The Effects of Exercise on Synaptic Plasticity in Individuals With Mild Cognitive Impairment and in Healthy Aging.

Background: The proposed research will investigate the hypothesis that exercise improves cognition by enhancing synaptic plasticity in individuals with MCI and in aging. Synaptic plasticity refers to changes in synaptic efficacy that are the consequence of the inherent activity of a neuron. Synaptic plasticity is fundamental to preserving and creating memories and at the level of the synapse, is the result of a high influx of postsynaptic Ca2+ that yields long-term potentiation (LTP) (7,8,9). The proposed research is the first to assess whether synaptic plasticity is enhanced after exercise training in individuals with MCI. If confirmed, future work will identify substitutes for exercise that alter synaptic plasticity since not all individuals are capable of exercise.

Mild cognitive impairment (MCI) is the stage before the more serious decline of dementia. MCI affects an estimated 15% to 20% of people over age 65 with ~10%-15% of those progressing to dementia each year (1, 2). Amidst ongoing challenges in developing disease-modifying drugs, non-pharmacological interventions including exercise are recommended as part of overall MCI management (3) based on the positive effects of exercise on cognitive performance (4-6).

In humans, synaptic plasticity can be assessed in vivo by delivery of two forms of Transcranial magnetic stimulation (TMS). These are called intermittent theta-burst stimulation (iTBS) and 5Hz repetitive TMS (5Hz rTMS). Both forms delivered over the motor cortex induce synaptic plasticity as measured by short-term increases in the efficacy of the corticospinal pathway from cortex to muscle (10, 11). These effects are analogous to animal models of LTP, since they are mediated by glutamate and require glutamate binding at NMDA receptors (11). Thus, in humans, iTBS and 5Hz rTMS are non-invasive tools to assess whether 1) aging and MCI populations demonstrate synaptic plasticity and 2) interventions such as exercise can enhance the magnitude of synaptic plasticity. Compared to controls, individuals with MCI demonstrate blunted synaptic plasticity as indicated by a reduced response to 5Hz rTMS (12) and iTBS (13). The question posed herein is whether synaptic plasticity can be enhanced by exercise in individuals with MCI and in the aging population.

Many studies have investigated the impact of acute exercise on neurobiology. Brain-derived neurotrophic factor (BDNF) is a key regulator of processes crucial for cognition, learning and memory (14 -16). Similarly, a bone-derived hormone called osteocalcin (OCN) increases following exercise (17, 18) and increases the number of BDNF vesicles transported to the synapse (19 - 21). Osteocalcin is found in several different isoforms in serum, and the form involved in exercise effects is not known (22, 23). The proposed research will also test the hypothesis that exercise training increases serum BDNF and OCN in MCI and the aging population and that these changes will correlate with increases in synaptic plasticity. If true, this would suggest that exercise-induced increases in BDNF and OCN are a consequence of altering synaptic efficacy.

Self-Determined Intensity Interval training involves intermittent bouts of challenging exercise interspersed with short recovery intervals (24,25). This training is unique in that intensity is determined by the participants themselves. They are required to identify a pace that is a physical challenging and rate their perceived exertion. This type of training can be achieved by participants of all ages with various underlying conditions such as Type 2 Diabetes (38,39,40) and coronary artery disease (41,42) and obesity (43). Exercise promotes cognitive improvement (26) and can be performed in individuals with MCI (27). In a case study, twelve weeks of interval training improved cognition in one female living with MCI (28).

The proposed research will determine whether a Self-Determined Intensity Interval Training exercise protocol will enhance synaptic plasticity in individuals with MCI and the aging population.

There are two Specific Aims of the proposed study.

1. To quantify synaptic plasticity following a Self-Determined Intensity Interval Training in aging and in individuals with MCI.
2. To determine whether changes in synaptic plasticity correlate with changes in serum BDNF, osteocalcin and in cognition.

Methods Exercise Intervention (Groups A and C only) Individuals will participate in 3 sessions of Self Determined Intensity Interval training, using a cycle ergometer, for 4 weeks in line with our previous experience in participant retention (25). Ratings of Perceived exertion (RPE) will be measured using a Borg's 6-20 scale. (44). Users will be asked to exercise on a stationary bike at an intensity whereby there RPE is challenging. This number on the RPE scale will be different for different participants with varying levels of pre-existing fitness. The important aspect is individual feel that the exercise is challenging for themselves. The cycling protocol includes five, 1-minute cycling intervals, interspersed with 1.5 minutes of recovery (cycling at a slow pace to bring the individuals heart rate down). Participants will also perform a 3-minute warm-up and a 2-minute cool-down for a total exercise duration of 17.5 minutes as we have reported (25, 31). The RPE will be accquired by asking the participant to provide their rating at the end of the last interval.

Dependent measures

1. Synaptic Plasticity Surface electrodes (9mm Ag-Cl) will be used to record activity from the first dorsal interosseous muscle (FDI) muscle of the right hand. The active electrode will be placed over the muscle belly. To reduce signal noise, wet ground will be wrapped around the forearm. EMG signals will be magnified x1000 and bandpass filtered between 20-2.5 kHz (Intronix Technologies Corporation Model 2024F, Bolton, Canada). An analog-digital converter will be used to digitize data at 5 kHz (Power1401; Cambridge Electronics Design, Cambridge, UK), prior to being analyzed through commercial software (Signal v7.01; Cambridge Electronics Design, Cambridge, UK). The hotspot of the right FDI muscle is defined as the location on the left motor cortex that, when stimulated with TMS, consistently led to the largest MEP in the muscle. This point will be found and registered using Brainsight Neuronavigation and TMS (Rogue Research, Montreal, Canada). To assess synaptic plasticity, repetitive TMS will be performed using a 70mm inner diameter figure-of-eight coil with a Magstim Super Rapid2 Plus Stimulator (Magstim, Whitland, UK). Biphasic magnetic pulses will be delivered over the primary motor area of the dominant hemisphere to find the optimal position for eliciting a MEP in the contralateral first dorsal interosseous (FDI) muscle. Intermittent theta burst stimulation (iTBS) protocol will be delivered using biphasic pulses in burst of three pulses delivered at 30Hz, in 6Hz trains that will last 2s, this will be followed by 8s with no pulse delivered. iTBS will be repeated for a total of 612 pulses at 80% of active motor threshold (32).The average of twenty single-pulse motor evoked potentials (MEPs) will be recorded from first dorsal interosseous muscle of the hand before and immediately following iTBS delivered over the primary motor cortex. Further, a second protocol will be used to assess synaptic plasticity. For this, participants will receive approximately 10 trains of 10 stimuli at a frequency of 5Hz. The stimulation intensity will be set to 120% of rMT with an inter-train interval of 2 minutes (11). MEPs will be recorded during the first and tenth bout of a 5Hz rTMS protocol (12). Both TMS protocols (iTBS and 5 Hz rTMS) yield increases in MEPs in non-MCI populations, and these are glutamate-driven effects mediated by LTP at NMDA receptors. Group C (non-MCI) will serve as a control to ensure that the synaptic plasticity protocols are effective.
2. Cognitive function Cognitive functions will be assessed using the National Alzheimer's Coordinating Center Uniform Data Set Neuropsychological Battery, Version 3 (UDSNB-3) comprised of the Montreal Cognitive Assessment, Semantic and Verbal Fluency, Trail-making Tests, Digit Span, Benson Complex Figure Test and the Multilingual Naming Task.
3. Brain Derived Neurotropic Factor and Osteocalcin Blood will be collected by a trained and certified phlebotomist in the fasted state from an antecubital vein using our standard procedures. From a total sample of 6 ml of whole blood, 2.5 ml will be collected into PAXgeneTM Blood RNA tubes (Qiagen/BD Diagnostics), and the remainder will be collected into a 4-ml, additive-free SST gold blood collection tube. After clotting and centrifugation to obtain the serum fraction, ELISA assays will be carried out for the determination of serum BDNF (Human BDNF DuoSet, DY248) and total intact osteocalcin (Human Osteocalcin ELISA kit KAQ1381). After incubation with 5 mg/ml hydroxylapatite (391947, Sigma-Aldrich) to remove carboxylated OCN (33), serum uncarboxylated osteocalcin (unOCN) will be measured by ELISA. Carboxylated OCN will be determined by subtracting uncarboxylated OCN from total OCN.
4. Participant experience A likert-type scale (0 to 4) for participants to rate their enjoyment of the Self-Determined Intensity Interval training intervention (Groups A and C) and the overall research experience.

References

1. Farias ST, Mungas D, Reed BR, Harvey D, DeCarli C. Arch Neurol. 2009;66(9):1151-7.
2. Roberts R, Knopman DS. Clin Geriatr Med. 2013;29(4):753-72.
3. Petersen RC, Lopez O, Armstrong MJ, Getchius TS, Ganguli M, Gloss D, et al. Neurology. 2018;90(3):126-35.
4. Chang Y-K, Labban JD, Gapin JI, Etnier JL. Brain Res. 2012;1453:87-101.
5. Lambourne K, Tomporowski P. Brain Res. 2010;1341:12-24.
6. McMorris T, Hale BJ. Brain Cogn. 2012;80(3):338-51.
7. Abraham, WC and Bear, MF. Trends Neurosci. 1996; 19:126-130.
8. Frey, U, Schollmeier, K, Reymann, KG, et al. Neuroscience. 1995; 67:799-807.
9. Abraham, WC. Nat Rev Neurosci. 2008; 9:387.
10. Premji, A, Ziluk, A, and Nelson, AJ. BMC Neurosci. 2010; 11:91.
11. Fitzgerald PB, Fountain S, Daskalakis ZJ.Clin Neurophysiol. 2006 Dec;117(12):2584-96.
12. Trebbastoni A, Pichiorri F, D'Antonio F, Campanelli A et al.,
13. Colella D, Guerra A, Paparella G, Cioffi E et al., Clin Neurophysiol 2021 Feb;132(2):315-322.
14. Miranda M, Morici JF, Zanoni MB, Bekinschtein P. Front Cell Neurosci. 2019;13:363.
15. Bechara RG, Lyne R, Kelly ÁM. Behav Brain Res. 2014;275:297-306.
16. Griffin ÉW, Mullally S, Foley C, Warmington SA, O'Mara SM, Kelly ÁM. Physiol Behav. 2011;104(5):934-41.
17. Khrimian L, Obri A, Ramos-Brossier M, Rousseaud A, Moriceau S, Nicot A-S, et al. J Exp Med. 2017;214(10):2859-73.
18. Kosmidis S, Polyzos A, Harvey L, Youssef M, Denny CA, Dranovsky A, et al. Cell reports. 2018;25(4):959-73. e6.
19. Hiam D, Voisin S, Yan X, Landen S, Jacques M, Papadimitriou ID, et al. Bone. 2019;123:23-7.
20. Levinger I, Jerums G, Stepto NK, Parker L, Serpiello FR, McConell GK, et al. J Bone Miner Res. 2014;29(12):2571-6.
21. Levinger I, Zebaze R, Jerums G, Hare DL, Selig S, Seeman E. Osteoporos Int. 2011;22(5):1621-6.
22. Nicolini C, Michalski B, Toepp S, et al., Neuroscience. 2020 Jun 15;437:242-255.
23. Nicolini C, Fahnestock M, Gibala M, Nelson AJ. Neuroscience. 2021 Mar 1;457:259-282.
24. El-Sayes J, Turco CV, Skelly LE, Nicolini C, Fahnestock M, Gibala MJ, et al. Neuroscience. 2019;410:29-40.
25. Nicolini C, Toepp S, Harasym D, Michalski B, Fahnestock M, Gibala MJ, et al. Physiological reports. 2019;7(11):e14140.
26. Kujach S, Byun K, Hyodo K, Suwabe K, Fukuie T, Laskowski R, et al. Neuroimage. 2018;169:117-2
27. Halikas A, Gibas KJ. Diabetes Metab Syndr. 2018 Nov;12(6):1141-1146.
28. Dahlgren H, Gibas KJ. Diabetes Metab Syndr. 2018 Sep;12(5):819-822.
29. Little JP, Gillen JB, Percival ME, Safdar A, Tarnopolsky MA et al.,2011. J Appl Physiol 111:1554-1560.
30. Percival ME, Martin BJ, Gillen JB, Skelly LE et al., J Appl Physiol 119:1303-1312.
31. Phillips BE, Kelly BM, Lilja M, Ponce-González JG, et al., 2017.) Front Endocrinol (Lausanne) 2017. 8:229.
32. Fassett HJ, Turco CV, El-Sayes J, Lulic T, Baker S, Richardson B, Nelson AJ. Front Neurol. 2017. 4;8:380.
33. S.A.P. Chubb, E. Byrnes, L. Manning, J.P et al., J Clin Endocrinol Metab, 100 (2015), pp. 90-99.
34. Premji, A., Ziluk, A., \& Nelson, A. J. (2010).. BMC neuroscience, 11, 91.
35. Jones, C. B., Lulic, T., Bailey, A. Z., Mackenzie, T. N., Mi, Y. Q., Tommerdahl, M., \& Nelson, A. J. (2016). Journal of neurophysiology, 115(5), 2681-2691.
36. Fassett, H. J., Turco, C. V., El-Sayes, J., Lulic, T., Baker, S., Richardson, B., \& Nelson, A. J. (2017). Frontiers in neurology, 8, 380.
37. Premji, A., Rai, N., \& Nelson, A. (2011). PloS one, 6(5), e20023.