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arxiv: 2606.27956 · v1 · pith:JSDVE5BFnew · submitted 2026-06-26 · ⚛️ physics.soc-ph

Linking the "inner" and "outer" self to mental health and brain networks

Cosimo Agostinelli , Ivan Casanovas , Lochan Chaudhari , Arda Ergin , Pablo Est\'evez-Guti\'errez , Akanksha Gupta , Juliane T. Moraes , Mario Edoardo Pandolfo
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Carlos Gershenson Haily Merritt Andreia Sofia Teixeira
This is my paper

Pith reviewed 2026-06-29 02:05 UTC · model grok-4.3

classification ⚛️ physics.soc-ph
keywords personality traitssocial supportmental healthbrain functional connectivitydefault mode networkk-means clusteringhuman connectome project
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The pith

Individuals with socially desirable profiles show higher life satisfaction and lower default mode network connectivity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses Human Connectome Project survey and resting-state fMRI data to examine links between personality traits as inner-self measures, social support variables as outer-self measures, mental health outcomes, and brain functional connectivity. Correlation matrices on z-score standardized variables indicate that social indicators group more by their impact on social experience than by the inner-outer distinction. K-means clustering separates participants into two groups, and the cluster with the more socially beneficial profile scores higher on positive mental health aspects while exhibiting lower interconnectivity, especially in the default mode network.

Core claim

Clustering individuals by social and personality profiles produces a socially desirable group that scores higher on life satisfaction and purpose in life and displays lower functional interconnectivity in the default mode network compared with the other group.

What carries the argument

k-means clustering on z-score standardized personality trait and social support measures to define two social profile groups, then compared against mental health scores and resting-state fMRI connectivity matrices.

Load-bearing premise

That the two groups produced by k-means on the chosen variables reflect genuine differences in mental health and brain connectivity rather than artifacts from variable selection, standardization, or the fixed choice of two clusters.

What would settle it

Repeating the k-means analysis with a different number of clusters or an alternative clustering algorithm and finding no corresponding differences in life satisfaction scores or default mode network interconnectivity.

Figures

Figures reproduced from arXiv: 2606.27956 by Akanksha Gupta, Andreia Sofia Teixeira, Arda Ergin, Carlos Gershenson, Cosimo Agostinelli, Haily Merritt, Ivan Casanovas, Juliane T. Moraes, Lochan Chaudhari, Mario Edoardo Pandolfo, Pablo Est\'evez-Guti\'errez.

Figure 1
Figure 1. Figure 1: A schematic of the conceptual framework and analysis pipeline of the study. Our project leverages multimodal data to understand mental health outcomes and their associations with brain organization. In particular, we consider mental health as influenced by personal and contextual features, here represented in (a) with the inner-self (henceforth purple) and outer-self (henceforth green) variables indexed by… view at source ↗
Figure 2
Figure 2. Figure 2: Characterization of psychosocial measures. (a) Distributions of standardized inner-self (purple) and outer￾self (green) measures expressed as z-scores. (b) Cross-correlation matrices of all psychosocial measures, separated by inner-self (purple) and outer-self (green). (c) Loadings of psychosocial measures on the first principal component. (d) Pairwise similarity between subjects according to their scores … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of PLS Analyses. Color indicates the percentage of LV1 covariance explained across resolutions of functional connectivity (x-axis) and sets of psychosocial measures (y-axis). Each PLS analysis yielded at most one significant latent variable (LV1), with p-values determined by permutation tests (variables with p-values < 0.05 are bold highlighted). None of the remaining PCs exhibits a systematic pre… view at source ↗
Figure 4
Figure 4. Figure 4: Behavioral Partial Least Squares analysis of associations between psychosocial measures and the coarsest resolution of functional connectivity. The leftmost column corresponds to the analysis using only inner-self measures, the center column corresponds to the analysis using only outer-self measures, and the rightmost column corresponds to the analysis using all psychosocial measures. (a) Loadings on the f… view at source ↗
Figure 5
Figure 5. Figure 5: Psychosocial profiles and their distinct mental health outcomes and functional connectivity patterns. All profiles were identified with k = 2 using either the inner-self measures (a), outer-self measures (b), or all psychosocial measures (c). Within each panel, the top figure depicts cluster profiles, the bottom figure shows mental health outcomes, and the right figure indicates where there were difference… view at source ↗
Figure 6
Figure 6. Figure 6: Accumulated explained variance by number of principal components considered for combined (a), inner (b), [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Loadings of the psychosocial measures on the first six principal components of the PCA applied to all [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Loadings of the psychosocial measures on the first two principal components of the PCA applied to the [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Results for the complementary validity indices for selection of the optimal [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
read the original abstract

How are psychosocial profiles, mental health, and brain functional connectivity related? Studies have been dedicated to unraveling the associations of social support perception and neural functional connectivity. Additionally, personality traits have been explored by examining brain networks. Research on mental health has been developed using a broad range of methods and different approaches. However, little attention has been devoted to understanding how personality traits and social variables are related, and to what extent these components are reflected in brain functional connectivity and mental health outcomes. In this work, we aim to address these complex relations by using data from the Human Connectome Project, both from surveys and resting-state fMRI. The survey data includes personality traits measures and self-reported social support-related variables, which we will refer to as inner- and outer-self, respectively. It also includes data on mental health outcomes. Using z-score standardized measures, we analyze correlation matrices to evaluate the association between the inner- and outer-self domains. Our results show that the social indicators are more evidently grouped by impact on social experience than by the duality of inner-outer selves. Using a $k$-means clustering algorithm, we separate individuals into two groups according to social profiles. When confronting these results with the mental health outcomes, we show that the more socially desirable cluster exhibited a higher score on positive aspects such as life satisfaction and purpose in life. In the functional brain connectivity, we observe that the cluster with a more socially beneficial profile exhibits lower interconnectivity, especially in the default mode network. The pipeline we present uses a combined analysis of both fMRI and psychosocial variables, which could open the path for more extensive analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript uses HCP survey and resting-state fMRI data to examine relations among personality traits (inner-self), social support variables (outer-self), mental health outcomes, and functional connectivity. After z-score standardization and correlation analysis, the authors apply k-means clustering (k=2) to the social-profile variables, partition subjects into two groups, and report that the more socially desirable cluster exhibits higher life satisfaction and purpose in life together with lower interconnectivity, particularly in the default mode network.

Significance. If the reported cluster differences survive proper validation and statistical controls, the integrative pipeline could offer a useful template for linking psychosocial profiles to brain networks in large cohorts. The absence of cluster validation, however, leaves the central claims vulnerable to the possibility that the observed mental-health and DMN differences are induced by the arbitrary bipartition rather than reflecting stable data structure.

major comments (2)
  1. [Methods (clustering)] Methods section on clustering: the manuscript states that k-means with k=2 is used to separate individuals into two groups according to social profiles, yet supplies no elbow plot, silhouette analysis, gap statistic, or stability check across random initializations or bootstrap samples. Because the subsequent claims about elevated life satisfaction/purpose and reduced DMN interconnectivity rest entirely on this bipartition, the lack of validation makes it impossible to rule out that the group differences are artifacts of the chosen k rather than genuine associations.
  2. [Results (group comparisons)] Results section on mental-health and connectivity comparisons: no description is given of the statistical tests, multiple-comparison correction, or effect-size measures used to establish the reported cluster differences in life satisfaction, purpose in life, or DMN interconnectivity. In addition, fMRI preprocessing details (motion correction, nuisance regression, parcellation) are omitted, preventing assessment of whether the lower interconnectivity finding is robust.
minor comments (2)
  1. [Abstract] Abstract: the sentence describing the clustering result does not indicate how the two clusters were labeled 'socially desirable' or 'socially beneficial,' nor does it mention any post-hoc validation of the partition.
  2. [Methods] The correlation-matrix analysis is presented before the clustering step, but the manuscript does not clarify whether the same variables enter both analyses or whether any variable-selection step precedes clustering.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important gaps in methodological reporting and validation. We address each major comment below and will revise the manuscript to incorporate the requested details and analyses.

read point-by-point responses

  1. Referee: [Methods (clustering)] Methods section on clustering: the manuscript states that k-means with k=2 is used to separate individuals into two groups according to social profiles, yet supplies no elbow plot, silhouette analysis, gap statistic, or stability check across random initializations or bootstrap samples. Because the subsequent claims about elevated life satisfaction/purpose and reduced DMN interconnectivity rest entirely on this bipartition, the lack of validation makes it impossible to rule out that the group differences are artifacts of the chosen k rather than genuine associations.

    Authors: We agree that explicit validation of the k=2 choice is necessary to support the central claims. The bipartition was selected because the correlation structure indicated that social indicators grouped more strongly by overall social desirability than by the inner/outer-self distinction, but this rationale alone is insufficient without quantitative checks. In the revised manuscript we will add elbow plots, silhouette scores, gap statistics, and cluster stability assessments across multiple random initializations and bootstrap resamples. These results will be reported in the Methods and Results sections; if they support k=2 we will retain the bipartition, otherwise we will discuss sensitivity to k. revision: yes

  2. Referee: [Results (group comparisons)] Results section on mental-health and connectivity comparisons: no description is given of the statistical tests, multiple-comparison correction, or effect-size measures used to establish the reported cluster differences in life satisfaction, purpose in life, or DMN interconnectivity. In addition, fMRI preprocessing details (motion correction, nuisance regression, parcellation) are omitted, preventing assessment of whether the lower interconnectivity finding is robust.

    Authors: The original submission omitted these details. Group differences were assessed with two-sample t-tests, Bonferroni-corrected for the number of mental-health and connectivity measures examined, and effect sizes were computed as Cohen’s d. The resting-state fMRI data were preprocessed following the standard HCP minimal pipeline (motion correction, nuisance regression of six motion parameters plus physiological signals, and Glasser 360-region parcellation). We will insert a dedicated Methods subsection describing the full preprocessing pipeline and will report the exact statistical tests, correction procedure, and effect sizes alongside the cluster-comparison results in the revised Results section. revision: yes

Circularity Check

0 steps flagged

No circularity: clustering on psychosocial variables followed by independent comparisons to separate mental-health and connectivity measures

full rationale

The paper standardizes inner/outer-self survey variables, computes correlations, applies k-means (k=2) to produce clusters, and then reports differences in separately collected mental-health scores and resting-state fMRI connectivity. No equation, fitted parameter, or self-citation reduces the reported group differences to quantities defined by the clustering step itself. The analysis contains no self-definitional loops, fitted-input predictions, or load-bearing self-citations that would force the central claims by construction. This is the normal non-circular case for an exploratory clustering study.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The analysis rests on public data and routine statistical procedures; the principal free parameter is the number of clusters, and the key assumptions concern the validity of the survey constructs and the relevance of resting-state connectivity differences.

free parameters (1)
  • number of clusters k = 2
    k-means is used to divide participants into two groups; the choice of k=2 determines the reported social profiles and is not justified in the abstract.
axioms (2)
  • domain assumption Survey items labeled as inner-self (personality traits) and outer-self (social support) validly measure the intended psychological constructs
    The correlation and clustering steps treat these labeled variables as faithful representations of the inner-outer distinction.
  • domain assumption Differences in resting-state functional connectivity, especially in the default mode network, are meaningfully related to the social profile clusters
    The comparison of brain interconnectivity across clusters assumes the fMRI measures capture relevant neural correlates of the psychosocial variables.

pith-pipeline@v0.9.1-grok · 5879 in / 1557 out tokens · 55334 ms · 2026-06-29T02:05:36.087673+00:00 · methodology

discussion (0)

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