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Extensive learning is associated with gray matter changes in the right hippocampus
YNIMG-12686; No. of pages: 6; 4C: 3, 4 NeuroImage xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
NeuroImage
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Kathrin Koch a,b,c,⁎, Tim Jonas Reess a,b,c, Oana Georgiana Rus a,b,c, Claus Zimmer a,b
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Extensive learning is associated with gray matter changes in the right hippocampus
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Article history: Received 2 September 2015 Accepted 21 October 2015 Available online xxxx
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Keywords: Gray matter volume Hippocampus Subiculum Neuroplasticity
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Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaningerstrasse 22, 81675 Munich, Germany TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München TUM, Ismaninger Strasse 22, 81675 Munich, Germany Graduate School of Systemic Neurosciences GSN, Ludwig-Maximilians-Universität, Biocenter, Groβhaderner Strasse 2, 82152 Munich, Germany
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Longitudinal voxel-based morphometry studies have demonstrated increases in gray matter volume in hippocampal areas following extensive cognitive learning. Moreover, there is increasing evidence for the relevance of the subiculum in the context of learning and memory. Using longitudinal FreeSurfer analyses and hippocampus subfield segmentation the present study investigated the effects of 14 weeks of intensive learning on hippocampal and subicular gray matter volume in a sample of medical students compared to control subjects not engaged in any cognitive learning activities. We found that extensive learning resulted in a significant increase of right hippocampal volume. Volume of the left hippocampus and the subiculum remained unchanged. The current findings emphasize the role of the hippocampus in semantic learning and memory processes and provide further evidence for the neuroplastic ability of the hippocampus in the context of cognitive learning. © 2015 Published by Elsevier Inc.
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Introduction
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An increasing amount of evidence supports the notion that the brain undergoes continuous activity-dependent neuroplastic changes across the life-span. In structural imaging studies these changes have been demonstrated as a consequence of various activities, such as learning to juggle (Boyke et al., 2008; Draganski et al., 2004), learning of mirror reading (Ilg et al., 2008), learning of new color names (Kwok et al., 2011), motor exercise (Niemann et al., 2014), video gaming (Kuhn et al., 2014) or meditation (Kurth et al., 2014). Surprisingly few evidence, on the other hand, has been provided for structural changes in association with learning of abstract information (Ceccarelli et al., 2009; Draganski et al., 2006). Ceccarelli et al. (2009) explored the effects of two weeks of intensive learning and found a fronto-parietal gray matter (GM) volume increase. Draganski et al. (2006) used voxel-based morphometry at three different time points to investigate the effects of intensive learning in students preparing for their medical exam. They found a significant gray matter increase in the posterior and lateral parietal cortex bilaterally and a significant increase in the right hippocampus during the learning period which augmented even further during the subsequent semester break. The primary function of the hippocampus is clearly memory-related. The hippocampus plays a key
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⁎ Corresponding author at: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaningerstrasse 22, 81675 Munich, Germany. Fax: +49 89 41404887. E-mail address: [email protected] (K. Koch).
role in the consolidation of information from short-term to long-term memory and a number of studies have shown a correlation between hippocampus volume and memory performance (Arlt et al., 2013; Avery et al., 2013; Gimenez et al., 2004; Pohlack et al., 2014; Siraly et al., 2015). Thus, Arlt et al. (2013) and Gimenez et al. (2004) reported a correlation between left hippocampal volume and verbal working memory, Avery et al. (2013) demonstrated an association between total hippocampal volume and relational working memory and Pohlack et al. (2014) reported an association between total hippocampal volume and verbal working memory. The hippocampus or hippocampal formation can be subdivided into several subfields (dentate gyrus, areas CA3 and CA1, entorhinal cortex and subiculum) (Amaral and Witter, 1995), out of which the subiculum constitutes one of the largest subfields and the major output structure. There is increasing evidence indicating that the subiculum is the subfield that is most strongly involved in basic memory processes and semantic learning (O'Mara et al., 2009). It receives the majority of efferent information from the CA1 region of the hippocampus thus being in a position to integrate, transfer and resolve information from other parts of the hippocampus related to learning and memory (Amaral et al., 1991; Deadwyler and Hampson, 2006). Nevertheless, changes in gray matter volume in association with learning of semantic information have only been reported for the hippocampus formation as a whole whereas potential gray matter changes in the subiculum have not been specifically investigated up to now. Learning-related changes in gray matter volume of the hippocampus
http://dx.doi.org/10.1016/j.neuroimage.2015.10.056 1053-8119/© 2015 Published by Elsevier Inc.
Please cite this article as: Koch, K., et al., Extensive learning is associated with gray matter changes in the right hippocampus, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.10.056
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Materials and methods
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We recruited 35 medical students from the Medical School of our University and 24 healthy control subject. 7 medical students and 6 control subjects dropped out after the first scan resulting in a final sample size of 28 right-handed healthy students (m:f = 14:14, mean age = 19.3 years, SD = 1.0 years) and 18 right-handed healthy control subjects (m:f = 6:12, mean age = 18.6 years, SD = 0.5 years) with no history of neurological or psychiatric disorders or other serious medical conditions. Groups were matched according to their level of education (i.e., both had the German “Abitur”) and they were carefully selected regarding the amount of physical exercise and musical activities they were doing (i.e., professional athletes or musicians or subjects excessively engaged in sports or playing an instrument were not included). In both groups, T1-weighted magnetic resonance imaging (MRI) scans were performed at two time points, TP1 and TP2 (i.e., 14 weeks after TP1). In the student group, the first scan was performed at the beginning of their first semester of medical school, the second scan was performed fourteen weeks later shortly before their first semester medical exams. During this time period the students spent on average about 190 h in class and 280 h outside of class with learning facts and information related to anatomy, chemistry, and biology. The control subjects had recently started a voluntary social year. They were not attending any lesson or studying for any exams between TP1 and TP2. Handedness was assessed using Annett's handedness inventory. All participants gave written informed consent to the study protocol which is in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee of the Technische Universität München, Medical School.
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We used the digit span task to assess cognitive performance and to investigate a potential association between memory performance and gray matter increase. The test consists of a forward and a backward version. In the forward version of the test, a list of random numbers is read out which the participant has to recall in the correct order. The test begins with two numbers, increasing a number at a time until two errors are committed in a row. In the backward version, participants are asked to recall the digits in backward order. Thus, the forward version mainly reflects working memory performance, the backward version assesses predominantly manipulation of stimulus material and executive processing. Memory performance was investigated using a repeated measures one-way ANOVA with digit span forward performance as the dependent variable, group as a between subject factor and measurement time point (TP1, TP2) as a within subject factor. Digit span data of one medical student and one control are missing. Gray matter volume was assessed using the FreeSurfer software package (version 5.3.0, http://surfer.nmr.harvard.edu). The initial processing of T1 high-resolution images includes several steps which have been described in previous papers (Dale et al., 1999; Fischl et al., 1999). Briefly, the implemented processing stream contains removal of non-brain tissue, transformation to Talairach-like space, and segmentation of gray/white matter tissue. White and gray matter boundary is tessellated and topological defects are automatically corrected. After intensity normalization and transition of gray/white matter, pial boundary is indicated by detecting the greatest shift in intensity through surface deformation. Segmented data were then parcellated into units based on gyral and sulcal structure, resulting in values for gyrification and volume. Maps were smoothed using a Gaussian kernel of 10 mm. Subsequently, for the longitudinal processing, an unbiased within-subject template is created using robust, inverse consistent registration to estimate average subject anatomy across both measurement time points (Reuter et al., 2012). Finally, each time point is processed “longitudinally”, where information from the subjecttemplate and from the individual runs are used (see Fig. 1, for details please refer to Reuter et al. (2012)). This procedure has been demonstrated to significantly increase reliability and statistical power in longitudinal studies (Ibarretxe-Bilbao et al., 2012; Kwan et al., 2012; Reuter et al., 2012). Then, an automated subfield segmentation of the hippocampus was performed using Bayesian inference and a probabilistic atlas of the hippocampal formation. The hippocampal subfield volumes obtained with this method have been compared to manual hippocampal subfield tracings, and reliability measures were good for the larger subfields (CA2/3, CA4/DG, subiculum) and only acceptable for the smaller ones (CA1, presubiculum, fimbria) (Van Leemput et al., 2009). Seven hippocampal subfield volumes were automatically calculated including the fimbria (white matter), presubiculum, subiculum, CA1, CA2–3, and CA4-DG fields (gray matter) as well as the hippocampal fissure (cerebrospinal fluid). The procedures for subfield segmentation have been described elsewhere (Van Leemput et al., 2009). The present analyses focus on the subiculum. To investigate the hypothesized changes in hippocampal and subicular gray matter volume, for each subject and time point,
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High-resolution anatomical T1-weighted volume scans (MP-RAGE) were collected on a 3 T whole body system equipped with a 12element receive-only head matrix coil (INGENIA, Philips). They were obtained in sagittal orientation (TR = 9 ms, TE = 4 ms, TI = 900 ms, flip angle = 8°, FOV = 240 × 240 mm2, matrix = 240 mm × 240 mm, number of sagittal slices = 170) with an isotropic resolution of 1 × 1 × 1 mm3.
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are most likely the consequences of synaptic or dendritic sprouting or increases in synaptic strength and neuronal growth (i.e., increases of neuronal somae and nuclei) and, as such, very small (Neves et al., 2008). Hence, high image resolution is needed to detect the aggregated effects of such changes on the macroscopic level across time. Against this background, in the present study we used FreeSurfer (http:// surfer.nmr.mgh.harvard.edu) which is a software package capable of detecting sub-millimeter changes in gray matter volume. The software has a longitudinal image processing framework (i.e., FreeSurfer longitudinal). This framework is based on unbiased, robust, within-subject template creation which has been demonstrated to successfully reduce variability, avoid over-regularization and increase power to detect structural changes across time by initializing the processing in each time point with common information from the within-subject template (Reuter et al., 2012). We used this longitudinal framework to investigate the effects of intensive learning on gray matter volume and expected increases in the hippocampus and more specifically the subiculum as a consequence of learning. In addition, we investigated whether baseline gray matter volume predicted learning-associated increases in hippocampal gray matter volume. Finally, based on the above mentioned findings of an association between memory performance and hippocampus volume, we used the digit span task, an established and frequently employed working memory task, to explore whether memory performance at the first measurement time point predicted learningassociated increases in hippocampal gray matter volume. Thus, we intended to extend previous findings of a mere association between working memory performance and hippocampal volume and to explore a directed association by investigating whether working memory performance predicts hippocampal volume change. Identifying parameters which allow the prediction of neuroplastic processes on an individual basis would be of high practical use, for instance in the field of cognitive remediation and training.
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Removal of non-brain tissue Automated Talairach Transformation Intensity normalization Tesselation of GM/WM boundary Automated topology correction Surface deformation
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Fig. 1. Simplified diagram of the three processing steps of the longitudinal processing approach. First, all high resolution volumes of all subjects are preprocessed independently. Second, based on inverse consistent robust registration an individual within-subject template is created for each subject from both measurement time points to estimate the average individual anatomy. Third, each high resolution volume from each measurement time point is processed longitudinally using information from the within-subject template and from the first processing step.
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volume data were extracted for the left/right hippocampus and left/ right subiculum. As hippocampus subfields in one medical student could not be segmented properly this subject was excluded from the subiculum analyses. To put emphasis on the volumetric change across time and to minimize confounding effects of a priori volumetric differences between subjects and groups, we investigated the percent change across time. To investigate the hypothesized differences in hippocampal gray matter change between the groups we used two (i.e., one for each hemisphere) one-way ANCOVAs with percent volumetric change (i.e., percent change between TP1 and TP2 hippocampus volume) as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate (to control for the effect of total individual ICV). For the structure with a significant group difference (i.e., the right hippocampus, see Results section) we subsequently used another one-way ANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2 right subicular volume) as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate to investigate differences in subicular gray matter change between the groups. On an exploratory basis, we additionally performed a MANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2) for all additional subfields of the automated subfield segmentation (i.e., presubiculum, CA1, CA2/3, fimbria, CA4/DG, hippocampal fissure) as dependent variables, group as between subject factor and total intracranial volume (ICV) as covariate to also investigate potential differences in gray matter change of other subfields between the groups. To exclude a potential effect of gender on the group difference in volumetric change a one-way ANCOVA with gender as between subject factor and ICV as covariate was performed post hoc for those structures that showed a significant group difference. To investigate a potential association between baseline gray matter volume and learning-associated increases in hippocampal gray matter volume we performed a correlation between hippocampal volume at TP1 and change in hippocampal volume across time. Finally, to investigate a potential association between memory performance and gray matter change (or, in other terms, whether memory performance at TP1 predicts subsequent volumetric gray matter change) a correlation between digit span forward performance and volumetric change was calculated using Pearson's correlation.
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The repeated measures ANOVA on working memory performance (i.e., digit span forward performance) yielded a significant group effect (F(1,42) = 7.5, p b 0.01) indicating significantly better working memory performance in the group of the students, no significant effect of measurement time point (F(1,42) = 0.001, p = 0.97) and no significant interaction (F(1,42) = 0.091, p = 0.76).
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The one-way ANCOVA with percent volumetric change of the right hippocampus as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded a significant group effect (F(1,43) = 5.3, p = 0.03) indicating a larger volumetric change in the student group compared to the control subjects (Fig. 2). Post hoc one-sample t-tests corroborated that the percent change of the right hippocampus volume was significant (i.e., significantly different from zero) in the student group (t(27) = 2.1, p = 0.045), but not in the control group (t(17) = − 1.4, p = 0.196). The one-way ANCOVA with percent volumetric change of the left hippocampus as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant group effect (F(1,43) = 0.01, p = 0.94). The one-way ANCOVA with percent volumetric change of the right subiculum as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant group effect (F(1,42) = 0.91, p = 0.35, Fig. 2). Hippocampal and subicular volumetric changes (percent volume change and mean volumetric change in mm3) for both groups are presented in Table 1. The MANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2) for all additional subfields of the automated subfield segmentation as dependent variables, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant overall group effect (F(1,37) = 0.76, p = 0.61) and, accordingly, no significant group effects for the separate subfields. Gender had no effect on the percent volumetric change in the right hippocampus, i.e., the one-way ANCOVA with gender as between subject factor and ICV as
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Please cite this article as: Koch, K., et al., Extensive learning is associated with gray matter changes in the right hippocampus, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.10.056
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Fig. 2. Percentage change between TP1 and TP2 in gray matter volume of the right hippocampus and right subiculum in students and control subjects (hippocampus indicated in yellow, subiculum indicated in light yellow, mean percentage change in each group indicated by red line).
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findings by Draganski et al. (2006) who used voxel-based morphometry to investigate the effects of intensive learning in a group of medical students and found a significant increase in GM volume in the parietal cortex and the right hippocampus during the learning period which augmented even further during the subsequent semester break. Thus, these previous and the present findings underline the relevance of the right hippocampus for the acquisition and consolidation of semantic knowledge. They provide evidence for an activity related plasticity of the hippocampus and corroborate the assumption that earlier findings of increased volume in line with better memory performance are not reflective of an innate relationship but can be assumed to indicate an activity dependent increase in gray matter volume. In keeping with this assumption the hippocampus is known as a structure that possesses the ability to generate neurons from stem cells (Aimone et al., 2014). This neurogenetic ability may explain the peculiar plasticity of this brain structure. Hippocampus-dependent learning is one of the major regulators of hippocampal neurogenesis (Gould et al., 1999). Thus, it has been demonstrated in rats that learning of hippocampusdependent tasks but not hippocampus independent tasks increases the number of new neurons in the subventricular zone and the subgranular zone of the hippocampus (Deng et al., 2010; Leuner et al., 2004, 2006). It should be noted, however, that certain influencing factors such as task difficulty or sex differences are to be taken into consideration when describing the effects of learning (i.e., in rats mainly spatial learning) on hippocampal neuroplasticity (Epp et al., 2013). For instance, in a study by Chow et al. (2013) stronger effects of spatial learning on hippocampal neurogenesis have been found in male compared to female rats. Once neuroplastic changes which can seemingly take place throughout life have become manifest, these adult generated neurons are functional and exhibit morphological and physiological properties that are practically indistinguishable from developmentally originated neurons (van Praag et al., 2002). In humans, a recent study by Kuhn et al. (2014) provides additional indication of hippocampal plasticity as a result of intensive motorcognitive activity. They investigated the effects of excessive 2-month video gaming which demanded complex cognitive and motor skills and found a significant gray matter increase in hippocampus, dorsolateral prefrontal cortex and bilateral cerebellum in the gaming group compared to a control group. Of note, as in the present study the increase in hippocampal volume was restricted to the right side. Structural hippocampal increases have also been reported in a recent study by Miskowiak et al. (2014) who studied the effects of 8 weekly Erythropoietin (EPO) infusions on cognition and hippocampus volume in moderately depressed or bipolar patients. EPO is known to increase neuroplasticity and to improve cognitive performance. They found that EPO was associated with mood-independent memory improvement and reversal of gray matter loss in several hippocampal subfields. Here, however, changes were detectable in the left hippocampus only. One can only speculate on the reasons for the differential structural changes of left and right hippocampus. As also discussed by Draganski et al. (2006) the learning period is a highly stressful time for medical students. Stress is known to counteract neuroplastic processes in the hippocampus (Saaltink and Vreugdenhil, 2014) including neurogenesis and synaptic growth or sprouting. The present data give reason to assume that the stress-related negative effects on neuroplasticity may affect the left hippocampus more strongly than the right. This hypothesis, however, remains speculative and has to be underpinned by further studies.
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Correlation between gray matter volume change and memory performance
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The Pearson's correlation investigating a potential association between gray matter volume of the right hippocampus at TP1 and change in right hippocampal volume (i.e., TP2–TP1) yielded no significant result (medical students: r = − 0.01, p = 0.95, control group: r = −0.10, p = 0.69).
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Finally, the Pearson's correlation investigating a potential association between memory performance (i.e., digit span forward performance) and the right hippocampal volumetric change of the student group showed no significant result (r = 0.3, p = 0.12).
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The present study investigated the effects of intensive learning on hippocampal and subicular gray matter volume employing, as one of the first studies, longitudinal FreeSurfer analyses. We found that 14 weeks of intensive learning resulted in a significant increase of right hippocampal volume whereas volume of the left hippocampus and the subiculum remained unchanged. The result of a right hippocampal volume increase as a result of intensive learning is in line with
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Table 1 Difference in hippocampal and subicular volume between both time points (i.e., TP2–TP1) in control subjects and students (percent volume change / average absolute volume change in mm3 ± SD percent volume change).
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covariate showed no significant effect of gender, neither in the whole group (F(1,43) = 0.09, p = 0.76) nor in the group of the students only (F(1,25) = 0.05, p = 0.83).
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0.77 / 34.6 mm3 (1.92) −0.59 / −26.4 mm3 (1.86)
−0.03 / −1.2 mm3 (1.65) −0.11 / −4.2 mm3 (1.90)
0.38 / 19.9 mm3 (2.31) −0.45 / −24.9 mm3 (3.03)
Please cite this article as: Koch, K., et al., Extensive learning is associated with gray matter changes in the right hippocampus, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.10.056
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Groups differed significantly in memory performance with the control group demonstrating significantly less correct responses in the forward digit span which assesses mainly working memory performance. However, to minimize the effects of potential a priori differences we investigated the percentage volumetric change across time. Hence, a priori differences in cognitive performance or hippocampal gray matter structure do not explain the significantly larger hippocampal increase we found in the student group. This is also substantiated by the results of the correlation between baseline right hippocampal gray matter volume and change in right hippocampal gray matter volume which
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was not significant in either of the two groups. Taken together, data of the present study, which is – to our best knowledge – the first study using longitudinal surface based analyses to investigate the effects of intensive learning on gray matter structure indicate a significant learning associated volumetric increase in the right hippocampus. As such, the current findings emphasize the role of the hippocampus in semantic learning and memory processes and provide further evidence for the neuroplastic ability of this structure in the context of learning.
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There was no statistically significant differential volumetric change in the subiculum (although – descriptively – subicular volume increased in the group of the students and decreased in the control group). This was an unexpected finding given mounting evidence for the relevance of the subiculum for memory and learning (de la Prida et al., 2006; O'Mara et al., 2009). The subiculum represents an interface between major parts of the hippocampal formation and core cortical and subcortical regions critically involved in memory processes. Thus, it occupies a strategic position in which to integrate, transfer and resolve activity from other parts of the hippocampus relating to memory and performance (Deadwyler and Hampson, 2004). Excitotoxic lesions of the subiculum have been demonstrated to disrupt object recognition and spatial memory processes in rats (Liu et al., 2001) and the temporal coupling between CA1 and subicular neurons has been shown to underlie retention of trial-specific information during delayed non-match to sample (DNMS) tasks indicating that subicular neurons might be able to integratively process spatial and working memory information (Deadwyler and Hampson, 2006). Moreover, earlier studies by Commins et al. (1998) have demonstrated that long-term potentiation (LTP) can be induced at the subicular pyramidal cell synapse likewise indicating that the subiculum may play a pivotal role in the hippocampal memory system. Against the background of these findings the lack of a learning-associated increase in gray matter volume of the subiculum may be contrary to expectation. Considering, however, that there were also no significant changes in the other hippocampal subregions (i.e., presubiculum, CA1, CA2/3, fimbria, CA4/DG, hippocampal fissure) separately, the lacking significance may have methodological reasons, i.e., the statistically significant change of the hippocampal formation seems to be driven by an overall change of the whole structure while the change in a single subregion may per se not be large enough to reach statistical significance. Regarding the lacking effect of the subiculum it is moreover conceivable that structural changes in hippocampus formation occur fast, whereas subiculum changes become manifest only at a later stage. Thus, the duration of the learning period is certainly of major relevance although little is known about the temporal characteristics of structural plasticity. Some studies demonstrated gray matter changes as a consequence of cognitive learning after only two weeks (Ceccarelli et al., 2009; Kwok et al., 2011) and in the context of motor learning even after a few days (Nudo, 2006). Studies in spinal-cord injured mice suggest that a time period of several months seems to be necessary for newly generated stem cells to differentiate into neurons. More data, however, are needed to get a better idea of the temporal aspects of learning related gray matter changes. There was no statistically significant correlation between memory performance at the beginning of the learning period and right hippocampal volume increase. Although the correlation was positive this finding suggests that digit span working memory performance does not predict individual hippocampal plasticity. The rather small sample size of the present study may, however, have contributed to the lacking significance. Hence, further studies with a larger sample size are needed to identify reliable predictors for learning associated neuroplastic changes.
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Leuner, B., Mendolia-Loffredo, S., Kozorovitskiy, Y., Samburg, D., Gould, E., Shors, T.J., 2004. Learning enhances the survival of new neurons beyond the time when the hippocampus is required for memory. J. Neurosci. 24, 7477–7481. Leuner, B., Waddell, J., Gould, E., Shors, T.J., 2006. Temporal discontiguity is neither necessary nor sufficient for learning-induced effects on adult neurogenesis. J. Neurosci. 26, 13437–13442. Liu, P., Jarrard, L.E., Bilkey, D.K., 2001. Excitotoxic lesions of the pre- and parasubiculum disrupt object recognition and spatial memory processes. Behav. Neurosci. 115, 112–124. Miskowiak, K.W., Vinberg, M., Macoveanu, J., Ehrenreich, H., Koster, N., Inkster, B., Paulson, O.B., Kessing, L.V., Skimminge, A., Siebner, H.R., 2014. Effects of erythropoietin on hippocampal volume and memory in mood disorders. Biol. Psychiatry. Neves, G., Cooke, S.F., Bliss, T.V., 2008. Synaptic plasticity, memory and the hippocampus: a neural network approach to causality. Nat. Rev. Neurosci. 9, 65–75. Niemann, C., Godde, B., Voelcker-Rehage, C., 2014. Not only cardiovascular, but also coordinative exercise increases hippocampal volume in older adults. Front. Aging Neurosci. 6, 170. Nudo, R.J., 2006. Plasticity. NeuroRx 3, 420–427.
O'Mara, S.M., Sanchez-Vives, M.V., Brotons-Mas, J.R., O'Hare, E., 2009. Roles for the subiculum in spatial information processing, memory, motivation and the temporal control of behaviour. Prog. Neuropsychopharmacol. Biol. Psychiatry 33, 782–790. 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 Struct. Funct. 219, 255–267. Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B., 2012. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418. Saaltink, D.J., Vreugdenhil, E., 2014. Stress, glucocorticoid receptors, and adult neurogenesis: a balance between excitation and inhibition? Cell. Mol. Life Sci. 71, 2499–2515. Siraly, E., Szabo, A., Szita, B., Kovacs, V., Fodor, Z., Marosi, C., Salacz, P., Hidasi, Z., Maros, V., Hanak, P., Csibri, E., Csukly, G., 2015. Monitoring the early signs of cognitive decline in elderly by computer games: an MRI study. PLoS ONE 10, e0117918. Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L.L., Augustinack, J., Dickerson, B.C., Golland, P., Fischl, B., 2009. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557. van Praag, H., Schinder, A.F., Christie, B.R., Toni, N., Palmer, T.D., Gage, F.H., 2002. Functional neurogenesis in the adult hippocampus. Nature 415, 1030–1034.
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Contents lists available at ScienceDirect
NeuroImage
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Kathrin Koch a,b,c,⁎, Tim Jonas Reess a,b,c, Oana Georgiana Rus a,b,c, Claus Zimmer a,b
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Extensive learning is associated with gray matter changes in the right hippocampus
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Article history: Received 2 September 2015 Accepted 21 October 2015 Available online xxxx
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Keywords: Gray matter volume Hippocampus Subiculum Neuroplasticity
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Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaningerstrasse 22, 81675 Munich, Germany TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München TUM, Ismaninger Strasse 22, 81675 Munich, Germany Graduate School of Systemic Neurosciences GSN, Ludwig-Maximilians-Universität, Biocenter, Groβhaderner Strasse 2, 82152 Munich, Germany
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Longitudinal voxel-based morphometry studies have demonstrated increases in gray matter volume in hippocampal areas following extensive cognitive learning. Moreover, there is increasing evidence for the relevance of the subiculum in the context of learning and memory. Using longitudinal FreeSurfer analyses and hippocampus subfield segmentation the present study investigated the effects of 14 weeks of intensive learning on hippocampal and subicular gray matter volume in a sample of medical students compared to control subjects not engaged in any cognitive learning activities. We found that extensive learning resulted in a significant increase of right hippocampal volume. Volume of the left hippocampus and the subiculum remained unchanged. The current findings emphasize the role of the hippocampus in semantic learning and memory processes and provide further evidence for the neuroplastic ability of the hippocampus in the context of cognitive learning. © 2015 Published by Elsevier Inc.
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An increasing amount of evidence supports the notion that the brain undergoes continuous activity-dependent neuroplastic changes across the life-span. In structural imaging studies these changes have been demonstrated as a consequence of various activities, such as learning to juggle (Boyke et al., 2008; Draganski et al., 2004), learning of mirror reading (Ilg et al., 2008), learning of new color names (Kwok et al., 2011), motor exercise (Niemann et al., 2014), video gaming (Kuhn et al., 2014) or meditation (Kurth et al., 2014). Surprisingly few evidence, on the other hand, has been provided for structural changes in association with learning of abstract information (Ceccarelli et al., 2009; Draganski et al., 2006). Ceccarelli et al. (2009) explored the effects of two weeks of intensive learning and found a fronto-parietal gray matter (GM) volume increase. Draganski et al. (2006) used voxel-based morphometry at three different time points to investigate the effects of intensive learning in students preparing for their medical exam. They found a significant gray matter increase in the posterior and lateral parietal cortex bilaterally and a significant increase in the right hippocampus during the learning period which augmented even further during the subsequent semester break. The primary function of the hippocampus is clearly memory-related. The hippocampus plays a key
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⁎ Corresponding author at: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaningerstrasse 22, 81675 Munich, Germany. Fax: +49 89 41404887. E-mail address: [email protected] (K. Koch).
role in the consolidation of information from short-term to long-term memory and a number of studies have shown a correlation between hippocampus volume and memory performance (Arlt et al., 2013; Avery et al., 2013; Gimenez et al., 2004; Pohlack et al., 2014; Siraly et al., 2015). Thus, Arlt et al. (2013) and Gimenez et al. (2004) reported a correlation between left hippocampal volume and verbal working memory, Avery et al. (2013) demonstrated an association between total hippocampal volume and relational working memory and Pohlack et al. (2014) reported an association between total hippocampal volume and verbal working memory. The hippocampus or hippocampal formation can be subdivided into several subfields (dentate gyrus, areas CA3 and CA1, entorhinal cortex and subiculum) (Amaral and Witter, 1995), out of which the subiculum constitutes one of the largest subfields and the major output structure. There is increasing evidence indicating that the subiculum is the subfield that is most strongly involved in basic memory processes and semantic learning (O'Mara et al., 2009). It receives the majority of efferent information from the CA1 region of the hippocampus thus being in a position to integrate, transfer and resolve information from other parts of the hippocampus related to learning and memory (Amaral et al., 1991; Deadwyler and Hampson, 2006). Nevertheless, changes in gray matter volume in association with learning of semantic information have only been reported for the hippocampus formation as a whole whereas potential gray matter changes in the subiculum have not been specifically investigated up to now. Learning-related changes in gray matter volume of the hippocampus
http://dx.doi.org/10.1016/j.neuroimage.2015.10.056 1053-8119/© 2015 Published by Elsevier Inc.
Please cite this article as: Koch, K., et al., Extensive learning is associated with gray matter changes in the right hippocampus, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.10.056
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We recruited 35 medical students from the Medical School of our University and 24 healthy control subject. 7 medical students and 6 control subjects dropped out after the first scan resulting in a final sample size of 28 right-handed healthy students (m:f = 14:14, mean age = 19.3 years, SD = 1.0 years) and 18 right-handed healthy control subjects (m:f = 6:12, mean age = 18.6 years, SD = 0.5 years) with no history of neurological or psychiatric disorders or other serious medical conditions. Groups were matched according to their level of education (i.e., both had the German “Abitur”) and they were carefully selected regarding the amount of physical exercise and musical activities they were doing (i.e., professional athletes or musicians or subjects excessively engaged in sports or playing an instrument were not included). In both groups, T1-weighted magnetic resonance imaging (MRI) scans were performed at two time points, TP1 and TP2 (i.e., 14 weeks after TP1). In the student group, the first scan was performed at the beginning of their first semester of medical school, the second scan was performed fourteen weeks later shortly before their first semester medical exams. During this time period the students spent on average about 190 h in class and 280 h outside of class with learning facts and information related to anatomy, chemistry, and biology. The control subjects had recently started a voluntary social year. They were not attending any lesson or studying for any exams between TP1 and TP2. Handedness was assessed using Annett's handedness inventory. All participants gave written informed consent to the study protocol which is in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee of the Technische Universität München, Medical School.
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We used the digit span task to assess cognitive performance and to investigate a potential association between memory performance and gray matter increase. The test consists of a forward and a backward version. In the forward version of the test, a list of random numbers is read out which the participant has to recall in the correct order. The test begins with two numbers, increasing a number at a time until two errors are committed in a row. In the backward version, participants are asked to recall the digits in backward order. Thus, the forward version mainly reflects working memory performance, the backward version assesses predominantly manipulation of stimulus material and executive processing. Memory performance was investigated using a repeated measures one-way ANOVA with digit span forward performance as the dependent variable, group as a between subject factor and measurement time point (TP1, TP2) as a within subject factor. Digit span data of one medical student and one control are missing. Gray matter volume was assessed using the FreeSurfer software package (version 5.3.0, http://surfer.nmr.harvard.edu). The initial processing of T1 high-resolution images includes several steps which have been described in previous papers (Dale et al., 1999; Fischl et al., 1999). Briefly, the implemented processing stream contains removal of non-brain tissue, transformation to Talairach-like space, and segmentation of gray/white matter tissue. White and gray matter boundary is tessellated and topological defects are automatically corrected. After intensity normalization and transition of gray/white matter, pial boundary is indicated by detecting the greatest shift in intensity through surface deformation. Segmented data were then parcellated into units based on gyral and sulcal structure, resulting in values for gyrification and volume. Maps were smoothed using a Gaussian kernel of 10 mm. Subsequently, for the longitudinal processing, an unbiased within-subject template is created using robust, inverse consistent registration to estimate average subject anatomy across both measurement time points (Reuter et al., 2012). Finally, each time point is processed “longitudinally”, where information from the subjecttemplate and from the individual runs are used (see Fig. 1, for details please refer to Reuter et al. (2012)). This procedure has been demonstrated to significantly increase reliability and statistical power in longitudinal studies (Ibarretxe-Bilbao et al., 2012; Kwan et al., 2012; Reuter et al., 2012). Then, an automated subfield segmentation of the hippocampus was performed using Bayesian inference and a probabilistic atlas of the hippocampal formation. The hippocampal subfield volumes obtained with this method have been compared to manual hippocampal subfield tracings, and reliability measures were good for the larger subfields (CA2/3, CA4/DG, subiculum) and only acceptable for the smaller ones (CA1, presubiculum, fimbria) (Van Leemput et al., 2009). Seven hippocampal subfield volumes were automatically calculated including the fimbria (white matter), presubiculum, subiculum, CA1, CA2–3, and CA4-DG fields (gray matter) as well as the hippocampal fissure (cerebrospinal fluid). The procedures for subfield segmentation have been described elsewhere (Van Leemput et al., 2009). The present analyses focus on the subiculum. To investigate the hypothesized changes in hippocampal and subicular gray matter volume, for each subject and time point,
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High-resolution anatomical T1-weighted volume scans (MP-RAGE) were collected on a 3 T whole body system equipped with a 12element receive-only head matrix coil (INGENIA, Philips). They were obtained in sagittal orientation (TR = 9 ms, TE = 4 ms, TI = 900 ms, flip angle = 8°, FOV = 240 × 240 mm2, matrix = 240 mm × 240 mm, number of sagittal slices = 170) with an isotropic resolution of 1 × 1 × 1 mm3.
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are most likely the consequences of synaptic or dendritic sprouting or increases in synaptic strength and neuronal growth (i.e., increases of neuronal somae and nuclei) and, as such, very small (Neves et al., 2008). Hence, high image resolution is needed to detect the aggregated effects of such changes on the macroscopic level across time. Against this background, in the present study we used FreeSurfer (http:// surfer.nmr.mgh.harvard.edu) which is a software package capable of detecting sub-millimeter changes in gray matter volume. The software has a longitudinal image processing framework (i.e., FreeSurfer longitudinal). This framework is based on unbiased, robust, within-subject template creation which has been demonstrated to successfully reduce variability, avoid over-regularization and increase power to detect structural changes across time by initializing the processing in each time point with common information from the within-subject template (Reuter et al., 2012). We used this longitudinal framework to investigate the effects of intensive learning on gray matter volume and expected increases in the hippocampus and more specifically the subiculum as a consequence of learning. In addition, we investigated whether baseline gray matter volume predicted learning-associated increases in hippocampal gray matter volume. Finally, based on the above mentioned findings of an association between memory performance and hippocampus volume, we used the digit span task, an established and frequently employed working memory task, to explore whether memory performance at the first measurement time point predicted learningassociated increases in hippocampal gray matter volume. Thus, we intended to extend previous findings of a mere association between working memory performance and hippocampal volume and to explore a directed association by investigating whether working memory performance predicts hippocampal volume change. Identifying parameters which allow the prediction of neuroplastic processes on an individual basis would be of high practical use, for instance in the field of cognitive remediation and training.
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Fig. 1. Simplified diagram of the three processing steps of the longitudinal processing approach. First, all high resolution volumes of all subjects are preprocessed independently. Second, based on inverse consistent robust registration an individual within-subject template is created for each subject from both measurement time points to estimate the average individual anatomy. Third, each high resolution volume from each measurement time point is processed longitudinally using information from the within-subject template and from the first processing step.
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volume data were extracted for the left/right hippocampus and left/ right subiculum. As hippocampus subfields in one medical student could not be segmented properly this subject was excluded from the subiculum analyses. To put emphasis on the volumetric change across time and to minimize confounding effects of a priori volumetric differences between subjects and groups, we investigated the percent change across time. To investigate the hypothesized differences in hippocampal gray matter change between the groups we used two (i.e., one for each hemisphere) one-way ANCOVAs with percent volumetric change (i.e., percent change between TP1 and TP2 hippocampus volume) as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate (to control for the effect of total individual ICV). For the structure with a significant group difference (i.e., the right hippocampus, see Results section) we subsequently used another one-way ANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2 right subicular volume) as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate to investigate differences in subicular gray matter change between the groups. On an exploratory basis, we additionally performed a MANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2) for all additional subfields of the automated subfield segmentation (i.e., presubiculum, CA1, CA2/3, fimbria, CA4/DG, hippocampal fissure) as dependent variables, group as between subject factor and total intracranial volume (ICV) as covariate to also investigate potential differences in gray matter change of other subfields between the groups. To exclude a potential effect of gender on the group difference in volumetric change a one-way ANCOVA with gender as between subject factor and ICV as covariate was performed post hoc for those structures that showed a significant group difference. To investigate a potential association between baseline gray matter volume and learning-associated increases in hippocampal gray matter volume we performed a correlation between hippocampal volume at TP1 and change in hippocampal volume across time. Finally, to investigate a potential association between memory performance and gray matter change (or, in other terms, whether memory performance at TP1 predicts subsequent volumetric gray matter change) a correlation between digit span forward performance and volumetric change was calculated using Pearson's correlation.
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The repeated measures ANOVA on working memory performance (i.e., digit span forward performance) yielded a significant group effect (F(1,42) = 7.5, p b 0.01) indicating significantly better working memory performance in the group of the students, no significant effect of measurement time point (F(1,42) = 0.001, p = 0.97) and no significant interaction (F(1,42) = 0.091, p = 0.76).
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The one-way ANCOVA with percent volumetric change of the right hippocampus as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded a significant group effect (F(1,43) = 5.3, p = 0.03) indicating a larger volumetric change in the student group compared to the control subjects (Fig. 2). Post hoc one-sample t-tests corroborated that the percent change of the right hippocampus volume was significant (i.e., significantly different from zero) in the student group (t(27) = 2.1, p = 0.045), but not in the control group (t(17) = − 1.4, p = 0.196). The one-way ANCOVA with percent volumetric change of the left hippocampus as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant group effect (F(1,43) = 0.01, p = 0.94). The one-way ANCOVA with percent volumetric change of the right subiculum as dependent variable, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant group effect (F(1,42) = 0.91, p = 0.35, Fig. 2). Hippocampal and subicular volumetric changes (percent volume change and mean volumetric change in mm3) for both groups are presented in Table 1. The MANCOVA with percent volumetric change (i.e., percent change between TP1 and TP2) for all additional subfields of the automated subfield segmentation as dependent variables, group as between subject factor and total intracranial volume (ICV) as covariate yielded no significant overall group effect (F(1,37) = 0.76, p = 0.61) and, accordingly, no significant group effects for the separate subfields. Gender had no effect on the percent volumetric change in the right hippocampus, i.e., the one-way ANCOVA with gender as between subject factor and ICV as
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Fig. 2. Percentage change between TP1 and TP2 in gray matter volume of the right hippocampus and right subiculum in students and control subjects (hippocampus indicated in yellow, subiculum indicated in light yellow, mean percentage change in each group indicated by red line).
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findings by Draganski et al. (2006) who used voxel-based morphometry to investigate the effects of intensive learning in a group of medical students and found a significant increase in GM volume in the parietal cortex and the right hippocampus during the learning period which augmented even further during the subsequent semester break. Thus, these previous and the present findings underline the relevance of the right hippocampus for the acquisition and consolidation of semantic knowledge. They provide evidence for an activity related plasticity of the hippocampus and corroborate the assumption that earlier findings of increased volume in line with better memory performance are not reflective of an innate relationship but can be assumed to indicate an activity dependent increase in gray matter volume. In keeping with this assumption the hippocampus is known as a structure that possesses the ability to generate neurons from stem cells (Aimone et al., 2014). This neurogenetic ability may explain the peculiar plasticity of this brain structure. Hippocampus-dependent learning is one of the major regulators of hippocampal neurogenesis (Gould et al., 1999). Thus, it has been demonstrated in rats that learning of hippocampusdependent tasks but not hippocampus independent tasks increases the number of new neurons in the subventricular zone and the subgranular zone of the hippocampus (Deng et al., 2010; Leuner et al., 2004, 2006). It should be noted, however, that certain influencing factors such as task difficulty or sex differences are to be taken into consideration when describing the effects of learning (i.e., in rats mainly spatial learning) on hippocampal neuroplasticity (Epp et al., 2013). For instance, in a study by Chow et al. (2013) stronger effects of spatial learning on hippocampal neurogenesis have been found in male compared to female rats. Once neuroplastic changes which can seemingly take place throughout life have become manifest, these adult generated neurons are functional and exhibit morphological and physiological properties that are practically indistinguishable from developmentally originated neurons (van Praag et al., 2002). In humans, a recent study by Kuhn et al. (2014) provides additional indication of hippocampal plasticity as a result of intensive motorcognitive activity. They investigated the effects of excessive 2-month video gaming which demanded complex cognitive and motor skills and found a significant gray matter increase in hippocampus, dorsolateral prefrontal cortex and bilateral cerebellum in the gaming group compared to a control group. Of note, as in the present study the increase in hippocampal volume was restricted to the right side. Structural hippocampal increases have also been reported in a recent study by Miskowiak et al. (2014) who studied the effects of 8 weekly Erythropoietin (EPO) infusions on cognition and hippocampus volume in moderately depressed or bipolar patients. EPO is known to increase neuroplasticity and to improve cognitive performance. They found that EPO was associated with mood-independent memory improvement and reversal of gray matter loss in several hippocampal subfields. Here, however, changes were detectable in the left hippocampus only. One can only speculate on the reasons for the differential structural changes of left and right hippocampus. As also discussed by Draganski et al. (2006) the learning period is a highly stressful time for medical students. Stress is known to counteract neuroplastic processes in the hippocampus (Saaltink and Vreugdenhil, 2014) including neurogenesis and synaptic growth or sprouting. The present data give reason to assume that the stress-related negative effects on neuroplasticity may affect the left hippocampus more strongly than the right. This hypothesis, however, remains speculative and has to be underpinned by further studies.
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The Pearson's correlation investigating a potential association between gray matter volume of the right hippocampus at TP1 and change in right hippocampal volume (i.e., TP2–TP1) yielded no significant result (medical students: r = − 0.01, p = 0.95, control group: r = −0.10, p = 0.69).
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Finally, the Pearson's correlation investigating a potential association between memory performance (i.e., digit span forward performance) and the right hippocampal volumetric change of the student group showed no significant result (r = 0.3, p = 0.12).
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The present study investigated the effects of intensive learning on hippocampal and subicular gray matter volume employing, as one of the first studies, longitudinal FreeSurfer analyses. We found that 14 weeks of intensive learning resulted in a significant increase of right hippocampal volume whereas volume of the left hippocampus and the subiculum remained unchanged. The result of a right hippocampal volume increase as a result of intensive learning is in line with
t1:1 t1:2 t1:3
Table 1 Difference in hippocampal and subicular volume between both time points (i.e., TP2–TP1) in control subjects and students (percent volume change / average absolute volume change in mm3 ± SD percent volume change).
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covariate showed no significant effect of gender, neither in the whole group (F(1,43) = 0.09, p = 0.76) nor in the group of the students only (F(1,25) = 0.05, p = 0.83).
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0.77 / 34.6 mm3 (1.92) −0.59 / −26.4 mm3 (1.86)
−0.03 / −1.2 mm3 (1.65) −0.11 / −4.2 mm3 (1.90)
0.38 / 19.9 mm3 (2.31) −0.45 / −24.9 mm3 (3.03)
Please cite this article as: Koch, K., et al., Extensive learning is associated with gray matter changes in the right hippocampus, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.10.056
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The authors declare no competing financial interests.
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Groups differed significantly in memory performance with the control group demonstrating significantly less correct responses in the forward digit span which assesses mainly working memory performance. However, to minimize the effects of potential a priori differences we investigated the percentage volumetric change across time. Hence, a priori differences in cognitive performance or hippocampal gray matter structure do not explain the significantly larger hippocampal increase we found in the student group. This is also substantiated by the results of the correlation between baseline right hippocampal gray matter volume and change in right hippocampal gray matter volume which
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was not significant in either of the two groups. Taken together, data of the present study, which is – to our best knowledge – the first study using longitudinal surface based analyses to investigate the effects of intensive learning on gray matter structure indicate a significant learning associated volumetric increase in the right hippocampus. As such, the current findings emphasize the role of the hippocampus in semantic learning and memory processes and provide further evidence for the neuroplastic ability of this structure in the context of learning.
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There was no statistically significant differential volumetric change in the subiculum (although – descriptively – subicular volume increased in the group of the students and decreased in the control group). This was an unexpected finding given mounting evidence for the relevance of the subiculum for memory and learning (de la Prida et al., 2006; O'Mara et al., 2009). The subiculum represents an interface between major parts of the hippocampal formation and core cortical and subcortical regions critically involved in memory processes. Thus, it occupies a strategic position in which to integrate, transfer and resolve activity from other parts of the hippocampus relating to memory and performance (Deadwyler and Hampson, 2004). Excitotoxic lesions of the subiculum have been demonstrated to disrupt object recognition and spatial memory processes in rats (Liu et al., 2001) and the temporal coupling between CA1 and subicular neurons has been shown to underlie retention of trial-specific information during delayed non-match to sample (DNMS) tasks indicating that subicular neurons might be able to integratively process spatial and working memory information (Deadwyler and Hampson, 2006). Moreover, earlier studies by Commins et al. (1998) have demonstrated that long-term potentiation (LTP) can be induced at the subicular pyramidal cell synapse likewise indicating that the subiculum may play a pivotal role in the hippocampal memory system. Against the background of these findings the lack of a learning-associated increase in gray matter volume of the subiculum may be contrary to expectation. Considering, however, that there were also no significant changes in the other hippocampal subregions (i.e., presubiculum, CA1, CA2/3, fimbria, CA4/DG, hippocampal fissure) separately, the lacking significance may have methodological reasons, i.e., the statistically significant change of the hippocampal formation seems to be driven by an overall change of the whole structure while the change in a single subregion may per se not be large enough to reach statistical significance. Regarding the lacking effect of the subiculum it is moreover conceivable that structural changes in hippocampus formation occur fast, whereas subiculum changes become manifest only at a later stage. Thus, the duration of the learning period is certainly of major relevance although little is known about the temporal characteristics of structural plasticity. Some studies demonstrated gray matter changes as a consequence of cognitive learning after only two weeks (Ceccarelli et al., 2009; Kwok et al., 2011) and in the context of motor learning even after a few days (Nudo, 2006). Studies in spinal-cord injured mice suggest that a time period of several months seems to be necessary for newly generated stem cells to differentiate into neurons. More data, however, are needed to get a better idea of the temporal aspects of learning related gray matter changes. There was no statistically significant correlation between memory performance at the beginning of the learning period and right hippocampal volume increase. Although the correlation was positive this finding suggests that digit span working memory performance does not predict individual hippocampal plasticity. The rather small sample size of the present study may, however, have contributed to the lacking significance. Hence, further studies with a larger sample size are needed to identify reliable predictors for learning associated neuroplastic changes.
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