Ralf Mekle1, Lara Fleck2, Martin Bauer2, Dinesh K. Deelchand3, Claudia Buss2,4, Sonja Entringer2,4, Jochen B. Fiebach1, Matthias Endres1,5,6, and Christine Heim2,5
1Center for Stroke Researech Berlin (CSB), Charite Universitätsmedizin Berlin, Berlin, Germany, 2Institute of Medical Psychology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, 3Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, 4Development, Health, and Disease Research Program, University of California, Irvine, Orange, CA, United States, 5Neurocure Cluster of Excellence, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany, 6Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
Synopsis
Keywords: Spectroscopy, Neuro, early-life stress, adverse childhood experiences, age-related, brain metabolites
Motivation: Adversity experienced during early life termed early-life stress (ELS) might increase the risk for neuroinflammation and neurodegenerative disorders in the adult human brain. An enhanced understanding of these relationships will aid in diagnosis and intervention.
Goal(s): Our goal was to investigate whether ELS is associated with changes in brain metabolism by using 1H MR spectroscopy.
Approach: The interaction between metabolite concentrations obtained from MRS, acquired in adult women, age, and scores of ELS was modeled using non-linear statistics.
Results: Higher concentrations with increasing age in individuals exposed to ELS were found for specific metabolites suggesting long-term effects of ELS on the human brain.
Impact: Understanding the role of early-life
stress (ELS) in driving neuroinflammatory processes and identification of
specific biomarkers to assess the risk for accelerated cognitive decline and
neurodegenerative disorders in individuals exposed to ELS will aid in early
identification and targeted interventions.
Introduction
Experience shapes the developing brain, and adversity during sensitive periods of developmental plasticity is termed early-life stress (ELS). Results from preclinical and clinical studies suggest that ELS causes major alterations in neural circuits implicated in emotion regulation and stress adaptation1. ELS induces sensitization of endocrine and autonomic stress responses and hinders the ability to fine-regulate these responses due to glucocorticoid receptor (GR) resistance. Via activation of GR in the hippocampus (HC)2, ELS impairs the structure and function of this brain region that undergoes significant development in early life. A second region of interest is the posterior cingulate gyrus (PCG), a highly connected and metabolically active brain area3 that is also indicated in cognitive impairment4. In addition, it is a central node of the default mode network (DMN)5 known to be affected by stress6. Despite overwhelming and mechanistically informed evidence for an impact of ELS on brain structure and immune activation, it is not known whether systemic inflammation after ELS translates to the adult brain in humans, thereby facilitating neuroinflammatory processes with impact on cognitive decline and age-related brain disease. Thus, the aim of this study was to elucidate whether ELS is associated with changes in brain metabolism in adult women as measured using 1H MR spectrosocopy (MRS).Methods
Seventy-nine adult healthy women (aged 30 – 60 yrs), including subjects with and without exposure to ELS, participated in structured interviews and questionnaires and MRS acquisitions.
ELS Assessment: Exposure to ELS (occurring prior to the onset of menstruation) was ascertained using the Childhood Trauma Questionnaire (CTQ)7 and the Comprehensive Trauma Interview (CTI)8. Five dimensions of childhood maltreatment were assessed by using cut-off values for moderate or severe exposure provided by the CTQ manual. Other stressors were included by counting additional items in the CTI that correspond to Adverse Childhood Experiences (ACE)9. A comprehensive ACE-score ranging from 0 to 11 was created by combining the five childhood maltreatment categories with six stressors.
MRS: Scans were performed on a 3T PrismaFit system (Siemens Healthineers, Erlangen, Germany) using a 64 channel radiofrequency (RF) coil. For 1H MRS, localized RF calibration was performed, and first- and second-order shims were adjusted using FAST(EST)MAP10. Voxel placement was facilitated using high-resolution T1-weighted MPRAGE images. Single volume spectra were acquired from the HC and PCG using the semi-LASER technique11 (TR/TE = 3000/23 ms, spectral width = 2000 Hz, VOIHC = 10x12x35 mm3, number of averages (NA)HC = 256, VOIPCG = 20x20x20 mm3, NAPCG = 128). The FID-A toolkit12 was used for pre-processing of MRS data. Resulting spectra were analyzed using LCModel13 with a simulated basis set.
Statistics: Statistical analyses were conducted using Generalized Additive Models (GAMs)14. GAMs were fitted to the data using tensor product smoothing to model the interaction between age and ACE scores – both as continuous predictors – using the Restricted Maximum Likelihood method. All statistical analyses were run in R Project for Statistical Computing (R Core Team) with the significance level set at p<0.05.Results
Shimming resulted in water linewidths of 8.0 ± 0.8 Hz (HC) and 6.0 ± 0.4 Hz (PCG), respectively. Cases with poor shim in the HC (LWH2O > 10 Hz) were excluded, such that NHC = 71. Analysis of high-quality spectra (Fig. 1) allowed quantification of neurochemical profiles in both regions including several metabolites (Fig. 2). Using GAMs, significant effects were found for myo-inositol (Ins) (p < 0.001), taurine (Tau) (p = 0.007), and glutamine (Gln) (p < 0.001), in the PCG (Fig. 3) indicating higher concentrations with increasing age in individuals exposed to ELS. No significant effects were observed for metabolites in the HC.Discussion
This study investigated whether neurochemical correlates of ELS (assessed by comprehensive ACE-scores) can be identified in the adult human female brain. Metabolite concentrations were successfully modelled by the interaction between age and ACE scores for Ins, Tau, and Gln in the PCG. Note that particularly with increasing age, the impact of ELS on metabolite levels become apparent, suggesting long-term effects of ELS on the human brain. Tau is reported to modulate the action of neurotransmitters15, and Gln is involved in neurotransmitter metabolism via the Gln-Glu cycle16. Hence, results for Tau and Gln might hint at a dysfunctional regulation of excitatory neuron-gliatransmission17. Finally, the finding of elevated Ins, considered a marker of neuroinflammation18, may confirm that neuroinflammatory processes are facilitated by ELS. Examining correlations with other markers of neuroinflammation will be crucial for a more comprehensive understanding of these observations.Conclusion
Exposure to ELS can be linked to age-related alterations in specific brain metabolite concentrations in healthy adult women.Acknowledgements
Funded
by DFG EXC 2049 BrainLab Grant to Professor Christine Heim and Professor
Matthias Endres.References
1. Lupien SJ, McEwen BS, Gunnar MR, Heim C. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat Rev Neurosci 2009;10:434-445.
2. McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med 1998;338:171-179.
3. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain 2014;137:12-32.
4. Talwar P, Kushwaha S, Chaturvedi M, Mahajan V. Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease. Clin Neuroradiol 2021;31:953-967.
5. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676-682.
6. Zhang W, Hashemi MM, Kaldewaij R, et al. Acute stress alters the 'default' brain processing. Neuroimage 2019;189:870-877.
7. Bernstein DP, Fink L, Handelsman L, et al. Initial reliability and validity of a new retrospective measure of child abuse and neglect. Am J Psychiatry 1994;151:1132-1136.
8. Shenk CE, Noll JG, Griffin AM, et al. Psychometric Evaluation of the Comprehensive Trauma Interview PTSD Symptoms Scale Following Exposure to Child Maltreatment. Child Maltreat 2016;21:343-352.
9. Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 1998;14:245-258.
10. Gruetter R, Tkac I. Field mapping without reference scan using asymmetric echo-planar techniques. Magn Reson Med 2000;43:319-323.
11. Scheenen TW, Klomp DW, Wijnen JP, Heerschap A. Short echo time 1H-MRSI of the human brain at 3T with minimal chemical shift displacement errors using adiabatic refocusing pulses. Magn Reson Med 2008;59:1-6.
12. Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J. Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit. Magn Reson Med 2017;77:23-33.
13. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993;30:672-679.
14. Wood SN. Generalized Additive Models: An Introduction with R, Second Edition (2nd ed.) ed. Boca Raton: Chapman and Hall/CRC, 2017.
15. Hardy DL, Norwood TJ. Spectral editing technique for the in vitro and in vivo detection of taurine. J Magn Reson 1998;133:70-78.
16. Rae CD. A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem Res 2014;39:1-36.
17. Westergaard N, Sonnewald U, Schousboe A. Metabolic trafficking between neurons and astrocytes: the glutamate/glutamine cycle revisited. Dev Neurosci 1995;17:203-211.
18. Chang L, Munsaka SM, Kraft-Terry S, Ernst T. Magnetic resonance spectroscopy to assess neuroinflammation and neuropathic pain. J Neuroimmune Pharmacol 2013;8:576-593.