# How important are inter-dataset interactions for large scale analysis of fMRI data: A multi-dimensional comparison

Clément Cosserat, Lucas Gois, Emilie Chouzenoux, Vince Calhoun, Tülay Adali

<figure><img src="image_1.png" /></figure>

## To cite this version:

Clément Cosserat, Lucas Gois, Emilie Chouzenoux, Vince Calhoun, Tülay Adali. How important are inter-dataset interactions for large scale analysis of fMRI data: A multi-dimensional comparison. ISBI 2026 - IEEE International Symposium on Biomedical Imaging, Apr 2026, London, United Kingdom. hal-05458756

HAL Id: hal-05458756

https://inria.hal.science/hal-05458756v1

Submitted on 14 Jan 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
HOW IMPORTANT ARE INTER-DATASET INTERACTIONS FOR LARGE SCALE ANALYSIS
OF FMRI DATA: A MULTI-DIMENSIONAL COMPARISON

Clément Cosserat*, Lucas Gois**, Emilie Chouzenoux*, Vince Calhoun***, Tülay Adali**

* Université Paris-Saclay, CentraleSupélec, Inria, CVN, Gif-sur-Yvette, France

** Dept of CSEE, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

*** TReNDS Center, Georgia State, Georgia Tech, Emory, Atlanta, GA

ABSTRACT

Analyzing multi-subject functional magnetic resonance imaging (fMRI) data requires methods that can jointly capture shared and individual patterns of brain activity across participants. Joint blind source separation (JBSS) techniques, such as independent vector analysis (IVA), provide a principled framework for this purpose by modeling dependencies across subjects while identifying distinct functional networks. Constrained IVA variants, including adaptive-reverse cIVA-G (ar-cIVA-G) and threshold-free cIVA-G (tf-cIVA-G), further enhance interpretability through the use of reference templates and inter-subject correlation constraints. Alternatively, regression-based methods like IVA-G regression (regIVA-G) and reference-guided component analysis (RGCA) process subjects individually, aligning their components to references with improved computational efficiency. Despite their potential, systematic evaluations of reference-based JBSS approaches for fMRI analysis remain limited. In this work, we present a comparative study of these methods to assess their capacity for identifying schizophrenia-related biomarkers using real fMRI data from subjects with schizophrenia and healthy controls. Our results demonstrate that both constrained IVA and regression-based methods effectively extract meaningful biomarkers while the latter achieve comparable performance at substantially reduced computational cost.

***Index Terms***— Gaussian independent vector analysis, multi-subject fMRI data, functional network connectivity, regression-based methods.

1. INTRODUCTION

Large scale studies like the bipolar-schizophrenia network for
intermediate phenotypes (B-SNIP) [1,2] and the adolescent
brain cognitive development (ABCD) [3] have been using
neuroimaging methods such as functional magnetic resonance
imaging (fMRI) to find biomarkers of mental disorders or

the developing brain. The extraction of functional networks
and their corresponding time courses from fMRI data can
be formulated as a blind source separation (BSS) problem.
Data-driven BSS methods such as independent component
analysis (ICA) [4–6] are widely used in the field.

Independent vector analysis (IVA) extends ICA to analyze
multiple subjects simultaneously by defining source component
vectors (SCVs) that model the statistical dependence across
datasets [7–9]. Assuming the SCVs are Gaussian reduces their
description to their second order statistics (SOS), yielding
the so-called Gaussian IVA (IVA-G) [8, 10] which has been
shown to allow more efficient source reconstruction than
approaches using only higher order statistics (HOS) [11].
IVA-G can incorporate prior knowledge into the model in
the form of reference spatial templates, to make the source
components more interpretable and implicitly take their HOS
into account as well. The constrained IVA (cIVA) method [12]
imposes a correlation between the source components and their
references greater than a fixed threshold using an augmented
Lagrangian framework. As choosing the threshold is a
complex problem by itself, adaptive-reverse cIVA (ar-cIVA)
uses subject-specific thresholds that are recomputed at each
iteration. In contrast, threshold-free cIVA (tf-cIVA) bypasses
the need for a threshold by including a regularization term to
control the correlations between the estimated sources and the
references [13]. When using Gaussian SCVs, we call these
methods ar-cIVA-G and tf-cIVA-G respectively.

IVA algorithms become computationally costly as the
number of datasets increases [8]. Recently, regression
methods were proposed as an alternative to IVA algorithms.
In [14], the authors introduced a regression-based extension of
IVA-G (regIVA-G), and [15] proposed the reference guided
component analysis (RGCA) that guides the separation
subject-wise. Using such smart regression approaches changes
the way we identify statistical links across the datasets. Instead
of aligning the components of all datasets within each SCV,
they correlate those components to the same reference source.

In this paper, we propose an analysis of the underlying
assumptions on the components RGCA, regIVA-G, ar-cIVA-G
and tf-cIVA-G. In this comparison study, we rely on 98
resting-state fMRI datasets from B-SNIP, composed patients
with schizophrenia (SZ) and healthy control (HC). We compare
the products of each method using statistical tests to assess
their usefulness for biomarkers identification.

C.C., and É.C. acknowledge support from the European Research Council
Starting Grant MAJORIS ERC-2019-STG850925, and T.A. from the US
National Science Foundation Grant NSF 2316420.
This paper is organized as follows. Section 2 briefly introduces the linear mixing model of IVA and presents the four algorithms of interest with an emphasis on the different assumptions made about the reconstructed spatial sources. Section 3 describes our experimental protocol to test the methods on real fMRI data from the B-SNIP dataset and their results. We conclude in section 4 with a synthesis of the differences between the methods in terms of approach and performance.

## 2. FOUR REFERENCE-BASED DATA ANALYSIS METHODS FOR FMRI

Let $K, T, V, N, M$ be positive integers with $M \le N \le \min(T, V)$. For each $k \in \{1, \dots, K\}$, we represent by $\mathbf{X}^{[k]} \in \mathbb{R}^{T \times V}$ the fMRI session of a subject $k$, recorded over $T$ time steps and $V$ voxels. We assume that these sessions are linear mixtures of latent sources, following the model:

$$ \mathbf{X}^{[k]} = \mathbf{A}^{[k]} \mathbf{S}^{[k]}, \quad (1) $$

where the rows of $\mathbf{S}^{[k]} \in \mathbb{R}^{N \times V}$ represent the respective spatial distribution of functional networks and the columns of the mixing matrix $\mathbf{A}^{[k]} \in \mathbb{R}^{T \times N}$ are the time courses of these networks. Using principal component analysis (PCA) to reduce the number of rows of $\mathbf{X}^{[k]}$ if necessary, we can assume that $\mathbf{A}^{[k]}$ is square ($T=N$) and non-singular and that the datasets $\mathbf{X}^{[k]}$ are row-wise centered and white (i.e., $\mathbf{X}^{[k]} \mathbf{X}^{[k]T} = \mathbf{I}_N$).

Demixing matrices $\mathbf{W}^{[k]} \in \mathbb{R}^{N \times N}$ allow to estimate $\mathbf{A}^{[k]}$ and $\mathbf{S}^{[k]}$ by $(\mathbf{W}^{[k]})^{-1}$ and $\mathbf{Y}^{[k]} = [\mathbf{y}_1^{[k]T}, \dots, \mathbf{y}_N^{[k]T}]^T = \mathbf{W}^{[k]} \mathbf{X}^{[k]} \in \mathbb{R}^{N \times V}$ respectively. Thus, $(\forall n \in \{1, \dots, N\})$, $\mathbf{y}_n^{[k]T} = \mathbf{w}_n^{[k]T} \mathbf{X}^{[k]}$. We note $\mathcal{W} = [\mathbf{W}^{[1]}, \dots, \mathbf{W}^{[K]}] \in \mathbb{R}^{N \times N \times K}$ the tensor of all demixing matrices. IVA models the rows of the $\mathbf{S}^{[k]}$ as $V$ samples of $N$ mutually independent random vectors $(\mathbf{s}_n = [\mathbf{s}_n^{[1]}, \dots, \mathbf{s}_n^{[K]}]^T)_{1 \le n \le N}$ called SCVs [8]. It thus computes estimated SCVs $\mathbf{y}_n = [\mathbf{y}_n^{[1]}, \dots, \mathbf{y}_n^{[K]}]^T$ by minimizing their mutual information (MI). IVA-G models SCVs with zero-mean multivariate Gaussian distributions (MGD) and thus formulates their MI cost function using only SOS of the estimated source components. Finally, we dispose of references $\mathbf{R} = [\mathbf{r}_1^T, \dots, \mathbf{r}_M^T]^T \in \mathbb{R}^{M \times V}$ to guide the reconstruction of the spatial source components for all subjects. These references have zero-mean and unit variance.

We will present the two following sets of methods. The cIVA-G approaches, first introduced in [12], are based on IVA-G cost minimization, with additional constraints [12, 13] or regularizations [13] to promote correlation between the source components and their corresponding references<fnref n="1" />. In comparison, regression-based methods, whose principle was first used in [14] to accelerate IVA-G, primarily aim to maximize these correlations, processing each dataset separately without leveraging the internal dependence of the

SCVs. To make things more understandable, we begin with regression-based methods.

### 2.1. Regression methods

For all $(\mathbf{u}, \mathbf{v}) \in (\mathbb{R}^V)^2$, we note $\text{Cov}(\mathbf{u}, \mathbf{v}) = \frac{1}{V-1} \mathbf{u}^\top \mathbf{v}$ and $\epsilon(\mathbf{u}, \mathbf{v}) = \frac{\text{Cov}(\mathbf{u}, \mathbf{v})}{\sigma(\mathbf{u}) \sigma(\mathbf{v})}$ their empirical covariance and correlation, where $\sigma(\cdot) = \sqrt{\text{Cov}(\cdot, \cdot)}$ is the empirical standard deviation. Since the $\mathbf{X}^{[k]}$ are whitened, for all $1 \le n, p \le N$ and $1 \le m \le M$, $\text{Cov}(\mathbf{y}_n^{[k]}, \mathbf{y}_p^{[k]}) = \mathbf{w}_n^{[k]T} \mathbf{w}_p^{[k]}$ and $\text{Cov}(\mathbf{y}_n^{[k]}, \mathbf{r}_m) = \mathbf{w}_n^{[k]T} \boldsymbol{\kappa}_m^{[k]}$ where $\boldsymbol{\kappa}_m^{[k]} = \frac{1}{V-1} \mathbf{X}^{[k]} \mathbf{r}_m$.

We note for all $k \in \{1, \dots, K\}$, $\mathbf{W}_{\text{ref}}^{[k]} \in \mathbb{R}^{M \times N}$ the part of the mixing matrix that accounts for the constrained components. RGCA [15] consists in minimizing with respect to each $\mathbf{W}^{[k]}$:

$$ \begin{aligned} \mathcal{J}_{\text{RGCA}}(\mathbf{W}^{[k]}) &= \frac{1}{2V} \|\mathbf{R} - \mathbf{W}_{\text{ref}}^{[k]} \mathbf{X}^{[k]}\|_{\text{F}}^{2} + \frac{\lambda}{4} \|\mathbf{W}_{\text{ref}}^{[k]} \mathbf{W}_{\text{ref}}^{[k]T} - \mathbf{I}_{M}\|_{\text{F}}^{2} \\ &= \sum_{n=1}^{M} \left( \frac{1}{2V} \|\mathbf{r}_n - \mathbf{y}_n^{[k]}\|_{2}^{2} + \frac{\lambda}{4} \sum_{p=1}^{N} (\text{Cov}(\mathbf{y}_n^{[k]}, \mathbf{y}_p^{[k]}) - \delta_{n,p})^{2} \right), \end{aligned} $$

where $\lambda > 0$ is a fixed parameter and $\delta_{p,n}$ equals 1 if $p=n$, 0 otherwise. This reformulation highlights that RGCA searches components $\mathbf{y}_n^{[k]}$ that yield a compromise between their proximity to $\mathbf{r}_n$ and their orthogonality with other components, controlled by $\lambda$.

In regIVA-G [14], the cost function acts on each demixing vector independently, so they can be computed in parallel:

$$ \mathcal{J}_{\text{regIVA-G}}(\mathcal{W}) = \sum_{k=1}^{K} \sum_{n=1}^{M} \mathbf{w}_n^{[k]T} \left( \sum_{\substack{m=1 \\ m \neq n}}^{M} \mathbf{R}_m^{[k]} - \mathbf{R}_n^{[k]} \right) \mathbf{w}_n^{[k]} $$

under the constraint that the $\mathbf{w}_n^{[k]}$ have a unit norm, and where $\mathbf{R}_n^{[k]} = \frac{1}{(V-1)^2} \mathbf{X}^{[k]} \mathbf{r}_n \mathbf{r}_n^T \mathbf{X}^{[k]T} = \boldsymbol{\kappa}_n^{[k]} \boldsymbol{\kappa}_n^{[k]T}$. The solutions are thus also minimizers of this alternative regIVA-G cost:

$$ \tilde{\mathcal{J}}_{\text{regIVA-G}}(\mathcal{W}) = \sum_{k=1}^{K} \sum_{n=1}^{M} \sum_{\substack{m=1 \\ m \neq n}}^{M} \epsilon^2(\mathbf{y}_n^{[k]}, \mathbf{r}_m) - \epsilon^2(\mathbf{y}_n^{[k]}, \mathbf{r}_n). $$

In contrast to RGCA, regIVA-G promotes orthogonality of the components with the other references rather than seeking their mutual orthogonality, in addition to their correlation with their corresponding reference.

Both methods have closed form solutions. RGCA solves a cubic equation to cancel the gradient of $\mathcal{J}_{\text{RGCA}}$ [15], while regIVA-G sets $\mathbf{w}_n^{[k]}$ as a unit eigenvector associated with the largest eigenvalue of $\sum_{m=1}^{M} \mathbf{R}_m^{[k]} - \mathbf{R}_n^{[k]}$ [14].

Regression-based methods can thus be orders of magnitude faster than the IVA algorithms. However, they are unable to produce meaningful free components, i.e., source estimates with index $n > M$ that can capture noise or functional networks outside the set of references.

<footnote n="1">¹Matlab codes for regIVA-G, ar-cIVA-G and tf-cIVA-G are available on the MLSP website: https://mlsp.umbc.edu/resources.html</footnote>
## 2.2. Constrained IVA-G methods

Constrained IVA-G methods inherit from IVA-G the ability to leverage the information shared between the datasets and can yield free components. As explained in [14], a possible formulation of the IVA-G cost function is

$$ \mathcal{J}_{\text{IVA-G}}(\mathcal{W}) = \frac{1}{2} \sum_{n=1}^{N} \log \det(\hat{\Sigma}_n) - \sum_{k=1}^{K} \log |\det(\mathbf{W}^{[k]})|, \quad (2) $$

where $\hat{\Sigma}_n$ is the empirical covariance matrix of $y_n$. The first term promotes an inner alignment between the $(y_n^{[k]})_{1 \le k \le K}$ for each $n \in \{1, \dots, N\}$ while the second term promotes orthogonality of the family $(w_n^{[k]})_{1 \le n \le N}$ for each $k \in \{1, \dots, K\}$. The IVA-G cost used in [13] differs from (2), but can be easily proven to share the same set of minimizers.

As IVA based cost functions have no known closed form solutions, iterative algorithms are necessary. Both methods presented here rely on gradient updates of the demixing vectors as in IVA-G-V [8]. In ar-cIVA-G, the authors introduced an augmented Lagrangian function [13], adding a constraint term to the IVA-G cost to force the correlations $\epsilon(y_n^{[k]}, r_n)$ to be above a threshold $\rho_n^{[k]}$ that is dynamically recomputed at each iteration for all $n$ and $k$. The tf-cIVA-G algorithm [13] is equivalent to minimize $\mathcal{J}_{\text{tf-cIVA-G}}(\mathcal{W}) = \mathcal{J}_{\text{IVA-G}}(\mathcal{W}) + \frac{\mu}{2} \tilde{\mathcal{J}}_{\text{regIVA-G}}(\mathcal{W})$ where $\mu > 0$ controls the strength of the regularization. Therefore, the combined effects of IVA-G and regIVA-G apply.

In a nutshell, the four aforementioned methods reconstruct estimated source components with various properties, namely correlation with the corresponding reference (A), uncorrelation with the other references (B), inner alignment within the SCVs (C) and independence between the SCVs (D). Tab. 1 summarizes which algorithm yields which properties.

## 3. EXPERIMENTS

Now that we explained in what different ways the four methods shape the reconstructed components, we propose a comparative study performed on real datasets to analyze their ability to extract biomarkers from resting-state fMRI sessions. For our experimental set-up, we used a set of $K = 98$ subjects, from the B-SNIP cohort collected in the Baltimore site [1, 2]. This set contains $K_{SZ} = 49$ SZ subjects and $K_{HC} = 49$ HC subjects. The fMRI data is preprocessed as described in [13]. We use $M = 53$ references $R$ from Neuromark 1.0 [16] organized into seven domains: subcortical (SC), auditory (AU), sensorimotor (SM), visual (VI), cognitive control (CC), default mode (DM), and cerebellar (CB). We used $N = 85$ components for this study. For RGCA, we set $\lambda = 1$ as suggested in [15]. Likewise, we set $\mu = 100$ for tf-cIVA-G following [13]. All data processing, is performed using the GIFT toolbox in Matlab<fnref n="2" />, on a Dell Latitude 7420 laptop with 1th Gen Intel(R) Core(TM) i7-1165G7 at 2.80GHz,

equipped with 16GB Ram. The computation times of the source separation step for each method are indicated in Tab. 1.

First, we compute the absolute correlation of all components of each subject to their closest reference. We separate the values between the two cohorts HC and SZ, then we run a two-sample t-test and a Kolmogorov-Smirnov (KS) test to verify if their distributions are significantly different. The violin plots in Fig. 1 show the two distributions for each constrained component. We put a blue (resp. red) star if the t-test (resp. KS-test) rejects the null hypothesis. In Fig. 2, we do the same with the free components for ar-cIVA-G and tf-cIVA-G. For each algorithm, we count the number of components, (constrained ones + free ones) that reject the null hypothesis for the KS test ($N_{KS}^{\text{Corr}}$) and the t-test ($N_{t}^{\text{Corr}}$). The results are summarized in Tab. 1.

<figure><img src="image_3.png" /><figcaption>Fig. 1: Distributions of the $|\epsilon(y_n^{[k]}, r_n)|$ for HC subjects (green) and SZ subjects (red).</figcaption></figure>

<footnote n="2">²The code of GIFT is available at: https://github.com/trendscenter/gift</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 2:** Distributions of the absolute correlations between free source components and their closest reference for HC subjects (green) and SZ subjects (red).</figcaption></figure>

For each algorithm, and each subject, we also compute the temporal FNC that is, the correlation matrix of the time courses after applying the following corrections to them. The time courses are first despiked, then detrended (as in [17]) and filtered from their highest frequencies. We then run a two-sample t-test between the FNCs of SZ and HC subjects coefficient-wise. The matrices of the t-values are shown in Fig. 3. Only the coefficients for which we reject the null hypothesis are represented under the diagonal, and they are counted in 1 ($N^{\text{FNC}}$). This study includes free components for cIVA-G methods, we did not represent them in the figure for clarity.

<figure><img src="image_5.png" /><figcaption>**Fig. 3:** 2-sample t-test between the HC and SZ FNCs.</figcaption></figure>

<table><caption>**Table 1:** Properties sought for the components and performance of each method on extracting biomarkers.</caption><thead><tr><th>Algorithm</th><th>(A)</th><th>(B)</th><th>(C)</th><th>(D)</th><th>Time (s)</th><th>N<sub>t</sub><sup>Corr</sup></th><th>N<sub>KS</sub><sup>Corr</sup></th><th>N<sup>FNC</sup></th></tr></thead><tbody><tr><td>RGCA</td><td>✓</td><td></td><td>✓</td><td></td><td><strong>4.8</strong></td><td>12</td><td>7</td><td>329</td></tr><tr><td>regIVA-G</td><td>✓</td><td>✓</td><td></td><td></td><td>3.8 × 10<sup>2</sup></td><td>13</td><td>7</td><td>368</td></tr><tr><td>ar-cIVA-G</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>9.9 × 10<sup>3</sup></td><td>13 + 7</td><td><strong>9 + 4</strong></td><td>369</td></tr><tr><td>tf-cIVA-G</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>7.9 × 10<sup>3</sup></td><td><strong>14 + 7</strong></td><td><strong>6 + 7</strong></td><td><strong>408</strong></td></tr></tbody></table>

For the datasets we used, RGCA is about 100 times faster than regIVA-G which is itself about 20 times faster than the cIVA-G algorithms. From columns $N_t^{\text{Corr}}$ and $N_{KS}^{\text{Corr}}$, it appears that the cIVA-G methods can extract more biomarkers than regression-based methods from the spatial source components, this is mainly due to the free components. Moreover, $N^{\text{FNC}}$ shows that this difference is much less important for temporal FNCs. Overall, taking into account the inter-subject dependence (C) is costly and does not seem to improve significantly the reconstruction of the sources for which we have references.

## 4. CONCLUSION

In this paper, we compared four state-of-the-art algorithms to extract information from fMRI datasets and reference templates. Those algorithms use different approaches and therefore have varying complexities and performances. The main observation is that regIVA-G and RGCA enabled a much faster analysis, however they do not provide free components. Those results are encouraging for regression-based methods as a way to dramatically accelerate fMRI studies for a diagnostic perspective. In future work, we would like to further investigate these methods and develop them to keep their efficiency while recovering more information.

## 5. COMPLIANCE WITH ETHICAL STANDARDS

All individuals in this study provided informed consent under an approved IRB protocol at each data collection site.
## 6. REFERENCES

[1] C. A. Tamminga, G. Pearlson, M. Keshavan, J. Sweeney, B. Clementz, and G. Thaker, "Bipolar and Schizophrenia Network for Intermediate Phenotypes: Outcomes Across the Psychosis Continuum," *Schizophrenia Bulletin*, vol. 40, no. Suppl_2, pp. S131–S137, Feb. 2014.

[2] C. A. Tamminga, E. I. Ivleva, M. S. Keshavan, G. D. Pearlson, B. A. Clementz, B. Witte, D. W. Morris, J. Bishop, G. K. Thaker, and J. A. Sweeney, "Clinical phenotypes of psychosis in the bipolar-schizophrenia network on intermediate phenotypes (B-SNIP)," *American Journal of Psychiatry*, vol. 170, no. 11, pp. 1263–1274, 2013, pMID: 23846857.

[3] B. J. Casey, T. Cannonier, M. I. Conley, A. O. Cohen, D. M. Barch, M. M. Heitzeg, M. E. Soules, T. Teslovich, D. V. Dellarco, H. Garavan et al., "The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites," *Developmental Cognitive Neuroscience*, vol. 32, pp. 43–54, 2018.

[4] M. J. McKeown, S. Makeig, G. G. Brown, T.-P. Jung, S. S. Kindermann, A. J. Bell, and T. J. Sejnowski, "Analysis of fMRI data by blind separation into independent spatial components," *Human Brain Mapping*, vol. 6, no. 3, pp. 160–188, 1998.

[5] P. Comon, "Independent component analysis, a new concept?" *Signal Processing*, vol. 36, no. 3, pp. 287–314, 1994.

[6] Q. Long, S. Bhinge, Y. Levin-Schwartz, Z. Boukouvalas, V. D. Calhoun, and T. Adali, "The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics," *Human Brain Mapping*, vol. 40, no. 2, pp. 489–504, 2019.

[7] T. Kim, T. Eltoft, and T.-W. Lee, "Independent vector analysis: An extension of ICA to multivariate components," in *International conference on independent component analysis and signal separation*. Springer, 2006, pp. 165–172.

[8] M. Anderson, T. Adali, and X.-L. Li, "Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis," *IEEE Transactions on Signal Processing*, vol. 60, no. 4, pp. 1672–1683, Apr. 2012.

[9] T. Adali, M. Anderson, and G.-S. Fu, "Diversity in independent component and vector analyses: Identifiability, algorithms, and applications in medical imaging," *IEEE Signal Processing Magazine*, vol. 31, no. 3, pp. 18–33, May 2014.

[10] C. Cosserat, B. Gabrielson, E. Chouzenoux, J.-C. Pesquet, and T. Adali, "An Effective Iterative Solution for Independent Vector Analysis with Convergence Guarantees," *IEEE Transactions on Signal Processing (to appear)*, 2025.

[11] S. Bhinge, R. Mowakeaa, V. D. Calhoun, and T. Adali, "Extraction of time-varying spatiotemporal networks using parameter-tuned constrained IVA," *IEEE Transactions on Medical Imaging*, vol. 38, no. 7, pp. 1715–1725, 2019.

[12] S. Bhinge, Q. Long, Y. Levin-Schwartz, Z. Boukouvalas, V. D. Calhoun, and T. Adali, "Non-orthogonal constrained independent vector analysis: Application to data fusion," in *Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017)*, 2017, pp. 2666–2670.

[13] T. Vu, F. Laport, H. Yang, V. D. Calhoun, and T. Adali, "Constrained independent vector analysis with reference for multi-subject fMRI analysis," *IEEE Transactions on Biomedical Engineering*, vol. 71, no. 12, pp. 3531–3542, 2024.

[14] B. Gabrielson, M. Sun, M. A. B. S. Akhonda, V. D. Calhoun, and T. Adali, "Independent vector analysis with multivariate gaussian model: a scalable method by multilinear regression," in *ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2023, pp. 1–5.

[15] T. Vu, H. Yang, F. Laport, B. Gabrielson, V. D. Calhoun, and T. Adali, "A robust and scalable method with an analytic solution for multi-subject fMRI data analysis," *Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)*, pp. 1831–1835, 2024.

[16] Y. Du, Z. Fu, J. Sui, S. Gao, Y. Xing, D. Lin, M. Salman, A. Abrol, M. A. Rahaman, J. Chen et al., "Neuromark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders," *NeuroImage: Clinical*, vol. 28, p. 102375, 2020.

[17] E. A. Allen, E. B. Erhardt, E. Damaraju, W. Gruner, J. M. Segall, R. F. Silva, M. Havlicek, S. Rachakonda, J. Fries, R. Kalyanam et al., "A baseline for the multivariate comparison of resting-state networks," *Frontiers in Systems Neuroscience*, vol. 5, p. 2, 2011.