Experimental design. This study used a repeated-measures design with nine total sessions to examine the effects of RLIC combined with motor and cognitive training on learning in neurologically intact adults (Fig. 1). This study was approved by the Washington University Human Research Protection Office and was conducted in compliance with the Helsinki Declaration. All participants provided informed consent prior to beginning the study and were compensated for their time. Fig. 1.Experimental design. There were 9 total sessions: 7 consecutive weekday sessions (D1–D7) and 2 follow-up sessions (FU1, FU2) 2 and 4 wk after D7. *Items were completed in a randomized order. BDNF, brain-derived neurotrophic factor. Download figureDownload PowerPoint



Participants. Eighteen neurologically intact adults were recruited for participation in this study. Participants were included if they were 1) 18 yr of age or older and 2) had sufficient cognitive skills to actively participate, determined during a standardized interview. Exclusion criteria were determined by self-report and included 1) history of neurological condition, balance impairment, or vestibular disorder; 2) history of attentional disorders that could affect learning (ADD, ADHD); 3) history of sleep apnea (Drager et al. 2010; Yang et al. 2013); 4) history of lower extremity condition, injury, or surgery that could compromise performance on the motor task; 5) any extremity soft tissue, orthopedic, or vascular condition or injury that may contraindicate RLIC (uncontrolled hypertension, peripheral vascular disease, hematological disease, severe hepatic or renal dysfunction); 6) any cognitive, sensory, or communication problem that would prevent completion of the study; 7) current use of medication for systemic inflammation or spasticity or treatment with selective serotonin reuptake inhibitors, which could decrease nervous system excitability (Koch and Gonzalez 2013); 8) current weight lifting or interval training exercise, which could alter serum neurotrophic factors and confound the effects of RLIC (Ding et al. 2011; Yarrow et al. 2010); and 9) current substance abuse or dependence. These criteria were established to ensure a relatively homogeneous sample that optimized our potential to detect an effect of RLIC on learning.

Order of experiment. This experiment included seven consecutive weekday sessions (D1–D7) and two follow-up sessions (FU1, FU2), shown in Fig. 1. During the first session (D1), participants provided informed consent and demographic data. Each participant then completed pretest measurements of 1) serum BDNF concentration, 2) the motor task, 3) the cognitive task, 4) finger flexor muscle activation, and 5) grip strength. Serum BDNF concentration was measured first to avoid the confounding effects of physical activity on BDNF concentrations. The sequence of pretest motor task performance, cognitive task performance, and muscle activation/grip strength performance was randomized. After pretesting, participants were randomly assigned to the RLIC or sham conditioning group via a random numbers generator in MATLAB. Participants were blinded to their group assignment, meaning that they were not told whether they were receiving RLIC or sham conditioning. Once randomized, participants underwent one set of RLIC or sham conditioning. During the second session (D2), which took place the following day, participants underwent RLIC or sham conditioning. Participants returned for the next five consecutive weekdays for sessions that included conditioning plus training (D3–D7). These sessions consisted of RLIC or sham conditioning, immediately followed by 15–20 min of both motor and cognitive training, in a randomized order. Details of motor and cognitive training are outlined below. Five sessions that included conditioning plus training were selected because this period of time is adequate to assess learning but is unlikely to result in performance plateaus and does not present an excessive time burden for participants (McNevin et al. 2003; Schaefer and Lang 2012; Wulf et al. 2003). Learning on the motor and cognitive tasks as well as changes in muscle activation, muscle strength, and serum BDNF concentration were evaluated through posttests at the end of the seventh session (D7). Finally, two follow-up sessions (FU1, FU2) took place 2 and 4 wk after session D7. Follow-up sessions, consisting of assessments of performance on the motor and cognitive tasks, were included to evaluate retention.

Remote limb ischemic and sham conditioning. RLIC was achieved via blood pressure cuff inflation to 200 mmHg on the nondominant upper extremity (Botker et al. 2010; Hausenloy et al. 2007; Koch et al. 2011; Meng et al. 2012). Sham conditioning was achieved via blood pressure cuff inflation to 10 mmHg below the individual's diastolic blood pressure, also on the nondominant upper extremity (Koch and Gonzalez 2013). The nondominant extremity was determined by self-report and was chosen as a practical matter, in order to ensure consistency in administration. Periodically, a pulse oximeter was placed on the index finger of the conditioned arm in order to confirm that ischemia was being achieved in participants in the RLIC group (oxygen saturation reading of 0 or “error”) and that ischemia was not occurring in participants in the sham conditioning group (oxygen saturation reading equivalent to preconditioning measure). In addition to periodic oxygen saturation checks, visual inspection of the conditioned limb was also done intermittently to ensure that the color and temperature of the limb remained unchanged in participants undergoing sham conditioning whereas the distal limb of participants in the RLIC group was cool and pale, indicating that ischemia was occurring. One set of RLIC or sham conditioning consisted of five cycles of alternating 5 min of inflation and 5 min of deflation. These are the timing and dose of RLIC that were shown to produce effects in human cardio- and neuroprotective trials (Botker et al. 2010; Hougaard et al. 2014) and are well tolerated by participants. One set of RLIC or sham conditioning was performed in sessions D1–D7. The goal of including conditioning without training during the first two sessions (D1, D2) was to prepare the central nervous system for subsequent motor and cognitive training. To assess the effect of participant blinding, participants were asked to identify whether they believed that they had been assigned to the RLIC or sham conditioning group upon completion of the study. We monitored participants for pain and/or adverse cardiovascular effects before, during, and after RLIC or sham conditioning. Treatment was terminated if 1) pain levels were >6 on a 10-point numerical pain rating scale, 2) oxygen saturation levels were <80%, 3) heart rate was <40 or >110 beats/min, or 4) systolic blood pressure was <80 mmHg or >150 mmHg. For the purpose of safety monitoring, oxygen saturation, heart rate, and blood pressure were measured on the arm not undergoing conditioning.

Motor task. The motor task used for this study was a stability platform balance task (Lafayette Instrument model 16030L) (Cherry et al. 2014; Taubert et al. 2010). The stability platform itself features integrated electronic tilt angle measurements (angle measurement resolution 1.0°), experimenter-selectable balance thresholds (angle limit setting resolution 1.0°), digital angle readouts, platform re-zero ability, and built-in timing functions for test and rest timing (timing resolution 0.001 s). This task engages multiple systems of the brain including the vestibular system, visual system, motor system, somatosensory system, and cognitive centers. Successful performance on the stability platform task requires that a subject anticipate changes in posture and coordinate muscle activation in response to self-induced perturbations. The stability platform task was selected because decreased dynamic balance is a significant contributor to fall risk (Toraman and Yildirim 2010) and maintaining stability during dynamic activities is required for daily activities such as walking and reaching. Moreover, we chose this motor task that engages a broad range of brain systems in order to distinguish the systemic neuroplastic effects of RLIC from focal changes in the sensorimotor cortex that could result from brief periods of limb deafferentation. Participants were instructed to stand on the platform with feet facing forward and to keep the platform level for as many seconds as possible during a 30-s trial. Performance was quantified by measuring the cumulative amount of time (to the tenth of a second) that a participant maintained the stability platform ±3° of horizontal during each trial. Participants performed 90 total trials including 5 pretest trials during D1, 15 trials on each during D3–D7, and 5 trials during each of the two follow-up sessions (FU1, FU2). The final five trials during D7 (trials 76–80) served as the posttest measure of performance on the stability platform. Trials were separated by 30 s of rest. A hand rail was positioned in front of the stability platform for safety. Use of the handrail was permitted for initial balance and positioning; however, no upper extremity support was allowed during a trial. After each trial on the stability platform, participants were told how many seconds they maintained the platform in balance. No feedback was given regarding balance or postural strategies, allowing participants to develop their own techniques and adapt their movement strategies based on trial and error.

Cognitive task. An associative recognition task was used to measure hippocampus-based learning (Bunge et al. 2004; Duzel et al. 2003). Participants were given a study period during which they were shown 60 “target” nonword-image pairs in a random order (stimulus presentation = 750 ms, interstimulus interval = 1,000 ms). Participants were instructed to associate and remember the 60 target pairs. After the study period, participants completed a trial in which they were shown 60 pairs in a random order and were instructed to indicate whether each pair was a target pair or a novel “foil” pair by pressing the 1 or 2 key, respectively, on a standard keyboard (stimulus presentation + response time = 750 ms, interstimulus interval = 1,000 ms). Foil pairs were pairs in which the nonword, the image, or both were not target items or in which the nonword and the image were incorrectly paired target items. The number of target pairs included in a trial ranged from 15 to 35 pairs. This range was established to increase task difficulty and prevent participants from discerning a pattern of the number of “target” and “foil” responses for a given trial. Each participant performed one pretest (D1), five training (D3–D7), and two follow-up (FU1, FU2) trials of the associative recognition task. The trial in D7 served as the posttest measure of performance on the associative recognition task. The participants were not given a study period before the follow-up assessments and thus were required to remember target pairs for approximately 2 and 4 wk. Instructions, stimuli, and feedback were presented electronically on a laptop computer screen with E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA). Nonwords (e.g., “piction”) were displayed in size 18 Courier New font on the left side of the screen while the image of a familiar object (e.g., apple, chair) appeared on the right side of the screen. Nonwords were selected from a database of nonwords provided by the English Lexicon Project (Balota et al. 2007). All nonwords were pronounceable and five to seven letters in length and had no more than two syllables. Images were selected from a bank of Microsoft Clip Art. All images were single items, considered to be familiar to most American adults. Performance on the associative recognition task was evaluated with a measure of discriminability (Pr) and reaction time (RT) (Snodgrass and Corwin 1988). Pr reflects the degree of accuracy with which a participant is able to discriminate between target pairs and foil pairs. Higher values indicate greater discriminability. RT was defined as the amount of time between stimulus presentation and subject response, averaged across all correct responses. Response times shorter than 150 ms were considered anticipatory responses and were excluded from RT analysis. Participants received immediate feedback about the accuracy of each response as the word(s) “correct,” “incorrect,” or “no response detected” appeared on the screen for 500 ms after each stimulus. These variables were computed with the following formulas (Snodgrass and Corwin 1988; Wolf et al. 2011): Pr=hit rate − false alarm rate (1) where hit rate = (0.5 + no. of correct targets)/(1 + no. of total targets) and false alarm rate = (0.5 + no. of incorrect foils)/(1 + no. of total foils). RT = sum of amount of time between stimulus presentation and subject response for all correct responses / no . of correct responses (2) Data from two participants (1 in each group) were excluded from cognitive task analysis. Reasons for exclusion were 1) an abundance of anticipatory responses during all trials, indicating a lack of task engagement (sham conditioning participant), and 2) limited command of the English language (RLIC participant). where hit rate = (0.5 + no. of correct targets)/(1 + no. of total targets) and false alarm rate = (0.5 + no. of incorrect foils)/(1 + no. of total foils).Data from two participants (1 in each group) were excluded from cognitive task analysis. Reasons for exclusion were) an abundance of anticipatory responses during all trials, indicating a lack of task engagement (sham conditioning participant), and) limited command of the English language (RLIC participant).

Measures of muscle activation and strength. To distinguish the effects of learning from more generalized increases in muscle activation and strength, we measured finger flexor electromyographic (EMG) activity and grip strength of the upper extremity. We chose measures of the upper extremity in order to dissociate the effects of RLIC alone from neuromuscular changes in the lower extremity that may occur as a consequence of motor training on the stability platform. The dominant side was selected because the muscles of the dominant upper extremity did not experience direct limb ischemia and that arm was not explicitly trained by other tasks. Muscle activation of the long finger flexors was measured during three trials of maximal isometric force measurements of grip strength. Participants were instructed to squeeze a handheld dynamometer with as much force as possible and to maintain that force for ∼5 s, until the auditory cue “relax” was given. Approximately 10 s of rest was given between trials to prevent muscle fatigue. Muscle activation and strength measurements were taken for each participant at the pretest, before and after RLIC/sham conditioning in session D3 (within-session changes), and at posttest in session D7 (across-sessions changes). Muscle activation was measured via EMG with surface electrodes positioned over the flexor digitorum superficialis and profundus muscle bellies on the dominant arm (Janda et al. 1987). EMG was recorded with a sampling rate of 1,000 Hz and a tethered EMG system (Noraxon U.S.A.). EMG data were processed off-line in MATLAB R2012a (MathWorks, Natick, MA) with custom-written software. Signals were full-wave rectified and smoothed with a second-order Butterworth low-pass filter with a cutoff frequency of 20 Hz (Farina et al. 2004; Reaz et al. 2006). EMG data were quantified by averaging the amplitude of the EMG activation during the middle 500 ms of each trial. The three trials were then averaged to yield a single value representing mean finger flexor muscle activation for a given measurement time. EMG electrodes remained in place on the participant's forearm between assessments in D3 (within session) for consistency. Grip strength performance was quantified as kilograms of force generated with the dominant hand when squeezing the handheld dynamometer (JAMAR hydraulic hand dynamometer, model SMP-5030J1, Sammons Preston) set on the third rung. Three grip strength trials were averaged to obtain a single score for a given session (Hamilton et al. 1994; Mathiowetz et al. 1984).

Serum BDNF. We measured serum BDNF because animal models of intermittent hypoxia have shown increases in BDNF measured directly in the respiratory and nonrespiratory motor nuclei in the cervical spinal cord (Baker-Herman et al. 2004; Lovett-Barr et al. 2012; Mitchell et al. 2001; Satriotomo et al. 2012; Wilkerson and Mitchell 2009). Additionally, BDNF is the most commonly used serum marker in human learning studies at the present time (Cunha et al. 2010; Edelmann et al. 2014). A 10-ml blood withdrawal via aseptic technique from the antecubital vein was performed by a trained patient care technician from the Washington University Clinical Research Unit. Blood samples were processed by staff at the Washington University CORE Laboratory. Blood was allowed to clot at room temperature for 30 min after collection (BD Vacutainer) and was then centrifuged (Jouan GR422) for 15 min at 2,780 g. The separated sample was stored in a −80°C freezer until analysis. Serum BDNF concentration was detected in an antibody sandwich format with a BDNF enzyme-linked immunosorbent assay (ELISA) kit (R&D Systems). BDNF values are the concentration (ng/ml) in the serum of each sample. Each participant underwent a blood draw at pretest, before conditioning in session D3, and at posttest in session D7. A comparison of pretest and D3 values allowed us to evaluate the effects of 2 days of conditioning alone on serum BDNF concentration, while comparing values from pretest and posttest allowed us to evaluate the effects of repeated sessions of conditioning plus training on serum BDNF concentration.