An individual's experiences and behavior shape their brain, thereby building and refining a network of connections between neurons. This unique network may affect an individual's brain resilience in the face of aging, injury or disease. Understanding how individual experiences shape brain connections could help scientists develop personalized treatments. It may also have important implications for preventing brain disease.

Studying mice can provide a window into some of these brain processes. By using inbred mice, scientists can rule out the role of genetics in brain differences. Scientists can also control the animals’ environments and track the activity of individuals to study their behavior.

Bogado Lopes et al. show that more active mice living in enriched environments have signs of more complex networks of brain connections. In the experiments, the researchers placed genetically identical mice in either standard laboratory mouse housing or in enriched environments. Mice in the enriched housing had access to multi-level enclosures connected with tubes and supplied with a rotating array of toys. A tiny tracking device was inserted under the skin of the mice to follow their movements. Finally, all mice underwent structural magnetic resonance imaging to assess their brain anatomy and connections. This revealed that the most active and adventurous mice in the enriched enclosures had the most robust signs of increased brain connectivity. However, mice with declining activity levels in the enriched enclosures had fewer brain connections. Brain connection patterns in these creatures of habit were nearly identical to the ones in mice housed in small unenriched enclosures.

The results show that how individual mice respond to their environments affects their brain structure. More active behavior patterns lead to more robust networks of brain connections. Larger studies in mice could provide more about lifestyle-dependent brain resilience. It may also help scientists to develop individualized approaches to optimizing brain health.

The ‘non-shared environment’ (Plomin and Daniels, 2011), the elusive component of the non-genetic factor in phenotypic variation (Morgante et al., 2015), contributes to interindividual differences and neurobiological individuality. In humans, in vivo neuroimaging studies have demonstrated a complex relationship between behavioral performance and brain structure associated with learning (Draganski et al., 2014, Taubert et al., 2010), personality traits (Nostro et al., 2017), and political orientation (Kanai et al., 2011), supporting the idea of a causal, yet individual relationship between brain function, structure, and behavior. The structural connectome, which can be estimated based on the correlation between regional brain volumes (across subjects) or white matter links between specific brain regions (within subjects) partially recapitulates known functional networks and represents an individual ‘fingerprint’ in humans and non-human animals (Alexander-Bloch et al., 2013; Melozzi et al., 2019; Pagani et al., 2016; Yee et al., 2018). Open questions about the exact nature of the connectome and how it is represented in covariance patterns as assessed by imaging studies abound, especially with respect to the crucial issue of causes and consequences.

We have reported that environmental enrichment (ENR) increases variability in activity-related behaviors (roaming entropy, RE), or total object exploration in a novel object recognition task (Körholz et al., 2018). The ENR mice also showed greater variability in measures linked to brain plasticity, such as adult hippocampal neurogenesis (Körholz et al., 2018) accompanied by distinguishing epigenetic patterns in the hippocampal dentate gyrus (Zocher et al., 2021; Zocher et al., 2020). From these results we have developed the Individuality paradigm that exposes the influence of the non-shared environment on phenotypic variation (Kempermann, 2019; Kempermann et al., 2022). Its key feature is that both genes and environment are kept constant, while behavior is continuously measured to assess the emergence of differential behavioral trajectories.

Structural magnetic resonance imaging (sMRI) studies of mice exposed to different enrichment paradigms have confirmed historical post-mortem findings, such as increased hippocampal volumes (Zhang et al., 2018), and extended these to mouse brain regions involved in sensorimotor processing (Scholz et al., 2015). We here go a decisive step further and examine the relationships between individual behavioral trajectories and regional brain volumes as well as whole-brain structural networks (‘connectomics’). These data may provide neurobiological foundations for concepts such as cognitive reserve or brain maintenance, which attempt to capture individual differences in healthy cognitive aging and resilience to neurodegenerative disease (Kempermann, 2019; Scholz et al., 2015).

We longitudinally tracked the behavior of enriched female mice (n = 38) in our Individuality cage system, equipped with radio-frequency identification (RFID) technology (Evans, 2013; Figure 1A). During 12 weeks of exposure to the ENR, mean values of RE as a measure of exploration and territorial coverage (Freund et al., 2013Zocher et al., 2020) were calculated per night and aggregated across four time blocks of 21 nights each. Increasing interindividual component of variance (Figure 1B) confirmed the emergence of individual behavioral differences. Two distinguishable patterns were observed (Figure 1C): mice with consistently high levels of RE and those with habituation and decreased RE values over time. Mice were stratified according to this criterion and grouped into ‘flat’ roamers (n = 15) or ‘down’ roamers (n = 15) depending on the slope of the linear regression line through a set of four RE time blocks. The slopes for ‘flat’ roamers ranged from −0.003 to 0.004, while slopes lower than −0.006 (to −0.049) were considered to represent ‘down’ roamers. Mice that showed in-between slope values (n = 2) and those with positive slopes (n = 6) were excluded from the clustering to achieve a sharper distinction between the extremes and equally sized groups of 15 mice were formed. The actual slopes are shown in Figure 3B. This is stratification serves the purpose of allowing the visualization of patterns at the top of a rank order based on changes in RE vs. at the bottom.

Figure 1

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The number of doublecortin (DCX)-positive cells in the dentate gyrus, a proxy measure of adult neurogenesis, increased in ENR compared to standard conditions, but did not differ between the ‘flat’ vs. ‘down’ ENR subgroups (Figure 1D). As previously shown (Evans, 2013; Gong et al., 2012), individual variability in behavior positively correlated to adult hippocampal neurogenesis (R² = 0.20, p = 0.0046; Figure 1E). The positive correlation between end-point behavior (RE at the time block 4) and immature neurons was statistically significant in the ‘down’ roamers subgroup (R² = 0.40, p = 0.012), but missed conventional statistical significance in the ‘flat’ roamers (R² = 0.20, p = 0.095; Figure 1F, G). However, the correlations in the flat and down subgroups were not statistically different (one-sided Fisher’s z test, z = −0.63, p = 0.27), which might point to an insufficient power for this comparison.

We next explored neuroanatomical changes between ENR- and standard-housed (STD) mice using ex vivo sMRI. Total brain volumes did not differ significantly (STD: 450.3 ± 11.7 mm³ vs. ENR: 456.5 ± 16.0 mm³; t = 1.14, df = 46; p > 0.05; q > 0.05; Figure 2—figure supplement 1). Using atlas-based segmentation (ABS), statistically significant (5% false discovery rate [FDR]) group differences in absolute volume (mm³) were found for 12% (22/182) of mouse brain atlas regions of interest (ROIs) with effect sizes (SMD) ranging from +2.7 in CA1 oriens (CA1Or) to +1.15 in the molecular layer of the dentate gyrus (Figure 2A; Supplementary file 1). Most of these ROIs (68%; 15/22) were located in the mouse hippocampus, but absolute volume increases were also observed in the infralimbic cortex (cingulate cortex area 25), olfactory nuclei, the anterior commissure, and the orbital cortex (Figure 2A; Supplementary file 1). The majority of these ROIs were conserved as significant group differences when considering relative volumes (% of whole brain), suggesting normal scaling (Figure 2—figure supplement 2a; Supplementary file 1). Complementing the atlas-based analysis, voxel-wise assessment of volume changes using tensor-based morphometry (TBM) revealed statistically significant (family-wise error [FWE] rate p < 0.05) clusters of voxels with apparent increases in both absolute volumes (Figure 2—figure supplement 2b) and relative volumes (Figure 2—figure supplement 2c) localized within the mouse hippocampus when comparing ENR to STD mice. Collectively, these data confirm the expected effects of ENR on mouse brain structure, as evidenced by prior mouse neuroimaging (Scholz et al., 2015; Zhang et al., 2018) and historical post-mortem studies in rats (Diamond et al., 1966).

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We next explored to what extent our key behavior metric of RE relates to local volume changes (as measured by sMRI) in the ENR group alone. Using the average RE slope for each individual mouse in the ENR group across all four time blocks as a continuous variable, we performed a voxel-wise correlation of this measure against log-Jacobian measures of local volume change. This analysis revealed that RE slope values correlated positively with clusters of local volume changes in the mediodorsal and parafascicular thalamic nuclei, the periaqueductal grey, medial geniculate nucleus, deep mesencephalic nuclei, gray and white matter layers of the superior colliculus, and the central nuclei of the inferior colliculus (FWE p < 0.05, Figure 2B). At an exploratory threshold of p < 0.01 uncorrected for multiple comparisons additional positive correlations were seen between RE slope and the volumes of the sensory and motor cortices, striatum, ventral thalamus, brainstem nuclei, and cerebellar white matter. These data suggest that in mice with greater RE behavior in the ENR group the absolute volumes of these regions were larger than in the mice that displayed less RE behavior.

We next explored whether the subgroups within the ENR group that reflected continued high vs. declining (‘flat’ vs. ‘down’ roamers) differed anatomically, as measured by sMRI. Total brain volumes did not differ between the two ENR subgroups (‘down’: 451.7 ± 11.8 mm³ vs. ‘flat’: 459.2 ± 5.2 mm³; t = 1.24, df = 28; p > 0.05; q > 0.05; Figure 2—figure supplement 1). Voxel-wise TBM to map localized neuroanatomical differences between Flat and Down subgroups of ENR-housed mice (Figure 2—figure supplement 3) did not show statistically significant localized volume differences between the groups after a stringent correction for multiple comparisons (FWE p < 0.05). We additionally ran a voxel-wise regression between the RE slope across all four time blocks against local volume changes for each individual mouse in the ‘flat’ and ‘down’ subgroups. In contrast to the correlation across all mice in the ENR group, there were no statistically significant correlations after correction for multiple comparisons (FWE p < 0.05). At an exploratory threshold of p < 0.01 uncorrected for multiple comparisons, however, RE slope values correlated positively with the absolute volumes of the cingulate, motor, somatosensory, insular, visual, and auditory cortices, as well as ventral thalamic, mid and hind-brain nuclei (Figure 2—figure supplement 4) largely replicating the pattern in Figure 2B. Collectively, these data suggest that individual differences in RE behaviors are likely supported by widely distributed mouse brain circuitry.

To study whether the emergence of interindividual variation in RE was associated with increased variability in the volumes of mouse brain regions, we calculated the coefficient of variation (CV) for each individual brain region. To determine if there are different degrees of overall variability between ENR subgroups, we averaged the CVs across all 182 ROIs in the mouse brain atlas to yield an average variability measure for each group (Figure 3—figure supplement 1). Comparing the sum of ranks between ‘down’ and ‘flat’ mice, we found a highly statistically significant difference (Mann–Whitney U = 11,076; p < 0.0001; Figure 3—figure supplement 1). These data are indicative of an increase in regional brain volume variance as a function of behavioral response to the enriched environment.

Structural covariance is defined as the correlated variation in volumes between pairs of brain regions, which, as human neuroimaging studies suggest, reflects both structural (Gong et al., 2012) and functional brain connectivity (Segall et al., 2012). Structural covariance networks are conserved in the mouse brain, providing an opportunity to explore the impact of ENR compared to standard housing on this cross-species measure of brain connectivity (Pagani et al., 2015; Yee et al., 2018). Correlation matrices of mouse brain structural covariance (Figure 3A) revealed a broad effect of housing, which was found to be statistically significant (Chi-square tests for equality of two correlation matrices; Chi-square = 17,997.7, df = 16471, p < 0.0001, where prob is the probability of observing the Chi-square under the null hypothesis) (Steiger, 1980). Furthermore, comparison of structural covariance matrices within the ENR group revealed distinctive effects between ‘down’ and ‘flat’ roamers (Chi-square = 19,997.29, df = 16,471, p < 0.0001; Figure 3B). The structural covariance matrix of ‘flat’ roamers (Figure 3C) differed from that of STD animals (Chi-square = 17,938.04, df = 16,471, prob <0.0001), whereas ‘down’ roamers (Figure 3D) were highly similar to STD group (Chi-square = 14,327.89, df = 16,471, prob <1; Figure 3A). In general, brain regions in the ’flat’ roamers were highly correlated with each other, whereas this was not the case in ‘down’ roamers and STD mice (Figure 3). These correlation patterns support the hypothesis of a link between behavioral trajectories and distinct levels of brain structural covariance, which develops independently of genetic variation, or the idea that emerging complex behavioral patterns are dependent on changes in covariance between widely distributed regions.

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To test hypotheses regarding specific network connections we used network-based statistics (NBS). To describe the graph model, an appropriate set of nodes was defined using the mouse brain ROIs from the DSURQE atlas that differed significantly in volumes based on the ENR > STD contrast after correction for multiple comparisons (5% FDR; Figure 2A, Supplementary file 1). Values of bilateral regions were averaged and plotted in both hemispheres. Comparing ENR and STD mice, NBS analysis detected a single statistically significant subnetwork of increased structural covariance, comprising 23 structural connections (t = 2.4; p < 0.05). By contrast, comparing ‘flat’ vs. ‘down’ mice in the ENR group, NBS identified a larger statistically significant subnetwork of increased structural covariance comprising 49 structural connections (t = 2.4; p < 0.05; Figure 4A, B). Figure 4C, D depicts how the size of these subnetwork depends on the chosen t thresholds.

Figure 4

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The NBS analysis supports the conclusion that interindividual differences in behavioral trajectories (here clustered into the ‘flat’ vs. ‘down’ roamer subgroups of ENR) surpassed the housing effects, and identifies specific subnetworks of structural covariance in the mouse brain.

Correlative analyses of magnetic resonance imaging (MRI) data provide evidence of integrated networks of brain regions, both structurally and functionally. These provide non-invasive insights into the organization of the brain at the macroscale, going beyond the analysis of local changes within individual brain regions as revealed by univariate analysis methods (Bullmore and Sporns, 2009). Within this framework, ‘structural covariance’, permits the study of variation in the organization of brain regional structures within networks that might emerge across a population of individuals in both humans (Alexander-Bloch et al., 2013; Evans, 2013) and mice (Bruce et al., 2021; Mueller et al., 2021). Applying structural covariance and NBS analysis to our individuality model, we observed patterns of structural covariance across the mouse brain that are highly distinct between STD- and ENR-housed mice, which may reflect synchronized plastic changes occurring across multiple brain regions over time. Remarkably, within the ENR group itself, the structural covariance matrix for the ‘down’ roamers was similar to the matrix generated for standard-housed mice (Figure 1F, G). The structural covariance matrix for those mice with a ‘flat’ RE suggests a much higher degree of inter-regional correlation in comparison to ‘down’ or STD mice, findings confirmed and extended by the NBS analysis. Variation in mouse brain structural covariance in the ENR group suggests that individual behavioral trajectories (as here first approximated by RE, but in reality encompassing the full scope and complexity of behaviors; Freund et al., 2015) are associated with a certain degree of individualization of brain structural networks at the macroscale and that this largely explains differences observed between standard and ENR mice per se.

The two groups of flat vs. down roamers were formed in order to be able to calculate structural covariance matrices at the ends of the behavioral spectrum within the ENR group. We used a within-group effect to point to the existence of interindividual differences, but by necessity, structural covariance has to be calculated in groups and cannot be calculated for individuals with the available structural MR images from this dataset. We excluded two animals with borderline slopes at the boundary between the groups and as a measure of stringency, removed mice with a clearly positive slope from the ‘flat’ group. Chi-square statistics support the qualitative impression that the visualization conveys: the patterns differ strongly.

Our data suggest that the behaviors reflected in RE are supported by widely distributed brain circuitry, in particular the hippocampal formation, cingulate and temporal cortices, white matter tracts and olfactory areas. Consistent with this view, neuroanatomical and/or structural covariance changes within these mouse brain circuits have been previously shown to be associated with spatial memory, navigation and cognitive strategies (Lerch et al., 2011), social behaviors (Bruce et al., 2021), and susceptibility or resilience to stress exposure (Anacker et al., 2016), all domains of mouse behavior that may contribute to the individual estimate of RE.

Environmental enrichment has always been by and large a ‘black box’ paradigm and discussions about the relative contribution of the various factors that make up enrichment (e.g., group size, space, changing objects) have filled volumes, without reaching a unified theory. In the center of our own considerations are learning and social interaction as key components. While the deconstruction of enrichment is an important research avenue, a case can also be made for leaving the black box closed and appreciate enrichment for its ability to apply a complex combination of inanimate and social stimuli. For the present study we have to leave open, to which extent and how exposure to the enriched conditions is causal for the observed behavioral trajectories and the associated changes in structural covariance. Nevertheless, the associations are strongly indicative of at least shared (indirect) causality.

The mice in our study are inbred, thus the genetic background and the ‘nominal’ environment are identical for all mice in the ENR group. Hence, phenotypic variation at the level of brain and behavior must arise from the non-shared component of the environmental factors (‘non-shared environment’). Collectively, our data support a view that a continuum exists between RE and experience-dependent plasticity in brain structure (as measured by ex vivo sMRI). We speculate that exposure to ENR may build on initial variance in mouse brain volumes observable from early in postnatal life (Qiu et al., 2013) and amplifies these differences by providing the opportunity for the development of individual behavior (Freund et al., 2013; Kempermann, 2019; Körholz et al., 2018; Zocher et al., 2020). Longitudinal in vivo multimodal MRI studies are however required to definitively confirm this view and define the relative contributions of pre-existing individual differences in both functional and structural mouse brain networks as compared to the experience of the shared enriched environment and link these changes to their cellular and molecular correlates.

A certain limitation to our current study is the fact that the stratification of the ENR group into behaviorally distinct subgroups involved arbitrary decisions. With these, however, we intended to maximize the N number within each subgroup to increase power, use equally sized groups, and base the definition of the two group on a simple, rational, and plausible parameter (i.e., slope of the behavioral trajectory). The observed differences in covariance are large and occur in a group of mice, which are genetically homogenous and share the same environment. The data are consistent with the hypothesis that greater activity results in greater brain plasticity and, by consequence, a richer connectome. While Figure 4 offers first insight into which connections are involved, we here primarily emphasize the fact that the number of connections increases with greater activity. Actual effect sizes, specific network properties, and the role of specific connections will have to be examined in larger follow-up studies based on more and different behavioral measures and stringently objective stratification strategies. As first step into this direction, we however present the continuous correlation between RE slopes and gray matter volumes in Figure 2B.

Because ‘flat’ roamers show stronger structural brain connectivity (based on structural covariance in MRI), it is tempting to speculate that keeping a stable level of territorial coverage has a positive effect on brain networks. In fact, our RE measure has already been used in human studies, providing evidence that greater levels of RE (assessed with a smartphone app) are associated with a greater positive affect and greater hippocampal–striatal functional connectivity (Heller et al., 2020). Our animal studies allow the generalization of such observations and ultimately will enable identification of the underlying mechanisms at the cellular and molecular levels.

The by now extensive literature on environmental enrichment covers different overarching mechanistic ideas, including the ‘learning theory’, the ‘arousal theory’, and the ‘developmental theory’. As argued recently, all of these seem to be correct to a certain extent and contribute to a multifactorial network of causes across scales (Kempermann, 2019), RE captures only a small facet of this complexity but appears to be a remarkably strong factor. We hypothesize learning and social factors to make important contributions to the individuality effect. It is, however, an unresolved question, to which extent these enrichment factors are necessary for behavioral and structural individualization to emerge.

Even now, however, our study demonstrates how whole mouse brain structures are distinctively affected by the non-shared component of environmental enrichment and can be linked to stable behavioral trajectories.

The structural MR images used in this manuscript are publicly available on the OSF platform (https://osf.io/m7gpd/). The volumetric MRI data are found in Supplementary Files 1 (absolute values) and 2 (relative values). The behavioral data from the cage (animal IDs with time-stamped raw antenna contacts) are assessible at Dryad: https://doi.org/10.5061/dryad.bzkh189ds.

1. 1. Bogado Lopes J
   2. Senko AN
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   4. Geisler D
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   (2022) Open Science Framework

   ID m7gpd. Individual behavioral trajectories shape whole-brain connectivity in mice: MRI data.

   https://osf.io/m7gpd/
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   (2023) Dryad Digital Repository

   Individual behavioral trajectories shape whole-brain connectivity in mice.

   https://doi.org/10.5061/dryad.bzkh189ds

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Author details

1. Jadna Bogado Lopes

   1. German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany
   2. Center for Regenerative Therapies Dresden (CRTD), TU Dresden, Dresden, Germany

   Contribution

   Conceptualization, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

2. Anna N Senko

   1. German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany
   2. Center for Regenerative Therapies Dresden (CRTD), TU Dresden, Dresden, Germany

   Contribution

   Data curation, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0885-0440

3. Klaas Bahnsen

   Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Dresden, Germany

   Contribution

   Formal analysis, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5413-0359

4. Daniel Geisler

   Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Dresden, Germany

   Contribution

   Investigation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

5. Eugene Kim

   Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience King's College, London, United Kingdom

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0066-7051

6. Michel Bernanos

   Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience King's College, London, United Kingdom

   Contribution

   Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Diana Cash

   Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience King's College, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Stefan Ehrlich

   1. Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Dresden, Germany
   2. Department of Child and Adolescent Psychiatry, Faculty of Medicine, Eating Disorder Treatment and Research Center, Dresden, Germany

   Contribution

   Formal analysis, Investigation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2132-4445

9. Anthony C Vernon

   1. Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, United Kingdom
   2. MRC Centre for Neurodevelopmental Disorders, King's College, London, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing – review and editing

   For correspondence

   anthony.vernon@kcl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7305-1069

10. Gerd Kempermann

    1. German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany
    2. Center for Regenerative Therapies Dresden (CRTD), TU Dresden, Dresden, Germany

    Contribution

    Conceptualization, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

    For correspondence

    gerd.kempermann@dzne.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5304-4061

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