Functional Connectivity in the Mouse Brainstem Represents Signs of Recovery from Concussion

Abstract

Mild traumatic brain injury (mTBI) is one of the most frequent neurological disorders. Diagnostic criteria for mTBI are based on cognitive or neurological symptoms without fully understanding the neuropathological basis for explaining behaviors. From the neuropathological perspective of mTBI, recent neuroimaging studies have focused on structural or functional differences in motor-related cortical regions but did not compare topological network properties between the post-concussion days in the brainstem. We investigated temporal changes in functional connectivity and evaluated network properties of functional networks in the mouse brainstem. We observed a significantly decreased functional connectivity and global and local network properties on post-concussion day 7, which normalized on post-concussion day 14. Functional connectivity and local network properties on post-concussion day 2 were also significantly decreased compared with those on post-concussion day 14, but there were no significant group differences in global network properties between days 2 and 14. We also observed that the local efficiency and clustering coefficient of the brainstem network were significantly correlated with anxiety-like behaviors on post-concussion days 7 and 14. This study suggests that functional connectivity in the mouse brainstem provides vital recovery signs from concussion through functional reorganization.

Introduction

Mild traumatic brain injury (mTBI), also called a concussion, is one of the most frequent neurological disorders in humans; about 80% of TBIs, an estimated 27 million cases per year, are categorized as mTBI based on initial symptom presentation.^1
–3 An mTBI—caused mostly by direct external impact on the brain—is characterized by a temporary dysfunction of the brain from structural abnormalities.^4,5 The Glasgow Coma Scale score defines mTBI as a minor head injury with a score of 13–15.⁶

Representative mTBI symptoms are cognitive or neurological disorders such as loss of consciousness,^7,8 memory loss,^9,10 headaches,^11,12 anxiety,^13,14 and depression.^13,15 Although these symptoms can manifest immediately, most of them resolve spontaneously over weeks. Prolonged symptoms over weeks after the initial injury can develop in about 15 − 30% of patients, however.

Post-concussive syndrome continues for weeks to months^16
–18 and may be associated with an increased risk of dementia¹⁹ and other neurodegenerative diseases.^20,21 Further, repeated minor brain injuries can sometimes lead to chronic, progressive neurodegeneration, known as chronic traumatic encephalopathy.^22
–24 The period of recovery from a concussion is extremely important because the concussion impact may last across time. This allows us to tailor rehabilitative interventions.²⁵

The effects of concussion on the brain vary according to characteristics of the head injury. Large-scale human concussion studies, such as the TRACK-TBI in the United States and CENTER-TBI in Europe,^26,27 have attempted to characterize the location and degree of damage,^28
–30 outcomes,^31

–34 regional difference,³² and recovery.^35

–38 In contrast, animal studies enable reproducible traumatic injury, and systematic variations in experimental parameters and diverse characteristics can be controlled in a laboratory environment, thus providing valuable insights in the pathology of concussion.^39

-42

According to previous studies using animal models, one of the main causes of mTBI is axonal injury derived from rapid stretching of axons by external mechanical forces on the brain parenchyma.⁴³ In addition, animal studies that combined neuroimaging and histological evaluation in mTBI suggest that structural alterations may be because of loss of neurons and synapses,⁴⁴ and that diffusion and metabolic abnormalities may be from neuroinflammation and axonal injury.^45,46 Studies on proton magnetic resonance spectroscopy in animals confirmed findings from human studies regarding alterations in several metabolite concentrations (e.g., choline [Cho], creatine [Cr], N-acetylaspartate [NAA], taurine [Tau]) hours to a few days after mTBI.^47,48

The manifestation of mTBI in the brain is still controversial because of the anatomical injury to a nerve. Neuroinflammation is reportedly observed in the cortex and the optic tract of the mTBI model without skull fracture or structural brain damage.^40,42 Other studies reported that repetitive mTBI causes strong astrogliosis, axonal degeneration, and cognitive deficits in the cortex, hippocampus, and corpus callosum over six months after injury.³⁹ Similarly, repeat concussive injury impaired short-term and long-term recognition memory and induced neuroinflammation in the corpus callosum.⁴¹ These studies have shown several behavioral and pathological alterations in the mTBI animal models using molecular experiments. Little is known, however, about the ability of the brain to change its function after a concussion.

Recent neuroimaging studies on humans and animals using diffusion tensor imaging or resting-state functional magnetic resonance imaging (rsfMRI) provide neurobiological evidence for understanding the manifestation of mTBI in the brain. According to previous studies, the effects of concussion were observed mainly in the frontal, temporal, and hippocampus regions involved in memory, executive function, and attention.^49
–51 These regions are expected to play an important role as cognitive function deficits, such as consciousness, memory, and attention, are manifested immediately after a concussion.

Other previous studies have demonstrated that after concussion, the structural volume of the brainstem⁵² and white matter integrity of the midbrain⁵³ is reduced and functional connectivity in the thalamus⁵⁴ and hypothalamus⁵⁵ is decreased while it increases in the medial prefrontal cortex.⁵⁶ Although both structural and functional connectivity were used to investigate concussion effects over time, a mismatch was reported between structural and functional connectivity after a concussion.⁵⁷ Detection of the recovery time after a concussion may be difficult because of the mismatch in the temporal evaluation of structure and function.

In the present study, we focused on the brainstem, using functional connectivity analysis because most concussions cause loss of consciousness, and the brainstem is the center of consciousness as well as the cornerstone of cognitive deficits. Functional changes in the brainstem are associated with consciousness, memory, attention, and autonomic functions after a concussion.⁵³ This can be attributed to the fact that functional changes are more demonstrative than structural changes in the brain affected by concussion.

Considering these facts, we hypothesized that functional connectivity in the brainstem changes across concussion time, and recovery signs would be visible in its stabilization. It would seem to be a reasonable hypothesis because the brain is a topologically well-organized module for the global integration of local functions^58,59 and the brainstem is also organized for topological characteristics.⁶⁰

Therefore, to test whether functional connectivity of the brainstem decreased at certain time points and recovered from a moment in time, we compared the functional connectivity of the brainstem between concussion days. We then tested functional network properties (e.g., global and local connectivity) in the brainstem using graph theory-based network analysis. Finally, we investigated the relationship between functional network properties and behavioral scores. This study revealed to what extent functional connectivity changes of the brainstem may provide neurobiological clues for functional recovery.

Methods

MRI data acquisition

We used structural MRI, rsfMRI, and behavioral data of concussion mouse models from the data repository in the University of Queensland: “MRI collection of mouse model concussion.”⁶¹ All MRI data were obtained from a pre-clinical 9.4T MRI scanner (Bruker Biospin, Germany) in the Queensland Brain Institute. The obtained cross-sectional dataset was composed of Sham (n = 14), post-concussion day 2 (CON day 2, n = 9), post-concussion day 7 (CON day 7, n = 10), and post-concussion day 14 (CON day 14, n = 10).

We excluded two mice each in Sham, CON day 7, and CON day 14, and one mouse in CON day 2 because of errors in image normalization to a template. We used a total of 36 male mice aged 3 − 4 months. The CON mice were placed upside down with the body and tail secured and then received the impact to the head by the piston and brass impactor.⁶¹ The same procedure was applied to the Sham mice without any real impact to the head.

Although detailed information on the MRI acquisition is available in To and Nasrallah's study,⁵⁷ the MRI sequences have been described below. A structural MRI was obtained using a T2-weighted TurboRARE (turbo rapid acquisition with refocused echoes) sequence with the following parameters: a 192 × 192 acquisition matrix, 19.2 mm field-of-view (FOV), voxel size of 0.1 × 0.1 × 0.3 mm³, repetition time (TR) of 7200 msec, and echo time (TE) of 39 msec. The rsfMRI data were obtained using a gradient-echo echo-planar-imaging (GE-EPI) with the following parameters: a 64 × 64 acquisition matrix, a 19.2 mm FOV, 20 slices, a voxel size of 0.3 × 0.3 × 0.6 mm³, a TR 1000 msec, a TE 14 msec, and a slice gap of 0.1 mm. A total of 600 volumes were obtained for rsfMRI.

Data pre-processing

All fMRI data were pre-processed using SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK⁶²) and FSL^78,79. The data underwent seven data pre-processing steps: orientation correction, voxel scaling ( × 10), slice timing, motion correction, distortion correction (FSL's topup),⁸⁰ co-registration of T2 image, and spatial normalization of fMRI to a template space Allen Mouse Brain Common Coordinate Framework (CCFv3),⁴⁵ using non-linear transformation. Finally, normalized data were interpolated to 0.8 × 0.8 × 0.8 mm³ voxels. Spatial smoothing was not conducted to avoid inflation effects of local connectivity.⁶³

Functional brainstem network

To construct a functional brainstem network including the interbrain (or diencephalon) (thalamus, hypothalamus) and midbrain, we used the Allen Mouse Brain Atlas.⁶⁴ All the 161 brain regions were extracted by overlapping the brainstem of the Allen mouse brain atlas and EPI. The following nuisance parameters were regressed out from the fMRI time-series for each of the 161 brain regions: six rigid motion parameters and their derivatives, three principal components of the white matter and the cerebrospinal fluid masks, linear, and quadratic regressors, and band-pass filtering at 0.01 − 0.3 Hz. We computed the correlation coefficient of all pairs of the filtered time-series and transformed them into Fisher Z scores. Functional connectivity matrices were constructed with 161 × 161 (Fig. 1).

FIG. 1.

Overview of experimental design and functional connectivity analysis. (A) A cross-sectional study was conducted in To and Nasrallah's work.⁶¹ The cross-sectional dataset were composed of sham (n = 14), post-concussion day 2 (CON day 2, n = 9), post-concussion day 7 (CON day 7, n = 10), and post-concussion day 14 (CON day 14, n = 10). (B) Functional brain networks were constructed on functional connectivity in the mouse brainstem. The functional brainstem network was derived from Pearson correlation analysis of rsfMRI time series at 161 regions after data preprocessing and signal processing. Graph-theoretic network properties of functional connectivity were compared between post-concussion days.

Brainstem network analysis

To investigate the change of functional connectivity in the brainstem network, we compared functional connectivity on post-concussion days and Sham. Then we conducted a graph theoretical analysis of functional connectivity using the Brain Connectivity Toolbox⁶⁵ to test the findings at the network level. We calculated global and local network properties.

As measures for global properties, we calculated global node degree and global node strength, which are described below:

Node degree is the total number of edges connected to a node. Global node degree $D_{g l o b}$ is defined as the average of the degrees of all nodes. $D_{g l o b} = \frac{1}{N} \sum_{j \in G} n (i, j)$ (1)

where N is the total number of nodes and G is the adjacency matrix.

Node strength is defined as the sum of the weights of all edges connected to a node. Global node strength $S_{g l o b}$ is the average of the node strengths of all nodes. $S_{g l o b} = \frac{1}{N} \sum_{j \in G} w (i, j)$ (2)

where w is the weights of edges between nodes i and j, N is the total number of nodes, and G is the adjacency matrix.

For local properties, we calculated local efficiency and clustering coefficient.

Local efficiency $E_{l o c}$ , which is the ratio of path-length (fewest number of edges between nodes i and j), is defined as the average of the global efficiency $E_{g l o b}$ of node neighborhoods. $E_{l o c} = \frac{1}{N} \sum_{i \in G} E_{g l o b}$ (3)

Global efficiency $E_{g l o b}$ is measured as: $E_{g l o b} = \frac{1}{N (N - 1)} \sum_{i \neq j \in G} \frac{1}{d (i, j)}$ (4)

where d is the geodesic path between nodes i and j.

Clustering coefficient C is defined as the ratio of the number of edges connected to a node with its neighboring nodes. $C = \frac{N}{k_{i} (k_{i} - 1)}$ (5)

in which k_i is the degree of node i.

Statistical analysis of functional connectivity and network properties

To evaluate group differences in functional connectivity, we conducted two-sample t tests and used the threshold of false discovery rate (FDR) <0.05 as a criterion of statistical significance. A one-way analysis of variance (ANOVA) was used to evaluate group differences in global and local network properties, and two-sample t tests were used as a post hoc analysis.

Relationships between functional network properties and behavior scores

In To and Nasrallah's study,⁵⁷ before MRI scanning open field activity was measured using an elevated circular arena where four annuli were divided. Each mouse in Sham, CON day 2, CON day 7, and CON day 14 was placed in the center of the circular arena and their activity was tracked using a camera and a Tracker software (Bio Signal Group, Brooklyn, NY) for 10 min.

From the previous work, the authors defined thigmotaxis index as the tendency of a mouse to stick to the wall and represents an index for anxiety-like behavior.⁵⁷ The thigmotaxis index was calculated as the ratio of the difference between the time spent in the peripheral (annulus 4), center (annulus 1), and middle (annulus 2 and annulus 3) zone and the sum of the time spent in the peripheral, center, and middle zone. Using the thigmotaxis index for anxiety-like behaviors, we investigated the relationship between neural and behavioral measures on concussion days. Based on the analysis performed in a previous study,⁵⁷ we conducted regression analysis to investigate the fit of the thigmotaxis index and network properties across combined CON day 7 and CON day 14 and combined Sham and CON day 2.

Results

In the functional connectivity analysis, we observed that the functional connectivity of the brainstem network changes according to post-concussion days and returns to normal. Based on these observations, we investigated whether concussion led to functional connectivity changes in the brainstem network. Group-average functional connections are displayed in Figure 2A. Compared with Sham, functional connections were decreased on CON day 2 and CON day 7 but increased on CON day 14. Statistical group differences in functional connectivity are summarized in Figure 2B. Significantly higher connectivity was observed on CON day 14 than in Sham, CON day 2, and CON day 7.

FIG. 2.

Functional connectivity differences in the brainstem across concussion time. (A) Group-average functional connectivity was presented for sham, CON day 2, CON day 7, and CON day 14. Compared with sham, functional connectivity was decreased in CON day 2, more decreased in CON day 7, but increased in CON day 14. (B) Significantly increased edges with FDR <0.05 were mainly found in CON day 14 compared to sham, CON day 2, and CON day 7. C2: CON day 2; C7: CON day 7; C14: CON day 14. Red lines indicate an increase, and blue lines indicate a decrease.

Figure 3 shows the global and local network properties of the brainstem. Global network property revealed significant group differences (one way ANOVA, global node degree: F(1,3) = 3.31, p = 0.0325; global node strength: F(1,3) = 4.71, p = 0.0078). Significantly lower global node degree and global node strength were observed in CON day 7 compared with those in CON day 2 and CON day 14 (global node degree: CON day 7 vs. CON day 2: p = 0.0221; CON day 7 vs. CON day 14: p = 0.0453; global node strength: CON day 7 vs. CON day 2: p = 0.0400; CON day 7 vs. CON day 14: p = 0.0052).

FIG. 3.

Global and local network properties in the brainstem. (A) Global node degree and global node strength in CON day 14 were significantly higher than in CON day 7. (B) Local efficiency and clustering coefficient were significantly higher in CON day 14 than in CON day 2 and CON day 7. *p < 0.05, **p < 0.01, ***p < 0.005.

In local network property, there were significant group differences (one-way ANOVA, local efficiency: F(1,3) = 8.02, p = 0.0004; clustering coefficient: F(1,3) = 5.45, p = 0.0038). CON day 7 showed significantly lower local efficiency and clustering coefficients compared with Sham and CON day 14 (local efficiency: CON day 7 vs. Sham: p = 0.0008; CON day 7 vs. CON day 14: p = 0.0007; clustering coefficient: CON day 7 vs. Sham: p = 0.0067; CON day 7 vs. CON day 14: p = 0.0036). CON day 2 showed significantly lower local efficiency and clustering coefficient compared to CON day 14 (local efficiency: p = 0.0323; clustering coefficient: p = 0.0471). The statistical results of global and local properties are summarized in Table 1.

Table 1.

Global and Local Network Properties of Functional Connectivity in the Brainstem

Network properties	Mean (Standard error of the mean)				p-value of ANOVA (F-value)	p-values adjusted for multiple comparison across groups
Network properties	Sham	CON 2	CON 7	CON 14	Sham vs. CON 2, 7, 14	Sham vs. CON 2	Sham vs. CON 7	Sham vs. CON 14	CON 2 vs. CON 7	CON 2 vs. CON 14	CON 7 vs. CON 14
Global node degree	23.75 (0.64)	25.91 (0.92)	22.32 (1.05)	25.37 (0.91)	^*0.0325 (3.31)	†0.0616	0.2308	0.1507	^*0.0221	0.6803	^*0.0453
Global node strength	10.58 (0.35)	10.84 (0.33)	9.41 (0.54)	11.78 (0.48)	^*0.0078 (4.71)	0.6267	†0.0723	†0.0553	^*0.0400	0.1290	^*0.0052
Local efficiency	0.3211 (0.0052)	0.3094 (0.0043)	0.2917 (0.0043)	0.3327 (0.0085)	^*0.0004 (8.02)	0.1416	^*0.0008	0.2324	^*0.0165	^*0.0323	^*0.0007
Clustering coefficients	0.2394 (0.0058)	0.2288 (0.0051)	0.2149 (0.0042)	0.2538 (0.0103)	^*0.0038 (5.45)	0.2183	^*0.0067	0.2072	†0.0540	^*0.0471	^*0.0036

ANOVA, analysis of variance; CON, concussion.

Significant group difference (p < 0.05, one-way analysis of variance). †Tendency.

Behavioral analysis for the thigmotaxis index revealed a statistical group difference (one way ANOVA, F(1,3) = 3.49, p = 0.0273). Specifically, CON day 14 showed a significantly higher thigmotaxis index than CON day 7 (CON day 7: 0.5701 ± 0.1160 (mean, std); CON day 14: 0.7792 ± 0.0774; CON day 7 vs. CON day 14: p = 0.0008) (Fig. 4A). We performed a regression analysis to test the relationship between neural and behavioral measures across the combined CON day 7 and CON day 14. The local efficiency and clustering coefficient of the brainstem functional network were significant positively correlated with the thigmotaxis index (Fig. 4B).

FIG. 4.

Relationship between local network properties and behavioral scores. (A) The thigmotaxis index for anxiety-like behaviors in CON day 14 was significantly higher than in CON day 7. (B) In the regression analysis, the thigmotaxis index was positively correlated with local efficiency and clustering coefficient in CON day 7 and CON day 14. However, no significant correlation was noted between the thigmotaxis index with local efficiency and clustering coefficient in the sham and CON day 2. Red regression lines (solid line, mean; dot line, 95% confidence intervals) represent the fit of the thigmotaxis and local properties in CON day 7 and CON day 14. Blue regression lines represent the fit of the thigmotaxis and local properties in Sham and CON day 2. Asterisk (*) indicates significance (p < 0.05). N.S. indicates non-significance.

Discussion

We reanalyzed the MRI data of mouse models to investigate functional changes of the brainstem across concussion days. This is a critical issue for clinical neuroscience because it enables us to understand the temporal effects of mTBI and provides useful information for recovery planning. This study adds to a previous work⁵⁷ with the finding that the brainstem represents functional changes after a concussion by recovering the functional network.

After a concussion, functional connectivity of the brainstem was decreased on CON day 2 and CON day 7 but was recovered on CON day 14 (Fig. 2). These functional connectivity changes (reduction-recovery) have been observed in the motor-related cortical regions⁵⁷ and in the whole-brain network.⁶⁶ The brainstem was excluded in these cases, however.

We observed an increased functional connectivity on CON day 14 compared with Sham, CON day 2, and CON day 7 (Fig. 2). We also observed higher global and local network properties on CON day 14 than on CON day 7 (Fig. 3). Further, both global and local network properties decreased on CON day 7 compared with those on CON day 2 but increased on CON day 14 compared with those on CON day 7. These results support that functional connectivity of the brainstem can be recovered after CON day 14 and may imply a sign of functional recovery from a concussion. We observed similar results in the gradient of functional connectivity that accounts for the variability of connectivity patterns within the brainstem on each post-concussive day (see Supplementary Figure S1 and Supplementary Methods and Results).

Our findings are in line with previous microscopic studies demonstrating a change in functionalities from the structural network in the brainstem. The midbrain dopamine system was damaged by repetitive mild head impacts.⁶⁷ Although the microglial activity increased, loss of nigrostriatal dopaminergic neurons was caused by TBI.⁶⁸ The dopaminergic activity was increased in compensatory reorganization for functional recovery in the nigrostriatal system.⁶⁹ These previous findings support the importance of the brainstem in evaluating functional changes after concussion⁷⁰ together with motor-related cortical regions.⁵⁷

We also found that the neural plasticity in the brainstem is related to the normalization of anxiety-like behaviors after concussion. In the brainstem, the local network properties of functional connectivity are significantly positively correlated with the behavioral score (e.g., thigmotaxis index; local efficiency, R² = 0.3168; clustering coefficient, R² = 0.2663) (Fig. 4B).

Our results revealed that the thigmotaxis index decreased with a decrease in the functional connectivity. This is consistent with the previous finding demonstrating that reduced thigmotaxis increases anxiety and reduces dopaminergic transmissions.⁷¹ Our findings indicated that the recovery timeline of function is similar to that of behavior. This may be true because, according to previous studies, the thigmotaxis index was decreased on CON day 7 and normalized on CON day 14, and there was an increase in resting-state functional connectivity.^57,72

This study demonstrated signs of recovery in behaviors through functional reorganization in the brainstem after rotational, acceleration/deceleration concussive brain injury as suggested in the previous works.^57,61 This is consistent with the reduction of the white matter integrity in the midbrain from rotational acceleration.⁵³ After acceleration-deceleration brain injury from car accidents, falls, sports, or military activity, functional and behavioral deficits (e.g., loss of consciousness and cognitive deficits) may be naturally normalized with behavior after some time.

This study suggests the involvement of functional connectivity in confirming when brain function is recovered and how it is correlated with behavioral recovery at the macroscopic level. This may provide clues about the cognitive and behavioral mechanisms underlying functional recovery after a concussion.

This study differs from the previous mTBI studies^57,66 by using resting-state functional connectivity because we focused on the brainstem networks. Although the brainstem revealed important clues about diffuse axonal injury in the histopathological mTBI studies,^43,53 neuroimaging studies have not been much reported on mTBI in both animal models and human investigations.

Therefore, the importance of translational brain injury studies that link neuroimaging findings in humans to pathophysiological results in animals should be re-emphasized,^73,74 because animal models enable the temporal study of brain injuries that are difficult to conduct for human patients. Despite the idiosyncratic features of human mTBI, translational neuroimaging findings may be comparable between humans and animals for mTBI.⁷⁴ For example, some rsfMRI studies demonstrated functional changes after mTBI in humans^66,75 and mice.⁵⁷

This study had some limitations. First, there is currently no established reference or gold standard validated against neuropathology in diagnosing mTBI. While various protein macromolecules and small molecules that can be detected in blood and cerebrospinal fluid are being investigated as biomarkers for mTBI, the molecular diagnosis based on fluid biomarkers is still in its infancy. Much work is required to optimize sample collection and storage, identify and mitigate pre-analytical and analytical confounders, and optimize the assays to maximize their analytical sensitivity and specificity.^76,77

Although structural MRI-based approaches have been used to monitor axonal integrity, structural changes are still insufficient as the conclusive biomarker for mTBI because they are not sensitive enough to detect axonal damages caused by mTBI. Functional MRI-based approaches, however, such as functional connectivity and its network properties, can represent functional changes in mTBI. Considering that most symptoms of mTBI develop dynamically and spontaneously resolve over time, global and local functional connectivity can be utilized as functional biomarkers for mTBI diagnosis. Although this study provides the recovery timeline of function and behavior in animal models of mTBI, future studies are needed to validate our findings in patients with mTBI.

Conclusion

We investigated the changes of functional connectivity in the mouse brainstem over concussion time using functional network properties. We observed that the functional global and local connectivity were decreased on CON day 7 but normalized on CON day 14. We also observed that the normalization of anxiety-like behaviors has the same trend as the normalization of functional connectivity. Our data suggest that functional connectivity in the mouse brainstem represents signs of recovery from concussion through functional reorganization.

Footnotes

Acknowledgments

The authors would like to thank Xuan To and Fatima Nasrallah for the access to head injury mouse models.

Authors' Contributions

Dongha Lee: conceptualization (lead); formal analysis; funding acquisition; methodology; supervision (equal); writing–original draft (equal); writing–review and editing (equal). Yujeong Lee: investigation; writing–original draft (equal); writing–review and editing (equal). Yoonsang Lee: investigation; writing–original draft (supporting). Kipom Kim: conceptualization (supporting); supervision (equal); writing–original draft (equal); writing–review and editing (equal).

Funding Information

This research was supported by the KBRI basic research program through the Korea Brain Research Institute funded by the Ministry of Science and ICT (22-BR-05-02) and Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (22YB1200).

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Figure S1

Supplementary Methods and Results

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