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Keywords:

  • BDNF polymorphism;
  • motor cortex;
  • motor skill;
  • transcranial magnetic stimulation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The purpose of this study was to investigate how healthy young subjects with one of three variants of the brain-derived neurotrophic factor (BDNF) gene modulate motor cortex excitability following experimentally induced and use-dependent plasticity interventions. Electromyographic recordings were obtained from the right first dorsal interosseous (FDI) muscle of 12 Val/Val, ten Val/Met and seven Met/Met genotypes (aged 18–39 years). Transcranial magnetic stimulation of the left hemisphere was used to assess changes in FDI motor-evoked potentials (MEPs) following three separate interventions involving paired associative stimulation, a simple ballistic task and complex visuomotor tracking task using the index finger. Val/Val subjects increased FDI MEPs following all interventions (≥ 25%, < 0.01), whereas the Met allele carriers only showed increased MEPs after the simple motor task (≥ 26%, < 0.01). In contrast to the simple motor task, there was no significant change in MEPs for the Val/Met subjects (7%, = 0.50) and a reduction in MEPs for the Met/Met group (−38%, < 0.01) following the complex motor task. Despite these differences in use-dependent plasticity, the performance of both motor tasks was not different between BDNF genotypes. We conclude that modulation of motor cortex excitability is strongly influenced by the BDNF polymorphism, with the greatest differences observed for the complex motor task. We also found unique motor cortex plasticity in the rarest form of the BDNF polymorphism (Met/Met subjects), which may have implications for functional recovery after disease or injury to the nervous system in these individuals.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Brain-derived neurotrophic factor (BDNF) is a growth factor that is highly expressed throughout the central nervous system (CNS; Pearson-Fuhrhop et al., 2009). BDNF is crucial for development but is also important in adulthood, by facilitating long-term potentiation (LTP) and mediating use-dependent plasticity (Schinder & Poo, 2000; Gottmann et al., 2009). In humans, a single nucleotide polymorphism of the BDNF gene (BDNF Val66Met) results in reduced BDNF release in cortical neurons (Egan et al., 2003), which has been associated with altered cortical morphology (Pezawas et al., 2004) and behavioural deficits primarily related to hippocampal functions (Egan et al., 2003). Furthermore, the Met allele has been associated with altered susceptibility to some neurological and psychiatric disorders (Bath & Lee, 2006), and may influence CNS repair and functionally beneficial neuroplasticity after neurological injury (Pearson-Fuhrhop et al., 2009). The BDNF polymorphism is relatively common (30–50% in the general population; Bath & Lee, 2006), indicating that any physiological differences might be used to guide features of therapy following neurological injury in these individuals.

Motor skill learning and recovery from brain injury requires plasticity in many areas of the brain, including primary motor cortex (M1; Sanes & Donoghue, 2000). At a systems level, recent studies with transcranial magnetic stimulation (TMS) have shown reduced experimentally induced and use-dependent M1 plasticity in healthy subjects with the BDNF polymorphism (Kleim et al., 2006; Cheeran et al., 2008). Similar observations were found using neuroimaging techniques, with a greater reduction of brain activation volume in the Met allele carriers after index finger training (McHughen et al., 2010). However, the impact on M1 plasticity is not always consistent, with some protocols failing to reveal differences in M1 plasticity in healthy subjects with different BDNF genotypes (Li Voti et al., 2011; Nakamura et al., 2011). We expect that these inconsistent effects are related to important inter-study differences in the intervention used and its interaction with BDNF to induce M1 plasticity.

The purpose of this study was to examine experimentally induced and use-dependent plasticity in healthy subjects with different BDNF genotypes. Three interventions of similar duration (12–16 min) were used, as BDNF effects are activity-dependent and require at least 6 min of neuronal discharge for BDNF to be released and to modify cellular function (Poo, 2001; Balkowiec & Katz, 2002; Tanaka et al., 2008). Experimentally induced plasticity was performed with paired associative stimulation (PAS), as it shares many physiological properties of synaptic plasticity in animal preparations (Ziemann et al., 2008), with the PAS response shown previously to be influenced by BDNF genotype (Cheeran et al., 2008; Missitzi et al., 2011). Use-dependent plasticity was assessed using two motor tasks: simple ballistic movement and complex visuomotor tracking. These tasks were chosen because they differ in the amount of skill required to achieve the task, with complex motor tasks more likely to modify the expression of BDNF in M1 (Klintsova et al., 2004). We therefore expect greater differences in M1 plasticity between different BDNF genotypes for the complex visuomotor tracking task compared with that induced by the simple ballistic task and PAS.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Experiments were performed on the right hand of 29 subjects (12 women, 17 men; mean ± SD age 24 ± 4 years; range 18–39 years) with no known history of peripheral or neurological impairment. All subjects were right handed (median Laterality Quotient = 0.83 ± 0.18, range 0.5–1.0) as assessed by the Edinburgh Handedness Questionnaire (Oldfield, 1971). No subjects reported long-term specialized use of the hands, such as playing a musical instrument, as this may influence cortical plasticity (Rosenkranz et al., 2007). All experiments were performed in the afternoon or evening to minimize variations in circulating cortisol and its effect on plasticity induction (Sale et al., 2008). A minimum visual capability (with corrective lenses if required) of 20/40 vision assessed using a Snellen Eye Chart was required for participation. Subjects also completed the long version of the International Physical Activity Questionnaire (IPAQ), consisting of 31 items describing the extent of leisure time physical activity involving aerobic exercises such as running, cycling and walking (Craig et al., 2003; Fogelholm et al., 2006). All subjects gave written informed consent prior to participation in the study, which was approved by the University of Adelaide Human Research Ethics Committee and was conducted in accordance to the standards established by the Declaration of Helsinki. Following informed consent, subjects provided a buccal swab for genotyping of the BDNF Val66Met polymorphism (see below). Initially 27 subjects were recruited and provided a buccal swab sample. From this pool, 12 subjects were identified with the Val/Val genotype, ten with the Val/Met genotype and five with the Met/Met genotype. Two additional subjects who were identified as having the Met/Met genotype from our other ongoing studies were recruited to selectively increase the sample size in this rare population. These subjects provided a buccal sample to verify that they possessed the BDNF Met/Met genotype. Therefore, a total of seven BDNF Met/Met subjects participated in this study. Experimenters were blind to the BDNF genotype of all subjects, with the exception of the last two BDNF Met/Met subjects.

Genotyping

Genomic DNA was prepared from buccal swabs using an Isohelix DNA Isolation Kit (Cell Projects, Kent, UK). PCR was performed to amplify a 197-bp product with the Val66Met polymorphism, when present, located at 73 bp. Primers (ACTCTGGAGAGCGTGAATGG/AGAAGAGGAGGCTCCAAAGG) were designed using Primer 3 software (Rozen & Skaletsky, 2000). PCR reaction conditions were: denaturation at 95 °C for 2 min, 35 cycles of 95 °C for 15 s, 60 °C for 15 s and 72 °C for 30 s, with a final extension at 72 °C for 5 min. PCR products were purified using a Qiaquick PCR Purification Kit (Qiagen) and digested with Eco721. Restriction digests were resolved on a 2% agarose gel and because the Val66Met polymorphism destroys the Eco721 site, the samples could be classified as Val/Val, Val/Met or Met/Met based on the observed banding pattern. Control digests using Taq1 were performed to ensure purified DNA was in a digestible form. Every sample was genotyped from two independent PCR reactions to ensure fidelity.

Experimental arrangement

Each subject participated in three experimental sessions, each involving a different intervention (see below), separated by at least 24 h with the order of intervention selected randomly. For each experiment, subjects were seated comfortably with their right shoulder abducted approximately 45° to allow the hand and arm to rest on a manipulandum, with the forearm pronated and the palm facing down. Surface electromyography (EMG) was recorded from the first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles throughout the experiment using bipolar surface electrodes (Ag-AgCl, 8 mm diameter) placed approximately 2 cm apart with a muscle belly-tendon montage. A grounding strap placed around the wrist was used as a common reference for all electrodes. The EMG signals were amplified (× 100–1000), bandpass filtered (high pass at 13 Hz, low pass at 1000 Hz), digitized online at 2 kHz with a CED interface system (Cambridge Electronic Design Co. Ltd, Cambridge, UK) and recorded onto computer for offline analysis. The EMG signals of both muscles were displayed on an oscilloscope to assist the subject in maintaining EMG silence when required.

Experimental procedures

The timeline for the experimental procedures are shown in Fig. 1. All measurements were the same in each of the three sessions.

image

Figure 1.  Description of experimental procedures and examples of motor performance before and after training. (A) Schematic representation of the experimental protocol with measures obtained before and after each intervention. Baseline measures include assessment of maximum voluntary contraction (MVC), maximal compound muscle action potential (M-wave) and resting motor threshold (RMT). Motor evoked potentials (MEPs) at approximately 10%Mmax for FDI at rest were obtained from 15 single-pulse transcranial magnetic stimulation (TMS) trials. The intervention consisted of experimentally induced plasticity with paired associative stimulation, or an assessment of use-dependent plasticity by examining changes in maximal index finger abduction acceleration (IFAAcc) or visuomotor (VM) tracking performance. (B) Example of maximal index finger abduction in one subject at the start and end of training. (C) Example of visuomotor tracking performance in the same subject (Met/Met) at the start and end of the visuomotor tracking task.

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Maximal compound muscle action potential

Supramaximal electrical stimulation was administered to the ulnar nerve at the wrist using a constant-current stimulator (DS7AH, Digitimer, UK) and bipolar surface electrodes (separated by 20 mm) with the cathode distal. Stimuli were square wave pulses with 100-μs pulse duration. Stimulator intensity was set at 120% of that required to elicit a maximal compound muscle action potential (Mmax) from FDI. Five stimuli were delivered before and after the intervention.

Transcranial magnetic stimulation

TMS was applied using a figure-of-eight coil (external wing diameter 90 mm) with two Magstim 2002 magnetic stimulators connected to a Magstim Bistim Module (Magstim, Whitland, Dyfed, UK). The coil was held tangentially to the skull with the handle pointing backwards and laterally at an angle of 45° to the sagittal plane. With this coil orientation, current flow within the cortex was induced in a posterior–anterior direction. The coil was placed at the optimal scalp position over the left hemisphere for eliciting a motor-evoked potential (MEP) in the relaxed right FDI muscle. The optimal position was then marked on the scalp with a pen for reference. TMS was delivered at 0.2 Hz for all conditions (unless stated otherwise) and optimal coil position was continually monitored by checking scalp position throughout the experiment.

Threshold

Resting motor threshold (RMT) was determined as the minimum stimulus intensity required to elicit an MEP in the relaxed FDI of at least 50 μV in amplitude in three out of five consecutive trials. RMT was expressed relative to maximum stimulator output (MSO) and the stimulus intensity was altered in 1% increments of MSO throughout this process until the appropriate threshold level was achieved.

Test intensity

The stimulus intensity that produced an MEP of approximately 10%Mmax during rest in FDI was determined before training. Using this TMS intensity, 15 trials were recorded to investigate MEP amplitude before and after each intervention.

Interventions

Three different protocols to induce plasticity, one experimentally induced with TMS and two that were based on motor training interventions (use-dependent), were tested. PAS was selected to experimentally induce plasticity in cortical circuits (Stefan et al., 2000), with previous studies showing changes induced by PAS may be different in Val/Val and Val/Met subjects (Cheeran et al., 2008; Missitzi et al., 2011). For the motor training interventions, a simple index finger abduction task and a more complex visuomotor tracking task were used to compare use-dependent plasticity and motor learning between different BDNF genotypes. The different interventions used lasted for a similar duration (12–16 min).

Paired associative stimulation

PAS was performed as described by Rosenkranz et al. (2007). The PAS protocol consisted of percutaneous electrical stimulation of the ulnar nerve at the right wrist (300% of perceptual threshold) followed by suprathreshold TMS (10%Mmax) 25 ms later over the optimal scalp position for the index finger of the left motor cortex. The interstimulus interval of 25 ms between the peripheral and TMS pulse has previously been shown to induce an LTP-like MEP increase (Stefan et al., 2000, 2002). The intervention consisted of 200 paired stimuli delivered at 0.25 Hz. Electrical stimuli were applied to the ulnar nerve at the wrist using a constant-current stimulator (DS7A stimulator, Digitimer Co. Ltd, Hertfordshire, UK) with bipolar surface electrodes, separated by 30 mm, and with the cathode proximal. Stimuli were square wave pulses with a pulse width of 200 μs.

The attentional focus of the subject has been shown to be an important factor influencing PAS effectiveness (Stefan et al., 2004). To quantify this, subjects received a total of 80 intermittent weak (200% perceptual threshold) electrical stimuli to their right index finger via ring electrodes (Stefan et al., 2004). Subjects were instructed to count and report the number of index finger stimuli they received. The level of attention was then assessed as the absolute error in the number of stimuli counted during PAS. The index finger stimulus was always delivered at the mid-point of the interval between successive paired stimuli in the PAS protocol.

Index finger abduction acceleration

The simple motor training task was similar to that described previously (Rogasch et al., 2009; Cirillo et al., 2010), requiring the subject to maximize peak index finger abduction acceleration (IFAAcc) during ballistic movement of the right index finger (see Fig. 1B). Subjects sat with their forearm placed in a custom designed splint and their arm abducted at the shoulder and bent at approximately 90° at the elbow. The thumb was restricted and all other digits (other than the index finger) were immobilized. This allowed the index finger to move freely for abduction–adduction movements. IFAAcc consisted of 150 ballistic index finger abduction movements paced at 0.5 Hz by an audible tone from a metronome. Subjects rested their index finger for 30 s after ten trials to avoid fatigue. A bi-axial accelerometer (sensitivity ± 6 g, LIS3L06AL, STMicroelectronics, Switzerland) placed over the interphalangeal joint of the index finger was used to assess index finger acceleration in the abduction–adduction and flexion–extension planes. Index finger acceleration > +3 m/s2 in the abduction–adduction plane triggered a recording sweep of ± 500 ms, and each movement recorded acceleration data in the abduction–adduction and flexion–extension planes. Continual verbal encouragement and visual feedback of IFAAcc displayed on a computer screen was provided to the subject throughout training to improve and maximize index finger acceleration. Acceleration signals were digitized online at 2 kHz using a CED interface system and recorded on computer for offline analysis.

Visuomotor tracking

A visuomotor tracking task was used as a more complex motor training task requiring an increased attentional demand to achieve accurate task performance. The experimental arrangement for this task has been described previously (Todd et al., 2009; Cirillo et al., 2011). Briefly, a potentiometer was attached to the right index finger at the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints. The task required subjects to match, as accurately as possible, MCP joint angle of the index finger with a moving target on a computer screen (for example see Fig. 1C). The moving target consisted of 18 unique 10-s frames that moved automatically down the screen while making unpredictable left and right movements. A left movement of the target corresponded to abduction of the index finger and a right movement of the target corresponded to adduction. The maximum MCP joint angle movement was ± 10° from neutral. Training consisted of two 6-min blocks where each frame was repeated twice and the order of the frames was the same for each block. To avoid fatigue, there was a 4-min rest period between blocks. Visual feedback of the MCP joint angle relative to the target was provided to subjects along with continual verbal instructions to follow the moving target as closely as possible. Target and tracking lines were amplified (× 3000), digitized online at 2 kHz and recorded onto computer for offline analysis.

Data analysis

All MEP and Mmax trials that contained pre-stimulus EMG activity (100 ms before stimulation) during rest conditions were discarded from the analysis, and repeated at the appropriate intensity following the data block. MEP and Mmax amplitudes (expressed in mV) were measured peak-to-peak in each individual trial. The MEP size was then expressed as a percentage of the peak-to-peak amplitude of Mmax.

For each IFAAcc movement trial, a baseline period from 400 to 200 ms before abduction acceleration was used to calculate the mean baseline acceleration in the abduction–adduction and flexion–extension planes. The mean acceleration over this baseline period was subtracted so that baseline acceleration equalled 0 m/s2 in both planes. Peak IFAAcc magnitude was then assessed, along with flexion–extension acceleration magnitude at the time of peak abduction acceleration. Abduction acceleration for the 150 ballistic index finger abduction movements was subdivided into blocks of 50 trials representing a start, middle and end (three blocks of 50 trials in total) for detailed analysis.

Performance during the visuomotor tracking task was assessed for each individual trial over the entire training block. For each block, the maximum cross-correlation coefficient and the lag time between the actual finger position and the target were calculated (for details see Cirillo et al., 2011). Tracking error was calculated by subtracting the MCP joint angle (finger position) from the target line, with the mean absolute tracking error for each training block reported.

Statistical analysis

A one-way anova was used to examine the effect of BDNF genotype (Val/Val, Val/Met, Met/Met: between-subject factor) on subject characteristics: Mmax amplitude, RMT, test TMS intensity (before training), attention during PAS, physical activity (IPAQ) and cognitive mental state (mini-mental state examination, MMSE). A Kruskal–Wallis one-way anova was used to compare non-parametric handedness scores between genotypes. Two-way repeated-measures anova was used to examine the effect of genotype and training (Blocks 1, 2 and 3: within-subject factor) on maximum IFAAcc, visuomotor tracking error, maximum cross-correlation coefficient and lag time. Measures of Mmax amplitude and TMS were separately analysed into experimentally induced (PAS) and motor training (IFAAcc and visuomotor tracking) interventions. Two-way repeated-measures anova was used to analyse the effect of genotype and time (before, after: within-subject factor) on Mmax amplitude, RMT, test MEP amplitude and PAS. Three-way repeated-measures anova was used to analyse the effect of genotype, time and task (IFAAcc and visuomotor tracking: within-subject factor) on Mmax amplitude, RMT and test MEP amplitude. A Fisher’s LSD post-hoc test that performed all possible comparisons was used to analyse significant main effects and interactions. The significance level was set at < 0.05 for all comparisons and all group data are provided as mean ± SEM.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The subject characteristics before training for each BDNF genotype are provided in Table 1. There was no significant difference between the BDNF Val/Val, Val/Met and Met/Met genotypes in their physical characteristics (handedness, physical activity levels), electrically induced muscle responses (M-waves) or baseline responses to TMS (RMT and 10% maximal M-wave test intensity). However, there was a tendency for RMT to be approximately 10% of maximal stimulator output higher in Met/Met subjects compared with the other two genotypes.

Table 1.   Group characteristics for different BDNF genotypes
 Val/Val (12)Val/Met (10)Met/Met (7) P-value
  1. Values are mean (SD). LQ, Laterality Quotient; IPAQ, International Physical Activity Questionnaire; RMT, FDI resting motor threshold; MSO, maximum stimulator output.

Handedness (LQ)0.85 (0.19)0.82 (0.17)0.82 (0.22)0.60
Physical activity (IPAQ)3689 (4540)4396 (4037)2821 (2935)0.86
Gender6 M, 6 F6 M, 4 F4 M, 3 F 
M-wave (mV)16.2 (4.0)17.5 (4.2)19.7 (4.4)0.23
RMT (% MSO)40.8 (8.0)41.5 (8.5)50.4 (11.0)0.07
Test intensity (% MSO)61.9 (19.1)64.2 (18.5)71.5 (20.6)0.58

There was a significant difference between muscles (FDI, ADM; F1,26 = 20.30, < 0.001) for all data examining maximal M-waves. Therefore, maximal M-wave data were averaged for interventions (PAS, simple ballistic, complex visuomotor; F2,52 = 0.21, = 0.81) and time (before, after; F1,26 = 0.51, = 0.48) with analysis focused on genotype effects separated between muscles. Maximal M-waves did not significantly differ between BDNF genotypes for FDI (genotype effect, F2,26 = 1.58, = 0.23) or ADM (genotype effect, F2,26 = 1.58, = 0.22).

For all data examining MEP amplitudes, there was a significant difference between interventions (PAS, simple ballistic, complex visuomotor, F2,52 = 3.35, = 0.04) and muscles (FDI, ADM; F1,26 = 73.96, < 0.001). Therefore, all subsequent analyses of MEP amplitudes focused on the Genotype and Time effects, which were separated between interventions and muscles.

PAS and BDNF genotype

Figure 2A shows averaged MEP recordings in relaxed FDI from one Val/Val and one Met/Met subject before and after PAS. Both subjects participated in experiments in the afternoon and had similar baseline characteristics of handedness (LQ: Val/Val = 0.8, Met/Met = 1), physical activity (IPAQ: Val/Val = 2183 MET-min, Met/Met = 1575 MET-min), maximal M-wave amplitude (Val/Val = 19.2 mV, Met/Met = 17.4 mV), RMT (Val/Val = 45% MSO, Met/Met = 46% MSO) and test TMS intensity (Val/Val = 72% MSO, Met/Met = 74% MSO). The original recordings from FDI in Fig. 2A show that there was a large MEP increase following PAS in the Val/Val subject (45% increase), whereas there was no change in MEP amplitude after PAS for the Met/Met subject (6% increase).

image

Figure 2.  Changes in MEPs before and after PAS in healthy subjects with different BDNF genotypes. (A) Mean MEP in resting FDI from one Val/Val (upper panel) and one Met/Met (lower panel) subject before (left) and after (right) PAS. (B and C) Mean MEPs at rest in 12 Val/Val, ten Val/Met and seven Met/Met subjects for the FDI and ADM muscles. Baseline MEPs were obtained at approximately 10% of maximal M-wave for the target FDI muscle. *< 0.05 compared with Before; < 0.05 compared with Val/Met and Met/Met genotypes.

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Figure 2B and C show the mean MEP responses before and after PAS in FDI (target) and ADM (control) muscles in subjects with different BDNF genotypes. Although there were no main effects between groups (Genotype effect, F2,26 = 0.79, P = 0.46) and following PAS (Time effect, F1,26 = 2.32, = 0.14), there was a significant group × time interaction (F2,26 = 4.87, < 0.02). Post-hoc analysis indicated that the FDI MEP amplitude was significantly greater in the Val/Val subjects after PAS (= 0.003), but there was no effect of PAS in the Val/Met (= 0.79) or Met/Met subjects (= 0.81). For the non-target ADM muscle, there was a significant increase in MEP amplitude for all subjects combined (Time effect, F1,26 = 6.29, = 0.02), but no difference between groups (Genotype effect, F2,26 = 0.19, = 0.83) and no group × time interaction (F2,26 = 1.30, = 0.29).

The level of attention during PAS was assessed as the absolute error in the number of attention stimuli (total of 80) counted during PAS. The mean error score was not significantly different between Val/Val (2.7 ± 0.7), Val/Met (2.5 ± 0.7) and Met/Met (2.9 ± 0.9) subjects (Genotype effect, F2,26 = 0.05, = 0.95). Linear regression analysis indicated that there was no significant correlation between FDI MEP facilitation induced by PAS and attention-related errors (r2 < 0.01, = 0.80). Furthermore, RMT for FDI did not significantly change following PAS (Time effect, F1,26 = 6.75, = 0.07) and remained similar between BDNF genotypes (genotype × time interaction, F2,26 = 0.46, = 0.64).

Motor performance and motor learning in different BDNF genotypes

Motor performance was assessed during a simple ballistic index finger task and a complex visuomotor tracking task in all subjects, and these data are shown in Fig. 3. For the simple ballistic task (Fig. 3A), there was a significant increase in peak IFAAcc throughout the training period (Time effect, F2,52 = 30.22, < 0.001), but there was no difference between BDNF genotypes (F2,26 = 0.40, = 0.68) and no genotype × time interaction (F4,52 = 0.84, = 0.51). Furthermore, the improvement in motor performance (i.e. motor learning) from the start (first minute of block 1) to the end (block 3) of training was similar between the different BDNF genotypes (Val/Val, 131%; Val/Met, 138%; Met/Met, 138%). For the complex visuomotor task (Fig. 3B), there was a decrease in visuomotor tacking error (improved performance) across training blocks (Time effect, F1,26 = 111.88, < 0.001), but this was not different between BDNF genotypes (Genotype effect, F2,26 = 0.95, = 0.40) and there was no genotype × time interaction (F2,26 = 1.12, = 0.34). The improvement in motor performance (block 2 performance normalized to the first minute of block 1) was similar for all BDNF genotypes (Val/Val, 11%; Val/Met, 11%; Met/Met, 13%). For this task, additional assessments of tracking accuracy were obtained by calculating the cross-correlation coefficient between the target line and index finger position, and identifying the maximum cross-correlation coefficient and its associated lag time (Table 2). Although there were significant improvements with training in maximum cross-correlation coefficient (Time effect, F1,26 = 27.51, < 0.001) and lag time (F1,26 = 23.10, < 0.001), these measures were not influenced by BDNF genotype.

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Figure 3.  Assessment of motor performance for simple ballistic (A) and complex visuomotor tasks (B) in different BDNF genotypes. (A) Group changes in peak index finger abduction acceleration (IFAAcc) for all three training blocks. (B) Visuomotor tracking error for all two training blocks. *< 0.05 compared with Block 1; #< 0.05 compared with Block 2.

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Table 2.   Group visuomotor tracking data for different BDNF genotypes
GenotypeMaximum cross-correlation (ρ)Lag time (ms)
Block 1Block 2Block 1Block 2
  1. Values are mean (SD).

Val/Val0.88 (0.07)0.91 (0.06)228 (46)208 (42)
Val/Met0.87 (0.11)0.90 (0.07)234 (57)210 (50)
Met/Met0.85 (0.08)0.91 (0.05)261 (19)242 (23)

Use-dependent plasticity in different BDNF genotypes

The change in FDI MEP amplitude after motor training was used as a marker of use-dependent plasticity. Figure 4 shows mean resting FDI MEP amplitude before and after a simple ballistic (Fig. 4A) and complex visuomotor (Fig. 4B) task in healthy subjects with different BDNF genotypes. For the simple ballistic task, there was an increase in MEP amplitude in all BDNF genotypes after training (Time effect, F1,26 = 20.77, = 0.001), with a 38% increase in Val/Val subjects, a 26% increase in Val/Met subjects and a 39% increase in Met/Met subjects. There was no significant difference between genotypes (F2,26 = 0.53, = 0.6) and no genotype × time interaction (F2,26 = 0.15, = 0.9). In contrast, for the complex visuomotor tracking task there were clear differences in MEP amplitude between genotypes (F2,26 = 6.42, = 0.005) and a significant genotype × time interaction (F1,26 = 9.77, < 0.001). Post-hoc analysis showed that there was a 26% increase in MEP amplitude after training in the Val/Val group (= 0.008), no change in MEP in the Val/Met group (= 0.5) and a 38% decrease in MEP amplitude in the Met/Met group (= 0.002).

image

Figure 4.  Resting FDI MEP amplitude in different BDNF genotypes obtained before and after simple ballistic (A) and complex visuomotor training (B). Baseline MEP amplitudes were approximately 10% of maximal M-wave before training in each subject. *< 0.05 compared with before training; < 0.05 compared with Val/Met and Met/Met genotypes; §< 0.05 compared with Val/Val and Val/Met genotypes.

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Changes in MEP amplitude before and after motor tasks were also assessed for the control ADM muscle. As expected, there were no genotype-related differences in MEP amplitude for the ADM muscle under any condition (IFAAcc: F2,26 = 0.36, = 0.70; visuomotor: F2,26 = 0.77, = 0.47) and no genotype × time interactions (IFAAcc: F2,26 = 0.25, = 0.78; visuomotor: F2,26 = 1.23, = 0.31). However, there was a significant increase in ADM MEP amplitude after the simple ballistic task (F1,26 = 4.91, = 0.04), but no change after the complex visuomotor task.

Using linear regression of data from individual subjects, we examined whether the training-related changes in excitability (MEP amplitude) in relaxed FDI were associated with the extent of motor learning (improvement in motor task performance). For the simple ballistic task, there was no association between the change in MEP amplitude and motor learning for Val/Val (r2 = 0.03, = 0.62), Val/Met (r2 = 0.06, = 0.50) or Met/Met (r2 = 0.02, = 0.78) subjects. Similarly, there was no association between the change in MEP amplitude and motor learning for Val/Val (r2 < 0.01, = 0.81), Val/Met (r2 = 0.03, = 0.63) or Met/Met (r2 < 0.01, = 0.96) subjects with the complex visuomotor task. We also correlated PAS-induced changes with behavioural gains in the motor tasks. For the different BDNF genotypes, there was no association between the change in PAS MEP amplitude and motor learning of the simple ballistic task (Val/Val, r2 = 0.03, P = 0.62; Val/Met, r2 = 0.11, = 0.34; Met/Met, r2 = 0.02, = 0.78) or the complex visuomotor task (Val/Val, r2 < 0.01, P = 0.94; Val/Met, r2 = 0.17, = 0.24; Met/Met, r2 = 0.08, = 0.53).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The present study investigated how healthy subjects with one of three different BDNF genotypes modulate M1 excitability following experimentally induced (PAS) and use-dependent (simple ballistic movement and complex visuomotor tracking) plasticity interventions. The main finding of this study was that the modulation of motor cortex excitability in healthy subjects with different BDNF genotypes is dependent on the intervention used, with Val/Val subjects showing increased MEPs for all interventions, whereas the Met allele carriers only showed increased MEPs after the simple ballistic task. Furthermore, we provide new evidence showing that M1 plasticity in the rare Met homozygotes (Met/Met) differs from Met heterozygotes (Val/Met) following complex visuomotor training.

In the present study, the different subject groups were well matched for gender (Inghilleri et al., 2004), hand dominance (Cirillo et al., 2010) and physical activity levels (Cirillo et al., 2009), so these factors are unlikely to contribute to differences in experimentally induced and use-dependent plasticity between BDNF genotypes (see Ridding & Ziemann, 2010). Furthermore, all studies were conducted in the afternoon, which removes any influence of the circadian rhythm on the assessment of cortical plasticity with PAS (Sale et al., 2007). However, there was a tendency for RMT to be approximately 10% MSO greater in Met/Met carriers compared with Val/Met and Val/Val subjects. This is in contrast to a previous study, which found no difference in RMT between the three different BDNF genotype groups (Kleim et al., 2006). Testing additional BDNF Met/Met subjects to improve statistical power may help to determine whether corticospinal excitability is altered in BDNF Met/Met subjects.

In animal models, BDNF has been shown to play an important role in the survival, health and functioning of glutamatergic neurons (Mattson, 2008), and is crucial in modulating synaptic plasticity by LTP and long-term depression (LTD) (Patterson et al., 1996; Ikegaya et al., 2002; Gottmann et al., 2009). However, the role of BDNF in modulating human cortical plasticity is less well established, presumably because it is not possible to obtain measures of cortical BDNF in humans. As an alternative, we are able to use TMS to examine the role of BDNF on motor cortex plasticity by comparing healthy subjects with different BDNF genotypes, as it is known that there is an 18% (Val/Met subjects) to 30% (Met/Met subjects) reduction in activity-dependent secretion of cortical BDNF in Met allele carriers (Egan et al., 2003). Along with abnormal cortical morphology and hippocampal function (Egan et al., 2003; Pezawas et al., 2004), more recent studies have suggested that the BDNF Met allele also contributes to impaired M1 plasticity (Kleim et al., 2006; Cheeran et al., 2008; McHughen et al., 2010), although this is not always a consistent finding (Li Voti et al., 2011; Nakamura et al., 2011). In the present study, we examined M1 plasticity in the same subjects using three different plasticity-inducing protocols of similar duration. We found that M1 plasticity was evident in Val/Val subjects for all tasks, whereas the Met allele carriers only showed M1 plasticity after the simple ballistic task. We therefore suggest that BDNF is important for human M1 plasticity induction, but the magnitude of the effect is dependent on the intervention used.

PAS response in different BDNF genotypes

PAS is an experimental technique in humans that has been deliberately adapted from similar protocols used in brain slices and neuronal cultures, which demonstrate bidirectional spike-timing-dependent plasticity (Dan & Poo, 2004; Caporale & Dan, 2008). The long-lasting (< 60 min) increase in MEP amplitude that is typically observed after PAS is thought to occur through LTP-like mechanisms (Stefan et al., 2000, 2002) in cortical circuits (Di Lazzaro et al., 2009), although spinal circuits may also be affected (Meunier et al., 2007). Only one previous study has investigated the effect of the BDNF polymorphism on PAS-induced plasticity. From nine Val/Val and nine Met allele carriers, Cheeran et al. (2008) reported an increase in MEPs in the homotypic target (median nerve innervated abductor pollicis brevis) muscle after PAS that just failed to reach statistical significance (= 0.07), although there was a significant increase in MEPs in the heterotopic (ulnar innervated) ADM muscle. Using a larger sample size, we provide additional support for a BDNF-dependent effect on the change in MEPs after PAS, showing a significant increase in MEPs in Val/Val subjects but no change in Val/Met subjects (Fig. 2). Furthermore, we have extended this observation by examining the ‘dose’ effect of BDNF on PAS-induced plasticity by comparing the responses in three different BDNF genotypes that are known to differ in their activity-dependent release of BDNF from cortical neurons (Egan et al., 2003). With this comparison we show that there is no change in MEPs after PAS in BDNF Met/Met carriers, with similar responses in BDNF Val/Met individuals. We can therefore conclude that BDNF plays an important role in PAS-induced plasticity in humans, but the effect is not directly related to the putative reduction in activity-dependent BDNF release from cortical neurons in Met allele carriers.

BDNF genotype and use-dependent plasticity

Along with experimentally induced plasticity, the BDNF polymorphism has been shown to dramatically influence use-dependent plasticity in human M1. For example, subjects with the BDNF (Val/Met) polymorphism exhibit less motor map reorganization and reduced changes in M1 excitability following training on several motor tasks (Kleim et al., 2006), although this deficit can be overcome with intense training over multiple days (McHughen et al., 2011). Neuroimaging techniques also support reduced short-term plasticity in BDNF Val/Met subjects, with a greater reduction in brain activation volume in the Met allele carriers after index finger training (McHughen et al., 2010). We sought to further explore these training-dependent effects by examining the change in MEPs after simple ballistic and complex visuomotor tracking movements in the three different BDNF genotypes. Interestingly, we found striking differences in the extent of use-dependent plasticity between the two motor tasks in BDNF Met allele carriers (Fig. 4). For the simple ballistic task, there was an increase in MEPs in all subjects that was independent of BDNF genotype. In contrast, the complex visuomotor tracking task resulted in no change in MEPs for the BDNF Val/Met subjects and a reduction in MEPs for the BDNF Met/Met group. These divergent findings between tasks may result from the greater task demands during visuomotor tracking, which requires the integration of attentional, memory, visual and motor systems for accurate task performance. In contrast, the simple ballistic task relies less on sensory feedback and more on feedforward mechanisms for optimizing the motor command to produce a rapid movement in the desired direction. This task-related difference is also in line with a recent report showing that cortical mechanisms are likely to make a greater contribution to use-dependent plasticity during the complex visuomotor task compared with the ballistic task (Giesebrecht et al., 2012). It is known from animal studies that complex motor tasks are more likely to modify the expression of BDNF in M1 (Klintsova et al., 2004), and that activity-dependent increases in BDNF are observed in multiple brain regions and not restricted to the primary motor cortex (Ploughman et al., 2005), suggesting that differences in activity-dependent BDNF release from cortical neurons (Egan et al., 2003) may contribute to the BDNF genotype differences in use-dependent plasticity during the complex motor task. Furthermore, it is unclear at this stage why the MEPs were reduced in the BDNF Met/Met subjects after complex visuomotor tracking. However, this may reflect LTD-like processes in the synapses involved (Bütefisch et al., 2000), with some studies showing that a shortage or blockade of BDNF increases susceptibility to LTD (Kinoshita et al., 1999; Kumura et al., 2000). Nonetheless, given that Met/Met subjects demonstrate the capacity for increased motor cortex plasticity after the ballistic training task, this effect is unlikely to be due to a lifetime of exposure to reduced BDNF secretion from cortical neurons in these subjects, but is more likely related to the task-specific requirements of complex visuomotor performance.

Motor cortex plasticity and motor learning

Motor skill learning can be considered the acquisition of new patterns of muscle activation in time and space to improve performance of a motor task (Sanes & Donoghue, 2000). Motor learning consists of several phases, with the initial phases involving mechanisms of synaptic plasticity such as LTP and LTD (Karni et al., 1998). This use-dependent plasticity plays a beneficial role in functional recovery from CNS injury and motor learning (Nudo et al., 1996). Several studies in M1 have shown that the mechanisms of motor skill learning all depend on protein synthesis, in which BDNF seems to play a major role (Kleim et al., 2003; see Adkins et al., 2006). LTP, LTD and activity-dependent increases in BDNF are all seen in M1, which is considered a crucial site for motor learning (Sanes & Donoghue, 2000; Klintsova et al., 2004). Furthermore, LTP is BDNF-dependent (Patterson et al., 1996) and inhibition of BDNF in rat M1 disrupts motor reorganization and impairs skilled motor performance (see Adkins et al., 2006). In humans, several lines of evidence using TMS also provide strong support that motor learning induces plasticity in M1 (e.g. Classen et al., 1998; Muellbacher et al., 2002).

Despite obvious differences in motor cortex plasticity between different BDNF genotypes, the functional significance of this remains unclear. The findings on motor behaviour from previous studies have been inconsistent, with one study showing reduced short-term (single session) motor performance (McHughen et al., 2010), whereas most studies have found no difference in motor performance between different BDNF genotypes (Kleim et al., 2006; Fritsch et al., 2010; Li Voti et al., 2011; McHughen et al., 2011). One factor that we thought might contribute to these disparate findings is the type of task used to assess motor performance. However, we found no difference in short-term motor performance or learning between different BDNF genotypes in either the simple ballistic or the complex visuomotor tracking task (Fig. 3 and Table 2). Given that the BDNF polymorphism is known to be associated with cognitive deficits (Egan et al., 2003) and anxiety-related behaviours (Hashimoto, 2007), it is possible that performance differences between BDNF genotypes are only observed during cognitively demanding tasks that act to heighten anxiety levels, such as in the driving-based simulation used by McHughen et al. (2010). Furthermore, the reduced use-dependent plasticity in Met allele carriers may be more closely related to deficits in the consolidation and retention of motor skills that have been detected over multiple training sessions (Fritsch et al., 2010; McHughen et al., 2010), which are likely to be more relevant to situations involving the recovery of motor skills after neurological injury, such as in stroke (Siironen et al., 2007).

In conclusion, we have used TMS to examine the role of BDNF in promoting experimentally induced and use-dependent motor cortex plasticity in healthy subjects with different BDNF genotypes. We found that the modulation of motor cortex excitability was strongly influenced by the BDNF polymorphism, but the effect was dependent on the intervention used. Although motor cortex excitability was increased after all tasks in BDNF Val/Val subjects, this increase was only observed after simple ballistic training of the index finger in BDNF Met allele carriers. Interestingly, we also found unique plasticity in the rare Met/Met subjects, who showed an increase in MEPs after the simple ballistic task, no change after PAS and a reduction in MEPs after complex visuomotor tracking. However, these differences in use-dependent plasticity between BDNF genotypes did not affect short-term motor performance and learning for either task. This lack of effect on motor performance and learning, particularly in Met/Met subjects, may have occurred because the experiments were performed on healthy young subjects, who are likely to have multiple mechanisms at their disposable to overcome deficits in specific aspects of motor system function. Given that the BDNF Met allele (see Bath & Lee, 2006) and deficits in BDNF levels (see Zuccato & Cattaneo, 2009) have been associated with altered susceptibility to some neurological and psychiatric disorders, future work will need to establish the effect of BDNF genotype on motor cortical plasticity during the performance of complex motor tasks in motor systems that have been compromised by disease or injury.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This work was partially supported by the Adelaide Centre for Neuroscience Research. This study forms part of the PhD of J.C., who is supported by a University of Adelaide Postgraduate Research Scholarship. P.Q.T. is a Pfizer Australia Research Fellow. M.C.R. is supported by a National Health and Medical Research Council Senior Research Fellowship.

Abbreviations
ADM

abductor digiti minimi

BDNF

brain-derived neurotrophic factor

FDI

first dorsal interosseous

IFAAcc

index finger abduction acceleration

IPAQ

international physical activity questionnaire

LQ

laterality quotient

LTD

long-term depression

LTP

long-term potentiation

M1

primary motor cortex

MCP

metacarpophalangeal

MEP

motor-evoked potential

M max

maximal compound muscle action potential

MMSE

mini-mental state examination

MSO

maximum stimulator output

PAS

paired associative stimulation

PIP

proximal interphalangeal

RMT

resting motor threshold

TMS

transcranial magnetic stimulation

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References