BDNF Val66Met gene polymorphism modulates brain activity following rTMS-induced memory impairment

Kilian Abellaneda-Pérez, Pablo Martin-Trias, Catherine Cassé-Perrot, Lídia Vaqué-Alcázar, Laura Lanteaume, Elisabeth Solana, Claudio Babiloni, Roberta Lizio, Carme Junqué, Núria Bargalló, Paolo Maria Rossini, Joëlle Micallef, Romain Truillet, Estelle Charles, Elisabeth Jouve, Régis Bordet, Joan Santamaria, Simone Rossi, Alvaro Pascual-Leone, Olivier Blin, Jill Richardson, Jorge Jovicich, David Bartrés-Faz, Kilian Abellaneda-Pérez, Pablo Martin-Trias, Catherine Cassé-Perrot, Lídia Vaqué-Alcázar, Laura Lanteaume, Elisabeth Solana, Claudio Babiloni, Roberta Lizio, Carme Junqué, Núria Bargalló, Paolo Maria Rossini, Joëlle Micallef, Romain Truillet, Estelle Charles, Elisabeth Jouve, Régis Bordet, Joan Santamaria, Simone Rossi, Alvaro Pascual-Leone, Olivier Blin, Jill Richardson, Jorge Jovicich, David Bartrés-Faz

Abstract

The BDNF Val66Met gene polymorphism is a relevant factor explaining inter-individual differences to TMS responses in studies of the motor system. However, whether this variant also contributes to TMS-induced memory effects, as well as their underlying brain mechanisms, remains unexplored. In this investigation, we applied rTMS during encoding of a visual memory task either over the left frontal cortex (LFC; experimental condition) or the cranial vertex (control condition). Subsequently, individuals underwent a recognition memory phase during a functional MRI acquisition. We included 43 young volunteers and classified them as 19 Met allele carriers and 24 as Val/Val individuals. The results revealed that rTMS delivered over LFC compared to vertex stimulation resulted in reduced memory performance only amongst Val/Val allele carriers. This genetic group also exhibited greater fMRI brain activity during memory recognition, mainly over frontal regions, which was positively associated with cognitive performance. We concluded that BDNF Val66Met gene polymorphism, known to exert a significant effect on neuroplasticity, modulates the impact of rTMS both at the cognitive as well as at the associated brain networks expression levels. This data provides new insights on the brain mechanisms explaining cognitive inter-individual differences to TMS, and may inform future, more individually-tailored rTMS interventions.

Conflict of interest statement

A.P.-L. serves on the scientific advisory boards for Starlab Neuroscience, Neuroelectrics, Axilum Robotics, Constant Therapy, NovaVision, Cognito, Magstim, Nexstim and Neosync, and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging. The remaining authors declare no competing interests.

© 2022. The Author(s).

Figures

Figure 1
Figure 1
Study design overview. Participants underwent an encoding visual memory task while receiving rTMS over the LFC (MNI coordinates: X = − 42; Y = 10; Z = 30, according to Martin-Trias and colleagues; pointed with a red arrow) or the vertex area (MNI coordinates: X = 4; Y = − 16; Z = 70, from Cz location according to Rojas et al., created for visual purposes, pointed with a yellow arrow). After a 30 min rest, participants completed the recognition of the visual memory task (i.e., discrimination between seen/not seen items in encoding phase) within the MRI scanner. RMT, resting motor threshold; rTMS, repetitive transcranial magnetic stimulation; LFC, left frontal cortex; fMRI, functional magnetic resonance imaging.
Figure 2
Figure 2
TMS effects on cognition as a function of BDNF Val66Met gene polymorphism. Cognitive performance results considering (A) accuracy during encoding as well as (B) hits during recognition. Statistical analyses were performed via one-way repeated measures ANOVAs with experimental conditions as within-subject factor and BDNF Val66Met gene polymorphism as between-subject factor. Subsequent pair-wise analyses were conducted with t-tests. * Significant differences (p < 0.05). LFC, left frontal cortex.
Figure 3
Figure 3
TMS effects on brain activity as a function BDNF Val66Met gene polymorphism and its relationships with cognitive performance. (A,B) fMRI activity maps for the HF > HV contrast at both group differences (Val > Met) and mean Val group. (C,D) Scatter plots showing Pearson correlations between hits difference (i.e., LFC hits—vertex hits) and BOLD signal values within the ROIs displayed in (A,B), only considering the Val group. HF, hits frontal cortex; HV, hits cranial vertex; Diff, difference; LFC, left frontal cortex; BOLD, blood oxygen level dependent.

References

    1. Luber B, Lisanby SH. Enhancement of human cognitive performance using transcranial magnetic stimulation (TMS) Neuroimage. 2014;85:961–970.
    1. Martin-Trias P, et al. Translational challenge models in support of efficacy studies: Neurobehavioral and cognitive changes induced by transcranial magnetic stimulation in healthy volunteers. CNS Neurol. Disord. Drug Targets. 2016;15:802–815.
    1. Paus T. Imaging the brain before, during, and after transcranial magnetic stimulation. Neuropsychologia. 1998;37:219–224.
    1. Abellaneda-Pérez K, et al. Age-related differences in default-mode network connectivity in response to intermittent theta-burst stimulation and its relationships with maintained cognition and brain integrity in healthy aging. Neuroimage. 2019;188:794–806.
    1. Ozdemir RA, et al. Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition. Proc. Natl. Acad. Sci. USA. 2020;117:8115–8125.
    1. Rossi S, et al. Prefontal cortex in long-term memory: an “interference” approach using magnetic stimulation. Nat. Neurosci. 2001;4:948–952.
    1. Rossi S. Age-related functional changes of prefrontal cortex in long-term memory: A repetitive transcranial magnetic stimulation study. J. Neurosci. 2004;24:7939–7944.
    1. Rossi S, et al. Prefrontal and parietal cortex in human episodic memory: An interference study by repetitive transcranial magnetic stimulation. Eur. J. Neurosci. 2006;23:793–800.
    1. Rossi S, et al. Temporal dynamics of memory trace formation in the human prefrontal cortex. Cereb. Cortex. 2010;21:368–373.
    1. Martin-Trias P, et al. Adaptability and reproducibility of a memory disruption rTMS protocol in the PharmaCog IMI European project. Sci. Rep. 2018;8:9371.
    1. Hamada M, Murase N, Hasan A, Balaratnam M, Rothwell JC. The role of interneuron networks in driving human motor cortical plasticity. Cereb. Cortex. 2012;23:1593–1605.
    1. López-Alonso V, Cheeran B, Río-Rodríguez D, Fernández-del-Olmo M. Inter-individual variability in response to non-invasive brain stimulation paradigms. Brain Stimul. 2014;7:372–380.
    1. Nettekoven C, et al. Inter-individual variability in cortical excitability and motor network connectivity following multiple blocks of rTMS. Neuroimage. 2015;118:209–218.
    1. Perellón-Alfonso R, et al. Similar effect of intermittent theta burst and sham stimulation on corticospinal excitability: A 5-day repeated sessions study. Eur. J. Neurosci. 2018;48:1990–2000.
    1. Ridding MC, Ziemann U. Determinants of the induction of cortical plasticity by non-invasive brain stimulation in healthy subjects. J. Physiol. 2010;588:2291–2304.
    1. Goldberg TE, et al. BDNF Val66Met polymorphism significantly affects d′ in verbal recognition memory at short and long delays. Biol. Psychol. 2008;77:20–24.
    1. Hariri AR, et al. Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J. Neurosci. 2003;23:6690–6694.
    1. Egan MF, et al. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell. 2003;112:257–269.
    1. Pezawas L, et al. The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J. Neurosci. 2004;24:10099–10102.
    1. McHughen SA, et al. BDNF val66met polymorphism influences motor system function in the human brain. Cereb. Cortex. 2009;20:1254–1262.
    1. Antal A, et al. Brain-derived neurotrophic factor (BDNF) gene polymorphisms shape cortical plasticity in humans. Brain Stimul. 2010;3:230–237.
    1. Cheeran B, et al. A common polymorphism in the brain-derived neurotrophic factor gene (BDNF) modulates human cortical plasticity and the response to rTMS. J. Physiol. 2008;586:5717–5725.
    1. Cirillo J, Hughes J, Ridding M, Thomas PQ, Semmler JG. Differential modulation of motor cortex excitability in BDNF Met allele carriers following experimentally induced and use-dependent plasticity. Eur. J. Neurosci. 2012;36:2640–2649.
    1. Di Lazzaro V, et al. Val66Met BDNF gene polymorphism influences human motor cortex plasticity in acute stroke. Brain Stimul. 2015;8:92–96.
    1. Jannati A, Block G, Oberman LM, Rotenberg A, Pascual-Leone A. Interindividual variability in response to continuous theta-burst stimulation in healthy adults. Clin. Neurophysiol. 2017;128:2268–2278.
    1. Lee M, et al. Interaction of motor training and intermittent theta burst stimulation in modulating motor cortical plasticity: influence of BDNF Val66Met polymorphism. PLoS ONE. 2013;8:e57690.
    1. Li Voti P, et al. Correlation between cortical plasticity, motor learning and BDNF genotype in healthy subjects. Exp. Brain Res. 2011;212:91–99.
    1. Mastroeni C, et al. Brain-derived neurotrophic factor—a major player in stimulation-induced homeostatic metaplasticity of human motor cortex? PLoS ONE. 2013;8:e57957.
    1. Nakamura K, et al. Quadri-pulse stimulation (QPS) induced LTP/LTD was not affected by Val66Met polymorphism in the brain-derived neurotrophic factor (BDNF) gene. Neurosci. Lett. 2011;487:264–267.
    1. Kowiański P, et al. BDNF: A key factor with multipotent impact on brain signaling and synaptic plasticity. Cell. Mol. Neurobiol. 2017;38:579–593.
    1. Lu B, Nagappan G, Lu Y. Neurotrophic Factors. Springer; 2014. BDNF and synaptic plasticity, cognitive function, and dysfunction; pp. 223–250.
    1. Lu B, Gottschalk W. Progress in Brain Research. Elsevier; 2000. Modulation of hippocampal synaptic transmission and plasticity by neurotrophins; pp. 231–241.
    1. Rossini PM, et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee. Clin. Neurophysiol. 2015;126:1071–1107.
    1. Martin-Trias, P. et al. A study of BOLD reproducibility: visual encoding, memory and resting state. In Organization for Human Brain Mapping, Hamburg, Germany. (2014).
    1. Jasper HH. Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. Suppl. 1958;10:370–375.
    1. Jovicich J, et al. Longitudinal reproducibility of default-mode network connectivity in healthy elderly participants: A multicentric resting-state fMRI study. Neuroimage. 2016;124:442–454.
    1. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782–790.
    1. Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage. 2001;14:1370–1386.
    1. Woolrich MW, Behrens TEJ, Beckmann CF, Jenkinson M, Smith SM. Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage. 2004;21:1732–1747.
    1. Toh YL, Ng T, Tan M, Tan A, Chan A. Impact of brain-derived neurotrophic factor genetic polymorphism on cognition: A systematic review. Brain Behav. 2018;8:e01009.
    1. Kennedy KM, et al. BDNF val66met polymorphism affects aging of multiple types of memory. Brain Res. 2015;1612:104–117.
    1. Yogeetha BS, et al. BDNF and TNF-α polymorphisms in memory. Mol. Biol. Rep. 2013;40:5483–5490.
    1. Santarnecchi E, Rossi S, et al. Advances in the neuroscience of intelligence: from brain connectivity to brain perturbation. Span. J. Psychol. 2016;19:1–7.
    1. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 2002;3:201–215.
    1. Corbetta M, Patel G, Shulman GL. The reorienting system of the human brain: From environment to theory of mind. Neuron. 2008;58:306–324.
    1. Szczepanski SM, Pinsk MA, Douglas MM, Kastner S, Saalmann YB. Functional and structural architecture of the human dorsal frontoparietal attention network. Proc. Natl. Acad. Sci. USA. 2013;110:15806–15811.
    1. Meister MLR, Hennig JA, Huk AC. Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J. Neurosci. 2013;33:2254–2267.
    1. Shah-Basak P, et al. Brain-derived neurotrophic factor polymorphism influences response to single-pulse transcranial magnetic stimulation at rest. Neuromodulation. 2020;24:854–862.
    1. Tulviste J, et al. BDNF polymorphism in non-veridical decision making and differential effects of rTMS. Behav. Brain Res. 2019;364:177–182.
    1. Hashimoto R, et al. Dose-dependent effect of the Val66Met polymorphism of the brain-derived neurotrophic factor gene on memory-related hippocampal activity. Neurosci. Res. 2008;61:360–367.
    1. Chen W, et al. Interaction effects of BDNF and COMT genes on resting-state brain activity and working memory. Front. Hum. Neurosci. 2016;10:540.
    1. Dennis NA, Cabeza R, Need AC, Waters-Metenier S, Goldstein DB, LaBar KS. Brain-derived neurotrophic factor val66met polymorphism and hippocampal activation during episodic encoding and retrieval tasks. Hippocampus. 2011;21:980–989.
    1. Molendijk ML, et al. A systematic review and meta-analysis on the association between BDNF val(66)met and hippocampal volume—A genuine effect or a winners curse? Am. J. Med. Genet. B. Neuropsychiatr. Genet. 2012;159B:731–740.
    1. Spaniol J, et al. Event-related fMRI studies of episodic encoding and retrieval: Meta-analyses using activation likelihood estimation. Neuropsychologia. 2009;47:1765–1779.
    1. Vaqué-Alcázar L, et al. Functional and structural correlates of working memory performance and stability in healthy older adults. Brain Struct. Funct. 2020;225:375–386.
    1. Cabeza R, Anderson ND, Locantore JK, McIntosh AR. Aging gracefully: Compensatory brain activity in high-performing older adults. Neuroimage. 2002;17:1394–1402.
    1. Cabeza R, et al. Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 2018;19:701–710.
    1. Reuter-Lorenz PA, Park DC. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol. Rev. 2014;24:355–370.
    1. Davis SW, Luber B, Murphy DLK, Lisanby SH, Cabeza R. Frequency-specific neuromodulation of local and distant connectivity in aging and episodic memory function. Hum. Brain Mapp. 2017;38:5987–6004.
    1. Cabeza R, Dennis NA. Principles of Frontal Lobe Function. Oxford University Press; 2002. Frontal lobes and aging: deterioration and compensation; pp. 628–652.
    1. Hartwigsen G, Volz LJ. Probing rapid network reorganization of motor and language functions via neuromodulation and neuroimaging. Neuroimage. 2021;224:117449.
    1. Solé-Padullés C, et al. Repetitive transcranial magnetic stimulation effects on brain function and cognition among elders with memory dysfunction. A randomized sham-controlled study. Cereb. Cortex. 2006;16:1487–1493.
    1. Abellaneda‐Pérez, K., Vaqué‐Alcázar, L., Solé‐Padullés, C. & Bartrés‐Faz, D. Combining non‐invasive brain stimulation with functional magnetic resonance imaging to investigate the neural substrates of cognitive aging. J. Neurosci. Res.10.1002/jnr.24514 (2019).
    1. Vidal-Piñeiro D, et al. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul. 2014;7:287–296.
    1. Park C, et al. The BDNF Val66Met polymorphism affects the vulnerability of the brain structural network. Front. Hum. Neurosci. 2017;11:400.
    1. Fjell AM, et al. Relationship between structural and functional connectivity change across the adult lifespan: A longitudinal investigation. Hum. Brain Mapp. 2016;38:561–573.
    1. Cassé-Perrot C, et al. Neurobehavioral and cognitive changes induced by sleep deprivation in healthy volunteers. CNS Neurol. Disord. Drug Targets. 2016;15:777–801.
    1. Rojas GM, et al. Study of resting-state functional connectivity networks using EEG electrodes position as seed. Front. Neurosci. 2018;12:235.

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