Altered resting-state brain activity at functional MRI during automatic memory consolidation of fear conditioning

brain research 1523 (2013) 59–67

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Research Report

Altered resting-state brain activity at functional MRI during automatic memory consolidation of fear conditioning Tingyong Fenga,b,n,1, Pan Fenga,1, Zhencai Chena a

School of Psychology, Southwest University, Chongqing, China Key Laboratory of Cognition and Personality, Ministry of Education

b

art i cle i nfo

ab st rac t

Article history:

Investigations of fear conditioning in rodents and humans have illuminated the neural

Accepted 24 May 2013

mechanisms of fear acquisition and extinction. However, the neural mechanism of

Available online 31 May 2013

automatic memory consolidation of fear conditioning is still unclear. To address this

Keywords:

question, we measured brain activity following fear acquisition using resting-state

Resting-state fMRI

functional magnetic resonance imaging (rs-fMRI). In the current study, we used a marker

ALFF

of fMRI, amplitude of low-frequency (0.01–0.08 Hz) fluctuation (ALFF) to quantify the

Fear memory consolidation

spontaneous brain activity. Brain activity correlated to fear memory consolidation was

vmPFC

observed in parahippocampus, insula, and thalamus in resting-state. Furthermore, after acquired fear conditioning, compared with control group some brain areas showed ALFF increased in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) in the experimental group, whereas some brain areas showed decreased ALFF in striatal regions (caudate, putamen). Moreover, the change of ALFF in vmPFC was positively correlated with the subjective fear ratings. These findings suggest that the parahippocampus, insula, and thalamus are the neural substrates of fear memory consolidation. The difference in activity could be attributed to a homeostatic process in which the vmPFC and ACC were involved in the fear recovery process, and change of ALFF in vmPFC predicts subjective fear ratings. & 2013 Elsevier B.V. All rights reserved.

1.

Introduction

Learning about potential dangers in the environment is critical for adaptive function, but at times fear learning can be maladaptive, resulting in excessive fear and anxiety. Researchers have argued that functional dysregulation of the fear system is

n

at the core of many psychiatric disorders (Schiller et al., 2009; Indovina et al., 2011). There are some works investigated the functional dysregulation or connectivity of the fear system or resting-state networks in psychiatric disorders (Indovina et al., 2011; Liao et al., 2010, 2011; Schiller et al., 2009). Understanding the neural mechanism of fear process is an important step to

Corresponding author at: School of Psychology, Southwest University, No. 1, Tian Sheng RD., Beibei, ChongQing 400715, China. Fax: +86 23 682 53629. E-mail address: [email protected] (T. Feng). 1 Pan Feng and Tingyong Feng contributed equally to this work. 0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.05.039

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brain research 1523 (2013) 59–67

translate basic research to the treatment of fear-related disorders. Classical fear conditioning is regarded as a model paradigm to study the neural systems of fear. Using this paradigm, researchers have been able to map the pathways of the neural mechanisms of fear learning and extinction (LeDoux, 2003).With regard to fear learning, cumulative evidence of several recent studies identified the amygdala, insula, dorsal anterior cingulate cortex (dACC), prefrontal cortex, striatum and temporal cortex as the key regions involved in fear learning. Specially, the amygdala is a brain structure that directly mediates aspects of fear learning and facilitates fear memory operations in other regions, including the hippocampus and prefrontal cortex (LaBar and Cabeza, 2006). Structural magnetic resonance imaging (MRI) showed the cortical thickness of dACC, insula and temporal lobe were also positively correlated with conditioned fear responses (Hartley et al., 2011; LaBar and Cabeza, 2006; Linnman et al., 2013; Milad et al., 2007a, 2007b). With respect to fear extinction, Extensive neurophysiological and brain imaging research has established several key regions involved in fear extinction processes, including the amygdala, hippocampus (Hip), ventromedial prefrontal cortex (vmPFC), and dACC (Milad and Quirk, 2002; Morgan and LeDoux, 1995; Quirk, 2002). Furthermore, structural imaging research also found that the cortical thickness of ventromedial prefrontal cortex (vmPFC)and dACC were closely associated with psychophysiological response during fear extinction (Hartley et al., 2011; Milad et al., 2005). Functional coupling of the vmPFC with the amygdala, dACC and hippocampus were also closely linked to the fear acquisition and extinction process (Kalisch et al., 2006; Lang et al., 2009; Milad et al., 2007b). From fear acquisition to extinction, there are another two steps of fear process: consolidation and reconsolidation. As the fear memory consolidation was automatic and the methodology was not fully developed yet, little was known about the neural mechanism of automatic memory consolidation of fear conditioning when the brain was “at rest”. Resting-state functional magnetic resonance imaging (rs-fMRI) was regarded as an important tool to examine spontaneous brain activity (Buckner and Vincent, 2007; Friston et al., 1997; Greicius et al., 2003; Raichle et al., 2001), and amplitude of low-frequency fluctuation (ALFF) may be a suggestive index of regional spontaneous neuronal activity (Yu-Feng et al., 2007). Using rs-fMRI, the present study attempted to examine neural substrates of automatic memory consolidation of fear conditioning by comparing ALFF between experimental group (fear conditions) and control group (neutral conditions). Based on previous researches investigating the neural mechanisms of the memory consolidation, we predicted that the fear memory consolidation was accompanied by changing activity in vmPFC, ACC, parahippocampus, insula, thalamus and striatal regions. Specifically, the vmPFC and ACC would yield greater activity, whereas the striatal regions (caudate, putamen) could decrease activity when the brain was “at rest” following fear acquisition.

2.

Results

2.1.

The neural circuits of fear acquisition

In order to investigate the brain activity related to fear acquisition, the neural correlates of differential fear learning

were identified by comparing activity of the CS+ relative to the CS− (t[35] ¼2.90, po0.05, FDR corrected), which revealed enhanced neural activity in the amygdala, dACC, mPFC, insula, thalamus and temporal lobe (see Fig. 1). To verify whether the fear matrix was only active in the experiment group, we performed the paired t-test between the CS+ and the CS− in the control group. The result revealed there was no significant difference in brain activity between CS+ and the CS− in the control group (p¼ 0.05, FDR corrected). The results suggested that the fear matrix was only active in the experiment group. These areas were commonly identified in human fMRI investigations of fear conditioning (Delgado et al., 2008; LaBar and Cabeza, 2006; Phelps and LeDoux, 2005), indicating that these regions would be the network commonly labeled as the “fear acquisition matrix”.

2.2. The neural substrates of automatic memory consolidation of fear conditioning To investigate the neural mechanisms of automatic memory consolidation of fear conditioning after a short period of fear acquisition, two-way mixed ANOVA analysis on the group (experimental group vs. control group) and the REST (REST1 vs. REST2) revealed that there was a significant interaction between two factors, F(1,86)¼ 6.94, p ¼0.01(a significant main effect for group, F(1, 86)¼ 11.60, p ¼0.001; a significant main effect of REST, F(1,86)¼8.30, p ¼0.005). To examine the effect of experimental treatment, we performed simple effect analysis. The following results were obtained: in the experimental group, some brain areas showed greater activation in the REST2 than in the REST1, including parahippocampus, insula and thalamus (t [28] ¼3.34, po0.05, FDR corrected) (see Fig. 2). In the control group, areas showing increased ALFF included thalamus, putamen and Lentiform Nucleus (t [15]¼ 4.58, po0.05, FDR corrected) (see Fig. 3).The findings supported the idea that enhanced activity in parahippocampus, insula in experimental group would be the consequence of fear memory consolidation. The whole brain regions activated for

Fig. 1 – Areas of brain activation in fear acquisition for the CS + vs. CS− condition. (Po0.05, FDR corrected; Voxels≥10).

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Fig. 2 – For ALFF, areas of brain activity in fear memory consolidation for the REST2–REST1 conditions in experimental group. (Po0.05, FDR corrected; Voxels≥10).

REST2–REST1 in experimental group and control group were displayed in Table 1 and in Table 2 respectively. To investigate whether the observed effects were the result of fear acquisition in experimental group, delta (Δ) image (REST2 images subtract REST1 images) was performed in experimental group and control group respectively. After that, a two sample t-test (Δ REST image for each group) was conducted. We found that significant difference between the two groups in some brain areas (see Fig. 4 and Table 3). Areas showing increased ALFF in the experimental group included vmPFC and ACC, whereas some areas showing decreased ALFF were in the striatal regions (the putamen, Lentiform Nucleus) (t [43]¼ 2.70, po0.05, FDR corrected). The difference between groups indicated the vmPFC and ACC may play a crucial role in modulation of conditioned fear during automatic memory consolidation of fear conditioning. These results also revealed that the brain is continuously and dynamically “active” even when at “rest” and the threatening stimuli are automatically processed.

2.3.

Brain–behavior correlation results

To investigate whether the change of ALFF could predict subjective fear ratings, we computed the correlation between the change of ALFF for REST2–REST1 and subjective fear ratings. The correlation analysis showed that the change of ALFF in vmPFC (REST2 vs. REST1) was positively correlated with the subjective fear ratings (r¼ 0.525, p¼ 0.004) (see Fig. 5). The findings suggested change of ALFF in vmPFC could predict subjective fear ratings.

3.

Discussion

Using rs-fMRI, we investigated the neural circuits of automatic memory consolidation of fear conditioning. Our study yielded two main findings. First, brain activity involved in fear memory consolidation was observed in insula, thalamus and parahippocampus. Second, we found during automatic

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Fig. 3 – For ALFF, areas of brain activation in fear memory consolidation for the REST2–REST1 conditions in control group. (Po0.05, FDR corrected; Voxels≥10). Table 1 – Areas of brain activation for REST2–REST1 in experimental group. Region

BA

No. Voxels

Peak t-value

x

y

z

L. Medial Frontal Gyrus L. Precentral/Medial/Middle Frontal Gyrus L. Superior/Middle Temporal Gyrus/Insula R. Superior Temporal/Inferior Frontal Gyrus R. Superior/Middle Temporal Gyrus/Insula L. Middle Temporal/Occipital Gyrus R.Inferior/Middle Temporal Gyrus/Middle Occipital Gyrus R. Lingual/Fusiform Gyrus R. Fusiform Gyrus R. Posterior Cingulate/Lingual/Parahippocampa Gyrus R. Right Cerebrum/Thalamus

6/31 3/4/6 41/22/13 38 42/22/41 39/19 37/19/39 17/18 37 19/30

10 746 226 18 215 43 59 103 11 46 135

3.73 5.10 5.09 4.46 5.89 4.76 4.66 4.80 4.20 4.84 6.68

−6 −33 −51 50 56 −50 48 6 48 12 3

−21 −23 −27 20 −17 −66 −64 −90 −50 −47 −8

45 62 9 −13 12 14 11 −1 −15 2 6

Table 2 – Areas of brain activation for REST2–REST1 in control group. Region

BA

No. Voxels

Peak t-value

x

y

z

L. Superior/Temporal Gyrus/Insula L. Thalamus R. Thalamus R. Putamen

41/13

48 93 76 10

7.19 7.18 7.12 5.78

−33 −15 12 33

−23 −14 −17 −18

4 9 6 −2

memory consolidation of fear conditioning in resting state, some brain areas showed ALFF increased in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC), whereas some brain areas showed decreased ALFF in striatal regions (caudate, putamen).. These findings suggested that the parahippocampus, insula, and thalamus would be the neural substrates of fear memory consolidation. The difference in activity could be attributed to a homeostatic process in which the vmPFC and ACC were involved in the fear recovery process. In line with prior researches, several key regions (amygdala, dACC, mPFC, insula, thalamus and temporal lobe) which labeled as the neural biomarkers of fear acquisition were emerged during fear acquisition. These findings and behavioral results showed that the experimental group acquired the conditioned fear. With regard to automatic memory

consolidation, the current research yielded two main findings. First, our results indicated brain activity correlated to fear memory consolidation was observed in insula, thalamus and parahippocampus in resting-state. With regard to studies that worked specifically on the consolidation of fear conditioning, recent studies in humans and rodents indicate the hippocampus and the amygdala play an important role in fear memory consolidation and the complementary and cooperative interplay between amygdala and hippocampus during encoding of emotional memory (Calandreau et al., 2006; Ito et al., 2006; Richardson et al., 2004). Specifically, hippocampal ERK/CREB activation is essential for the contextual fear memory consolidation (Trifilieff et al., 2006). Moreover, biphasic ERK1/2 activation in both the hippocampus and amygdala may be crucial for a systematic consolidation of contextual fear memory. BLA may exert a delayed

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modulation of memory consolidation processes mediated by the hippocampus (Trifilieff et al., 2007). However, in respect to human, activation in the insula has been reported to be associated with feeling states and interoceptive awareness of the body (Craig, 2009; Shelley and Trimble, 2004). Moreover, the insula may play a crucial role in supporting feedback representation of peripheral autonomic arousal that provides input to conscious awareness of emotional states (Critchley et al., 2002), conveying a cortical representation of fear to the amygdala (Phelps et al., 2001). The importance of the thalamus in emotion is not due to its status as a subcortical ‘labeled line’ conveying emotional information to the amygdala, but due instead to its pattern of connectivity with subcortical and cortical sites that have a role in determining the biological significance of a stimulus (Pessoa and Adolphs, 2010). Prior studies suggested that the parahippocampus gyrus played a crucial role in the recovery of perceptual information (Cabeza et al., 2001; Sharot et al., 2004). What is more, the current results suggested the parahippocampus were also engaged and required during fear memory consolidation processing. In all, these regions (insula, thalamus and parahippocampus) may be the neural substrates of fear memory consolidation in resting-state. In other words, we speculated that these regions may play an important role in fear memory consolidation.

Fig. 4 – ALFF differences between experimental and control groups. Yellow indicates that experimental subjects had increased ALFF compared with the controls and the blue indicates the opposite. (Po0.05, FDR corrected; Voxels≥10).

The second, more important finding was that some brain areas showed increased ALFF in ventromedial prefrontal cortex (vmPFC) and Anterior Cingulate cortex (ACC), whereas some brain areas showed decreased ALFF in striatal regions (caudate, putamen) during automatic memory consolidation of fear conditioning in resting state. Converging lines of research across species suggested that the vmPFC and ACC played a critical role in the consolidation and retention of extinction learning and the reduction of fear expression during extinction recall (Knight et al., 2004; Quirk and Mueller, 2007; Sotres-Bayon et al., 2006). Specifically, neuroimaging studies have shown that when exposed to traumarelated cues, PTSD patients showed a relative failure of activation in the mPFC (Bremner et al., 1999; Bremner et al., 2004; Lanius et al., 2002; Shin et al., 2004; Shin et al., 2001). These neuroimaging studies supported the hypothesis that dysfunctional mPFC activity may underlie the exaggerated fear responses commonly observed in PTSD. Decreased PFC cortical volumes have also been observed in panic disorder (Vythilingam et al., 2000). Regulatory mechanisms such as reappraisal and suppression, were thought to reside in the PFC and ACC. Specifically, based on fMRI studies, Levy and Anderson postulated that suppressing retrieval of unpleasant memories was accomplished by executive control mechanisms mediated by lateral PFC and ACC, which terminated recollection-related activity in the hippocampus (i.e., unwanted memories) and inhibited the expressions of conditioned fear (Levy and Anderson, 2008). Moreover, adaptive

Fig. 5 – The correlation between the change of ALFF in vmPFC and subjective ratings (r ¼ 0.525, p¼ 0.004).

Table 3 – Detailed information for clusters showing group ALFF differences (delta (Δ) image in experimental group vs. delta (Δ) image in control group) (Talairach coordinates). Region

BA

No. Voxels

Peak t-value

x

y

z

Medial Frontal/Anterior Cingulate L. Lentiform Nucleus /Putamen

11/25/32/24

109 109

4.15 3.58

0 −24

23 9

−11 8

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forms of uninstructed or “automatic” emotion regulation were proposed to involve recruitment of the vmPFC and ACC mechanisms (Carretié et al., 2005; Mauss et al., 2007; Westen et al., 2006). The vmPFC and ACC might implement their function by dampening the output of other brain regions involved in conditioned fear expression, such as the amygdala, dACC and the hippocampus. Ultimately, the current study supported that vmPFC and ACC were strongly correlated with emotion progress, which involved in emotion assessment, emotion-related learning, and autonomic regulation (Stevens et al., 2011). Moreover, our findings makes it reasonable to assume that the increased vmPFC and ACC activation following fear acquisition might be a means to downregulate internally generated intrusive and fear memories. It has a protective value for the individual in adapting to real-life situations (i.e., develop fewer fear symptoms). These data also supported previous results indicating that the ventromedial prefrontal cortex (vmPFC) and ACC were involved in the top-down regulation of attention and danger processing. With regard to the striatum, Seymour et al. showed a relative selectivity of the more anterior regions to reward and the posterior striatum to aversive outcomes (Seymour et al., 2007). The striatum has been ascribed various different functions such as reward prediction (Knutson et al., 2001), and more generally the processing of positive emotions (Burgdorf and Panksepp, 2006; Whittle et al., 2006). Our findings suggested that decreased activation in the striatum may associate with the negative emotions such as fear and anxiety. Interestingly, the brain–behavior correlation analysis showed that the change of ALFF in vmPFC was positively correlated with the subjective fear ratings. In accordance with prior research, activation of the vmPFC is thought to inhibit the amygdala and suppress fear(Milad et al., 2006; Milad et al., 2007b; Rauch et al., 2006). Moreover, as previously suggested, the vmPFC has a regulatory role involved in generating fear responses (Etkin et al., 2011; Roy et al., 2009). Schultz et al. found that behavioral measure of explicit memory performance and implicit autonomic measure of conditioning were significantly correlated with the change in amygdala connectivity with superior frontal gyrus, and ACC respectively by using the resting-state functional connectivity (RSFC). However, we found that the change of ALFF in vmPFC was positively correlated with the subjective fear ratings. Changes in RSFC and ALFF indicate that prior experience can modify neural networks at rest. These changes might also reflect some of the ongoing processes that support the consolidation of memory (Schultz et al., 2012). Additionally, our study may suggest that when normal human were in the fear memory consolidation process, the vmPFC was more active to dampen and regulate fear response. In summary, these findings suggested that the parahippocampus, insula, and thalamus would be the neural substrates of fear memory consolidation. However, activation in the vmPFC and ACC may primarily link to the regulation and inhibition the expression of conditioned fear following fear acquisition. Interestingly, change of ALFF in vmPFC could predict subjective fear ratings. Combined, the present study can help elucidate the neural mechanisms and regulatory

circuits of the automatic memory consolidation of fear conditioning. However, the current study did not include objective behavioral data such as startle amplitude or SCR which are common measures in the human conditioning task, so it may not be a strong evidence that the participants acquired the conditioned fear. A number of important questions wait to be addressed. Specifically, future studies should target the reconsolidation update mechanisms during the reconsolidation window from the development perspective. However, our study included the control group to eliminate the possibility that ALFF changes are due to non-associative factors.

4.

Experimental procedures

4.1.

Participants

Fifty-six right-handed college subjects (28 females; M¼ 21.68, SD¼ 1.70) were recruited for the study, and they were paid for their participation. They were divided into two groups. The experimental group consisted of 36 college students from a Chinese university. An additional group of 20 college students in control group was recruited from the same university. In experimental group, no participants were removed from the task analysis and seven participants were not included in the rest analysis due to excessive head movement artifact. The rest analysis included 16 participants in control group (four participants were removed from the rest analysis due to excessive head movement). Subjects were pre-assessed to exclude those with a previous history of neurological or psychiatric illness. All subjects gave informed consent, and the study was approved by the Institutional Review Board of the Southwest University.

4.2.

Stimuli

Another fifty participants rated 533 pictures chosen from the Internet and International Affective Picture System (IAPS) which was used in the fear conditioning task (Lang et al. 1999). In accordance with the previous literature, we used a dimensional model for measuring pictures along 3 dimensions, “valence”, “arousal” and “the degree of fear”. They rated the respective dimensions on a 7-point Likert scale. Finally we chose 60 fear pictures and 160 neutral pictures, in which the disparity of the degree of fear and the valence was as large as possible (the fear picture (fear): M¼5.72, SD¼ 0.51, the neutral picture (fear): M¼ 1.85, SD¼0.55, t(49) ¼33.38, po0.001; the fear picture (valence): M¼ 6.08, SD¼ 1.09, the neutral picture(valence): M¼ 2.86, SD¼0.50, t(49) ¼18.35, po0.001) and the arousal of fear pictures and neural pictures was as follows: the fear picture (arousal): M¼5.12, SD¼ 0.35, the neutral picture (arousal): M ¼4.74, SD¼ 0.27, t(49)¼ 1.36, p40.1. The conditioned stimulus (CS+, CS−) were yellow and blue squares and the unconditioned stimulus (US) were the fear pictures.

4.3.

Design and procedure

The experiment began with a baseline rest condition (REST1, 10 min), then participants completed fear acquisition task

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(40 min) before the experimental rest condition (REST2, 10 min) (see Fig. 6). During acquisition, all experimental subjects underwent a Pavlovian discrimination fearconditioning paradigm with partial reinforcement, whereas the control group underwent the same task without reinforcement. The conditioned stimulus (CS+, CS−) were yellow and blue squares (2 s), and the unconditioned stimulus was the fear picture (2 s). When the CS+ terminated the unconditioned stimulus set on. The inter-trial-interval (ITI) was 2–6 s. The CS+ was paired with the fear picture on a 62.5% partial reinforcement schedule and the CS− was always paired with neutral picture. Subjects were instructed to pay attention to the screen and try to figure out the relationship between the squares appearing on the screen and the fear picture. Moreover, when the CS+ appeared on screen, the subjects need to press key “1”, otherwise they should press “3”. Two orders were used to counterbalance for key and designations of colored squares (blue or yellow) as CS+ or CS−. In control group, all procedures were the same as the experimental group except for no reinforcement. During the resting state, subjects were instructed to keep their eyes closed, relax their mind, and remain motionless as much as possible. The resting scan lasted for 600 s. All participants informed that they had not fallen asleep during the scan. At last, subjective fear ratings (CS+ and CS–) were obtained immediately following REST2 using an 1–7 scale of fearfulness (on a 7-point Likert scale: 1, a little; 4, moderately; 7, extremely). Two-way mixed ANOVA analysis on the group (experimental group vs. control group) and the type of the conditioned stimulus (CS+ vs. CS−) revealed that there was a significant interaction between two factors, F(1,54)¼ 79.45, po0.001. To examine the effect of experiment treatment, we performed simple effect analysis. The following results were obtained: in the experimental group, the subjective fear ratings of CS+ and CS− was as follows: the CS+: M¼ 5.42, SD¼ 1.52, the CS−: M¼1.28, SD¼ 0.77, t(35)¼ 13.07, po0.001. In the control group, the subjective fear ratings of CS+ and CS− was as follows: the CS+: M¼ 1.75, SD¼ 0.55, the CS−: M ¼1.6, SD¼ 0.60, t(19) ¼1.14, p¼ 0.27. For another simple effects analysis, the following results were obtained: there was no significant difference between the subjective fear ratings of CS− in experimental group (M¼ 1.28, SD¼ 0.77) and the subjective fear ratings in the control group (M¼ 1.6, SD¼0.60), t(54)¼ 1.59, p ¼0.12; However, participants in the experimental group have greater fear ratings of CS+ (M¼5.42, SD¼ 1.52) than participants in

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the experimental group (M¼ 1.75, SD¼ 0.55), t(54)¼ 10.34, po0.001. The behavioral results showed that the experimental group acquired the fear conditioning.

4.4.

Image acquisition and analysis

4.4.1.

Task-state functional MRI

Images were acquired with a Siemens 3 T scanner (Siemens Magnetom Trio TIM, Erlangen, Germany). An Echo-Planar imaging (EPI) sequence was used for data collection, and 300 T2n-weighted images were recorded per run (TR¼ 2000 ms; TE¼ 30 ms; flip angle¼ 901; FOV¼192  192 mm2; matrix size¼64  64; 32 interleaved 3 mm-thick slices; in-plane resolution¼3  3 mm2; interslice skip¼ 0.99 mm). T1-weighted images were recorded with a total of 176 slices at a thickness of 1 mm and in-plane resolution of 0.98  0.98 mm2 (TR¼1900 ms; TE¼ 2.52 ms; flip angle¼91; FoV¼250  250 mm2). We used SPM8 to analysis the functional data (Friston et al. 1994). For T2n-weighted images, slice timing was used to correct slice order, the data was realigned to estimate and modify the six parameters of head movement, and first five images were discarded to achieve magnet-steady images. The T1-weighted images were co-registered to the EPI mean images and segmented into white matter, gray matter, and Cerebrospinal fluid (CSF).These images were then normalized to MNI space in 3  3  3 mm3 voxel sizes. The normalized data were spatially smoothed with a Gaussian kernel; the full width at half maximum (FWHM) was specified as 8  8  8 mm3. In the first-level specify the four functional scanning runs were modeled in one GLM (general linear model) in fear acquisition task. Five regressors (“+”, CS+, CS−, fear picture, and neutral picture) were created for each run after convolution with the Canonical Hemodynamic Response Function (HRF). These regressors further included six realignment parameters, and the resulted design matrix was filtered with a high-band pass of 128 s. After these we used the contrast of CS+ and CS− to explore the fear related brain regions in the second-level specify, and the threshold of p-value was 0.05, FDR corrected, voxels≥10.

4.4.2.

Resting-state functional MRI

The identical data acquisition parameters and preprocessing steps were employed here as they were in task-state. However, the spatially smoothing kernel was 6  6  6 mm3. The

Fig. 6 – Schematic illustration of the paradigm for the fMRI experiment. The experiment began with a baseline rest condition (REST1, 10 min), then participants completed fear acquisition task (40 min) before the experimental rest condition (REST2, 10 min). During acquisition, all experimental subjects underwent a Pavlovian discrimination fear-conditioning paradigm with partial reinforcement (62.5%), whereas the control group underwent the same task without reinforcement.

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REST and DPARSF software were further used for rest-state analysis (Chao-Gan and Yu-Feng, 2010; Song et al., 2011). We removed the linear drift and filtered the spectrum with the band pass of 0.01–0.08 Hz before the calculation of Amplitude of Low-Frequency Fluctuation (ALFF) for each subject (YuFeng et al., 2007). After achieving the ALFF images of the experiment and control groups, two-way ANOVA on the group (experimental group vs. control group) and the REST (REST1 vs. REST2) was performed to examine the interaction between two factors. To see the ALFF difference between REST1 and REST2 in each group, simple effects analysis was performed to investigated the effect of fear memory consolidation in the resting state through the comparison between REST1 and REST2 of each group with the threshold of po0.05, FDR corrected, Voxels≥10. Finally, delta (Δ) image (REST2 images subtract REST1 images) was performed in experimental group and control group respectively. After that, a two sample t-test (Δ REST image for each group) was performed to investigate whether the observed effects were the result of fear acquisition with a P-valueo0.05, FDR corrected, Voxels≥10. To examine whether change of ALFF (rest 2 vs. rest 1) in brain regions correlated to fear memory consolidation could predict subjective fear ratings (the score of the mean subjective fear rating of CS+), we performed the correlation analysis between brain activity and behavioral data. ROIs with the radius of 6 mm were selected on the basis of increased ALFF in the experimental group in rest functional MRI, that is, centering the ROI on the peak of activation including parahippocampus, insula,thalamus,vmPFC and ACC (MNI coordinate: R thalamus, 3,–9,6; L thalamus, -9, -18, 6; parahippocampus, 15, −36, −6; vmPFC, 3, 33, −15; L insula:−36,19,−8; R insula:36,−21,15).

Acknowledgments This research was supported by the Fundamental Research Funds for the Central Universities (SWU1309002), the National Natural Science Foundation of China (31271117) and the National Key Discipline of Basic Psychology in Southwest University of China (TR201207-2).

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