Fear acquisition and extinction deficits in amnestic mild cognitive impairment and early Alzheimer's disease

Journal Pre-proof Fear Acquisition and Extinction Deficits in Amnestic Mild Cognitive Impairment and Early Alzheimer’s Disease Sarah Nasrouei, Julina A. Rattel, Michael Liedlgruber, Josef Marksteiner, Frank H. Wilhelm PII:

S0197-4580(19)30392-6

DOI:

https://doi.org/10.1016/j.neurobiolaging.2019.11.003

Reference:

NBA 10710

To appear in:

Neurobiology of Aging

Received Date: 3 January 2019 Revised Date:

16 October 2019

Accepted Date: 1 November 2019

Please cite this article as: Nasrouei, S., Rattel, J.A., Liedlgruber, M., Marksteiner, J., Wilhelm, F.H., Fear Acquisition and Extinction Deficits in Amnestic Mild Cognitive Impairment and Early Alzheimer’s Disease, Neurobiology of Aging (2019), doi: https://doi.org/10.1016/j.neurobiolaging.2019.11.003. 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.

Fear Acquisition and Extinction Deficits in Amnestic Mild Cognitive Impairment and Early Alzheimer’s Disease

Sarah Nasroueia,b,*, Julina A. Rattela, Michael Liedlgrubera, Josef Marksteinerb, Frank H. Wilhelma

a

Division of Clinical Psychology, Psychiatry, and Health Psychology, Department of Psychology,

University of Salzburg, Salzburg, Austria b

Department of Psychiatry and Psychotherapy A, State Hospital Hall, Hall, Austria

*Address correspondence to Sarah Nasrouei, State Hospital Hall, Milser Strasse 10, 6060 Hall, Austria, Phone: +43 (0) 50504/ 88222, Email: [email protected]

Key Words: Alzheimer’s Disease; amnestic Mild Cognitive Impairment; Fear Conditioning; Amygdala; Memory; Emotional Learning

Abstract Impaired learning and memory functioning are prime markers for Alzheimer’s disease (AD). Although initial evidence points to impaired fear acquisition in later AD, no study has investigated fear conditioning in early stages and amnestic Mild Cognitive Impairment (aMCI), a condition often preceding AD. The present study examined if fear conditioning gradually decays from healthy elderly to aMCI, to AD. AD (n=43), aMCI (n=43), and matched healthy controls (HC, n=40) underwent a classical fear conditioning paradigm. During acquisition, a neutral face (conditioned stimulus, CS+) was paired with an electrical stimulus, whereas another face (unconditioned stimulus, CS-) was unpaired. Conditioned responses were measured by USexpectancy, valence, and skin conductance. Compared to HC, both patient groups showed less differential (CS+ vs. CS-) fear acquisition across all measures. Patients further displayed slowed extinction indexed by higher US-expectancy and reduced positive valence for CS+, declining from aMCI to AD. Groups did not differ in responses during a pre-conditioning habituation phase and in unconditioned responding. Diminished differential fear acquisition and slowed extinction could represent prognostic markers for AD onset.

2

1. Introduction Alzheimer’s disease (AD) is characterized by impaired learning and memory functioning (Goerlich et al., 2017; Salmon and Bondi, 2009; Solomon et al., 1991), like deficits in classical fear conditioning (e.g., Hamann et al., 2002; Hoefer et al., 2008; Solomon et al., 1991). Although a gradual decay in cognitive functioning is assumed with AD progression, transitional stages of AD have mostly been overlooked by past fear conditioning research. Amnestic Mild Cognitive Impairment (aMCI), often preceding AD (see Petersen et al., 2018), may constitute a transitional stage to AD. The present study was set out to investigate if fear conditioning gradually decays from healthy elderly, to aMCI, to AD. 1.1. Classical fear conditioning In fear conditioning paradigms, during acquisition, a conditioned fear response (CR) is acquired by repeatedly pairing a neutral stimulus (conditioned stimulus, CS+) with an aversive stimulus (unconditioned stimulus, US; Lonsdorf et al., 2017). Discriminative learning can then be assessed by comparing fear responses to the CS+ relative to the CS- (a second CS that has not been paired with the US). During subsequent fear extinction, the CS+ is presented several times without the US, resulting in decreased fear responding. 1.1.1. Younger individuals compared to healthy elderly Past research showed that compared to younger individuals, healthy elderly display decreased discriminative acquisition learning in eyeblink (Kimble and Pennypacker, 1963; Knuttinen et al., 2001; Woodruff-Pak and Thompson, 1988) as well as skin conductance responding (SCR; LaBar et al., 2004). In addition, healthy elderly display reduced responding to a white noise US (LaBar et al., 2004) and deficits in contingency awareness (CA; LaBar et al., 2004). Although LaBar and colleagues (2004) did not find any extinction learning deficits in healthy elderly, most studies did not assess extinction learning (Kimble and Pennypacker, 1963; Woodruff-Pak and Thompson, 1988). 1.1.2. Healthy elderly compared to AD Compared to healthy elderly, AD show decreased discriminative acquisition learning in eyeblink responding (Solomon et al., 1991; Woodruff-Pak et al., 1996, 1990). In addition, AD display diminished SCR to a white noise US (Hamann et al., 2002; Hoefer et al., 2008). No 3

group differences have been found during extinction (Hamann et al., 2002; Hoefer et al., 2008). Table A.1 in Appendix A provides an overview of human conditioning studies investigating either healthy elderly and/ or persons suffering from dementia. 1.1.3. APP-mice Decreased discriminative fear acquisition has also been found in young APP-mice (Mehla et al., 2018; Végh et al., 2014; Wang et al., 2004) – an AD mouse model characterized by mutant amyloid precursor protein. Those mice show fear conditioning deficits even before plaque detection (Bloom et al., 2006; Comery et al., 2005; Dineley et al., 2002) and in line with findings for human AD patients, display increased learning deficits with increasing age (Corcoran et al., 2002; Dineley et al., 2002; Dobarro et al., 2013). In contrast to human studies, APP-mice also display impaired fear extinction (Bonardi et al., 2011; Pardon et al., 2009) even before the onset of acquisition deficits. 1.2. Limitations of past studies Past research investigating fear conditioning deficits with disease progression primarily focused on later stages of AD (Hamann et al., 2002), including relatively small samples (see Table A.1, Appendix A). To our knowledge, no study has yet examined fear conditioning deficits in early stages of AD or aMCI, thereby investigating patients not yet receiving anti-dementia medication. Medication such as cholinesterase inhibitors used in AD treatment may interfere with SCR by increasing cholinergic functioning (Hoefer et al., 2008). This is particularly problematic, as past research primarily focused on physiological fear conditioning measures. Moreover, as physiological (e.g., SCR) and subjective fear conditioning responses (e.g., USexpectancy) represent conceptually different outcome measures of fear learning and dissociations of SCR and US-expectancy have been reported (McAndrew et al., 2012; Schultz and Helmstetter, 2010), both should be assessed and reported (Lonsdorf et al., 2017). Furthermore, there is an ongoing debate if deficits in contingency awareness (CA) could influence fear acquisition (Lovibond and Shanks, 2002; Sevenster et al., 2014; Wiens and Ohman, 2002). Although lower CA in healthy elderly compared to young and middle-aged participants has been reported (LaBar et al., 2004), possibly affecting group differences in fear conditioning, previous AD studies did not check for group differences in CA. 4

Moreover, reduced SCR reactivity has been observed in healthy aging participants, possibly manifest in AD (LaBar et al., 2004). Therefore, in order to account for individual electrodermal (and group) baseline differences not related to conditioning, a CS-habituation phase prior to fear acquisition should be included in conditioning paradigms. To our knowledge, only Hoefer et al. (2008) assessed SCR baseline differences; although non-significant, AD patients displayed somewhat higher SCR than frontotemporal lobar degeneration patients and HC. 1.3. The present study The present study investigated fear acquisition and extinction in early AD, compared to aMCI and HC. In line with the studies by Hamann et al. (2002) and Hoefer et al. (2008), we used a classical fear conditioning paradigm including a habituation, acquisition, and immediate extinction phase. In contrast to these studies, we implemented an electric stimulus instead of air puff or white noise as US and neutral faces in place of tones or geometric figures as CS. Extending past studies, differential fear-conditioning was measured by 1) valence ratings, an affective rating indexing predominantly implicit, evaluative conditioning (Lonsdorf et al., 2017), 2) US-expectancy ratings, indexing explicit likelihood estimation of imminent threat (Boddez et al., 2013), and 3) skin conductance (SC), a measure of sympathetic nervous system activity responsive to threat stimuli (Boucsein et al., 2012), thereby capturing different affective and cognitive learning mechanisms. We predicted early AD and aMCI, compared to HC, to display decreased differential valence and US-expectancy ratings, as well as decreased differential SC during acquisition. In line with research in APP-mice (Bonardi et al., 2011), we expected early AD and aMCI, compared to HC, to display extinction deficits in valence and US-expectancy ratings and SC. We hypothesized a gradual decay from HC to aMCI to early AD in terms of differential fear acquisition as well as extinction learning. Exploratory analyses investigated if contingency awareness gradually decays with disease progression.

5

2. Material and methods 2.1. Participants 126 participants (AD: n=43; aMCI: n=43; HC: n=40) were recruited at the Memory Clinic of the State Hospital of Hall, Austria. AD and aMCI participants were outpatients, diagnosed after extensive neuropsychological testing, clinical evaluation, and MR scanning of the brain by a multidisciplinary team. AMCI diagnosis was based on the criteria of Petersen (2004) and Winblad et al. (2004). All of the following criteria had to be met: 1) no dementia diagnosis (ICD-10; Clinical Dementia Rating Scale ≤0.5); 2) subjective cognitive impairment; 3) memory deficits in at least one of the memory subtests of CERAD (Consortium to Establish a Registry for AD; performance of more than 1.5 standard deviation below education- and ageadjusted norms); 4) no or minimal impairment of daily life activities (Bayer-Activities of Daily Life Scale ≤4). Early AD patients were diagnosed using the ICD-10 (World Health Organization, 1993) and the NINCDS-ADRDA criteria for probable AD (McKhann et al., 1984), including 1) progressive cognitive impairment in more than two cognitive domains; 2) hippocampus atrophy in the MR scan; 3) Clinical Dementia Rating Scale ≥0.5 and <2 and MMSE=20-26. Inclusion criteria were being 55 years or older, stable and controlled medical conditions, and not taking anti-dementia medication. Exclusion criteria were severe sensorimotor and/or neurological diseases (e.g., stroke, seizure disorders, cerebral tumor), severe behavioral disturbances interfering with test compliance, and poor health. HC were relatives of the participants or participants undergoing the examination as part of a medical checkup, free of any psychiatric or neurological disease. HC were age-, sex-, and education-matched. All participants were Caucasian. The study was approved by the ethics committee of the Medical University of Innsbruck, Austria (AN2015-0158 351/4.6) and all participants provided written informed consent. 2.2. Procedure 2.2.1. Psychological assessment 6

Participants underwent a detailed neuropsychological assessment. They completed the German Version of the Mini-Mental-State-Examination (MMSE; Folstein et al., 1975) and performed all subtests of the CERAD battery (Morris et al., 1988) including figural and verbal memory, verbal and semantic fluency, object naming, constructive abilities, processing speed, divided attention, and cognitive flexibility. Further, planning (CLOX Test part 1; Royall et al., 1998), numeric and working memory (digit span forwards and backwards, Nuremberg-AgingInventory, NAI: Oswald and Fleischmann, 1994), and discrimination ability were examined (subtest Reynolds-Intellectual-Assessment-Scales, RIAS; Hagmann-von Arx and Grob, 2014). Additional information was obtained from caregivers of AD and aMCI through the Clinical Dementia Rating Scale (Hughes et al., 1982) and the Bayer-Activities of Daily Life Scale (Erzigkeit and Lehfeld, 2010). Anxiety and depressive symptoms were assessed using the German version of the Geriatric-Depressions-Scale (GDS; Yesavage et al., 1982), the General-Depression-Scale (ADS-L; Hautzinger et al., 2012), and the State-Trait-Depression-Anxiety-Inventory (STADI; Laux et al., 2013). At the end of psychological assessment, participants evaluated pictures of same-sex faces with neutral expression of ten Caucasian individuals (i.e., matched to ethnicity of participants) from the Radboud Face Database (Langner et al., 2010) on a visual-analogue valence scale (ranging from 1 (very unpleasant) to 10 (very pleasant)); for each participant, the two most neutral faces were selected as CSs and randomly assigned as CS+ or CS- for the subsequent conditioning procedure. Note: healthy elderly and mild AD patients do not seem to differ in recognition of neutral faces (Wright et al., 2007). 2.2.2. Conditioning procedure The experimental procedure was similar to previous human fear conditioning studies (e.g., Blechert et al., 2007). After diagnostic assessment at the Memory Clinic, participants were seated in front of a computer monitor, electrodes for electric stimulation were attached at the back of the dominant hand, and instructions about the stepwise adjustment of the electric stimulation were given. Electric stimulation (US) was adjusted by increasing the intensity gradually until participants clearly stated that the shock level was “unpleasant and challenging 7

to tolerate but not painful”, reaching 1-10mA, followed by three-minute quiet sitting baseline assessment. Prior to habituation participants were instructed that two faces would be shown on the screen in random order and that only one of the faces would occasionally be accompanied by shock. The experimenter assured that participants understood the instructions and repeated them if necessary. The conditioning procedure consisted of a habituation, acquisition, and extinction phase, each of which contained six CS+ and six CS-. CSs were shown for 8s in pseudo-randomized order in each phase (not more than two CSs of the same type in sequence) and the intertrial interval (ITI) was 18 +/- 2s (determined at random). During acquisition, each CS+ was immediately followed by a 500ms US (100% reinforcement rate), remaining the same intensity for all six repetitions. During extinction, both CS+ and CS- were not paired with the US. Participants moved directly from habituation to acquisition to extinction, only interrupted by the ratings during the middle and end of these phases. 2.2.3. Rating procedures In the middle and the end of each conditioning phase, US-expectancy and valence ratings were collected for each CS (thus, every third CS was evaluated, resulting in 12 ratings of each question type). Four seconds after CS offset, the face reappeared and was rated on two visual-analogue scales displayed underneath for valence: “How pleasant is this face?”, from 1 (very unpleasant) to 10 (very pleasant); and US-expectancy: “How much do you believe that this stimulus will be followed by an electric stimulation?”, from 1 (not at all) to 10 (very likely). Immediately following extinction, contingency awareness was assessed by presenting each CS on the screen separately and asking: “Has this picture been followed by an electric stimulus at any time during the experiment. Yes/no.” 2.3. Apparatus and physiological recordings During conditioning, participants were seated on an armchair placed 50cm in front of a 22” monitor. Stimulus presentation and physiological data acquisition were controlled by two PCs running E-Prime 2.0 (Psychology Software Tools, Inc., Pittsburgh, PA, USA) and Acqknowledge software (Biopac Systems, Inc., Goleta, CA, USA). Recording of the physiological channels and rating slider information was performed using the Biopac MP150 8

system with a sampling rate of 1000 Hz. Skin conductance (SC) was measured using 11-mm inner diameter Ag/AgCl electrodes filled with isotonic electrode paste (Boucsein et al., 2012). Electrodes were placed on the middle phalanx of the index and middle finger of the nondominant hand. An electrical stimulator (constant current unit between 1mA and 10mA, Biopac Systems, Inc., Goleta, CA, USA) delivered the US via 11-mm inner diameter Ag/AgCl electrodes on the back of the dominant hand. Two channels were obtained as control measures: (a) an accelerometer attached to the shoulder of the non-dominant arm measured body movement and (b) a pneumographic belt placed at the lower thorax/upper abdomen recorded the respiration pattern, since movement and respiration irregularities may affect electrodermal measuresment. Psychophysiological data analysis and artifact editing was conducted using ANSLAB 2.6 (Blechert et al., 2016). 2.4. Data reduction and statistical analyses The average skin conductance level (SCL) for the 1s immediately before CS onset (stimulus baseline) was subtracted from the average SCL during the 8s CS-interval to calculate skin conductance level response (SCLR) as a measure of sympathetic nervous system reactivity related to CS presentation. This analysis deviates somewhat from the typical SCR trough-to-peak scoring. It was chosen because electrodermal data during the CS-interval often was noisy and contained multiple peaks. With noisy psychophysiological data average measures are typically more robust than point measures. The UR to the electric stimulus was determined by subtracting the stimulus baseline from the SCL recorded during the 8s following the US. Artefacts were manually edited after visual inspection of accelerometer and respiration channels. Due to movement and electric stimulation artefacts approximately 5% of data were edited in each group. We calculated repeated measures ANOVAs for accelerometry during CSintervals using “Group” (AD, aMCI, HC) as between-subjects factor, and CS-type (CS+, CS-) and “Time” (early and late halves of the conditioning phases) as within-subject factors and found no significant effect for any of the factors or their interactions (e.g., for Group: F(2,122)=.71, p=.493, η2=.01, for Group×CS-type: F(2,122)=.76, p=.470, η2=.01). To normalize the distribution, SCLRs were square root transformed (Blechert et al., 2007; Boucsein et al., 2012; Hamann et al., 2002). Mean SCLR values were computed for each CS-type (CS+/CS-) in 9

each conditioning phase (habituation/acquisition/extinction) by averaging SCLR values for three consecutive trials; this resulted in two blocks for each experimental phase, early and late respectively. Scores for US-expectancy and valence ratings were computed for the same time points. The average response to the last 3 habituation trials was used as individual preconditioning CS baseline and therefore subtracted from all acquisition and extinction responses. Although we found no significant group differences at the end of habituation for any response measure, participants substantially differed interindividually in their ratings and SCLRs (see also Dunsmoor et al., 2011; Wegerer et al., 2013). One participant was excluded because of technical problems; thus 125 participants were included in the final analyses. Group differences in demographics, control measures, CA, and other participant characteristics (e.g., US-intensity, UR) were examined using χ2-tests or univariate analyses of variance (ANOVAs) with “Group” (AD, aMCI, HC) as a between-subjects factor. Separate repeated measures ANOVAs were computed for each measure using “Group” (AD, aMCI, HC) as between-subjects factor, and “CS-type” (CS+, CS-) and “Time” (early and late halves of the conditioning phase) as within-subject factor, for each conditioning phase (habituation, acquisition, extinction). Significant main and interaction effects were followed by planned comparisons (AD vs. aMCI, AD vs. HC, aMCI vs. HC) using Group×CS-type×Time ANOVAs. As group differences were the main interest of the present study, only post-hoc tests on main and interaction effects containing the between-subject factor “Group” are reported. Effect sizes are provided (ANOVA: partial eta squared η2; t-test: Cohen’s d). Results were additionally checked non-parametrically (due to mild assumption violations); results were congruent. We additionally calculated all analyses excluding “unaware” participants, classified by two different options. In Option A, we excluded participants who did not correctly identify the CS+ at the end of extinction (either rating the CS-, the CS- and the CS+, or no CS to be followed by electric stimulation). In Option B, to reduce potential effects of forgetting, cognitive interference, or misunderstanding particularly in AD, we used a minimum criterion of end-of10

acquisition differential US-expectancies (CS+ minus CS-); this difference had to be ≥1 in order to be classified as “aware”. We used separate repeated measures ANOVAs for each conditioning phase (“Group” as between-subjects factor, and “CS-type” and “Time” as withinsubject factor; see Appendix B for the results of both options). Alpha-level was set at 0.05 for all analyses. All statistical analyses were conducted using IBM SPSS Statistics 24 for Windows. 3. Results 3.1. Demographics, psychometrics, and control variables Table 1 shows demographic data and control measures for the three groups. Groups did not differ in age, sex, and years of education. Trait anxiety and depressive symptoms were in the normal range. As expected, groups differed in B-ADL (F(2,122)=41.28, p<.001, η2=.41), CDR (F(2,122)=57.06, p<.001, η2=.48), MMSE (F(2,122)=63.60, p<.001, η2=.50) and CERAD Total Score (F(2,122)=76.94, p<.001, η2=.56). AD displayed highest scores in B-ADL and CDR as well as lowest scores in MMSE and CERAD Total Score, followed by aMCI and HC. Table 1. Means (SD) and statistical comparisons for demographic, psychometric, and control measures for the three groups Alzheimer’s Disease (AD), amnestic Mild Cognitive Impairment (aMCI), and healthy aging controls (HC).

Variables Age (years) Male/ Female (%) Education (years) B-ADL CDR MMSE CERAD Total Score GDS ADS STADI State Depression STADI Trait Depression STADI State Anxiety STADI Trait Anxiety US level (mA) UR mean (µS)

AD, M (SD)

aMCI, M (SD)

HC, M (SD)

Inferential statistics

76.67 (8.13) 15 (34.9%)/ 28 (65.1%) 10.00 (2.19) a,b 4.88 (2.44) a,b 0.82 (0.38) a,b 23.60 (3.34) a,b 62.08 (9.80) 7.83 (4.89) 7.51 (6.81) 16.40 (4.50)

75.72 (7.97) 17 (39.5%)/ 26 (60.5%) 10.60 (2.19) b,c 2.17 (1.74) b,c 0.37 (0.29) b,c 27.72 (2.11) b,c 76.21 (10.15) 8.42 (5.09) 9.21 (6.00) 16.49 (4.29)

75.70 (8.34) 15 (37.5%)/ 25 (62.5%) 10.88 (2.55) a,c 1.38 (0.99) a,c 0.08 (0.21) a,c 29.10 (0.87) a,c 88.31 (8.05) 8.40 (7.44) 8.83 (8.17) 16.13 (5.56)

F(2,122)=.20, p=.820 2 χ (2,122)=.132, p=.936 F(2,122)=1.57, p=.210 F(2,122)=40.93, p<.001 F(2,122)=55.50, p<.001 F(2,122)=61.45, p<.001 F(2,122)=76.94, p<.001 F(2,122)=.13, p=.870 F(2,122)=.69, p=.500 F(2,122)=.06, p=.940

20.23 (15.77)

18.63 (5.03)

18.80 (5.22)

F(2,122)=.32, p=.720

15.40 (4.59) a 15.05 (4.40) 6.31 (2.63) .116 (.14)

15.88 (4.54) 17.47 (4.86) 6.47 (2.87) .140 (.15)

16.10 (6.17) a 18.15 (6.43) 6.88 (2.76) .171 (.18)

F(2,122)=.21, p=.810 F(2,122)=3.93, p=.022 F(2,122)=.45, p=.641 F(2,122)=1.29, p=.280

11

Note. B-ADL, Bayer-Activities-of-Daily-Life-Scale; CDR, Clinical Dementia Rating Scale; MMSE, Mini Mental State Examination; CERAD Total Score, Consortium to Establish a Registry for Alzheimer’s Disease; GDS, Geriatric Depression Scale; ADS, General Depression Scale; STADI, State-Trait-AnxietyDepression-Inventory; US, unconditioned stimulus; UR, unconditioned response. Significant overall group effects were followed by pairwise post-hoc tests; different superscripts (a,b,c) indicate significant post-hoc differences (a: AD vs. HC, b: AD vs. aMCI, c: aMCI vs. HC).

3.2. Conditioning procedure Figure 1 depicts means for all groups during early and late halves of the conditioning phases (habituation, acquisition, extinction) for valence and US-expectancy ratings, and SCLR.

Figure 1. Valence ratings, US-expectancy ratings, and SCLR (skin conductance level response) for CS+ and CS- during habituation, acquisition, and extinction across study groups; Note: AD, Alzheimer’s dementia; aMCI, amnestic Mild Cognitive Impairment; HC, healthy aging controls; CS, conditioned stimulus. Error bars represent standard errors of the mean. 12

US-intensity and UR. Groups did not differ in their US-level or in their UR (see Table 1). UR decreased over acquisition, resulting in a significant main effect for Time (F(2,121)=8.92, p=.003, η2=.07). No Group×Time interaction was found. Habituation phase. Groups did not differ in US-expectancy (F(2,122)=.06, p=.943, η2=.00), positive valence (F(2,122)=1.11, p=.334, η2=.02), or SCLR (F(2,122)=1.32, p=.271, η2=.02), neither were other main and interaction effects found. Nevertheless, participants differed substantially in their individual means, especially for SCLR (see Fig. 1). Therefore, end of habituation SCLRs were used as individual preconditioning baseline. 3.2.1. Acquisition phase US-expectancy. Groups differed (F(2,122)=16.01, p<.001, η2=.21), ratings increased from early to late acquisition (F(1,122)=73.65, p<.001, η2=.38) and were higher for CS+ than CS- (F(1,122)=521.98, p<.001, η2=.81). AD showed lower US-expectancy than aMCI (F(1,83)=8.61, p=.004, η2=.09) and HC (F(1,80)=37.59, p<.001, η2=.32); aMCI showed lower US-expectancy than HC (F(1,81)=6.58, p=.012, η2=.08). No interaction effect Time×Group (F(2,122)=2.15, p=.121, η2=.03) emerged. Interactions CS-type×Group (F(2,122)=27.76, p<.001, η2=.31), CS-type×Time (F(1,122)=112.46, p<.001, η2=.48), and CS-type×Time×Group (F(2,122)=4.65, p=.010, η2=.07) were significant. During early acquisition, AD displayed less US-expectancy after CS+ compared to HC (t(80)=-7.53, p<.001, d=-1.66) and aMCI (t(83)=-4.75, p<.001, d=-1.03), whereas more after CS- compared to HC (t(80)=2.54, p=.014, d=.47). During late acquisition, AD rated the CS+ lower than HC (t(80)=-6.00, p<.001, d=-1.31) and aMCI (t(83)=-2.35, p<.021, d=-.51). Compared to HC, aMCI showed lower US-expectancy for CS+ during early t(81)=-2.63, p=.010, d=-.58) and late acquisition (t(81)=-3.76, p<.001, d=-.82). Valence ratings. Groups differed (F(2,122)=6.43, p=.002, η2=.10), ratings decreased from early to late acquisition (F(1,122)=23.13, p<.001, η2=.16), and were higher for CS- than for CS+ (F(1,122)=234.27, p<.001, η2=.66). AD rated the CSs more positive than HC (F(1,80)=12.49, p<.001, η2=.14) and aMCI rated more positive than HC (F(1,81)=8.00, p=.006, η2=.09); AD vs. aMCI did not differ. 13

Interaction effects Time×Group (F(2,122)=28.27, p<.001, η2=.32), CS-type×Group (F(2,122)=13.06, p<.001, η2=.18), and CS-type×Time (F(1,122)=53.59, p<.001, η2=.31) were found, but no three-way-interaction. During early acquisition, AD rated CS+ higher compared to HC (t(80)=8.76, p<.001, d=1.94) and aMCI (t(83)=2.65, p=.010, d=.58). During late acquisition, AD rated CS+ higher (t(80)=2.82, p=.006, d=.63) and CS- lower (t(80)=-2.80, p=.006, d=-.62) than HC. Compared to HC, aMCI rated the CS+ as more positive during early (t(81)=5.97, p<.001, d=1.31) and late acquisition (t(81)=3.99, p<.001, d=.89). SCLR. Groups differed (F(2,122)=3.62, p=.030, η2=.06), SCLR decreased from early to late acquisition (F(1,122)=34.70, p<.001, η2=.22) and CS+ elicited higher SCLR than CS(F(1,122)=7.77, p=.006, η2=.06). AD displayed lower SCLR than HC (F(1,80)=6.98, p=.010, η2=.08); AD vs. aMCI and aMCI vs. HC did not differ. A significant interaction effect CS-type×Time (F(2,122)=5.43, p=.006, η2=.08) was mainly due to lower SCLR to the CS+ for AD compared to HC during early (t(80)=-3.29, p=.002, d=.73) and late acquisition (t(80)=-2.81, p=.007, d=-.62). 3.2.2. Extinction phase US-expectancy. Groups differed (F(2,122)=23.12, p<.001, η2=.28), ratings decreased from early to late extinction (F(1,122)=76.25, p<.001, η2=.39), and were higher for CS+ than CS(F(1,122)=40.31, p<.001, η2=.25). AD displayed higher US-expectancy compared to aMCI (F(1,83)=15.59, p<.001, η2=.16) and HC (F(1,80)=44.33, p<.001, η2=.36); aMCI displayed higher US-expectancy than HC (F(1,81)=7.08, p<.009, η2=.08). Moreover, interaction effects Time×Group (F(2,122)=16.61, p<.001, η2=.21), CStype×Group (F(2,122)=8.13, p<.001, η2=.12), CS-type×Time (F(1,122)=65.28, p<.001, η2=.35), and CS-type×Time×Group (F(2,122)=22.80, p<.001, η2=.27) were found. During early extinction, AD displayed higher US-expectancy for both CSs than HC (CS+: t(80)=8.36, p<.001, d=1.83; CS-: t(80)=2.54, p=.014, d=.55) and aMCI (CS+: t(83)=4.40, p<.001, d=.95; CS-: t(83)=2.66, p=.010, d=.57). During late extinction, AD rated CS+ higher compared to HC (t(80)=2.92, p=.005, d=.64) and aMCI (t(83)=2.30, p=.025, d=.18); AD also rated the CS- higher than HC (t(80)=2.81, p=.007, d=.61). Compared to HC, aMCI also displayed higher US-

14

expectancy for CS+ during early (t(81)=3.20, p=.002, d=.69) and late extinction (t(81)=2.68, p=.010, d=.58). Valence ratings. Groups did not differ (F(2,122)=.98, p=.377, η2=.02), ratings increased from early to late extinction (F(1,122)=58.95, p<.001, η2=.33), and were higher for CS- than for CS+ (F(1,122)=77.83, p<.001, η2=.39). Interactions CS-type×Group (F(2,122)=11.56, p<.001, η2=.16), CS-type×Time (F(1,122)=6.57, p=.002, η2=.10) and CS-type×Time×Group (F(2,122)=10.16, p<.001, η2=.14) were found. During early extinction, AD (t(80)=-5.70, p<.001, d=-1.26) and aMCI (t(81)=-3.33, p<.001, d=-.74) rated CS+ lower than HC; AD rated CS+ lower than aMCI (t(83)=-2.29, p=.025, d=-.50). SCLR. For this phase, no main or interaction effects were found (p>.138). 3.3. Contingency awareness Option A. Twenty-one of 125 participants did not correctly identify the CS+ at the end of extinction. As expected, AD displayed less CA than HC (n=14 vs. 1; due to low sample size no inferential statistical analyses were computed) and aMCI (n=14 vs. 6; χ2(1)=4.43, p=.035); aMCI displayed less CA than HC (n=6 vs. 1). Option B. Only six AD, but no aMCI and HC reached differential expectancy scores lower one. Three of the six individuals were “aware” in Option A. 3.4. Adjustment for differences in trait anxiety Participants differed in their trait anxiety (F(2,122)=4.02, p=.020), mainly due to lower scores in AD than in aMCI and HC (see Table 1). To examine if these differences explain the between-group effects, we conducted separate ANCOVAs for acquisition and extinction using trait anxiety as covariate. Similar group differences were found for US-expectancy (Acquisition: F(2,121)=15.06, p<.001, η2=.20; Extinction: F(2,121)=21.60, p<.001, η2=.26), valence (Acquisition: F(2,121)=7.30, p<.001, η2=.11; Extinction: p=.523), and SCLR (Acquisition: F(2,121)=3.40, p=.037, η2=.05; Extinction: p=.148).

15

4. Discussion The present study is the first to investigate impaired differential fear acquisition and extinction as a prognostic marker for later AD development, indicating in a cross-sectional cohort design a gradual decay from HC, to aMCI, to early AD. For both differential acquisition and slowed extinction learning, group differences were most pronounced for US-expectancy and valence ratings; a similar, though less pronounced pattern emerged for electrodermal responding. 4.1. Fear conditioning as a prognostic marker for AD In line with Hamann and colleagues (2002), differential skin conductance learning was decreased in early AD compared to HC during acquisition, proposing deficient differential fear learning as a prognostic marker for AD (see also Hoefer et al., 2008). Being the first study to assess fear acquisition learning in AD across self-report measures (US-expectancy and valence), a similar and even more pronounced decay from HC, to aMCI, to early AD was found. Moreover, self-report measures revealed slowed extinction learning in early AD compared to aMCI and HC; skin conductance showed a similar, though weaker pattern. Those findings are in line with past research demonstrating attenuated eyeblink responses in AD during acquisition (Solomon et al., 1991; Woodruff-Pak et al., 1996, 1990) and both decreased fear acquisition and slowed extinction learning in AD mice (Bonardi et al., 2011; Mehla et al., 2018; Végh et al., 2014). In sum, present and past research highlight the important role of decreased acquisition and slowed extinction learning in AD. The present findings propose US-expectancy and valence ratings as particularly reliable (prognostic) markers, rarely assessed by past research. The conditioning deficits were not merely a byproduct of general unresponsiveness of patient groups, since their habituation phase responses and their unconditioned responses during acquisition did not differ from healthy controls. 4.1.1. Conditioned electrodermal responses vs. US-expectancy and valence ratings Weaker findings for SCLR compared to self-report could be explained by attenuated electrodermal responding in older (compared to younger) participants (Boucsein et al., 2012; Catania et al., 1980). In line with this, Hamann et al. (2002) reported attenuated unconditioned 16

SCR in AD, possibly explaining less pronounced AD conditioning results of studies relying solely on SCR (Hamann et al., 2002; Hoefer et al., 2008).The present study, however, did not find any group differences in unconditioned SCLR and US level. Compared to past studies, the present study took great care in controlling for anti-dementia (SCLR attenuating) medication. Therefore, we believe that the lack of SCLR group differences during extinction in the present study can likely be attributed to overall electrodermal attenuation in older age (Boucsein et al., 2012). This makes skin conductance a rather unreliable conditioning measure, highlighting the importance of self-report conditioning measures as promising prognostic markers of AD progression. 4.1.2. Contingency awareness There is ongoing debate whether contingency awareness is necessary for successful conditioning (Lovibond and Shanks, 2002; Sevenster et al., 2014; Wiens and Ohman, 2002). In the current study, participants without CA were not excluded from the main analyses, as implicit learning can appear without clear knowledge about the CS-US association (Esteves et al., 1994; Knight et al., 2006; Schultz and Helmstetter, 2010). Exploratory analyses revealed reduced retrospective contingency awareness in AD (33.3%) and aMCI (13.9%), compared to HC, who were almost fully aware (2.5%). Using US-expectancy as an index for contingency awareness, only few AD patients were unaware (14.3%). Importantly, when excluding unaware participants, present findings hardly changed (see Appendix B) and the very few results that did not reach significance anymore may be explained by lower power. Generally, assessing contingency awareness after extinction may be influenced by “forgetting” or “misunderstanding”, as during extinction CS and US are no longer paired (Lovibond and Shanks, 2002), and this may particularly apply to AD. Past studies reported reduced contingency awareness in elderly using a semi-structured interview with eight questions following the extinction procedure (LaBar et al., 2004). Possibly, our retrospective contingency awareness in AD would have been higher with this more elaborate assessment procedure due to explicit memory deficits often found in early stages of AD (Nestor et al., 2006). 4.2. Implications: Fear conditioning deficits and brain changes in AD

17

From a neuroscientific perspective, deficient fear acquisition and extinction could be linked to amygdala degeneration in AD. The amygdala, a part of the medial temporal lobe structure where histopathological changes and atrophy in AD have often been reported (Hamann et al., 2002; Scott et al., 1991), plays a central role in emotional learning (Seymour and Dolan, 2008) and has been shown to be involved in fear conditioning (Debiec et al., 2010; Maren et al., 2013; Phillips and LeDoux, 1992). Whereas the amygdala seems to be related to autonomic fear response measures like SCR, hippocampal functioning has been linked to conscious acquisition of CS-US contingencies and US-expectancy (Bechara et al., 1995; Clark et al., 2002). Amygdala lesions lead to attenuated SCRs but not to CS-US contingency learning deficits (Bechara et al., 1995; LaBar et al., 1995), whereas hippocampal damage impairs contingency learning (Bechara et al., 1995; Clark and Squire, 1998). This stresses the importance of capturing different learning measures in fear conditioning of AD. Supporting these findings, hippocampal activation has only been found during aware contingency trials (Knight et al., 2008), while amygdala activation also occurred without awareness (Morris et al., 1998). Measuring voxel-based morphometry to link brain tissue changes to fear conditioning, Hoefer et al. (2008) found a connection between decreased responses to the US and tissue loss in the anterior cingulate cortex in healthy elderly. They further linked acquisition deficits in frontotemporal lobar degeneration with amygdala damage, suggesting that similar deficits in AD may not be related to volume loss in these stages of disease. As the present study found deficits in differential acquisition and slowed extinction learning in AD, this could hint at early amygdala and hippocampus degeneration. Both neuropathological hippocampus and amygdala degeneration have been found in early AD and aMCI in humans (Klein-Koerkamp et al., 2014; Scott et al., 1991; Zanchi et al., 2017) and APP-mice (Lin et al., 2015). This suggests that additional assessment of amygdala functioning may be beneficial in MCI diagnosis (Lin et al., 2015). Future studies may investigate how deficits in in fear acquisition and extinction across aMCI and early AD may be explained by amygdala and hippocampus degeneration using MRI. 4.3. Implications: Fear conditioning deficits and progression to AD As AD is a progressive disease with pre-symptomatic stages spanning many years (Jack et al., 2013), it is difficult to draw a sharp line of conversion from MCI or aMCI to AD. Self18

perceived cognitive decline currently seems to be the earliest notable marker of AD (Wolfsgruber et al., 2014). MCI is proposed as a transitional stage between “normal” cognitive impairment in elderly and AD, diagnosed in patients not meeting the criteria for dementia and not interfering with activities of daily living (Petersen, 2004). Only 1-2% of the healthy elderly develop AD (Petersen et al., 1999), whereas MCI or aMCI to AD conversion rates are highly variable and range from 10.2-33.6% (observed over one year; Ward et al., 2013). Cognitive impairment and cerebral atrophy increase with AD progression (Brooks and Loewenstein, 2010), in line with present findings of increasing conditioning deficits from HC, to aMCI, to early AD. Nevertheless, future research extending to moderate and severe AD will be necessary to proof the progression of conditioning deficits to later stages of the disease. 4.4. Strengths and limitations The present study is the first to investigate a gradual decay from healthy elderly to AD in a fear conditioning paradigm. The present study took great care to control for AD medication, ensure accurate early AD diagnoses, to properly match HC by age, sex, and educational background, and to ensure sufficient ethnically homogenous group sample sizes; this resulted in large effect sizes (mean d=.85) that further emphasize the clinical importance of the results. Interpreting the present findings, some limitations should be considered. First, it should be recognized that our sample consists of memory clinic patients and is not necessarily representative for people suffering from dementia in the general population. Additionally, all of our participants were Caucasian due to the demographic pattern in this age group in the catchment area. Therefore, future research should expand to other ethnic groups to assess generalizability of current results. Additionally, although consistent with the majority of fear conditioning studies (Lonsdorf et al., 2017), we used an immediate extinction design instead of a delayed one, which would test more long-term conditioning effects. Thus, there is an ambiguity whether the observed extinction impairment represents a deficit in fear memory consolidation or a deficit in extinction memory formation. We explicitly chose a one-day immediate-extinction paradigm, as we believe that extended (two-day-paradigms separating acquisition and extinction) may be subject to

19

pronounced general memory deficits in AD (Carlesimo and Oscar-Berman, 1992) masking more subtle fear conditioning effects. Furthermore, measuring US-expectancy and valence only after every third CS, it is difficult to distinguish whether HC extinguish suddenly or gradually. As we used a 100% reinforcement schedule, HC may have learned particularly fast that the CS+ would not be followed by the US anymore. Compared to subjective ratings, SCLR was assessed in each trial, showing gradual extinction (Figure C.1, Appendix C). 4.5. Conclusion In sum, to the best of our knowledge, the current study is the first to examine fear conditioning in aMCI and early AD. We found that fear acquisition and extinction deficits increase from mild impairment in aMCI to moderate impairment in early stage AD. Results are congruent with findings of neurodegenerative amygdala and hippocampus changes in humans and mice in early stages of AD. Decreased differential acquisition and slowed extinction learning in AD could provide an early marker to predict later AD development and help identify people at risk. As several research groups investigate disease-modifying medication with first positive finding (e.g., Sevigny et al., 2016), this makes early and differential diagnosis of AD particularly important.

20

Acknowledgements The authors would like to thank the team of the Memory Clinical, Hall, Austria, for their help with data collection, the IT-team, and especially Michael Huber and Immo Curio for technical support as well as Christina Rainer for manual data input. Part of these data were presented at the Alzheimer Society International Congress and the annual meeting of the Austrian Alzheimer’s Society (Österreichische Alzheimer Gesellschaft, ÖAG) and the first author received the young investigator award.

Disclosures S. Nasrouei, M. Liedlgruber, J. Marksteiner, and F. H. Wilhelm report no actual or potential conflicts of interest relevant to the manuscript. J. A. Rattel was financially supported by the Doctoral College “Imaging the Mind” of the Austrian Science Fund (FWF; W1233-G17; PI: Wilhelm).

21

References Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., Damasio, A.R., 1995. Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science 269, 1115–8. https://doi.org/10.1126/science.7652558 Blechert, J., Michael, T., Vriends, N., Margraf, J., Wilhelm, F.H., 2007. Fear conditioning in posttraumatic stress disorder: evidence for delayed extinction of autonomic, experiential, and behavioural responses. Behav. Res. Ther. 45, 2019–33. https://doi.org/10.1016/j.brat.2007.02.012 Blechert, J., Peyk, P., Liedlgruber, M., Wilhelm, F.H., 2016. ANSLAB: Integrated multichannel peripheral biosignal processing in psychophysiological science. Behav. Res. Methods 48, 1528–45. https://doi.org/10.3758/s13428-015-0665-1 Bloom, F.E., Redwine, J.M., Morrison, J.H., Reinhart, P.H., Arias, R., Comery, T.A., Martone, R., Jacobsen, J.S., Bowlby, M., Wu, C.-C., Pangalos, M.N., 2006. Early-onset behavioral and synaptic deficits in a mouse model of Alzheimer’s disease. Proc. Natl. Acad. Sci. 103, 5161–6. https://doi.org/10.1073/pnas.0600948103 Boddez, Y., Baeyens, F., Luyten, L., Vansteenwegen, D., Hermans, D., Beckers, T., 2013. Rating data are underrated: Validity of US expectancy in human fear conditioning. J. Behav. Ther. Exp. Psychiatry 44, 201–206. https://doi.org/10.1016/j.jbtep.2012.08.003 Bonardi, C., de Pulford, F., Jennings, D., Pardon, M.C., 2011. A detailed analysis of the early context extinction deficits seen in APPswe/PS1dE9 female mice and their relevance to preclinical Alzheimer’s disease. Behav. Brain Res. 222, 89–97. Boucsein, W., Fowles, D.C., Grimnes, S., BenhShakhar, G., Roth, W.T., Dawson, M.E., Filion, D.L., 2012. Publication recommendations for electrodermal measurements Publication recommendations for electrodermal measurements. Psychophysiol. 49, 1017–34. https://doi.org/10.1111/j.1469-8986.2012.01384.x Brooks, L.G., Loewenstein, D.A., 2010. Assessing the progression of mild cognitive impairment 22

to Alzheimer’s disease: current trends and future directions. Alzheimers Res. Ther. 2, 28– 37. https://doi.org/10.1186/alzrt52 Carlesimo, G.A., Oscar-Berman, M., 1992. Memory deficits in Alzheimer’s disease: A comprehensive review. Neuropsychol. Rev. 3, 119–69. Catania, J.J., Thompson, L., Michalewski, H.A., Bowman, T.E., 1980. Comparisons of sweat gland counts, electrodermal activity, and habituation behavior in young and old groups of subjects. Psychophysiol. 17, 146–52. Clark, R.E., Manns, J.R., Squire, L.R., 2002. Classical conditioning, awareness, and brain systems. Trends Cogn. Sci. 6, 524–31. https://doi.org/10.1016/S1364-6613(02)02041-7 Clark, R.E., Squire, L.R., 1998. Classical conditioning and brain systems: The role of awareness. Sci. 280, 77–81. https://doi.org/10.1126/science.280.5360.77 Comery, T.A., Martone, R.L., Aschmies, S., Atchison, K.P., Diamantidis, G., Gong, X., Zhou, H., Kreft, A.F., Pangalos, M.N., Sonnenberg-Reines, J., Jacobsen, J.S., Marquis, K.L., 2005. Acute gamma-secretase inhibition improves contextual fear conditioning in the Tg2576 mouse model of Alzheimer’s disease. J. Neurosci. 25, 8898–8902. https://doi.org/10.1523/JNEUROSCI.2693-05.2005 Corcoran, K.A., Lu, Y., Scott Turner, R., Maren, S., 2002. Overexpression of hAPPswe impairs rewarded alternation and contextual fear conditioning in a transgenic mouse model of Alzheimer’s disease. Learn. Mem. 9, 243–252. https://doi.org/10.1101/lm.51002 Debiec, J., Diaz-Mataix, L., Bush, D.E., Doyere, V., LeDoux, J.E., 2010. The amygdala encodes specific sensory features of an aversive reinforcer. Nat. Neurosci. 13, 536–537. Dineley, K.T., Xia, X., Bui, D., David Sweatt, J., Zheng, H., 2002. Accelerated plaque accumulation, associative learning deficits, and up-regulation of Alpha7 nicotinic receptor protein in transgenic mice co-expressing mutant human presenilin 1 and amyloid precursor proteins. J. Biol. Chem. 277, 22768–80. https://doi.org/10.1074/jbc.M200164200 Dobarro, M., Gerenu, G., Ramírez, M.J., 2013. Propranolol reduces cognitive deficits, amyloid 23

and tau pathology in Alzheimer’s transgenic mice. Int. J. Neuropsychopharmacol. 16, 2245–57. https://doi.org/10.1017/S1461145713000631 Dunsmoor, J.E., Prince, S.E., Murty, V.P., Kragel, P.A., LaBar, K.S., 2011. Neurobehavioral mechanisms of human fear generalization. Neuroimage 55, 1878–88. https://doi.org/10.1016/j.neuroimage.2011.01.041 Erzigkeit, H., Lehfeld, H., 2010. Bayer ADL-Skala (B-ADL) [Bayer’s activities of daily life scale]. Pearson. Esteves, F., Parra, C., Dimberg, U., Ohman, A., 1994. Nonconscious associative learning: Pavlovian conditioning of skin conductance responses to masked fear-relevant facial stimuli. Psychophysiol. 31, 375–85. https://doi.org/10.1111/j.1469-8986.1994.tb02446.x Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–98. https://doi.org/10.1016/0022-3956(75)90026-6 Goerlich, K.S., Votinov, M., Dicks, E., Ellendt, S., Csukly, G., Habel, U., 2017. Neuroanatomical and neuropsychological markers of amnestic MCI: A three-year longitudinal study in individuals unaware of cognitive decline. Front. Aging Neurosci. 9, 34. Hagmann-von Arx, P., Grob, A., 2014. Reynolds Intellectual Assessment Scales (RIAS). German Version. Huber, Bern. Hamann, S., Monarch, E.S., Goldstein, F.C., 2002. Impaired fear conditioning in Alzheimer’s disease. Neuropsychol. 40, 1187–95. Hautzinger, M., Bailer, M., Hofmeister, D., Keller, F., 2012. Allgemeine Depressionsskala (ADS) [General Depression Scale], 2. ed. Hogrefe. Hoefer, M., Allison, S.C., Schauer, G.F., Neuhaus, J.M., Hall, J., Dang, J.N., Weiner, M.W., Miller, B.L., Rosen, H.J., 2008. Fear conditioning in frontotemporal lobar degeneration and Alzheimer’s disease. Brain A J. Neurol. 131, 1646–57.

24

Hughes, C.P., Berg, L., Danziger, W., Coben, L.A., Martin, R.L., 1982. A new clinical scale for the staging of dementia. Br. J. Psychiatry 140, 566–72. Jack, C.R., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Vemuri, P., Wiste, H.J., Weigand, S.D., Lesnick, T.G., Pankratz, V.S., Donohue, M.C., Trojanowski, J.Q., 2013. Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. The Lancet Neurol. 12, 207–216. https://doi.org/10.1016/S1474-4422(12)70291-0 Kimble, G.A., Pennypacker, H.S., 1963. Eyelid conditioning in young and aged subjects. J. Genet. Psychol. 103, 283–289. Klein-Koerkamp, Y., Heckemann, R.A., Ramdeen, K., Moreaud, O., Keignart, S., Krainik, A., Hammers, A., Baciu, M., Hot, P., 2014. Amygdalar atrophy in early Alzheimer’s disease. Curr. Alzheimer Res. 11, 239–52. Knight, D.C., Nguyen, H.T., Bandettini, P.A., 2006. The role of awareness in delay and trace fear conditioning in humans. Cogn. Affect. Behav. Neurosci. https://doi.org/10.3758/CABN.6.2.157 Knight, D.C., Waters, N.S., Bandettini, P.A., 2008. Neural substrates of explicit and implicit fear memory. Neuroimage 208–14. https://doi.org/10.1016/j.neuroimage.2008.11.015 Knuttinen, M.G., Power, J.M., Preston, A.R., Disterhoft, J.F., 2001. Awareness in classical differential eyeblink conditioning in young and aging humans. Behav. Neurosci. 115, 747– 57. https://doi.org/10.1037/0735-7044.115.4.747 LaBar, K.S., Cook, C.A., Torpey, D.C., Welsh-Bohmer, K.A., 2004. Impact of healthy aging on awareness and fear conditioning. Behav. Neurosci. 118, 905–15. https://doi.org/10.1037/0735-7044.118.5.905 LaBar, K.S., LeDoux, J.E., Spencer, D.D., Phelps, E.A., 1995. Impaired fear conditioning following unilateral temporal lobectomy in humans. J. Neurosci. 15, 6846–55. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., van Knippenberg, A., 2010. 25

Presentation and validation of the radboud faces database. Cogn. and Emot. 24, 1377–88. https://doi.org/10.1080/02699930903485076 Laux, L., Hock, M., Bergner-Köther, R., Hodapp, V., Renner, K.-H., 2013. Das State-Trait-AngstDepressions-Inventar (STADI) [The State-Trait-Anxiety-Depression-Inventory]. Hogrefe, Göttingen. Lin, T.-W., Liu, Y.-F., Shih, Y.-H., Chen, S.-J., Huang, T.-Y., Chang, C.-Y., Lien, C.-H., Yu, L., Chen, S.-H., Kuo, Y.-M., 2015. Neurodegeneration in amygdala precedes hippocampus in the APPswe/PS1dEp mouse model of Alzheimer’s disease. Curr. Alzheimer Res. 12, 951– 63. Lonsdorf, T.B., Menz, M.M., Andreatta, M., Fullana, M.A., Golkar, A., Haaker, J., Heitland, I., Hermann, A., Kuhn, M., Kruse, O., Meir Drexler, S., Meulders, A., Nees, F., Pittig, A., Richter, J., Römer, S., Shiban, Y., Schmitz, A., Straube, B., Vervliet, B., Wendt, J., Baas, J.M.P., Merz, C.J., 2017. Don’t fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear. Neurosci. Biobehav. Rev. 77, 247–85. Lovibond, P.F., Shanks, D.R., 2002. The role of awareness in Pavlovian conditioning: Empirical evidence and theoretical implications. J. Exp. Psychol. Anim. Behav. Process. 28, 3–26. https://doi.org/10.1037//0097-7403.28.1.3 Maren, S., Phan, K.L., Liberzon, I., 2013. The contextual brain: Implications for fear conditioning, extinction and psychopathology. Nat. Rev. Neurosci. 14, 417–28. https://doi.org/10.1038/nrn3492 McAndrew, A., Jones, F.W., McLaren, R.P., McLaren, I.P.L., 2012. Dissociating expectancy of shock and changes in skin conductance: An investigation of the Perruchet effect using an electrodermal paradigm. J. Exp.Psychol. Anim. Behav. Process. 38, 203–8. https://doi.org/10.1037/a0026718 McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E., 1984. Clinical 26

diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–44. Mehla, J., Lacoursiere, S.G., Lapointe, V., McNaughton, B.L., Sutherland, R.J., McDonald, R.J., Mohajerani, M.H., 2018. Age dependent behavioral and biochemical characterization of single APP knock-in mouse (APPNL-G-F/NL-G-F) model of Alzheimer’s disease. Neurobiol. of Aging 75, 25–37. https://doi.org/10.1016/j.neurobiolaging.2018.10.026 Morris, J., Mohs, R., Rogers, H., Fillenbaum, G., Heyman, A., 1988. Consortium to establish a registry for Alzheimer’ s disease (CERAD) clinical and neuropsychological assessment of Alzheimer’ s disease. Psychopharmacol. Bull. 641–52. Morris, J.S., Ohrnan, A., Dolan, R.J., 1998. Conscious and unconscious emotional learning in the human amygdala. Nature 393, 467–70. https://doi.org/10.1038/30976 Nestor, P.J., Fryer, T.D., Hodges, J.R., 2006. Declarative memory impairments in Alzheimer’s disease and semantic dementia. Neuroimage 30, 1010–1020. https://doi.org/10.1016/j.neuroimage.2005.10.008 Oswald, W.D., Fleischmann, U.M., 1994. Das Nürnberger Altersinventar (NAI) [Nuremberg aging inventory]. German Edition. Hogrefe, Göttingen. Pardon, M.C., Sarmad, S., Rattray, I., Bates, T.E., Scullion, G.A., Marsden, C.A., Barrett, D.A., Lowe, J., Kendall, D.A., 2009. Repeated novel cage exposure-induced improvement of early Alzheimer’s-like cognitive and amyloid changes in TASTPM mice is unrelated to changes in brain endocannabinoids levels. Neurobiol. of Aging 30, 1099–113. https://doi.org/10.1016/j.neurobiolaging.2007.10.002 Petersen, R.C., 2004. Mild cognitive impairment as a diagnostic entity, in: J. of Int. Med. pp. 183–94. https://doi.org/10.1111/j.1365-2796.2004.01388.x Petersen, R.C., Lopez, O., Armstrong, M.J., Getchius, T.S.D., Ganguli, M., Gloss, D., Gronseth, G.S., Marson, D., Pringsheim, T., Day, G.S., Sager, M., Stevens, J., Rae-Grant, A., 2018. 27

Practice guideline update summary: Mild cognitive impairment. Neurology 90, 1–10. https://doi.org/10.1212/WNL.0000000000004826 Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E., 1999. Mild cognitive impairment: clinical characterization and outcome. Arch. of Neurol. 56, 303–8. https://doi.org/10.1001/archneur.56.3.303 Phillips, R.G., LeDoux, J.E., 1992. Differential contribution of amygdala and hippocampus to cued and contextual fear conditioning. Behav. Neurosci. https://doi.org/10.1037/07357044.106.2.274 Royall, D.R., Cordes, J.A., Polk, M., 1998. CLOX: an executive clock drawing task. J. Neurol., Neurosurg. Psychiatry 64, 588–94. https://doi.org/10.1136/jnnp.64.5.588 Salmon, D.P., Bondi, M.W., 2009. Neuropsychological assessment of dementia. Annu. Rev. Psychol. 60, 257–82. https://doi.org/10.1146/annurev.psych.57.102904.190024 Schultz, D.H., Helmstetter, F.J., 2010. Classical conditioning of autonomic fear responses is independent of contingency awareness. J. Exp. Psychol. Anim. Behav. Process. 36, 495– 500. https://doi.org/10.1037/a0020263 Scott, S., DeKosky, S.T., Scheff, S.W., 1991. Volumetric atrophy of the amygdala in Alzheimer’s disease: quantitative serial reconstruction. Neurol. 41, 351–356. https://doi.org/10.1212/WNL.41.3.351 Sevenster, D., Beckers, T., Kindt, M., 2014. Fear conditioning of SCR but not the startle reflex requires conscious discrimination of threat and safety. Front. Behav. Neurosci. 8. Sevigny, J., Chiao, P., Bussière, T., Weinreb, paul H., Williams, L., Maier, M., Dunstan, R., Salloway, S., Chen, T., Ling, Y., O’Gorman, J., Qian, F., Arastu, M., Li, M., Chollate, S., Brennan, M.S., Quintero-Monzon, O., Scannevin, R.H., Arnold, H.M., Engber, T., Rhodes, K., Ferrero, J., Hang, Y., Mikulskis, A., Grimm, J., Hock, C., Nitsch, R.M., Sandrock, A., 2016. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature 537, 50–6. https://doi.org/10.1038/nature19323 28

Seymour, B., Dolan, R., 2008. Emotion, decision making, and the amygdala. Neuron 58, 662– 71. https://doi.org/10.1016/j.neuron.2008.05.020 Solomon, P.R., Levine, E., Bein, T., Pendlebury, W.W., 1991. Disruption of classical conditioning in patients with Alzheimer’s disease. Neurobiol. of Aging 12, 283–7. Végh, M.J., Heldring, C.M., Kamphuis, W., Hijazi, S., Timmerman, A.J., Li, K.W., van Nierop, P., Mansvelder, H.D., Hol, E.M., Smit, A.B., van Kesteren, R.E., 2014. Reducing hippocampal extracellular matrix reverses early memory deficits in a mouse model of Alzheimer’s disease. Acta Neuropathol. Commun. 2, 76. https://doi.org/10.1186/s40478-014-0076-z Wang, R., Dineley, K.T., Sweatt, J.D., Zheng, H., 2004. Presenilin 1 familial Alzheimer’s disease mutation leads to defective associative learning and impaired adult neurogenesis. Neurosci. 126, 305–12. https://doi.org/10.1016/j.neuroscience.2004.03.048 Ward, A., Tardiff, S., Dye, C., Arrighi, H.M., 2013. Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: A systematic review of the literature. Dement. and Geriatr. Cogn. Disord. Extra 3, 320–32. https://doi.org/10.1159/000354370 Wegerer, M., Blechert, J., Kerschbaum, H., Wilhelm, F.H., 2013. Relationship between fear conditionability and aversive memories: Evidence from a novel conditioned-intrusion paradigm. PLoS One 8. Wiens, S., Ohman, A., 2002. Unawareness is more than a chance event: comment on Lovibond and Shanks (2002). J. Exp. Psychol. Anim. Behav. Process. 28, 27–31. https://doi.org/10.1037/0097-7403.28.1.27 Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.O., Nordberg, A., Bäckman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., De Leon, M., Decarli, C., Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., Van Duijn, C., Visser, P., Petersen, R.C., 2004. Mild cognitive impairment - Beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment, in: J. of Intern. Med. pp. 240–6. 29

Wolfsgruber, S., Wagner, M., Schmidtke, K., Frölich, L., Kurz, A., Schulz, S., Hampel, H., Heuser, I., Peters, O., Reischies, F.M., Jahn, H., Luckhaus, C., Hüll, M., Gertz, H.-J., Schröder, J., Pantel, J., Rienhoff, O., Rüther, E., Henn, F., Wiltfang, J., Maier, W., Kornhuber, J., Jessen, F., 2014. Memory concerns, memory performance and risk of dementia in patients with mild cognitive impairment. PLoS One 9. Woodruff-Pak, D.S., Finkbiner, R.G., Sasse, D.K., 1990. Eyeblink conditioning discriminates Alzheimer’s patients from nondemented aged. Neuroreport 1, 45–8. Woodruff-Pak, D.S., Papka, M., Romano, S., Li, Y.T., 1996. Eyeblink classical conditioning in Alzheimer’s disease and cerebrovascular dementia. Neurobiol. of Aging 17, 505–12. https://doi.org/10.1016/S0197-4580(96)00070-X Woodruff-Pak, D.S., Thompson, R.F., 1988. Classical conditioning of the eyeblink response in the delay paradigm in adults aged 18-83 years. Psychol. Aging 3, 219–229. World Health Organization, 1993. The ICD-10 classification of mental and behavioural disorders, The ICD-10 classification of mental and behavioural disorders. World Health Organization, Geneva. Wright, C.I., Dickerson, B.C., Feczko, E., Negeira, A., Williams, D., 2007. A functional magnetic resonance imaging study of amygdala responses to human faces in aging and mild Alzheimer’s disease. Biol. Psychiatry 62, 1388–95. Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., Leirer, V.O., 1982. Development and validation of a geriatric depression screening scale: A preliminary report. J. Psychiatr. Ress 17, 37–49. https://doi.org/10.1016/0022-3956(82)90033-4 Zanchi, D., Giannakopoulos, P., Borgwardt, S., Rodriguez, C., Haller, S., 2017. Hippocampal and amygdala gray matter loss in elderly controls with subtle cognitive decline. Front. Aging Neurosci. 9, 50. https://doi.org/10.3389/fnagi.2017.00050

30

Figure 1. Valence ratings, US-expectancy ratings, and SCLR (skin conductance level response) for CS+ and CS- during habituation, acquisition, and extinction across study groups; Note: AD, Alzheimer’s dementia; aMCI, amnestic Mild Cognitive Impairment; HC, healthy aging controls; CS, conditioned stimulus. Error bars represent standard errors of the mean.

Figure A.1. SCLR (skin conductance level response) for CS+ and CS- during late acquisition (trials 4-6) and early extinction (trials 1-3) for the study groups; Note: AD, Alzheimer’s dementia; aMCI, amnestic Mild Cognitive Impairment; HC, healthy aging controls; CS, conditioned stimulus. Error bars represent standard errors of the mean

Highlights: •

fear conditioning in anti-dementia medication free early Alzheimer’s disease

•

acquisition deficits increase from healthy controls to early Alzheimer’s disease

•

slowed extinction in early Alzheimer’s disease compared to healthy controls

•

mild cognitive impairment as a transitional stage regarding conditioning deficits

•

fear conditioning deficits may be early marker to predict Alzheimer’s disease