reveal that DFBNs contain heterogeneous neural nodes with more extensive connections and more drastic temporal changes, which play pivotal roles in coordinating the reorganization of the brain. Moreover, the spatio- temporal patterns of these nodes are modulated by the brain's 's historical states. However, existing methods only ignore the spatio-temports heterogeneity of neural nodes, but also fail to effectively encode the temporal propagation mecha- nism heterogeneous activities. These limitations hinder the deep exploration of spatio-temporal relationships within DFBNs, preventing the capture of abnormal neural heterogeneity caused by brain diseases. To address these challenges, this paper proposes
Haarr---etrogeeeityy guided Temporal Graph Learning strategy (GeuroH-TGLL.). Specifically, we first develop a spatio-temporal pattern decoupling module to disentangle DFBNs into topological consistency networks and temporal trend networks that align with the brain's operational mechanisms. Then, we intro- duce heterogeneity mining module to identify pivotal heterogeneity nodes that drive brain reorganization from the two decoupled networks. Finally, we design temporal propagation graph convolution to simulate the influence of the historical states heterogeneity nodes on the current topology, thereby flexibly extracting heterogeneous ppatiottemporal information from the brain. Experiments show that our method surpasses several state-of-the-ane methods, and can identify abnormal heterogeneous nodes caused by brain diseases.
Introduction
Functional magnetic resonance imaging (fMRI) measures neural activity by detecting changes in blood oxygen level-dependent signals, and is commonly employed to construct functional brain networks [ 3 In fact, the brain constantly reorganizing even during the resting state 4 (]. Obviously, compared with the static functional brain network, the dynamic functional brain network (DFBN) can more comprehensively describe the topological evolution of the brain. Studies have shown that brain diseases such Alzheimer's disease and Parkinson's 's disease can change the spatio- temporal properties of DFBNs 7) Therefore, effectively analyzing the spatio-temporal structure of DFBNs crucial for brain disease diagnosis and biomarker mining.
To capture the varying structure DFBNs, they are usually modeled as series of dynamic brain graphs 8 9 TO II]. In dynamic brain graphs, nodes represent brain regions and edges
Cooresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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represent temporal connections between these regions. Existing dynamic brain graph analysis methods usually use graph convolution network (GCN) [22 to extract the topologiaa feature, and then use temporal convolution to capture the temporal correlation between brain regions [10 13 14] For example, STAGIN first uses GCN to extract structural information in DFBNs, and then introduces transformer to capture the temporal dependence between brain graphs. Although significant progress has been made in the analysis of dynamic brain graphs, most methods overlook the crucial fact that the brain exhibits significant spatio-temporal heterogeneity: Certain neural nodes in DFBNs exhibit extensive connectivity or more active temporal evolution due to their functional properties. For instance, the posterior cingulate cortex forms stable and tight connections with the frontal and parietal lobes, while the connection strength between the primary motor cortex and supplementary motor area exhibits heightened temporal variability [T]. These neural nodes with high -patioltemporal variability can flexibly adjust the reconstruction pattern of functional network, which a key factor driving the reorganization of the brain. Therefore, identifying the spatio-temporal heterogeneity of neural nodes is significant for elucidating the evolution mechanism of the brain.
However, accurately capturing the spatio-temporal heterogeneity of neural nodes faces dual challenges. (1) Spatio-temporal coordination of DFBNs. While maintaining stable connections of pivotal nodes, the brain network can dynamically adjust connections according to cognitive demands to achieve efficient information integration. This spatial consistency and temporal trend together constitute the
neural basis supporting complex cognitive functions [17 18 19] (2) Sequential dependence of
DFBNs. Due to the continuity of brain activity and the lag in information interaction [20] [2] [5], current connectivity patterns are systematically influenced by prior network states. There is rich
sequential dependence exhibited among neural nodes.
To address these challenges, we propose a Hettr-eeterrgyneityy guided Temporal Graph Learning strategy (NeuroH-TGL) to comprehensively capture the intrinsic evolution mechanism of DFBNs. Specifically, to simulate the spatio-temporal coordination of DFBNs, we design spatio-temporal pattern decoupling (STPD) module to disentangle the DFBNs into topological consistency networks and temporal trend networks. Then, we calculate the cross-window similarity of topologic consistency networks and temporal trend networks, and use them as the spatial and temporal heterogeneity weights, respectively. Subsequently, we apply spatio-temporal heterogeneity weighting to DFBNs, thereby highlighting the pivotal nodes driving network reorganization. Finally. we develop a temporal propagation graph convolution network (TPGCN) further capture the propagation mechanisms of heterogeneous neural information, that is, to simulate the impact of historical states on the current topology, thereby flexibly capturing the spatio-temporal features within heterogeneous DFBNs. In summary, the main contributions of this paper are as follows:
To accurately simulate the heterogeneous evolution mechanism of brain neural activities, we propose a NeuroH- -GLL to identify neural nodes with high spatio-temporal variability that drive network reorganization, and construct brain networks that integrate heterogeneity.
Since current functional network is persistently modulated by historical neural activity, we devise TPGCN to model the propagation of neural information in the temporal dimension, thereby effectively extracting the spatio-temporal features from heterogeneous DFBNs
Experimental results show that the proposed method outperforms the current state-of-the-an methods, and can provide effective biomarkers for brain disease diagnosis.
2 Related Work
Brain Network Analysis. Brain network analysis aims to understand the organizational structure of the brain, thereby identifying its working mechanisms and abnormalities caused by neurological disorders []. Current methods can be categorized into two types: static brain network analysis and dynamic brain network analysis. Static brain network analysis refers to extracting fixed connectivity between brain regions over a period of time. For example, BrainNetCNN (23 proposes edge-to-edge, edge-to-node node, and node-to- tolle-to-grahh convolutional filters extract the local properties of structural brain networks. BNTransformer [24] employs a graph transformer to learn pairwise connection strengths between brain regions, and incorporates orthogonal clustering to identify discriminative node embeddings. Unlike these methods, dynamic brain network analysis focuses on capturing tire-varying connectivity between brain regions. For instance, ACIFBN (55 leverages an attention
mechanism to learn spatio- temporal interactions among IMRI sub-sequences. OT-MCSTGCN (6]

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Figure I: The overall framework of the proposed NeuroH-TGL. model for brain disease diagnosis.
employs optimal transport to simulate the hubness propagation between adjacent brain graphs, thereby capturing high-order evolution in DFBNs. However, these methods overlook the inherent spatio- temporal heterogeneity of brain networks, failing effectively model realistic dynamic dependencies in the brain. In this work, we introduce spatio-temporal decoupling and heterogeneity mining module capture the connectivity density and temporal variability of brain structures, thereby accurately representing the heterogeneous brain activity.
Opatio-Temporse Graph Convolution for DFBNs. Spatio-temporal graph convolution typically integrate GCN and temporal convolution within a unified architecture to extract tare-narying struc- tures from DFBNs. For instance, STAGIN 4 employs GCN to extract structural features, followed by attention mechanisms to model temporal dynamics. ST-IMRI [26] integrates GCN with four parallel ID convolutional filters model long-range dynamic interactions between brain regions. ST-GCN [ combines GCN with temporal convolution to capture the non-stationary properties of functional connectivity. OT-MCSTGCN (6) proposes multi-channel opatio-temporis GCN to efficiently aggregate topological evolution information in DFBNs. Notably, DFBNs exhibit significant sequential dependence 225 21 Existing spatio-temporal models fail incorporate historical brain states' influence on current brain graphs, resulting in suboptimal diagnostic performance. In this paper, design the TPGCN model the temporal propagation mechanism of heterogeneous neural activity, comprchensively capturing spatio-temporal information within heterogeneous DFBNs.
Proposed Method
As shown in Figure I we develop a neuro-heterogencity guided temporal graph learning strategy. This framework aims identify neural nodes with high spatio-temporal heterogeneity that drive network reorganization, thereby enhancing the diagnostic performance of brain diseases.
3.1 Spatio-Temporay Temporal Patterns Decoupling
In this paper, we assume the rs-MMRI signal for each subject is represented as X [x1, "vy) E RVL where x, represents the time series signal of the 7th brain region, V denotes the number of neural nodes and L indicates the number of temporal signal points. To characterize the dynamic evolution patterns of brain activity, we employ overlapping sliding windows of length S partition the fMRI signal generating series of sub-signals F = (Fi, F2, Fr) x For the
sub-signals F; under the 1h window, we use the Pearson correlation coefficient brain network Ag;
4 to construct the
Cov(Frt)) FRO)
A((i))
(I)
Fuug Fie)
3

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where i and are indices of Fs, Cov indicates the cross covariance, and Fico is the standard deviation of Feje). Therefore, DFBNs can be represented as A (Ai, A2,..., Ar) RTxVxV To simulate the sparsity of brain networks, we further set elements with connection strengths lower than a O. Neuroscience research shows that the brain's cognitive function is supported by its intrinsic topological consistency and temporal trend
8 19]. Thus, decoupling spatio-temporal organizational patterns within DFBNs can help reveal network dysregulation caused by brain diseases. For each brain network A, (t = 1,2, . T)) we first employ two independent GCN [12] to extract topological consistency networks HPT and temporal trend networks H
H GCN(A, Fi) A. (ReLU (A.F.WH(olo)) Wo(oo(s)
(2)
Hoam GCN(A, F.) A, (ReTU (A.F.W/eom()) (efem(1)
where A, Dj * (Ae + T)D,,, self-loops, W Woto((0) Woor()) w/tem(0) and Weem(1)) all represent the learnable parameters in the graph convolutional layers, and ReL.U 27 denotes the nonlinear activation function.
+T) ,, I is the identity matrix, Dj denotes the degree matrix after adding
To enhance the discriminability between topological consistency networks and temporal trend net- works, encourage the similarity constraint Leca between them to gradually decrease throughout the training process:
V Lec. = V Σ
H
1
H
Hoop [(3]
ll
H ll
where the represents the dot product operation, and ||ll, denotes the L2 norm. Additionally, to ensure the complementarity of topological consistency networks and temporal trend networks, sum the decoupled features to obtain a reconstructed representation. Then, we introduce mean squared error (MSE) 28 as the reconstruction loss between the reconstructed representation and brain network Aç:
(5) where represents element-wise addition. Notably, the topological consistency refers to the high stability of certain network structures across different windows. Therefore, we further impose a similarity constraint on topological consistency networks across adjacent windows, and encourage this similarity LcC,a to increase as training progresses:
LRc. =I| H!"" e H -A, lla
H Hlop HoOp ((s) (+1(()
1 V
Leca
(6)
lop (
l2
H (+1(0) l12
The above operations ensure that the topological consistency networks and temporal trend networks within the same window are distinct and complementary, while also promoting the similarity of consistency networks across different windows, thereby better aligning with the intrinsic spatio-
temporal coordination of the brain 17 18 19
3.2 Spatio Heterogeneity Mining
Capturing the wross-winoww topological similarity messures spatial beterogeneily
Capturing cross window temporal similarity measures temporal beterogemeity
Denser spatial connectivity
SH Weight
TII Neight
unstable
temporal evolution
Time
Time
Win Topological Consistency
Win
Win 3
Win Temporal Trond Networls
Win 2
Win
Networks
(a) Spatial Heterogeneiit
Temporal Heterogoneity
Figure 2: The schematic diagram of spatio-temporal heterogeneity mining. We calculate cross- window topological similarity and temporal similarity respectively to measure spatial heterogeneity and temporal heterogeneity.
There are some neural nodes with high spatio-temporal variability DFBNs 6 Figure 2 the neural node marked by red has denser spatial connections than other nodes, while the
shown in

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node highlighted in orange shows more unstable temporal evolution. These nodes play an important role coordinating the evolution of the brain, which will form complex
network centered on these nodes. Moreover, the brain is continuously evolving dynamic system with extensive asynchronous interactions [21 6 Therefore, we calculate the cross-window similarity for topological consistency networks and temporal trend networks separately to measure the connectivity density and temporal variability of the brain, thereby exploring the spatio-temporal heterogeneity of neural activity. The detailed process of mining spatio-temporal heterogeneity is shown in Figure 2 For spatial heterogeneity (SH), we first calculate the average correlation of topological consistency networks across all paired windows:
2 SH
Σ j=i+1
sim H
oP H
T(T T(T-1) 1)
where sim() represents the cosine similarity (29]. Then, we apply spatial heterogeneity weighting to topological consistency networks: Z: SH & H! 1,2,. .... .....)..T) .T), where @ denotes the element-wiss multiplication.
contrast, for temporal heterogeneity (TH), lower cross-window similarity in temporal trend networks indicates more pronounced dynamic evolution. Therefore, we calculate TH using the following formula:
T-1
T
TH T(T
2
ΣE j=i+
Σ
sim H, H)em)
(8)
Then, we apply temporal heterogeneity weighting to temporal trend networks: Z TH & Spatio-temporal heterogeneity weighting not only preserves the dynamic topology of DFBNs, but also highlights important nodes and connections. Therefore, we can obtain
He (t = 1, ..... T). ,
topological consistency networks and temporal trend networks that fuse spatio-temporal heterogeneity.
3.3 Temporal Propagation Graph Convolution Network
The heterogeneous information in the brain propagate continuously along the temporal dimension, which means that each brain network gradually influences the state of the adjacent brain network 4 To flexibly capture cross-temporal interactions in heterogeneous brain networks, we further design a temporal propagation graph convolution network. This framework utilizes the heterogeneous spatio-temporal features of the brain networks from the previous moment to guide the information aggregation of the brain networks in the next moment. In this framework, each spatio-temporal convolutional block consists of two GCNs 12] and one 2D convolutional network (CNN) 30,, so as to efficiently aggregate dynamic structure information. To reduce the number of parameters, we use two parameter-sharing spatio-temporal convolutional blocks extract features from topological consistency networks and temporal trend networks, respectively. The topological consistency feature Et and temporal trend feature Efem can be learned as follows:
E GCN(A, CNN(GCN(A,.
Zi-1))
where i E {lop, t.m). Then, we add the two types of features to obtain the fused spatio-temporal representation: Eg = EdoP @ Etem After performing the same operation on the two networks for each window, we concatenate the features of all windows to obtain the global spatio-temporal representation E:
E Concatenatee (Eelt EE|t e (1,2, ,T))
(10)
Finally, we feed the features into a multi-layer perceptron to predict the diagnostic results, and use cross-entropy loss ICE to supervise the update of model parameters. The overall training objective can be formulated as: = I.C + > Lec + Xa Rte. where X and A2 are hyperparameters that control the relative importance of different loss terms, Loc and Le represent the similarity loss and
reconstruction loss for the decoupling of the STPD module across all windows, respectively.
Experiments
4.1 Experimental Settings
Datasets. We conduct experiments on both the public ADNI dataset (http::/adni.lon wow..usoooiuus.... low..usc..du/) and the Parkinson's is discase (PD) dataset collected by the Affiliated Hospital of Nanjing Medical
5

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University. The ADNI dataset comprises 140 normal controls (NC), 268 patients with mild cognitive impairment (MCT), and 102 patients with Alzheimer's disease (AD). The PD dataset includes 54 NC, 44 tremor dominant Parkinson's 8 disease (TDPD) patients, and 64 postural instability and gait disorder Parkinson's 's disease (PGPD) patients.
Preprocessing, All fMRI data are preprocessed using SPM8 implemented in the DPARSF toolbox B1]. Specifically, we first correct and align the original images based on the EPI template. Then, we utilize detrending techniques to alleviate the effects of head motion as well as the interference from cerebrospinal fluid and white matter. Finally, we use the automated anatomical labeling atlas [32 to divide the fMRI of ADNI dataset into 90 brain regions with 140 time points, and the fMRI of PD dataset into 90 brain regions with 220 time points.
Metrics. For the ADNI dataset, we conduct the following classification tasks: NC vs. MCI vs. AD, NC MCI, NC vs. AD, and MCI vs. AD. For the PD dataset, conduct the following classification tasks: NC vs. TDPD vs. PGPD, NC vs. TDPD, NC vs. PGPD, and TDPD vs. PGPD. We employ 10-fold cross-validation to evaluate the diagnostic performance of all methods on different tasks. For the three-class task, we adopt macro-averaged metries 33 to ensure equitable evaluation across all categories. We report the mean values of 10 runs.
Implementation Details. All experiments are implemented in PyTorch and trained on an NVIDIA GeForce RTX 3080 GPU with 12GB. We employ Adam optimizer [34 with learning rate of 7 to optimize the proposed method. The size of the convolution kernel is 3x3. Additionally, we adopt an early stopping mechanism that 80 epochs patience in total 300 epochs. The batch size is set 8. The threshold a is set to 0.6. For the ADNI dataset, T = 6 and S = 90. For the PD dataset, T 8 and 5 = 80. The hyperparameters X and X₂ varied within the range (1e-6, 1e-5, 1e-3, le-2, le-1, I, 10), with the optimal combination determined through grid search. The source code has been uploaded the supplementary material.
4.2 Performance Analysis
Comparison Methods. To validate the effectiveness of the proposed method, we compare it with 11 state-of-the-art brain network analysis approaches. These methods can be categorized into two types: static brain network analysis and dynamic brain network analysis. Static brain network analysis methods include BrainNetCNNN (23), FBNetGen (35), BNTransformer (24) BrainGNN (36), LSGNN
337. and ALTER 38 Dynamic brain network analysis methods include ACIFBN 25 DRAT 39
ST-GCN M ST-MMR 26). STAGIN
4 OT-MCSTGCN
6 and MGNN
40
Classification Result. Table I and Table 2 show the diagnostic results of different methods on the
ADNI and PD datasets, respectively. The standard deviations can be referred to in Appendix A Obviously, the proposed NeuroH-TGL. significantly outperforms the comparison methods. Specifi- cally, the ADNI dataset, NeuroH-TGL achieves accuracy improvements of 4.69% 1.04%, 0.15%
Table 1: Classification results of different methods on the ADNI dataset (%).
ACC
F1
AUC
ACC
Fl
AUC
ACC
Fl
AUC
ACC
F1
AUC
Type
Method
NC vs.
MCI
vs.
NC vs. MCI
NC vs.
AD
MCI vs.
AD
BrainNeuCNN
57.06
38.06
54.81
71.34
81.16
55,54
71.47
50.00
62.69
68.92
75.32
53.29
FBNetGen
57.50
56.97
67.75
61.25
66.46
58.47
63.33
58.50
57.53
63.83
73.71
64.07
BNTransfomer
60.21
40.94
61.96
69.75
79.65
57.58
74.00
71.72
75.89
74.50
81.56
57.64
Static
BrainGNN
58.82
45.33
61.66
74.25
83.20
62.88
74.05
62.90
68. 66
79.19
87.36
64.12
LSGNN
58.75
36.96
58.20
60.27
68.62
54.04
70.42
53.07
66.26
64.05
73.72
56.11
ALTER
63.73
51.68
68.98
76.42
82,20
71.11
75.23
59.18
74.25
82.43
88.45
73.50
ACIFBN
62.35
57.88
70.42
71.25
78.96
64.49
79.17
71.53
78.44
83.75
90.91
62.88
DRAT
60.83
51.09
70.11
71.50
79.78
57.93
72.92
55.79
68. 62
80.31
88.02
70.01
ST-GON
67.50
41.68
59.07
73.44
83.88
54.22
67.92
51.46
60.51
82.08
89.81
61.79
ST-MMRI
57.45
38.79
64.84
71.81
81.71
61.41
76.45
68.40
78.96
84.16
84.61
62.16
Dynamic
STAGIN
56.46
27.75
$4.76
69.25
80.97
56.44
67.08
47.99
55.49
74.44
85.35
53.71
MCCTCTGN
59.02
41.31
58.52
76.08
80.14
601.9
73.75
57.61
66.41
76.26
85.40
68.15
MGNN
61.76
51.38
66.74
77.46
83.16
70.51
81.35
77.46
80.84
80.54
86.80
75.43
NeuroH-TGL
72.19
57.81
70.49
78.50
84.35
72.61
81.50
72.12
83.01
86.67
92.46
76.01

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Table 2: Classification results of different methods on the PD dataset (%)).
ACC
ACC
ACC
F1
AUC
Type
Method
NC vs.
TDPD vs. PGPD
NC vs. TDPD
NC vs. PGPD
TDPD vs.
PGPD
BrainNetCNN
55.70
46.09
67.16
85,67
81,98
83.52
79.55
75.32
73.10
74.00
79.43
69.78
FBnetGen
62.50
57.65
69.23
74,56
52.80
65.65
74.55
78.39
51.82
72.00
73.05
66.14
Stutic
BNTranforrme
63.75
56.47
71.75
82.50
70.88
74.83
79.00
77.18
78.62
76.73
79.50
70.20
BrainGNN
63.49
57.58
72:51
83.67
78.47
84.85
83.93
75.70
73.67
75.00
74.37
63.06
LSGNN
56.88
46.44
64.46
73.75
74.84
77.08
74.17
76.93
68.47
72.00
76.61
58.62
ALTER
62.28
48.47
63,23
86.56
80.83
78,38
80.53
78.55
74,91
79,55
83,11
68.64
ACIFBN
61.25
48.72
66.33
82.89
79.80
76.97
73.90
74.18
73.36
63.79
DRAT
61.88
54.72
64.66
74.75
67.53
79.20
72.83
66.37
61.49
71.27
79.67
39.69
ST-GCN
58.13
54,93
65,56
82,67
81.21
80.18
79.70
72,80
77.15
74,45
72.67
67.44
Dynumic
ST-MMRI
58.75
52.10
75.33
73.64
78.33
81.79
74.27
71.67
72.96
44.33
STAGIN
55.63
43.52
63.71
81.89
74.20
76.55
82,20
80.89
79.30
78.00
63.00
39.85
OT-MCSTGCN
59.38
38.24
62.37
81.56
79.31
81.59
82.20
83.59
75.50
75.82
73.39
63.05
MGNN
51.56
85.78
81.10
78.79
80.14
73.22
78.73
74.98
69.43
NeuroH-TGL.
66.25 61.71
73.85
91.25
91.00
94.21
87.17
89,38
88.42
83.75
86.80
82.91
and % over the suboptimal results across four classification tasks, respectively. On the PD dataset, NeuroH-TGL. achieves accuracy improvements of 2.50%, 4.69%, 3.24%, and 4.20% over the suboptimal results across four classification tasks, respectively. The reason for the performance improvement is that the proposed method exploits the apatio-temporal heterogeneous activity patterns of the brain, thereby highlighting the pivotal neural nodes involved in the evolution of DFBN. On this basis, further design the TPGCN to model the sequential dependencies between heterogeneous neural nodes, thereby collaboratively extracting the time-varying topological features within them. Additionally, we conducte an analysis of computational efficiency. The proposed method requires only 0.0924M parameters and 0.2479M FLOPs, which indicates that the method can achieve excellent diagnostic performance with a minimal amount of computational resources.
T-SNE Visualization. To visually demonstrate the performance different methods, we use t-SNE 41 to visualize their learned features. In Figure aa),, the original feature distribution is chaotic. BrainNetCNN forms two clusters with confusion. The feature distributions learned by ACIFBN, DART and OT-MCSTGCN are loose and fail establish clear inter-class boundaries. In contrast, method effectively aggregates intra-class features while maintaining distinct inter-class separation. This is because BrainNetCNN ignores the dynamic features of the brain. Although DART, ACIFBN and OT-MCSTGCN capture spatio-temporal information to certain extent, they all neglect the heterogeneity of neural activity. Unlike them, the proposed NeuroH-TGl not only effectively captures
NC
MCI
Origin
BrainNetCNN
ACIFBN
NC vs. MCI
NC MCI
DART
OT-MCSTGON
NeuroH-TGL.
VS. AD (b)
NC vs. TDPD (c)
Figure 3: (a) T-SNE visualization for different methods on the NC vs. MCI task. (b) The impact of
A1 and A on different diagnostic tasks. (c) The impact of T and S on different diagnostic tasks.
7

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the pivotal heterogeneous nodes driving the network reorganization, but also comprchensively encodes the temporal dependence between historical neural activity and the current brain topology.
Parameter Sensitivity Analysis. The hyperparameters À and X2 in the objective function are vary within the range (1e-6, le-5, 1e-3, le-2, le-1, 1, 10]. In Figure [)), we systematically investigate the impact of different parameter combinations on disease diagnosis. The experimental results demonstrate that the accuracy remains stable across varying values of X₁ and X2. Therefore, the proposed method exhibits robustness and is not sensitive to hyperparameters. Additionally, we also explore the impact of T and on diagnostic performance. and the experimental results are shown in Figure B c). Specifically, T changes within the set [5, 6, 7, 8, 9, 10), and S varies within the set [60, 70. 80, 90, 100). We conduct a grid search on both S and T ensure the optimal parameter combination. For NC vs. MCI, the best performance is achieved when T=66 and S=90, For NC vs. TDPD, the best performance is achieved when 7=8 and S=80. Larger values of T and lead to longer overlapping sequences across windows, which might smooth out valuable dynamic information in the brain. Conversely, smaller values of T and S result in shorter time sequences per window, potentially making the statistical correlation between brain regions unreliable. Therefore, moderate window size can balance reliable statistical correlation with temporal evolution.
4.3 Ablation Study
To validate the effectiveness of each module, we conduct ablation experiments on ADNI and PD datasets, with the results presented in Table The standard deviations can be referred to in Appendix The simplified models included: (1) w/o STPD: The STPD module is removed (i.e., A λ== 0). (2) w/o STHW: Spatial and temporal heterogeneity weights (STHW) are replaced with all-ones matrices. (3) w/o TPGCN: The TPGCN is substituted with GCN. The experimental results indicate that removing any module will lead to decrease in performance. For instance, in the NC vs. MCI task, removing STPD, STHW, and TPGCN led to accuracy decreases of 3.75% 3,25% and 3.50%, respectively. The reasons for this phenomenon include: The STPD module effectively decouples the topological consistency features and temporal trend features aligned with brain dynamics from DFBN, thus laying foundation for spatio-temporal heterogeneity mining. The STHW module effectively captures the heterogeneous activity patterns of each brain region, making it possible to identify abnormal brain states associated with neurological disorders. Moreover, TPGCN outperforms GCN, proving its effectiveness in simulating the temporal propagation mechanisms of heterogeneous neural information, thereby comprehensively capturing the apatio-temport dependencies in heterogeneous DFBNs. Therefore, all the proposed modules are effective and promote each other.
Table 3: Ablation results of the proposed method on the ADNI and PD datasets (%).
ACC
F1
AUC
ACC
F1
AUC
ACC
F1
AUC
ACC
AUC
Datasets
Method
NC vs. MCI vs. AD
NC vs.
MC1
NC vs.
AD
MCI vs.
AD
wo STPD
66.88
35.00
57.22
73.75
83.20
62.16
76.67
71.42
76.87
81.67
88.77
72.95
w/ STHW
65.21
54.88
67.21
74.25
82.94
63.90
77.08
70.36
74.69
78 18.06
86.63
67.01
ADNI
w/o TPGCN
65.80
60.13
70.98
74.00
82.58
64.76
77.91
72.77
77.35
82.08
89.00
68.66
-GU
72.19
57.81
70.49
78.50
84,35
72.61
81.50
72.12
83.01
86.67
92.46
76.01
NC
TDPD
PGPD)
NC
TDPD
NC
PGPD
TDPD
PGPP
w/o STPD
65.00
55.15
64.57
86.25
87,06
83,54
83,75
85.76
86.12
81.25
86.21
70.50
w/o STHW
63.60
52.48
65.53
83,50
80.96
79.78
79.33
82.47
79.93
78.75
81.96
69.91
PD
w/o STPGC
64.38
55.18
64.40
84.75
83.73
88.53
80.50
82.64
81.16
76.00
78.23
70.65
NeuLoH-TGI.
66.25
61.71
73.85
91.25
91.00
94.21
87.17
89.38
88.42
83.75
86.80
82.91
w/o means without.
5 Discussions
Heterogeneity Weights Visualization. To explore the impact of brain diseases on neural activity, Figure displlas the spatial and temporal heterogeneity weights across all brain regions of different groups. Based on the experimental results, we can draw the following conclusions. First, brain diseases alter spatio-temporal properties of the brain. For instance, MCI and AD groups exhibit lower spatial heterogeneity but higher temporal heterogeneity compared to the NC group. Second,
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Spatial Heterogenelty
NC
MCI
AD
ProOG
MFC
ORBma
IrGupere
TPeimang
ORDin!
ROI
SMA
OL
SPGmaa
ORGGupmed
REC
INS
ACO
M
PCG
3IP
PHG
AMYG
CAL
CUN
LING
SOG
ICG
PPG
Par
SPG
IP1
SMG
ANG
PCUN
PCL
CAU
PUT
PAL
THA
HES:
STG
ThOsp
MTG
TPOmid
JTG
Tomporal Heterogemcity
NC
MCT
AD
PreOG
StG
ORBsup
MRG
ORtmm
GrGopae
IF FCGininng
ORSia
ROL
OLI
SFGmao
ORGsupmal
REC
INS
ACG
MCG
PCG
HIP
PHG
AMYG
CAL
LING
SOG
MOG
BOG
FRG
PoOG
SPG
IPL
SMG
ANG
PCUN
PCL
CAU
PUT
PAL
THA
DIS
SYG
TPD
TP0mmd ITG
Figure 4: Visualization of spatio-temporal heterogeneity weights across different groups. Each square represents a brain region, and abbreviations are provided for each brain region. 'L' indicates the left brain region, and 'R' indicates the right brain region.
the spatio-temporal heterogeneity of each brain region in the same group is different. The NC group exhibits higher spatial heterogeneity but lower temporal heterogeneity of brain regions, while the MCI and AD groups show the opposite pattern. This may be because neurodegenerative changes reduce the complexity of the brain, thus decreasing the spatial heterogeneity [25] 42). Moreover, brain discases can trigger compensatory mechanisms that increase variability in temporal activities, enhancing the temporal heterogeneity 43). Notably, we also find that the spatio-temporal heterogeneity of supplementary motor area, hippocampus and amygdala in the patient group is significantly different from that in the NC group. Therefore, these brain regions may serve potential biomarkers for the diagnosis of MCI and AD.
Discriminative Brain Regions. To futher evaluate the efficacy of the proposed method identifying biomarkers, employ t-test spatio-temporal feature of each brain region, thereby identifying the 10 most discriminative regions (p < 0.05). The visualization results are shown in Figure For NC vs. MCI, the significant brain regions gyrus, among others. This may be because MCI leads to visual impairments and memory deficits, thereby causing abnormalities in the related brain regions 44). For NC AD, key brain regions include the amygdala and superior frontal gyrus, which are responsible for emotion regulation and
concentrated in the middle temporal gyrus and parahippocampal
NC vs. MCI
NC vs. TDPD
NC vs. AD (a) ADNI datasct
NC vs. PGPD
(b) PD datasct
Figure 5: Distribution of the 10 most discriminative brain regions on different diagnostic tasks. Different colors indicate the relative importance of these brain regions.

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behavioral control and are closely related to the occurrence of AD 45 For NC vs. TDPD, important brain regions include the precentral gyrus and rolandic operculum, possibly because motor disorders in TDPD patients disrupt the normal functioning of these motor control areas [46]. For NC vs. PGPD, significant brain regions include the inferior occipital gyrus and lingual gyrus. This because PGPD patients exhibit abnormalities in processing complex visual scenes [7]. Therefore, the proposed method can provide reasonable biomarkers for brain disease diagnosis.
Conclusion. In this paper, we propose the NeuroH-TGL to collaboratively capture neural nodes in the brain that exhibit spatial density and significant temporal variability, addressing the shortcomings of existing methods that overlook the spatio-temporal heterogeneity of nodes. Specifically, we first decouple the DFBNs into topological consistency networks and temporal trend networks based on their apatio-temporal coordination. Then, we measure the spatial density of topological consistency networks and the temporal variability of temporal trend networks across global time domains, respectively, to emphasize the significant spatio-temporal associations driven by these heterogeneous nodes. Finally, we develop the TPGCN to model the influence of the historical state of heterogeneous nodes on the current network configuration, enabling comprehensive capture of dynamic topological features. Extensive experiments show that NeuroH-TGL not only significantly enhances diagnostic performance but also identifies abnormal spatio-temporal features caused by brain diseases.
Limitations and Future Work. The proposed method based solely on a single fMRI modality, overlooking the complementary information from other modalities. In future research, we will develop a heterogeneity reorganization mechanism for DFBNs under structural connectivity constraints. This framework will be capable of integrating complemen complementary heterogeneity features between function and structure to improve diagnostic accuracy and provide interpretability.
Acknowledgements
This work was supported in part by Key Research and Development Plan of Jiangsu Province (No. BE2022842), National Natural Science Foundation of China (Nos. 62371234, 62076129, 62136004, 62272226 and 62276130), Natural Science Foundation of Jiangsu Province (No. BK20231438, and also National Key R&D Program of China (No. 2023YFF1204803..
References
[1] Hongting Ye, Yalu Zheng, Yueying Li, Ke Zhang, Youyong Kong, and Yonggui Yuan. Rh- brainfs: regional heterogeneous multimodal brain networks fusion strategy. Advances in Neural Information Processing Systems, 36:59286-59303, 2023.
[2] Zuozhen Zhang. Junzhong Ji, and Jinduo Liu. Metarlec: Meta-reinforcemmnnt learning for discovery of brain effective connectivity. Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 10261-10269, 2024.
[3] Huzheng Yang, James Gee, and Jianbo Shi. Brain decodes deep nets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages
2024.
[4] Byung- Hoon Kim, Jong Chul Ye, and Jae-Jin Kim. Learning dynamic graph representation of brain connectome with patio-temporal attention. Advances Neural Information Processing Systems,
2021.
[5] Davide Momi, Zheng Wang, Sara Parmigiani, Ezequiel Mikulan, Sorenza P Bastiaens, Moham- mad P Oveisi, Kevin Kadak, Gianluca Gaglioti, Allison C Waters, Sean Hill, et al. Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks. Nature Communications, 16(1):3222, 2025.
[6] Qi Zhu, Shengrong Li, Xiangshui Meng, Qiang Xu, Zhiqiang Zhang, Wei Shao, and Dao- qiang Zhang. Spatio-temporal graph hubness propagation model for dynamic brain network classification. IEEE Transactions on Medical Imaging, (000:2331-2394 2024.
[7] Javier Pagonabarraga, Helena Bejr-Kasem Saul Martinez- Horta, and Jaime Kulisevsky. Parkin- son disease psychosis: From phenomenology to neurobiological mechanisms. Nature Reviews Neurology, 20(3):135-150, 2024.
10

---

[8] Soham Gadgil, Qingyu Zhao, Adolf Pfefferbaum, Edith V Sullivan, Ehsan Adeli, and Kilian M Pohl. Spatio-temporal graph convolution for resting-staae state fmri analysis. In Medical Image Computing and Computer Assisted Intervention-MICCA. 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VII 23, pages 528-538 Springer, 2020.
[9] Rui Liu, Yao Hu, Jibin Wu, Ka-Chun Wong, Zhi-An Huang, Yu-An Huang, and Kay Chen Tan. Dynamic graph representation learning for spatio-temporal neuroimaging analysis. IEEE Transactions on Cybernetics, 55(3): (5(3):121-1133) 2025.
[10] Youyong Kong, Xiaotong Zhang. Wenhan Wang, Yue Zhou, Yueying Li, and Yonggui Yuan. Multi-scale spatial temporall attention networks for functional connectome classification. IEEE Transactions on Medical Imaging. 44(1):475 488, 2025.
[11] Sin-Yee Yap, Junn Yong Loo, Chee-Ming Ting, Fuad Noman, Raphaël C.-W. Phan, Adeel Razi, and David L. Dowe. A deep probabilistic spatiotemporal framework for dynamic graph representation learning with application brain disorder identification. In IJCAI, pages 5353- 5361, 2024.
[12] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv: (ete:::09900000 2016.
[13] Dongdong Chen and Lichi Zhang. Fe-stgnn: Spatio-temporal graph neural network with func- tional and effective connectivity fusion for mci diagnosis. In Hayit Greenspan, Anant Mad- abhushi, Parvin Mousavi, Septimiu Saleudean, James Duncan, Tanveer Syeda-Mahmood, and Russell Taylor, editors, Medical Image Computing and Computer Assisted Intervention MIC- CAI 2023, pages 67-76, Cham, 2023. Springer Nature Switzerland.
[14] Tiago Azevedo, Alexander Camphell, Rafael Romero-Garcia, Luca Passamonti, Richard AT Bethlehem, Pietro Lio, and Nicola Toschi. A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional mri data. Medical Image Analysis, 79:102471, 2022.
[15] Matthew Ainsworth, Zhemeng Wu, Helen Browncross, Anna S Mitchell, Andrew H Bell, and Mark Buckley. Frontopolar cortex shapes brain network strueture across prefrontal and posterior cingulate cortex. Progress in Neurobiology, 217:102314, 2022.
[16] Sonia Turrini, Francesca Fiori, Naomi Bevacqua, Chiara Saracini, Boris Lucero, Matteo Can- didi, and Alessio Avenanti. poike-timing-dependee plasticity induction reveals dissociable supplementary-and premotor-motor pathways to automatic imitation. Proceedings of the National Academy of Sciences, (21(27):2204992521, 2024.
[17] Georg Northoff and Dusan Hirjak. Is depression global brain disorder with topographic dynamic reorganization? Translational Psychiatry, 14(1):278, 2024.
[18] Andrea Santoro, Federico Battiston, Maxime Lucas, Giovanni Petri, and Enrico Amico. Higher- order connectomies of human brain function reveals local topological signatures of task decod- ing, individual identification, and behavior. Nature Communications, 15(1):10244, 2024.
[19] Tal Seidel Malkinson, Dimitri Bayle, Brigitte C Kaufmann, Jianghao Liu, Alexia Bourgcois, Katia Lehongre, Sara Fernandez- Vidal, Vincent Navarro, Virginie Lambrecq, Claude Adam, et al. Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention. Nature Communications, 15(1):2586, 2024.
[20] Xiaolong Peng, Qi Liu, Catherine S Hubbard, Danhong Wang, Wenzhen Zhu, Michael D Fox, and Hesheng Liu. Robust dynamic brain coactivation states estimated in individuals, Science Advances, :3)ebbb8856,, 2023.
[21] Diego Vidaurre, Stephen M Smith, and Mark W Woolrich. Brain network dynamics are hierar- chically organized in time. Proceedings of the National Academy of Sciences, 128(47) 12832, 2017.
11

---

[22] Song Wang, Zhenyu Lei. Zhen Tan, Jiaqi Ding, Xinyu Zhao, Yushun Dong, Guorong Wu, Tianlong Chen, Chen Chen, Aiying Zhang, et al. Brainmap: Learning multiple activation pathways in brain networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 14432-14440, 2025.
[23] Jeremy Kawahara, Colin J Brown, Steven P Miller, Brian G Booth, Vann Chau, Ruth E Grunau, Jill G Zwicker, and Ghassan Hamarneh. Brainnetenn: Convolutional neural networks for brain networks; towards predicting neurodevelopment Newrolmage, 146:
2017.
[24] Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, and Carl Yang. Brain network transformer. Advances in Neural Information Processing Systems, 15:25586-25599 2022.
[25] Jianjia Zhang, Xiaotong Wu, Xiang Tang. Luping Zhou, Lei Wang, Weiwen Wu, and Dinggang Shen. Asynchronous functional brain network construction with spatiotemporal transformer for mei classification. IEEE Transactions on Medical Imaging, (663)1168-1111 2025.
[26] Simon Dahan, Logan ZJ Williams, Daniel Rueckert, and Emma C Robinson. Improving phenotype prediction using long-range spatio-temporal dynamics of functional connectivity. In Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCC 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 4, pages 145-154, Springer, 2021.
[27] Yuhan Bai. felu-function and derived function review. In SHS web of conferences, volume 144, page 02006. EDP Sciences, 2022.
[28] Jiawei Ren, Mingyuan Zhang, Cunjun Yu, and Ziwei Liu. Balanced mse for imbalanced visual regression. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, pages 7926-7935, 2022.
[29] Harald Steck, Chaitanya Ekanadham, and Nathan Kallus. Is cosine-similarity of embeddings really about similarity? In Companion Proceedings of the ACM Web Conference 2024, pages 887-890, 2024.
[30] An Yan, Shuo Cheng, Wang-Chen Kang, Mengting Wan, and Julian McAuley, Cosrec: 2d convolutional neural networks for sequential recommendation. In Proceedings of the 28th ACM international conference on information and knowledge management, pages 2173-2176, 2019.
[31] Shuo Yu, Shan Jin, Ming Li, Tabinda Sarwar, and Feng Xia. Long-range brain graph transformer. Advances in Neural Information Processing Systems, 37:24472-24495, 2024.
[32] Edmund T Rolls, Chu-Chung Huang, Ching-Po Po Lin, Jianfeng Feng, and Marc Joliot. Automated anatomical labelling atlas 3. Neurolmage, 206: 1 16 189, 2020.
[33] Yu Wang, Junxian Mu, Pengfei Zhu, and Qinghua Hu, Exploring diverse representations for open set recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 5731-5739, 2024.
[34] Zijun Zhang. Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th international symposium quality of service (IWQoS), pages 1-2. IEEE, 2018
[35] Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, and Carl Yang. Fbnetgen: Task-aware gon-based fmri analysis via functional brain network generation. In International Conference on Medical Imaging with Deep Learning, pages 618-637. PMLR, 2022.
[36] Xiaoxiao Li, Yuan Zhou, Nicha Dvomek, Muhan Zhang, Siyuan Gao, Juntang Zhuang, Dustin Scheinost, Lawrence Staib, Pamela Ventola, and James S Dunean. Braingnn: Interpretable brain graph neural network for fmri analysis. Medical Image Analysis, 14:102233, 2021.
[37] Dongdong Chen, Mengjun Liu, Zhenrong Shen, Xiangyu Zhao, Qian Wang, and Lichi Zhang. Learnable subdivision graph neural network for functional brain network analysis and inter- pretable cognitive disorder diagnosis. International Conference on Medical image Computing and Computer- Assisteed Intervention, pages 56-66. Springer, 2023.
12

---

[38] Shuo Yu, Shan Jin, Ming Li, Tabinda Sarwar, and Feng Xia. Long-range brain graph transformer. In A. Globerson, Mackey, D. Belgrave, A. Fan, Paquet, J. Tomczak, and Zhang, editors, Advances in Neural Information Processing Systems, volume 37, pages 24472-24499 Curran Associates, Inc., 2024.
[39] Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui, Ying Guo, and Carl Yang. Dynamic brain transformer with multi- levell attention for functional brain network analysis. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1-4. IEEE, 2023.
[40] Qianqian Wang, Wei Wang, Yuqi Fang, Pew-Thian Yap. Hongtu Zhu, Hong-Jun Li, Lishan Qiao, and Mingxia Liu. Leveraging brain modularity prior for interpretable representation learning of fmri. EEEE Transactions Biomedical Engineering, 71(8):2391-2401, 2024.
[41] Dmitry Kobak and Philipp Berens. The of using t-sne for single-cell transcriptomics. Nature Communications, 10(1):5416, 2019.
[42] Sebastian Moguilner, Sandra Baez, Hernan Hernandez, Joaquín Migeot, Agustina Legaz, Raul Gonzalez-Gomez, Francesca Farina, Pavel Prado, Jhosmary Cuadros, Enzo Tagliazucchi, al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nature Medicine, pages 3646-3657, 2024.
[43] Zhengwang Xia, Tao Zhou, Saqib Mamoon, and Jianfeng Lu. Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia. Medical Image Analysis, 94:103133, 2024.
[44] Zhijian Yang, Ila M Nasrallah, Haochang Shou, Junhao Wen, Jimit Doshi, Mohamad Habes, Guray Erus, Ahmed Abdulkadir, Susan M Resnick, Marilyn S Albert, et al. deep learning framework identifies dimensional representations of alzheimer's 's disease from brain structure. Nature Communications, 12(1):7065, 2021.
[45] Kaitlin Stouffer, Xenia Grande, Emrah Düzel, Maurits Johansson, Byron Creese, Menno Witter, Michael Miller, Laura EM Wisse, and David Berron. Amidst amygdala renaissance in alzheimer's s disease. Brain, 147(3):816-88)) 2024.
[46] Han Liu, Zeqi Hao, Shasha Qiu, Qianqian Wang, Linlin Zhan, Lina Huang. Youbin Shao, Qing Wang, Chang Su, Yikang Cao, et al. Grey matter structural alterations in anxiety disorders: A voxel-based meta-analysis. Brain Imaging and Behavior, 18(2):456-74,, 2024.
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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
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A Appendix A: Comparison Results with Standard Deviation
Due to text layout and page limitations, only the mean values from 10 tests are presented in the main text. To ensure comprehensive presentation of the experimental results, we list the mean values and standard deviations (std) for each evaluation metric in Table and Table S
Table 4: Classification results (mean/std) of different methods on the ADNI dataset (%).
ACC
F1
AUC
ACC
AUC
Type
Method
NC vs. MCI vs. AD
NC vs. MCI
BrainNe:CNN
57.06/03.87
38.06/05.63
54.81/05.50
71.34/04.51
81.16/04.21
55.54/07.73
FBNetGen
57.50/06.88
56.97/04.69
67.75/06.11
61.25/09.29
66.46/11.22
58.47/12.03
BNTransformer
60.21/03.78
40.94/07.51
61.96/06.90
69.75/06.17
79.65/04.68
57.58099.83
Static
BrainGNN
58.82/05.48
45.33/09.87
61.66/1M667
74,25/03.72
83.20/02.31
62.88/11.8 1,82
LSGNN
58.75/02.91
36.96/09.56
58.20/04.99
60.27/08.83
68.62/13.00
54.04/08.45
ALTER
63.73/05.70
51.68/07.54
68.98/05.91
76.42/08.96
82.20/07.31
71.11/12.26
ACIFBN 62.35/05.02
57. 17.
70.42/03.40
71.25/04.37
78.96/05.37
64.49/08.50
DRAT 60.83/06.44
51.09/09.14
70.11/08.88
71.50/05.15
79.78/09.00
57.93/19.88
ST-GCN 67.50/05.27
41, 68/09/99
59.07/09.93
73.44/02.52
83.88/17.67
54.22/1 1.43
ST-MMRI
57.45/04.30
38.79/12.07
64.84/07.52
7181/022.9
81.71/03.72
61.4//06.51
Dynamic
STAGIN
56.46/05.62
27.75/05.83
54.76/05.3
69.25/03.54
80.97/01.53
56.44/07.40
OT-MCSTGCN
59.02/06.82
41.31/10.78
58.52/09.15
76.08/7.28
10.14/03.83
60.89/08.05
MGNN
61.76/05.63
51,38/09.65
66.74/06.20
77.46/06.32
13/16/3
70.51/12.48
NeuroH-TGI.
72.19/06.31
57.81/13.21
70.49/12.30
78.50/05.27
84.35/03.88
72.61/07.13
ACC
F1
AUC
ACC
AUC
Type
Method
NC vs. AD
MCI vs. AD
BrainNexCNN
71.47/08.48
50.00/32.92
62.69/16.07
68.92/13.63
75.32/23.49
53.29/11.14
FBNetGen
63.33/07.86
58.50/10.03
57.53/11.74
65,83/10.40
73.71/08.32
64.07/12.72
BNTransformer
74.00/10.68
71.72/07.97
75.89/09.99
74.50/09.07
81.56/08.45
57.64/18.27
Static
BrainGNN
74.05/08.36
62.90/10.90
68.66/08.10
79.19/04.37
87.36/02.79
64. 64.12/12.39
LSGNN
70.42/06.83
53.07/18.88
66.26/13.00
64.05/09.21
73.72/10.06
56.11/07.38
ALTER
75.23/07.32
19.18/24.16
74.25/10.48
82.43/05.70
88.45/04.11
73.50/10.80
ACIFBN
79.17/05.21
71,53/00.66
78.44/08.56
83.75/05.09
90.91/02.81
62/85/65
DRAT
72.92/08.79
55.79/24.21
68.62/10.45
80.31/07.79
88.02/04.79
70.01/17.71
ST-GCN
67.92/04.19
51.46/20.23
60.51/10.43
82.08/0..67
89.81/02.28
61.79/05.12
ST-MMR
76.45/1 1.22
68.40/27.38
78.96/10.92
84.16/05.16
84.61/04.91
62. 16/09.9
Dynamic
STAGIN
67.08/04.73
47.99/12.31
55.49/15.81
74.44/01.11
85.35/00.73
53.71/11.38
OT-MCSTGCN
73.75/08.34
57.61/23.67
66.41/12.45
76.26/06.02
85,40/03.93
68.15/06.89
MGNN
81.35/06.62
77.46/07.68
80.84/08.44
80.54/04.65
86.80/03669
75.43/08.22
NeuroH-TGL.
81.50/06.73
72.12/11.49
83.01/10.12
86.67/04.08
92.46/02.25
76.01/16.12
21

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Table 5: Classification results (mean/std) of different methods on the PD dataset (%)
ACC
F1
AUC
ACC
AUC
Type
Method
vs. TDPD vs. PGPD
NC vs. TDPD
BrainNetCNN
55.70/1
46.09/12.90
67.16/10.13
85.67/06.80
81.98/08.77
83.52/11.72
FBnetGen
62.50/13.11
57.65/16.24
69.23/13.49
14.56/08.13
52.80/31.54
65.65/23.42
Static
BNTransformer
63.75/05.48
56.47/08.10
71.75/03.40
82.50/06.
70.88/24.91
74.83/12.12
BrainGNN
63.49/07.36
57.58/10.14
72.51/06.90
83.67/08.06
78.47/11.21
84.85/10.96
LSGNN
56.88/08.59
16.44/11.69
64.46/10.71
73.75/27.07
74.84/17.88
77.08/1643
ALTER
62.28/07.85
48.47/13.40
63.23/13.95
86.56/09.63
80.83/16.55
78.38/18.57
ACIFBN
61.25/10.75
48.72/17.28
66.33/16.23
82.89/10.93
77.91/16.31
79.80/18.10
DRAT
61.88/08.15
54.72/07.69
64.66/07.81
74.75/12.47
67.53/24.90
79.20/17.45
ST-GCN
58. 18.13/08.41
54.93/10.88
65.56/08.51
82.67/09.02
81,21/09.85
80. 18/18/13/04
Dynamic
ST-fMRI
58.75/08.45 52.10/10.15
66. 16.10/08.19
75.33/13.92
74.04/14.31
73.64/17.50
STAGIN
55.63/07663
43.52/10.77
63.71/04.28
81.89/09.71
74.20/26.
76.55/21.18
OT-MCSTGCN
59.38/08.95
38.24/08.67
62.37/14.32
81.56/10.89
79.31/13.10
81.59/13.40
MGNN
59.89/06.23
11.56/11.43
64.25/12.15
85.78/11.27
83.17/15.54
81.10/19.85
NeuroH-TGL.
66,25/14.58
61.71/16.88
73.85/15.14
91.25/08.00
91.00/08.20
94.21/08.27
ACC
F1
AUC
ACC
AUC
Type
Method
NC vs. PGPD
TDPD vs. PGPD
BrainNe:CNN
79.55/08.81
75.32/16.97
73.10/21.24
74.00/09.95
79.43/09.10
69.78/19.29
FBnetGen
74.55/07.51
78.39/04.73
51.82/25.05
72.00/12.49
73.05/14.63
66.14/19.14
Static
BNTransformer
79.00/09.60
77.18/15.50
78.62/11.08
76.73/09.82
79.50/09.28
70.20/14.43
BrainGNN
83.93/1
75.70/27.87
73.67/18.62
75.00/11883
74.37/25.70
63.06/19.10
LSGNN
74.17/08.98
76.93/09.53
68.47/16.28
72.00/07.48
76.61/08.45
58.62/13.83
ALTER
80.53/07.60
78.55/15.06
74.91/13.03
79.55/08.18
83.11/07.73
68.64/20.59
ACIFBN
76.97/13.27
73.90/26.74
68.59/24.62
74.18/14.29
73.36/26.45
63.79/10.20
DRAT
72.83/12.89
66.37/27.64
61.49/25.87
71.27/07.79
79.67/05.16
59.69/21.38
ST-GCN
79.70/10.05
72.80/20.98
77.15/13.92
74.45/14.45
72.67/27.77
67.44/18.90
Dynamic
ST-MMRI
78.33/09.25
81,79/08.50
74.27/11.15
71.67/07.93
72.96/13.02
44.33/23.31
STAGIN
82.20/06.04
80.89/10.86
79.30/09.40
78.00/13.27
63.00/36.08
59.85/21441
OT-MCSTGCN
82.20/08.69
83.59/09.09
75.50/15.85
75.82/11.14
73.39/25.59
63.05/18.53
MGNN
78.79/09.38
10.14/10.92
73.22/16.48
78.73/07.00
74.98/25.74
69.43/16.27
NeuroH-TGI.
87.17/08.40
89.38/05,29
88.42/12.64
83.75/09.76
86.80/08.57
82.91/15.43
22

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B Appendix B: Ablation Results with Standard Deviation
Similarly, to ensure comprehensive presentation of the ablation results, we list the mean values and standard deviations for each evaluation metric in Table and Table
Table 6: Ablation results (mean/std) of the proposed method on the ADNI dataset (%).
ACC
FI
AUC
ACC
AUC
Method
NC MCI
AD
NC vs. MCI
w/o STPD
66.88/02.50
35.00/05.06
57.22106.68
73.75/04.64
83.20/02.15
62.16/08.47
w/o STHW
65.21/04.85
54.88/11.18
67.21/04.89
74.25/05.25
82.94/02.96
63.90/07.58
w/o STPGC
65.80/04.33
60.13/06.91
70.98/05.18
74.00/04.06
82.58/02.27
64.76/09.32
NeuroH-TGL
72.19/06.31
57.81/13.21
70.49/12.30
78.50/05.27
84.35/03.88
72.61/07.13
ACC
Fl
AUC
ACC
F1
AUC
Method
NC vs. AD
MCI vs. AD
w/o STPD
76.67/06.24
71.42/10.93
76.87/09.74
81.67/03.33
88.77/02.59
72.9513.90
w/o STHW
77.08/05.97
70.36/10.05
74.69/08.72
78.06/04.72
86.63/02.69
67.01/13.40
w/o STPGC
77.91/06.47
72.77/08.43
77.35/09.11
82.08/06.47
89.00/03.65
68. 68.66/20.30
NeuLoH-TGL
81.50/06.73
72.12/11.49
83.01/10.12
86.67/04.08
92.46/02.25
76.01/16.12
Table 7: Ablation results (mean/std) of the proposed method on the PD dataset (%).
ACC
Fl
AUC
ACC
AUC
Method
NC
TDPD vs. PGPD
NC TDPD
w/o STPD
65.00/03.00
55. 15/0.44
64.57/06.56
86.2510.38
87.06/09.10
83.54/15.76
w/o STHW
63.60/03.56
52.48/09.00
65.53/06.12 12
83.50/07.66
80.96/08.49
79.78/12.56
w/o STPGC
64.38/07.42
55.18/08.81
64.40/09.95
84.75/07.94
83.73/07.99
88.53/09.50
NeuroH-TGL.
66.25/14.58
61.71/16.88
73.85/15.14
91.25/08.00
91.00/08.20
94.21/08.27
ACC
F1
AUC
ACC
F1
AUC
Method
NC vs. PGPD
TDPD vs. PGPD
w/o STPD
83.75/09.21
85.76/09.05
86. 86.12/09.29
81.25/10.08
86.21/08.81
70.50/14.04
w/o STHW
79.33/09.13
82.47/77.58
79.93/11.89
78.75/08.80
81.96/15.01
69.91/29.99
w/o STPGC
80.50/09.13
82.64/09.05
81.16/09.47
76.00/08.C
78.23/09.10
70.65/13.63
NeULoH-TGL.
87.17/08.40
89.38/05.29
88.42/12.64
83.75/09.76
86.80/08.57
82.91/15.43
23


Reviews:
[{"review_id": "CskxDwGmOn", "reviewer": "Reviewer_15Vy", "summary": "This paper proposes a strategy for learning temporal brain graphs guided by neural heterogeneity. Innovatively, it decomposes dynamic functional brain networks (DFBNs) into temporal trend networks and spatial consistency networks, thereby identifying key brain regions driving brain network reorganization. Additionally, inspired by brain dynamics, the paper designs temporal propagation graph convolution to fully capture the temporal association between historical states and current connections. The proposed method has been compared with various advanced brain network analysis methods, and demonstrates superior diagnostic performance. Overall, I find this to be an interesting and comprehensive paper.", "questions": "1. The author indicates X=(x_1,x_2,…,x_V) , but does not explain the meaning of x_i. In conjunction with the context, I believe it represents the time series signal for each brain region?\n2. The authors point out that λ_1 and λ_2 in the objective function are determined through grid search. However, there are numerous hyperparameters that need to be determined in the paper; How are they determined?\n3. The PD dataset is relatively small, with a total of only 162 subjects. However, the proposed method is complex. How did the authors mitigate the issue of overfitting when applying deep learning models to small sample datasets?", "limitations": "NA", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. This method, driven by neuroscience findings, proposes decomposing brain connectivity into topologically consistent networks that align with reorganization patterns and temporal trend networks. This paper also designs temporal propagation graph convolutional networks to further simulate the sequential dependencies in the brain.\n2. The authors validate the effectiveness of the proposed method on multiple datasets and classification tasks, and design ablation studies to analyze the reasons for the model's effectiveness. Lastly, the authors perform extensive interpretability analysis to confirm that the method can identify reasonable biomarkers.\n\nWeakness:\n1. The process for calculating spatial and temporal heterogeneity weights relies on cross-window cosine similarity. Have the authors considered the self-attention approaches, and how might these compare?\n2. The motivation of the paper is to identify key brain regions with high spatiotemporal heterogeneity that drive brain reorganization. This is essentially an interpretable approach inspired by neurodynamics. The proposed method should be compared with some of the latest interpretable methods available.\n3. The brain is a smoothly changing dynamic system. The number of sliding windows is a key hyperparameter in constructing dynamic functional brain networks, and the authors lack discussion on it.", "was_revised": true, "final_justification": "Thanks for your response. My concerns are addressed.", "rebuttal": ">**To Reviewer 15Vy:**  \n  \nWe sincerely appreciate the reviewer for acknowledging that the proposed method is interesting and for offering valuable suggestions. We provide detailed responses to the constructive comments.  \n   \n**Answers to Weaknesses:**  \n>**W1: Comparison of Self-Attention.**  \n  \nAs you suggested, we employ the self-attention mechanism (SA) to capture spatio-temporal heterogeneity. Tables R1 and R2 show that SA does not effectively improve diagnostic accuracy. In addition, the NeuroH-TGL based on cosine similarity requires only 0.09 million model parameters and 0.25 million FLOPs. After introducing SA, the model parameters and FLOPs increase to 0.11 million and 1.60 million, respectively. Therefore, we chose the efficient cosine similarity to compute spatio-temporal heterogeneity.  \n\n**Table R1 Accuracy of different methods on the ADNI dataset (%).**\n|          Method        |     NC vs. MCI vs. AD    |     NC vs. MCI    |     NC vs. AD    |     MCI vs. AD    |\n|:----------------------:|:------------------------:|:-----------------:|:----------------:|:-----------------:|\n|     NeuroH-TGL (SA)    |           64.69          |        75.75      |       76.00      |        85.50      |\n|        NeuroH-TGL       |           **72.19**          |        **78.50**      |       **81.50**      |        **86.67**      |\n\n**Table R2 Accuracy of different methods on the PD dataset (%).**   \n|          Method        |     NC vs. TDPD vs. PGPD    |     NC vs. TDPD    |     NC vs. PGPD    |     TDPD vs. PGPD    |\n|:----------------------:|:---------------------------:|:------------------:|:------------------:|:--------------------:|\n|     NeuroH-TGL (SA)    |             59.19           |        90.00       |        86.25       |         77.50        |\n|        NeuroH-TGL      |             **66.25**           |        **91.25**       |        **87.17**       |         **83.75**        |\n\n>**W2: Interpretability Methods.**  \n \nThanks for pointing this out. We compare NeuroH-TGL with two interpretability methods: ALTER [1] and MGNN [2]. The results are summarized in Tables R3 and R4. Evidently, NeuroH-TGL outperforms both baselines. ALTER captures long-range and short-range dependencies, but it overlooks the dynamic properties of the brain. MGNN offers module-level interpretability, but it ignores spatio-temporal heterogeneity, leading to less precise characterization of brain activity. By contrast, NeuroH-TGL not only identifies heterogeneous regions that drive network reconfiguration but also models the temporal propagation mechanism of neural activity, thereby achieving the best performance. These results will be included in the revised version.  \n\n**Table R3 Classification results on the ADNI dataset (%). task1: NC vs. MCI vs. AD; task2: NC vs. MCI; task3: NC vs. AD; task4: MCI vs. AD.**  \n|        Method      |                  |       task1      |                  |                  |       task2      |                  |                  |       task3      |                  |                  |       task4      |                  |\n|:------------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|\n|                    |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |\n|        ALTER       |       63.73      |       51.68      |       68.98      |       76.42      |       82.70      |       71.11      |       75.23      |       59.18      |       74.25      |       82.43      |       88.45      |       73.50      |\n|         MGNN       |       61.76      |       51.38      |       66.74      |       77.46      |       83.16      |       70.51      |       81.35      |       77.46      |       80.84      |       80.54      |       86.80      |       75.43      |\n|     NeuroH-TGL     | **72.19** | **57.81** | **70.49** | **78.50** | **84.35** | **72.61** | **81.50** | **72.12** | **83.01** | **86.67** | **92.46** | **76.01** |\n\n**Table R4 Classification results on the PD dataset (%). task1: NC vs. TDPD vs. PGPD; task2: NC vs. TDPD; task3: NC vs. PGPD; task4: TDPD vs. PGPD.**\n|           Method         |                  |       task1      |                  |                  |       task2      |                  |                  |       task3      |                  |                  |       task4      |                  |\n|:------------------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|\n|                          |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |        ACC       |         F1       |        AUC       |\n|     ALTER                |     62.28        |     48.47        |     63.23        |     86.56        |     80.83        |     78.38        |     80.53        |     78.55        |     74.91        |     79.55        |     83.11        |     68.64        |\n|     MGNN                 |     59.89        |     51.56        |     64.25        |     85.78        |     83.17        |     81.10        |     78.79        |     80.14        |     73.22        |     78.73        |     74.98        |     69.43        |\n| NeuroH-TGL | **66.25** | **61.71** | **73.85** | **91.25** | **91.00** | **94.21** | **87.17** | **89.38** | **88.42** | **83.75** | **86.80** | **82.91** |\n\n>**W3: Sensitivity Analysis of T and S.**  \n  \nThanks for the kinder reminder. The number of overlapping windows T and window length S jointly determine the construction of the brain network. Specifically, T changes within the set {5, 6, 7, 8, 9, 10}, and S varies within the set {60, 70, 80, 90, 100}. We conducted a grid search on both S and T to ensure the optimal parameter combination. Given space limitations, we explore the impact of T and S on accuracy using the NC vs. MCI and NC vs. TDPD tasks as examples, with the results recorded in Tables R5 and R6, respectively. For NC vs. MCI, the best diagnostic performance is achieved when T=6 and S=90. For NC vs. TDPD, the best diagnostic performance is achieved when T=8 and S=80. Larger values of T and S lead to longer overlapping sequences across windows, which might smooth out valuable dynamic information in the brain. Conversely, smaller values of T and S result in shorter time sequences per window, potentially making the statistical correlation between brain regions unreliable. Therefore, a moderate window size can balance reliable statistical correlation with temporal evolution. We will discuss this in the revised version.  \n\n**Table R5 Impact of T and S on accuracy for the NC vs. MCI task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     68.89    |     69.84    |     70.59    |     71.80    |     73.30    |     70.63    |\n|      70    |     70.30    |     73.50    |     73.75    |     74.50    |     72.25    |     71.81    |\n|      80    |     76.08    |     76.42    |     78.00    |     77.21    |     74.49    |     72.25    |\n|      90    |     75.50    |     **78.50**    |     78.15    |     77.50    |     76.25    |     73.27    |\n|     100    |     77.96    |     76.23    |     74.50    |     71.81    |     68.25    |     67.89    |\n\n**Table R6 Impact of T and S on accuracy for the NC vs. TDPD task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     83.75    |     85.25    |     86.78    |     88.75    |     87.50    |     86.25    |\n|      70    |     84.69    |     86.56    |     88.67    |     90.89    |     87.76    |     84.69    |\n|      80    |     88.75    |     90.00    |     90.00    |     **91.25**    |     89.80    |     86.89    |\n|      90    |     85.78    |     88.75    |     90.50    |     90.82    |     88.76    |     85.71    |\n|     100    |     85.00    |     86.75    |     86.73    |     86.25    |     84.67    |     83.67    |\n\n**Answers to Questions:**  \n>**Q1: $x_i $.**  \n  \nAs you expected, $x_i $ represents the time series signal of the $i^{th}$ brain region, and we will clarify this in the revised version.  \n\n>**Q2: Determination of Hyperparameters.**  \n  \nFor other hyperparameters, such as the brain network threshold $\\alpha$, we also determine it by grid search. Specifically, $\\alpha$ is searched within {0.3, 0.4, 0.5, 0.6, 0.7, 0.8}. We incorporated these parameters into an outer loop during model training so that the optimal setting could be identified through grid search. The results show that optimal performance can be achieved when $\\alpha$=0.6.\n\n>**Q3: Mitigating Overfitting.**  \n  \nWe understand your concern about overfitting. In fact, to mitigate overfitting, we have taken the following measures. First,  we adopt an early stopping mechanism that 80 epochs patience in total 300 epochs. Second, stratified cross-validation is used to evaluate model performance. It ensures that the proportions of different classes are consistent in both the training and validation sets, thereby preventing the model from overfitting to specific subsets.\n\n[1] Long-range Brain Graph Transformer. NeurIPS 2024  \n\n[2] Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI. IEEE TBME 2024"}, {"review_id": "2OtDBuYZ1H", "reviewer": "Reviewer_9ryA", "summary": "This paper highlights a significant gap in existing DFBN analysis methods, specifically the neglect of spatio-temporal heterogeneity in the brain, which limits the understanding of the brain network's evolutionary process. To bridge this gap, the authors propose a neuro-heterogeneity guided temporal graph learning framework. This framework includes a decoupling module that separates temporal and spatial information, a heterogeneity weighting module that identifies heterogeneous nodes based on the decoupled networks, and a temporal propagation graph convolution module that comprehensively extracts spatio-temporal features. The proposed method is validated on the Alzheimer's and Parkinson's diseases datasets. The experimental results demonstrate the effectiveness of the proposed method.", "questions": "(1) In Eq (4), Eq (5), and Eq (6), the authors lack a definition for .\n(2) According to the context, it should be modified from  to .\n(3) In the temporal propagation graph convolution section, the authors employ convolutional neural networks to extract temporal features. However, the size of the convolutional kernel is not clear.", "limitations": "See weaknesses and questions for details.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n(1) This work introduces a neuro-heterogeneity guided method for analyzing dynamic functional brain networks, considering the spatio-temporal heterogeneity of brain regions and encoding the relationship between their current and historical states.\n(2) This work conducts binary and ternary classification experiments, demonstrating that the proposed method is superior to current state-of-the-art approaches and has clinical significance.\n(3) The spatio-temporal analysis of DFBNs is an important topic in the field of neuroscience research, and the method is novel and well-designed. The authors make the source code publicly available, ensuring reproducibility.\n\nWeakness:\n(1) It is unclear whether all the dynamic brain network thresholds are the same.\n(2) In this work, most of the comparative methods are based on graph convolution approaches. In fact, Graph Transformers also perform well because of their ability to capture long-range node interactions. The authors should compare with the following methods:\n[1] Do transformers really perform badly for graph representation? NeurIPS 2021\n[2] Long-range Brain Graph Transformer. NeurIPS 2024\n(3) It is unclear how the experimental results of the comparison methods are obtained. are they directly excerpted from other papers? Is the code open-source? Additionally, how are the hyperparameters of these methods determined?\n(4) In Figure 5, different colors represent the relative importance of brain regions. It is unclear whether this importance refers to the P-value or the spatio-temporal connection strength of each brain region.", "was_revised": false, "final_justification": "", "rebuttal": ">**To Reviewer 9ryA**  \n  \nWe sincerely appreciate the reviewer's assessment that the proposed method is innovative and reasonable, and for providing valuable suggestions. We provide detailed responses to the constructive comments.  \n  \n**Answers to Weaknesses:**  \n>**W1: Threshold.**  \n  \nThe threshold $\\alpha$ for dynamic brain networks is the same. In the experiments, $\\alpha$ is varied within {0.3, 0.4, 0.5, 0.6, 0.7, 0.8}, and we use grid search to determine the optimal value. The experimental results show that optimal performance can be achieved when $\\alpha$=0.6 for different diagnostic tasks.  \n>**W2: Comparison based on Graph Transformer.**  \n  \nFollowing your suggestion, we have added Graphormer and ALTER as comparison experiments. As can be seen from Tables R1 and R2, our proposed method continues to demonstrate superior performance. This is because although the aforementioned two methods can depict the complex spatial structure of the brain, they both overlook the dynamic characteristics of brain networks. In contrast, our method not only effectively captures the inherent neural heterogeneity but also simulates the temporal propagation mechanism of the brain, thus achieving superior results.  \n\n**Table R1 Classification results on the ADNI dataset (%). task1: NC vs. MCI vs. AD; task2: NC vs. MCI; task3: NC vs. AD; task4: MCI vs. AD.**  \n\n|         Method       |              |     task1    |              |              |     task2    |              |              |     task3    |              |              |     task4    |              |   |\n|:--------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:-:|\n|                      |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |   |\n|       Graphormer     |     65.94    |     39.17    |     56.61    |     77.65    |     82.39    |     71.60    |     75.68    |     62.50   |     71.32   |     82.16    |     88.62    |     76.21    |   |\n|         ALTER        |     63.73    |     51.68    |     68.98    |     76.42    |     82.70    |     71.11    |     75.23    |     59.18    |     74.25    |     82.43    |     88.45    |     73.50    |   |\n|     NeuroH-TGL       |     **72.19**    |     **57.81**    |     **70.49**    |     **78.50**    |     **84.35**    |     **72.61**    |     **81.50**    |     **72.12**    |     **83.01**    |     **86.67**    |     **92.46**    |     **76.01**    |   |  \n\n**Table R2 Classification results on the PD dataset (%). task1: NC vs. TDPD vs. PGPD; task2: NC vs. TDPD; task3: NC vs. PGPD; task4: TDPD vs. PGPD.**  \n\n|        Metohd      |              |     task1    |              |              |     task2    |              |              |     task3    |              |              |     task4    |              |\n|:------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|                    |      ACC     |      F1      |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |\n|      Graphormer    |     59.23    |     48.05    |     61.73    |     84.67    |     74.95    |     83.24    |     78.86    |     77.99    |     73.00    |     77.73    |     77.86    |     62.09    |\n|        ALTER       |     62.28    |     48.47    |     63.23    |     86.56    |     80.83    |     78.38    |     80.53    |     78.55    |     74.91    |     79.55    |     83.11    |     68.64    |\n|     NeuroH-TGL     |     **66.25**    |     **61.71**    |     **73.85**    |     **91.25**    |     **91.00**    |     **94.21**    |     **87.17**    |     **89.38**    |     **88.42**    |     **83.75**    |     **86.80**    |     **82.91**    |  \n\n>**W3: Source of Comparison Results.**  \n  \nThanks for the thoughtful suggestion. Due to the use of different datasets, we are unable to directly excerpt experimental results from the original papers. The codes of the comparison methods are all open source, and we use the open source code to obtain experimental results on the datasets used in this paper. To ensure fairness, the hyperparameters of the comparison methods are also determined through grid search.  \n\n>**W4: Explanation of Figure 5.**  \n  \n In Figure 5, different colors indicate the relative importance of brain regions. This importance represents the spatio-temporal connectivity strength of the brain regions. It is worth noting that the statistical p-values of these brain regions are all less than 0.05. This means that the spatio-temporal connectivity strength of these brain regions can effectively distinguish different groups.  \n\n**Answers to Questions:**  \n>**Q1: Symbol Definition.**  \n  \nThanks for your question. Unfortunately, your question is not fully displayed. Considering that there are the same symbols $\\parallel \\parallel_2$ in Eq (4), Eq (5), and Eq (6), we speculate that you are asking about the meaning of this symbol. $\\parallel \\parallel_2$ represents the $L_2$ norm. We will clarify this in the revised version to facilitate reader understanding.  \n  \n>**Q2: Size of Convolution Kernel.**  \n  \n The size of the convolution kernel is 3×3. We will clarify this in the implementation details."}, {"review_id": "BBfo5QefmF", "reviewer": "Reviewer_k1cj", "summary": "This paper introduces NeuroH-TGL, a novel graph-based temporal learning framework for dynamic functional brain network (DFBN) analysis using fMRI data. The key innovation lies in explicitly modeling neuro-heterogeneity by decoupling DFBNs into topological consistency and temporal trend networks, then mining their spatio-temporal heterogeneity. A temporal propagation graph convolution network (TPGCN) is proposed to model the influence of historical neural states. The method is validated on two datasets (ADNI and a Parkinson’s dataset), demonstrating superior diagnostic accuracy and biomarker identification capability compared to 11 state-of-the-art baselines.", "questions": "•\tWhile the paper introduces TPGCN to capture historical dependencies, it would be helpful to compare it directly to recurrent alternatives (e.g., GRU, LSTM) in a small-scale ablation or discussion.\n•\tCan the method support subject-level interpretability? For example, can we visualize which brain regions were most influential for a given prediction?\n•\tHow does the method scale with higher-resolution parcellations or different brain models? A brief runtime/memory complexity analysis or report would help the discussion.\n•\tImpact of sliding window size (S): Since the window size governs network granularity, a sensitivity analysis on this hyperparameter would be insightful. (It will also be solved when the authors publish their codes and detail settings)", "limitations": "•\tYes. The authors have provided a thoughtful discussion on the unimodal nature of the input data and propose future integration with structural connectivity—a reasonable and commendable limitation statement.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "•\tThe paper demonstrates high technical quality. The formulation is solid, the modules are clearly motivated, and experimental evaluation is thorough. Results are statistically robust with detailed ablation, visualization, and reproducibility efforts. The introduction of heterogeneity weighting in both spatial and temporal dimensions is particularly well-justified and novel.\n•\tThe manuscript is generally clear and well-organized. Some terminology (e.g., \"neuro-heterogeneity\", \"topological consistency network\") may be unfamiliar to readers outside neuroimaging and might benefit from brief intuitive definitions early in the text. Figures (e.g., Fig.2 heterogeneity diagram) are informative, though captions could offer more interpretation.\n•\tThe work is significant in the context of fMRI-based brain disease diagnosis, a high-impact and medically relevant field. The framework addresses long-standing challenges of spatio-temporal heterogeneity in DFBNs, offering a methodological advancement that could generalize beyond the tested datasets.\n•\tThe paper’s originality lies in (1) decoupling DFBNs into distinct spatio-temporal substructures, (2) introducing heterogeneity mining via cross-window similarity analysis, and (3) a tailored propagation mechanism (TPGCN) to model sequential dependencies. These contributions are novel and nontrivial.\n•\tAs discussed in limitation part, the method performs well on two fMRI datasets, but It remains unclear how the method generalizes to other cognitive or psychiatric modalities, and different fMRI-based tasks (other than classification).\n•\tAlthough group-level biomarker identification is discussed, the model lacks a mechanism to generate subject-specific explanations, which are increasingly expected in neurodiagnostic tools.", "was_revised": true, "final_justification": "The authors' rebuttal is solid and provide enough information, but I wish to keep me original score since it was high enough and the dicussion did not persuade me for a strong acceptance.", "rebuttal": ">**To Reviewer k1cj:**  \n  \nWe sincerely appreciate the reviewer's recognition of the novelty of the proposed method and the comprehensiveness of the experimental analysis. We provide detailed responses to your constructive comments.  \n  \n**Answers to Weaknesses:**  \n>**W1: Terminology.**  \n  \nThanks for this kinder reminder. Neuro-heterogeneity indicates that certain brain regions have more extensive connections and more pronounced temporal changes. Topological consistency networks refers to the brain connections that maintain a stable state. Temporal trend networks refer to the brain will dynamically adjust the connections according to cognitive demands while maintaining stable connections of certain nodes. This spatio-temporal coordination forms the neural basis that supports complex cognitive functions. We will provide intuitive definitions in revised version to facilitate understanding for readers outside of neuroimaging. \n \n>**W2: Figure Caption.**  \n  \nIn the revised version, we will provide more detailed explanations for the figures to enhance their readability.\n\n>**W3: Generalization.**  \n \nWe understand your concern about the model's generalization. NeuroH-TGL shows excellent performance across many diseases, including MCI, AD, TDPD and PGPD, which proves its generalizability to some extent. In future research, we will extend this method to applications such as brain age prediction and brain decoding.\n\n**Answers to Questions:**  \n>**Q1: Small-Scale Ablation.**  \n  \nFollowing your suggestion, we replaced TPGCN with GRU and LSTM for an ablation study.  From Tables R1 and R2, We can find that TPGCN achieves superior performance. This is because GRU and LSTM focus more on the temporal evolution and overlook the topological properties of the brain. TPGCN is capable of modeling the temporal influence of historical brain topology on the current network structure, thereby more comprehensively capturing spatio-temporal dependencies.\n \n **Table R1 Results on the ADNI dataset (%). task1: NC vs. MCI vs. AD; task2: NC vs. MCI; task3: NC vs. AD; task4: MCI vs. AD.**  \n|        Method      |              |     task1    |              |              |     task2    |              |              |     task3    |              |              |     task4    |              |\n|:------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|                    |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |\n|       NeuroH-TGL (GRU)     |     66.67    |     60.79    |     69.92    |     77.21    |     83.46    |     69.28    |     76.02    |     60.93    |     73.35    |     82.70    |     88.57    |     75.69    |\n|      NeuroH-TGL (LSTM)     |     63.33    |     53.58    |     66.31    |     76.96    |     83.84    |     67.27    |     78.02    |     71.93    |     75.21    |     81.35    |     87.94    |     74.10    |\n|     NeuroH-TGL     |     **72.19**    |     **57.81**    |     **70.49**    |     **78.50**    |     **84.35**    |     **72.61**    |     **81.50**    |     **72.12**    |     **83.01**    |     **86.67**    |     **92.46**    |     **76.01**    |  \n\n**Table R2 Results on the PD dataset (%). task1: NC vs. TDPD vs. PGPD; task2: NC vs. TDPD; task3: NC vs. PGPD; task4: TDPD vs. PGPD.**  \n|        Method      |              |     task1    |              |              |     task2    |              |              |     task3    |              |              |     task4    |              |\n|:------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|                    |      ACC     |      F1      |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |      ACC     |       F1     |      AUC     |\n|       NeuroH-TGL (GRU)     |     65.29    |     53.97    |     64.84    |     85.89    |     73.79    |     74.79    |     80.45    |     79.71    |     73.27    |     77.64    |     82.03    |     77.23    |\n|      NeuroH-TGL (LSTM)      |     64.23    |     52.85    |     64.86    |     88.67    |     85.58    |     88.90    |     85.45    |     83.21    |     77.69    |     82.55    |     77.55    |     79.87    |\n|     NeuroH-TGL     |     **66.25**    |     **61.71**    |     **73.85**    |     **91.25**    |     **91.00**    |     **94.21**    |     **87.17**    |     **89.38**    |     **88.42**    |     **83.75**    |     **86.80**    |     **82.91**    |  \n\n>**Q2: Subject-Level Interpretability.**  \n  \nThanks for your valuable feedback. NeuroH-TGL offers subject-level interpretability by calculating the spatio-temporal heterogeneity across all brain regions for each subject, identifying areas with significant changes. Additionally, the spatio-temporal heterogeneity of corresponding brain regions varies among different subjects, which can characterize the diverse impacts of brain diseases on the same brain regions across subjects. Therefore, NeuroH-TGL can offer personalized biomarkers. We apologize that image or file uploads are not permitted in this rebuttal, preventing us from providing visual results.  \n\n>**Q3: Computational Efficiency.**  \n  \nThanks for the insightful comment. We compare the average time for 1 inference, number of model parameters, and floating-point operations (FLOPs) between NeuroH-TGL and some spatio-temporal models. The results are shown in Table R3. The inference time for NeuroH-TGL is 0.0236 seconds, longer than other spatio-temporal models. This may be because it performs spatio-temporal heterogeneity mining and weighting for multiple brain networks separately, and performs a large number of convolutions during feature extraction. Notably, NeuroH-TGL only requires 0.0924 million parameters and 0.2479 million FLOPs, making it the most efficient among the compared methods. Overall, NeuroH-TGL can achieve superior performance with fewer resources, indicating good scalability. We will clarify this in the discussion section.  \n\n**Table R3 Computational Efficiency Analysis.**  \n|       Method      |     Inference time (s)    |     #Parameter (M)    |     FLOPs (M)    |\n|:-----------------:|:-------------------------:|:--------------------:|:----------------:|\n|       ACIFBN      |           0.0123          |         3.9087       |      20.7483     |\n|       ST-GCN      |           0.0145          |         2.7978       |       5.8566     |\n|       ST-fMRI     |           0.0130          |         0.5023       |      264.2269    |\n|       STAGIN      |           **0.0044**          |         0.2723       |      24.1312     |\n|     OT-MCSTGCN    |           0.0050          |         4.1417       |       6.9713     |\n|     NeuroH-TGL    |           0.0236          |         **0.0924**       |       **0.2479**     |  \n\n>**Q4: Sensitivity Analysis of T and S.**  \n  \nWe understand your concerns about the parameters related to the window. In fact, the number of overlapping windows T and window length S jointly determine the construction of the brain network. Specifically, T changes within the set {5, 6, 7, 8, 9, 10}, and S varies within the set {60, 70, 80, 90, 100}. We conducted a grid search on both S and T to ensure the optimal parameter combination. Given space limitations, we explore the impact of T and S on accuracy using the NC vs. MCI and NC vs. TDPD tasks as examples, with the results recorded in Tables R4 and R5, respectively. For NC vs. MCI, the best performance is achieved when T=6 and S=90. For NC vs. TDPD, the best performance is achieved when T=8 and S=80. Larger values of T and S lead to longer overlapping sequences across windows, which might smooth out valuable dynamic information in the brain. Conversely, smaller values of T and S result in shorter time sequences per window, potentially making the statistical correlation between brain regions unreliable. Therefore, a moderate window size can balance reliable statistical correlation with temporal evolution. We will discuss this in the revised version.  \n**Table R4 Impact of T and S on accuracy for the NC vs. MCI task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     68.89    |     69.84    |     70.59    |     71.80    |     73.30    |     70.63    |\n|      70    |     70.30    |     73.50    |     73.75    |     74.50    |     72.25    |     71.81    |\n|      80    |     76.08    |     76.42    |     78.00    |     77.21    |     74.49    |     72.25    |\n|      90    |     75.50    |     **78.50**    |     78.15    |     77.50    |     76.25    |     73.27    |\n|     100    |     77.96    |     76.23    |     74.50    |     71.81    |     68.25    |     67.89    |\n\n**Table R5 Impact of T and S on accuracy for the NC vs. TDPD task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     83.75    |     85.25    |     86.78    |     88.75    |     87.50    |     86.25    |\n|      70    |     84.69    |     86.56    |     88.67    |     90.89    |     87.76    |     84.69    |\n|      80    |     88.75    |     90.00    |     90.00    |     **91.25**    |     89.80    |     86.89    |\n|      90    |     85.78    |     88.75    |     90.50    |     90.82    |     88.76    |     85.71    |\n|     100    |     85.00    |     86.75    |     86.73    |     86.25    |     84.67    |     83.67    |"}, {"review_id": "TJzUl0YRYc", "reviewer": "Reviewer_4GxD", "summary": "The authors propose a novel spatio-temporal network, NeuroH-TGL, designed for brain disease analysis. The core idea is to collaboratively capture spatial density and temporal variability in brain networks, which allows the model to better address node-level heterogeneity. Experimental results demonstrate that NeuroH-TGL improves diagnostic performance across multiple brain disease datasets.", "questions": "• Could the authors clarify the motivation for using a 2D CNN instead of a multilayer perceptron (MLP) in Equation (9)? Given that the output of a GCN is not grid-structured, it is more common to use an MLP for feature transformation in such settings. Additional justification for this architectural choice would help readers understand its intended advantage.\n\n• The paper uses a simple concatenation operation to obtain the global spatio-temporal representation. Could the authors elaborate on why this approach was chosen over more established methods for temporal modelling? For example, recurrent frameworks like GConvLSTM [1] or attention-based fusion strategies such as FE-STGNN [2] could provide more expressive modelling of temporal dependencies. A discussion or ablation comparing these alternatives would strengthen the paper.\n\n• If I understand correctly, the ADNI dataset is collected across multiple sites, which may introduce variability in temporal resolution. How does NeuroH-TGL address this potential issue when segmenting the fMRI data into graphs? Clarifying how the model accounts for or normalizes these temporal inconsistencies would be helpful.\n\nReference:\n1. Seo Y, Defferrard M, Vandergheynst P, Bresson X. Structured sequence modeling with graph convolutional recurrent networks. InNeural information processing: 25th international conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, proceedings, part I 25 2018 (pp. 362-373). Springer International Publishing.\n2. Chen D, Zhang L. Fe-stgnn: Spatio-temporal graph neural network with functional and effective connectivity fusion for mci diagnosis. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 67-76). Cham: Springer Nature Switzerland.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n• The authors effectively identify and address spatio-temporal heterogeneity, a crucial challenge that is often overlooked in recent brain network analysis methods. By decoupling spatial and temporal patterns and enhancing their discriminability, NeuroH-TGL demonstrates improved diagnostic capabilities compared to existing approaches.\n\n• To model temporal interactions, the authors introduce a Temporal Propagation Graph Convolutional Network (TPGCN). This component significantly contributes to classification performance by capturing meaningful temporal dependencies.\n\n• The overall performance of NeuroH-TGL is good.\n\nWeaknesses:\n• The sliding window length (S) and the total number of windows (T) are crucial hyperparameters that influence the construction of dynamic brain graphs. While the authors use different values of S and T for different diseases, these parameters are not discussed in the sensitivity analysis. This omission limits the reader’s understanding of the method’s robustness and reproducibility.\n\n• The performance of NeuroH-TGL exhibits higher variance compared to baseline methods. This raises concerns about the model's stability and consistency across runs or datasets, which warrants further analysis or justification.", "was_revised": true, "final_justification": "The proposed NeuroH-TGL demonstrates good performance; however, it appears somewhat sensitive to the sliding window configuration and exhibits higher variance across different settings.", "rebuttal": ">**To Reviewer 4GxD:**  \n  \nWe sincerely appreciate the reviewer's recognition of the proposed method and the experimental performance, as well as the valuable comments provided. We provide detailed responses to the constructive comments. \n \n**Answers to weaknesses:**  \n>**W1: Sensitivity Analysis of T and S.**  \n  \nWe understand your concerns about the sliding window. In fact, the number of overlapping windows T and window length S jointly determine the construction of the brain network. Specifically, T changes within the set {5, 6, 7, 8, 9, 10}, and S varies within the set {60, 70, 80, 90, 100}. We conducted a grid search on both S and T to ensure the optimal parameter combination. Given space limitations, we explore the impact of T and S on accuracy using the NC vs. MCI and NC vs. TDPD tasks as examples, with the results recorded in Tables R1 and R2, respectively. For NC vs. MCI, the best diagnostic performance is achieved when T=6 and S=90. For NC vs. TDPD, the best diagnostic performance is achieved when T=8 and S=80. Larger values of T and S lead to longer overlapping sequences across windows, which might smooth out valuable dynamic information in the brain. Conversely, smaller values of T and S result in shorter time sequences per window, potentially making the statistical correlation between brain regions unreliable. Therefore, a moderate window size can balance reliable statistical correlation with temporal evolution. We will discuss this in the revised version.  \n\n**Table R1 Impact of S  and T on accuracy for the NC vs. MCI task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     68.89    |     69.84    |     70.59    |     71.80    |     73.30    |     70.63    |\n|      70    |     70.30    |     73.50    |     73.75    |     74.50    |     72.25    |     71.81    |\n|      80    |     76.08    |     76.42    |     78.00    |     77.21    |     74.49    |     72.25    |\n|      90    |     75.50    |     **78.50**    |     78.15    |     77.50    |     76.25    |     73.27    |\n|     100    |     77.96    |     76.23    |     74.50    |     71.81    |     68.25    |     67.89    |\n \n**Table R2 Impact of S  and T on accuracy for the NC vs. TDPD task (%).**  \n|     S\\T    |       5      |       6      |       7      |       8      |       9      |       10     |\n|:----------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      60    |     83.75    |     85.25    |     86.78    |     88.75    |     87.50    |     86.25    |\n|      70    |     84.69    |     86.56    |     88.67    |     90.89    |     87.76    |     84.69    |\n|      80    |     88.75    |     90.00    |     90.00    |     **91.25**    |     89.80    |     86.89    |\n|      90    |     85.78    |     88.75    |     90.50    |     90.82    |     88.76    |     85.71    |\n|     100    |     85.00    |     86.75    |     86.73    |     86.25    |     84.67    |     83.67    |  \n\n>**W2: Model Stability.**  \n  \nThanks for the valuable comment. We agree that the proposed method exhibits higher variance, particularly in PD diagnosis tasks. This might be attributed to the following reasons. First, the sample size of the PD dataset is relatively small, which leads to unstable performance of the model on the test set. Moreover, as shown in Tables 4 and 5 in Appendix A, the standard deviation of dynamic brain network analysis methods is generally higher than that of static brain network analysis methods. This phenomenon may be due to the fact that dynamic analysis methods require simultaneous optimization of spatio-temporal feature extraction, leading to a loss function with more local optima. Consequently, the higher standard deviation associated with dynamic methods is a common characteristic in this field. Notably, although our method has a slightly higher standard deviation than the baseline methods, its average diagnostic performance is significantly better than theirs. Overall, the higher standard deviation of our method is acceptable. In future research, we plan to increase the sample size and the number of modalities to further enhance the model's stability.  \n\n**Answers to Questions:**  \n>**Q1: Motivation of CNN.**  \n  \nThanks for the great advice. We chose 2D CNN to process the output of GCN for two main reasons. First, the spatio-temporal heterogeneity of the brain is essentially a local property. After GCN extracts local spatial dependencies, 2D CNN can further model the local temporal correlations of the brain. However, MLP treats all node features equally, which leads to the neglect of local temporal dependencies. Therefore, combining 2D CNN with GCN can capture complementary spatio-temporal associations. Second, in order to intuitively compare the performance of CNN and MLP, we conduct ablation experiments on two datasets. As shown in Tables R3 and R4, CNN outperforms MLP in diagnostic accuracy. The evidence indicates that CNN is a more reasonable choice.  \n**Table R3 Ablation results on the ADNI dataset (%).**  \n|          Method         |     NC vs. MCI vs. AD    |     NC vs. MCI    |     NC vs. AD    |     MCI vs. AD    |\n|:-----------------------:|:------------------------:|:-----------------:|:----------------:|:-----------------:|\n|     NeuroH-TGL (MLP)    |           69.06          |        74.78      |       76.07      |        84.68      |\n|        NeuroH-TGL       |           **72.19**          |        **78.50**      |       **81.50**      |        **86.67**      |  \n  \n**Table R4 Ablation results on the PD dataset (%).**  \n  |          Method         |     NC vs. TDPD vs. PGPD    |     NC vs. TDPD    |     NC vs. PGPD    |     TDPD vs. PGPD    |\n|:-----------------------:|:---------------------------:|:------------------:|:------------------:|:--------------------:|\n|     NeuroH-TGL (MLP)    |             64.26           |        87.78       |        85.00       |         78.55        |\n|        NeuroH-TGL       |             **66.25**           |        **91.25**       |       **87.17**       |         **83.75**        |  \n\n >**Q2: Concatenation Operation.**  \n  \nIn fact, we have considered using established recurrent frameworks or attention to obtain global spatio-temporal features. Tables R5 and R6 illustrate the impact of GConvLSTM and attention-based fusion methods on accuracy. The results show that the simple concatenation operation performs comparably to the aforementioned methods, and even outperforms them in some cases. This is likely because the proposed TPGCN can effectively encode the dynamic influence of historical brain states on the current network, and concatenating the outputs of TPGCN directly is already sufficient to characterize global spatio-temporal features. If an additional complex framework is employed, it might cause the model to overfit to the noise within the data, thereby losing critical spatio-temporal patterns and increasing computational costs. Consequently, we employ the efficient concatenation operation to obtain the global spatio-temporal features.\n\n**Table R5 Ablation results on the ADNI dataset (%).**  \n|             Method            |     NC   vs. MCI vs. AD    |     NC   vs. MCI    |     NC   vs. AD    |     MCI   vs. AD    |\n|:-----------------------------:|:--------------------------:|:-------------------:|:------------------:|:-------------------:|\n|     NeuroH-TGL (GConvLSTM)    |            68.44           |         75.67       |        78.45       |         84.78       |\n|     NeuroH-TGL (Attention)    |            70.94           |         77.95       |        79.67       |         85.55       |\n|           NeuroH-TGL          |            **72.19**           |         **78.50**       |        **81.50**       |         **86.67**       |\n\n**Table R6 Ablation results on the PD dataset (%).**  \n|             Method            |     NC   vs. TDPD vs. PGPD    |     NC   vs. TDPD    |     NC   vs. PGPD    |     TDPD   vs. PGPD    |\n|:-----------------------------:|:-----------------------------:|:--------------------:|:--------------------:|:----------------------:|\n|     NeuroH-TGL (GConvLSTM)    |              63.64            |         87.50        |         78.93        |          78.82         |\n|     NeuroH-TGL (Attention)    |              64.71            |         **92.50**        |         84.58        |          83.75         |\n|           NeuroH-TGL          |              **66.25**            |        91.25       |         **87.17**        |          **83.75**         |  \n\n\n>**Q3: Temporal Resolution.**  \n  \nThanks for the insightful comment. As you mentioned, the ADNI is a multi-site dataset. NeuroH-TG does not address this temporal inconsistency. We mitigate this challenge from the perspective of data processing. The fMRI data used are acquired by 3T Philips scanners with consistent parameters, and all have the same acquisition duration. The specific scanning parameters are as follows: repetition time=3000ms, echo time=30ms, slice thickness=3.31mm, and the whole-brain images consist of 48 slices. After obtaining the raw fMRI data, we employ the same preprocessing pipeline for brain signal extraction. The preprocessing steps included correction, realignment, and detrending techniques to mitigate the interference from external factors. Finally, we use the same AAL template to parcellate all the fMRI data into 90 brain regions with 140 time points. Therefore, we ensure the temporal resolution consistency of brain graphs across different subjects from the data processing perspective."}]

Author rebuttal:

Meta-review: This paper proposes NeuroH-TGL, a neuro-heterogeneity guided temporal graph learning framework for dynamic functional brain network analysis. The method introduces a decomposition of brain connectivity into temporal and spatial components, a heterogeneity weighting mechanism, and a temporal propagation graph convolution module to model historical dependencies. Across multiple datasets (ADNI and PD), the approach consistently outperforms strong baselines, with thorough ablations, interpretability analyses, and additional experiments provided during rebuttal. Reviewers highlighted the paper’s novelty, solid technical formulation, and clinical significance, while concerns about sensitivity to hyperparameters and variance across runs were acknowledged but reasonably addressed. Overall, the contributions are novel, well-validated, and impactful for spatio-temporal brain network modeling.
Decision: Accept (poster)