Hippocampal Function and Sports:
Effects of Physical Exercise on Hippocampal Volume and Pattern Separation
David B. van de Merwe
Supervisor: Dr. Michelle M. Solleveld Co-assessor: Dr. Anouk G. Schrantee
Examiners: Dr H. Krugers, Prof.Dr. P.J. Lucassen, SILS, FNWI
Dept. Radiology AMC Amsterdam
25.05.2018 26 ECTs
rMSc Brain & Cognitive Sciences student number: 10891390 University of Amsterdam
Abstract
The purpose of this study was to investigate the effects of long-term physical exercise on hippocampal volume, memory parameters and their interactions. The method used was an exercise intervention study with young healthy subjects. Fifty-two participants were recruited and randomly assigned to either a high or low intensity exercise regimen. Participants were subjected to three sessions of exercise per week in addition to their typical exercise schedule for a period of 12 weeks. Preceding and following the exercise intervention, MRI scans were performed. Participants also performed a pattern separation task. Performance in pattern separation tasks is known to be affected by exercise mediated hippocampal neurogenesis in rodents.
The results for the pattern separation task indicated that participants were significantly more accurate in low similarity trials than in high similarity trials. In addition, accuracy significantly increased for high similarity trials following the exercise intervention. General Response Time (RT) significantly increased post-intervention compared to pre-intervention. However, the study did not establish stronger effects for a high intensity exercise manipulation than for a low intensity exercise manipulation with respect to pattern separation. A mediating effect of baseline hippocampus volume on exercise induced increases in pattern separation was not established.
For future intervention studies, the addition of a non-exercise control is suggested. A trial of longer duration could also be considered. This paper concludes that exercise can improve pattern separation in healthy adults, particularly concerning discriminability for highly similar information.
Hippocampal Function and Sports:
Effects of Physical Exercise on Hippocampal Volume and Pattern Separation
Previous studies have shown that prolonged periods of extensive aerobic exercise can lead to structural changes in brain anatomy such as general volume increases (Colcombe, Erickson et al, 2006; Mortimer et al, 2012). More specifically, hippocampal volume has shown to increase following prolonged periods of extensive exercise (Erickson et al., 2011; Hariprasad et al., 2013). The hippocampus is considered to be imperative to learning and memory capabilities. Through its structural and excitatory interactions with the cortex it supports the construction and consolidation of episodic, spatial and declarative memory (Bird & Burgess, 2008; Eichenbaum, 2000). Exercise induced structural volume increases of the hippocampus have been accompanied by improvements in performance on hippocampus related cognitive tasks (Erickson et al., 2011; Firth et al., 2017). These findings provoke the need for a deeper investigation concerning the interaction between physical exercise and structural changes in the hippocampus and changes in hippocampus related cognitive function.
In particular, high intensity aerobic exercise programs seem to increase both human and animal hippocampal volume (Erickson et al 2011; Firth et al., 2017) and hippocampal function (Griffin et al, 2011; Erickson et al, 2011). These increases may be attributed to an increase of growth factors and neurotransmitters which serve to protect against the possibility of bodily and neuronal harm due to aerobic exercise. Cotman & Berchtold (2002) explain that aerobic exercise primes the hippocampus to respond to environmental stimulation, which calls for an increase of information encoding. Congruently, neuronal resistance must be increased due to increased odds of physical insult to the hippocampus during physical activity. This in part may be attributed to an increase of brain-derived neurotrophic factor (BDNF). BDNF is known to be upregulated by noradrenaline and other peripheral exercise factors (Cotman & Berchtold, 2002). Increases of BDNF have been connected to neurogenesis increases induced by running (Vivar et. Al 2013; So et al., 2017). Conversely, tasks highly related to learning and memory are particularly sensitive to disruptions in neurogenesis (Leuner & Gould, 2010). Previous research suggests that nascent DCGs contribute to memory formation, consolidation and retrieval (Kempermann et al., 1997; Kitamura et al., 2009; So et al., 2012). Based on these notions, interventional exercise approaches have garnered positive results in patient groups affected by neurodegenerative diseases such as Multiple Sclerosis (MS), Parkinson’s disease and Alzheimer’s disease. These patient groups showed increases in hippocampal
volumes and depreciation or restriction of learning and memory deficits (Leavitt et al., 2013; Paillard et al., 2015).
The generation of new neurons from precursor cells and subsequent migration and differentiation of these neurons is known as neurogenesis. Neurogenesis is known to be upregulated by exercise and may prove explanatory in the observation of hippocampal volume and cognitive performance increases following long-term exercise interventions (Erickson et al., 2011). Neurogenesis is shown to be facilitated by a multitude of interventions within rodent studies. Research by Eriksson et al. (1998) suggests that new neurons are actively generated and incorporated within functional brain circuits. Within the Dentate Gyrus (DG) of adult animals, new neurons are continuously formed. They originate from Neural Stem Cells (NSCs) located in the subgranular zone of the Dentate Gyrus (DG), a sub region of the hippocampus. Every NSC develops through stages of proliferation, neural differentiation and synaptic integration (So et al., 2017). Physical exercise has been shown to strongly increase NSC proliferation and new neuron survival rates (Vivar, Peterson & van Praag, 2015; Marlatt, 2012; van Praag, Kempermann & Gage, 1997). In human adults, running enhanced proliferation of NSCs into dentate granular cells (DCGs) by speeding up NSC cell cycles (Fariochi-Vecchioli et al., 2014). The neurogenerative potency of NSC microenvironments may be positively affected by increased blood flow, which results in increased turnover of oxygen and nutrients to brain tissue (So et al., 2017).
One of the cognitive functions that is most relevant with respect to hippocampal neurogenesis – and in particular DG neurogenesis – is pattern separation (Deng et al., 2010; Gilbert, Kesner, & Lee, 2001; McHugh et al., 2007). Pattern separation resembles the ability to compare new information to information internalized earlier, aiding the capacity to distinguish between instances of highly similar information. The hippocampus – and particularly the dentate gyrus – respond strongly if a slightly different version of a previously shown event or object is presented (Bakker et al., 2012; Brickman et al., 2014). These findings suggest a constructive role for the hippocampus – and specifically the dentate gyrus – in performing cognitive tasks which are highly dependent on pattern separation.
It has been theorized that neurogenesis increases the capacity to form and store new memories. This increased capacity to form and store memories may diminish the interference between old and new information (Deng et al., 2010). Deng’s theory supplies theoretical vindication to the connection between hippocampal volume increases and increased performance in cognitive tasks which are highly dependent on pattern separation. In mouse studies where neurogenesis was
impaired through x-irradiation, task performance in pattern separation-dependent tasks dropped. Performance was particularly affected for tasks showing higher similarity of experimental stimuli in comparison to tasks with low similarity of the experimental stimuli (Clelland et al., 2009). Other studies have shown that mice subjected to artificial neurogenesis enhancement by way of Bax gene ablation (Sahay et al., 2011) and mice exhibited to exercise manipulations (Creer et. Al., 2010) outperformed controls in spatial pattern separation tasks. In the exercise manipulation study, an increased performance was reported primarily for tasks with high similarity.
In accordance with neurogenesis mediating pattern separation, we hypothesize that participants that start with a larger hippocampus and DG have more room for new neurons and neuronal connections to form. This opens the possibility to form more intricate neuronal pathways. Which can mean two things: I) participants that start with larger hippocampi perform better in pattern separation tasks than participants with smaller hippocampi II) larger baseline hippocampi could mediate the supportive effects of exercise induced increases in neurogenesis on abilities such as pattern separation. These ideas concur with the positive relationship between smaller hippocampi and associative memory (Daugherty et al., 2017). Associative memory resembles a conceptual opposite to pattern separation, because it constitutes the ability to remember associations between qualitatively unrelated objects or instances of information, for instance name-face combinations. Pattern separation however calls for high discriminability as qualitatively highly similar objects or instances of information must be distinguished from each other. Larger hippocampi have been connected to increased declarative memory (for which discriminability is of great importance) (Pohlack et al., 2014). Hippocampus-dependent memory tasks in short-delay retention, long-delay retention and discriminability have shown positive relations to hippocampal volume. No other brain substrates have shown a positive relation to performance on such tasks. More importantly, pattern separation shows strong conceptual overlap to such tasks in which the load on memory and discrimination is highly dependent.
The current study is part of a broader research project named NEURO-SHAPE, which aims to establish the neurophysiological basis of exercise-induced changes on hippocampal functioning in young adults. The current study aims to disentangle the effects of high intensity and low intensity exercise interventions on pattern separation dependent tasks. By subjecting participants to either a high intensity or low intensity workout regimen of 12 weeks, a discrepancy in cognitive performance will be assessed. Baseline hippocampal volumes are included as a mediating factor Based on the research discussed above, it is hypothesized that increasing exercise for a prolonged period will improve accuracy in pattern separation dependent tasks, particularly for tasks with high similarity.
This effect is specifically expected for participants exposed to a high intensity workout regimen. Finally, it is expected that participants starting with larger baseline hippocampus and dentate gyrus volumes will exhibit a stronger increase in pattern separation. Although RT is not a primary parameter in pattern separation, it will be analyzed as it is a typical cognitive performance metric.
Methods Parameters
The primary independent parameters are: instance of measurement; exercise group; baseline hippocampal and DG volume; and similarity. Instance of measurement discriminates between measurements at either pre-intervention or post-intervention and is later referred to as Time. Exercise group defines to which intervention participants have been exhibited, pertaining the high intensity experimental group or the low intensity control group. Baseline hippocampal and DG volumes reflect the hippocampal and DG volumes in mm3 at the start of the experiment as semi-automatically segmented by Freesurfer. Similarity defines either high similarity or low similarity of the material used in a set of trials in the pattern separation task.
The primary dependent parameters revolve around task performance in the pattern separation task and are reflected in Accuracy and Response Time (RT).
Secondary parameters used to check adequate similarity in group formation at baseline were: BMI, VO2, Work-Out time self reported and Workout time measured. BMI indicates the body-mass index as calculated from participants length in meters and weight in kilograms. VO2 as calculated from a VO2-max endurance test reflects the maximum oxygen consumption per unit body volume. Work -out time self reported indicates the amount of time a participant has stated to have worked out during the 12 week intervention based on the weekly questionnaires. Workout time measured indicates the amount of time worked-out as measured by the applied heart monitor.
Workout time measured above 75% of maximum heart rate is a secondary parameter that guarantees the dissimilarity in exercise between groups during the exercise intervention. It reflects the time within which participants exhibited a heartrate above 75% of their maximum heart-rate during the VO2-max endurance test. A derivative of this parameter is the Percentage Workout Time measured above 75% of maximum heart-rate.
Participants
At the start of the study 52 participants between the age of 18 and 30 were selected based on extensive screening after which they were randomly divided in two groups. The experimental group (N=26) consisted of 10 males and 16 females. The control group (N=26) consisted of 12 males and 14 females. The study was completed in its entirety by 47 participants.
Selection
Participants had to be healthy, both mentally and physically, and willing and able to add three bouts of exercise to their weekly schedule. Further inclusion criteria pertained that BMI ≤ 30 kg/m2; for male participants VO2 max ≤ 55 ml/kg/min and for female participants VO2 max ≤ 45 ml/kg/min; use of oral contraceptive or intrauterine device for female participants; a stable exercise history 3 months prior to study inclusion.
Exclusion criteria were deemed as follows: general contraindications for MRI (such as claustrophobia), history of chronic renal insufficiency; history of allergic reaction to Gadolinium-containing compounds; history of any psychiatric disorder; intensive sports (>3/week); excessive smoking (>1 pack/day); alcohol (>21 units/week), and other regular drug use.
After the screening, each participant was randomly assigned to either a high intensity experimental group or a low intensity control group.
Hippocampal Volume
Before starting the exercise intervention, a 7T structural MRI (T1-weighted) scan was performed. A 7T Philips whole body MR-scanner (Philips Healthcare, Best, The Netherlands) running under software release 3.2.1 has been used.
For the segmentation of hippocampal substrates, the segmentation program Freesurfer (version 6.0) was used. After segmentation several quality checks were performed focused on outlier detection and correction of ill-performed segmentations in line with ENIGMA protocol (http://enigma.ini.usc.edu/protocols/imaging-protocols/).
Intervention
During a 12-week period, participants had to partake in some form of exercise three times a week on top of their baseline weekly workout count, each exercise routine taking between 45 minutes and a full hour. During exercise participants always wore a cordless heartrate measuring device (Polar H1 Heartrate sensor, Polar Electro Europe B.V., Schiphol-Rijk, The Netherlands).
For the experimental group, the exercises consisted of workouts. The main goal for the participants in the experimental group is to sustain a prolonged and significantly increased heart rate during their workouts. This meant that participants had to endure exercise exertion leading to a heartrate 75% of their maximum measured heartrate as measured in a cardiovascular assessment for at least 30 minutes of an exercise bout consisting of at least 45 minutes.
For the control group, the exercises consisted of typical low intensity workouts such as yoga. It was important that, although they should work out 3 times a week, they should not show signs of physical activity similar to that of the experimental group.
Each week participants answered a questionnaire concerning the extent and nature of their exercises during this week. At the start and at the end of the intervention all participants performed a VO2-max endurance test and their heights and bodyweight were measured.
Pattern Separation Task Design
Participants performed a pattern separation task both pre- and post- exercise intervention. This task was specifically constructed for the research group NEURO-SHAPE as described by Guijt (unpublished data). Participants were exhibited to one of two versions before and after the exercise intervention. Both versions of the pattern separation task consisted of a practice round of 16 trials and two experimental rounds of 55 trials each. In every trial three images of one of the image sets were presented sequentially for 1000ms each. The delay between the first two images was 2500ms and the delay between the last two images was 5000ms (Figure 1). Following the last image each participant was asked: whether the third image was the same as the first image (condition 1; button press 1); whether the third image was the same as the second image (condition 2; button press 2); whether the third image was the same as both the first and the second image (repeat condition; button press 3), or if the third image was different from image 1 and 2 (new condition; no button
press required). In the practice round, participants got acquainted with the task and the buttons they needed to press. After every practice trial, feedback was given on the performance. In the experimental rounds no feedback was provided.
Figure 1. A depiction of the chain of events during a round of the pattern separation task, as adapted from Guijt (unpublished data).
Procedure
All subjects were asked to sit down in a separate experimental room. After the experimenter left the room, subjects started by reading the task description. Subjects were not orally informed about the procedure of the task to limit interference of the experimenter. When subjects were finished with the task description, they could continue to the practice round by pressing the space bar. After the practice round, the participants were able to ask the experimenter for clarification of the task. When the task was completely clear, subject could start the experimental rounds by pressing the space bar. There was a break between the two experimental rounds to ensure participants were fully focused throughout the experiment. Participants were able to end the break and start the last experimental round by pressing the spacebar.
Measure
The primary performance measure for pattern separation is reflected by accuracy, defined as the ratio of correct scores amongst all responses. Response time (RT) is a secondary performance measure and refers to the time of response since presentation of the response stimulus. To note, all responses within 100ms RT were excluded from further analysis.
Data Analysis
To ensure that experimental group demographics were equivalent, a t-test has been performed on Age, Sex, Body Mass Index (BMI), and Maximum Volume Oxygen per body unit (VO2Max). Interventional outcomes were also compared to assess adequacy of the intervention based on self-reported Work out time, Measured Work out time, Hours of Heartrate above 75% of baseline heartrate, and Percentage of Workout Hours of Heartrate above 75% of baseline heartrate.
Paired t-tests were performed on the general pattern separation accuracy and RT at baseline and post intervention. One-way Analyses of Variance (ANOVAs) were performed on Δ-accuracy and Δ-RT for group differences.
For the comparison between High and Low similarity pattern separation, linear mixed effects models (lme) were applied. Within which the initial model were compared to theoretically extended versions of this model. If an extension was indeed an improvement of the model, the next extension was added to the model and compared to the current best fitting model. If this was not the case extension was substituted by another theoretical extension. In this case an intercept model - which reflected the mean as the predictor of subject test performance - was compared to the subsequent model. Order of the extensions was informed by the preceding t-test’s and ANOVAs. Model comparison and selection was based on Maximum Likelihood Analysis.
The theoretical extensions as referred to are; Time, Similarity, and Group. Time reflecting either “baseline” or “post experimental” phase, Similarity reflecting either “low” or “high similarity” within the pattern separation task, and Group reflecting participants from the “experimental” or “control” group. The best fitting model was compared to an extended version of this model including volume metrics of the left and right Hippocampi and the left and right Dentate Gyri.
In a similar fashion RT was subjected to a pattern of lme analyses. These were informed by previous t-tests and based on Time, Similarity and Group. Following the selection of the best fitting model this model was compared to models which include the aforementioned volume metrics.
Results
Within this study 52 adults between the age of 18 and 30 participated, of which 47 finished the entire study. Exclusion was based on non-compliance with experimental participation guidelines, particularly not participating in the right amount of exercises per week and failure to fill out weekly monitoring forms. Table 1. shows the demographics per intervention group. No differences between the groups were found based on age, sex, BMI, or VO2Max The instance of measurement being either, pre-intervention or post intervention, is captured in the variable labeled “Time”. Interventional group designation is reflected by the variable “Group”. Response time and accuracy are referred to as “RT” and “Accuracy” respectively. Hippocampal volume size at baseline is referred to as “Hippocampal Volume”.
Table 1. Baseline demographics
High (N=26) Low (N=26) p
Age 24.3 (SD=2.44) 25.4 (SD=3.22) 0.78
Sex (m/f) 10 / 16 12 / 14 0.19
BMI 23.14 (SD=2.78) 24.05 (SD=3.00) 0.28
VO2 37.14 (SD=7.33) 36.79 (SD=5.80) 0.86
Mean age in years per group; Sex distribution; BMI as mean “weight in kilograms/(length)2”; BMI =
Body Mass Index. VO2 exertion test score Oxygen ml/kg/min. SD = standard deviation. High = High
Table 2 shows the intervention assessment scores. The experimental and control groups did not significantly differ on self-reported mean sport duration or mean polar duration. The groups were significantly different based on t-tests for the intervention manipulation over aerobic activity, time of HR above 75% of maximum heartrate in percentage of workout time and total hours.
Table 2. Intervention Assessment
High (N=26) Low (N=26) p
Work out time self-reported 30.40h (SD=15.74) 29.62h (SD=11.84) 0.85
Work out time measured 16.90h (SD=10.82) 19.60h (SD=9.84) 0.36
Work time measured over 75% of heartrate
7.43h (SD=4.75) 2.64h (SD=1.75) <0.01
Percentage work out time measured over 75% of heartrate
47.74% (SD=15.54) 13.91% (SD=8.54) <0.01
Workout time is depicted in hours. “Work out time self-reported” depicts what participants have reported in the weekly questionnaires during the study. “Work out time measured” depicts the hours measured by the Polar heart-monitor. “Work out time measured over 75% of heartrate” shows the amount of time within working out which showed a heart rate above 75% of maximum heartrate. The final message shows a calculated percentage of the measured hours in which a heartrate higher than 75% of a participant’s maximum heartrate.
General Pattern Separation
All accuracy values are depictions of accuracy ratios as calculated based on the correct count divided by the total active responses. A paired t-test on general pattern separation performance between baseline and post intervention showed no significant change in accuracy, (t(41 )= -0.094095, p = 0.93 and Δ-accuracy= -0.001190476. With mean baseline accuracy being 0.69 (SD =0.11) and mean post intervention accuracy being 0.68 (SD=0.10) When performing a one-way ANOVA on group and Δ-accuracy no significant difference was found (F(1,40) = 1.253, p = .27). RT showed to be significantly affected by Time, t(41) = -3.4318, p < .01, baseline RT being 466.78 ms (SD = 107.11) and post RT being 408.59 ms (SD = 102.83). This effect can be seen in figure 2. When comparing Δ-RT for groups no significant difference in change was found, (F(1,43) = 0.0468, p = .83)
Figure 2. Effect of Time on Response Time (RT) in the pattern separation task. Including a 95% confidence interval. A visualization of the significant decrease in RT between baseline (base) and post-intervention (post) based on t-test results, t(41) = -3.4318, p < .01.
Accuracy – Similarity, Time, and Group
Based on a Maximum Likelihood analysis, “Accuracy ~
Similarity * Time + (1|Subject)”
was the best fitting model as can be seen in table 3.
When compared to the intercept model, the model proved to be significantly different, χ2(6,3) = 104.07 and p <.001
. Within this model the “Intercept” fixed at accuracy ratio of 0.60 (SE=0.02), “Low-similarity” increased accuracy with 0.13 (SE = 0.01), “Time Post” increased accuracy ratio with 0.04 (SE = 0.01), the interaction “Low Similarity-Time Post” affected accuracy with -0.06 (SE = 0.02) as expressed in figure 3.Table 3. Accuracy - High vs. Low Similarity models
Model DF, d AIC BIC χ2 p
Intercept + (1|Subject) 3 -291.46 -281.79
Similarity + (1|Subject) 4,1 -381.57 -368.67 92.109 2.2e-16 ***
Similarity * Time + (1|Subject) 6,2 -389.54 -370.18 11.964 0.002524 **
Similarity * Time * Group + (1|Subject)
10,4 -384.72 -352.47 3.1883 0.5268
Shows the linear mixed effects model Maximum Likelihood comparison for Time, Similarity, and Group effects. All models include a random intercept for Subject. For models including interactions individual effects are implied. Every model is compared to the model above it. “DF, d” = degrees of freedom, difference in degrees of freedom to compared model, “AIC” = Aikake Information Criterion, “BIC” = Bayesian Information Criterion, “χ2” = χ2 test, ** = ”p < 0.01”, *** = ”p < 0.001”.
Figure 3. Interaction effect of Time and Similarity of the task on response accuracy in pattern separation tasks. Including 95% confidence interval. The interaction effect proved to be significant, χ2(6,3) = 104.07 and p < 0.001.
Accuracy – Similarity, Time, and Hippocampal Volume
For the assessment of baseline Hippocampal volume effects on Accuracy, models which included different hippocampal volume measures were compared to the “Accuracy ~ Similarity * Time + (1|Subject)” model. As can be seen in Table 4, none of the tested models which included hippocampal and dentate gyrus volumes showed to be a significant improvement compared to the previous model.
Table 4. Accuracy and Similarity with Hippocampal Volumes
Model DF, d AIC BIC χ2 p
Similarity * Time + (1|Subject) 5 -369.27 -353.36 88.935
Similarity * Time + (1|Subject) + Hippocampus L 6,1 -367.37 -348.28 0.1063 0.7444 Similarity * Time + (1|Subject) + Hippocampus R 6,1 -367.27 -348.18 0.0068 0.9343 Similarity * Time + (1|Subject) + DG-L 6,1 -369.32 -350.23 2.055 0.1517 Similarity * Time + (1|Subject) + DG-R 6,1 -367.42 -348.33 0.1516 0.697
Shows the comparison of the “Accuracy ~ Similarity * Time + (1|Subject)” with hippocampal volume added models. “DF, d” = degrees of freedom, difference in degrees of freedom to compared model, “AIC” = Aikake Information Criterion, “BIC” = Bayesian Information Criterion, “χ2” = χ2 test.
RT – Time, Similarity, and Group
As Time showed to significantly affect RT, Time was the first extension on the intercept model. This also showed to be the best fitting model, was found to be significant based on a Maximum likelihood comparison to the intercept model. Neither Similarity nor Group showed to improve RT modeling. Within this model the “Intercept” was 494.71 ms (SE = 14.01) and Time Post decreased RT with 77.55 ms (SE = 8.56). The results are reflected in the table 5.
Table 5. RT and Similarity
Model DF, d AIC BIC χ2 p
Intercept + (1|Subject) 3 2216.3 2226.0
Time + (1|Subject) 4,1 2152.6 2165.4 65.755 5.1e-16***
Time * Similarity + (1|Subject) 6,2 2156.5 2175.8 0.0851 0.9584
Time * Group + (1|Subject) 6,2 2154.4 2173.7 2.1612 0.3394
The linear mixed effects model Maximum Likelihood comparison for Time, Similarity, and Group effects. All models include a random intercept for Subject. For models including interactions individual effects are implied. Model 2 is compared to model 1. Model 3 and 4 are compared to model 2. “DF,d” = degrees of freedom, difference in degrees of freedom to compared model, “AIC” = Aikake Information Criterion, “BIC” = Bayesian Information Criterion, “χ2” = χ2 test,** = ”p < 0.01”, *** = ”p < 0.001”.
RT – Time and Hippocampal Volume
The model which predicted RT based on Time and was compared to extended versions of this model, the extensions reflected different hippocampal volumes. As can be seen in table 6, these extensions did not show any significant improvement in prediction as compared to the original model.
Table 6. RT and Time with Hippocampal Volumes
Model DF, d AIC BIC χ2 p
Time + (1|Subject) 4 2063.0 2075.8
Time + (1|Subject) + Hippocampus L 5,1 2063.1 2079.0 1.9126 0.1667
Time + (1|Subject) + Hippocampus R 5,1 2063.3 2079.2 1.6985 0.1925
Time + (1|Subject) + DG-L 5,1 2064.6 2080.5 0.4533 0.5008
Time + (1|Subject) + DG-R 5,1 2064.1 2080.0 0.8858 0.3466
Shows the comparison of the “RT ~ Time + (1|Subject)” with models extended with Hippocampal Volume metrics. “DF, d” = degrees of freedom, difference in degrees of freedom to compared model, “AIC” = Aikake Information Criterion, “BIC” = Bayesian Information Criterion, “χ2” = χ2 test.
Discussion
In the current study we aimed to disentangle the effects of different exercise interventions on pattern separation in healthy young adults. Our study showed that a 12-week sports intervention significantly improved cardiorespiratory fitness in healthy young adults and that a high intensity sports intervention affects cardiorespiratory fitness more positively than a low intensity sports intervention. In this paper we provide evidence that long-term exercise increases can positively affect pattern separation. This is reflected by both a significant increase in pattern separation accuracy for trials with highly similar images and a significant decrease of the Response Time (RT) for all pattern separation trials. We could not establish that a high intensity sports intervention would have a better effect on hippocampal function than a low intensity sports intervention. The postulation that a priori larger hippocampus volumes and dentate gyrus volume would mediate the positive effects of exercise was not confirmed either.
One positive outcome was that exercise interventions can positively affect cardiorespiratory fitness and high intensity exercise regimens are more effective than low intensity exercise regimens. Multiple arguments can be posed based on the quality and nature of the exercise manipulation. The intervention results reflect the rigorous demands which were initially set for the intervention concerning the difference in aerobic demand. Extensive monitoring was performed with accessible remote sensors (Polar heart sensor) and questionnaires. However, in order to reliably assess long-term effects of exercise interventions, both a larger test population and a longer intervention are advisable, for instance during a full year period as was the case in the study performed by Erickson et al. (2011). In addition, whilst in the current study the low intensity exercise manipulation conceptually served as a control manipulation, some participants reported low intensity exercise to be quite intensive compared to their baseline physical activity. Furthermore, exercise has shown to positively affect pattern separation performance, as will be discussed in more depth in the following paragraphs. It is important to note that much of the divergence in positive hippocampal function results in previous research has been reported for long-term aerobic exercise amongst elderly subjects or neurodegenerative patient groups (Erickson 2011; Mortimer, 2012). Firth et al. (2017) suggest that such effects amongst patient groups and elderly subjects can be attributed to the protective influence of aerobic exercise against the neuronal mass decreases that come with healthy aging (Ding et al., 2016).
The results have not affirmed all expectations concerning long-term exercise effects on hippocampal function. Regarding accuracy during a pattern separation task, the results showed no
general effects nor were there any exercise group effects. The absence of general accuracy effects does conform to Pohlack’s theory (2014) that hippocampal neurogenesis may primarily affect discriminability. However, the hypothesized specific improvement for trials based on highly similar images showed a significant increase in accuracy. Participants were less accurate during high similarity trials in general, however for these trials accuracy significantly improved following the exercise intervention whilst this was not the case for low similarity trials. This also concurs with findings by Creer et al. (2010) which showed that in particular performance on high similarity tasks improved.
While there was no specific hypothesized effect, long-term exercise seems to improve RT. In contrast to accuracy, RT diminished significantly following the exercise intervention across all trial types. Again, no intervention group effects were found. Exercise has been shown to positively affect RT during working memory related tasks; in healthy, elderly, and pathological subjects. However, these results were reflected in studies concerning acute exercise effects rather than long term exercise effects (McMorris et al 2011; Moriya, Aoki, & Sakatani 2016).
Mediating effects of a priori larger hippocampal volumes on exercise induced increases in hippocampal functioning for young and healthy adults cannot be claimed on the basis of the current study. While some papers have hinted at increased effects of baseline hippocampus volumes, these are often the case for elderly subjects (Erickson 2011; Mortimer, 2012). It remains difficult to distill effects amongst neurodegenerative patient groups and elderly subjects and apply these to the young and healthy adults.
Concerning hippocampal function results, a consideration of speed-accuracy trade-off (SAT) may clarify the current and future research. While accuracy is the prime parameter and RT is regarded as a secondary parameter for the applied pattern separation, one can coarsely define cognitive performance as a function of task execution accuracy and speed. When accuracy remains at level and speed increases we can speak of a general increase in performance (Heitz, 2014). Previous research (Heitz, 2014; Heitz & Schall, 2012) has reflected SAT effects of exercise on working memory related tasks, to which pattern separation belongs. At the same time SAT effects are not typically attributed to the hippocampus but rather to other brain substrates, namely the basal ganglia, frontal eye field, lateral intraparietal area, and superior colliculus (Heitz & Schall 2012). For a deeper understanding of RT within pattern separation tasks, it may be informative to further investigate SAT effects for the applied pattern separation task as constructed by Guijt et al.(unpublished) and subject it to drift decision model (DDM) analyses (Turner, Van Maanen, &
Forstmann, 2015). The latent parameters applied in this model can help to distinguish task specificity, distribution of and balance between speed and accuracy. Such an approach would be particularly useful for research with young and healthy adults, where effects might be sparse, a problem which is stated in previous exercise related research (Gourgouvelis et. al, 2018).
The absence of the hypothesized high intensity vs. low intensity exercise effects on hippocampal function can be somewhat resolved considering the constitution of the current test population and that of previous research. A large portion of promising research was reflected in animal studies (Griffin et al, 2011) and studies focusing on neurodegenerative patient groups and elderly subjects (Erickson et al, 2011; Leavitt et al., 2013; Paillard et al., 2015; Mortimer 2012; Duzel et al 2016). Whilst there is biological similarity between human and animal subjects, comparing results or even similarity of applied test framework demands the utmost care (Premack, 2007). For instance, the lab mice used in research lead a very sedentary lifestyle compared to healthy young adult human subjects. In a similar fashion, compared to neurodegenerative patients and elderly subjects, young and healthy adults may show hippocampal function that is close to its functional ceiling. Even minor exercise may raise hippocampal functioning to ceiling level. As neurodegenerative patients and elderly subjects are further removed from this functional ceiling to begin with, they may have more to benefit from exercise interventions and be more susceptible to increased benefits of high intensity workouts. Keeping hippocampal functional ceiling proximity for young healthy adults in mind, similar future studies are advised to include a non-exercise control for which exercise is substituted by an activity other than exercise.
In summary, the current study has reflected some of the theoretical expectations that exercise interventions may positively affect pattern separation in healthy adults. Accuracy was shown to significantly increase for trials with highly similar stimuli. The improved accuracy for these specific tasks reflects an increase in discriminability. However, no general accuracy increases on pattern separation were found. RT, a secondary performance measure during the pattern separation task, showed a general improvement across all trial types. We deliberated on the nature of cognitive performance as a function of task accuracy and speed, suggesting that our results also reflect a general improvement in pattern separation. To solidify such claims in future research we suggest performing speed-accuracy trade-off analyses on the applied pattern separation task. This study could not provide any evidence concerning mediating effects of baseline hippocampus volumes on functional increases. Furthermore, no high versus low intensity exercise intervention effects were found concerning hippocampal functioning. We have posed the idea of a hippocampal functional ceiling, which addresses the absence of exercise intensity effects in healthy adults in comparison to
studies focused on neurodegenerative patient groups and the elderly subjects, where exercise intensity effects have been found.
Conclusion
Based on the established quality measures, the current study provides a promising foundation for the application of similar exercise intervention methods in future research. Such research is advised to include a non-exercise control group in the case of research which wishes to distill high-intensity from low-intensity effects. Finally, it may be concluded that exercise interventions can positively affect pattern separation in healthy young adults, particularly for the discriminability of highly similar information.
References
Ament, W., & Verkerke, G. J. (2009). Exercise and Fatigue. SPORTS MEDICINE, 39(5), 389-422 Bakker, A., Krauss, G. L., Albert, M. S., Speck, C. L., Jones, L. R., Stark, C. E., … Gallagher, M. (2012).
Reduction of Hippocampal Hyperactivity Improves Cognition in Amnestic Mild Cognitive Impairment. Neuron, 74(3), 467–474. http://doi.org/10.1016/j.neuron.2012.03.023 Bird, C. M., & Burgess, N. (2008). The hippocampus and memory: insights from spatial processing.
Nature Reviews: Neuroscience, 9, 182 - 194
Brickman, A. M., Khan, U. A., Provenzano, F. A., Yeung, L.-K., Suzuki, W., Schroeter, H., Small, S. A. (2014). Enhancing dentate gyrus function with dietary flavanols improves cognition in older adults. Nature Neuroscience, 17(12), 1798–803. http://doi.org/10.1038/nn.3850 Colcombe, S. J., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R., McAuley, E., Elavsky, S., Marquez, D.
X., Hu, L., & Kramer, A. F. (2006). Aerobic exercise training increases brain volume in aging humans. The Journals of Gerontology: Series A, 61(11), 1166-1170.
https://doi.org/10.1093/gerona/61.11.1166
Cotman, C.W., & Berchtold, N.C. (2002). Exercise: a behavioral intervention to enhance brain health and plasticity. Trends Neuroscience, 25(6), 295-301. Review. PubMed PMID: 12086747. Creer, D. J., Romberg, C., Saksida, L. M., van Praag, H., & Bussey, T. J. (2010). Running enhances
spatial pattern separation in mice. Proceedings of the National Academy of Sciences of the United States of America, 107(5), 2367–2372. http://doi.org/10.1073/pnas.0911725107 Deng, W., Aimone, J. B., & Gage, F. H. (2010). New neurons and new memories: how does adult
hippocampal neurogenesis affect learning and memory? Nature Reviews. Neuroscience, 11(5), 339–50. http://doi.org/10.1038/nrn2822
Ding, X. Q., Maudsley, A. A., Sabati, M., Sheriff, S., Schmitz, B., Schütze, M., ... & Lanfermann, H. (2016). Physiological neuronal decline in healthy aging human brain—An in vivo study with MRI and short echo-time whole-brain 1H MR spectroscopic imaging. NeuroImage, 137, 45-51.
Duzel, E., van Praag, H., & Sendtner, M. (2016). Can physical exercise in old age improve memory and hippocampal function? Brain, 139(3), 662–673. http://doi.org/10.1093/brain/awv407
Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., … Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences, 108(7), 3017–3022.
http://doi.org/10.1073/pnas.1015950108
Eriksson, P. S., Perfilieva, E., Björk-Eriksson, T., Alborn, A. M., Nordborg, C., Peterson, D. A., & Gage, F. (1998). Neurogenesis in the adult human hippocampus. Nature Medicine, 4(11),
1313-1317. http://doi.org/10.1038/3305
Farioli‐Vecchioli, S., Mattera, A., Micheli, L., Ceccarelli, M., Leonardi, L., Saraulli, D., ... & Tirone, F. (2014). Running rescues defective adult neurogenesis by shortening the length of the cell cycle of neural stem and progenitor cells. Stem Cells, 32(7), 1968-1982.
Firth, J., Stubbs, B., Vancampfort, D., Schuch, F., Lagapoulos, J., Rosenbaum, S., & Ward, P.B. (2017). Effect of aerobic exercise on hippocampal volume in humans: A systematic review and meta-analysis. Neuroimage, 166, 230-238.
https://doi.org/10.1016/j.neuroimage.2017.11.007
Gourgouvelis, J., Yielder, P., Clarke, S. T., Behbahani, H., & Murphy, B. (2018). You can’t fix what isn’t broken: eight weeks of exercise do not substantially change cognitive function and
biochemical markers in young and healthy adults. PeerJ, 6, e4675.
Hariprasad, V. R., Varambally, S., Shivakumar, V., Kalmady, S. V., Venkatasubramanian, G., & Gangadhar, B. N. (2013). Yoga increases the volume of the hippocampus in elderly subjects. Indian Journal of Psychiatry, 55(Suppl 3), S394–S396.
http://doi.org/10.4103/0019-5545.116309
Heitz, R. P., & Schall, J. D. (2012). Neural Mechanisms of Speed-Accuracy Tradeoff. Neuron, 76(3), 616–628. http://doi.org/10.1016/j.neuron.2012.08.030
Heitz, R. P. (2014). The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Frontiers in Neuroscience, 8, 150. http://doi.org/10.3389/fnins.2014.00150
Kempermann, G., Kuhn, H. G., & Gage, F. H. (1997). More hippocampal neurons in adult mice living in an enriched environment. Nature, 386(6624), 493. http://doi.org/10.1038/386493a0 Kitamura, T., Saitoh, Y., Takashima, N., Murayama, A., Niibori, Y., Ageta, H., ... & Inokuchi, K. (2009).
Adult neurogenesis modulates the hippocampus-dependent period of associative fear memory. Cell, 139(4), 814-827.
Leavitt, V. M., Cirnigliaro, C., Cohen, A., Farag, A., Brooks, M., Wecht, J. M., ... & Sumowski, J. F. (2014). Aerobic exercise increases hippocampal volume and improves memory in multiple
sclerosis: preliminary findings. Neurocase, 20(6), 695-697. DOI: 10.1080/13554794.2013.841951
Lee, I. M., Hsieh, C. C., & Paffenbarger, R. S. (1995). Exercise intensity and longevity in men: the Harvard Alumni Health Study. Jama, 273(15), 1179-1184.
Leuner, B., & Gould, E. (2010). Structural plasticity and hippocampal function. Annual review of psychology, 61, 111-140. http://doi.org/10.1146/annurev.psych.093008.100359
Marlatt, M. W., Potter, M. C., Lucassen, P. J., & van Praag, H. (2012). Running throughout middle‐age improves memory function, hippocampal neurogenesis, and BDNF levels in female
C57BL/6J mice. Developmental neurobiology, 72(6), 943-952. http://doi.org/10.1002/dneu.22009.Running
Manganas, L. N., Zhang, X., Li, Y., Hazel, R. D., Smith, S. D., Wagshul, M. E., ... & Maletić-Savatić, M. (2007). Magnetic resonance spectroscopy identifies neural progenitor cells in the live human brain. Science, 318(5852), 980-985. http://doi.org/10.1126/science.1147851 McMorris, T., Sproule, J., Turner, A., & Hale, B. J. (2011). Acute, intermediate intensity exercise, and
speed and accuracy in working memory tasks: a meta-analytical comparison of effects. Physiology & behavior, 102(3-4), 421-428.
https://doi.org/10.1016/j.physbeh.2010.12.007.
Mortimer, J. A., Ding, D., Borenstein, A. R., DeCarli, C., Guo, Q., Wu, Y., … Chu, S. (2012). Changes in Brain Volume and Cognition in a Randomized Trial of Exercise and Social Interaction in a Community-Based Sample of Non-Demented Chinese Elders. Journal of Alzheimer’s Disease : JAD, 30(4), 757–766. http://doi.org/10.3233/JAD-2012-120079
Moriya, M., Aoki, C., & Sakatani, K. (2016). Effects of physical exercise on working memory and prefrontal cortex function in post-stroke patients. In Oxygen Transport to Tissue XXXVIII (pp. 203-208). Springer, Cham.
Nokia, M. S., Lensu, S., Ahtiainen, J. P., Johansson, P. P., Koch, L. G., Britton, S. L., & Kainulainen, H. (2016). Physical exercise increases adult hippocampal neurogenesis in male rats provided it is aerobic and sustained. The Journal of Physiology, 594(7), 1855–1873.
Paillard, T., Rolland, Y., & de Souto Barreto, P. (2015). Protective Effects of Physical Exercise in Alzheimer’s Disease and Parkinson’s Disease: A Narrative Review. Journal of Clinical Neurology (Seoul, Korea), 11(3), 212–219. http://doi.org/10.3988/jcn.2015.11.3.212
Pereira, A. C., Huddleston, D. E., Brickman, A. M., Sosunov, A. A., Hen, R., McKhann, G. M., ... & Small, S. A. (2007). An in vivo correlate of exercise-induced neurogenesis in the adult dentate
gyrus. Proceedings of the National Academy of Sciences, 104(13), 5638-5643. http://doi.org/10.1073/pnas.0611721104
Premack, D. (2007). Human and animal cognition: Continuity and discontinuity. Proceedings of the National Academy of Sciences of the United States of America, 104(35), 13861–13867. http://doi.org/10.1073/pnas.0706147104
Pohlack, S. T., Meyer, P., Cacciaglia, R., Liebscher, C., Ridder, S., & Flor, H. (2014). Bigger is better! Hippocampal volume and declarative memory performance in healthy young men. Brain Structure & Function, 219(1), 255–267. http://doi.org/10.1007/s00429-012-0497-z Turner, B. M., Van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through
neuroimaging: the neural drift diffusion model. Psychological review, 122(2), 312. http://dx.doi.org/10.1037/a0038894
Van Praag, H., Kempermann, G., & Gage, F. H. (1999). Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nature neuroscience, 2(3), 266. http://doi.org/10.1038/6368
Vivar, C., Peterson, B. D., & van Praag, H. (2016). Running rewires the neuronal network of born dentate granule cells. Neuroimage, 131, 29-41.
http://doi.org/10.1016/j.neuroimage.2015.11.031
Yassa, M. A., Lacy, J. W., Stark, S. M., Albert, M. S., Gallagher, M., & Stark, C. E. L. (2011). Pattern separation deficits associated with increased hippocampal CA3 and dentate gyrus activity in nondemented older adults. Hippocampus, 21(9), 968–979.
