Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
Authors: Hwa Hui Tew
Junn Yong Loo
Leong Fang Yu
Julia K. Lau
Ding Fan
Hernando Ombao
Raphael CW Phan
Chee Pin Tan
Chee-Ming Ting
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Models
Time Series
Flow Matching
Primary area: applications to neuroscience & cognitive science

Abstract: Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the resource-intensive nature of fMRI acquisition limits the availability of high-fidelity samples required for data-driven brain analysis models. While modern generative models can synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals. To address these challenges, we propose Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. Specifically, our framework first converts BOLD signals into a wavelet decomposition map via a discrete wavelet transform (DWT) to capture globalized transient and multi-scale variations, and projects into the discrete cosine transform (DCT) space across brain regions and time to exploit localized energy compaction of low-frequency dominant BOLD coefficients. Subsequently, a spectral flow matching model is trained to generate class-conditioned cosine-frequency representation. The generated samples are reconstructed through inverse DCT and inverse DWT operations to recover physiologically plausible time-domain BOLD signals. This dual-transform approach imposes structured frequency priors and preserves key physiological brain dynamics. Ultimately, we demonstrate the efficacy of our approach through improved downstream fMRI-based brain network classification.

Published as a conference paper at ICLR 2026
FUNCTIONAL MRI TIME SERIES GENERATION VIA WAVELET-BASED IMAGE TRANSFORM AND SPECTRAL FLOW MATCHING FOR BRAIN DISORDER IDENTIFICATION
Hwa Hui Tew!; Junn Yong Loo'r, Fang Yu Leong', Julia K. Lau', Ding Fan', Hernando Ombao², Raphaäl C.-W. Phan', Chee Pin Tan?, Chee-Ming Ting"
1 Schooo of Information Technology, Monash University Malaysia School of Engineering, Monash University Malaysia
"Statistics Program, King Abdullah University of Science and Technology
fhwa. tew, 1oo. hoo.jooooooonng.. junnyong, ting. cheemi wtweeeiggeemmmaaeeeeeeeeeeeeeaee @monash. edu
ABSTRACT
Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) sig- nals over time. However, the resource-intensive nature of fMRI acquisition lim- the availability
high-fidelity samples required for data-driven brain anal- ysis models. While modern generative models synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals. To address these challenges, we propose Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. Specifically, our framework first converts BOLD signals into a wavelet decomposition map via discrete wavelet transform (DWT) to capture globalized transient and multi-scale variations, and projects into the discrete cosine transform (DCT) space across brain regions and time to exploit localized energy compaction of low-frequency dominant BOLD coefficients. Subsequently, spectral flow matching model trained to gener- ate conditiond cosine-frequency representation. The generated samples are reconstructed through inverse DCT and inverse DWT operations to recover phys- iologically plausible time-domain BOLD signals. This dual-transform approach imposes structured frequency priors and preserves key physiological brain dynam- ics. Ultimately, demonstrate the efficacy of approach through improved downstream fMRI-based brain network classification. The code is available at http://ggihbb.commheew/////M/.f
INTRODUCTION
Recent advances in deep generative modeling have shown promising capability in synthesizing real- istic yet diverse variations of neuroimaging modalities (Yap et al., 2024). Among available modali- ties, functional MRI (fMRI) signals offer non-invasive view of neuronal activity, critical for diag- nosing neuropsychiatric and neurodevelopmental disorders (Noman et al., 2022 2024). However, IMRI data collection is costly and yields limited, often imbalanced datasets (Tew et al. 2025b). These shortcomings limit the generalizability of data-driven brain analysis models, ultimately af- fecting the reliability of computer-aiee aided clinical tools for neurological and psychiatric conditions (Bollmann & Barth 2021 Ting et al.) (022). To address these challenges, generative models have been explored for iMRI signal synthesis to support data augmentation and downstream applications
(Power et al. 2014
Tew et al. 2025b).
"Equal contribution.
1 "oorrsponniig author.

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Most existing approaches generate brain connectivity directly in the functional connectivity (FC) space, where BOLD signal dependencies are summarized by a single correlation matrix (Biswal & Oddin 2025). For instance, Tan et al. (2024b) proposes a DCGAN that preserves connectomic structure and improves the performance of downstream FC classifiers. Similarly, BrainFC-CGAN jointly trains adversarial and supervised loss components to preserve the subject identity of real FC on synthetic samples (Tan et al. (2024). However, such FC representations encode static pair- wise relations into dyads and do not effectively capture transient network states within human brain networks (Shabestari et al. 2025).
Recent works have revisited iimein modeling of fMRI as an alternative to correlation-basee functional connectivity (FC). Yuan & Qiao (2024) designs diffusion-TS, a denoising diffusion prob- abilistic model (DDPMM for tMRI time series data generation, showing improved robustness over GANs and (Variational Autoencoder) VAE-based generative models. [Hu et al. (2024) proposes FM- TS that accelerates the sampling step yet provides quality synthetic samples via a flow matching framework. While these methods shift focus from traditional FC to time-series data generation, their feasibility and effectiveness for neuroimaging tasks remain largely unexplored. We argue that limiting generative modeling to FC matrices the raw time series is inadequate to faithfully produce the brain's transient state, multiscale oscillations, and cross-frequency interactions due to difficulties in disentangling physiologically driven fluctuations (e.g., cardiac pulsation, respiratory cycles, motion-induced artifacts) (Biswal & Uddin (025). In contrast, a fimeenrequencyyscale repre- sentation that captures time and spectral BOLD information can fully reproduce the rich spatiotem- poral dynamics of BOLD signals. Motivated by T2I-Diff and ImagenTime. both of which frame time-series signals as an image-generation task (Tew al. 2025b Naiman al., 2024). T2I-Diff specifically remodeled and validated the feasibility of this time- freeufryy image- based approach for generating BOLD signals. Crucially, the performance gains were modest due to the rixouutesolttion Short-Time Fourier Transform (STFT) representation, which neglects fine-grained transients and attenuates frequency amplitude modulations, leading to artifacts during the image-to-signal recon- struction (Tew et al., 2025b).
To address these issues, in this paper, we propose Dual-Spectral Flow Matching (DSFM), an fMRI generation framework that cascades two spectral transformations of BOLD signals and integrates a spectral flow matching for generative modeling. Our framework first decomposed BOLD signals using the discrete wavelet transform (DWT) to form multiresolution time-scale scalogram images. Subsequently, we compute a discrete cosine transform (DCT) that exploits low- lw-frreuuency BOLD coefficients. These transforms produce a dual-spectral view in which local and global dynamics are jointly represented. Additionally, our framework introduces a spectral-domat flow matching for efficient and high-fidelity generation of the time-scale fMRI scalograms conditioned on subject classes. The generated fimeffrequency scalograms are then reverted to BOLD signals via image-to- time series transforms. Our main contributions are summarized as follows:
1. Our proposed DSFM framework is the first to jointly leverage DWT and DCT, forming a unified dual-spectral image transform to capture both global and local spatiotemporal and spectral features for fMRI BOLD signal generation and brain disorder classification.
2. We develop spectral flow matching to model a heat dissipation process in the DCT domain achieve efficient, coarse-to-fine generation aligned with the frequency hierarchy of the dual-spectral representation. This enables DSFM to leverage the spectral sparsity inherent in iMRI signals effectively capture diverse brain profiles.
3. Our results show that DSFM demonstrates strong performance on unconditional and con- ditional spectral image synthesis, and achieves improvement in brain disorder classification compared to recent time-series and fMRI generation baselines.
METHODS
2.1 DISCRETE WAVELET TRANSFORM AND ITS INVERSION
Fig. provides an overview of proposed framework. Given high- digh-dimensional fMRI signals from subjects, denoted as X = {2,15-1 where each subject x, E R DxT consists of D regions of interest (ROIs) recorded over T time points, objective is to learn the underlying real data
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Proceesing
Step 2: Dusi Spectrol Transformation
DCT- OCT-Space Flow Matching and ODE Sampling
Petch spintig Frequecc Compachios en OCT
Spectrul Menchhin
Step Eveluation
Cassifiiation
Step 4. FMRI BOLD Signals Reconstruction
Recall
Precision
F1 Call
HIDO
RDC
Figure I: The pipeline of DSFM. ROI-based BOLD time series are first extracted, followed by DWT- based multiresolution decomposition and blockwise 2D DCT for localized spectral encoding. U-ViT used to model the velocity field in the DCT domain for ODE-based sampling. The reconstructed signals (via IDCT and IDWT) then used for data augmentation, FC matrix construction, and classification. Finally, fidelity and downstream performance are evaluated.
distribution Pdama (X) and generate a synthetic distribution (2') that is statistically indistinguishable from the real data. Unlike conventional time-series generative tasks that operate exclusively in the time domain, our approach transforms fMRI time series into time-scale images using the DWT, defined as follows:
W (Kk
Σ ((n) (.]] n=1
(1)
where (n) is the BOLD signal at local time index n 21.2....,N? N). Here, d 2-k/2 (=2 the dyadic wavelet basis function, where scale (1, [log, NJ) controls the frequency resolution, and translation index (1,2,..., N/22} determines the time location, derived from the mother wavelet d [n]. To construct a wavelet decomposition map, we upsam- ple each wavelet subband to the original time length and stack them along the scale axis, form- ing a multiresolution wavelet decomposition map. Thus forming a full wavelet coefficient tensor W ji k) D where TĄ N/2 and C llog, NJ. that captures both low-frequency trends and high-frequency transients in the fMRI BOLD signals. We further perform component- wise normalization to accentuate the difference between high and low coefficients over brain regions and time. As shown in Fig. 2 this allows the time-series signals to be represented as multichannel images with preserved
characteristics.
To reconstruct the original signals from the generated scalogram representation, we first denormal- ize the predicted wavelet components (i k) E TTC of each 7th ROI. The coefficients are then downsampled according their corresponding dyadic scales and computed the inverse DWT (IDWT) to obtain the fMRI BOLD signals as follows:
C T
a(n) = N
Σ
k=1j=1
Finally, these wavelet subbands are reconstructed through a hierarchical combination of approxima- tion and detail components across all scales to obtain the reconstructed time-domain signal â, for each subject. This process ensures that the inherited spectral-tempora characteristics the original fMRI BOLD signals pree-preserved.
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Prontal_Sup Sup L.
Incerst (KO1) intorsti (Otg) (ndu)ioo (P04)
Derail (1H1)
Detail 2 0 H21
Denail (LH3)
Detail (1
Detail H5)
Approsimation (1
-00-
Bapion el Ragiin
80
100
Syn.KC
500
Began
0
NO0
Bagen of
Tirs
Tims
Temm
Tirr
Tirrs
Timr
Tins
Figure 2: Original (Rows 1&3) vs. synthetic BOLD signals (Rows 2 &4) and generated normalized scalograms. Our framework generates new synthetic BOLD signals as opposed to correlation matri- or functional connectivity, with distributional statisties that closely match the original samples.
2.2 DISCRETE COSINE TRANSFORM FOR BOLD SIGNALS
To extract localized energy compactions of low-frequency spontaneous BOLD coefficients. We divide each subband map (i.j) e PxT, of each Athh wavelet scale into non-overlapping 2D blocks of size B, resulting in a set of blocks (patches):
w
with (x,y) B
p=1
where P is the number of blocks per subband image and B is the block size. Each block is then transformed via 2D type-1l DCT, as follows:
(2x 1)uT'
(2y + 1)UT
(u, a(u) a(v)
ΣΣ
w (x, cos
cos
(4)
2B
where au)) / if and a(u) otherwise. The resulting ((k) (u, v) E BxB
each scale k represents the DCT coefficients within each block.
To recover the full image representation, apply the inverse 2D DCT (IDCT) each block (u, v). The signal block is reconstructed via the following inverse transform:
B (2,9) = ΣΣ (()) (u, v) cos
(2x 1)ux
(2y + 1)UT
cos
(5)
2B
2B
Once all blocks have been transformed back into the original spatial domain, stitch the patches to recover the full subband map. Since the DCT is applied to non-overlapping blocks, the recon- struction involves simply tiling the inverse-transformed blocks back into their original positions within the subband image. This blockwise DCT preserves localized low-frequency structure in the ROI-time space while discarding high-frequency noise components. The resulting of filtered subbands can then be passed back into the IDWT to recover the time-domain fMRI BOLD signal it (n),, ensuring global and local spectral characteristies are retained.
2.3 SPECTRAL FLOW MATCHING IN DCT DOMAIN
Recent studies have empirically demonstrated that pixel-based diffusion models exhibit approximate autoregressive behavior in the frequency domain (Dieleman, 2024; Falck et al. 2025). Specifically, diffusion models (Ho et al. 2020; Song et al.) 2021) tend to eliminate high- high-frequency components early in the forward process, followed by progressively nower-frequenee components as the diffusion timestep advances. While prior studies focus the Fourier basis, this property also holds in the
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DCT domain (Skorokhodov al. 2025 Ning et al. 2025). which offers practical advantages: real- valued orthogonality, energy compaction in low-frequency bands, and compatibility with blick-wise architectures.
Modeling diffusion directly in the frequency domain enables the exploitation of spectral sparsity for designing frequency-anne aware noise schedules. However, existing frequency-domain generative models (Hoogeboom & Salimans, 2023 Rissanen et al. (023) remain constrained to the diffusion framework, which relies on stochastic differential equation (SDE) sampling and typically requires hundreds to thousands of steps for high-quality synthesis. In contrast, flow-matching approaches based on ordinary differential equations (ODEs) provide deterministic alternative with significantly lower sampling complexity. In this work, we introduce spectral flow www-machhing framework that extends frequency-band -aased generative modeling beyond the diffusion paradigm.
First, consider forward-time heat dissipation process (Rissanen et al., 2023) as an alternative to the conventional isotropic diffusion, deseribed by the following stochastic partial differential equation (SPDE):
dæ r7(t) =x)) + (t),
where xe 122 1R+ + R is an idealized continuous-space representation of a single image channel at time E [0, 11, and A. V. Ve is the Laplace operator with respect to the spatial image coordinates e; (t)) and G(t) are time-dependent scalar drift and matrix-valued diffusion coefficients, respectively. The corresponding reverse-timg probability flow ODE (Song et al. (02I) is given by:
dxa dt
1
= n(t) (.()). - G(t)G(t)) Va, logp(xz))
Subsequently, define the forward and inverse DCT transforms formally as
VT - DCT(#)), 24 = Va, IDCT(=;),
(8)
where denotes the matrix of orthonormal DCT basis eigenvectors. It then follows that the Lapla- cian operator in equation 6 can be diagonalized via the eigendecomposition A = V A VT, where denotes the diagonal matrix of DCT mode-specific Laplacian eigenvalues. Applying DCT to the forward-time SPDE e yields
d2p = (t)) A 2 dt + G(t) (t),,,
where W(t) is standard Wiener process, but in the DCT domain. To obtain a frequency-ordered representation, we apply zig-zag flattening, which maps the two-dimensional DCT coefficient grid into one-dimensional sequence sorted from low (upper-left) to high (bottom-right) frequencies. Subsequently, we apply per-DCT-mode signal-to-noiss scaling to ensure that the DCT coefficients conform the forward perturbation process of the proposed DCT flow governed by the SPDEs in equation Band equation Moreover, given that V is orthonormal, the following change-of-variable holds for any differentiable function f:
0z dx
xz)((())= = V
V vef(2)= VTV V v.f((²) v.f(2).
By letting f (xxe) log p(x)), the score transforms V logp(z;) = V., log Since A is diagonal and the DCT basis orthogonalizes the frequency modes, applying DCT to equation 7 the reverse-time probability flow ODE admits the following mode-wise decomposition:
dz dl
1 2 g(t, A:)² V 2a[M 2. logp(=)).
n(t) d [k]
(10)
where d the k-th diagonal entry of A, corresponding the Laplacian eigenvalue of the 1 DCT basis component, which evolves independently under the ODE dynamics.
The following proposition bridges between this DCT mode-wise probability flow ODE and the conditional velocity (vector) field in flow matching (Lipman et al. (202)).
Proposition A mode-wise conditional perturbation kernel (isotropic in each DCT mode) is
p(z [k] za [k]) N Nu(t, k) 20 o(, k))))
(11)
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Table 1: Comparison of unconditional Netsim dataset generation across SOTA and proposed model.
CoT-GAN
DiftTime
DiffWave
TimeVAE
TimeGAN
Diffusion-TS
T2I-Diff
DSFM (Ours)
cFID
7.8134.550
0.340 015
0.244:.018
14.4494.969
0.126::002
0.108:.006
1.384:.107
0.193:1017
Cor:.
26.824#.449 449
1,501 + 048
3,927+-049
17.296#.520
23,502+ 039
1,411+.022
4.121 094
4.552++041
Disc.
0,492:.018
0.245#.051
0.402:.029
0,4761.044 044
0.4841.042
0,1674.023
1.400::.099
0.4972.001
Pred.
0.185:4.003
0.100=0000
0.101:1.00
0.113:4.003
0.126:1.002
0.099:1.000
0.102:+.001
0.104:1.000
with mean and standard deviation (std) schedules pt(t, a() wtt, k), w(t,k)) e AuT(e)
= -1
(12)
where att) is the mean schedule of variance-preserving (VP) diffusion process and r(t)
So (s) ds, satisfies the heat dissipation process O and (9). The mode-wise diffusion coefficients are then given by
g(t, 20 (o(t, f(t,k) ot,
(13)
where k(),k) d(l)) a(T) n(t) Ab. and k)),,k), (1,, denote fime-derivatives of the mean and std
schedules in 72
Proof. Refer to the Supplementary Material.
Proposition A mode-wise conditional velocity field
d2e
[k]
u(zelzoit, k) ju(t, k) [k] + ött, k) e,
(14)
dt
zo [k]
where c ~ (O,,)), 1), equivalent to the conditional probability flow ODE
[z([k]
1 --( A 21[k]] 9 V log
(15)
[0|0]
Furthermore, it follows that the marginal velocity field
dz vzz,t, (psm((=olze) [0(2)] 20: t.k
(16)
dt
given by the law of the unconscious statistician (Lipman al. 2024), satisfies the marginal mode- wise probability flow ODE (TO).
Proof. Refer to the Supplementary Material.
Given this correspondence between the probability flow ODE from diffusion models and flow match- ing, we parameterize the velocity field vo using deep neural network (U-ViT (Bao et al., 2023)
and train it via the following conditional spectral flow matching (CSFM) loss:
CCSFM Ex, ==)pant==)]]lop (Ze5 beeett,A - k))l?
(17)
where u2 20: is the conditional velocity field in (14). with 2, sampled from the por- conditional perturbation kernel (TI). and ~ 24 1) is uniformly sampled. Notably, this CSFM loss recovers the standard flow matching loss under the OT-CFM schedules p(t) (t) t, where the time convention adopted here is the reverse of that in (Lipman et al. 2023). Hence, our framework generalizes flow matching to a heat dissipation process in the DCT domain. In our experiments, we use a<t) from the variance-preservinn (VP) cosine schedule and set 7()
f and
Tmax sin? (5)) following (Hoogeboom & Salimans, 2023). which observes optimal performance with max 20.
To enable class-conditioned generation, we employ classifier-free guidance (Ho & Salimans 2021) by conditioning the velocity model on the class label c, i.e., vp(2;; t, ki c) and set c Ø for the unconditional model. The conditional and unconditional models are trained jointly by randomly replacing the class label with the null token Ø with probability Pa. During sampling, the classifier- free guided velocity is obtained weighted combination of the model outputs (Zheng et al. 2023). Finally, DCT samples are generated by numerically integrating the learned flow velocity using an adaptive ODE solver.

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Table 2: The best generation quality and classification performance the MDD dataset for different generative models using the AAL atlas parcellation, trained on ground-truig data augmented at three levels. "_" denotes FC-based generation. Full results refer the Supplementary Material.
WiO Aug.
Vanilla GAN
2D 3D.DCGAN
TimeGAN
Diffusion
T21 Diff
DSFM (Ours)
Metric
Real (R.)
R. + Synth. 3x
R. Synth. 3x
R. Symth. 1x
R. Synth. 1x
R. Synth. 1x
R. R.+ Synth. 1
Context-FID
4,98 0.65
2.06 + 0.21
7.45 + 0.42
1,51 + 0.41
Correlational
197.05 + 17.75
64.16
3.92
62.32 1.04
57.30 +-000++00 2.80
Accuracy
58.90
2.98
08.86
2.24
62.88
+
66. = 1.06
67.29
1.81
66.87 3.222
70.84 5.89
Recall
58.90
2.98
58.80
2.24
62.88
4.99
86.78 1.66
67.29
1.81
68.87 + 4-3.2
70.84 +-44+:10 5.89
Precision
59.56
2.74
59.91
2.57
63.12 5.02
67.14 1 1.70
67.55
1.97
67.06 3.34
70.99 1.800
FI-Score
58.39 = 3.09
37.64
1.96
62.
5.25
66.48 = 1.96
67.21 1.87
68.83 4: 3:21
70.77
5.97
ROC
59.00 =0:00:2:0 2.50
08.57
2.05
62.67
5.15
67. = 2.81
61.57
2.76
67.26
71-19
5.73
Table 3: The best generation quality and classification performance on the ABIDE dataset for dif-
ferent generative models using the Schaefer parcellation, trained on ground-truth data augmented at three levels. denotes FC-ba generation. Full results refer to the Supplementary Material.
W/ Aug.
Vanilla GAN
2D 3D-DCGAN
TimeGAN
Dillassoo TS
T2I-Dii
DSFM (Ours)
Metrie
Real (R.)
R Synth. 2x
R. + Synth. 1x
Synth. 1x
R. Synth. 1x
R.+ Syath. 1x
R.+ Synth. 1x
Context-HID
4.25 1.30
0.31 0.97
0.82
Concelational
238. 70 + 5.49
26 26.13-2 83
25.75 1.67
13.42 2.17
Accuracy
155
61.87
66.39
66
1.55
1.87
Rocall
64.67 + 1.56
61.87 + 1.05
65.63 = 1.91
66.59 1.00:22.0 2.50
06.60 100+141 1.41
69.09 1.55
71.11 1.87
Precision
65.77 2.77
65.06 + 1.28
66.03 2.44
67.93 + 1.87
68.60 + 1.41
69.78
1.74
73.08
3.00
FI-Soore
64.12 1.39
61.75
+ 0.93
65.26 = 1.90
65.92 + 2.92
66.58 1/11
69.65 = 1.53
70.98 + 2,30
ROC
67.28 4.
68.07 + 2.88
68.44 1.23
67.49 3.26
68.85 3.64
71.88 2.47
73.78 + 3.64
EXPERIMENT
3.1 SETTINGS
Data Acquisition and Pre-processing. In our experiments, we evaluated the proposed method on one simulated and two real-world brain disorder datasets. (1) Major Depressive Disorder (MDD): We preprocessed the resting-state fMRI (rs-fMRI) dataset from the REST-meta-MDD Consortium database Yan al. 2019) using the Data Processing Assistant for Resting-State fMRI (DPARSF)
Yaa & Zang 2010). This dataset comprises 250 Healthy Controls (HC) subjects and 227 individuals diagnosed with Major Depressive Disorder (MDD). All scans were acquired using a Siemens Tim Trio 3T scanner TR/TE- 2000/30 ms, and slice thickness of 3mm. The brain was parcellated into 116 ROIs, covering cortical and subcortical areas, and the mean BOLD signal for each ROI was extracted across 232 time points using the Automated Anatomical Labeling (AAL) atlas. (2) Autism Brain Imaging Data Exchange (ABIDE): We preprocessed rs-IMRI scans from multiple international sites. This dataset includes 488 Autism Spectrum Disorder (ASD) patients and 537 normal controls (NC) from the ABIDE database. The brain was parcellated into 100 ROIs using the Schaefer atlas, We then extracted mean BOLD signal for each ROI over 200 time points (Di Martino et al. 2014). (3) NetSim: We used the NetSim dataset, a simulated benchmark for evaluating causal discovery neuroimaging. NetSim provides biologically realistic simulations of BOLD time series, we selected Simulation 4 with 50 channels from the original dataset (Smith el al. 2011
3.2 IMPLEMENTATION DETAILS
DSFM Training. The proposed DSFM framework generates fMRI signals corresponding to the subjects' condition. The classifiers then discriminate between control and clinical groups for each dataset. We train the DSFM using an Adam optimizer with learning rate of 2e over 300k iterations. All experiments employ Haar wavelet basis with a 5-level decomposition, yielding real- valued images of size 116x232 (MDD) and 100 (ABIDE). We compare numbers function evaluations (NFE) of 20, 50, and 100 steps. Connectivity Network Construction. The subject-speciffe functional connectivity is derived using the Ledoit- Wolf (LDW) regularized shrinkage covariance estimator to preserve the strongest 40% connections, resulting in a sparse 116x 116 (MDD) and 100 100 (ABIDE) FCs with all other connections zero.
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Table 4:
Ablation analysis of frequency-specifie FC classification by incorporating individual and
grouped wavelet subbands on the MDD dataset.
Wavelet Subtands
Accuracy
Precision 1
F1-Score T
ROC'
Setting
LHI
LH2
LH3
LH4
LH5
LL
Value
Drop (%)
Value
Drop (1)
Value
Drop (5)
Value
Drop (%)
Full-band
/
70.84
70.99
70.77
71.49
x
66.89
-5.58
66.96
-5.68
66.77
-5.65
65.79
-7.97
Mid-pase
-0.64
Highopass
x
x
65.40
-7.68
65.55
-7.66
65.18
-7.90
63.66
-11.0
Rand-pass
/
-6.20
Band pass 2
/
X
66.66
-5.90
66.88
5.79
66.60
-5,89
66.74
6.64
Bund-pass-
/
67.77
Data Augmentation and Classifier Training. The trained DSFM used to augment real fMRI signals by factors of xx, 2x, and 3x. For our classifier, the L2 regularization weight decay is from 10- to 10-2, the scheduler learning rate reduction factor is from 0.1 to 0.9, and the batch size from 5 to 16, the same in (Tew al. 2025b). All hyperparameters are selected based on a 5-fldd stratified cross- vrossvaalidtiin.
3.3 OVERALL PERFORMANCE
We first trained our proposed DSFM unconditionally to produce comparable outputs in Table ! Then, followed the standard setting for the quality and classification evaluation of the conditional time-series generation as described in section D Our primary goal is to achieve better cFID score to model complex spatiotemporal patterns and excel in capturing conditional distribution over time.
Classification Score. To validate the fidelity of the generated samples, evaluate the classifica- tion performance BrainNetCNN (Kawahara et al.] 2017), comparing DSFM GAN and diffusion- based baselines on our fMRI dataset. Here, we use the parameter setting of NFE 100 in sub- sequent downstream analyses, as supported by the quality metries of distinguishing HC and MDD sub- jects in Table 7 Table [ and 3 reports the clas- sification results on the 5-fold cross-validation test set. Notably, DSFM achieves the highest accuracy under I x (MDD) and 1 x (ASD) data augmen- tation setting. Moreover, variance across increased augmentation levels, indi- cating strong generalization and robustness. These results confirm that DSFM not only enriches sam- ple diversity but also preserves discriminative neu- rophysiological functional patterns critical for clin- ical tasks. Figure further demonstrates that our proposed DSFM model excels in generating class- conditioned synthetic data whose statistical distribu- tion closely matches that of the original samples.
ELLAN thtt MIDII
Dtermmncc 2011
X3H
model exhibits lower
Figure 3: We plot the 2D t-SNE embedding of HC and MDD synthetic data generated by our method (top left). Then, we compare with the distributions using Sensen-Shannon Divergence and probability density functions (top right and bottom).
3.4 ABLATION STUDIES
We first conducted ablation study on six wavelet detail bands, i.c., LHI: 0.125 0.250Hz. LH2: 0.0625 0.125Hz, LH3: 0.03125 0.0625Hz, LH4: 0.015625 0.03125Hz, LH5: 0.007825 0.015625Hz, and a coarse approximation LL: 0 0.007825Hz, contrasting each setting with the full 0 to 0.25Hz spectrum. Table 4 assesses the impact of different wavelet subbands on model performance. The steepest decline occurred when the mid-frequency LH3-LH4 pair was removed, highlighting the pivotal role of 0.01- Hz oscillations to capture disease-specific interactions due to insufficient contextual information. Suppressing either the highest (LHI-LH2) or the very lowest components (LL and LH5) produced a comparable, still significant degradation (5 (5-8%), indicating that both rapid fluctuations and slow drifts provide complementary cues. Conversely, removing in-
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Avtrage Front
Medl 0000
Hil Crigulam
HRF:
L2:0,0031
12:0.0017
12:0.0086
Anolaaca JuAt
2
E
30
13
-6
Average TSD: Pront Sup Ned
Aetrage PSD: Front Sup Med
Average FSD: Ceguuum Post
Average PSD Origu um Post
1..3
Prokson
Speere Oue cosematte
6.22
3.0
300
300
C:00
3.5
8.50
6.33
0.23
0.53
6.35
6:23
8.5
6.23
025
5.10
Figure 4: Visualization of the average resting-state hemodynamic response function (rsHRF) and power spectral density (PSD) of real and synthetic BOLD in the Medial Prefrontal Cortex (mPFC) and Posterior Cingulate Cortex (PCC) region of the Default Mode Network (DMN). Highlighted L.2 norm quantifies the generation and synthetic results closely resemble the real physiological profiles.
dividual bands such as LHI and LL also reduced performance by 5-7%, indicating that long-range and slow drifts carry global synchrony patterns essential for classification. Interestingly, we observe that although BOLD fluctuations predominantly lie in the cow-frequency band, removing any sub- bands impaired performance, indicating that relast- features distributed across the entire frequency spectrum.
Table 5 presents ablation analyses of different configurations evaluating normalization strate- gies, block sizes and wavelet bases influence the generative quality of our dual-spectral represen- tation. In particular, parison of MinMax normalization (MM) with the Entropy- Scaling (ECS). Notably, MinMax scales each wavelet coefficient inde- pendently, broadening the distribution of high- frequency coefficients, which results in slower training and reduced performance. In contrast, ECS preserves the global spectral coefficient by normalizing DCT frequency components using a percentile-trimmet bound derived from the low- est frequency component, providing better cFID and correlation scores by maintaining the origi- nal coefficient distribution.
Table 5: Ablation studies of block sizes, wavelet bases, normalization strategies, different spec- tral representations and generative models. Full results refer to the Supplementary Material.
and 2) show com-
Configurations
cFID4
Corr
1) B = 4. MM
1.505=0.:41
57.3
2) B 4, ECS
0.098++0.01
18.2+1.41
3) B 2, Haar
0.121+0003
19.7#3.03
4) B Haar
0.098+0001
18.2+1.41
5) B
dB-4
0.199:+0.10
20.7+3.25
DSFM (Ours)
0.098
18.2
Experiments with smaller and larger block size B in 3) and 4) achieve comparable generation performance, with a tradeoff of a smaller B will lead to slower training, larger B leads to the loss of fine ane-grained local dependencies. Finally, the ablations in 4) and 5) using different mother wavelets produces similar generation results on the MDD dataset. This further exemplifies that the underlying fMRI signals do not exhibit strong wavelet-specific bases sensitivity. Additional ablations in Section F.2 further illustrate results for different spectral representations (Fourier and wavelet transforms) and types of generative models (flow matching and diffusion models), against our proposed DSFM.
Table 6: Similarity between synthetic and real FC networks across FC edges, node strength, and
edge betweenness centrality. Higher values indicate better preservation of real FC topology.
Metric
Vanilla-GAN
ID-DCGAN
2D-DCGAN
WGAN
WGAN-GP
DSFM (Ours)
FC Edges 1
0.53 0.05
0.10 0.11
0.54 + 0.49
0.51 4: 0.47
0.52 0.52::0.11
0.99 0.00
Node Strength ?
0.67 + 0.08
0.30 + 0.16
0.53 = 0.08
0.64 4: 0.09
0.62 :: 0..3
0.99 + 0.00
Edge Betweenness Centrality 1
0.11 + 0.02
0.06 + 0.02
0.14 + 0.02
0.14 + 0.02
0.15 + 0.02
0.77 + 0.09

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MCC
Diffrrncc
Real: MEDI
Sym MDDI
Real: MKC
Real:
HI!
Somt MDD
Figure 5: a) Group-averaged connectivity patterns of real and synthetic HC/MDD connectivity pat- terns and their differences. b) and c) Subject-level connectivity patterns of real and synthetic data from HC and MDD, respectively. d) 3D cortical surface and brain networks visualizations showing node strength (top) and network organization (bottom) for both real and synthetic HC/MDD data.
4 NEUROPHY
PLAUSIBILITY ANALYSIS
Figure 4 presents the qualitative and quantitative comparisons of resting-state hemodynamic sponse function (rsHRF) and power spectral density (PSD) between real and synthetic signals in two key hubs of the Default Mode Network (DMN). The near-perfect overlap of the HRF plots in- dicates that DSFM preserves the canonical temporal dynamics the hemodynamic process, rather than merely matching marginal statistics. Likewise, the close alignment of the PSD curves indicates that the synthetic samples exhibit realistic fMRI spectral characteristics, accurately capturing the dominant low-freque cowffreuuency peaks and the spectral decay across low- and high-frequency components. The low L2 error for both HRF and PSD provides evidence that DSFM effectively learns underlying spectral-temporal dynamics of the BOLD signals. Overall, these analyses suggest that our model generates peurophysiologically plausible synthetic signals that are suitable for different downstream tasks, as further supported by the brain disorder classification performance Tables and
FUNCTIONAL CONNECTIVITY (FC) ANALYSIS AND VISUALIZATION
Table 6 further evaluates the fidelity of the generated data FC matrices derived from real and syn- thetic fMRI BOLD signals. Across all graph similarity metrics, DSFM shows higher Pearson cor- relation with the real data than other GAN-based models, indicating more realistic synthesis of FC networks in both connectivity edges and network topology. These results demonstrate that DSFM not only reproduces plausible spectral-temporal BOLD signals dynamies but also faithfully cap- tures higher-order network transformations, reflecting more coherent interdependencies among FC edges than existing BAN--ased generative models. Figure [ visualizes group-averaged connec- tivity, thresholded at 0.6 to highlight significant edge connections. Our analysis reveals that the synthetic FC closely aligns with functional changes observed in the real FC distribution. Fur- thermore, the HC and MDD connectograms between both real and synthetic FC indicate reduction in intra-network connectivity within the left superior frontal gyrus (FrontalSupL) and weakened coupling between the left middle frontal gyrus (FrontalMidLL) and the left anterior cingulate cortex (CingulumAmtL.). The results suggest impaired cognitive functions associated with difficulties in decision-making and emotion regulation, indicating the biological plausibility of the generated data.
CONCLUSIONS AND FUTURE WORK
In this paper, propose DSFM, which effectively captures both temporal dynamics and spectral evolution underlying the ground-trun truth data distribution for accurate brain signals generation. Future work will further validate MDD and ASD classification using graph-based deep learning models
(Tew et al. 2024 2025a) and incorporate energy-based models to identify out-of-distribution (OOD)
patterns in brain spectogramms/calogrrass associated with neurological disorders (Loo al.
2025
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ACKNOWLEDGMENTS
This work was supported part by the Monash University Malaysia and the Ministry of Higher Ed- ucation, Malaysia under Fundamental Research Grant Scheme FRGS///2023ICCO 2/MUSM/02/1, and by the King Abdullah University of Science and Technology (KAUST) Grant. The authors also acknowledge the support of the Advanced Computing Platform (ACP), Monash University Malaysia, for providing computational resources.
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A APPENDIX
This appendix provides self-contained additional material for the submission titled "Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification". It includes a detailed about related works, proofs and derivations, the evaluation metrics, full experimental results, limitations, reproducibility statement, well as the use of large language models (LLMs).
B RELATED WORKS
B.1 GENERATIVE MODELING OF FMRI TIME SERIES.
Synthesizing fMRI BOLD signals is challenging due to the complex spatiotemporal dependencies, non-stationarity. and interferences arising from neurophysiological fluctuations. Existing time-series generation is principally based on generative adversarial networks (GANs), variational autoencoders (VAEs), and liffusion-based frameworks.
GAN-based approaches: Yoon et al. 2019) proposes TimeGAN by extending GAN framework with an embedding function and supervised loss to better capture temporal dynamics, successfully preserving both the static and dynamic characteristics of synthetic time-series data. COT-GAN introduces a causality-aware optimal transport cost, further aligning real and synthetic samples over time and reducing dependenn discrepancy between them (Xu et al. 2020).
VAE-based approaches: TimeVVE incorporates temporal components into its encoder-decoder network, improving the interpretability of generated time series. Furthermore, it demonstrates suc- cess in reducing overall training time compared to adversarial methods (Desai et al. 2021).
Diffusion-based approaches: DiffTime improves time-series generation by applying hard con- straints to enforce fixed points and global minima; alongside soft constraints introduce penalties to guide the model towards desired temporal trends (Coletta al. 2023). DiffWave achieves high- fidelity time-series generation by replacing autoregressive dependencies with a diffusion denoising chain (Kong al., 2020). More recently, ImagenTimme and T21-Diff demonstrated its capability in modelling long-tere sergtermm time-series benchmarks by converting signals into short-time Fourier transform (STFT) as the image representation, offering an alternative for modelling longer continuous signals using spectral components (Naiman et al., 2024 Tew et al. 2025b). In contrast, the wavelet trans- form provides multi-resolution bands by using adaptive windows that narrow at high frequencies and widen at low frequencies. These adaptive methods make the wavelet transform better at capturing short transients continuous signals while still capturing slower trends (Murad et al. 2025 WU et al. 2025).
C PROOFS AND DERIVATIONS
C.I PROOF OF PROPOSITION
Proof. The forward-time SPDE (9) in the DCT domain admits the following mode-wise decompo- sition:
dz,[k] 17t A [ik dt - g(t, k) dW
(18)
where Wi[k] is the per-mode standard Wiener process. Subsequently, introduce the variance- preserving (VP) scaling
2, kk] a(t) [k]
(19)
where ax(t) is a scalar applied equally to every mode, and the DCT basis remains unchanged, i.c., the scaled 2, still obeys the heat dissipation SPDE. Substituting this into (18) and applying Itô's lemma gives
dz.[k] = S(1,) 2x[k]] di + g(1, k) dW.(k]
(20)
where we have defined
a(l)
f(t,k) a(t) -n(t) A
(21)
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Taking the conditional expectation of the drift term in (20) and integrating with respect time yields
d
I8 [<][k]]]
20 [k]] = f(t, k) [1(k)] 20[A]]
dt
(22)
zo [K]] S. s(t,k) dt a(t) AoT(t) = plt, k)
I3 [=]]]]]]]]
which is exactly the mean schedule defined in (12). From we also have jut, k) f(t, k) p(t,k)
(23)
which we will use to derive the standard deviation.
Applying Itô's lemma once again the square of (20) and taking conditional expectations yields
[|= = 2 ftt, E
2 [+]]]
+ A(²
(24)
dt
Additionally, taking the time-derivative and substituting j f(t, μ from 23 we have
E [2]] At)² ott,k) k-)² Var [b] 20 [:]]
(25)
= 2 [t, + glt, k)²
(26)
where we use the shorthand notations μ4, and ji, ā for brevity. Since the conditional perturbation kernel is variance- seriance-presevingg we also have
ott, k)² = 1 k((
(27)
Differentiating this gives
- -2,() -1 - 0²)
(28)
Equating 26 and 28 gives
g(t,k)² k)² = 2 2 k) (ō(t, f(t,k)o(tt,k)
(29)
which is exactly (13). This completes the proof.
C.2 PROOF OF PROPOSITION
Proof. The Gaussian reparameterization trick
24 [k]]|2,]k]] = p)t, 2o[4] + (t, k)
(30)
follows from the mode-wist wise conditional perturbation kernel (11) and its time-derivative gives the conditional vector field (14). Using the results (21) 233 and (29) from the proof of Proposition 1,
and substituting 30 we can reformulate the conditional vector field (14) as follows:
die [k]
v(22 20: k)
dt
=o[M:]
ja [k(k] + à
P (z|[k] o)) + +
f(t, k) (zelki] [k( - oc) + ge
(31)
(t, k) =+(k)]
+ (ā - f(t,k)
f(t, A-) 24 [k]]
+ + =
1
1
f(t k) 2/ [k]]
2 9 k)²² (M log log p(2][k] 20[4])
which arrives at the conditional probability flow ODE (15). Here, we again use the shorthand nota- tions for brevity.
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Applying the law of the unconscious statistician from (16)
(zol [0(26/20; ( [
(32)
to the score v,, log p(2) 20), we have
/ 2, log p(zr 20) Paaaa (20( (x) deo
/ Vx, log pogp(zz ( 20) p( zt 20) Passa
dzo
J& p(ze 20) Pauaa (20) dzo
/ Va,p(=z 20) p( 20) panta dzo p(z))
(33)
p(z 20)
((=) Ve, 1/2 p(zz [20 Penta (zo) dzo
p(z) Vz.P(zz) = Vz. log
where we have repeatedly apply the log-derivative trick p(z) Vp(2) V llgp(22) This gives the marginal score and the same applies to the drift term [tt k) 21 [k]] (31), thus completing the proof.
D EVALUATION PROTOCOL
D.1 BASELINES.
Time-series Generative Models. Our proposed DSFM model is first assessed in the unconditional setting using standard metrics used by T2I-Diff (Tew et al., 2025b), against seven time-series and fregeenyy generative model baselines such as CoT-GAN (Xu et al., 2020), DiffTime (Coletta
cct al. 2023), DiffWave (Kong et al. 2020). TiE (Desai et al. (021). TimeGAN (Yoon et al, 2019 Diffusion-TS Yuaan & Qiao 2024). and T21-Diff (Tew al 2025b). In the conditional setting, we computed the image-domain FID score on subject-specifer DCT and DWT image repre- sentations (Heusel al. 2017). To ensure image-to-signal reconstruction quality, we evaluate the time-domain using the context-FID (cFID) score (Jeha et al. 2022).
fMRI Generative Models. We further compared DSFM with FC-based GAN models, such as Vanilla-GAN (Goodfellow et al. 2020), ID-DCGAN (Radford et al. 2015), 2D-DCGAN (Tan et al.
2024b), WGAN and WGAN-GP (Gulrajani al. 2017).
D.2 TIME-SERIES METRICS.
We utilize the standard time-series generation metries from Naiman al. 2024). We employ the following four metrics and provide their mathematical formulations to ensure comparable evaluation across multiple aspects:
Discriminative (Disc.) & Predictive score (Pred.). We adopt the same experimental setup of Yoon al., 2019) for both the discriminative and predictive scores. Both the classifier and sequence- prediction model use two-layer GRU-based architecture. The discriminative score is computed as Jaceuracy 0.5|, where lower scores indicate better indistinguishability. and higher scores reflect greater divergence. The predictive score is the mean absolute error (MAE) of the one-step-ahead predictions and the ground-trutt truth values.
Context-FID score (cFID). Context-FID score is time-series adaptation of the image-based Frechet Inception Distance (FID) that measures how close in distribution synthetic data to the real data in learned embedding space (Jeha et al. 2022). Instead of image features, it uses trained encoder called TS2 to capture temporal context. Lower scores indicate higher fidelity and have been shown to correlate with better downstream tasks.
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Correlational score (Corr.). Following (Liao et al. 2020), first estimate the covariance of the ith and jth feature of time series as follows:
T
COV -
Σ x
7
x
T
x
(34)
Then, the correlation score is defined as the average absolute difference between corresponding pairwise correlations in the real and synthetic data:
Corr 1 10
Σ JJ
COV COV. 4,
cov 5 coV 1,8 35
(35)
COV
COV
JJ
D.3 CLASSIFICATION METRICS.
We quantify classification performance using accuracy, precision, recall, F1-score, and the area under the ROC curve, with larger values indicating better performance; their definitions are given in equation [qqaatoo 40
TP TN TP + TN + FP EN
Accuracy (ACC)
(36)
TP
Precision (PRE)
(37)
TP + FP TP
Recall (REC)
(38)
TP + FN
PRE x REC
Fl FI-score
(39)
PRE + REC
ROC
(PR(T) (FPR(T))
(40)
E ADDITIONAL EXPERIMENTAL RESULTS
Table 7: Evaluation of our proposed DSFM with class-conditional (HC vs MDD) generation under
varying NFE.
Discrete Cosine Transform (DCT)
Discrete Wavelet Transform (DWT)
Signal Transform (Time)
NFE
HC
MDD
HC
MDD
HC
MDD
5.303=1.890
5.352-+2.126
4.552=10.150
4.529
1.70344.332
1.428
50
5.481 + 1.892
1.580+1.5522
4.567-40.303
4.624+0.100
1,327+00.08
1.312+0.118
100
5.079=1.507
5.386:1
4.520=10.191
4.569:10.14
1.237:10.519
1.255
E.1 CONDITIONAL GENERATION QUALITY ACROSS TIME AND FREQUENCY DOMAINS.
Table 7 compares the generative fidelity our DSFM framework across three domains: frequency (DCT), time-scale (DWT), and the raw time-series representations. Overall, DSFM demonstrates competitive performance in the DWT domain by achieving the lowest FID across HC and MDD sub- jects with hyperparameter settings of NFE 100, indicating precise reconstruction of scale-specific BOLD dynamics. Consistently low cFID values the time domain further confirm that the synthetic signals remain well aligned with in-distribution temporal patterns, outlining that the model is com- plementary with additional spectral features. contrast, we also observe that increasing the number of NFE from 20 100 consistently reduces error across subjects. These results validate DSFM effective time- teme-series-ie to- image framework for synthesizing biologically plausible, frequency- alliged fMRI signals across representations.
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Table 8: Ablation of block size, wavelet basis, normalization strategy and spectral representations
(Fourier and wavelet transforms) and type of generative models (flow matching and diffusion).
Configurations
Context- Correlational
Discriminative
Predictive |
1) B MM
1.51 0.41
57.30 2.89
0.49 + 0.00
0.05 + 0.00
2) B = 4, ECS
0.10 + 0.01
18.20 + 1.41
0.17 + 0.05
0.04 + 0.00
3) B 2, Haar
0.12 + 0.03
19.70 + 3.03
0.13 + 0.04
0.04 + 0.00
B Haar
0.10 0.01
18.20 + 1.41
0.17 + 0.05
0.04 + 0.00
5) B = 4, dB-4
0.20 + 0.10
20.70 + 3.25
0.13 + 0.04
0.04 + 0.00
6) Flow Matching, Fourier
1.00 + 0.20
107.90 + 14.35
0.46 + 0.02
0.05 + 0.00
7) Flow Matching, Wavelet
2.19 = 0.34
41.88
0.04
8) Diffusion, Wavelet
3.57 = 0.68
0.48 + 0.02
0.04
DSFM (Ours)
0.10 = 0.01
18.20 = 1.41
0.17 + 0.05
0.04 + 0.00
E.2 FULL RESULTS OF ABLATION STUDIES.
Table 8 presents the complete ablation results on the MDD dataset with additional experiments on different spectral representations and type of generative models. comparison, the Fourier repre- sentation achieves better context-FID score, indicating strength in capturing global distributional characteristics, but it falls short on the other metries relative to the wavelet representation. This observations exemplify that the time-freqqency localization property in wavelet representation is more effective at preserving local and multi-scale structural information. Moreover, the better re- sults of flow matching model with wavelet representation across all metrics suggest smoother and stable noise-to-data distribution alignment than that of the diffusion- based wavelet approach. Over- all, these results demonstrate that DSFM outperformed all the configurations across all time-series generative metrics with the advantage of dual-spectral transformation and spectral flow matching.
E.3 FULL RESULTS OF CLASSIFICATION PERFORMANCE.
Method
Train Set
Accuracy f
Recall f
Precision t
F1-Score f
ROC 1
W/O Augmentation
Real
58.90 2.98
58.90 2.98
59.56 = 2.74
58.39 3.09
59.00 = 2.56
Real + Synth 1x
$6.90 1.66
56.90 1.66
56.40 2.86
53.68 # 3.31
$6.29 + 1.92
Vanila-GAN
Real + Synth 2x
50.71 + 3.69
50.71 + 3.69
48.60 + 7.23
46.74 + 5.18
50.81 = 4.13
Real + Synth 3x
58.86 = 2.24
59.91 2.57
57.64 1.96
58.57 = 2.05
Real + Synth 1x
62.94 2.01
62.94 + 2.01
63.43 2.20
62.23 2.68
62.71 # 2.26
ID-DCGAN
Real + Synth 2x
65.04 + 2.02
65.04 + 2.02
2.13 66.35
64. 2.10
64.74 + 2.08
Real + Synth 3x
58.21 = 2.98
58.21 = 2.98
55.70 6.58
52.86 = 4.14
57.38 = 3.11
Real + Synth 1x
60.78 4,98
60.78 + 4.98
61.30 = 5.34
60.01 5.00
60.33 +: 5.03
2D-DCGAN
Real + Synth 2x
61.41 + 2.59
61.41 = 2.59
61.99 3.73
62.18 3.29
61.04 + 2.79
Real + Synth 3x
62.88 + 4.99
62.88 + 4.99
63.12 + 5.02
62,48 *: 5.25
62.67 + 5.15
Real Synth 1x
64.98 5.54
64.98 + 5.54
65.19 + 5.34
64.86 5.61
64,95 5.39
WGAN
Real + Synth 2x
60.59 + 1.81
60.59 + 1.81
60.89 1.96
60.35 1.78
60.53 + 1.84
Real + Synth 3x
61.83 + 3.03
61.83 + 3.03
62.27 + 3.29
61.44 * 2.70
61.58 * 2.73
Real + Synth 1x
66.0 4.25
66.02 + 4.25
66.22 4.24
65.93 4.20
65.95 +: 4.13
WGAN-GP
Real + +yytth 2x
64.76 = 4.25
64.76 = 4.25
65.67 = 4.08
64.23 # 4.52
64.73 = 4.14
Real + Synth 3x
64.56 + 3.
64.56 + 3.18
64.78 + 3.17
64.38 *: 3.15
64.41 #: 3.08
Real + Synth 1x
66.87 + 3.22
66.87 = 3.22
67.06 + 3.34
66.83 3.21
67.26 + 6.00
T2I-Dift
Real + Synth 2x
65.41 = 2.37
65.41 = 2.37
66.30 = 1.67
64.73 # 2.80
65.75 + 3.22
Real + Synth 3x
66.03 + 1.75
66.03 # 1.75
66.50 + 1.32
65.85 1.82
66.58 + 5.33
Real + Synth 1x
70.84 + 5.89
70.84 + 5.89
70.99 + 5.80
70.77 # 5.97
71.49 + 5.73
DSEM (Ours)
Real + Synth 2x
69.58 = 3.89
69.58 = 3.89
69.75 + 3.72
69.43 3.86
69.91 = 4.23
Real + Synth 3x
69.80 + 3.13
69.80 + 3.13
69.61 * 3.02
69.80 3.13
69.00 + 4.29
Table 9: Classification performance (MDD) of different generative models trained on the ground- truth data with an increasing amount of augmented time series data using our proposed model.
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Figure illustraass the forward and inverse processes of ImagenTime/T2I-Dii and DSFM applied proposed fMRI signals. The top row shows univariate STFT real-valued coefficients, and Published as a conference paper at ICLR 2026
Method
Train Set
Accuracy f
Recall f
Precision 1
F1-Score f
ROC't
W/O Augmentation
Real
64.67 1.56
64.67 1.56
65.77 +577
64.12 1.39
67.28 + 4.15
Real + Synth
64.10 3.28
64.10
64.33 3.34
65.59 3.83
Vanila-GAN
Real Synth
64.87 1.05
64.87 1.05
65.06 1.28
64.75
68.07 2.88
Real Synth
62.94 4.68
62.94 4.68
62.94 4.69
62.91 4.70
64.48 5.20
Real + Synth
65.63 + 1.91
65.63
66.63 2.44
68 1.23
2D-DCGAN
Real Synth
65.64 1.41
65.64
66.37 2.56
65.32
67.80 77.80 3.32
Real Synth 3x
64.86 2.87
64.86 2.87
65.10 2.82
64.66 2.96
67.92 + 4.46
Real + Synth 66.59 2.50
66.59
2.50
67.93 1.87
65.92
3.26
TimeGAN
Real Synth
66.24 1.07
66.24 1.07
66.89 1.37
65.84
69.21 2.03
Real Synth 3x
65.26 1.80
65.26 1.80
65.37 1.83
1.81 65.21
64.80 + 1.81
Real + Synth 66.60 + 1.41
66.60 + 1.41
66.58
68.85 3.64
DiffusionTS
Real Synth
66. + 2.18
66.02 2.18
66.20 2.01
2.18 65.98
66. 2.09
Real Synth 3x
64.87 1.48
64.87 1.48
65. 1.54
64.78 1.41
66.24 + 2.92
Real Synth 69.69 1.55
69.69
69.78 1.74
69.65
71.88 2.47
T21-Diff
Real Synth
69.49 2.14
69.49 2.14
69.76 1.90
69.31 = 2.38
71.23 2.84
Real Synth 3x
68.91 1.57
68.91 1.57
69.22 1.80
68.79 1.49
68.79 2.40
DSFM (Ours)
Real Synth 71.54 1.87
71.54 1.87 73.08 3.00
70.98 2.30
73.78 3.64
Classification performance (ABIDE) of different generative models trained on the ground- truth data with increasing amount of augmented time series data using our proposed model.
Table 10:
Table 9 and 10 presents the complete classification results on the MDD and ABIDE datasets across three augmentation levels. The consistent performance gains each augmentation level indicate that synthesized functional conductivity (FC) matrices accurately capture brain connectivity patterns and that the data augmentation strategy significantly improves classifier's generalization to unseen samples. Notably, DSFM achieved better performance even with only a single augmentation level.
ADDITIONAL VISUALIZATION
Univartate Time Series
ImagenTime
Mu tiveriate Time Series
DSFM
100
10-
00
-35
Figure 6: Comparison of univariate and multivariate spectral representations: Imagen Time/T21-Diff and our proposed DSFM.
F.1 SPECTRAL IMAGE TRANSFORMATIONS
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Figure 7: Frequency-speciffc functional connectivity (FC) matrices for healthy controls (HC) and patients with major depressive disorder (MDD), alongside their differences. The FCs are shown under four different conditions: full-band: removal of the highest-frqqueccy subbands (D1 D2) and the lowest-freee fereeneeryeencyy component (A5), which both yield the two highest classification scores; and removal of the mid-band subbands (D3 + D4), which produces the greatest deviations and the lowest score.
the bottom row presents single subband (Detail 1) of multivariate DWT coefficient map. Our framework directly transforms multivariate BOLD signals into single image representation.
F.2 FREQUENCY-SPECITE FC ANALYSIS
Figure compares the HC and MDD FC matrices against the ground-truth data correlation across different wavelet subbands. Consistent with the full-band correlation, removing the highest- frequency subbands (D1 and D2), or combining DI with the lowest band (A5) preserves dense edge connections the main diagonal. In contrast, removing the mid-frequency subbands (D3 and D4) results in sparser connectivity, particularly in the lower-right region of the matrices.
G COMPUTATIONAL COST
The training of the proposed DSFM required 22 hours, 40 minutes, and 52.698 seconds of wall- clock time, while inference for generating the full samples took 48 minutes and 48.98 seconds with 1x A100 GPU. The model contains 044,244,552 parameters.
H LIMITATIONS
Currently, DSFM is specially designed for the generation of resting-state fMRI signals. This opens valuable opportunity expand our work other human brain activity signals, such electroen- cephalography (EEG), functional near-infrared spectroscopy (INIRS), and magnetoenceehaaoogra phy (MEG). Our spectral flow matching framework offers flexibility to capture dynamics of other neural signals with frequency-specine representation.
I REPRODUCIBILITY STATEMENT
We provide the datasets, source code, and configurations for all key experiments. including instruc- tions on how to preprocess data and train the models at http:///hhhibbccmm/h//www///////
THE USE OF LARGE LANGUAGE MODELS (LLMs)
We used L.LMs solely for grammar correction. All ideas, analyses, and results are by the authors.
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Reviews:
[{"review_id": "XmHN9Hgs17", "reviewer": "Reviewer_btKb", "summary": "This paper proposes a novel approach to fMRI time-series generation based on dual spectral transformations and a learned flow in spectral space. The authors detail the spectral transforms and the flow’s training procedure, and they experimentally validate the method on a single dataset.", "questions": "1/ Why choose a heat dissipation process? Does avoiding conventional isotropic diffusion help capture interactions between patches (~ ROIs) more effectively?\n\n2/ At a general level, the paper seems to suggest that learning a complex generative distribution (and sampling from it) is easier than discriminating between two of its marginals. This is not a typical results (cf. On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes), can you elaborate?\n\n3/ Please report the computational cost of your approach: approximate training time, hardware used, and runtime for sample generation.\n\n4/ How do you denormalize the wavelet coefficients (c.f. \"We further perform componentwise normalization to accentuate\"). Please detail the inverse step.\n\n5/ Do you have an explanation for the discrepancy between Table 1 and Table 3 (non-SOTA generation quality vs. SOTA prediction performance)?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1/ The subject of study is of utmost interest.\n\n2/ The approach is well described, and most technical details are provided. Figure 1 is an excellent summary.\n\n3/ The range of evaluation metrics and the ablation study make the validation comprehensive (apart from the limited number of datasets).", "weaknesses": "Weaknesses summary\n\nSome rationale for the design is missing, and the approach lacks comprehensive empirical validation across additional datasets.\n\nMajor concerns\n\n1/ The choice to work in the spectral domain and to cascade spectral transforms is not clearly justified. I am trying to understand why you chose the DCT after a wavelet transform. Apart from the diagonalization of the Laplacian and the simpler truncation it enables, why introduce a second spectral projection and why this one?\n\n2/ If B is too large, it will squash multiple ROIs into a single patch, which in turn will make it impossible for the flow to learn the interactions between those ROIs (since they are collapsed). This is not specifically discussed in the paper, and the experimental values for B are not provided.\n\n3/ In the contributions, you state that you \"forming a unified dual-spectral image transform to capture both global and local spatiotemporal and spectral features for fMRI BOLD signal generation\"; It is not clear what specifically differentiates your method from prior approaches in capturing ROI-to-ROI interactions (see 2/). Why should these interactions be better captured in spectral space rather that in the time domain? Note that classical fMRI analysis choose to capture the ROIs' interplays in the time domain.\n\n4/ Truncation is crucial since it removes part of the information (note that, at this stage, most artifacts should already have been filtered out by preprocessing); yet it is not specifically discussed in the paper, and the experimental values are not provided. Additionally, for short TRs (< 0.7 s), it remains unclear whether truncation would not be detrimental.\n\n5/ Lines 60 to 63: It is not clear why your approach, which operates in the spectral domain, differs from methods that operate on preprocessed time series in its ability to filter out confounds; the preprocessing pipeline should have already remove cardiac pulsations, respiratory cycles, and motion-induced artifacts. Please clarify which components remain to be removed (see 4/).\n\n6/ The main issue with this paper is the lack of an exhaustive experimental validation. It should be evaluated on at least half a dozen datasets, spanning different TRs, spatial resolutions, and tasks (e.g., multiclass subject prediction).\n\nMinor comments\n\n1/ In the abstract, \"preserves key physiological brain dynamics\" is an over-claims evidence.\n\n2/ There are numerous typos, for example: \"we stitches the patches\", \"Fig. 1 provide an overview\", etc\n\n3/ Some acronyms are undefined or defined too late, for example: MDD, STFT, etc. Even for some common acronyms, a brief reminder would improve readability\n\n4/ Please cite Kawahara et al., 2017 when mentioning BrainNetCNN so the provenance is clear.\n\n5/ The results are somewhat ambiguous: the proposed approach is not SOTA in terms of \"generation quality\" (Table 1), yet it achieves SOTA prediction performance (Table 3). This discrepancy is surprising and is not discussed.\n\nGrading explanation\n\nThe paper lacks comprehensive empirical validation, and some key rationales for the proposed approach are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Xnsq0RdNJQ", "reviewer": "Reviewer_YXrp", "summary": "This paper proposes a framework named Dual-Spectral Flow Matching (DSFM) for fMRI BOLD signal generation. The method jointly constructs dual-spectral representations through Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), then introduces Spectral Flow Matching (SFM) in the DCT domain to achieve fMRI generation by modeling the heat diffusion process in the frequency domain.", "questions": "Check the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Innovative dual-spectral architecture:\nThe paper introduces a dual-spectral generative framework combining DWT (Discrete Wavelet Transform) and DCT (Discrete Cosine Transform).\nThis design elegantly captures both temporal–frequency representations and spectral sparsity of fMRI signals, offering a novel perspective distinct from previous time-domain or FC-based generation approaches.\n\nSolid theoretical derivation:\nThe work provides a rigorous and mathematically sound derivation linking stochastic partial differential equations (SPDE) to frequency-domain probability flow ODEs, establishing a coherent theoretical foundation for the proposed spectral flow matching.\nThe formulation is complete and clearly connects to existing flow-matching paradigms.\n\nThe experimental design is comprehensive, and the analysis is detailed.", "weaknesses": "Lack of cross-dataset validation and statistical significance tests:\nExperiments are conducted only on the REST-meta-MDD dataset, without evaluation on other publicly available fMRI datasets such as ABIDE [1] and HCP [2].\nThis raises concerns about the model’s generalizability across acquisition protocols and subject populations.\nMoreover, the reported improvements lack statistical significance testing, making it difficult to assess the robustness of the observed gains.\n\n\n[1] Heinsfeld A S, Franco A R, Craddock R C, et al. Identification of autism spectrum disorder using deep learning and the ABIDE dataset[J]. NeuroImage: clinical, 2018, 17: 16-23.\n\n[2] Smith S M, Beckmann C F, Andersson J, et al. Resting-state fMRI in the human connectome project[J]. Neuroimage, 2013, 80: 144-168.\n\nLimited physical and physiological interpretability:\nAlthough the proposed DSFM introduces a dual-spectral flow matching framework with physically inspired dynamics, its neuroscientific meaning remains limited. The model is clean and physically inspired from a signal engineering perspective; however, it lacks true mechanistic grounding in neuroscience. Although the paper claims to generate “physiologically plausible” fMRI signals, no quantitative evidence is provided to support this claim. Metrics such as power spectral density (PSD) alignment, hemodynamic response function (HRF) consistency, or spatiotemporal smoothness are not evaluated. Including these analyses would substantially strengthen the physiological credibility of the proposed approach. In essence, DSFM produces spectrally and statistically plausible fMRI-like time series rather than neurodynamically meaningful brain processes. I would encourage the authors to further elaborate on the neuroscientific significance of generating such fMRI signals, for example, what new understanding or insight about brain dynamics can be obtained from this generation process, or how such generated fMRI signals could be practically useful?\n\nOverly dense pipeline illustration:\nFigure 1 is visually overloaded. Several components such as \"Reorder & Truncation\", \"SNR scale + Add Noise\" and \"Zig Zag Flattening\" are insufficiently explained in the text, which reduces clarity. Simplifying or modularizing the figure, or adding explicit textual references, would improve readability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "smknvhPreq", "reviewer": "Reviewer_7U7B", "summary": "The paper proposes Dual-Spectral Flow Matching (DSFM) for fMRI time-series generation. It converts BOLD signals into a dual-spectral representation using DWT and DCT, then applies a standard flow-matching model to synthesize time-series data for data augmentation on a depression vs control classification task.", "questions": "1. The paper fix the wavelet type to Haar. Did author experiment with different wavelet bases (e.g., Daubechies, Coiflet, Symlet)? Since different wavelets have distinct time–frequency localization and smoothness properties, this omission makes the transform choice look arbitrary. \n2. What's the rationale of using zig zag flattening rather than normal flattening in Figure 1(step 3)? What would be the difference in model performance\n3. What could be the possible reason that, in Figure 4, the synthetic connectivity matrices appear more clustered and uniformly distributed compared to the real ones?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Combining DWT and DCT with flow-matching for fMRI synthesis is a creative approach.\n\n2. The demonstrated use of fMRI data augmentation for enhancing downstream classification performance is interesting, and the proposed application appears promising.", "weaknesses": "Major:\n\n1. The experiment scope is a bit limited. All experiments are done on a single dataset (REST-meta-MDD). There is no cross-dataset or cross-site validation, so the generalization and robustness claims are weak.\n\n2. The paper repeatedly mentions that the proposed spectral-domain flow matching is more efficien, but this claim is not supported by theoretical analysis or empirical evidence. It would strengthen the work if the authors could elaborate on what “efficiency” specifically refers to (e.g., faster sampling, fewer function evaluations, or reduced computational cost) and provide quantitative comparisons to substantiate this point.\n\n3. The paper does not include experiments that clarify whether transforming fMRI signals into the frequency domain is strictly necessary. A direct comparison with a latent-space flow matching baseline, i.e., encoding fMRI ROI time series into latent embeddings with transformer encoder without explicit DWT/DCT transforms, would help determine whether the spectral transforms genuinely contribute to the observed improvements in performance or efficiency, or if similar gains could be achieved through compact latent representations.\n\n4. The authors note that their primary goal is not to achieve the best sample quality metrics but rather to improve cFID and conditional modeling of spatiotemporal patterns. However, this statement seems to suggest that the proposed DSFM does not outperform recent baselines (e.g., Diffusion-TS) in unconditional generation quality (Table 1). Can the author elaborate more on this, why cFID is more important than other metrics in this scenario?\n\n\nMinor:\n1. For the connectivity matrices in Figure 4, would be more informative to include colorbar to show the value scale.\n\n2. There might be format error at line 291 and line 308\n\n3. While the technical content is interesting, the overall presentation could be improved for clarity, especially the method and theory parts.\n\n4. Potential typo at line 136-137 \"Thus forming a full wavelet...\"\n\n5. There is another recent baseline for fMRI synthesis [1]. Although it is not open-sourced yet, it would still be worthwhile to discuss it in the related work section to provide a more complete context\n\n[1] Synthesizing Realistic fMRI: A Physiological Dynamics-Driven Hierarchical Diffusion Model for Efficient fMRI Acquisition, ICLR 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ufEAHKLTib", "reviewer": "Reviewer_UQBR", "summary": "This paper presents a method to generate fMRI time-series data. The authors propose a three-step process.\nThe first step computes a time-frequency representation from the training data using a wavelet transform. Then the transformed signals are then transformed using a 2D Discrete Cosine Transform to further localize the low-frequency BOLD spectral components. Then they use a flow-matching approach that operates in the DCT space that relies on an image generation model (U-ViT) to parameterize and train the model that minimizes the \"spectral flow-matching loss\" to learn velocity fields in this space. This process further allows to sample the DCT coefficients by integrating the velocity flow, which is then inverse-transformed (both DCT and wavelet) to generate the reconstructed fMRI signals. The authors compare their method with several state of the art models. They also present an application that performs classification between HC and MDD patients.", "questions": "In the reproducibility statement, the authors have stated, \"We provide the datasets, source code, and configurations for all key experiments, including instruc-tions on how to preprocess data and train the models at https://anonymous.4open.science/r/DSFM-\n123C\" However the link returned \"File not Found\".\n\nWhat happens if the DCT step is short-circuited? I.e. the flow velocity fields are learnt on the DWT components?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Synthetically generating fMRI time-series data is still an open problem. Most approaches have tackled synthetic data generation in modalities such as ECG, EKG, etc., but fMRI time-series generation is complex and thus still remains challenging. Thus this paper makes a good contribution to this problem. \n\nWhile the DCT and DWT (more so for time-series) have been applied to fMRI time-series data individually, they have been done so primarily for filtering, preprocessing, noise/motion removal etc type applications. To my knowledge, the joint approach, i.e. first perform DWT for time-frequency analysis and then low-frequency signal compaction using DCT is new, especially for fMRI time-series application.\n\nThe idea of recasting flow-matching in the spectral domain instead of relying on diffusion models is somewhat novel. This is especially important for fMRI applications, as 4D time-series data is huge and the deterministic ODE-based flow matching idea is magnitudes in order more efficient than diffusion modeling. \n\nThe authors present a method for learning the velocity fields of probability flows directly in the DCT space. They derive the spectral flow-matching loss by diagonalizing the Laplacian operator and then propose the probability flow in the DCT space (proposition 1). This is novel. \n\nThe authors have provided the code for their method including experimental results and validation. This is a strength. However, also see the Questions below.", "weaknesses": "The main weaknesses in the paper are in the experimental results and aspects related to evaluation of the method on BOLD fMRI time-series applications. \n\nThe generated fMRI signals are not validated using any physiological or neuroscientific basis. They are simply validated by context FID scores. Thus the validation is relying on comparing data distributions where the synthetic data is supposed to have come from. This is weak. \n\nFurthermore, using classification accuracy as a metric to discriminate HC against MDD is a complex high-level application, which may mask underlying subtleties of scanner, population, processing methods etc. Thus it is hard to make any judgement on the actual reconstructed time-series signals.\n\nAblation study is performed on omitting wavelet sub-bands. However, the authors don't comment on the dual-formulation advantage. I.e. is the real gain coming from the spectral representation itself or from flow-matching process, generating the velocity fields in the DCT space. \n\nOnly a single dataset (REST-MDD) is used. The dataset contains 250 Healthy Controls (HC) subjects and 227 individuals diagnosed with MDD. This is a much limited dataset to test the method on. \n\nThe claim \"brain disorder identification\" is both pretty general and strong. One could at best say, the method showed improved performance in classification after using the synthetic time-series on the MDD dataset. On that note, the authors don't mention what MDD is. It is supposed to stand for major depressive disorder. Instead they wrongly use the acronym MDD to denote a general brain disorder. Line 88 says: Our results show that DSFM demonstrates strong performance on unconditional and conditional spectral image synthesis, and achieves improvement in brain disorder (MDD) classification. This is an incorrect terminology. \n\nThe paper visualizes the average connectivity patterns of real and synthetic connectivity patterns. Without knowing the error bars or any quantitative tests, it is difficult to tell if the synthetic data generation method has worked. Visually, one can see differences in anatomical locations in the connectivity patterns from real and synthetic data and even in their differences. Thus the method is not as accurate, but it is not clear by how much. \n\nFor validation purposes, the method does not comment on frequency bands (LF vs HF) that are captured or accurately reflected in the synthetic data. Absence of such low-level measures makes the experimental impact of the contribution, difficult to judge. \n\nThe method is compared to several state of the art time-series generation methods. But comparisons to the more recent rectified-flow matching methods are missing. This is important especially as this particular method does rely on a flow-based formulation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)