Basolateral Amygdala Connectivity With Subgenual Anterior Cingulate Cortex Represents Enhanced Fear-Related Memory Encoding in Anxious Humans

Journal Pre-proof Basolateral amygdala connectivity with subgenual anterior cingulate cortex represents enhanced fear-related memory encoding in anxious humans Hakamata Yuko, PhD, Mizukami Shinya, BSc, Izawa Shuhei, PhD, Moriguchi Yoshiya, MD, PhD, Hori Hiroaki, MD, PhD, Kim Yoshiharu, MD, PhD, Hanakawa Takashi, MD, PhD, Inoue Yusuke, MD, PhD, Tagaya Hirokuni, MD, PhD PII:

S2451-9022(19)30305-2

DOI:

https://doi.org/10.1016/j.bpsc.2019.11.008

Reference:

BPSC 513

To appear in:

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Received Date: 7 September 2019 Revised Date:

31 October 2019

Accepted Date: 15 November 2019

Please cite this article as: Yuko H., Shinya M., Shuhei I., Yoshiya M., Hiroaki H., Yoshiharu K., Takashi H., Yusuke I. & Hirokuni T., Basolateral amygdala connectivity with subgenual anterior cingulate cortex represents enhanced fear-related memory encoding in anxious humans, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2019), doi: https://doi.org/10.1016/j.bpsc.2019.11.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc on behalf of Society of Biological Psychiatry.

Title: Basolateral amygdala connectivity with subgenual anterior cingulate cortex represents enhanced fear-related memory encoding in anxious humans

Running title: Basolateral amygdala connectivity and fear encoding

Authors: Yuko Hakamata, PhD;a,b,* Shinya Mizukami, BSc;c Shuhei Izawa, PhD;d Yoshiya Moriguchi, MD, PhD;a Hiroaki Hori, MD, PhD;a Yoshiharu Kim, MD, PhD;a Takashi Hanakawa, MD, PhD;e Yusuke Inoue, MD, PhD;f Hirokuni Tagaya, MD, PhDb

Affiliations: a Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry; b Department of Health Science, Kitasato University School of Allied Health Sciences; c Department of Radiological Technology, Kitasato University School of Health Sciences; d Occupational Stress Research Group, National Institute of Occupational Safety and Health; e Integrative Brain Imaging Center, National Center of Neurology and Psychiatry; f Department of Diagnostic Radiology, Kitasato University School of Medicine

*Corresponding author: Yuko Hakamata, Ph.D. Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry. 4-1-1, Ogawahigashi, Kodaira, Tokyo, 187-8553, Japan Phone: +81 42 341 2711; Fax: +81 42 346 1986 Email: [email protected] 1

Abstract (250 words) Background: The amygdala can enhance emotional memory encoding as well as anxiogenesis, via corticotropin-releasing factor neurons. However, amygdala’s explicit role in emotional encoding remains unclarified in humans. We examined how functional connectivity (FC) of amygdala subnuclei affects emotional encoding, considering its mechanism in which anxiety, attention, and cortisol conceivably participate. Methods: Sixty-five healthy humans underwent resting-state fMRI scans and saliva collection at 10 points-in-time over 2 days. FC analysis was performed for basolateral (BLA) and centromedial (CEM) subnuclei. We assessed attentional control via emotional stroop task and emotional encoding via facial identification task that examines how strongly a neutral face is memorized when accompanied by an emotional face (fearful, sad, or happy). FC and task performance were compared between high-anxious (HA) and non-high anxious (NA) groups classified by anxious personality scores. Results: BLA connected with the subgenual anterior cingulate cortex (sgACC) in proportion to the strength of fear-related encoding, whereas CEM connected with caudate nucleus for happy-related encoding. HA showed more enhanced fear-related encoding but impaired happy-related encoding compared to NA. BLA-sgACC FC was more intensified in HA than in NA; however, CEM-caudate FC did not differ between them. Although emotional encoding was uncorrelated with either attentional control or cortisol, BLA-sgACC positively correlated with cortisol increase after awakening. Conclusions: The study revealed that neural interactions of BLA, specifically with sgACC, might play a critical role in fear-related memory encoding, depending on the individual’s level of anxiety. These findings aid to understand the complicated mechanisms of emotional memory in anxiety disorders.

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Keywords: cognitive bias; memory; attention; anxious personality trait; rostral anterior cingulate cortex; total cortisol output

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Introduction Emotion biases the cognitive processing of its relevant information by facilitating attention and memory encoding, which leads to anxiety and stress when exaggerated (1-3). For example, impoverished attentional control, such as over-engagement of attention to potential threat, is closely associated with anxious personality (4-8) and with cortisol, a representative stress hormone (9-12). Biased attention towards a negative stimulus can promote subsequent encoding processes (13), resulting in its preferential retrieval (14-17). Previous studies revealed that negative emotion generates an enhancing effect on related memory processing (18-20). However, little is known about mechanisms underlying this phenomenon.

The amygdala plays a crucial role in the formation of emotional memory by binding an item and emotion (21-23) and purportedly strengthens encoding (or consolidation) processes of emotion-related information (23) by activating cortisol release (24-27) as a major expression site of corticotropin-releasing factor (CRF) neurons (28, 29). Two principal subnuclei compose the human amygdala: basolateral (BLA) and centromedial (CEM) (30, 31). BLA contains primary sensory input cells and can mediate neural plasticity in fear-related associative learning. CEM is an output terminal responsible for expression of fear-responses (21, 32, 33). Previous animal studies reported that BLA plays a vital role in learning an association between fearful and non-fearful stimuli (34, 35), which is often exaggerated in humans with anxious personality as a form of the fear generalization (36). Further, BLA provokes emotional arousal (37) and augments encoding (or consolidation) processes of emotional memory due to corticosteroid-binding to CRF receptors (38-40). Nevertheless, BLA’s involvement in enhanced memory encoding and associative learning remains undetermined in humans.

The amygdala also contributes to anxiogenesis through the disrupted connectivity of BLA (and nearby intercalated cells) with the rostral anterior cingulate cortex (rACC) extending 4

to medial prefrontal cortex (mPFC) (41-44), which is partly implicated in emotional attentional control deficits (45). A large-scale, meta-analytic study of resting-state functional magnetic resonance imaging (fMRI) in humans revealed that internalizing symptoms, such as anxiety, are associated with altered intrinsic functional connectivity (FC) of the amygdala to rACC, which converges into subgenual and perigenual parts rather than mPFC (46). Recent animal studies confirmed that neural interactions between these regions affect anxiety (47-49). Yet, an increase or decrease in the connectivity is inconsistent in literature (46), which may be attributed to differences in the anatomical definition of the amygdala (entire amygdala vs. subdivisions). Furthermore, studies suggest that amygdala-centered FC with rACC is also related to endogenous cortisol during the resting-state and emotional tasks (50, 51), supporting the comparability between the resting-state and task-related fMRI (52). Given the BLA’s presumable role in fear-related learning and memory, the intrinsic FC of BLA with rACC may represent an anxiety-associated functional aspect of enhanced encoding in which a fearful emotion readily creates an association with a non-fearful object. Further, endogenous cortisol and emotional attentional control may underlie the neural mechanisms of enhanced emotional encoding. However, no study has yet investigated the relationships between them comprehensively.

This present study’s objective was to unravel whether intrinsic FC of BLA is associated with emotional encoding, considering anxious personality, emotional attentional control, and endogenous cortisol in humans. Hence, using a behavioral task, we first examined how strongly a neutral stimulus target is encoded when paired with an emotional stimulus (vs. neutral stimulus). Next, we used a resting-state fMRI to investigate whether intrinsic FC of amygdala subnuclei explains the task performance. We subsequently compared the performance and FC between individuals with high-anxious personality trait scores and those without. Furthermore, we explored whether the FC of each amygdala sub-nucleus—which is associated with emotional 5

encoding—is related to cortisol (measured at 10 points-in-time across 2 typical weekdays) or attentional control. We hypothesized that (1) fear-related encoding task performance is significantly associated with the intrinsic FC of BLA, specifically with rACC; (2) high-anxious individuals would more correctly encode a neutral target stimulus, especially when paired with a fearful stimulus (vs. a neutral stimulus); (3) BLA-rACC FC is correlated with increased endogenous cortisol levels and poor emotional attentional control.

Methods Participants The present study was carried out under the approval of the Kitasato University Medical Ethics Organization (No.C17-126) and following the ethical guidelines issued by the National Ministry of Health, Labor and Welfare and the Declaration of Helsinki. Participants were recruited via website and magazine advertisements and billboards at Kitasato University. The eligibility criteria were (1) 18–59 years old, (2) no current Axis-I psychiatric disorders or substance-abuse history as determined using the Mini-International Neuropsychiatric Interview (53), (3) no major medical illnesses, and (4) no regular intake of psychotropics, steroids, or opioids or irregular intake of these medications in the 30 days prior to experimentation. Of those who registered, 65 individuals met the criteria and provided written informed consent: 52% women; mean age 29.8 years, SD 12.4 years, range 18–59 years; and 92% right-handed as determined by the Edinburgh Handedness Inventory (54). Inclusion criteria for MRI scan were as follows: (1) no metal embedded in nor any medical appliance attached to the body; (2) no history of brain injury or trauma with loss of consciousness over 10 minutes; (3) no habitual intake of cold medicine or excessive caffeine (>400mg/day) that affects blood-oxygen-level-dependent (BOLD) signals (55). Of the 65 participants, three were excluded due to a dental implant (n=1), history of concussion over 10 6

minutes (n=1), and epilepsy (n=1). Thus, 62 participants received MRI data analysis: 52% women; mean age 30.0 years, SD 12.4 years, range 18–59 years; 92% right-handed. Study procedures All participants received psychological assessment and collected their saliva. The MRI-eligible participants underwent the assessment and MRI scans on the same day. The following demographic and physical status data were collected: age, gender, body mass index (BMI), daily caffeine intake, monthly alcohol consumption, and years of education (for scoring procedures of these variables, see Supplementary material and (27)). Women were requested to disclose the presence of menstrual irregularity, their typical menstrual period, and last menstruation date. Anxious personality assessment Anxious personality traits were assessed with the Japanese version of NEO-Personality Inventory Revised (NEO-PI-R) (56), whose reliability and validity are well established (57). NEO-PI-R is a widely known self-reported questionnaire, consisting of 240 items that measure five basic personality traits including neuroticism—a tendency to experience more frequent and intense negative emotions such as anxiety and sadness (58). Forty-eight items assessed neuroticism, each rated on a 5-point scale (from 0: “Do not agree at all” to 4: “Strongly agree”). We focused on ‘anxiety’ facets of neuroticism (susceptibility to fear, worry, or nervousness). High-anxious participants (HA) were those scoring more than 1SD from the mean of Japanese population in a standardization study (n=1024) (57). The other participants were defined as non-high anxious individuals (NA). Experimental tasks We used E-prime version 2.0 for Professionals (Psychology Software Tools, Inc., Pittsburgh, PA) to construct experimental tasks. Emotional encoding: facial identification task We created a facial identification task based on the stimulus layout of previous studies (59-61). Eight different facial stimuli (4 women and 4 men) were selected from the NimStim set of facial 7

expressions (62). Each individual face had 4 expressions including fearful, sad, happy, and neutral, which resulted in a total of 32 pictures (152×118 pixels). After a 1500-ms presentation of a white fixation on a black screen, 3 different individual faces appeared randomly for 3,000 ms within 3 of the 8 frames and located at an equal distance from the screen’s center (Figure 1). When the faces vanished, the participants were asked to indicate, by pressing a correspondent number button, which face (among 5 faces to choose from) was identical to the face they saw on the previous screen. The task consisted of 144 trials with 4 experimental conditions (24 trials each), (1) ‘w/Happy’ (1 happy and 2 neutral faces, and the correct answer is a neutral face; i.e., 1 of the neutral faces), (2) ‘w/Fearful’ (1 fearful and 2 neutral faces, and the correct answer is a neutral face), (3) ‘w/Sad’ (1 sad and 2 neutral faces, and the correct answer is a neutral face), and (4) ‘Control’ (all faces are neutral and the correct answer is a neutral face). Also, 48 filler trials (in which the correct answer is an emotional face) were embedded between the trials to prevent participants from forming a mindset towards neutral faces. The presentation order was randomized. Before the task, the participants performed 18 practice trials composed of neutral faces of individuals that would not appear in the actual task. These faces came from the Computer Vision Laboratory Face Database (63). Correct response (CR) ratio and response time (RT) were recorded. The groups’ CR ratios in 4 experimental conditions were compared. RT data are summarized in Table S1. Emotional attentional control A traditional emotional Stroop task (EmoStroop) was used in this study (64). EmoStroop consisted 48 trials where each word was displayed randomly in red, blue, or green color on a white screen. As previous studies found, the emotional stroop effect appears specifically when neutral and negative words were presented in separate blocks (65-67). We thus presented 24 neutral and 24 negative words in 2 separate sequential blocks. To prevent a presentation order 8

effect, order of the 2 blocks was counterbalanced between participants and controlled for in related analysis. The index of emotional attentional control (emoAC) was calculated as follows: [mean of RTs in negative word trials] – [mean of RTs in neutral word trials]. Positive values indicate poorer attentional control for emotional interference. RTs outside of 5SD based on each participant’s mean were excluded as in our previous study (68). RT data are summarized in Table S1. Saliva collection and cortisol assay Salivary cortisol was measured 5 times per day: upon awakening (T1); 30 min after awakening (T2); around noon (11:30–12:30) (T3); late afternoon (17:30–18:30) (T4); and at bedtime (T5). Detailed saliva collection procedures are described elsewhere (9, 27, 50) (also see Supplementary material). Each participant was instructed to passively drool their saliva into a micro-tube and complete a customized web-entry form for recording the exact collection time. They recorded their sleep duration, perceived stress, and physical condition on the same form. Within 2 weeks of the assessment, saliva was collected on 2 consecutive, typical weekdays using personalized kits with 20 tubes color-coded for each collection time (e.g., blue for bedtime on the first day). During the collection days and 1 night prior, participants were required not to consume any alcohol or smoke cigarettes. For 1 hour prior to saliva collection, they were instructed not to eat or drink anything except water, exercise, brush teeth, shower, or bathe. Within 3 days after completing saliva collection, the frozen samples were sent to our research institute and delivered to an author of this study (S.I.). For the salivary assay, slowly thawed samples were centrifuged at 3,000 rpm (g-force=1710) for 10 minutes. Cortisol level was determined by enzyme immunoassay using an EIA Kit (Salimetrics LLC, USA). Inter-assay and intra-assay concentration variations were below 11% and 7%, respectively. Area under the curve with-respect-to ground (AUCg) as total cortisol output and area under the curve with-respect-to increase between T1 and T2 (AUCiT1-T2) as estimated cortisol

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awakening response (CAR) were calculated based on a standard formula (69) and averaged across sampling days. Acquisition and preprocessing of MRI data We acquired anatomical and functional MRI scans with a 32-channel phased-array head coil, 3.0T scanner (Discovery MR750; GE Healthcare, USA). The participants were placed supine in the scanner, motionless with closed eyes, and awake. We used the CONN Functional Connectivity Toolbox version 17c (http://www.nitrc.org/projects/conn), which is compatible with SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12) for preprocessing and statistical analyses and applied band-pass filtering with a frequency window of 0.008–0.090 Hz (default setting for resting-state fMRI in CONN). Supplementary material describes the scanning parameters and preprocessing procedures.

Data analyses FC of amygdala subnuclei: its association with emotional encoding strength We performed amygdala-seeded correlation analysis between the CR ratio in the experimental condition of interest (w/Fearful condition) and averaged values of a time-series of BOLD signals in each whole-brain voxel. Effects of age, gender, and CR ratios in other conditions were covariates of no interest in the model. To assure that BLA-centered FC had specific relevance to fear-related encoding, we performed amygdala-seeded correlation analyses for CR ratios in the other conditions.

Using the Jülich histological atlas based on cyto- and myelo-architectonic segmentations (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases/Juelich), we defined the 2 major amygdala subnuclei, CEM and BLA, as seed regions (Figure S1). We used the ACC (including dorsal and perigenual parts) and subcallosal cortex (encompassing as subgenual part), in addition to mPFC and hippocampus due to memory encoding relevance (25, 33, 70), as regions 10

of interest (ROIs). ROIs were defined by the CONN default atlas based on the FSL Harvard-Oxford atlas (http://www.cma.mgh.harvard.edu/fsl atlas.html). Small volume correction was applied to the ROIs. According to the number of seed-regions and ROIs, a statistical threshold was set at p<0.006 (peak level) with family-wise error (FWE) corrections for multiple tests to satisfy FWE-correctedp<0.05. For regions outside the ROI, the threshold was FWE-corrected

p<0.05 (peak level) throughout the brain. The estimated values (βs) of

amygdala-centered FC with the region that was significantly associated with emotional encoding were extracted with MarsBaR ver.0.43 (http://marsbar.sourceforge.net/) for subsequent analyses. Relationship between strength of emotional encoding and anxious personality

We performed a 2-way, mixed-model analysis of covariance (ANCOVA) for condition (within-subject factor) and group (between-subject factor), controlling for effects of age and gender. When a significant interaction effect was observed, the simple main effects were calculated (71).

Relationships between amygdala-centered FC, attentional control, and endogenous cortisol Provided that the amygdala-centered FC was significantly correlated with fear-related encoding as hypothesized, we performed a correlation analysis between the extracted FC values, emoAC, and cortisol levels, as well as the CR ratio in w/Fearful condition and controlled for age, gender, CR ratios of other conditions, EmoStroop presentation order, interval between assessment/MRI scans and saliva collection, and the confounders of cortisol that were identified in potential confounder analyses (see Results and Supplementary material). We applied the Benjamini-Hochberg method to correct for multiple testing (72). Except for fMRI, analyses were conducted with SPSS version 25.0J (IBM Inc., Japan). The statistical significance threshold was p<0.05 (2-sided).

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Results Participants’ characteristics Descriptive statistics for demographics and cortisol data are shown in Table 1. The study found no group difference in these variables between the HA- and NA-groups. Additionally, there was no group difference between all participants and MRI-scanned participants (Table S2). Sixty-four participants participated in the cortisol-related correlation analysis because 1 participant provided insufficient saliva. Of these participants, 2 had a smoking habit, which was too small a number to conduct a statistical analysis to examine its potential confounding effect on the overall results. We thus excluded these 2 participants’ data in the correlation analysis. The average interval between saliva collection and MRI was 5.0±4.7 days. Because sleep duration, physical conditions, and menstruation had confounding effects on cortisol measures (see Supplementary material), these effects were controlled in the correlation analysis, along with interval between saliva collection and assessment/MRI scans. None were engaged in shift work or had jet lag. No woman used oral contraceptives. FC of amygdala subnuclei: its association with emotional encoding strength FC analysis revealed that CR ratio in w/Fearful condition had a significantly positive relationship with FC of the right BLA with a subgenual part of the ACC (sgACC) (MNI peak coordinate:–6 26 –6, volume 288 mm3, T= 4.40, FWE-correctedp=0.005) (Figure 2A), indicating that stronger BLA-sgACC FC represents more correct identification of a neutral face appearing with a fearful face (Figure 2B). No significant FC was found for CEM. The results of the other predefined ROIs are provided in Table S4. For CR ratio in w/Happy condition, no correlation existed with any BLA-centered FC. However, it showed a significant correlation with CEM-centered FC with caudate nucleus (MNI -12 8 2, 32 mm3, T=5.58, FWE-correctedp=0.023). As for w/Sad and Control conditions, no

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significant FC was observed in either BLA or CEM. These results indicated the specific relevance of BLA to fear-related encoding. When these FC values were compared between HA and NA, BLA-sgACC FC values were significantly higher in HA than in NA (Figure 2C: F1,58=10.62, η2=0.15, p=0.002), whereas CEM-caudate FC did not differ between them (F1,58=0.04, η2=0.001, p=0.84). The relationship between emotional encoding strength and anxious personality A 2-way mixed ANCOVA (controlled for age and gender) showed a significant interaction effect (condition×group; F3,59=4.6, η2=0.20, p=0.006). No significant main effect was found either for condition (F3, 59=0.39, p=0.76) or group (F1,61=0.001, p=0.97). When we parsed the interaction effect into simple main effects, significant group differences were observed in the w/Fearful (F1,208= 5.0, p=0.03) and w/Happy (F1,208=4.27, p=0.04) conditions. HA group had a significantly higher CR ratio identifying a neutral face appearing with a fearful face (HA: mean±SD = 63.4±19.5; NA: 56.6±17.7) but lower CR ratio for a neutral face with happy face (HA: 59.6±18.2; NA: 65.9±16.3) (Figure 3). No group difference was found in other conditions: w/Sad: F1,208=2.30, p=0.13 (HA: 63.2±19.9; NA: 67.8±14.8) or Control: F1,208=1.8, p=0.19 (HA: 69.9±15.7; NA: 73.9±13.2). The results for all participants were comparable to those of the MRI-scanned participants (Supplementary material). Correlations between amygdala-centered FC, attentional control, and cortisol The CR ratio in w/Fearful condition alone had no correlation with emoAC or cortisol, but BLA-sgACC FC showed a significant positive correlation with AUCiT1-T2 (r=0.37, p=0.008; corrected

p=0.026) (Table 2).

Discussion The present study revealed that the intrinsic connectivity of BLA, specifically with sgACC, was associated with fear-related emotional encoding in humans. Anxious individuals, as compared to 13

non-anxious individuals, possessed more strengthened BLA-sgACC connectivity. Further, they more correctly identified a neutral face appearing with a fearful face; however, they more often misidentified a neutral face accompanied with a happy face as compared to non-anxious individuals. The fear-related encoding-associated BLA-sgACC connectivity was positively correlated with AUCiT1-T2.

The facial identification task in this study assessed participants’ correct discrimination, among 5 choices, of a neutral face presented with an emotional face on the previous screen. Since it randomly included filler trials targeted at an emotional face, participants necessarily encoded all 3 presented faces (1 emotional and 2 neutral) in a given trial. The current findings―higher CR ratio in fear-related condition and lower CR ratio in happy-related condition, in HA versus NA―suggest that fearful stimulus more easily creates association with non-emotional stimulus, during learning, for anxious individuals, whereas happy stimulus might not. This notion corresponds to only the CR ratio in the fear-related condition specifically correlated with connectivity of the BLA that is implicated in fear-related associative learning (21, 32, 33) and emotional memory encoding (37, 38, 40, 73).

Interestingly, anxious individuals exhibited greater values of intrinsic BLA-sgACC FC than non-anxious individuals, which indicated that the more intensified the FC, the more enhanced the fear-related emotional encoding. Neural interactions of BLA, especially with rACC, are implicated in the discrimination between threat and safety (48, 70), as well as fear-related associative learning (74). The cingulate cortex contains extensive regions, but above all, sgACC has close relevance to emotion (75). Animal studies have revealed that the rACC’s neural inputs are predominantly targeting BLA and not CEM (76-78), which influences fear encoding (78) as well as innate fear (77). Considering these findings, neural connectivity between sgACC and BLA might represent how strongly a fearful stimulus is tied to a non-fearful stimulus at encoding, depending on each anxious personality. Further investigations 14

are needed to clarify the direction of the BLA-sgACC connectivity in enhanced fear-related encoding in anxious humans.

However, the amygdala was unconnected with the hippocampus or mPFC for emotional encoding. The current task’s lack of the context, with which the hippocampus binds items rather than emotion (23), might have led to the absence of a significant relationship between amygdala-hippocampus connectivity and CR ratios. For the mPFC, together with BLA, it reportedly involves the acquisition (but not extinction) of fear conditioning in animal studies (79-81). In humans with more differentiated brains, recent systematic meta-analytic studies have revealed that functional alternations in sgACC, but not mPFC, occur during fear acquisition (but not extinction) (82-84). That suggests that sgACC might play a more predominant role in fear acquisition and related associative learning than mPFC and, therefore, is specifically and preferentially connected to the BLA in fear-related encoding (85).

Seeking a clue to possible neural mechanisms underlying enhanced emotional encoding, we further examined relationships between fear encoding-associated BLA-sgACC FC, emoAC, and cortisol. Unexpectedly, there was no correlation between the BLA-sgACC FC and emoAC. Further, we investigated whether RT in filler trials, which could reflect facilitated attention to a fearful face target, would be correlated with fear-related encoding itself (w/Fearful condition CR ratio). We, however, found no correlation between them (Supplementary material). These results concur with earlier studies showing that arousal, and not selective attention, affected enhanced encoding of negative material (86, 87). Still, the present study was different from these earlier studies because a neutral, and not an emotional, face was the target. Considering these findings, arousal, rather than selective attention, might have a stronger influence on emotional encoding, even of non-emotional material if it accompanies emotional material. Indeed, it has been shown that amygdala-rACC strength parallels autonomic arousal magnitude during fear acquisition (88, 89), which correlated positively with neuroticism (90). Therefore, 15

BLA-sgACC might affect fear-related encoding due to a potent relevance to arousal, whose sensitivity relies on individual anxious personality.

Cortisol increase after awakening showed a positive correlation with BLA-sgACC FC, but not with emotional encoding. The lack of a correlation of cortisol with emotional encoding might be because it was not stress elicited, although we previously found a general enhancing effect of unstimulated cortisol on memory encoding (27). Emotional arousal through noradrenergic activation might be needed for cortisol to participate in enhanced emotional encoding (37, 39), despite our observations that encoding was enhanced by a fearful face, but not necessarily by fear-conditioned stimulus provoking apparent autonomic arousal, in anxious individuals. Notwithstanding an undetermined, precise function of CAR, its relevance to stress (91, 92) and alertness or arousal is substantiated (93, 94). Presumably, that is why BLA-sgACC FC was associated with AUCiT1-T2 in the present study; although, the index was measured from two, not three, points as recommended (95). Thorough investigation is needed to clarify the possibility.

Limitations This study had several limitations. First, the target stimulus was presented with no contextual information; encountering an emotional face on the black screen is an unlikely event in real-life situations. Second, the current cross-sectional study did not reveal the causal relationship between amygdala-centered FC, emotional encoding, and cortisol, although BLA-sgACC FC might have affected the enhanced fear-related encoding and cortisol because the MRI scans were performed prior to the task and saliva collection. Third, the noradrenaline levels, which are an accurate reflection of the arousal index, were not measured in the present study. Such measurements might have shown a possible interaction with cortisol and an involvement in selective attention (37). Lastly, the encoding immediately after stimulus presentation was a 16

focus of this study, which made it difficult to follow temporal changes of emotional memory itself. With time, memory of emotional material can be facilitated, whereas contextual information is gradually lost (20, 96, 97). Reportedly, in patients with posttraumatic stress disorder who show exaggerated fear-related associative learning (98), fearful stimuli are subject to intrusive recollection, while contexts are recalled with difficulty (99, 100). Cortisol has similar time-dependent memory-enhancing effects (101). Future research is needed to longitudinally investigate how contextual information, encoded with fearful material, changes over time, interacting with amygdala-hippocampal-ACC connectivity. Conclusion This study provided the first evidence showing that intrinsic connectivity of BLA, specifically with sgACC, is associated with fear-related emotional encoding in humans. Anxious individuals, possessing more strengthened BLA-sgACC connectivity than non-anxious individuals, showed more enhanced fear-related encoding. The emotional encoding-associated BLA-sgACC connectivity was related to cortisol. Neural interactions between BLA and sgACC might play a critical role in emotional memory encoding, even of neutral material if it accompanies fearful material. These findings would provide a clue to understand the complicated neural mechanisms of emotional memory formation in anxiety disorders.

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Supplementary data Supplementary information is available at the website of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

Acknowledgments We wish to thank all study participants and Mr. Soshi Hori, Ms. Yasuko Omura, Ms. Yumiko Oniduka, Ms. Miho Hide, Mr. Takashi Abe, Mr. Kuniaki Yamadera, Mr. Kaito Takabayashi, Mr. Masumi Fukusaka, Mr. Hiroki Mori, Mr. Takeo Katakura, Ms. Mari Abe, and Mr. Yuki Asano for data entry and research assistance.

Declaration of interest All authors report no biomedical financial interests or potential conflicts of interest.

Funding sources A Grant-in-Aid for Scientific Research (B) (No. 18H01094 to Y.H.) and Grant-in-Aid for JSPS Fellows (No. 17J40250 to Y.H.) was received from the Japanese Society for the Promotion of Science and research grants were received from the Koyanagi Foundation and the Nakatomi Foundation (to H. H.). Y.H. is a research fellow at the Japanese Society for the Promotion of Science, who supported this work. Funding sources did not influence any aspect of the work detailed in the current manuscript (design, data collection, analyses, interpretation, writing, or submission).

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26

Figure legends

Figure 1: Experimental design of a facial identification task.

Figure 2: BLA-sgACC FC significantly associated with CR ratio in w/ Fearful condition of the facial identification task. A. sgACC significantly connected with right BLA (sgACC: MNI peak coordinate: -6 26 -6, volume: 288 mm3, T = 4.40, FWE-correctedp = 0.005). Color bar indicates T-value. B. Scatter plot between BLA-sgACC FC and CR ratio in w/ Fearful condition of facial identification task (Z scores in which effects of age, gender, and CR ratios in the other conditions). C. Significantly greater BLA-sgACC FC values in HA than in NA group: F1,58 = 10.62, η2 = 0.15, p = 0.002. The effects of gender and age were controlled for. Abbreviations: BLA, basolateral sub-nucleus of the amygdala; sgACC, subgenual part of the anterior cingulate cortex; FC, functional connectivity; CR, correct response; HA, high anxious group; NA, non-high anxious group.

Figure 3: Results of a 2-way mixed design ANCOVA for correct responses in a facial identification task. Significant interaction effect between group and experimental conditions: F3,59 = 4.6, η2 = 0.20, p = 0.006. Significant group differences were observed in the w/ Fearful (F1,208 = 5.0, p = 0.03) and w/ Happy (F1,208 = 4.27, p = 0.04) conditions. Abbreviations: ANCOVA, analysis of covariance; HA, high anxious group; NA, non-high anxious group.

27

Table 1. Descriptive statistics of demographic information in each participant’s group test statistics (t or χ2)

p

1.79 0.46

0.08 0.50

3.0 69.7 36.6

1.63 -0.20 1.09 0.26

0.26 0.84 0.28 0.80

15.1

2.0

1.60

0.11

101.4 38.2 1.3 1.4

89.9 1.1

26.4 1.3

1.33 0.37

0.19 0.71

Time 1 (at awakening) Time 2 (30 min after awakening) Time 3 (12:00)

8.3 13.4 5.6

3.6 4.7 3.2

8.1 12.7 5.3

3.0 5.6 2.5

0.15 0.44 0.44

0.88 0.66 0.66

Time 4 (18:00) Time 5 (bedtime)

4.2 2.5

2.9 1.3

3.9 2.3

2.2 0.9

0.42 0.88

0.67 0.38

HA (n = 15)

NA (n = 50)

Mean SD

Mean

SD

Age Gender (% of females)

34.7 50.0

13.8

28.3 40.0

11.7

Handedness (% of right-handedness) BMI Daily caffeine intake amount (mg) Monthly alcohol consumption (unit)

90.0 20.7 3.3 114.2 78.4 25.5 38.8

0.0 20.9 91.1 22.7

Years of education Cortisol (nmol/L)1 Total cortisol output (AUCg) CAR (AUCiT1-T2)

16.3

4.0

1

As in the text, cortisol data of a participant were unavailable due to the lack of sufficient number of saliva tubes. The cortisol values are based on n = 64. Abbreviations: HA, high-anxious; NA, non-anxious; BMI, body mass index; AUCg, area under the curve with respect to ground; CAR, an estimate of approximate estimate of cortisol awakening response (calculated based on AUCi between Time 1 and 2); AUCi, area under the curve with respect to increase.

28

Table 2. Correlations between BLA-sgACC FC, w/ Fearful CR ratio, emotional attentional control, and endogenous cortisol CR ratio in w/ Fearful condition

BLA-sgACC FC BLA-sgACC FC

―

CR ratio in w/ Fearful condition

0.62 **

Emotional attentional control AUCg AUCi

T1-T2

Emotional attentional control

AUCg

―

–0.11

–0.11

―

0.06

–0.10

0.02

―

0.12

0.03

0.53**

*

0.37

AUCiT1-T2

*

―

**

p<0.001, *p<0.01

Notes: Effects of age, gender, CR ratios of the other experimental conditions, presentation order in EmoStroop, interval days between assessment/MRI scans and saliva collection, sleep duration, physical condition, and menstrual status were controlled for. Two participants were excluded from the analysis due to smoking habits. Abbreviations: BLA: latero-basal nucleus of the amygdala; sgACC, subgenual part of the anterior cingulate cortex; CR, correct response; AUCg (total cortisol output), area under the curve with respect to ground based on 5 time points, AUCi (cortisol awakening response estimate), area under the curve with respect to increase between T1 and T2.

29

KEY RESOURCES TABLE Resource Type

Specific Reagent or Resource

Source or Reference

Identifiers

Add additional rows as needed for each Include species and sex when applicable. resource type

Include name of manufacturer, company, repository, individual, or research lab. Include PMID or DOI for references; use “this paper” if new.

Include catalog numbers, stock numbers, database IDs or accession numbers, and/or RRIDs. RRIDs are highly encouraged; search for RRIDs at https://scicrunch.org/resources.

Biological Sample

Saliva

National Institute of Mental Health, National Center for Neurology and Psychiatry

UBERON: 0001836

Chemical Compound or Drug

Cortisol

National Institute of Mental Health, National Center for Neurology and Psychiatry

CHEBI: 17650

Commercial Assay Or Kit

Cortisol Enzyme Immunoassay

Salimetrics

#1-3002, SCR:002343

Organism/Strain

Human

National Institute of Mental Health, National Center for Neurology and Psychiatry

https://www.ncnp.go.jp/english/index.html

Software; Algorithm

MATLAB R2019a

Mathworks

RRID:SCR_001622

CONN v.17c

Gabrieli Lab. McGovern Institute for Brain Research Massachusetts Institute of Technology RRID:SCR_009550

Software; Algorithm Software; Algorithm

SPM 12

University College London

RRID:SCR_007037

Software; Algorithm

SPSS v.25 fMRI

IBM

RRID:SCR_002865

GE Healthcare

Discovery MR750, BIRNLEX: 2250

Other

Additional Information Include any additional information or notes if necessary.

Key Resource Table The journals of the Society of Biological Psychiatry support efforts in the biomedical research community to improve transparency and reproducibility in published research. Thus, Biological Psychiatry and Biological Psychiatry: Cognitive Neuroscience and Neuroimaging are pleased to participate in the initiative to include a Key Resources Table in published articles. Authors are asked to submit this table at first revision, which may be uploaded using the "Key Resources Table" item type. This table will then be published as supplemental information. The Key Resources Table is designed to promote reproducibility and thus, should include the resources and relevant details necessary to reproduce the study's results. It does not need to be exhaustive. Extensive lists (e.g., oligonucleotides, etc.) may be supplied in a supplementary table and the table referenced here. We strongly encourage the use of RRID identifiers that provide persistent, unique identifiers to key study resources. Search for RRIDs at https://scicrunch.org/resources. Resource categories Note: For all categories, indicate sex and species when applicable ●

Antibody - include host organism common name and clonality (e.g., “mouse monoclonal”)

●

Biological sample - any other biological entity, ranging from isolated tissue to defined population

●

Cell line - if a primary cell line, describe in Additional Information

●

Chemical compound, drug - commercially available reagents

●

Commercial assay/kit - detection assays; labeling and sample preparation kits

●

Deposited data or public database - include both raw data from this paper deposited into a repository and public repository databases (postmortem tissue; genetic consortia data; etc.)

●

Genetic reagent - applies to mutations and variants in whole organism, including transgenically introduced constructs

●

Peptide, recombinant protein - commercially available reagents

●

Recombinant DNA reagent - traditional cultured clones, plasmids, cDNAs, etc., including recombinant DNA libraries

●

Sequence-based reagent - oligonucleotides, primers, etc.; indicate sequence

●

Software, algorithm - include version number and URL for download

●

Organism/Strain - applies to whole organism

●

Transfected construct - in cell line; indicate species of cell line or construct component

●

Other - miscellaneous other categories, including histological stains

EXAMPLE KEY RESOURCES TABLE Resource Type

Specific Reagent or Resource

Add additional rows as needed for each Include species and sex when applicable. resource type

Source or Reference

Identifiers

Additional Information

Include name of manufacturer, company, repository, individual, or research lab. Include PMID or DOI for references; use “this paper” if new.

Include catalog numbers, stock numbers, database IDs or accession numbers, and/or Include any additional information or RRIDs. RRIDs are highly encouraged; search notes if necessary. for RRIDs at https://scicrunch.org/resources.

Antibody

rabbit anti-E2F2

Abcam

Abcam Cat# ab50917, RRID:AB_869541

Antibody

total actin

MP Biomedicals

Cat#8691002, RRID:AB_2335304

Antibody

E2F3

Bacterial or Viral Strain

C-18, Cat#SC-878, RRID:AB_2096807 N/A

Bacterial or Viral Strain

AAV-hSyn-DIO-hM3D(Gq)-mCherry HSV-wtSmurf1

Santa Cruz University of North Carolina Vector Core PMID: 10458166

Biological Sample

postmortem brain tissue

Addgene plasmid # 11752 RRID:SCR_003316

Cell Line

control 03231 iPSC line

Harvard Brain Tissue Resource Center National Institute of Neurological Disorders and Stroke repository

Chemical Compound, Drug

Terazosin

Sigma-Aldrich

N/A

Commercial Assay Or Kit

Bio-Rad DC Protein Assay

Bio-Rad Laboratories, Inc.

# 5000111

Commercial Assay Or Kit

Illumina, Inc.

Deposited Data; Public Database

TruSeq Stranded mRNA GSE35978, Sample Prep Kit v2 GSE17806, GSE53987, GSE13564, GSE80655, and GSE25219

NCBI GEO DataSets

Cat. No. RS-122-2101 RRID:SCR_005012; https://www.ncbi.nlm.nih.gov/gds

Organism/Strain

Mouse: C57BL/6J, male

The Jackson Laboratory

RRID:IMSR_JAX:000664

Sequence-Based Reagent

Primers for RT-qPCR, see Table S1

This paper

N/A

Software; Algorithm

HTSeq Python package

https://doi.org/10.1093/bioinformatics/btp120; https://doi.org/10.1093/bioinformatics/btu638 RRID:SCR_005514

Software; Algorithm

MATLAB v9.1

Mathworks

NINDS # ND03231; RRID:SCR_004520

RRID:SCR_001622

＋ Stimulus layout

1500 ms

+

+ Three faces were randomly presented in 3 of the 8 locations. Dotted-line frames were not shown in the actual slides.

3000 ms

Which picture did you see?

1

3

2

4

5

Presented until response

A

B

p = 0.002

C 0.4

BLA-sgACC functional connectivity (β values)

BLA-sgACC functional connectivity (β values)

0.16 0.3

0.2

0.1

0

-0.1

-0.2

-0.3

-0.4 -2.5

y = 0.08x + 0.03

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 -0.02

-1.5

-0.5

0.5

CR ratio in w/ Fearful condition

1.5

2.5

NA (n = 48)

HA (n = 14)

Significant group×condition effect (F3,59 = 4.6, η2 = 0.20, p = 0.006) HA (n = 15)

0.80 80.0

NA (n = 50)

Correct response ratio

75.0 0.75

70.0 0.70

*

*

65.0 0.65

60.0 0.60 55.0 0.55 50.0 0.50

w/ Fearful

w/ Happy

w/ Sad

Control (all Neutral)

Experimental Condition