# Subject fingerprinting and task classification rely on distinct functional connectivity features

Himanshu Aggarwal, Ana Fernanda Ponce, Swetha Shankar, Yasmin Mzayek, Agnés Pérez-Millan, Ana Luísa Pinho, Alexis Thual, Bertrand Thirion

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## To cite this version:

Himanshu Aggarwal, Ana Fernanda Ponce, Swetha Shankar, Yasmin Mzayek, Agnés Pérez-Millan, et al.. Subject fingerprinting and task classification rely on distinct functional connectivity features. Brain Structure and Function, 2026, 231 (1), pp.12. 10.1007/s00429-025-03068-3. hal-05469826

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# Subject fingerprinting and task classification rely on distinct functional connectivity features

Himanshu Aggarwal,¹* Ana Fernanda Ponce,¹ Swetha Shankar,¹

Yasmin Mzayek,¹ Agnès Pérez-Millan,² Ana Luísa Pinho,³,⁴,⁵

Alexis Thual,¹,⁶,⁷ Bertrand Thirion¹

¹Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France

²Alzheimer's Disease and Other Cognitive Disorders, Neurology Service, Hospital Clínic de Barcelona, Spain

³Department of Computer Science, Western University, London, Ontario, Canada

⁴Department of Psychology, Western University, London, Ontario, Canada

⁵Western Centre for Brain and Mind, Western University, London, Ontario, Canada

⁶Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Gif sur Yvette, France

⁷Inserm, Collège de France, Paris, France

*Correspondence: himanshu.aggarwal@inria.fr

December 24, 2025

**Keywords:** functional connectivity, subject fingerprinting and task classification

**Abstract:** Functional connectivity (FC) measured by functional magnetic resonance imaging (fMRI) has been shown to be a marker of individual brain characteristics, and also to reflect cognitive tasks. However, it remains unclear how the choice of FC measures affects the encoding of both subject and task properties. We address this question using a high-quality deep-phenotyping dataset consisting of multiple naturalistic tasks (listening to a story, watching three different movies and playing a video game) and resting-state, while working on two classification problems: subject fingerprinting and task classification. We compare the performance of a combination of two FC measures and three covariance estimation methods. We then examine the similarity and subject specificity of FC across tasks and
with structural connectivity (SC). We find that sparse partial correlation, obtained from the Graphical-Lasso estimator, performs best in subject identification tasks and is most similar to SC; it stands out as a marker of identity. In contrast, task information is better captured by Pearson correlation measures, as they account for distributed brain activity. Overall, we find that pairwise interactions captured by partial correlation are optimal for fingerprinting, while multi-way relationships underlying full correlation are an accurate marker of cognitive function.

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## Highlights

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* Subject identity and task can be decoded simultaneously from FC.

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* Partial correlation-based FC, which captures pairwise interactions, is more similar to SC and more accurate for subject fingerprinting.

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* This implies that pairwise functional interactions are better representations of structure and identity.

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* Task classification is more accurate with full correlation-based FC.

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* Large-scale and multi-way interactions are thus a marker of task-specific cognitive function.

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# 1 Introduction

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The last decade has witnessed a surge in functional connectivity (FC)-based predictions of individual characteristics, such as behavioral phenotypes (Amico & Goñi, 2018; Chen et al., 2022; Dadi et al., 2019; Finn et al., 2015; Nielsen et al., 2013; Shen et al., 2017) and subject identification (also known as subject fingerprinting) (Amico & Goñi, 2018; Finn et al., 2015; Hannum et al., 2023; Jalbrzikowski et al., 2020; St-Onge et al., 2023; Vanderwal et al., 2017). Simultaneously, it has been shown to successfully identify cognitive tasks (Demirtaş et al., 2019; Hannum et al., 2023; Richiardi et al., 2011; Shirer et al., 2012; Xie et al., 2018). Overall, previous literature suggests that FC carries information specific to *both* individual identity and cognitive tasks (Bernstein-Eliav & Tavor, 2024). In this work, we seek to examine *how* individual identity and cognitive tasks are jointly encoded in FC. To do so, we employ a supervised learning approach (similar to Hannum et al. (2023)) to perform subject fingerprinting across tasks and task classification across individuals. In particular, we examine how the choice of FC measures affects the encoding of both subject and task properties. Therefore, it is important to note that this work does

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not aim to be a benchmark for performance of subject fingerprinting or task classification using FC, but
rather a comparison of the relative performance of different FC measures in these two tasks.

**Different flavors of FC** Accuracy of subject identification, task classification or any other downstream inferences drawn from FC can vary depending on decisions made at different steps of the FC estimation pipeline. One such aspect is the choice of different methods to estimate the interactions of fMRI signal time courses between these regions (S. M. Smith et al., 2011; Wang et al., 2014). We hypothesize that the choice of these connectivity measures is critical to understanding the underpinnings of task identification vs fingerprinting. We refer to the statistics used to quantify connectivity as FC *measures*, and the algorithmic procedures used to reach these statistics as covariance *estimators*. Full (aka Pearson) correlation and partial correlation between the time series of brain regions are the most commonly used FC measures. Partial correlation is informative about direct links between regions, so it is meant to outline physical connections, while full correlation reflects both direct and indirect links (S. M. Smith et al., 2011) and may thus better capture network-level multivariate interactions. Due to this, cross-regional partial correlation matrices are sparser and only encode pairwise interactions, while full correlation matrices are denser and are informative about multi-way and large-scale interactions.

We also have to take into account the choice of covariance estimators. Within the context of FC, unregularized empirical covariance estimators are the default choice to estimate these measures (Finn et al., 2017; Hannum et al., 2023; Kong et al., 2021; Vanderwal et al., 2017). However, as is usually the case in neuroscientific studies, the number of samples (length of time series) is small and the number of features (number of regions) is large. This leads to noisier estimates of full correlation or even undefined partial correlation, given that it involves calculating the inverse of the covariance matrix. To address this issue, various regularization methods have been proposed to reach a stable solution (Friedman et al., 2008; Ledoit & Wolf, 2004), resulting in different covariance estimators. These methods are underused in FC studies, although some benchmarks have been carried out in the context of predicting phenotypes, diagnostic information, or behavioral measures (Dadi et al., 2019; Pervaiz et al., 2020) and one comparing their correlation with SC (Liégeois et al., 2020).

So in the present study, we compare the performance of two connectivity measures (full correlation and partial correlation), obtained from three estimators: an unregularized estimator, the Ledoit-Wolf (Ledoit & Wolf, 2004) shrinkage estimator, and the Graphical-Lasso (Friedman et al., 2008) $L_1$-regularized estimator, which enforces sparsity on the inverse covariance. This allows us to compute the relative importance of local and global interactions in both fingerprinting and task classification.
73 Leveraging a deep-phenotyping dataset and naturalistic stimuli Vanderwal et al. (2017) showed 74 that naturalistic tasks (such as movie-watching) are more effective in capturing the individual-specific 75 information encoded in FC than resting-state or cognitive-domain specific tasks. Note that first-order 76 effect of task-evoked activations is introduced due to time-locked events that are common in cognitive- 77 domain specific tasks (both event-related and block design paradigms). This effect is unrelated to FC 78 and is known to substantially inflate task-state FC estimates (Cole et al., 2019). Consequently, these 79 effects can further lead to inflated scores when classifying between cognitive-domain specific tasks. For 80 instance, as noted by Hannum et al. (2023), the removal of these effects with task regression leads to a 81 drop in task classification accuracy in the HCP dataset. However, they also note an unexpected decrease 82 in subject fingerprinting accuracy, indicating that this task regression could remove some subject-specific 83 information as well. This indicates that the downstream effects of task regression have a noticeable 84 impact on between-subject and between-task similarity (Finn, 2021). But, in the case of naturalistic 85 tasks, such first-order effect is no longer separable from FC effects.

86 The Individual Brain Charting (IBC) dataset (Pinho et al., 2018, 2020, 2024) is a unique deep-phenotyping 87 dataset that provides multi-modal data (fMRI and diffusion MRI) for multiple naturalistic tasks and 88 resting-state, for the same set of subjects. These tasks include resting-state and five naturalistic tasks: a 89 story listening, three different movie watching and a video game playing task. All these tasks consist of 90 multiple runs, allowing us to perform task classification not merely between different task combinations 91 but also across the runs of each task. This level of granularity in task classification is not possible with 92 most other datasets, which typically consist of either only one movie watching task or only one run per 93 movie watching task (for example, the CamCAN dataset (Shafto et al., 2014) or the HCP dataset (Van 94 Essen et al., 2012)). However, these benefits come at the cost of a small subject pool of eight to twelve 95 subjects, depending on the task. For this reason, the absolute performance of subject fingerprinting and 96 task classification should not be considered a benchmark for performance achievable with FC. As stated 97 before, the goal of this work is to compare the relative performance of different FC measures in subject 98 fingerprinting and task classification when a diverse set of naturalistic tasks is used.

99 Correspondence between FC and SC When considering the encoding of subject identity in FC, it 100 is interesting to compare it to the structural connectivity (SC) of the brain. However, large variations in 101 group-level FC-SC similarity have been observed across studies (Honey et al., 2009; Honey et al., 2010; 102 Mansour L et al., 2021; Straathof et al., 2019). This variability can be explained by the choice of FC 103 measures, parcellation schemes, small sample sizes and limitations of diffusion-weighted imaging (DWI) 104 and tractography methods to quantify SC (Maier-Hein et al., 2017; Straathof et al., 2019; Thomas et al., 105 2014). For example, Zimmermann et al. (2018) have studied the subject-specificity of FC-SC correspon-
dence by comparing the FC-SC correlation within and across subjects, in six different combinations of three datasets and six parcellation schemes. However, they found that the FC-SC correlation between the same subjects was significantly higher than the FC-SC correlation between different subjects in only one dataset-parcellation combination: the HCP dataset (Van Essen et al., 2012) with the Glasser parcellation (Glasser et al., 2016).

Furthermore, in terms of the task used for estimating FC in most of these studies comparing FC with SC, resting-state has been the default and only choice, except for Hermundstad et al. (2013) who also used FC from attention and memory tasks. This work also showed that, in relative terms, resting-state FC is stronger in inter-hemispherical and in shorter intra-hemispherical connections. In contrast, attentional and memory tasks are associated with stronger FC in longer intra-hemispherical connections. This suggests that the choice of task can also affect the FC-SC correspondence. Furthermore, the choice of FC measure can affect the FC-SC correspondence. In principle, partial correlation should correspond better to SC than full correlation FC. This has been confirmed empirically by Liégeois et al. (2020).

What is missing in the literature is a comparison of the subject specificity of FC-SC correspondence across different tasks and FC measures. Therefore in the present study, we examine the strength and subject-specificity of FC-SC correlations, across naturalistic tasks as well as FC measures mentioned above.

Overall, based on previous literature, we hypothesize that full correlation-based FC measures will be better suited for task classification, while partial correlation-based FC measures will be more suitable for subject fingerprinting and will be more similar to SC. The remainder of the paper provides a comprehensive examination of this hypothesis through a series of analyses. We start by presenting the experimental procedures and models in section 2. The experiments include subject fingerprinting and task classification, the analysis of the classifier coefficients across functional networks, and eventually the FC-SC similarity and subject specificity analyses. In section 3, we compare all these metrics across two FC measures and three covariance estimators. Finally, we discuss how the varying performance of different FC measures in both classification tasks highlights the relative importance of pairwise and multi-way interactions in encoding subject identity and task information, respectively. This is followed by a discussion of all the factors that could contribute to the performance of FC measures. Some additional experiments are provided in the appendix.
<table><caption>Table 1: List of the naturalistic fMRI tasks used in the study</caption><thead><tr><th>Task</th><th>Subjects</th><th>Runs</th><th>Total duration (in mins)</th><th>Task Label</th></tr></thead><tbody><tr><td>Resting State</td><td>12</td><td>4</td><td>60</td><td>RestingState</td></tr><tr><td>LePetitPrince (Li et al., 2022)</td><td>11</td><td>9</td><td>106</td><td>LePetitPrince</td></tr><tr><td>Raiders of the Lost Ark (Haxby et al., 2011)</td><td>12</td><td>10</td><td>116</td><td>Raiders</td></tr><tr><td>The Good, the Bad, and the Ugly (Mantini et al., 2012)</td><td>11</td><td>18</td><td>188</td><td>GoodBadUgly</td></tr><tr><td>Monkey Kingdom</td><td>8</td><td>5</td><td>78</td><td>MonkeyKingdom</td></tr><tr><td>Mario</td><td>8</td><td>6</td><td>60</td><td>Mario</td></tr></tbody></table>

# 2 Methods

## 2.1 Data acquisition and preprocessing

The Individual Brain Charting (IBC) dataset (Pinho et al., 2018, 2020, 2024) is used in this study. The data were collected using a Siemens Magnetom Prisma-fit 3 Tesla magnetic resonance scanner. Details about the procedures and dataset can be found on IBC documentation: https://individual-brain-charting.github.io/docs/.

**fMRI** We use fMRI data for FC estimation from two resting-state, one story-listening, three movie-watching, and one video-gaming session. Each participant was scanned for either all or a subset of these tasks (see supplementary Figure 7). The movie-watching tasks were composed of several different runs of varying durations. See Table 1 for a detailed list of the tasks, the number of runs and the number of subjects for each task. In each run, a different section of the movie or the story was played while preserving the narrative. Regarding the resting-state analysis, the four runs were acquired in two different sessions, hence the first run of each session is considered as *first run*, while the second is handled as *second run*, although there are four runs in total. The video-gaming task involved playing the *Super Mario Bros* game for 60 minutes, which was divided into six runs of 10 minutes. The game consisted of eight different worlds, each with three levels. Participants were instructed to play freely and complete as many levels as possible within the session, resulting in varying time spent on each level as well as different behaviors per run across participants.

It is important to note that all these tasks are naturalistic and different from cognitive-domain specific tasks as explained in section 1. In practice, task-specific contrasts cannot be computed for these tasks. Instead, the full time series (albeit with some preprocessing) is used to estimate FC as explained in
<table><caption>Table 2: fMRI acquisition parameters for story-listening, movie-watching, video-gaming and resting-state sessions. Note that resting-state acquisition parameters were chosen to optimize temporal resolution, while those for other acquisitions are meant to optimize spatial resolution.</caption><thead><tr><th>Parameter</th><th>Story-listening / Movie-watching / gaming</th><th>Resting-state</th></tr></thead><tbody><tr><td>Sequence</td><td>Gradient Echo EPI</td><td>Gradient Echo EPI</td></tr><tr><td>TR</td><td>2000 ms</td><td>760 ms</td></tr><tr><td>TE</td><td>26.8 ms</td><td>29 ms</td></tr><tr><td>Flip angle</td><td>74 deg</td><td>53 deg</td></tr><tr><td>Fat suppr.</td><td>Fat sat.</td><td>Fat sat.</td></tr><tr><td>FOV</td><td>192 mm</td><td>194 mm</td></tr><tr><td>Matrix</td><td>128 × 128</td><td>88 × 88</td></tr><tr><td>In-plane resolution</td><td>1.5 × 1.5 mm²</td><td>2.2 × 2.2 mm²</td></tr><tr><td>Slice thickness</td><td>1.50 mm</td><td>2.20 mm</td></tr><tr><td>Number of slices</td><td>93 slices</td><td>66 slices</td></tr><tr><td>GRAPPA iPAT</td><td>2</td><td>1</td></tr><tr><td>Multiband accel. factor</td><td>3</td><td>6</td></tr><tr><td>Echo spacing</td><td>0.65 ms</td><td>0.55 ms</td></tr><tr><td>BW</td><td>1776 Hz/Px</td><td>2470 Hz/Px</td></tr><tr><td>Phase partial Fourier</td><td>None</td><td>None</td></tr><tr><td>Multi-slice mode</td><td>Interleaved</td><td>Interleaved</td></tr><tr><td>Series</td><td>Interleaved</td><td>Interleaved</td></tr></tbody></table>

section 2.3.

Acquisition parameters for all tasks are given in Table 2. It should be noted that the acquisition parameters for the resting-state session were chosen to optimize temporal resolution, while those for other acquisitions were meant to optimize spatial resolution. Hence the task classification between resting-state and other tasks may be confounded by this parameter choice. The other task classification experiments do not suffer from such confounding.

All fMRI images, i.e. Gradient-Echo (GE) EPI volumes, were collected either with Anterior-Posterior (AP) or Posterior-Anterior (PA) phase-encoding direction, resulting in images distorted in opposite directions. Two separate Spin-Echo (SE) EPI volumes were also collected in reversed phase-encoding directions which were used to estimate the susceptibility-induced off-resonance field and to correct the distortions in GE-EPI volumes using the *Topup* method. Details about the *Topup* method are described in (Andersson et al., 2003). Further, the GE-EPI volumes were aligned within each participant. A rigid-body transformation was employed to register all volumes to the average volume of all images (Friston et al., 1995). Raw data were preprocessed with *PyPreprocess* (https://github.com/neurospin/pypreprocess), using SPM12
<table><caption>Table 3: DWI acquisition parameters. A total of 120 combinations of b-values and b-vectors were used in each individual.</caption><thead><tr><td>Parameter</td><td>Value</td></tr></thead><tbody><tr><td>TR</td><td>7000 ms</td></tr><tr><td>TE</td><td>76 ms</td></tr><tr><td>Flip angle</td><td>90 deg</td></tr><tr><td>Refocusing flip angle</td><td>180 deg</td></tr><tr><td>FOV</td><td>240 mm</td></tr><tr><td>Slice thickness</td><td>1.30 mm</td></tr><tr><td>Number of slices</td><td>112 slices</td></tr><tr><td>GRAPPA iPAT</td><td>2</td></tr><tr><td>Multiband accel. factor</td><td>2</td></tr><tr><td>Echo spacing</td><td>0.71 ms</td></tr><tr><td>BW</td><td>1598 Hz/Px</td></tr><tr><td>Phase partial Fourier</td><td>6/8</td></tr><tr><td>B-values</td><td>[1500, 3000] s/mm2</td></tr></tbody></table>

170 routines for the main steps: realignment, coregistration with anatomy and spatial normalization. No 171 slice-timing correction was performed. The preprocessing pipeline for the fMRI IBC data is described in 172 more detail in (Pinho et al., 2021).

173 **Diffusion-weighted Imaging (DWI)** We use diffusion-weighted magnetic resonance imaging data 174 for SC estimation. These data were acquired in 12 subjects, and the acquisition parameters are given in 175 Table 3.

176 The DWI data were preprocessed using MRtrix3 (Tournier et al., 2019) and FSL (S. M. Smith et al., 177 2004). The images were first denoised using the *Marchenko-Pastur* PCA method (Cordero-Grande et al., 178 2019; Veraart et al., 2016) implemented with the MRtrix *dwidenoise* function. This denoising procedure 179 identifies the principal components corresponding to noise and then removes them from the DWI data 180 (Veraart et al., 2016). After denoising, to correct the distortions due to inhomogeneities of the magnetic 181 field, FSL's *Topup* (Andersson et al., 2003) and eddy correction (Andersson & Sotiropoulos, 2016) were 182 used. The *Topup* method is the same as described in the fMRI section. The eddy correction step 183 corrects for eddy current-induced distortions, which are a consequence of rapid switching of the diffusion 184 gradients. No bias field correction was done.
**Cohort** A detailed description of the age, sex and handedness of the group is provided in (Pinho et al., 2020). Age varied between 26 and 40 years old (median = 30 years) upon recruitment and handedness was determined with the *Edinburgh Handedness Inventory* (Oldfield, 1971). For more information on the cohort's recruitment, consult Pinho et al. (2018) and Pinho et al. (2020).

**Ethical aspects** The experimental protocols were approved by a local ethical committee in the context of the IBC project ("Comité de Protection des Personnes" - no. 14-031), and a specific committee to ensure compliance with data-protection rules ("Commission Nationale del'Informatique et des Libertés" - DR-2016-033). Experiments were carried out with the informed written consent of each participant according to the Declaration of Helsinki and French public health regulations.

**Generalization datasets** The IBC dataset reuses tasks from previous studies. This means that we could find external datasets with a different set of subjects, whose data is acquired with different acquisition parameters, but performing the same tasks. Therefore, we leveraged this feature to mitigate the limitation of the small subject pool in the IBC dataset. The details of this analysis are provided in section 2.5.

We used four different datasets for this, as listed in Table 4. All the task labels in Table 4 (the *Task* column) are as per the names in the IBC dataset, and the details about each task can be found on the IBC documentation: https://individual-brain-charting.github.io/docs/tasks.html

## 2.2 Fiber orientation density estimation and tractography

From the preprocessed DWI data, the response functions (required for fiber orientation density estimation) for all tissue types (white matter, grey matter, and cerebrospinal fluid) were estimated using the *Dhollander* algorithm (Dhollander et al., 2019), implemented in *MRtrix* function *dwi2response dholander*. These derived response functions were then used to estimate the amount of diffusion in three orthogonal directions (known as fiber orientation density estimation) using a multi-shell, multi-tissue constrained deconvolution method implemented under *dwi2fod* function in *MRtrix*.

Then, to seed the streamlines from the grey matter-white matter interface in the next step, a mask of this interface was first generated using the high-resolution segmented T1 image with the *5tt2gmwmi* function in *MRtrix*. Finally, using the grey matter-white matter interface mask and the estimated white-matter fiber orientation density, the second-order integration over fiber orientation distributions (*iFOD2*) method (Tournier et al., 2010) was used to estimate the streamline tracts. For this, the *MRtrix* function *tckgen*
<table><caption>Table 4: List of external fMRI datasets used for testing generalizability</caption><thead><tr><th>Dataset</th><th>Subjects</th><th>Runs</th><th>Task</th><th>Total duration (in mins)</th></tr></thead><tbody><tr><td rowspan="4">ARCHI<br>(Pinel et al., 2019)</td><td rowspan="4">78</td><td rowspan="4">1</td><td>ArchiStandard</td><td>5</td></tr><tr><td>ArchiEmotional</td><td>7</td></tr><tr><td>ArchiSpatial</td><td>8</td></tr><tr><td>ArchiSocial</td><td>8.5</td></tr><tr><td rowspan="7">HCP<br>(Van Essen et al., 2012)</td><td rowspan="7">728</td><td rowspan="7">2</td><td>HcpEmotion</td><td>4</td></tr><tr><td>HcpRelational</td><td>5.5</td></tr><tr><td>HcpGambling</td><td>6</td></tr><tr><td>HcpSocial</td><td>6.5</td></tr><tr><td>HcpMotor</td><td>7</td></tr><tr><td>HcpLanguage</td><td>7.5</td></tr><tr><td>HcpWM</td><td>10</td></tr><tr><td>HumanMonkeyGBU<br>(Mantini et al., 2012)</td><td>25</td><td>3</td><td>GoodBadUgly</td><td>30</td></tr><tr><td>TheLittlePrince (Li et al., 2022)</td><td>28</td><td>9</td><td>LePetitPrince</td><td>106</td></tr></tbody></table>

214 was used to generate 10⁷ streamlines with a maximum length of 250 mm and the fiber orientation density 215 amplitude cut-off set at 0.6. The resulting set of streamlines represents a dense and high-resolution 216 individual structural tractogram.

## 2.3 Functional and structural connectivity estimation

**FC estimation** As presented in the upper half of Figure 1, the preprocessed voxel-wise BOLD fMRI time series were extracted from *p* regions of the Schaefer et al. (2018) atlas using Nilearn (Abraham et al., 2014). We report the results for *p* = 400 regions in the main text, while the results for *p* = 200 regions are presented in supplementary Table 6 and section 5. We specifically chose the *p* = 200 and *p* = 400 regions because several previous studies have noted that the classification performance maximises in that range (Dadi et al., 2020; Dohmatob et al., 2021; Vanderwal et al., 2017). The time series extracted from these regions were filtered in the 0.01–0.2 Hz range, standardized and regressed to remove motion and high-variance confounds (via *CompCor* method Behzadi et al., 2007). We also tested the effect of changing the frequency band to 0.01–0.1 Hz and 0.01–0.249 Hz, but found no difference in the results (see supplementary Figure 20). Two FC measures were estimated from the region-wise time series: full and partial correlation. Three different covariance estimators were used: the unregularized
<figure><img src="image_3.png" /><figcaption>**Figure 1: Connectivity estimation and downstream analyses: (a) Connectivity estimation:** fMRI data from each of 5 tasks (1 resting-state, 3 movie-watching, 1 video-gaming) were preprocessed and parcellated into *p* = 200 or 400 regions of the Schaefer et al. (2018) atlas. FC was estimated from *p* region-wise time series using six different measures: full correlation and partial correlation, each derived from three different covariance estimators – the unregularized empirical estimator, Ledoit-Wolf regularized shrinkage estimator, Graphical-Lasso *L*₁-regularized estimator. The DWI data was preprocessed and subjected to fiber orientation density estimation and tractography, yielding 10⁷ streamlines in each subject. These streamlines were then used to estimate the SIFT2 (R. E. Smith et al., 2015) SC matrix for the *p* regions of the Schaefer et al. (2018) atlas. Finally, since all the connectivity matrices are symmetric, only lower triangular matrices (without the diagonal) were kept and flattened into one-dimensional vectors for further analysis. **(b) Analyses:** FC matrices from each task and each subject were used to perform subject fingerprinting and task classification. FC-FC similarity was calculated using Pearson's correlation between each pair of tasks, within and across subjects (results in supplementary material). FC-SC similarity was also calculated using Pearson's between each FC task and SC, within and across subjects.</figcaption></figure>

empirical covariance estimator, the Ledoit-Wolf regularized shrinkage estimator, and the Graphical-Lasso $L_1$-regularized estimator from Scikit-Learn (Pedregosa et al., 2011). For Graphical-Lasso, we used the GraphicalLassoCV implementation from Scikit-Learn, which performs an internal cross-validation for each task (region-wise time series for each run of each task) to pick the best sparsity parameter. Overall, this yielded a total of six combinations of FC measures and estimators with shape $p \times p$ per fMRI run. However, for clarity, we only report the results from the three best-performing measures *i*) unregularized full correlation, *ii*) Ledoit-Wolf regularized full correlation, and *iii*) the Graphical-Lasso partial correlation FC in the main text. All the performance metrics for the rest of the FC measures are presented in
supplementary Table 6. There were 463 runs available (9 for LePetitPrince in 11 subjects, 10 for Raiders in 12 subjects, 18 for GoodBadUgly in 11 subjects, 5 for Monkey Kingdom in 8 subjects, 4 for resting-state in 12 subjects and 6 for Mario in 8 subjects) so that $463 \times 6 = 2778$ FC connectivity matrices were built. Since these matrices were symmetrical, only the lower triangular matrices were retained, which were subsequently flattened into one-dimensional vectors for further analysis.

**SC estimation** The tractograms generated from the DWI data were in the native space of each subject. So for estimating the SC matrices, the Schaefer et al. (2018) atlas was first transformed from MNI152 to the native space using ANTs image registration (Avants et al., 2008) as described on MR-trix wiki (https://community.mrtrix.org/t/use-of-volumetric-templates-parcellation-atlases/2253). Finally, the SC matrices were calculated for *p* regions of this transformed atlas using the *tck2connectome* function from MRtrix. Similar to FC, we report the results for *p* = 400 regions in the main text, while the results for *p* = 200 regions are presented in the supplementary Table 6 and section 5.

Each value in these SC matrices is the sum of the contribution (SIFT2 weights (R. E. Smith et al., 2015) calculated using *tcksift2*) of each streamline (between any two given regions) to the overall fiber orientation density. This value was then normalized by the volume of the two regions (using parameter *-scale-invnondevol* with *tck2connectome*). This finally gave us twelve *p* × *p* SC matrices, each corresponding to one subject. Similar to FC matrices, the upper triangular matrices for SC were retained and flattened into one-dimensional vectors for further analysis. The SC estimation pipeline is presented in the lower half of Figure 1.

## 2.4 Analyses

**Subject fingerprinting** Once we had obtained the FC vectors, we used a linear SVC classifier (from Scikit-Learn (Pedregosa et al., 2011)) to predict if an FC vector obtained from a given task corresponds to a specific subject. We refer to this as *within-task* subject fingerprinting because the classifier is trained and tested on FC vectors from one task at a time. This means that the classifier learned to classify across 8-12 subjects (because not all 12 subjects performed each task) *within each task* – *RestingState*, *LePetitPrince*, *Raiders*, *GoodBadUgly*, *MonkeyKingdom*, and *Mario*. We used a group-*k*-fold cross-validation scheme from Scikit-Learn (Pedregosa et al., 2011), with run labels as groups, subject labels as classes and *k* was the number of unique groups. Using run labels as groups and number of groups as *k* ensured that all samples corresponding to one of the run labels was left out of the training set and used as the test set in each fold. This implies that in the testing phase, the classifier could not merely rely on trivial run differences that had been memorized during the training phase.
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We also performed across-task subject fingerprinting, i.e., we classified between subjects across all tasks simultaneously. In this fingerprinting scenario also, we used the same cross-validation scheme, except that the data were grouped according to task labels, to prevent the classifier from relying on task differences memorized during the training phase.

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We used a train-test split over a train-test-validation split throughout this study because: (i) the number of subjects in the IBC dataset is small (12 subjects), and the train-test split maximizes the number of samples used for training (ii) we used a linear SVC with default hyperparameters and did not perform any hyperparameter tuning; hence, we did not need a separate validation set.

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### Task Classification
We used the same linear SVC classifier to predict if a given FC corresponds to a resting-state, story-listening, one of the movie-watching, or gaming session. To do so, we considered all pairs of tasks among the six available (RestingState, LePetitPrince, Raiders, GoodBadUgly, Mon-keyKingdom, and Mario), leading to fifteen different binary classification scenarios. We used the same group-k-fold cross-validation scheme as before except here the groups correspond to subjects. This ef-fectively translates to a leave-one-subject-out cross-validation scheme. In this scheme, the classifier is trained on all runs of all tasks from all subjects except one. Then, it is tested on the runs of the task from the left-out subject. Finally, we posed the same problem in a multi-class classification scenario, where we classified across all six tasks simultaneously. We used the same cross-validation scheme here as in the binary classification scenarios.

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### Run Classification
We used the linear SVC classifier to predict if an FC vector belongs to a specific run, corresponding to a section of a given movie. This experiment tests the specificity of information encoded in FC. This setup was identical to that of the (within-task) subject classification except that the run labels were the classes and subjects were groups during cross-validation.

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### Evaluation metric
We used macro-averaged F1 score for evaluating the performance of the classification tasks. The macro-averaged F1 score is the average of the F1 scores of each class, where the F1 score is the harmonic mean of precision and recall. It is considered to give the best account of classifier performance in one single number. We compared these F1 scores against the baseline F1 score of a dummy classifier that predicts the most frequent class label. For each classification task, we estimated uncertainty in the average F1 score using 95% confidence intervals (CIs) of a bootstrap distribution constructed by randomly resampling with replacement 10⁴ times. These CIs are represented as error bars in the figures plotted via Seaborn (Waskom, 2021).
We also tested the significance of the F1 scores by performing permutation tests. We permuted the class labels 50 times and performed the classification with the permuted labels to get a distribution of F1 scores at chance-level. We then compared the actual F1 score with this distribution to check if the actual F1 score was significantly higher than chance-level. We only did this for unregularized and Ledoit-Wolf full correlation, and Graphical-Lasso partial correlation FC estimates for $p = 400$ regions. The results of these permutation tests are presented in supplementary Figure 21.

**Classifier parameters** As mentioned previously the classifier used was a linear SVC from Scikit-Learn (Pedregosa et al., 2011). The parameters of the classifier were set to the default values, except for `max_iter` which was set to $10^5$ to avoid convergence warnings and `dual` which was set to `auto`.

**Network-wise breakdown of classifier coefficients** We examined the classifier coefficients for a given classification task to find which connections were most important for each FC measure at a given classification task. We conducted this study to determine if the differential information encoded in the two functional connectivity (FC) measures (higher within-network connectivity for partial correlation versus both between- and within-network connectivity for full correlation) translates into how the classifier uses these connections to distinguish between subjects or tasks. Apart from that, this analysis also allows us to assess whether the specific networks involved in subject fingerprinting and task classification are similar to those previously reported in the literature. We calculated these coefficients by fitting the linear SVC classifier on the entire dataset (without cross-validation). We only did so for the classification across all tasks pooled together and subject fingerprinting across all tasks. These classifier coefficients $X = (x)_{(i,j),c}$ were of size $N_{\text{features}} \times N_{\text{classes}}$, where $N_{\text{features}}$ was the number of pairs of regions – i.e. $p \times (p-1)/2$ – and $N_{\text{classes}}$ was 6 for task classification and 12 for subject classification. So, we calculated the $L_2$ norm across $N_{\text{classes}}$ classes for each coefficient corresponding to regions $i$ and $j$ as follows:

$$ \|x\|_{i,j} = \sqrt{\sum_{c=1}^{N_{\text{classes}}} x_{(i,j),c}^2} $$

Then, given that the Schaefer et al. (2018) regions can be integrated into networks (those defined in (Thomas Yeo et al., 2011)), we averaged the coefficient norm for region pairs within each network pair to get the network-wise classifier coefficients.

**Group averaged similarity between FC and SC** The similarity between FC and SC was calculated as Pearson's correlation between each possible pair of FC and SC vectors. Since we had only one SC
vector per subject, and 2-6 FC vectors per subject (depending on the number of tasks performed), the SC vector was the same in those pairs. The similarities reported in the results section are for these pairwise correlations (for example, between Resting-State FC and SC) and are averaged within and between subjects. Note that to avoid confusion with the full correlation as an FC measure, we refer to the pairwise correlations between FC and SC as similarity in this paper.

**Subject specificity of similarities** To assess whether FC-SC similarities within subjects were significantly higher than those between subjects, we compared the average similarities within and between subjects. To correct for across-subject variability in measurement quality, we double mean-centered each similarity value. To do so, from similarity between subject *i* and *j* ($s_{i,j}$), we subtracted the average similarity of subject *i* with others ($\bar{s}_i$) and the average similarity of subject *j* with others ($\bar{s}_j$) and then added back the overall average $\bar{s}$, as follows:

$$s_{i,j}^{\text{corrected}} = s_{i,j} - \bar{s}_i - \bar{s}_j + \bar{s}$$

Finally, we used the non-parametric Mann-Whitney U test to check if the within-subject similarities were significantly higher than the between-subject similarities. Since we are doing multiple comparisons here (six tasks × three FC estimates), we corrected for it using the false discovery rate (FDR) method (Benjamini & Hochberg, 1995).

## 2.5 Supplementary analyses

**Similarity between FCs from pairs of tasks** We also analyzed the similarity of functional connectivity (FC) between tasks by calculating the Pearson correlation coefficient between the FC vectors of each pair of tasks. This analysis is redundant with respect to the task classification experiments, in that if two tasks are similar, then their FCs should be easier to classify and have a higher F1 score. However, this analysis allowed us to test whether we could reach the same conclusions as in the task classification experiments without relying on the classifier. Furthermore, as with FC-SC similarity, we compared within- and between-subject correlations using the double mean-centering method described above.

**Effect of run length on classification performance** To test whether the varying run lengths contribute to the classification performance, we trimmed all the runs to the shortest run length (293 volumes) and then performed the run classification analysis. The rest of the classification setup was the same as explained earlier for run classification.
**Effect of motion on classification performance** To test whether the variable motion artifacts across subjects and runs contribute to the classification performance, we used frame-wise displacement (FD) as the feature for classification analyses (similar to Chyzhyk et al. (2022)). Specifically, we performed the subject fingerprinting and run classification analyses with FD as features and compared the performance with the FC-based classification. The rest of the classification setup was the same as explained earlier for FC-based classification.

The FD was calculated using the motion parameters obtained from the realignment step in the pre-processing of the fMRI data. It is the sum of the absolute values of the six motion parameters (three translations and three rotations) at each time point:

$$FD(t) = \sum_{i=1}^{3} |T_{i}(t) - T_{i}(t-1)| + \sum_{j=1}^{3} 50 \cdot |R_{j}(t) - R_{j}(t-1)|$$

Here $T_{i}(t)$ and $R_{j}(t)$ are the translations and rotations at time point $t$ and $i$ and $j$ are the indices for the three translations and rotations, respectively. Assuming a 50 mm radius for the head, we multiplied the absolute values of the rotations by 50 to convert them to millimeters.

**Generalization in external datasets** As mentioned before, to mitigate the limitation of small subject pool of the IBC dataset, we used external datasets listed in Table 4. We did this in two ways:

1. **Replication on other datasets:** Here, we tested whether our observations for relative performance of full correlation FC vs. partial correlation FC in the IBC dataset could be replicated in two of the external datasets, namely TheLittlePrince dataset (Li et al., 2022) and the HCP dataset (Van Essen et al., 2012).

* TheLittlePrince dataset had 28 subjects, each listening to 9 runs of the LePetitPrince story, which is the same as in the IBC dataset. This allowed us to perform the same within-task subject fingerprinting and run classification analyses as in the IBC dataset and compare the results. We specifically chose this dataset because it had the same naturalistic task (story-listening) as in the IBC dataset and had more subjects (28 vs. 12 in IBC). All the other datasets listed in Table 4 were not suitable because they either had cognitive-domain specific tasks (which would require removal of first-order effect of task evoked activity) or had fewer subjects than the TheLittlePrince dataset.

* Next, we replicated the results for across-task subject fingerprinting. However, none of the external datasets included more than 12 subjects across multiple naturalistic tasks. There-
fore, we used the HCP900 dataset (Van Essen et al., 2012), which has 728 subjects and seven different cognitive-domain-specific tasks. To avoid trivial performance saturation, we performed the subject fingerprinting analysis across two tasks instead of all of them. This meant that we trained the classifier to learn the subject labels from one task and tested it on another. The purpose of this analysis was to create a more challenging classification to detect differences between full correlation and partial correlation FC.

2. **Classification generalization:** Here, we tested whether a classifier trained on FC from one dataset could predict the labels for the FC of the other dataset. We did this for each of the datasets listed in Table 4 paired with the IBC dataset. The details are explained below.

* The ARCHI dataset (Pinel et al., 2019) included 78 subjects, each of whom performed four different tasks with one run each. Here, we tested the generalizability of task classification. We only used data from the ARCHI subjects to train a classifier to learn the mapping between their FC and task labels (*ArchiStandard*, *ArchiSpatial*, *ArchiSocial*, *ArchiEmotional*). Then, we used the trained classifier to predict the task labels from FC of subjects from the IBC dataset (ARCHI → IBC). Similarly, we also did the reverse, i.e. trained another classifier on IBC data and predicted on ARCHI data (IBC → ARCHI).

* We also did the same task classification generalization across IBC and the HCP dataset (Van Essen et al., 2012). Here we first learned the mapping between HCP subjects' FC and task labels (*HcpEmotion*, *HcpGambling*, *HcpSocial*, *HcpMotor*, *HcpLanguage*, *HcpRelational*, *HcpWM*) and then predicted these task labels from IBC subjects' FC (HCP → IBC) and also the reverse (IBC → HCP).

* For the HumanMonkeyGBU dataset (Mantini et al., 2012) we had 25 subjects who each watched three runs of the Good, Bad and Ugly movie (3rd, 4th and 5th run in the IBC dataset). Here, we tested the generalizability of classification of these runs. First, we trained a classifier to learn the mapping between the run labels (*run-03*, *run-04*, *run-05*) and FC of subjects in the HumanMonkeyGBU dataset. Then, we used this pretrained classifier to predict the run labels from the FC of IBC subjects (HumanMonkeyGBU → IBC) and also vice versa (IBC → HumanMonkeyGBU).

* Similarly, we tested the run classification generalization across the IBC and TheLittlePrince dataset (Li et al., 2022). In this case, 28 subjects listened to all 9 runs of the LePetitPrince story as in the IBC dataset. We repeated the same procedure as for HumanMonkeyGBU: we trained a classifier on TheLittlePrince subjects and predicted on IBC subjects (TheLittlePrince → IBC) and vice versa (IBC → TheLittlePrince).
## 3 Results

Across all classification tasks, we found that the full correlation FC measures performed better at task and run classification and partial correlation FC measures performed better at subject fingerprinting. An overall summary of the classification scores are presented in Table 5. In the following subsections, we present these results in detail.

<table><caption>Table 5: **Average classification performance of all FC estimates:** These scores are averaged across all classification tasks, for each parcellation resolution, and FC measure. A detailed breakdown of the classification performance for each task, parcellation resolution, and FC measure is presented in supplementary Table 6</caption><thead><tr><th rowspan="2">Classification task</th><th rowspan="2">Parcellation resolution</th><th colspan="2">Unregularized</th><th colspan="2">Ledoit-Wolf</th><th colspan="2">Graphical-Lasso</th><th rowspan="2">Dummy score</th></tr><tr><th>correlation</th><th>partial correlation</th><th>correlation</th><th>partial correlation</th><th>correlation</th><th>partial correlation</th></tr></thead><tbody><tr><td rowspan="2">Subject fingerprinting</td><td>400 parcels</td><td>100</td><td>33</td><td>100</td><td>98</td><td>99</td><td>100</td><td>2</td></tr><tr><td>200 parcels</td><td>99</td><td>94</td><td>99</td><td>99</td><td>98</td><td>100</td><td>2</td></tr><tr><td rowspan="2">Task classification</td><td>400 parcels</td><td>90</td><td>36</td><td>90</td><td>53</td><td>85</td><td>89</td><td>43</td></tr><tr><td>200 parcels</td><td>90</td><td>51</td><td>90</td><td>69</td><td>86</td><td>89</td><td>43</td></tr><tr><td rowspan="2">Run classification</td><td>400 parcels</td><td>37</td><td>10</td><td>37</td><td>18</td><td>25</td><td>29</td><td>7</td></tr><tr><td>200 parcels</td><td>37</td><td>14</td><td>37</td><td>24</td><td>26</td><td>30</td><td>7</td></tr></tbody></table>

### 3.1 Subject fingerprinting

Subject fingerprinting *within* each task achieves a perfect F1 score of 100% across cross-validation splits for each of the three FC measures presented in Figure 2 (a). On the other hand, fingerprinting *across* tasks achieves a maximum F1 score of 98% with Graphical-Lasso partial correlation, and both full correlation measures show similar performance at 96% (Figure 2 (b)).
F1-score for subject fingerprinting

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Performance of subject fingerprinting using FC: (a) Within each task: Bars represent the F1 score of subject fingerprinting across runs, *within each task*. (b) Across tasks: Bars represent the F1 score of subject fingerprinting *across* all tasks. The chance-level F1 scores are from a dummy classifier that always predicts the most frequent class label. The error bars represent 95% confidence intervals of a bootstrap distribution, which estimates the uncertainty in the average F1 scores plotted in the bars.</figcaption></figure>
## 3.2 Task classification

<figure><img src="image_5.png" /><figcaption>Figure 3: Performance of task classification using FC: (a) Between tasks pairs: Bars represent the F1 score for classifying between pairs of tasks. (b) Across all tasks: Bars represent the F1 score of task classification across all six tasks. The chance-level performance is from a dummy classifier that always predicts the most frequent class label. The error bars represent 95% confidence intervals of a bootstrap distribution, which estimates the uncertainty in the average F1 scores plotted in the bars.</figcaption></figure>
The supervised classification between resting-state vs. story-listening/movie/gaming FCs achieves F1 score of 96-100% (blue bars in Figure 3 (a)) across the three selected FC measures. It is followed by the classification between story-listening/movie vs. gaming FC where F1 score ranges between 87-98% (green bars in Figure 3 (a)). Followed by these is the classification between story-listening vs. movie FCs where F1 score ranges between 81-97% (purple bars in Figure 3 (a)). Finally, the classification between pairs of movie FCs achieves lowest F1 scores between 57-87% (orange bars in Figure 3 (a)). So, as expected, the classification between different tasks, i.e., resting-state vs. story-listening/movie/gaming or story-listening, is easier than the classification between similar tasks, i.e., between two movies or between story-listening and movie-watching.

On the other hand, the multi-class classification (Figure 3 (b)) between all tasks at once is inherently more difficult than the previous binary classification scenario. Consequently, the F1 scores for this multi-class classification ranges between 72-77% across the FC measures. In this case, the Graphical-Lasso partial correlation performs worse than the two full correlation measures (although still much higher than chance, 15%).

## 3.3 Run classification

The runs represent different 10-15 minute sections of the same story or movie. The classification performance of runs varies a lot across the tasks, partly because of the variability in the number of runs (see Table 1 and the varying chance levels in Figure 4). A maximum of 59% F1 score is achieved for Raiders with the Ledoit-Wolf full correlation FC. Both full correlation measures perform better than the Graphical-Lasso partial correlation in all the tasks. This is difference between the FC measures is particularly noticeable when the classification is relatively difficult – as is the case not only for run classification here, but also for task classification between similar tasks (two movies or movies and story-listening) as observed previously in Figure 3.

We also pooled runs from the four naturalistic tasks and performed a classification between them. The F1 score for this classification scenario is 32% using the full correlation FC estimates, and 16% for Graphical-lasso partial correlation (supplementary Table 6), both still better than the chance-level accuracy of 1%.

Furthermore, in order to assess whether classification performance was driven by time series length difference, we performed the identical run classification analysis using FC estimated from time series truncated to 293 time points (the minimum number of time points across all runs). We found that the classification performance was not significantly different from the original analysis (supplementary
<figure><img src="image_6.png" /></figure>

Figure 4: **Performance of classifying story or movie sections (or runs) from FC:** The F1 score of classifying story or movie sections (or runs) within each naturalistic task. The chance-level F1 score are from a dummy classifier that always predicts the most frequent class label. The error bars represent 95% confidence intervals of a bootstrap distribution, which estimates the uncertainty in the average F1 scores plotted in the bars.

Figure 12).

## 3.4 Network-wise breakdown of classifier coefficients

Classifier's coefficients straightforwardly represent the importance of connections between each pair of regions for a given classification task. The matrix values in Figure 5 are the averages of those coefficients for regions constituting a network as defined in (Thomas Yeo et al., 2011). Along the diagonal, the values represent the importance of connections within a network, and off-diagonal values represent the importance of connections between networks.

Connections within the visual network stand out as an important feature in all classification problems. The coefficients for the full correlation measures (the first two columns) are similarly distributed across networks within each classification task. Notably, within-network connections (along the diagonal) are significantly more important than between-network connections for subject fingerprinting (Mann-Whitney U test, *p* < 0.05 for both full correlation measures). In addition, the connections between and within fronto-parietal, default-mode, and dorsal attention networks are the most important. The connections between ventral and dorsal attention networks and the rest of the networks except for the limbic network
### Network-wise breakdown of classifier coefficients

<figure><img src="image_7.png" /></figure>

Figure 5: **Network-wise breakdown of classifier coefficients:** Classifier's coefficients represent the importance of connections between pairs of regions for a given classification task. The matrix values here are the averages of those coefficients for regions constituting a network. The top row shows the importance of networks for subject fingerprinting across all tasks. The bottom row shows the importance of networks for classifying between all five tasks. The columns show the coefficients obtained from classifiers using different FC measures.

are also important.

In contrast, for task classification, the within and between network coefficients do not exhibit large differences (two-tailed Mann-Whitney U test, *p* > 0.05) for either full correlation measure. The connections within and between the visual, default-mode, and dorsal attention networks are more important than the others. There are, however, some noteworthy observations when we look at these coefficients for each task separately, as reported in supplementary section 5 and Figure 17.

With Graphical-Lasso partial correlation, within-networks connections are significantly dominant over between-networks ones for both classification tasks (Mann-Whitney U test, *p* < 10⁻⁷) and connections between fronto-parietal and dorsal attention networks are slightly more important for subject fingerprinting.
FC-SC similarity and its subject-specificity

<figure><img src="image_8.png" /><figcaption>(b) Comparison between within and across subject FC-SC similarity</figcaption></figure>

<figure><img src="image_9.png" /></figure>

Figure 6: **Similarity between FC and SC:** (a) The group averaged similarity between FC and SC is measured through Pearson's correlation. Different colors correspond to FC from different tasks, and different FC measures are along the y-axis. (b) Comparison between within and across-subject FC-SC similarities. Different colors correspond to FC from different tasks (along y-axis). Different FC measures are along the columns. The hatched distributions correspond to across-subject similarity, while solid-colored ones correspond to within-subject similarity. The FDR-corrected significance of the difference between within and across-subject similarity is represented as stars (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, ns = not significant) on the right of the distributions.

## 3.5 Similarity between FC and SC

The group averaged similarity between FC and SC is measured by Pearson's correlation between FC and SC and then taking an averaging within and between all subjects (Figure 6 (a)). Overall, the Graphical-Lasso partial-correlation FC is more similar to SC than other full correlation FC measures. Additionally, the Graphical-Lasso partial correlation FC from *resting state* is more similar to SC than the FC from *movie-watching*. However, full correlation-based FC estimates from *movie-watching* tasks are more similar to SC than *resting-state* FC estimates. All FC-SC similarities from different tasks are significantly different from each other within each FC measure (Mann-Whitney U test, FDR corrected *p* < 10⁻⁴) except LePetitPrince and MonkeyKingdom.

The subject-specificity of similarity between FC and SC is assessed by comparing the within-subject
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and across-subject FC-SC similarities (Figure 6 (b)). The subject-specificity of FC-SC depends on the FC measures considered and the task. Only GoodBadUgly show significant subject-specificity for full correlation FC estimates. By contrast, Graphical-Lasso partial correlation FC estimates achieve significant subject-specificity for Raiders and GoodBadUgly. Notably, these are the tasks with most runs.

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## 3.6 Effect of motion on classification performance

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We also compared the performance of subject fingerprinting and run classification using frame-wise displacement as features with the FC-based classification. We found that although the classification performance using frame-wise displacement was higher than that of dummy classifier always predicting the most frequent class label, it was still lower than the FC-based classification (supplementary Figure 13) in both subject fingerprinting and run classification.

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## 3.7 Generalizability in external datasets

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To assess the generalizability of our findings, we conducted additional experiments on external datasets, as explained in detail in section 2.5.

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First we tested the replicability of our observations in the TheLittlePrince dataset (Li et al., 2022), which had 28 subjects, each listening to 9 runs of the LePetitPrince story. We performed the same *within-task* subject fingerprinting and run classification analyses as in the IBC dataset. In Figure 14, we can see that the F1 scores for subject fingerprinting *within* the task are 100% for all three FC measures, and the full correlation FC estimates outperform the Graphical-Lasso partial correlation FC estimates at run classification. This replicates our observations in the IBC dataset, indicating that the relative performance of full correlation FC estimates vs. Graphical-Lasso partial correlation FC estimates is consistent across datasets.

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Then, to test the replicability of the *across-task* subject fingerprinting analysis, we used the HCP900 dataset (Van Essen et al., 2012) which had 728 subjects and 7 different cognitive-domain specific tasks. As shown in Figure 15, when we performed the subject fingerprinting across only two HCP tasks, we achieved an F1 score of 70.4% with the full correlation and 99.7% with the Graphical-Lasso partial correlation FC. The almost perfect performance with Graphical-Lasso partial correlation FC indicates that it encodes the subject-specific information more reliably than the full correlation FC and this information persists even when the classification is more challenging.

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Next, we tested whether a classifier trained on FC from one dataset could predict the labels for the
FC of another dataset, meaning we tested the generalization of classification across datasets. This is explained in detail in section 2.5. As can be seen in supplementary Figure 16, the F1 scores for all these classification scenarios are above chance for all the FC estimates. This indicates that even though the IBC subject pool is small, the FC estimates are generalizable across datasets. Furthermore, the full correlation FC estimates continue to outperform the graphical Lasso partial correlation FC estimates. This suggests that the previous observations about the relative performance of the two FC measures remain valid.

# 4 Discussion

In the present study, we set out to understand whether FC encodes the subject and task-specific information in the same way. To do so, we used linear SVC classifiers to perform subject fingerprinting and task classification using two different FC measures from three different covariance estimators.

FC measures (full correlation and partial correlation) have previously been shown to encode different types of information (S. M. Smith et al., 2011), and have been shown to display different statistical behavior in practice (Dadi et al., 2019; Liégeois et al., 2020; Pervaiz et al., 2020). Indeed, since full correlation FC captures both direct and indirect connections, it encodes interactions both between and within functional networks. By contrast, partial correlation only captures direct connections, which are mostly found within-networks (see supplementary Figure 18). On top of these fundamental properties, it should be noted that *i*) regularization always improves the stability of connectivity estimates in practice, and *ii*) reliable partial correlation estimates are hard to obtain and call for a structure-inducing regularization, such as Graphical Lasso. Despite these observations we found that Ledoit-Wolf regularized full correlation FC estimates performed the same as unregularized full correlation FC estimates in all metrics. Indeed, the computation of Pearson correlations is numerically stable and thus minimally affected by regularization. We suspect that this is because the long time series available (10-15 minutes of acquisition per run typically) in the IBC dataset allowed us to estimate FC reliably even without regularization at both chosen parcellation resolutions. Therefore this observation does not necessarily imply that regularization is ineffective in general, but rather a consequence of the specific dataset used in this study. More complex penalties that enforce group sparsity were considered (Varoquaux et al., 2010), but the additional computation cost was found too high to consider the method.

**Subject fingerprinting** For subject fingerprinting, the linear SVC classifier achieved an F1 score of 100% for subject fingerprinting *within* each task and 98% *across* all tasks with the Graphical-Lasso partial
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correlation FC (Figure 2). This provides evidence for efficacy of supervised machine learning approaches
for subject fingerprinting in naturalistic tasks.

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Graphical-Lasso partial correlation FC (98%) performed better than full correlation FC estimates (96%) when subject fingerprinting was done across all six tasks. Given the small difference and the fact IBC dataset has a small subject pool, we tested the replicability of this observation in the HCP dataset (Van Essen et al., 2012) (Figure 15). Doing the subject fingerprinting with only 2 tasks, the difference between the F1 score with the full correlation FC and the Graphical-Lasso partial correlation FC was much larger (70.4% vs. 99.7% respectively). This shows that Graphical Lasso partial-correlation FC is less perturbed by the particular cognitive state or task performed by participants; it is thus a better fingerprinting marker.

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Looking at the network-wise breakdown of classifier coefficients (Figure 5), we find that with Graphical-Lasso partial correlation FC, the within-network coefficients are significantly more important than the between-network coefficients for subject fingerprinting. This shows that relying more on within-network connections, as with Graphical-Lasso partial correlation, proves to be more accurate for subject fingerprinting. Specifically, the connections within and between fronto-parietal, default-mode, and dorsal attention networks are more important for subject fingerprinting than the other connections, corroborating similar observations from previous studies on other datasets (Amico & Goñi, 2018; Finn et al., 2015; Jalbrzikowski et al., 2020; Vanderwal et al., 2017).

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**Task classification** For task classification, we achieved an F1 score of 96-100% for classification between resting-state vs. story-listening/movie/gaming, 87-98% for story-listening/movie vs. gaming, 81-97% for story-listening vs. movie FCs and 57-87% for classification between pairs of movie FCs. When classifying across all six tasks, we achieved an F1 score of 77% with both the full correlation FC estimates and 72% with the Graphical-Lasso partial correlation FC estimates (Figure 3). Further, we also managed to uniquely identify 10-15 minute sections of movies with an F1 score of 31-59% with both the full correlation FC estimates and 24-48% with the Graphical-Lasso partial correlation FC estimates (Figure 4). This adds to the growing body of evidence that FC is a reliable marker of task-related information even across highly similar naturalistic tasks (Finn et al., 2015; Hannum et al., 2023; Richiardi et al., 2011; Shirer et al., 2012; Vanderwal et al., 2017).

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Notably, we find that the Graphical-Lasso partial correlation FC estimates are more similar across tasks than both full correlation FC estimates (supplementary Figure 8 and section 5). This observation is consistent with the lower accuracy of task classification with Graphical-Lasso partial correlation FC estimates relative to the full correlation FC estimates, especially when classifying between two movies

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or movie sections or between story-listening and movies. We hypothesize that this is because the full correlation FC estimates better reflect between-network interactions than partial correlation, which are important for discriminating between more similar tasks (such as two movies). This is also evident from the network-wise breakdown of classifier coefficients for full correlation FC (Figure 5), where the within and between network coefficients do not exhibit large differences for task classification. When tasks are more dissimilar, as in the case of *resting-state* vs. *story-listening movie/gaming*, or *story-listening movie* vs. *gaming* all FC estimates perform similarly. Furthermore, since full correlation FC estimates capture both direct and indirect interactions between brain regions, there might exist a redundancy of information that renders more features to differentiate between tasks.

**FC-SC similarity** We also corroborate some of the previous findings concerning the FC-SC correspondence (Hermundstad et al., 2013; Liégeois et al., 2020). The first one is the fact that partial correlation FC estimates are more similar to SC than full correlation FC estimates (from Figure 6 (a)) as also observed by Liégeois et al. (2020). Further, similar to Hermundstad et al. (2013) we also find that full correlation FC measures (that encode large-scale interactions) from movie-watching tasks are more similar to SC than the resting-state FC estimates. By contrast, partial correlation FC (that encodes local interactions) from resting-state is more similar to SC than the movie-watching FC, indicating that the resting-state activity more closely reflects the structural backbone of brain connectivity.

We hypothesize that this dissociation occurs because resting-state brain activity does not favor any particular system. Therefore, it reflects the constraints of underlying anatomical connectivity. This could explain why this association is better captured by partial correlation FC, which encodes direct connections, resulting in a higher similarity between resting-state FC and SC. Conversely, while the partial correlation FC of other tasks is still more similar to SC than full correlation FC, it is less similar to resting-state FC. This is likely because the stimuli in these tasks are more complex and induce more large-scale interactions between different brain regions. Hence, the similarity with SC is lower relative to resting-state. Conversely, although the partial correlation FC of other tasks with SC is higher than full correlation FC, it is lower than resting-state FC. This is likely because these tasks' stimuli are more complex, inducing more large-scale interactions between different brain regions and resulting in lower similarity with SC than with the resting state. However, with video gaming, the FC is the least similar to SC for both measures, possibly because the task leads to noisier FC estimates.

**Subject-specificity of FC-SC similarity** As suggested previously by Deslauriers-Gauthier et al. (2020) we also observed that SC estimates are highly similar across subjects (average across subject SC-SC similarity = 0.84, supplementary Figure 19). The relative inability to capture individual anatomical
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characteristics with SC hints at a bias induced by the design of tractography methods (Thomas et al., 2014) rather than intrinsic brain characteristics.

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However, our interest here is to understand if SC could explain some of the subject-specific information in FC. Therefore, when we compare the within-subject and across-subject functional connectivity (FC) similarities, we find that the within-subject similarity is significantly higher than the across-subject similarity for Raiders and GoodBadUgly, but only when using Graphical-Lasso partial correlation FC estimates. For full correlation FC estimates, the difference is only significant for GoodBadUgly (Figure 6 (b)). Importantly, this is only the case when we consider parcellation resolution at *p* = 400 regions. At *p* = 200 regions, the FC-SC similarity is subject-specific for all tasks except LePetitPrince with Graphical-Lasso partial correlation FC and all naturalistic tasks except LePetitPrince when using full correlation FC measures (supplementary Figure 11).

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The improvement in the subject-specificity of FC-SC similarity with a lower parcellation resolution (higher signal-to-noise ratio) further points toward bias in SC estimates. Despite that, given enough data, it can still account for some of the subject-specific information in regularized partial correlation FC, further implying that it is a more reliable measure of both structure and identity.

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**Parcellation resolution** Finally, we extended all of our analyses to a lower parcellation resolution of *p* = 200 regions to check whether the inferences were solely driven by our choice of parcellation resolution (see supplementary Table 6 and section 5). The comparisons between FC measures remain consistent with the ones presented in the main text. This includes the observation that Graphical-Lasso partial correlation FC outperforms full correlation FC estimates for subject fingerprinting, and vice-versa for task classification. However, the absolute values of the classification accuracies are slightly lower for 200 regions than for 400 regions (supplementary Table 6). This can be put in perspective with the observation that FC similarity across tasks and subjects is significantly higher for 200 regions than for 400 regions (see supplementary Figure 9).

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In addition, the patterns observed in the network-wise breakdown of classifier coefficients also remain consistent. In particular, subject fingerprinting relies on full correlation FC estimates from all within-network connections and only some between-network connections, while task classification more heavily relies on between-network connections. On the other hand, Graphical-Lasso partial correlation FC estimates show a dependence on within-network connections only, for both classification tasks.

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SC is more similar to Graphical-Lasso partial correlation FC estimates than full correlation FC estimates for 200 regions also. In addition, the relative FC-SC similarities across tasks are also consistent, i.e. full correlation FC estimates from movie-watching tasks are more similar to SC than the resting-state FC

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estimates, and the Graphical-Lasso partial correlation FC from resting-state is more similar to SC than movie-watching FC (supplementary Figure 10).

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**Limitations** The primary limitation of the present study is the small subject pool of 8-12 subjects (depending on the task). This results in a less diverse subject pool in terms of age, and other demographic factors, which might limit the generalizability of the findings. This is mostly due to the design of the IBC project, which is a deep-phenotyping study aiming to capture the variability across several cognitive tasks and MRI modalities, leading to higher costs per subject. This design choice, however, is what made possible the availability of five naturalistic tasks in the same subjects, which is the main strength of the present study.

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Nevertheless, to test the generalizability of our findings, we performed additional analyses on external datasets, as described in section 2.5. We found that the observations regarding the relative performance of full correlation FC vs. Graphical-Lasso partial correlation FC can be replicated in TheLittlePrince and the HCP datasets (subsection 3.7 and supplementary Figure 14 Figure 15). In addition, we also found that the FC estimates are generalizable across datasets because the classifiers trained on FC from one dataset could predict the labels for the FC of the other dataset, as shown in supplementary Figure 16.

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It is also worth noting that due to this choice the IBC dataset has several observations per subject, which makes the cross-validation results more reliable. Besides, we are using linear SVC classifiers with limited capacity, which avoids overfitting to the small sample size. Furthermore, we also replicated several previous findings from studies with larger cohorts (Amico & Goñi, 2018; Finn et al., 2015; Hannum et al., 2023; Vanderwal et al., 2017), including the subject fingerprinting and task classification accuracies and the FC-SC correspondence.

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Another issue that could contribute to relatively low FC-SC correspondence is the inaccuracy of atlas-based region definition across subjects which might make connectivity estimates less informative. Therefore, instead of an atlas, using individualized parcellations might help model the subject-specific spatial organization better (Bijsterbosch et al., 2019; Thirion et al., 2023).

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Further, the >95% task classification F1 scores between movie-watching and resting-state could be partially attributed to the differences in MRI sequence parameters between the two tasks. However, the classification of runs within each task, as well as the classification between the movie-watching and gaming tasks, performed well. Both tasks have identical acquisition parameters, indicating that classification accuracy is not solely dependent on differences in acquisition parameters. Apart from MRI sequence parameters, we particularly tested the effect time series length (supplementary Figure 12), motion (supplementary Figure 13), and the low pass filter cutoff frequency (supplementary Figure 20) on
the classification performance. While time series length and low-pass filter cutoff frequency changed the classification performance of individual functional connectivity (FC) measures, the observations regarding the relative performance of full-correlation FC versus Graphical-lasso partial correlation FC remained consistent across these variations. Regarding motion, we found that classification performance using frame-wise displacement as features was lower than FC-based classification. This indicates that our results are not solely driven by motion artifacts.

## Conclusion

Partial correlation FC and full correlation FC open two different windows on brain organization. Partial correlation FC, which aims at recovering direct connections, leads to more accurate subject fingerprinting performance, underscoring the importance of direct connections for this classification task. On the other hand, full correlation FC estimates encode both local and global organization of FC. This provides higher discriminative power across tasks (as indicated by lower similarity across tasks) and higher task classification performance with full correlation FC relative to partial correlation FC. Finally, we also find that the subject-specificity of correspondence between FC and SC is overall stronger with partial correlation FC than with full correlation FC. This further indicates that partial correlation FC is a more reliable measure of not only identity but also structure.

## Data and code availability

* The IBC data is periodically released via EBRAINS: https://search.kg.ebrains.eu/instances/c10859c9-536f-45c0-a1d1-442f79f2a66e.

* Detailed walkthroughs for downloading the data, task descriptions, analysis pipelines, etc., are available on IBC documentation: https://individual-brain-charting.github.io/docs.

* The code for replicating all IBC task protocols is available on GitHub: https://github.com/individual-brain-charting/public_protocols.

* All the code for the analyses and figures in this paper is available on GitHub: https://github.com/individual-brain-charting/connectivity. It is written in Python v3.11.4, and uses Scikit-Learn v1.3.0 (Pedregosa et al., 2011), Nilearn v0.10.1 (Abraham et al., 2014), MRtrix v3.0.4 (Tournier et al., 2019), ANTs v2.5.0 (Avants et al., 2009), FSL v5 (S. M. Smith et al., 2004), SPM12 (Penny et al., 2011), Numpy v1.25.2 (Harris et al., 2020) and Scipy v1.11.1 (Virtanen et al., 2020) for
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preprocessing and analyses and Seaborn v0.13.0 (Waskom, 2021), and Matplotlib v3.8.0 (Hunter, 2007) for plotting the figures.

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## Author Contributions

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**H.A.:** Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing - Original Draft, Visualization, Investigation.

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**A.F.P.:** Conceptualization, Software, Data Curation, Writing - Review and Editing.

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**S.S.:** Software, Data Curation, Writing - Review and Editing.

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**A.P.M.:** Software, Writing - Review and Editing.

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**A.L.P.:** Software, Data Curation, Writing - Review and Editing.

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**A.T.:** Software, Data Curation, Writing - Review and Editing.

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**B.T.:** Conceptualization, Methodology, Software, Data Curation, Writing - Original Draft, Supervision, Project administration, Funding acquisition.

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## Declaration of Competing Interests

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The authors declare no competing interests.

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## Acknowledgements

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This work benefited from state aid managed by the Agence Nationale de la Recherche under the France 2030 program, reference ANR-22-PESN-0012 and it has received funding from the European Union's

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Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement

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HORIZON-INFRA-2022-SERV-B-01. A.L.P. is the recipient of a BrainsCAN Postdoctoral Fellowship at

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Western University, funded by the Canada First Research Excellence Fund (CFREF). A.P.M is a recipient

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of the French Embassy in Spain / Institut français de España fellowship.

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# 5 Supplementary Material

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## Tasks done/not done by each subject

In IBC, each subject performed a subset of the tasks, as shown in Figure 7.

<figure><img src="image_10.png" /><figcaption>Figure 7: **Tasks done/not done by each subject:** Black squares indicate that the subject performed the task, and white squares indicate that the subject did not perform the task.</figcaption></figure>

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## Classification performance of all FC measures

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The classification performance of all FC estimates is presented in Table 6. As mentioned in the main text, due to the unstable nature of the inverse covariance matrix, the unregularized partial correlation measure is very noisy, leading to the worst performance in all classification tasks. The Ledoit-Wolf partial correlation measure manages to achieve some stability due to the shrinking of the covariance matrix, but it is still worse than (both unregularized and Ledoit-Wolf) full correlation measures. The full correlation FC measures from unregularized and Ledoit-Wolf estimators perform similarly across all classification tasks. This indicates that the shrinkage of the covariance matrix does not affect the full correlation FC estimates much: actually, it shifts the values, but does not alter the information carried by the matrix. Moreover there are enough samples (points in BOLD time series) to estimate the covariance matrices at both 400 and 200 region resolutions with a limited amount of shrinkage. The full correlation FC measure from the Graphical-Lasso estimator performs worse than the other two full correlation FC measures, indicating that the sparsity induced by Graphical-Lasso causes some loss of information in the
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full correlation FC. The Graphical-Lasso partial correlation FC, on the other hand, performs better than the full correlation FC estimates in subject fingerprinting across all tasks, and worse in multiple task classification scenarios and run classification. The performance of the Graphical-Lasso partial correlation FC and full correlation FC estimates is comparable in subject fingerprinting within single tasks as well as binary task classification scenarios.

<table><caption>Table 6: Classification performance of all FC estimates: All values are reported as average F1 score across all cross-validation splits for each classification task, parcellation resolution, and FC measure. The best-performing measure has been highlighted in yellow. The right-most column contains the average performance of a dummy classifier that classifies with the most frequent class label in each cross-validation split; it is always close to $1/n_{classes}$.</caption><thead><tr><th rowspan="2">Classification task</th><th rowspan="2">Parcellation resolution</th><th rowspan="2">Cognitive tasks</th><th colspan="2">Unregularized</th><th colspan="2">Ledoit-Wolf</th><th colspan="2">Graphical-Lasso</th><th rowspan="2">Dummy score</th></tr><tr><th>correlation</th><th>partial correlation</th><th>correlation</th><th>partial correlation</th><th>correlation</th><th>partial correlation</th></tr></thead><tbody><tr><td rowspan="10"></td><td rowspan="10">400 parcels</td><td>Within GoodBadUgly</td><td>100</td><td>63</td><td>100</td><td>100</td><td>100</td><td>100</td><td>2</td></tr><tr><td>Within LePetitPrince</td><td>100</td><td>44</td><td>100</td><td>100</td><td>100</td><td>100</td><td>2</td></tr><tr><td>Within Mario</td><td>100</td><td>41</td><td>100</td><td>100</td><td>100</td><td>100</td><td>3</td></tr><tr><td>Within MonkeyKingdom</td><td>100</td><td>15</td><td>100</td><td>100</td><td>100</td><td>100</td><td>3</td></tr><tr><td>Within Raiders</td><td>100</td><td>35</td><td>100</td><td>99</td><td>97</td><td>100</td><td>1</td></tr><tr><td>Within RestingState</td><td>100</td><td>17</td><td>100</td><td>94</td><td>100</td><td>100</td><td>1</td></tr><tr><td>Between five naturalistic tasks (all except RestingState)</td><td>97</td><td>18</td><td>97</td><td>95</td><td>93</td><td>98</td><td>2</td></tr><tr><td>Within GoodBadUgly</td><td>100</td><td>98</td><td>100</td><td>100</td><td>100</td><td>100</td><td>2</td></tr><tr><td>Within LePetitPrince</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>2</td></tr><tr><td>Within Mario</td><td>100</td><td>86</td><td>100</td><td>100</td><td>97</td><td>100</td><td>3</td></tr><tr><td>Within MonkeyKingdom</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>3</td></tr><tr><td>Within Raiders</td><td>100</td><td>97</td><td>100</td><td>100</td><td>97</td><td>100</td><td>1</td></tr><tr><td>Within RestingState</td><td>100</td><td>84</td><td>100</td><td>100</td><td>100</td><td>100</td><td>1</td></tr><tr><td>Between five naturalistic tasks (all except RestingState)</td><td>96</td><td>91</td><td>96</td><td>96</td><td>95</td><td>98</td><td>2</td></tr><tr><td rowspan="10">Subject fingerprinting</td><td rowspan="10">200 parcels</td><td>GoodBadUgly vs Mario</td><td>90</td><td>60</td><td>91</td><td>59</td><td>89</td><td>98</td><td>58</td></tr><tr><td>GoodBadUgly vs MonkeyKingdom</td><td>57</td><td>17</td><td>58</td><td>58</td><td>65</td><td>65</td><td>58</td></tr><tr><td>LePetitPrince vs GoodBadUgly</td><td>93</td><td>39</td><td>93</td><td>41</td><td>86</td><td>92</td><td>38</td></tr><tr><td>LePetitPrince vs Mario</td><td>94</td><td>57</td><td>94</td><td>63</td><td>88</td><td>96</td><td>55</td></tr><tr><td>LePetitPrince vs MonkeyKingdom</td><td>92</td><td>22</td><td>93</td><td>56</td><td>84</td><td>81</td><td>56</td></tr><tr><td>LePetitPrince vs Raiders</td><td>96</td><td>50</td><td>96</td><td>89</td><td>91</td><td>97</td><td>32</td></tr><tr><td>MonkeyKingdom vs Mario</td><td>91</td><td>32</td><td>89</td><td>89</td><td>73</td><td>87</td><td>35</td></tr><tr><td>Raiders vs GoodBadUgly</td><td>87</td><td>38</td><td>87</td><td>72</td><td>75</td><td>77</td><td>36</td></tr><tr><td>Raiders vs Mario</td><td>98</td><td>54</td><td>98</td><td>61</td><td>91</td><td>98</td><td>58</td></tr><tr><td>Raiders vs MonkeyKingdom</td><td>76</td><td>16</td><td>77</td><td>59</td><td>78</td><td>69</td><td>59</td></tr><tr><td>RestingState vs GoodBadUgly</td><td>97</td><td>24</td><td>97</td><td>40</td><td>95</td><td>100</td><td>40</td></tr><tr><td>RestingState vs LePetitPrince</td><td>97</td><td>30</td><td>97</td><td>38</td><td>91</td><td>100</td><td>38</td></tr><tr><td>RestingState vs Mario</td><td>99</td><td>52</td><td>99</td><td>25</td><td>99</td><td>100</td><td>19</td></tr><tr><td>RestingState vs MonkeyKingdom</td><td>97</td><td>54</td><td>97</td><td>24</td><td>93</td><td>98</td><td>54</td></tr><tr><td>RestingState vs Raiders</td><td>96</td><td>25</td><td>96</td><td>40</td><td>94</td><td>99</td><td>40</td></tr><tr><td>Between all tasks</td><td>77</td><td>5</td><td>77</td><td>37</td><td>65</td><td>72</td><td>9</td></tr><tr><td rowspan="10">Task classification</td><td rowspan="10">200 parcels</td><td>GoodBadUgly vs Mario</td><td>94</td><td>58</td><td>92</td><td>67</td><td>86</td><td>98</td><td>64</td></tr><tr><td>GoodBadUgly vs MonkeyKingdom</td><td>57</td><td>15</td><td>56</td><td>58</td><td>61</td><td>61</td><td>64</td></tr><tr><td>LePetitPrince vs GoodBadUgly</td><td>94</td><td>38</td><td>94</td><td>85</td><td>88</td><td>89</td><td>50</td></tr><tr><td>LePetitPrince vs Mario</td><td>95</td><td>55</td><td>94</td><td>81</td><td>91</td><td>100</td><td>64</td></tr><tr><td>LePetitPrince vs MonkeyKingdom</td><td>92</td><td>31</td><td>92</td><td>57</td><td>92</td><td>79</td><td>64</td></tr><tr><td>LePetitPrince vs Raiders</td><td>95</td><td>61</td><td>95</td><td>87</td><td>96</td><td>95</td><td>46</td></tr><tr><td>MonkeyKingdom vs Mario</td><td>92</td><td>31</td><td>92</td><td>97</td><td>76</td><td>94</td><td>50</td></tr><tr><td>Raiders vs GoodBadUgly</td><td>84</td><td>30</td><td>83</td><td>77</td><td>78</td><td>75</td><td>46</td></tr><tr><td>Raiders vs Mario</td><td>97</td><td>58</td><td>97</td><td>81</td><td>99</td><td>99</td><td>67</td></tr><tr><td>Raiders vs MonkeyKingdom</td><td>82</td><td>43</td><td>82</td><td>59</td><td>74</td><td>75</td><td>67</td></tr><tr><td>RestingState vs GoodBadUgly</td><td>97</td><td>68</td><td>97</td><td>49</td><td>95</td><td>99</td><td>46</td></tr><tr><td>RestingState vs LePetitPrince</td><td>96</td><td>93</td><td>96</td><td>42</td><td>93</td><td>100</td><td>46</td></tr><tr><td>RestingState vs Mario</td><td>99</td><td>52</td><td>99</td><td>92</td><td>97</td><td>100</td><td>33</td></tr><tr><td>RestingState vs MonkeyKingdom</td><td>98</td><td>54</td><td>98</td><td>72</td><td>94</td><td>97</td><td>67</td></tr><tr><td>RestingState vs Raiders</td><td>96</td><td>93</td><td>96</td><td>44</td><td>93</td><td>98</td><td>50</td></tr><tr><td>Between all tasks</td><td>79</td><td>29</td><td>78</td><td>57</td><td>70</td><td>72</td><td>17</td></tr><tr><td rowspan="10">Run classification</td><td rowspan="10">200 parcels</td><td>Within GoodBadUgly</td><td>32</td><td>8</td><td>32</td><td>9</td><td>19</td><td>18</td><td>1</td></tr><tr><td>Within LePetitPrince</td><td>15</td><td>5</td><td>15</td><td>16</td><td>10</td><td>14</td><td>2</td></tr><tr><td>Within MonkeyKingdom</td><td>39</td><td>11</td><td>39</td><td>8</td><td>11</td><td>24</td><td>7</td></tr><tr><td>Within Raiders</td><td>58</td><td>12</td><td>59</td><td>21</td><td>28</td><td>48</td><td>3</td></tr><tr><td>Between four naturalistic tasks (all except RestingState and LePetitPrince)</td><td>32</td><td>0</td><td>32</td><td>7</td><td>15</td><td>19</td><td>0</td></tr><tr><td>GoodBadUgly</td><td>30</td><td>6</td><td>30</td><td>13</td><td>16</td><td>19</td><td>1</td></tr><tr><td>LePetitPrince</td><td>16</td><td>3</td><td>17</td><td>14</td><td>12</td><td>15</td><td>2</td></tr><tr><td>MonkeyKingdom</td><td>36</td><td>11</td><td>36</td><td>8</td><td>21</td><td>33</td><td>7</td></tr><tr><td>Raiders</td><td>58</td><td>12</td><td>59</td><td>34</td><td>40</td><td>40</td><td>3</td></tr><tr><td>Between four naturalistic tasks (all except RestingState and LePetitPrince)</td><td>32</td><td>1</td><td>32</td><td>13</td><td>16</td><td>16</td><td>0</td></tr></tbody></table>
## Similarity of FC between tasks

### Within and across subject similarity between FC from pairs of tasks

<figure><img src="image_11.png" /><figcaption>Figure 8: **Similarity of FC and its subject-specificity between pairs of tasks:** The similarity of FC between two tasks is measured as Pearson's correlation represented over the color bar. The upper triangular matrix shows within-subject similarity, and the lower one shows across-subject similarity.</figcaption></figure>

The similarity of FC from tasks pairs is measured as Pearson's correlation between the FC vectors (Figure 8). As expected, the similarity is maximum between *movie* FCs, followed by *story-listening and movies*, then *resting-state and movies*, then *movie / story-listening and gaming*, and lowest between *resting-state and gaming* FCs. On average, Graphical-Lasso partial correlation estimates are significantly more similar across tasks than both full correlation estimates (pairwise Mann-Whitney U test, $p < 10^{-129}$). This reduced task dependence of partial correlation probably contributes to the lower task classification accuracy with this connectivity measure.

The subject-specificity of similarity of FC from different tasks is assessed by comparing the within-subject and across-subject similarities (the upper and lower triangular matrices respectively in Figure 8). All the comparisons show that within-subject similarity is significantly higher than across-subject similarity, implying that the similarity between FC from different tasks is subject-specific.

## Comparison between 200 vs. 400 regions

In Figure 9 we show that the similarity between FC from different tasks and subjects is significantly higher for 200 regions than for 400 regions for each FC measure. This is because the higher parcellation
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resolution leads to more variability in FC across subjects and tasks. This also leads to a lower accuracy of subject fingerprinting and task classification for 200 regions than for 400 regions (see Table 6).

FC-FC similarity, 200 regions vs. 400 regions

<figure><img src="image_12.png" /></figure>

Figure 9: **Comparison of similarity between FC across tasks, across subjects at different parcel- lation resolutions:** The similarity of FC between two tasks and two subjects is measured as Pearson's correlation. The histograms shown above are the distributions of FC-FC similarity across all pairs of tasks and subjects, at 200 region resolution (red) and 400 region resolution (blue). The lowest one percentile of the values has been removed for better visualization. The dotted lines are the mean of those thresholded distributions. The Mann-Whitney U tests of significance, however, were done for complete distributions. The p-values are shown in the top right corner. The alternative hypothesis of the test is that the FC-FC similarity is higher at 200 regions than at 400 regions.

<figure><img src="image_13.png" /></figure>

Figure 10: **Similarity between FC and SC for 200 (left) vs. 400 (right) regions:** The similarity between FC and SC is measured as Pearson's correlation between the two. Different colors correspond to FC from different tasks, and different FC measures are along the y-axis.
<figure><img src="image_14.png" /><figcaption>Figure 11: **Subject specificity of similarity between FC and SC for 200 (top) vs. 400 (bottom) regions:** Subject specificity is determined by a comparison between within- and across-subject FC-SC similarities. Different colors correspond to FC from different tasks (along y-axis). Each column corresponds to a different FC measure. The hatched distributions correspond to across-subject similarity, and the solid-colored ones correspond to within-subject similarity. The significance of the difference between within and across-subject similarity is represented as stars (* = $p < 0.05$, ** = $p < 0.01$, *** = $p < 0.001$, ns = not significant) on the right of the distributions.</figcaption></figure>
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Effect of run length on run classification

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To test whether the varying run lengths contribute to the classification performance, we cut down all the runs to the shortest run length (293 volumes) and repeated the run classification experiment. The F1 scores for the run classification with same run lengths (Figure 12 (a)) and comparable to that with the original different run lengths (Figure 12 (b)).

<figure><img src="image_15.png" /><figcaption>Figure 12: **Effect of run length on run classification:** F1 score of run classification when (a) all the runs were cut down to 293 volumes vs. (b) all runs had the original and different run lengths. The chance-level accuracy is from a dummy classifier that always predicts the most frequent class label; it is close to 1/$n_{classes}$.</figcaption></figure>

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Subject fingerprinting and run classification with frame-wise displacement

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To test whether the variable motion artifacts across subjects and runs contribute to the classification performance, we used the frame-wise displacement as the feature for the classification (Chyzhyk et al., 2022). The F1 scores for the subject fingerprinting (Figure 13 (a)) and run classification (Figure 13 (b)) with frame-wise displacement are lower than those with unregularized full correlation FC in all classification scenarios.
Classification with frame-wise displacement vs. FC

<figure><img src="image_16.png" /></figure>

Figure 13: **Classification with frame-wise displacement:** F1 score of (a) subject fingerprinting with frame-wise displacement (red) vs. fingerprinting with unregularized full correlation FC (blue) (b) run classification with frame-wise displacement (red) vs. with unregularized full correlation FC (blue). The chance-level accuracy is from a dummy classifier that always predicts the most frequent class label; it is close to 1/*n*<sub>classes</sub>.

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We assessed whether our findings regarding relative performance of different FC measures at *within-task* subject fingerprinting and run classification in IBC dataset could also be replicated in TheLittlePrince dataset, as explained in section 2.5. In Figure 14, we see that all FC estimates can achieve a 100% F1 score for subject fingerprinting within a single task (same as in the IBC dataset Figure 2(a)), and the full correlation FC estimates perform better than the Graphical-Lasso partial correlation FC estimates at run classification (same as in the IBC dataset Figure 4).

Replication in TheLittlePrince dataset

<figure><img src="image_17.png" /></figure>

Figure 14: **Replicability in TheLittlePrince dataset:** (a) *Within-task* subject fingerprinting (b) Run classification in TheLittlePrince dataset. The chance-level accuracy is from a dummy classifier that always predicts the most frequent class label; it is close to 1/*n*<sub>classes</sub>.
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## Replicability of across-task subject fingerprinting in HCP dataset

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To assess whether we can replicate the results of subject fingerprinting across tasks in IBC dataset, we performed the same analysis on the HCP dataset (Van Essen et al., 2012) with 728 subjects performing 7 tasks (see section 2.5 for more details). We made this classification more challenging by using *only two tasks* at a time. This meant that we trained the classifier to learn the subject labels from one task and tested it on another task This resulted in a larger difference in the performance between the unregularized full correlation and the Graphical-Lasso partial correlation FC estimates (70.4% vs. 99.7% respectively) (Figure 15). This shows that the full correlation FC depends more on the task-specific information to classify the subjects, while the Graphical-Lasso partial correlation FC captures more subject-specific information that is common across tasks. This is consistent with the observations in IBC dataset, where the Graphical-Lasso partial correlation FC performed better than full correlation FC for subject fingerprinting across tasks, but not within tasks (Figure 2). We think that we see this difference even *across all 6 tasks* in IBC because the tasks are all naturalistic and are hence more similar to each other than the cognitive tasks in HCP dataset, which tend to add more task-specific information to the FC estimates.

## Replication of across-task subject fingerprinting in the HCP dataset

<figure><img src="image_18.png" /><figcaption>Figure 15: **Replicability in HCP dataset:** Average F1-score for subject fingerprinting *across 2 tasks*, meaning the classifier is trained on 1 task and tested on 1 task and so on for all pairs of tasks.</figcaption></figure>

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## Generalizability of task and run classification across datasets

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One of the features of the IBC dataset is that it reuses the tasks from previous studies, allowing for the generalization of the task discrimination across datasets. This means that we have a different set of
subjects, whose data is acquired with different acquisition parameters, performing the same tasks. So we used this feature to mitigate the limitation of the small subject pool in the IBC dataset and tested the generalizability of task and run classification across datasets (see section 2.5 for more details.)

As can be seen in Figure 16, the F1 score for all classification scenarios is above chance-level, indicating that even though the IBC subject pool is small, the classification scores are generalizable across datasets. Furthermore, we still see that the full correlation FC estimates outperform the Graphical-Lasso partial correlation FC estimates, indicating that the previous observations about the relative performance of the two FC measures still hold.

Generalization between external datasets and IBC

<figure><img src="image_19.png" /><figcaption>**Figure 16: Generalizability of task and run classification across datasets:** F1 score of a classifier trained on FC from external datasets and tested on IBC dataset (in green) and vice-versa (in orange). The external dataset is ARCHI in (a) HCP in (b), HumanMonkeyGBU in (c) and TheLittlePrince in (d). The chance-level accuracy is from a dummy classifier that always predicts the most frequent class label; it is close to 1/$n_{classes}$.</figcaption></figure>
Network-wise classifier coefficients for each task

In Figure 17 we look at the network-wise classifier coefficients for task classification for each task separately. The higher the coefficient the more important the connection between those networks were for classifying the given task. The major observations from subsection 3.4 and Figure 5 still hold. First is the fact that visual network stands-out as the most important network for classifying each task. Secondly, we again see that both between and within network connections are important when classifying with full correlation FC while only within-network connections are important with partial correlation FC.

There are, however some additional noteworthy observations from Figure 17 but only from full correlation FC which could not be observed in Figure 5:

1. For classifying RestingState, connections between and within visual network are relatively less important as compared to the other tasks (that are more visual, by design). On the other hand, connections within the limbic network are most important for classifying RestingState task.

2. For classifying MonkeyKingdom, only the connections within the visual network are important.

3. For classifying Mario, connections between and within default-mode, dorsal attentional, limbic and somato-motor networks are relatively more important.

Network-wise classifier coefficients for each task

<figure><img src="image_20.png" /><figcaption>Figure 17: **Network-wise classifier coefficients for each task:** The matrix values are the classifier coefficients between given networks for each task. On the left, we have the classifier coefficients for unregularized full correlation FC, and on the right, we have the classifier coefficients for Graphical-Lasso partial correlation FC.</figcaption></figure>
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## Network-wise average FC

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Figure 18 shows the network-wise average FC across all subjects, runs and tasks. The off-diagonal values are the average FC between networks, and the diagonal values are the average FC within networks. While within-network FC is relatively higher overall, the full correlation measures capture both between-network and within-network FC, whereas the Graphical-Lasso partial correlation FC captures within-network FC only.

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<figure><img src="image_21.png" /><figcaption>Figure 18: **Network-wise average FC:** The matrix values are the average FC between given networks across all subjects, runs and tasks.</figcaption></figure>

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## Across subject SC-SC similarity

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Figure 19 shows the SC-SC similarity across subjects. On an average, across subject SC-SC similarity is 0.84.

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<figure><img src="image_22.png" /><figcaption>Figure 19: **Across subject SC-SC similarity:** Each value represents the Pearson's correlation between SC estimates from two subjects.</figcaption></figure>
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Effect of changing lowpass filter cutoff frequency during time series extraction

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We also tested the effect of changing the lowpass filter cutoff frequency during time series extraction on the classification performance. We compared the classification performance across time series extracted with different lowpass filter cutoff frequencies: 0.1 Hz, 0.2 Hz (the original results presented in the main paper), and 0.249 Hz. The 0.249 Hz cutoff frequency is the Nyquist limit for the 2 seconds TR used for the movie-watching and story-listening tasks in the IBC dataset. While a higher cutoff frequency was possible for the resting-state task, we did not use it to avoid classification performance being driven due to the differences caused by the different lowpass filter cutoff frequencies alone. Figure 20 shows that changing the lowpass filter cutoff frequency does change the classification performance, but the relative performance of the different FC measures remains consistent. The full correlation FC estimates outperform the Graphical-Lasso partial correlation FC estimates for task classification and run classification, while the Graphical-Lasso partial correlation FC estimates outperform the full correlation FC estimates for subject fingerprinting at all cutoff frequencies.

(1) Within each task

<figure><img src="image_23.png" /></figure>

(2) Across tasks

<figure><img src="image_24.png" /></figure>

Figure 20: **Changing lowpass filter cutoff frequency:** Classification performance (F1 score) for different lowpass filter cutoff frequencies: 0.1 Hz (blue), 0.2 Hz (grey), and 0.249 Hz (red). **(1) Within each task:** (a) Scores for subject fingerprinting (equivalent to Figure 2 (a)). (b) Average scores for several binary task classifications (equivalent to average of all bars in Figure 3 (a)). (c) Average scores for run classification (equivalent to average of all bars in Figure 4). **(2) Across all tasks:** (d) Scores for subject fingerprinting (equivalent to Figure 2 (b)). (e) Scores for task classification (equivalent to Figure 3 (c)). (f) Scores for run classification across all tasks. The chance-level accuracy is from a dummy classifier that always predicts the most frequent class label; it is close to 1/n_classes.
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Permutation tests for significance of classification scores against chance-level

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To test the significance of the classification scores against chance-level, we performed permutation tests for each classification task, for unregularized and Ledoit-Wolf full correlation, and Graphical-Lasso partial correlation FC estimates at 400 parcellation resolution. We permuted the class labels 50 times and performed the classification with the permuted labels to get a distribution of F1 scores at chance-level. All of the classification scores on original class labels were significantly higher than chance-level (p-value < 0.05), except for the task classification between GoodBadUgly and MonkeyKingdom with unregularized and Ledoit-Wolf full correlation FC (see red histograms in Figure 21).
<figure><img src="image_25.png" /><figcaption>Figure 21: Permutation tests for significance of classification scores against chance-level: The green and red histograms are the F1 scores with permuted class labels. The dotted vertical lines are the F1 scores with the original class labels. Wherever the histograms are red, p-value > 0.05, meaning the classification scores are not significantly different from chance-level. First column and second column of plots under each classification heading: unregularized and Ledoit-Wolf full correlation and third column: Graphical-Lasso partial correlation FC.</figcaption></figure>
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