老年人 功能连接

the encoding of posi Donna R. Addisa, Christina M aDepartment of Psychology, University of Auc bDepartment of Psychology, Boston College, C edical related changes in the interactions between affect-processing regions and the hippo- Though many aspects of memory change with aging, within the realm of emotional memory, one of the most intriguing effects of age has been a ‘‘positivity effect’’ in memory. Compared to younger adults, proportionally more of what Carstensen, 2005). Though there is active debate about the generality of this effect (e.g., Murphy and Isaacowitz, 2008), it also is clear that there are some circumstances that reliably elicit a positivity effect (reviewed by Mather, 2006). The posi- tivity effect generally has been interpreted within the * Corresponding author. Department of Psychology, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA. ava i lab le at www.sc ienced i rec t . com .e l c o r t e x 4 6 ( 2 0 1 0 ) 4 2 5 – 4 3 3 E-mail address: elizabeth.kensinger@bc.edu (E.A. Kensinger). campus during the encoding of positive information. ª 2009 Elsevier Srl. All rights reserved. 1. Introduction older adults remember is positive (reviewed by Mather and fMRI Memory Structural equation modeling Results: Aging did not impact the connectivity among regions engaged during the encoding of negative information, but age differences did arise during the encoding of positive information. Most notably, in older adults, the ventromedial prefrontal cortex and amyg- dala strongly influenced hippocampal activity during the encoding of positive information. By contrast, in young adults, a strong thalamic influence on hippocampal activity was evident during encoding. Conclusions: These findings suggest that older adults’ ‘‘positivity effect’’ may arise from age- cAthinoula A. Martinos Center for Biom a r t i c l e i n f o Article history: Received 13 November 2008 Reviewed 13 February 2009 Revised 10 March 2009 Accepted 13 April 2009 Published online 18 May 2009 Keywords: Aging Connectivity Emotion 0010-9452/$ – see front matter ª 2009 Elsevi doi:10.1016/j.cortex.2009.04.011 tive, but not negative, information . Leclercb, Keely A. Muscatellb and Elizabeth A. Kensingerb,c,* kland, New Zealand hestnut Hill, MA, USA Imaging, Massachusetts General Hospital, Charlestown, MA, USA a b s t r a c t Introduction: Older adults often show sustained attention toward positive information and an improved memory for positive events. Little is known about the neural changes that may underlie these effects, although recent research has suggested that older adults may show differential recruitment of prefrontal regions during the successful encoding of emotional information. In the present study, effective connectivity analyses examined the network of regions that college-age and older adults recruited during the encoding of positive and negative images. Methods: Participants viewed positive and negative images while undergoing a functional magnetic resonance imaging (fMRI) scan. Structural equation modeling was used to compare young and older adults’ connectivity among regions of the emotional memory network while they encoded negative or positive items. There are age-rel d changes in neural connectivity during ate Special issue: Research report journa l homepage : www er Srl. All rights reserved sev ier . com/ loca te / cor tex . tion and process it more deeply than young adults or because c o r t e x 4 6 ( 2 0 1 0 ) 4 2 5 – 4 3 3426 framework of Socioemotional Selectivity Theory (Carstensen et al., 1999); this theory states that when adults view time as limited (as occurs with advancing age), they are more likely to prioritize their emotional wellbeing. The positivity effect has been hypothesized to reflect the increased importance that older adults place on emotional gratification: older adults are more motivated than young adults to process information in a manner that will provide them with emotional fulfillment, and because of this motivational change, older adults are more likely to attend toward, and to remember, positive information (reviewed by Carstensen and Mikels, 2005; Mather and Carstensen, 2005). The hypothesis that older adults’ positivity effect in memory is tied to motivational changes in the processing of emotional information is a plausible one, particularly because the effect is noted most readily when older adults have extensive cognitive resources available to devote toward pro- cessing emotional information (Mather and Knight, 2005). The hypothesis is also consistent with a number of studies that have suggested an age-related shift away from amygdala- based emotion processing and toward prefrontal-based pro- cessing (where this prefrontal-based processing may reflect a more deliberative, controlled type of processing; e.g., Satpute and Lieberman, 2006). Most of the studies that have revealed these age-related changes in neural activity have examined the processing of facial expressions that convey negative emotion. In these studies, older adults tend to under-activate the amygdala and to over-activate the prefrontal cortex (as compared to young adults) when processing negative facial expressions (e.g., Gunning-Dixon et al., 2003; Fischer et al., 2005; Tessitore et al., 2005; Williams et al., 2006). A recent set of studies has suggested that this shift from amygdala-driven to prefrontal-based processing may also occur when older adults view negative photographs (rather than facial expressions), and that these changes may be tied to older adults’ poor memory for negative information (see also Mather et al., 2004; St Jacques et al., 2009; St Jacques et al., 2010). Because these studies only examined the processing of negative information, they could not clarify whether the age- related changes in neural activity are specific to negative valence or whether they extend to positive information as well. There is reason to believe that valence may be an important factor to consider, with a few studies suggesting that older adults’ recruitment of the amygdala may be stronger during the processing of positive as compared to negative information (e.g., Mather et al., 2004; Leclerc and Kensinger, 2008) and that their prefrontal recruitment may also be heightened during the presentation of positive as compared to negative information (e.g., Leclerc and Ken- singer, 2008; Leclerc and Kensinger, in press). In fact, old age sometimes results in a valence reversal: whereas young adults often activate the amygdala and medial prefrontal cortex more strongly in response to negative as compared to positive items, older adults sometimes show the opposite pattern of activity (e.g., Mather et al., 2004; Leclerc and Kensinger, 2008). These studies suggest that older adults’ processing of both positive and negative information may be altered, but that the alterations may diverge for the two valences of information: The strength of older adults’ recruitment of the amygdala and of medial prefrontal regions may be stronger for positive they direct less attention and devote fewer processing resources toward negative information? Neuroimaging can provide a viable way to address this issue, by examining whether aging primarily impacts the processes recruited during the encoding of positive information or if it also influences the processes recruited during the encoding of negative information. The present study used effective connectivity analyses of functional magnetic resonance imaging (fMRI) data to reveal how aging affects the connections among brain regions recruited during encoding of negative and positive items. We focus on age-related connectivity changes within a network of brain regions including the amygdala, the prefrontal cortex, and the hippocampus. These regions have been linked to the successful encoding of emotional information by neuro- imaging studies that have used a ‘‘subsequent memory’’ paradigm (reviewed by Paller and Wagner, 2002). In such paradigms, neural activity during the encoding phase is sorted based upon whether the items are later remembered or later forgotten, and regions are considered to have a link to successful encoding if their activity is greater for items that are later remembered than for those that are later forgotten. A large literature has revealed a consistent network of regions that are implicated in the successful encoding of emotional information. Some of these regions seem to be recruited specifically when information is emotional (e.g., the orbito- frontal cortex and amygdala), whereas others serve a general role in the encoding of information with or without emotional content, though their activity may be modulated by the emotional salience of information (e.g., the hippocampus; see Hamann, 2001; LaBar and Cabeza, 2006; Kensinger, 2009 for reviews). Our primary question was how aging would affect the connections between the amygdala, the prefrontal regions, and the hippocampus, a region that is essential for the successful encoding of long-term memories (see Postle, 2009 for recent review). We were especially interested in whether age-related changes in connectivity would be comparable whenever participants were processing emotional information (regardless of its valence) or whether the age-related differences would be more pronounced for one valence of information. 2. Methods 2.1. Participants The participants comprised 17 younger adults (12 women) between 19 and 31 years of age and 20 older adults (13 women) between 61 and 80 years of age (see Table 1 for addition information than for negative information. It is not clear from this prior research whether, or how, these age-related changes connect to older adults’ ‘‘positivity effect’’ in memory. In particular, it is widely debated whether older adults’ ‘‘positivity effect’’ is best characterized as a shift toward the positive or as a shift away from the negative (see Murphy and Isaacowitz, 2008). For example, do older adults remember proportionally more positive information because they attend to that informa- participant characteristics). All participants were right- handed, native English speakers, with no history of depression c o r t e x 4 6 ( 2 0 1 0 ) 4 2 5 – 4 3 3 427 or other psychiatric or neurological disorder. All participants had scores on the Mini Mental Status Examination (Folstein et al., 1975) of greater than 27. No participant was taking any medication that would affect the central nervous system. Informed consent was obtained from all the participants in a manner approved by the Boston College and Massachusetts General Hospital Institutional Review Boards. Participants were compensated $75 for their participation. 2.2. Materials The photo objects used in the present study were a subset of those previously rated by a separate group of young and older adults on valence and arousal dimensions, using 9-point Lik- ert-type scales (1¼negative valence or low arousal and 9¼ positive valence or high arousal). One-third of the images selected were negative and high arousal (valence ratings less than 3.5, arousal ratings higher than 5), one-third were posi- tive and high arousal (valence higher than 5.5, arousal higher than 5), and one-third were neutral (valence ratings between 3 and 6, arousal less than 5; see Leclerc and Kensinger, 2008 for more details on the stimuli). The participants in this study also rated the stimuli for valence and arousal, and their clas- sifications of the stimuli generally agreed with those based upon prior ratings. In the rare instances where classifications did not agree (fewer than 2% of all items), those items were not Table 1 – Participant characteristics. Measure Young Older Years Education 15.2 (.38) 16.8 (.32) Digit Symbol Substitution 71.4 (3.3) 49.1 (2.2) Vocabulary .82 (.02) .89 (.02) Digit Span – Forward 5.9 (.45) 5.3 (.25) Digit Span – Backward 5.12 (.25) 4.65 (.33) Note: Digit Symbol Substitution, Digit Span Backward, & Arithmetic from Wechesler Adult Intelligence Scale (WAIS)-III Wechesler Adult Intelligence Scale; Wechsler, D. Technical Manual for the Wechsler Adult Intelligence and Memory Scale, 3rd ed. New York: The Psychological Corporation, 1997. Vocabulary reflects the percentage correct on the Shipley (1986) measure. All values represent the mean (SE) raw, nonstandardized scores. included in the analyses. 2.3. Procedure While in the fMRI scanner, participants viewed 108 positive, 108 negative, and 108 neutral photo objects for 1000 msec apiece. Images from each emotion category were pseudor- andomly intermixed with one another. After each image was presented, an intertrial fixation cross was presented for a variable duration, ranging between 5 and 13 sec, to provide jitter (Dale and Buckner, 1997). The order of the images, and the length of each intertrial fixation interval, was determined using a program that optimizes for detection of the hemody- namic response associated with each image (the optseq program available from http://www.nitrc.org/projects/ optseq). While viewing the photos, participants were asked to indicate whether each object would fit inside of a file cabinet drawer. This task ensured that participants were attending to each item but did not direct them to process its emotional relevance nor to memorize the items for a later memory task (no participant indicated that they were aware that a memory assessment would follow the scan), a set of conditions that facilitates the revelation of a positivity effect (see Mather, 2006). Once participants were settled outside of the scanner (after approximately a 20 min delay), they per- formed a recognition memory task. Participants were pre- sented with a series of studied items intermixed with novel items; items were presented one at a time, and for each item, participants were asked to indicate whether it had been studied. The recognition test was self-paced, so that as soon as a participant made a response, the next item was pre- sented. The present analyses focus only on the neural activity during the encoding of items that were later recognized successfully, so that age differences in connectivity could not be attributed to differences in the proportion of items that were successfully encoded (see Leclerc and Kensinger, 2008 for presentation of the neuroimaging data for all items, regardless of later memory performance). Because successful encoding was operationalized as the encoding of items that were later recognized, it is possible that a small proportion of items was mistakenly considered to be ‘‘successfully enco- ded’’ because participants correctly guessed about those items’ presentations. 2.4. Image acquisition and data analysis Images were acquired on a 3.0 T Siemens Allegra magnetic resonance imaging (MRI) scanner. Stimuli were back- projected onto a screen in the scanner bore, and the partici- pants viewed the images through an angled mirror attached to the head coil. Detailed anatomic images were acquired using a multiplanar rapidly acquired gradient echo sequence. Functional images were acquired using a T2*-weighted echo planar imaging sequence (repetition time – TR¼ 3000 msec, time to echo – TE¼ 30 msec, field of view – FOV¼ 200 mm; flip angle¼ 90�). Twenty-eight axial-oblique slices (3.2 mm thick- ness, .6 mm skip between slices), aligned in a plane along the axis connecting the anterior commissure and the posterior commissure, were acquired in an interleaved fashion. All preprocessing and data analysis were conducted within SPM2 (Wellcome Department of Cognitive Neurology). Stan- dard preprocessing was performed on the functional data, including slice-timing correction, rigid body motion correc- tion, normalization to the Montreal Neurological Institute template (resampling at 3-mm cubic voxels), and spatial smoothing (using a 7.6-mm full-width half maximum isotropic Gaussian kernel). 2.5. Effective connectivity analyses In order to examine the interactions between the amygdala and other limbic and prefrontal regions during the processing of negative and positive items, structural equation modeling (SEM) was carried out using Lisrel software (Joreskog and Sorbom, 1993). Unlike simple correlations, SEM allows for a consideration of connections across multiple nodes of a network, and it provides information about the direction- ality of influences between different regions. The first step of the SEM analysis was to specify the anatomical model. Regions were included in the model if they were of theoretical relevance to emotional memory (see reviews by Hamann, 2001; Phelps and LeDoux, 2005; LaBar and Cabeza, 2006) and were revealed in a whole-brain analysis comparing activity elicited during the successful encoding of positive or negative information to the activity elicited during the unsuccessful encoding of that emotional information. This analysis was conducted collapsing across the age groups, so as to define the regions in an unbiased fashion with regard to age. Because of the contrast used to define the regions, all regions included in the model were related to memory for emotional items rather than to more general emotion processing. Note, however, that we did not require regions in this model to be implicated only in memory for emotional (and not neutral) information, because we expect there should be overlap between nodes of the emotional memory network and nodes of a more general- purpose mnemonic network that is not specific to emotional information (and see Hamann, 2001; LaBar and Cabeza, 2006 for discussion). The regions selected were: ventromedial prefrontal cortex (Talairach coordinates, x, y, z¼ 0, 39, �5; Brodmann area – BA 10/32), dorsomedial prefrontal cortex (x, y, z¼ 8, 61, 15; BA 10), left orbitofrontal cortex (x, y, z¼�36, 44, �8; spanning BA 10/11/47), left amygdala (x, y, z¼�28, �3, �12), left hippocampus (x, y, z¼�36, �7, �23), thalamus (x, y, z¼�6,�20,�2), and left fusiform gyrus (x, y, z¼�46,�48,�18; BA 37; see Fig. 1 for the anatomic model). Table 2 presents c o r t e x 4 6 ( 2 0 1 0 ) 4 2 5 – 4 3 3428 additional information on the pattern of activity revealed within these regions; this information was determined by analysis of variances (ANOVAs), computed separately for each region, which compared the maximum signal change reached Fig. 1 – Anatomical model used in the SEM. (summing across 4–8 sec post-stimulus onset) as a function of subsequent memory (remembered, forgotten), valence (positive, negative, neutral), and age (young, older adult). On the basis of anatomical research in nonhumans (e.g., Swanson and Petrovich, 1998; Patterson and Schmidt, 2003), an anatomical connectivity model was created to specify the anatomically plausible connections (including multi-synaptic connections) between the specified network nodes and the potential directions of those connections (McIntosh, 1999; Addis et al., 2007). Next, a functional model was constructed for each group. Signal change was extracted from all active voxels within a 5 mm sphere (centered on the peak voxel) in each region of interest, using the MarsBar toolbox implemented within SPM2 (Brett et al., 2002). The sum of the signal change across the 4–8 sec period post-stimulus onset was extracted for remem- bered items only, and this signal change was extracted sepa- rately for each person, for each valence type, and for each region. We focus on the successful encoding of information (i.e., on signal change to subsequently remembered items) so that differences in the models cannot be accounted for by age differences in encoding effectiveness. Correlation matrices were then cr