New neurons stand out from the crowd


Editor's Introduction

Emergence of individuality in genetically identical mice

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We have noticed for a long time that identical twins, who share the same genes and grew up in the same family, do not act exactly the same. But, since we also know that our environment and genetic makeup influence how our bodies and brains develop, where do these individual differences come from? When genetically identical mice live in the same environment for a long time, they develop individual patterns of exploring their home. How do you get unique behavior from the same DNA and the same environment?

Paper Details

Original title
Emergence of individuality in genetically identical mice
Original publication date
Vol. 340 no. 6133 pp. 756-759
Issue name


Brain plasticity as a neurobiological reflection of individuality is difficult to capture in animal models. Inspired by behavioral-genetic investigations of human monozygotic twins reared together, we obtained dense longitudinal activity data on 40 inbred mice living in one large enriched environment. The exploratory activity of the mice diverged over time, resulting in increasing individual differences with advancing age. Individual differences in cumulative roaming entropy, indicating the active coverage of territory, correlated positively with individual differences in adult hippocampal neurogenesis. Our results show that factors unfolding or emerging during development contribute to individual differences in structural brain plasticity and behavior. The paradigm introduced here serves as an animal model for identifying mechanisms of plasticity underlying nonshared environmental contributions to individual differences in behavior.


Plasticity, or the reciprocal interaction between brain structure and function, draws on genetic and nongenetic sources of variation and forms the neurobiological basis of individuality. Behavioral-genetic studies with humans provide statistical tools for estimating the additive and interactive contributions of genetic and environmental variations to individual differences in behavioral development (1). In the case of monozygotic twins reared together, sibling differences reflect the influence of individual responses, based on the same genetic makeup, to a nominally identical environment. Somewhat paradoxically, this source of variation is generally referred to as the “nonshared environment” in behavior genetics. As Turkheimer has noted, “exactly what the nonshared environment consists of has been a matter of mystery and controversy for some time” [(2) p. 826]. We developed an animal model for studying the nonshared environment and examined its effects on behavioral and neural development.

In rodents, enriched environments are among the tools of choice for addressing the influence of a given environment on individuals with identical genetic background (34). However, with some exceptions [(5), see also (36)] the emergence of experience-based individual differences within groups of genetically identical animals exposed to the same enriched environment has rarely been addressed. We used a large group of animals and a particularly complex environment to capture the emergence of individual differences in brain and behavior over time. We used exploration as a marker of behavioral development, and adult neurogenesis in the hippocampus as a marker for continued brain development.

Adult hippocampal neurogenesis allows lifelong plastic adaptation of the hippocampal neural network in the face of environmental complexity and novelty, is regulated by physical and cognitive aspects of behavioral activity, and can be quantified in a straightforward way and captured with numerical key parameters (7). We used the individual regulation of adult hippocampal neurogenesis in response to individual differences in experiencing a nominally identical environment as a proxy for individual brain plasticity. What happens when genetically identical mice inhabit the same environment? How much individuality emerges over time, and is it related to adult hippocampal neurogenesis? Do initial individual differences in behavior dominate later individual differences, or will novel variance emerge?

Forty female inbred mice (C57BL/6N), 4 weeks old at the beginning of the experiments, were kept in a large enriched environment (ENR) for 3 months [(8); see Fig. 1A and detailed description in the supplementary materials]. The mice were tagged with radio-frequency identification (RFID) transponders, and 20 antennas, distributed over the entire environment, monitored their current locations.


Fig. 1.  Experimental setup and effects on body and brain weight.  (A) Schematic illustration of the large enrichment enclosure housing 40 mice including RFID antenna positions (shown as red rings). Positions of levels, water sources, nesting boxes, and connecting tubes are drawn to scale. (Inset) Schematic illustration of animal tracking; an RFID passive integrated transponder (PIT) is implanted in mouse’s neck. The electromagnetic field issued by the antenna induces the PIT to emit the number identifying the animal. This information is then picked up by the antenna and stored into a database together with spatial and temporal annotations. (B) Experimental time line. (C) Body weight development: weights (in grams) of CTR (blue) and ENR (red) mice at the beginning and end of the experiment. (D) Brain weights at perfusion (in grams). The difference in variance between CTR and ENR missed conventional statistical significance at P = 0.057.


Can mouse individuality be measured in an enriched environment such that differences in behavior can be related to differences in the generatioin of new neurons?


Genetically identical mice will develop individual activity patterns within the same enriched environment, which will correlate with physical differences between individual mice, such as body or brain weight, and possibly even the generation of new neurons in the hippocampus.


Panel A: Implant radio transponders in mice that can be read by antennas inside a large enriched environment cage to track when and where the mice move over time.

Panel B: Mice will be put into the enriched or control environment at 5 weeks old. Animals designated for baseline neuron generation measurements are injected with BrdU at an age of 5 weeks and their brains are examined at 8 weeks. Mice that will live the entire 3 months in the enriched or control environment will be injected with BrdU at 17 weeks to measure neuron generation at the end of the experiment. Note: “perfusion” is a chemical fixation technique used to preserve tissue for analysis.

Panel C: Measurements of body weight between control and enriched environment mouse groups at baseline (mice were in the environments for 3 weeks) and at the endpoint (after 3 months).

Panel D: Measurements of brain weight between control and enriched environment mouse groups after 3 months.


Both groups of mice gained weight as they aged from 8 weeks to 20 weeks, and though the variance (variability) of the enriched environment mice tends to be greater than that of control mice, the results are not statistically significant for either total body weight or brain weight.


The authors conclude that there is no significant difference of the total body weight and total brain weight between the control and enriched environment groups based on data presented in C and D.

Over the experimental period of 3 months (Fig. 1B), we saw an increase in mean body weight (Fig. 1C) compared with the baseline group (N = 8). Besides this age effect, variability of both body and brain weight (Fig. 1D) seemed larger in ENR mice than in control (CTR) mice at the end of the experiment (but P = 0.057 for brain-weight variance, Bartlett-box F test, F11,39 = 0.338; and P = 0.154 for body-weight variance, F11,39 = 0.449).

Adult neurogenesis was assessed at the end of the experiment by counting proliferating precursor cells that had been labeled with bromodeoxyuridine (BrdU) 3 weeks before (Fig. 1B). Average adult neurogenesis was increased in the ENR group compared with the CTR group (Fig. 2A), decreasing the physiological age-related decline in adult neurogenesis (as shown in the comparison with the baseline value at 8 weeks). In line with previous studies, ENR animals also had significantly more new astrocytes than CTR animals (t = 4.321, P = 0.0002) but there was no significant difference in cells with undefined phenotype (t = 0.736, P = 0.5) (fig. S1) (9).


Fig. 2 Adult hippocampal neurogenesis and RE.  (A) Total number of new neurons (BrdU+ and NeuN+ cells) in the hippocampal dentate gyrus. Both CTR and ENR groups show the typical age-related decrease in neurogenesis. Compared with CTR mice, ENR mice increased adult hippocampal neurogenesis and resulted in a relative increase in variability. (Baseline versus CTR: P = 5.82 × 10−9; baseline versus ENR: P = 7.17 × 10−12, CTR versus ENR: P = 7.68 × 10−5.) Exemplary histological images. (Top) “ENR low” shows the dentate gyrus of an animal with an adult neurogenesis level in the range of CTR, whereas the mouse in “ENR high” is at the upper end of neurogenesis levels in ENR. Scale bar, 150 μm. All analyses were done on continuous data, not by classifying animals into categories of high or low neurogenesis. (B) Exemplary heat maps of the RE of two mice in the large ENR enclosure (see also supplementary materials). The two panels are heat maps of explorative behavior for two mice assessed during one night depicting the probability of a mouse being in a specific location when viewed from above (i.e., aggregating across the four levels of the cage). Low probabilities are shown in blue, medium probabilities in green, and high probabilities in orange (see arrow on the left). Antenna positions are shown in black. (Left) A mouse with low RE (animal no. 2 at day 19); (right) a mouse with high RE (animal no. 93 at day 9). All analyses were done on continuous data, not by classifying animals into categories of high or low RE. (C) Measurements of RE were aggregated into four adjacent time periods to obtain an index of cumulative RE (cRE). Each line displays the cRE for a single mouse. Corresponding levels of neurogenesis are continuously color-coded from low (blue) to high (yellow). For exact values, see also the scatterplots in Fig. 2D. The mice differed in cRE at T1 (variance T1 = 0.001, χ2 = 28.91, df = 1, P < 0.0001). At the same time, they differed markedly in rates of linear change (χ2 = 18.76, df = 1, P < 0.0001) and exponential change (χ2= 27.80, df = 1, P < 0.0001). As a result, the variance in cRE at T4 (variance T4 = 0.022, χ2 = 35.12) increased by a factor of 22 relative to the variance in cRE at T1 (for the difference, χ2 = 31.73, df = 1, P < 0.0001). Individual differences in linear change predicted individual differences in cRE at the end of the observation period, r = 0.98 (χ2 = 118.742, P < 0.0001), whereas individual differences in cRE at the beginning of the observation period did not predict individual differences in linear change (χ2 = 3.19, P = 0.074). (D) Individual differences in cRE are associated with individual differences in adult hippocampal neurogenesis. The number of new neurons correlated significantly with cRE at T4, r = 0.46 (t = 3.227, P = 0.0026).


Are there differences in adult neurogenesis in the hippocampus that correlate with individual animal movement patterns?


Mice that have higher cumulative Roaming Entropy will have higher rates of new neuron generation than mice with lower cumulative Roaming Entropy.


Panel A: Count the number of BrdU labeled “new” NeuN-positive neurons in the hippocampus of mice at baseline and at the endpoint, and compare the total numbers between the control and enriched environment mouse groups. Additionally, within the enriched environment mouse group, compare the number of BrdU positive cells in the hippocampus of low and high Roaming Entropy mice to see if there is a correlation.

Panel B: Measure the cumulative Roaming Entropy scores for each mouse and plot the probability that a given mouse is in a particular location of the enriched environment. Confirm that the cumulative Roaming Entropy scores reflect the measured activity patterns of the mice.

Panel C: Plot the cumulative Roaming Entropy scores for each mouse over time, and color code each mouse’s trace line according to how many new neurons were counted in the hippocampus at the end of the experiment to test for a correlation of cRE and neurogenesis.

Panel D: Plot each mouse’s cRE versus the number of BrdU-positive neurons counted in the hippocampus and determine whether there is a mathematical correlation between the two measures.

For more on Linear Regression Correlation, see Khan Academy's explanations:


The authors conclude from the data presented in A that there is an age-related decrease in new hippocampal neuron generation in both control and ENR mouse groups as compared to baseline. However, the ENR mice generated more hippocampal neurons as compared to the control group.  There was also more variability within the ENR group in how many new neurons were counted in the hippocampus, where some low cRE ENR mice had few new neurons (as many as the control group), but high cRE mice tended to have many more new neurons than either low cRE ENR mice or control mice.

The probability graphs in B show that mice with low cRE are more likely to be in fewer areas of the habitat, whereas high cRE mice have a high probability of being in many areas of the habitat.

In C, the plot showing each mouse’s cRE over time shows that the cRE measures become more different from each other over time, and that mice with high new-neuron generation also tend to be the mice with high cRE. This observation is mathematically tested in part D, where the correlation between the two measures is statistically significant.


The authors conclude from these data that adult neuron generation in the hippocampus decreases with age, but can be partially upheld by exposure to an enriched environment (part A). They also note that different mice adopt individual movement patterns that become more different from each other over time (part B and C). Finally, the authors determine that individual activity patterns and adult neuron generation are mathematically correlated, such that mice with higher cRE behavior patterns also tend to have higher levels of adult hippocampal neurogenesis (part D).

New neurons in the hippocampus are assumed to enable the hippocampus to flexibly cope with novelty and complexity (10). Physical activity and locomotion provide subjective proxy feedback signals that tend to indicate situations potentially rich in cognitive challenges requiring plasticity (1112). Our specific hypothesis was that mice with a greater range of experiences, as reflected in more explorative behavior corresponding to a larger and more intensive coverage of the territory, would show higher levels of adult hippocampal neurogenesis. Within the ENR group, individual differences in the total number of antenna contacts as proxy for the sheer amount of locomotion were not associated with individual differences in neurogenesis [correlation coefficient (r) = 0.133; t = 0.829; P = 0.412] (fig. S2) (13). Hence, we sought to identify a trait marker of activity that would be more likely to be associated with adult neurogenesis.

To obtain an ethologically valid index of explorative behavior, we derived a new measure, called “roaming entropy” (RE; for more details, see methods in the supplementary materials). RE is the entropy of the probability distribution of finding a mouse at a given antenna at a given time and, thus, is an indicator of the territorial range covered by a given mouse in a given period of time. RE is a continuous measure. RE is low when a mouse has a stable and small home range, independent of the amount of locomotion within that area. But even a relatively large range can be covered with low RE if few stable spots of attendance are spread out over larger distances. RE is high, in contrast, if coverage is evenly distributed over the entire area of the cage (Fig. 2B; see also movies S1 and S2).

Measurements of RE were aggregated into four adjacent time periods (T1, T2, T3, and T4), each representing the average RE over 24 calendar days, and summed over time periods to obtain an index of cumulative roaming entropy (cRE; i.e., cRET1 = RET1; cRET2 = cRET1 + RET2; cRET3 = cRET2 + RET3; cRET4 = cRET3 + RET4). A three-factor latent–growth curve model was fit to the data to obtain estimates of intercept, linear change, and exponential change (for details, see methods in the supplementary materials). The fit of the model was acceptable, comparative fit index (CFI) = 0.986, root mean square error of approximation (RMSEA) = 0.099. Although reliable individual differences in cRE were present at baseline, these differences were completely wiped out by individual differences that emerged during the observation period (Fig. 2C). The correlation between cRE and neurons labeled with BrdU and neuronal marker NeuN did not differ significantly from zero at baseline, r = 0.24 (P = 0.144). In contrast, at the end of the experiment, the number of new neurons (BrdU and NeuN double-positive cells) correlated significantly with cRE at T4, r = 0.46 (t = 3.227, P = 0.0026). Mice who explored their habitat more broadly also grew more new neurons in the hippocampus (Fig. 2D). An estimate of distance traveled (i.e., number of unique, nonrepetitive antenna contacts over the period of the experiment) showed a weaker but still significant association with adult neurogenesis (r = 0.345; t = 2.268; P = 0.029), which explained 12% of the variance (fig. S2 and related information).

This study shows that adult neurogenesis, as an instantiation of brain plasticity, is linked to individual differences in experience among genetically identical individuals who live in a nominally identical environment. About one-fifth of the experiential effects on adult neurogenesis was captured by a measure of roaming through an enriched environment. This finding supports the idea that the key function of adult neurogenesis is to shape hippocampal connectivity according to individual needs and thereby to improve adaptability over the life course and to provide evolutionary advantage (111415). The observed individual differences in behavioral trajectories were reliable. This is in line with the observation that behavioral traits can be strongly influenced by external stimuli that vary between individuals or populations of individuals, as evidenced by diverging results of behavioral testing across different laboratories (1617).

The molecular mechanisms driving individual differences in behavioral and neural plasticity await further study. From the present results, three routes seem worth pursuing: (i) As full inbreeding is impossible, minimal residual segregation remains, and ontogeny may amplify the functional consequences of this residual genetic variation. However, this variability due to novel mutations corresponds to only 8 to 12 single-nucleotide polymorphisms across the entire genome (18). In addition, inbred mice might possibly genetically vary with respect to variable number tandem repeats and transposon insertions (1920) (ii) Stochastic gene regulation may lead to individual differences in molecular states, which are further amplified through experience. (iii) Animals might show small changes in the epigenetic state of their genome and may drift epigenetically apart over time, which reflects the cumulative effects of the choices they make in the course of their lives. This last explanation would be particularly in line with Turkheimer’s argument (2) and consistent with data from human monozygotic twins, according to which epigenetic differences increase from young adulthood to old age and contribute to a growing discordance of monozygotic twins with advancing age (21). In addition, Lathe has suggested the following sources of initial individuality in rodents (19): intrauterine position, nutrition and interaction, imprinting errors, maternal stress and disease, and early postnatal interactions (including handling). Presumably, all of these would result in differences in the epigenome. When we obtained the animals for our study, we received mice randomly picked from as many litters as possible to achieve the best possible randomization.

As foreshadowed by psychological (22) and neurobiological (23) theories of ontogenetic development and in line with general theories of neural self-organization (24), small perturbations, possibly related to the factors mentioned above, may lead to initial individual differences in action tendencies. These differences, in turn, may trigger differences in experience that accumulate over time, result in differential plasticity, and correspond to different epigenetic states and developmental trajectories.

Environmental enrichment does not seem generally to increase variability, although some controversy exists with regard to parameters such as body weight (172526). Most studies, however, have followed smaller cohorts of animals over shorter periods of time than in our study. Whether long-term enrichment in large groups and seminaturalistic conditions have a general variance-increasing effect across a wide range of parameters remains to be determined.

Three months of living in a complex environment led to a massive magnification of individual differences in explorative behavior among genetically identical individuals over time, and these differences were related to adult hippocampal neurogenesis. The rich environment lost its “sameness” over time and gave way to the emergence of a personalized “life space” (27) and a “mouse individuality,” similar to what has been observed in humans for personality traits (28). Hence, the magnitude of individual differences observed in replications of this experiment is likely to vary across studies: As the members of each new cohort individualize, their “society” will also be shaped in a slightly different, individual way. The present paradigm serves as an animal model for addressing the “mystery and controversy” (2) of the nonshared environment, or the ways in which living our lives makes us who we are (29).

Supplementary Materials

Materials and Methods

Figs. S1 and S2

References (3035)

Movies S1 and S2

References and Notes

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Acknowledgments: This study was financed from basic institutional funds. J.F. and I.K. were fellows of the International Max Planck Research School on the Life Course (LIFE), Berlin. M.K. has been supported by the International Research Training Group on Semantic Integration of Geospatial Information, funded by the German Research Foundation (DFG). The authors thank S. Vogler for drawing the cage in Fig. 1A and D. Lasse for technical support. G.K. and U.L. would also like to thank J. Nesselroade for inspiring discussions that helped shape the ideas presented in this report.