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. 2023 Jul 7:12:e85188.
doi: 10.7554/eLife.85188.

Functional and microstructural plasticity following social and interoceptive mental training

Sofie Louise Valk  1   2 , Philipp Kanske  3   4 , Bo-Yong Park  5   6   7 , Seok-Jun Hong  7   8   9 , Anne Böckler  10 , Fynn-Mathis Trautwein  11 , Boris C Bernhardt #  5 , Tania Singer #  12
Affiliations

Functional and microstructural plasticity following social and interoceptive mental training

Sofie Louise Valk et al. Elife. .

Abstract

The human brain supports social cognitive functions, including Theory of Mind, empathy, and compassion, through its intrinsic hierarchical organization. However, it remains unclear how the learning and refinement of social skills shapes brain function and structure. We studied if different types of social mental training induce changes in cortical function and microstructure, investigating 332 healthy adults (197 women, 20-55 years) with repeated multimodal neuroimaging and behavioral testing. Our neuroimaging approach examined longitudinal changes in cortical functional gradients and myelin-sensitive T1 relaxometry, two complementary measures of cortical hierarchical organization. We observed marked changes in intrinsic cortical function and microstructure, which varied as a function of social training content. In particular, cortical function and microstructure changed as a result of attention-mindfulness and socio-cognitive training in regions functionally associated with attention and interoception, including insular and parietal cortices. Conversely, socio-affective and socio-cognitive training resulted in differential microstructural changes in regions classically implicated in interoceptive and emotional processing, including insular and orbitofrontal areas, but did not result in functional reorganization. Notably, longitudinal changes in cortical function and microstructure predicted behavioral change in attention, compassion and perspective-taking. Our work demonstrates functional and microstructural plasticity after the training of social-interoceptive functions, and illustrates the bidirectional relationship between brain organisation and human social skills.

Keywords: brain organization; human; intrinsic function; microstructure; neuroscience; plasticity; social cognition.

Plain language summary

Navigating daily life requires a number of social skills, such as empathy and understanding other people’s thoughts and feelings. Research has found that specific parts of the brain support these abilities in humans. For instance, the brain areas that support compassion are different from the regions involved in understanding other people’s perspective and thoughts. It is unclear how learning and refining social skills alters the brain. Previous studies have shown that learning new motor skills restructures the areas of the brain that regulate movement. Could acquiring and improving social skills have a similar effect? To investigate, Valk et al. trained more than 300 healthy adults in different social skills over the course of three months as part of the ReSource project. The program was designed to enhance abilities in compassion and perspective through mental exercises and working in pairs. Participants were also trained using different approaches to see whether changes to the brain are influenced by how a skill is learnt. The brains of the participants were repeatedly pictured using magnetic resonance imaging (MRI). This revealed that different types of training caused unique changes in specific parts of the brain. For example, teaching mindfulness made parts of the brain less functionally connected, whereas training to understand other people’s thought increased functional connections between various regions. These functional alterations were paralleled by changes in brain structure. They could also predict improvements in social skills which were measured throughout the study using behavioural tests. These findings suggest that training can help to improve social skills even in adults, which may benefit their quality of life through stronger social connections. Better knowledge of how to develop social skills and their biological basis will help to identify people who need support with these interactions and develop new therapies to nurture their abilities.

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Conflict of interest statement

SV, PK, BP, SH, AB, FT, BB, TS No competing interests declared

Figures

Figure 1.
Figure 1.. Study design.
(A) Training design of the ReSource study; (B) Training modules; (C) Task-based meta-analytical maps, and a legend of the color-coding of the maps; (D) Functional cortical organization: i. functional connectivity matrix, gradient 1–3, eccentricity metric; ii. task-based network embedding; iii. intracortical microstructure, mean qT1 values as a function of task-based meta-analytical maps and cortical depth and relative values (z-scored per depth-compartment).
Figure 2.
Figure 2.. Training-induced changes in cortical functional organization.
(A) upper: T-maps of TM-specific changes in functional eccentricity; lower: TM-specific change in functional eccentricity, p<0.01, FDRq <0.05 outlined in black, below: alterations of eccentricity in the FDRq <0.05 regions, right: mean changes in FDRq <0.05 eccentricity regions as a function of G1-G2-G3; (B) A-priori network functional eccentricity change in networks that showed TM-specific change.
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. TM-specific change in functional eccentricity as a function of training cohort and a-priori network.
X-axes depict days of training, y-axes change in functional eccentricity, colors reflect the TM (yellow = Presence; red = Affect; green = Perspective).
Figure 2—figure supplement 2.
Figure 2—figure supplement 2.. TM-specific change in functional Gradient 1.
Trends at p<0.01, FDRq <0.05 outlined in black.
Figure 2—figure supplement 3.
Figure 2—figure supplement 3.. TM-specific change in functional Gradient 2.
Trends at p<0.01, FDRq <0.05 outlined in black.
Figure 2—figure supplement 4.
Figure 2—figure supplement 4.. TM-specific change in functional Gradient 3.
Trends at p<0.01, FDRq <0.05 outlined in black.
Figure 3.
Figure 3.. Changes in depth-varying microstructure as a function of TM.
(A). Change in cortical microstructure, per TM, red indicates positive change in qT1, blue negative change. FDRq <0.05 findings are outlined in black on top of t-values per parcel; (B) TM specific change in cortical microstructure. Red indicates positive change in qT1, blue negative change. FDRq <0.05 findings are outlined in black in combination with semi-transparent trends (p<0.01).
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Training-specific change in qT1 from baseline to T3 as a function superficial (1:4) mid (5:8) and deep (8:12) depth compartment.
(A). Left: Change in retest controls, right: Change in training versus retest controls, trends at p<0.01, FDRq <0.05 outlined in black. (B) Network-specific change in qT1 as a function of training cohort.
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. Cohort-specific change in qT1 from baseline to T1 as a function superficial (1:4) mid (5:8) and deep (8:12) depth compartment.
(A). First row: Left: Change in Presence TC1 vs retest controls, middle: change in Presence TC1 versus Affect TC3, right: Change in Affect TC3 versus retest controls; second row: Left: Change in Presence TC2 vs retest controls, right: change in Presence TC2 versus Affect TC3, trends at p<0.01, FDRq <0.05 outlined in black; (B) Network-specific change in qT1 as a function of training cohort in T0-T1.
Figure 4.
Figure 4.. Dissociable microstructural alterations following mental training.
(A).TM-specific changes in cortical microstructure; qT1 in regions showing eccentricity change (y-axis: depth, x-axis: qT1 change); (B) Network-specific change in cortical microstructure as a function of depth, mean change per TM, pFDR <0.05 have black outline (y-axis: depth, x-axis: qT1 change). The boxes on the right of each plot display the statistics (t-values) of the respective difference between TM, with the contrast color coded as upper minus lower TM (defined by color); (C) Correspondence of functional versus microstructural change; i. Spatial correlation of mean alterations in each TM, black outline indicates pspin <0.05, as a function of cortical depth.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. TM-specific change in superficial (1:4) mid (5:8) and deep (8:12) depth compartment microstructure as a function of training cohort and a-priori network.
Yellow = Presence, Red = Affect, Green = Perspective, Blue = Retest Control. Changes are displayed as a function of days past baseline (MRI measurement points).
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. TM-specific change in qT1 as a function superficial (1:4) mid (5:8) and deep (8:12) depth compartment.
Network specific changes in microstructure as a function of depth per training cohort/retest control cohort, and time point.
Figure 5.
Figure 5.. Behavioral change prediction.
(A).Attention change; from left to right: attention task, behavioral change, predicting weights, nMAE and holdout r distribution; (B). Compassion change; from left to right: compassion task, behavioral change, predicting weights, nMAE and holdout r distribution; (C). Perspective-taking change; from left to right: perspective-taking task, behavioral change, predicting weights, nMAE and holdout r distribution (left side; within TM, right side; other TM (yellow: Presence; red: Affect; Green: Perspective) or RCC (grey)).
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  • doi: 10.1101/2020.11.11.377895

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References

    1. Adolphs R. The social brain: neural basis of social knowledge. Annual Review of Psychology. 2009;60:693–716. doi: 10.1146/annurev.psych.60.110707.163514. - DOI - PMC - PubMed
    1. Amunts K, Schleicher A, Bürgel U, Mohlberg H, Uylings HB, Zilles K. Broca’s region revisited: cytoarchitecture and intersubject variability. The Journal of Comparative Neurology. 1999;412:319–341. doi: 10.1002/(sici)1096-9861(19990920)412:2<319::aid-cne10>3.0.co;2-7. - DOI - PubMed
    1. Baars BJ. The conscious access hypothesis: origins and recent evidence. Trends in Cognitive Sciences. 2002;6:47–52. doi: 10.1016/s1364-6613(00)01819-2. - DOI - PubMed
    1. Barbas H. General cortical and special Prefrontal connections: principles from structure to function. Annual Review of Neuroscience. 2015;38:269–289. doi: 10.1146/annurev-neuro-071714-033936. - DOI - PubMed
    1. Barrett LF. The theory of constructed emotion: an active inference account of Interoception and Categorization. Social Cognitive and Affective Neuroscience. 2017;12:1–23. doi: 10.1093/scan/nsw154. - DOI - PMC - PubMed

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