Imaging genetics of cognitive functions: Focus on episodic memory

NeuroImage 53 (2010) 870–877

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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g

Review

Imaging genetics of cognitive functions: Focus on episodic memory B. Rasch a,b,c,d,e,⁎, A. Papassotiropoulos b,c, D.-F. de Quervain a,d,e,⁎ a

University of Basel, Division of Cognitive Neuroscience, Birmannsgasse 8, 4055 Basel, Switzerland University of Basel, Division of Molecular Psychology, Missionsstr 60/62, 4055 Basel, Switzerland c University of Basel, Biozentrum, Life Sciences Training Facility, Klingelbergstr 50/70, 4056 Basel, Switzerland d University of Basel, Psychiatric University Clinic, Wilhelm Klein-Strasse 27, 4055 Basel, Switzerland e Center for Integrative Human Physiology, University of Zürich, 8057, Zürich, Switzerland. b

a r t i c l e

i n f o

Article history: Received 1 September 2009 Revised 2 December 2009 Accepted 2 January 2010 Available online 11 January 2010

a b s t r a c t Human cognitive functions are highly variable across individuals and are both genetically and environmentally influenced. Recent behavioral genetics studies have identified several common genetic polymorphisms, which are related to individual differences in memory performance. In addition, imaging genetics studies are starting to explore the neural correlates of genetic differences in memory functions on the level of brain circuits. In this review we will describe how functional magnetic resonance imaging (fMRI) can be used to validate and extend findings of behavioral genetics studies of episodic memory and give examples of recent advances in this new and exciting research field. In addition, we will present advantages and problems related to the different sensitivity of behavioral- vs. imaging genetics studies and discuss possible methodological approaches for an appropriate evaluation and integration of the results. Although the field of imaging genetics of episodic memory is still young, it already became clear that imaging methods have a large potential to enhance our understanding of the neural mechanisms that underlie genetic differences in memory. © 2010 Elsevier Inc. All rights reserved.

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imaging episodic memory. . . . . . . . . . . . . . . . . . . . . . . . Imaging genotype-dependent differences in episodic memory . . . . . . . Matched memory performance . . . . . . . . . . . . . . . . . . . . . Unmatched memory performance . . . . . . . . . . . . . . . . . . . . Genetic complexity of episodic memory . . . . . . . . . . . . . . . . . General problems of group comparisons in genetic imaging studies . . . . Neural compensation vs. encoding efficiency . . . . . . . . . . . . . . . The consequences of different sensitivity of imaging vs. behavioral genetics Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction Human memory is a cognitive trait that is influenced by both genetic and environmental factors. Twin studies have estimated that genetic factors account for approximately 50% of the variability in human memory capacity (McClearn et al., 1997), indicating that naturally occurring genetic variations must have a significant impact on this cognitive ability. In support of this assumption, several recent ⁎ Corresponding authors. E-mail addresses: [email protected] (B. Rasch), [email protected] (D.-F. de Quervain). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.01.001

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behavioral- and imaging-genetics studies have successfully identified and characterized genetic variations significantly associated with human memory performance. In the present review, we will focus on episodic memory, but similar developments are taking place for other forms of memory, such as working memory (Goldberg and Weinberger, 2004; Meyer-Lindenberg and Weinberger, 2006). In 2003, two genetic factors associated with episodic memory in healthy humans were identified: the Val66Met polymorphism in the gene encoding the brain-derived neurotrophic factor (BDNF) (Egan et al., 2003) and the His452Tyr polymorphism in the gene encoding the serotonin 2A receptor (HTR2A) (de Quervain et al., 2003). Following these two studies, several other polymorphisms related to

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individual differences in episodic memory performance have been recognized (e.g. polymorphisms in COMT (de Frias et al., 2004); GRM3 (Egan et al., 2004); PRNP (Papassotiropoulos et al., 2005b); CHRFAM7A (Dempster et al., 2006); KIBRA (Papassotiropoulos et al., 2006); CAMTA (Huentelman et al., 2007); CPEB3 (Vogler et al., 2009); PDYN (Kolsch et al., 2009)) and the investigation of the genetic basis of human episodic memory is developing in an exciting and fast growing research field. In addition and combination to purely behavioral measures, several groups have started to use neuroimaging methods to investigate the neural underpinnings of genotypedependent differences in human episodic memory performance. In this review, we will describe how functional imaging technology can be used to validate and extend findings in the field of cognitive genetics of episodic memory, and discuss advantages and limitations of different methodological approaches. We will start by briefly reviewing some major neuroanatomical findings with regard to episodic memory. Imaging episodic memory Episodic memory refers to memory for particular events or autobiographical episodes in a life of an individual, which includes information about the content of the experience and the spatial and temporal context in which it occurred (Tulving, 1983). Episodic memories together with non-contextual semantic memories are termed “declarative” or “explicit” memories, because they can be intentionally, consciously and in most cases verbally assessed (Squire and Zola, 1996). Consequently, testing episodic memory usually involves asking the participant to describe a past event or stimulus presented at a certain time in the experimental procedure using freeor cued recall procedures. Recognition paradigms are sometimes also used, although performance in recognition tests is confounded by correct answers given based on non-episodic feelings of familiarity (Tulving, 1993; Yonelinas, 2001). Several functional imaging studies have investigated cerebral activation during memory tasks. This large body of evidence suggests that particularly the medial temporal lobe (MTL), but also frontal and parietal brain areas are involved in episodic memory processes (Cabeza et al., 2008; Moscovitch et al., 2006; Spaniol et al., 2009; Squire et al., 2004). Within the medial temporal lobe, numerous lesion studies indicate that the functional integrity of the hippocampal complex (encompassing the CA fields, the dentate gyrus, the subiculum and the parahippocampal gyrus) is critical for encoding as well as most retrieval processes of episodic memories (Milner, 1972; Moscovitch et al., 2006; Nadel et al., 2000; Squire, 1992; Squire et al., 2004). Brain activity in the hippocampal complex as recorded by positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) has been consistently associated with memory encoding and retrieval processes also in the healthy brain (Cabeza and Nyberg, 2000; Cohen et al., 1999; Eichenbaum and Lipton, 2008; Schacter and Wagner, 1999). In particular, higher activity in medial temporal lobe regions during encoding of episodic memories is typically associated with better memory for the encoded events, as shown by comparing encoding-related brain activity of subsequently remembered vs. subsequently forgotten items (subsequent memory effect) (Rugg et al., 2002; Spaniol et al., 2009). During the retrieval phase, successful recollection of old vs. correctly rejected new memories is similarly accompanied by increased activity in hippocampal and parahippocampal areas, whereas recognition processes based on feelings of familiarity are less dependent on the hippocampus (Eichenbaum et al., 2007). In addition to MTL regions, lesion- as well as imaging studies provide evidence for an involvement of medial and lateral prefrontal regions in episodic memory encoding and retrieval (Rugg et al., 2002; Spaniol et al., 2009). The prefrontal cortex (PFC) plays a putative role in memory storage (Jung et al., 2008), but is also involved in cognitive

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control processes accompanying episodic memory formation, like stimulus selection and organization, attention direction and semantization during encoding and initialization of retrieval search as well as maintaining and monitoring the search result during the retrieval process (Fletcher and Henson, 2001). Activity in prefrontal regions (in particular in the dorso-lateral and ventro-lateral cortices) is strongly related to successful memory encoding and retrieval (Spaniol et al., 2009). However, higher activity in these regions might also reflect increased task difficulty or recruitment of additional cognitive control resources to achieve a certain performance level. For example, aging is often accompanied by impaired memory performance, but agerelated increases as well as decreases in memory-related PFC activity have been reported (Han et al., 2009; Prvulovic et al., 2005; Rajah and D'Esposito, 2005). Furthermore, changes in lateralization and reduced hemispheric asymmetry frequently occur in the elderly (Hedden and Gabrieli, 2005). Consequently, age-related changes need to be considered in imaging genetics studies of episodic memory, and age should be routinely included as covariate in the analyses (Papassotiropoulos et al., 2005a). More recently, several authors have implicated parietal regions in episodic memory processes, highlighting the importance of attention–memory interaction for forming and retrieving memory traces (Cabeza et al., 2008; Uncapher and Wagner, 2009). Considering emotional aspects, increased activity in the amygdala, orbitofrontal cortex and insula is associated with the robust memory enhancement induced by emotional stimuli (Cahill and McGaugh, 1998; Labar and Cabeza, 2006; Phelps, 2004). In sum, a network of brain regions encompassing the medial temporal lobe, prefrontal cortex as well as parietal and limbic regions underlies episodic memory encoding and retrieval. It is primarily in this network, where we expect genotype-dependent differences in brain activity during episodic memory tasks. Imaging genotype-dependent differences in episodic memory The main rationale for combining behavioral- and neuroimaging methods in studies investigating genetic polymorphisms of episodic memory is to validate and extend purely behavioral studies by providing insight into the genetic differences in memory processes at the level of neural circuits. There are mainly two strategies of subject selection with regard to memory performance used in imaging genetics studies, which are detailed in the following sections (see Supplementing Table 1, for examples of imaging genetics studies on episodic memory using fMRI). Matched memory performance One strategy is to select a subpopulation of participants for fMRI from a larger sample used for behavioral genetics in a way that the genotype groups of the fMRI subpopulation are exactly matched for memory performance. The rationale for this matching procedure is to avoid measuring genotype-unrelated performance effects on brain activations, but to instead capture solely genotype-dependent differences in brain activation patterns. We used this approach in an imaging genetics study of KIBRA (Papassotiropoulos et al., 2006). In that genome-wide association study, we identified a single nucleotide polymorphism (an intronic C to T substitution) in the gene encoding KIBRA as significantly associated with recall of words in three independent samples (Fig. 1a). In a subsequent fMRI Study, 15 carriers of the T allele, which was associated with better episodic memory in the three large samples, and 15 non-carriers were selected, so that genotype groups were matched according to their memory performances (P = 1). During the retrieval phase in the fMRI, non-carriers of the T allele exhibit increased activity in the hippocampus and parahippocampal gyrus (Fig. 1b), as well as in the medial frontal gyrus and inferior parietal gyrus. All these regions are

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opoulos et al., 2005b). After matching genotype groups for 24 h recall, carriers of the genetic variant associated with poorer episodic memory retrieval had increased brain activity in the hippocampus, middle temporal gyrus, middle and inferior frontal gyrus, suggesting that also in that study, carriers of the genetic variant associated with poorer performance needed more activity to achieve the same level of performance. In sum, comparing brain activity during retrieval in genotype groups matched for memory performance consistently reveals increased activity in memory-related brain regions for the genotypes associated with poorer episodic memory (but see Egan et al., 2004, for discrepant results and interpretation). A careful matching of the genotype groups according to memory performance is a prerequisite for this approach. The advantage of such a matching procedure is that differences in brain activity can be fully attributed to genetic differences (i.e. genotype-unrelated performance effects on brain activations are avoided). The disadvantage is that the matching procedure prevents measuring genotype-dependent differences in brain activity that are actually related to the genotype-dependent differences in behavior. For that purpose, using performanceunmatched genotype groups is advantageous. Unmatched memory performance

Fig. 1. KIBRA and episodic memory. (a) A single-nucleotide polymorphism (SNP) in the KIBRA gene was significantly associated with episodic memory performance in a genome-wide association analysis (Cohort I). The effect was replicated in two independent cohorts II and III. (b) After matching subjects for performance, noncarriers of the T allele, who had poorer episodic memory in the large samples, exhibited increased activity in the hippocampus (H) and parahippocampal gyrus (P) as compared to T allele carriers during memory retrieval. This finding suggests that non-carriers need more activation in these memory-related brain regions to reach the same level of memory performance as T allele carriers. Adapted from Papassotiropoulos et al. (2006).

part of the network involved in memory retrieval. Therefore, these findings suggested that non-carriers of the T allele need more activation in these memory retrieval-related brain regions to reach the same level of retrieval performance as T allele carriers. Huentelman et al. (2007) applied a similar approach. After identifying an association between a polymorphism in the gene encoding calmodulin-binding transcription activator 1 (CAMTA1) and episodic memory performance in two independent samples, the additional fMRI study in genotype groups matched for memory performance revealed increased activity in medial temporal lobe regions during retrieval testing in carriers of the allele associated with poorer memory performance. A third example is the study by Buchmann et al. (2008), who investigated differences in retrievalrelated brain activity associated with genetic variations of the prion protein gene (PRNP). Participants were selected from a previous study that reported a link between these polymorphisms and recall of word pairs after 24 h, but not after 5 min, suggesting a role for PRNP in long-term consolidation of episodic memories (Papassotir-

Several imaging genetics studies examined memory-related brain activity without matching genotype groups for performance. For example, Hariri et al. (2003) recorded brain activity in 28 healthy participants during encoding and recognition of complex visual scenes and investigated the role of the memory-related Val66Met polymorphism in the gene encoding BDNF (Egan et al., 2003). On the behavioral level, Val homozygotes (as compared to Met allele carriers) were significantly more accurate at recognizing encoded scenes. With fMRI, the authors observed increased brain activity in the posterior hippocampal complex during encoding as well as recognition testing for Val homozygotes as compared to Met allele carriers. The interaction between the Val66Met polymorphism and hippocampal activity accounted for 25% of the variance in recognition performance. Using basically the same paradigm, Hashimoto et al. (2008) examined dosedependent effects of the Val66Met polymorphism in 58 Japanese. They similarly reported a significant positive association with the number of Val alleles in the right parahippocampus and bilateral hippocampus during encoding, although with more anterior peak activations. In contrast to the previous study, no reliable genotype effect on medial– temporal activity during retrieval and no genotype effect on memory performance were observed. The Val allele of the Val66Met BDNF polymorphism has been previously associated with better episodic memory performance in a larger sample (Egan et al., 2003), and the observed increases in hippocampal activity in Val homozygotes during encoding might indicate a generally higher sensitivity and responsivity of the medial temporal lobe network to external cues, which is in line with the known role of BDNF in hippocampal plasticity (Bath and Lee, 2006; Poo, 2001). However, the genotype effects on hippocampal activity during retrieval as well the relation between activity differences and performances are not consistent. Other examples for genotype-dependent activity differences in the memory network in performance-unmatched fMRI samples are provided with regard to genetic variations in the dopaminergic system. Dopamine availability is modulated by the activity of the catalyzing enzyme Catechol-O-Methyltransferase (COMT), and the gene encoding COMT harbors a functional Val158Met polymorphism that alters enzyme activity (Lachman et al., 1996). In a cognitive genetics study in 286 healthy subjects, Met homozygotes (having the low activity enzyme and putatively higher dopamine availability) had higher episodic and semantic memory scores as compare to Val allele carriers (de Frias et al., 2004). In an independent fMRI study (n = 27) (Bertolino et al., 2006), the Met allele was associated

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with increased recognition performance and with increased activity within the hippocampal complex during encoding and recognition of complex visual scenes as compared to Val allele carriers, together with reduced activity in ventro-lateral prefrontal regions. Furthermore, task-independent coupling between the hippocampal complex and the ventro-lateral prefrontal cortex over time was less negative in Met allele carriers, and the strength of negative coupling predicted recognition performance. A similar fMRI study (n = 49) also observed changes in prefrontal activity as well as functional coupling between hippocampal and prefrontal regions depending on COMT Val158Met genotype (Schott et al., 2006). In contrast, neither genotype-dependent increases in hippocampal activity nor differences in recognition performance were observed in this study. However, the two studies differed in the analysis method: While Bertolino et al. (2006) compared general levels of hippocampal activity (encoding or retrieving scenes vs. baseline), Schott et al. (2006) examined activity for successful encoded vs. subsequently forgotten items between genotype groups, which may lead to very different results and interpretations (see Rasch et al., 2009 for a discussion of this topic). The genotype-dependent differences in hippocampal-prefrontal coupling together with the reduced prefrontal activity in Met allele carriers might reflect reduced effort to encode and retrieve the information, possibly indicating a higher efficiency of memory formation processes in Met allele carriers of the COMT Val158Met polymorphism. Genetics can also affect memory by influencing modulators of episodic memory. For example, emotional arousal is well known to enhance episodic memories, which has obvious adaptive value in evolutionary terms, but can also be maladaptive in the context of aversive experiences as starting point for the development of anxiety disorders, such as posttraumatic stress disorder (de Quervain et al., 2009). In a recent study, a functional polymorphism in the gene encoding the alpha-2B-adrenergic receptor (ADRA2B) was significantly associated with strength of emotional memories in healthy humans and with the strength of traumatic memories in war victims (de Quervain et al., 2007). A subsequent fMRI study (n = 57, unmatched) revealed that carriers of the genetic variant associated with enhanced emotional memories exhibited increased amygdala activation in response to negative pictures (Rasch et al., 2009). In addition, functional coupling between the amygdala and the insula, brain regions that are known to be hyperactive in post-traumatic stress disorders (PTSD) (Liberzon and Martis, 2006), was significantly enhanced in this genotype group. Emotional memory performance did not differ significantly between genotype groups, although direction and size of the effect were similar to the previous behavioral study. The ADRA2B genotype-dependent amygdala responsivity and interaction with the insula already during the acquisition of memories may affect the strength of emotional and traumatic memories in these genotype groups, which might have important implications for PTSD. Taken together, numerous imaging genetics studies—using either performance-matched or -unmatched genotype groups—observe significant differences in brain activity as well as functional coupling between brain regions involved in memory processing. While genotype-dependent activity differences in MTL regions in performance-matched studies are generally interpreted as compensatory activity, similar increases in unmatched genotype groups are often interpreted as improved processing underlying memory formation, even though these findings are not always paralleled by significant increases in performance. Genetic complexity of episodic memory It is important to emphasize at this point that the described findings capture only a very small part of the genetic complexity underlying human episodic memory. Human memory is a polygenic

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trait and depends on gene × gene and gene × environment interactions, which requires to move on from single SNP analyses to more sophisticated methodology. Some studies have started to include more than one genetic variant in fMRI studies of human

Fig. 2. Prediction of memory performance and brain activity by a gene score. (a) A cluster of seven genetic variations (i.e., polymorphisms and haplotypes) was significantly associated with episodic memory performance. The seven-SNP cluster was used for the calculation of an individual's memory-related genetic score, termed individual memory-associated genetic score (IMAGS). (b) Regression analysis revealed a significant positive correlation between the IMAGS and learning-induced brain activations in the medial temporal lobe (MTL), including the hippocampus and parahippocampal gyrus. (c). Scatter plot illustrating the positive correlations between IMAGS and learning-induced brain activations in the hippocampus at coordinate position [24 12 20]. Adapted from de Quervain and Papassotiropoulos (2006).

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memory and investigate their genetic interaction (e.g., Schott et al., 2006). In a recent study we applied a compound genetic analysis (de Quervain and Papassotiropoulos, 2006), which goes beyond the traditional SNP by SNP comparison. In a first step, we identified a cluster of seven genetic variations (i.e., polymorphisms and haplotypes) that were highly significantly associated with episodic memory performance in 302 healthy subjects (Fig. 2a) and generated an individual's compound genetic score, which correlated positively with individual memory performance. In an independent fMRI experiment, the individual genetic score correlated also positively with brain activity in the hippocampus and parahippocampal gyrus during the encoding phase (Figs. 2b and c). Using genetic scores instead of single SNP comparisons may greatly increase the sensitivity and power of imaging genetic studies, because this approach accounts better for the high genetic complexity of human episodic memory. Similar approaches have been successfully applied in the context of Alzheimer's disease (Papassotiropoulos et al., 2005c; Reiman et al., 2005; Reiman, 2007). In addition to these analytical tools, exciting new high-throughput technologies allow for the initiation of genome-wide association analyses, which represent an unbiased approach of identifying association with a certain phenotype, e.g. hippocampal activity during memory processing, at high genomic resolution (Potkin et al., 2009). However, large study samples are required to reliably apply this method. General problems of group comparisons in genetic imaging studies Differences in brain activity between genotype groups (or any other groups) may be confounded by several factors (see Han et al., 2009; Samanez-Larkin and D'Esposito, 2008 for reviews). First, brain activity differences between groups may be due to changes in hemodynamics altering the coupling between neural activity and the blood-oxygen level dependent (BOLD) response. Second, morphological changes in certain brain regions may account for differences in strength and extend of a signal. Third, activity differences may be due to increased variance or noise of the BOLD signal depending on the genotype. Samanez-Larkin and D'Esposito (2008) have recommended using interaction or parametric designs instead of simple comparisons of activity as well as to include appropriate control tests (e.g., short, simple visual or motor tasks) to control for possible hemodynamic differences between groups. Morphological differences should be analyzed and accounted for by selecting the best normalizing algorithm and individual adjustment of regions of interest. Testing for variance equality as well as reporting the time course of activation can help to clarify the contribution of increased noise or changes in shape, height or latency of the BOLDsignal to the observed differences between genotypes.

compensatory activity to achieve the same level of performance as the high-memory group. In theory, such an activity difference should disappear or go in the opposite direction when the same genotypes are compared without prior matching. In contrast, increased activity for the high-memory genotype is more difficult to explain in performance-matched groups: Is this activity unrelated to memory? Or does it reflect deeper processing, which does not translate in differences in performance? The use of reaction time analysis in addition to recall success might be one way to increase sensitivity of memory measures on the behavioral level. Also, long-term (N24 h) as compared to short-term recall could lead to more pronounced differences between genotypes with regard to episodic memory performance, because of the reduced influence of short-term/ working memory processes on successful retrieval. In the case of significant performance differences between genotype groups examined with fMRI, one would expect higher activity in memory-related brain regions in the high-memory genotype. Here, the question remains whether increased activity is actually related to the differences in genetics, or due to the differences in performance between genotype groups. If sufficient participants are available, a reanalysis using post-hoc matching according to performance or including performance as covariate might help to answer this question (Murphy and Garavan, 2004b). Compensatory increases in brain activity might also occur in groups with different performance levels. For example in the elderly, more extended MTL-activation and/or recruitment of additional prefrontal regions are observed during memory tasks in spite of lower memory scores as compared to young controls (Han et al., 2009; Rajah and D'Esposito, 2005). However, decreased memoryrelated activity in the elderly paralleling the age-related changes in performance have been similarly reported (Prvulovic et al., 2005). To account for these apparent discrepancies, it has been proposed that activity of a less efficient processing unit can only increase until its maximum processing capacity is reached. Thus, compensatory increases only occur when task difficulty is low and sufficient “reserve” capacity is available. When processing capacity is reduced (e.g., by degeneration) or task difficulty is too high, decreased brain activity occurs (see Prvulovic et al., 2005). As a consequence, tasks with various difficulty levels should be used in imaging genetics studies of episodic memory to map the course of compensatory activity and its limits more accurately. The most problematic case for interpretations occurs when genotype-dependent activity differences are reported in performance-unmatched groups, which are accompanied by non-significant differences in memory performance. We will turn to this problem in the next section.

The consequences of different sensitivity of imaging vs. behavioral genetics studies

Neural compensation vs. encoding efficiency In addition to the general problems of group comparisons, interpretation of genotype-dependent activity differences in the context of memory processing should be done with caution. Increased activity in the medial temporal lobe during memory tasks can either reflect deeper and improved encoding of events or compensatory activity due to recruitment of additional neural resources or slower reaction times. Also in the prefrontal cortex, higher activity is related to successful encoding, but might reflect increased cognitive control indicative for less efficient processing. Thus, taking into account genotype-dependent differences in memory performance is absolutely crucial for reliably interpreting results of imaging genetics studies in the context of memory. In the case of genotype groups matched for performance, activity increases for the low-memory genotype can be interpreted as

Interestingly, the number of subjects used in imaging genetics studies that found significant genotype-dependent differences in brain activity typically lies between 20 and 60 subjects, whereas behavioral genetics studies usually use hundreds of subjects to consistently produce significant results (Fig. 3). A possible explanation for this observation is that biological phenotypes like neural activity are more proximate to the direct effects of functional genetic polymorphisms on gene products and their function, and might therefore be more sensitive in estimating genotype-dependent differences in mental processing (Hariri et al., 2006; Mattay et al., 2008). Thus, the size of the effects of genetic variations on brain activity seems to be much larger as compared to behavioral measures. Consequently, in imaging genetics studies of episodic memory, fewer participants are required to achieve enough statistical power to detect a significant genotype effect, than in behavioral genetics studies.

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Fig. 3. Levels of analysis in genetic studies of episodic memory. At the level of genes, subjects are genotyped with regard to naturally occurring genetic variations in the human genome. On the level of brain circuits, neuroimaging is used to examine genotype-dependent differences in brain activity or functional coupling between brain regions. Twenty to sixty subjects are typically sufficient to detect differences for certain memory-related polymorphisms (a) (e.g., Bertolino et al., 2006; de Quervain and Papassotiropoulos, 2006; Hariri et al., 2003; Hashimoto et al., 2008; Schott et al., 2006). On the level of memory performance, usually hundreds of subjects are required to consistently produce significant results, suggesting that genotype effects are smaller on the level of performance as compared to brain circuits (b) (e.g., de Frias et al., 2004; de Quervain et al., 2003; Egan et al., 2004; Egan et al., 2003; Papassotiropoulos et al., 2006).

Although the increased sensitivity of neuroimaging parameters for gene effects is an advantage, it can also cause problems. While small sample sizes may be sufficient to detect significant genotype effect on brain activity, they are insufficient in reliably replicating genotype effects on memory performance in the same sample. Thus, it is very likely that significant genotype-dependent differences in brain activity are reported in small fMRI samples, which are accompanied by non-significant differences in memory performance. Statistical considerations imply that the probability to detect a certain effect (i.e. obtain a significant result) depends on the effect size, the sample size and the chosen significance level (Cohen, 1988). The lower the effect size, the more subjects are required to detect the effect. For example, the size of the effect of the BDNF Val66Met polymorphism on memory performance (as estimated from behavioral genetics studies) is approximately η2 = 7%,1 i.e., 7% of the sample variance in memory performance is explained by differences in BDNF genotype (Dempster et al., 2005; Egan et al., 2003; Miyajima et al., 2008). How high is the chance to replicate the BDNF effect of η2 = 7% on memory behavior with 50 subjects? As indicated in Fig. 4a, the probability to detect such an effect is as low as 37% (see e.g. software G⁎Power3; Faul et al., 2007 for calculation of effect sizes and statistical power). Importantly, these probabilities are calculated under the assumption that a true genotype effect of η2 = 7% on memory performance exists in the population. Effects of single genetic 1 η2 = (Sum of squareseffect/Sum of squarestotal). η2 indicates the amount of explained variance of the effect under investigation relative to the total variance on the level of the sample. The interpretation is similar to the effect size r2 used in regression analyses. We recommend using η2 as compared to partial η2p offered by several statistical software packages, because η2p—values can strongly overestimate the effect when more the one factor is included in the ANOVA (Levine and Hullet, 2002). For t-test or one-factorial ANOVAs, η2 and η2p do not differ.

Fig. 4. Statistical power to detect an effect of η2 = 0.07 (a) and η2 = 0.25 (b) depending on the total sample size N and the significance level α. An fMRI-sample of 50 subjects may have sufficient statistical power (70%) to detect a large genotype related difference in brain activity (b), but has low statistical power (37%) to detect small genotype effects on memory behavior (a). Statistical power is calculated here under the assumption that an effect of η2 = 0.07 of genotype on memory performance exists in the population, although these effects can also be smaller. Thus, significant genotype effects on brain activity are likely to be paired with non-significant genotype effects on behavioral measures in fMRI studies with small sample sizes. Power calculations were done with G⁎Power3 (Faul et al., 2007).

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variations on behavioral measures of memory are typically even smaller (e.g. η2 = 2% for COMT; de Frias et al., 2004). The described statistical phenomenon can be readily observed in the literature of imaging genetics of episodic memory. Two studies consistently observed significant differences in hippocampal activity between BDNF genotype groups, but only one reported also genotypedependent differences in recognition performance (Hariri et al., 2003; Hashimoto et al., 2008). Also with regard to COMT genotype, only one out of two studies observed a behavioral effect in their sample (Bertolino et al., 2006; Schott et al., 2006). According to the statistical considerations detailed above, these inconsistencies regarding the behavioral results are not surprising. How high is the chance to replicate the genotype-dependent activation difference in an fMRI study? For example, the highest MTL activity difference between BDNF genotypes during memory retrieval reported by Hariri et al. (2003) had an estimated effect size of approximately η2 = 25%. Applying the logic of univariate statistics, the chance of replicating this effect is 70% with 50 subjects when using an uncorrected threshold of P = 0.001 (Fig. 4b). However, estimates of effect sizes and statistical power in imaging studies are much more complex due to the high number of examined inter-correlated voxels (see e.g., (Desmond and Glover, 2002; Fox et al., 2001; Murphy and Garavan, 2004a; Reiman, 2007), for further discussion on this topic). Recently, Hayasaka et al. (2007) have proposed a framework to calculate power and sample size maps for fMRI data. Here, effect sizes estimates are based on spheres around voxels to account for data smoothing. It might be useful to report such effect sizes in future fMRI studies instead of only peak activations to better estimate sample sizes required for replication studies. In sum, non-significant effects of genetic variants on episodic memory performance are very likely in small fMRI samples, while genotype-dependent differences in brain activation are detected more reliably. However, how can we interpret significant genotypedependent effect on brain activity, if they do not translate in significant differences in episodic memory performance? Do they really reflect increased responsivity or efficiency of the memoryrelated brain regions, which underlies or at least partially explains genotype-dependent difference in memory processing? How are activity differences in the memory network related to the differential episodic memory performance in genotype groups? To fully answer these important questions, future imaging genetic studies will require larger sample sizes to achieve sufficient statistical power to reliably detect genetic effects on both brain activity as well as behavioral measures of memory. Furthermore, large samples will allow to investigate both performance-unmatched and performancematched genotype groups, to measure both genotype-dependent differences in brain activity that are related to the genotypedependent differences in behavior and genotype-dependent differences in brain activity under exclusion of behavioral differences. Finally, genetic imaging studies of episodic memory should included replication samples, as it is common in behavioral genetic studies to avoid reporting of false positives. Conclusions By today, numerous imaging genetics studies of episodic memory have shown that it is feasible to measure genotype-dependent differences in brain activity using fMRI. Although the field of imaging genetics of episodic memory is still young, it already became clear that imaging methods have a large potential to enhance our understanding of the neural mechanisms that underlie genetic differences in memory functions and help to validate results obtained from behavioral genetic studies. However, as with behavioral genetics studies, replications of the existing results are absolutely necessary for an appropriate evaluation of the genetic effects on brain activity.

Regarding the different sensitivity of imaging genetics studies and behavioral genetics studies, we recommend indicating effect sizes and statistical power estimations to avoid misinterpretations and inconsistencies due to statistical reasons. In spite of the potential, there are also important limitations: Imaging genetics studies using functional imaging techniques do not inform us about the underlying molecular mechanisms related to gene effects on memory, because recording of imaging data captures effects only on a broad system level of neural networks. Furthermore, the site of the largest genotype-dependent difference in brain activity does not necessarily indicate the major site of the gene effect on the molecular or cellular level. Finally, it is important to emphasize that studies examining genetic differences in memory performance or brain activity are correlational in nature and do not allow causal interpretations. Several important questions remain unanswered. For example, what does it mean in terms of memory functioning that similar effects on memory performance and memory-related brain activity are associated with different genetic variations? How do the effects of individual SNPs relate to each other, do they simply add up or do they interact? Can the reported genetic effect on memory be generalized to other memory tasks or even other memory systems? By investigating these and other questions, future imaging genetics studies of episodic memory will have a great potential to increase our understanding of the genetics of human episodic memory, and may also influence and re-shape our neurobiological concepts underlying memory functions. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.01.001. References Bath, K.G., Lee, F.S., 2006. Variant BDNF (Val66Met) impact on brain structure and function. Cogn Affect. Behav. Neurosci. 6, 79–85. Bertolino, A., Rubino, V., Sambataro, F., Blasi, G., Latorre, V., Fazio, L., Caforio, G., Petruzzella, V., Kolachana, B., Hariri, A., Meyer-Lindenberg, A., Nardini, M., Weinberger, D.R., Scarabino, T., 2006. Prefrontal–hippocampal coupling during memory processing is modulated by COMT val158met genotype. Biol. Psychiatry 60, 1250–1258. Buchmann, A., Mondadori, C.R., Hanggi, J., Aerni, A., Vrticka, P., Luechinger, R., Boesiger, P., Hock, C., Nitsch, R.M., de Quervain, D.J., Papassotiropoulos, A., Henke, K., 2008. Prion protein M129V polymorphism affects retrieval-related brain activity. Neuropsychologia 46, 2389–2402. Cabeza, R., Nyberg, L., 2000. Neural bases of learning and memory: functional neuroimaging evidence. Curr. Opin. Neurol. 13, 415–421. Cabeza, R., Ciaramelli, E., Olson, I.R., Moscovitch, M., 2008. The parietal cortex and episodic memory: an attentional account. Nat. Rev., Neurosci. 9, 613–625. Cahill, L., McGaugh, J.L., 1998. Mechanisms of emotional arousal and lasting declarative memory. Trends Neurosci. 21, 294–299. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, Erlbaum. Cohen, N.J., Ryan, J., Hunt, C., Romine, L., Wszalek, T., Nash, C., 1999. Hippocampal system and declarative (relational) memory: summarizing the data from functional neuroimaging studies. Hippocampus 9, 83–98. de Frias, C.M., Annerbrink, K., Westberg, L., Eriksson, E., Adolfsson, R., Nilsson, L.G., 2004. COMT gene polymorphism is associated with declarative memory in adulthood and old age. Behav. Genet. 34, 533–539. de Quervain, D.J., Papassotiropoulos, A., 2006. Identification of a genetic cluster influencing memory performance and hippocampal activity in humans. Proc. Natl. Acad. Sci. U. S. A. 103, 4270–4274. de Quervain, D.J., Henke, K., Aerni, A., Coluccia, D., Wollmer, M.A., Hock, C., Nitsch, R.M., Papassotiropoulos, A., 2003. A functional genetic variation of the 5-HT2a receptor affects human memory. Nat. Neurosci. 6, 1141–1142. de Quervain, D.J., Kolassa, I.T., Ertl, V., Onyut, P.L., Neuner, F., Elbert, T., Papassotiropoulos, A., 2007. A deletion variant of the alpha2b-adrenoceptor is related to emotional memory in Europeans and Africans. Nat. Neurosci. 10, 1137–1139. de Quervain, D.J., Aerni, A., Schelling, G., Roozendaal, B., 2009. Glucocorticoids and the regulation of memory in health and disease. Front Neuroendocrinol. 30, 358–370. Dempster, E., Toulopoulou, T., McDonald, C., Bramon, E., Walshe, M., Filbey, F., Wickham, H., Sham, P.C., Murray, R.M., Collier, D.A., 2005. Association between BDNF val66 met genotype and episodic memory. Am. J. Med. Genet. B Neuropsychiatr. Genet. 134B, 73–75.

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