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. Author manuscript; available in PMC: 2021 Sep 1.
Published in final edited form as: Schizophr Res. 2020 Jul 28;223:179–185. doi: 10.1016/j.schres.2020.07.007

Habituation during encoding: a new approach to the evaluation of memory deficits in schizophrenia

Suzanne N Avery a, Maureen McHugo a, Kristan Armstrong a, Jennifer U Blackford a, Simon Vandekar b, Neil D Woodward a, Stephan Heckers a
PMCID: PMC7704891  NIHMSID: NIHMS1616027  PMID: 32736836

Abstract

Background

Memory is significantly impaired in schizophrenia. However, memory measures are often complex and confounded by additional impairments such as motivation and task comprehension, which can affect behavioral performance and obscure neural function during memory tasks. Neural signatures of memory encoding that are robust to potential confounds may shed additional light on neural deficits contributing to memory impairment in schizophrenia.

Methods

Here, we investigate a potential neural signature of memory—habituation—and its relationship with healthy and impaired memory function. To limit potential confounds, we used a passive depth of encoding memory task designed to elicit neural responses associated with memory encoding while limiting other cognitive demands. To determine whether habituation during encoding was predictive of intact memory processing, we first compared neural habituation over repeated encoding exposures with subsequent explicit memory in healthy individuals. We then tested whether a similar relationship existed in patients with schizophrenia.

Results

Explicit memory performance was impaired in patients with schizophrenia relative to healthy control subjects. In healthy participants, more habituation over repeated exposures during encoding was associated with greater repetition-related increases in accuracy during testing. However, in patients with schizophrenia, better performance was associated with less habituation, or a more sustained neural response during encoding.

Conclusions

These results suggest that sustained neural activity is required for normal repetition-related improvements in memory performance in schizophrenia, in line with a neural inefficiency model. Habituation may serve as a valuable index of neural processes that underlie behavioral memory performance.

Keywords: encoding, hippocampus, repetition suppression, translational approach, neuropsychiatric disorders

1. Introduction

Memory impairments are among the largest and most replicable deficits found in schizophrenia (Aleman et al., 1999; Guo et al., 2019; Saykin et al., 1991). Although decades of memory research has expanded our understanding, memory deficits remain among the most intractable aspects of schizophrenia (Meltzer and McGurk, 1999), contributing to significant social, personal, and economic burden (Green et al., 2004; Kitchen et al., 2012; Liddle, 2003). The complexity of experimental design remains a major limitation—memory performance is substantially confounded by deficits in motivation and task compliance (Foussias et al., 2014), which can result in significant variability in both behavior and neural activity. Experimental designs that measure well-defined, reliable neural signatures of memory while limiting the influence of other cognitive functions are necessary. One candidate signature—habituation—is characterized within the human (Kreiman et al., 2000; Murty et al., 2013; Pedreira et al., 2010; Ranganath and Rainer, 2003; Rutishauser et al., 2006) and non-human (Dellu et al., 2000; Giles and Rankin, 2009; Glanzman, 2009; Pinsker et al., 1970; Rankin et al., 2009) memory literature, and may be a ssvaluable phenomenon in identifying the neural substrates of memory deficits (Thompson and Spencer, 1966).

Habituation, the decrease in response to a stimulus with repeated exposure, is ubiquitous across species and can be measured at both the behavioral and neural level (Groves and Thompson, 1970; Rankin et al., 2009; Thompson and Spencer, 1966). At the behavioral level, repeated encounters with a stimulus lead to decreased novelty response and increased familiarity behavior. Similarly, at the neural level, repeated exposures to a stimulus lead to a rapid decrease in activity within novelty-responsive brain regions (Fischer et al., 2003; Kafkas and Montaldi, 2018, 2014; Murty et al., 2013; Phan et al., 2003; Satpute et al., 2016; Yamaguchi et al., 2004), a phenomenon associated with increased familiarity for repeated information (Greene et al., 2007; Manelis et al., 2013) and hypothesized to be a marker of neural efficiency. As information is repeated and becomes less novel, novelty-responsive neurons become less active while sparse neuronal populations tuned to the specific properties of the repeated stimulus become more synchronized, leading to more efficient processing (Gilbert et al., 2010). At the level of neuroimaging, the transition from a strong to a sparse, synchronized response can be observed as habituation of functional magnetic resonance imaging (fMRI) response over time (Gotts et al., 2012). In contrast, failure to habituate may reflect sustained attempts to incorporate information into memory (Turk-Browne et al., 2008) or the continued experience of information as if it were novel.

These findings implicate habituation as a signature of normal memory function. The question remains whether habituation can be used to index memory deficits. We propose that basic processes such as habituation may provide novel insight into memory in schizophrenia (Jessen et al., 2002; Kumaran and Maguire, 2009). Although habituation is a basic process, individual differences have been reported as early as infancy (Bushnell, 1982; Snyder and Keil, 2008) and are hypothesized to fundamentally contribute to psychopathology (Braff et al., 1995; Davidson and Irwin, 1999; Freedman, 2010). Habituation is disrupted in schizophrenia (Bolino et al., 1994; Braff et al., 1995; Freedman, 2010; Freedman et al., 1996; Geyer and Braff, 1982; Holt et al., 2005; Horvath and Meares, 1979; Shakow, 1963). While habituation deficits have been linked to memory impairment in both early stage (Avery et al., 2019b) and chronic schizophrenia patients (Williams et al., 2013), in both studies, memory was measured in a separate behavioral task and thus was indirectly linked to neural habituation deficits. Therefore, the critical question of whether habituation to repeated information directly predicts subsequent memory remains unclear.

To address this question, we used a novel event-related repetition task designed to build graded levels of familiarity with stimuli. Participants were exposed to 1, 3, 5, or 7 repeated presentations of a stimulus, where stimuli presented one time would be the least familiar and those presented seven times the most familiar (Avery et al., 2016; Avery and Blackford, 2016). To minimize demands on executive function, participants passively viewed stimuli during encoding and responded with a button-press during subsequent memory testing. We characterized neural habituation as the slope of the fMRI signal change over time to repeated stimulus exposures (Figure 1A). To determine whether neural habituation was localized or widespread, we calculated habituation slopes in a network including the hippocampus, parahippocampal gyrus, visual cortex, amygdala, cingulate cortex, and precuneus. Subsequent recognition accuracy during testing was characterized as an accuracy slope, with higher slopes indicating a greater benefit from repeated exposure to a stimulus (Figure 1B). We hypothesized that more neural habituation to repeated stimuli during encoding would predict greater repetition-related accuracy increases at test in healthy control subjects, and that less habituation to repeated stimuli would predict less repetition-related accuracy increases in patients with schizophrenia.

Figure 1.

Figure 1.

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Behavioral and neural measures of memory during the repetition task. A) Habituation of the functional magnetic resonance (fMRI) signal was calculated as a slope of change over time with repeated exposures to a stimulus during the exposure phase. Negative habituation slopes indicated greater habituation over repeated exposures, while habituation slopes near zero indicated sustained signal over exposures. B) During testing, stimuli with one prior exposure were the least familiar and were expected to have the lowest accuracy while stimuli with seven prior exposures were the most familiar and expected to have the highest accuracy. The benefit of repeated prior exposures on subsequent accuracy was characterized as an accuracy slope over testing categories (1, 3, 5, or 7 prior exposures), with higher accuracy slopes indicating a greater benefit from prior exposures.

2. Materials and Methods

2.1. Participants

We studied 27 patients with a schizophrenia spectrum disorder and 28 healthy control participants (Table 1). Patients were recruited from the inpatient units and outpatient clinics of the Vanderbilt Psychiatric Hospital. At the time of study, patients met diagnostic criteria for: schizophreniform disorder (n = 14), schizophrenia (n = 13), schizoaffective disorder (n = 1), and brief psychotic disorder (n=1)). Follow-up clinical diagnoses were available for 13 patients with schizophreniform disorder: 10 patients maintained a schizophreniform diagnosis after at least 6 months, and 3 patients converted to a diagnosis of schizophrenia or schizoaffective disorder. The patient with brief psychotic disorder converted to a diagnosis of schizophreniform disorder. The majority of patients (67%) were treated with antipsychotic medication at the time of the study. Healthy control participants were recruited from the surrounding community.

Table 1.

Participant characteristics.

Sample Healthy control vs. Schizophrenia
Demographics Healthy control N = 28 Schizophrenia N = 27 Statistic df p
Age, years 24 ± 3.4 24 ± 3.4 0.00 55 0.99
Sex (% male) 64% 71% 0.33 1 0.57
Race (white/black/other) 22/3/3 21/7/0 4.62 2 0.10
Handedness (% right) 93% 96% 0.35 1 0.55
Participant education, years 15 ± 2.3 14 ± 2.3 2.41 55 0.13
Parental education, years 16 ± 2.5 16 ± 2.8 0.09 55 0.77
IQ, WTAR 112 ± 8.5 108 ± 14.9 1.42 55 0.24
Clinical Mean Range
Ham-D 11 ± 7.5 0 – 31
YMRS 3 ± 5.4 0 – 21
PANSS – total 51 ± 16.2 31 – 86
PANSS – positive 13 ± 6.6 7 – 30
PANSS – negative 12 ± 5.5 7 – 28
PANSS – general 26 ± 7.9 16 – 44
CPZ (mg / day) 246 ± 241.1 0 – 825
Duration of illness, months 29 ± 15.3 10.5 – 58.5
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Mean values ± standard deviations are shown for each group. WTAR, Wechsler Test of Adult Reading; Ham-D, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale; PANSS, Positive and Negative Syndrome Scale; CPZ, chlorpromazine equivalent; mg, milligram.

Exclusion criteria were age less than 16 or greater than 35, significant medical or neurological illness, or contraindication for MRI scanning (see Supplementary Methods for exclusions). The restricted age range was chosen to limit potential confounds of aging on memory and neural function (Hedden et al., 2014). Healthy control subjects were excluded for history of psychiatric illness, a first-degree relative with a psychotic illness, and current psychotropic medication use. A total of 30 patients and 31 healthy control participants were recruited for the study. Two healthy control participants were excluded due to data acquisition errors (e.g. gradient coil malfunction), two patients and one healthy control participant were excluded for motion during functional imaging (see Imaging data acquisition and processing below), and one patient was excluded for low behavioral response rate (see Experimental paradigm below). Participants were assessed for current mood, psychotic symptom severity, and intellectual function (see Supplementary Methods). Groups were similar in age, sex, race, handedness, and years of parental education (Table 1).

This study was conducted in accordance with the Vanderbilt Human Research Protection Program and all participants provided written informed consent prior to study procedures. Participants received financial compensation for their time.

2.2. Experimental paradigm

We used a repetition task to investigate neural response to repeated stimuli (Avery and Blackford, 2016) (Figure 2). The task consisted of an exposure phase followed by a testing phase. To assess response to social and non-social stimuli, participants completed the task twice, first with faces and again with objects. For consistency with prior studies (Avery and Blackford, 2016; Williams et al., 2013), faces were presented prior to objects. The exposure phase was designed to build a graded level of familiarity with a set of images (n = 32) by exposing participants to an image 1, 3, 5, or 7 times. During the exposure phase, images were presented in a jittered, event-related design over two fMRI runs (4 m, 50 s each) to allow modeling of neural response to each image exposure. Following the two exposure runs, participants completed one testing run (5 m, 24 s) where the 32 previously-exposed stimuli and 32 novel stimuli were presented for 2 s each. Participants were asked to indicate by button press whether each stimulus was “Old” or “New”. Images were neutrally-valenced and were randomly selected to stimulus sets. Stimulus sets were shown in pseudorandom order (see Supplementary Methods for further details). Participants were verbally checked for alertness between each fMRI run. One patient failed to respond to any object recognition test trials and was excluded. Binomial accuracy for each trial was recorded as 1 (hit) or 0 (miss). Average accuracy for previously-exposed and novel stimuli were calculated as the proportion of correct responses across trials. Response bias was measured by comparing the probability of correct responses for previously-exposed and novel stimuli (Supplementary Methods and Results). Change in accuracy over repeated exposures was similar for faces and objects; therefore, accuracy data were averaged across stimulus types (Supplementary Results).

Figure 2.

Figure 2.

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Participants completed the repetition task first with neutral faces, then with neutral objects. A) During the exposure phase, 32 unique stimuli were presented either 1, 3, 5, or 7 times to build tiered levels of familiarity. To enable modeling of neural response to each individual trial, stimuli were presented for 1 s followed by a black screen for a 2–4 s interstimulus interval (ISI). B) During the testing phase, 32 previously-exposed stimuli and 32 novel stimuli were presented for 2 s each and participants completed a forced-choice recognition test.

2.3. Accuracy slope

To characterize the change in recognition accuracy with repeated exposures, an accuracy slope was calculated across exposure categories (1, 3, 5, or 7 prior exposures; Figure 1). Accuracy slopes were calculated for each individual using linear regression, where recognition accuracy is predicted by the log-transformed block number. Log transforms accurately model the nonlinear change in recognition accuracy with repeated exposures, where initial repeated exposures result in the largest increases in recognition. Because the change in accuracy with repeated exposures can be distinct from average accuracy (e.g., a participant may have high average accuracy but only small improvements with repeated exposures), the proportion of correct responses was also calculated across all trials.

2.4. Imaging data acquisition and processing

Structural and functional data collection and quality control are detailed in the supplement. The first-level (participant) temporal model was estimated using a general linear model (GLM) (Friston et al., 1995). The design matrices included 4 task regressors, one for each face exposure category (1, 3, 5, 7). Motion parameters (rotation, translation, mean displacement, and outliers) were included as covariates of no interest. Data were high-pass filtered (1000 s) to attenuate low frequency signal (linear scanner drift) without removing habituation signal.

2.5. Regions of interest (ROIs)

Our goal was to measure neural responses during encoding across a network of novelty-responsive memory regions (Kafkas and Montaldi, 2018), including hippocampus (anterior, posterior), parahippocampal gyrus, visual cortex (calcarine and extrastriate cortex), amygdala, cingulate cortex (anterior and posterior), and precuneus. ROI selection is detailed in the supplement.

2.6. Habituation slope

Habituation slope (b′) values were calculated for each participant for faces and objects separately using linear regression analysis (Figure 1). Habituation is dependent on novelty response; that is, there is more opportunity for habituation over time if novelty response is high. Because we were interested in examining differences in the rate of habituation independent of differences in novelty response, we calculated a normalized habituation slope (b′), corrected for novelty response differences, for each participant (Avery and Blackford, 2016; Montagu, 1963; Plichta et al., 2014) (see Supplementary Methods for details). Novelty response was defined as the initial magnitude of signal during the first presentation of a stimulus. Percent signal change was extracted from each ROI using MarsBar (Brett et al., 2002). Signal in the left and right hemispheres and for faces and objects were highly correlated across ROIs. As such, data were averaged across hemisphere and stimulus type to increase statistical power and minimize type I error.

2.7. Validation analysis

To test the specificity of habituation effects within selected ROIs, we examined habituation in an a priori negative control region. We chose the primary motor cortex (precentral gyrus) as a negative control because 1) the exposure phase of the repetition task does not involve an explicit motor response, and 2) the primary motor cortex has been shown to be responsive to both novel and repeated stimuli (Yamaguchi et al., 2004), suggesting it does not habituate with memory. The precentral gyrus ROI was defined using the AAL standard mask and habituation slopes were calculated as described above (see 2.6. Habituation slope).

To test the specificity of associations between habituation and repetition task memory, we collected a standardized visual memory assessment outside of the scanner (Wechsler Memory Scale (Wechsler, 1945) Faces subtest, WMS-III; Supplementary Methods). Immediate and delayed standard memory scores were calculated for each participant.

2.8. Statistical analysis

Binomial accuracy and non-response data were modeled using a general linear mixed effects model, with prior exposures (1, 3, 5, 7) and group as fixed factors, and participant and trial as random factors. Post-hoc tests of simple effects of prior exposure were conducted using estimated marginal means. Habituation effects by region and group were modeled using a linear mixed model, with neural habituation slope (1st – 7th exposure) as the dependent variable, region and group as fixed factors, and participant as a random factor. We next tested whether neural habituation was associated with subsequent memory in healthy participants. To test for a unique relationship between habituation and accuracy slope, independent of effects of average accuracy, both memory measures (average accuracy and accuracy slope) were modeled as fixed factors in a linear mixed model, with participant as a random factor, and neural habituation slope as the dependent variable. Schizophrenia patients were then added to the model to test whether the same brain-behavior patterns existed in a memory-impaired group, with group included as a fixed factor. Posthoc analyses tested for associations within the schizophrenia group. Based on previous findings from our lab (Avery et al., 2016; Avery and Blackford, 2016) that show habituation and learning occur non-linearly, we performed planned secondary analyses within three discrete repetition windows (1st – 3rd, 3rd – 5th, and 5th – 7th exposure) to determine whether habituation during specific time windows (early, middle, or late) differed between groups and were associated with memory. Associations between neural habituation and WMS immediate memory were examined using a linear mixed model (Supplementary Methods). Statistical analyses were performed using SAS software v9.4 (SAS Institute Inc., Cary, NC) and RStudio v1.2 (RStudio, Inc., Boston, MA).

3. Results

3.1. Accuracy

We first examined behavior during the forced-choice memory test. Response rates were high (87% ± 17%) and were similar across exposure categories, suggesting level of familiarity did not influence non-responding (Supplementary Results). Non-response trials were excluded from accuracy calculations.

Patients were less accurate in identifying previously-seen stimuli (main effect of group, X2 1 = 5.21, p = 0.02; Figure 3A). All participants benefited from more exposures to a stimulus (main effect of exposure, X2 3 = 49.81, p < 0.001), although schizophrenia patients benefited less from more exposures than did healthy control subjects (group by exposure interaction, X2 3 = 9.79, p = 0.02; Figure 3B). Post hoc tests revealed that results were driven by lower recognition for stimuli seen five and seven time in schizophrenia patients compared to healthy control subjects (all p’s ≤ 0.04). Schizophrenia patients also tended to be less accurate at discriminating novel stimuli relative to healthy control subjects (p = 0.06, Supplementary Results). Results were similar after removal of one participant with near-chance level performance (Supplementary Methods and Results).

Figure 3.

Figure 3.

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Recognition accuracy during testing. A) Healthy control subjects were more accurate in identifying previously-seen items than schizophrenia patients. B) All participants were more accurate in identifying items with a greater number of previous exposures relative to fewer exposures. Schizophrenia patients benefited less from more exposures than healthy control subjects, with results driven by lower accuracy for items previously-seen 5 and 7 times.

3.2. Habituation

Habituation from the 1st to 7th exposure was similar between groups (no main effect of group, p = 0.36; Supplementary Table 2) and across regions (no main effect of region, p = 0.21). We next tested for a relationship between habituation and accuracy in healthy participants. More habituation during the exposure phase was associated with a stronger accuracy slope during testing in healthy control subjects (main effect of accuracy slope, F 1,25 = 6.96, p = 0.01; Figure 4). This relationship was specific to the repetition-related memory benefit, as habituation was not associated with average accuracy (no main effect of accuracy, p = 0.97). We then included patients in the model to test whether a similar relationship existed in schizophrenia. There was a main effect of group (F 1,51 = 19.48, p < 0.001) qualified by an accuracy slope by group interaction (F 1,51 = 24.37, p < 0.001), indicating a different brain-behavior relationship between groups. Posthoc tests revealed that schizophrenia patients also showed an association between habituation during encoding and accuracy slope (F 1,25 = 14.41, p < 0.001), although in the opposite direction from healthy control subjects. In schizophrenia patients, a sustained neural response at or above baseline (less habituation) during the exposure phase was associated with stronger accuracy slopes during testing. Habituation and accuracy slopes were not correlated with chlorpromazine equivalent dose or duration of illness (p’s ≥ 0.24).

Figure 4.

Figure 4.

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The association between neural habituation during encoding and subsequent memory. In healthy control subjects, more habituation over repeated stimulus exposures was associated with a stronger accuracy slope at test. In contrast, in schizophrenia patients, a more sustained signal (less habituation) was associated with a stronger accuracy slope at test.

Discrete exposure windows

Because habituation changes may be nonlinear, we conducted planned secondary analyses of habituation within specific windows (e.g., 1st – 3rd exposure). In healthy control subjects, the overall habituation (1st – 7th)-memory association was driven by habituation in the initial (1st – 3rd, main effect of accuracy slope, F 1,25 = 18.39, p < 0.001) and middle exposure window (3rd – 5th, main effect of accuracy slope, F 1,25 = 11.61, p = 0.002). More habituation across the early and middle exposure windows, but not later exposures (5th – 7th), was associated with stronger accuracy slopes (Supplementary Results, Supplementary Figure 1). Schizophrenia patients showed a different brain-behavior relationship from healthy control subjects in both time windows (Supplementary Results), where less habituation, particularly from the 3rd to 5th exposure, was associated with a stronger accuracy slope at test (Supplementary Figure 1).

Validation analysis

To rule out non-specific associations between habituation and memory, we also modeled the effect of habituation on memory in a negative control region, the precentral gyrus, which we do not expect to be involved in repetition-related learning. The linear mixed model including average accuracy and accuracy slope revealed no associations in healthy control subjects (p’s ≥ 0.38), and the inclusion of the schizophrenia group revealed no main effects or interactions (p’s ≥ 0.25), indicating that signal in non-task-related regions did not predict memory task performance (Supplementary Table 3).

3.3. Wechsler Memory Scale

To determine whether the association between habituation and memory were specific to the experimental stimuli, we also tested whether neural habituation predicted performance on a standardized visual memory test completed outside the scanner. Schizophrenia patients had worse immediate memory scores compared to healthy control subjects (Supplementary Results). The relationship between habituation and memory performance differed between groups (immediate memory by group interaction, F 1,50 = 4.53, p = 0.04, Supplementary Results). Consistent with repetition-task results, more habituation in healthy participants was associated with better immediate memory performance, while a more sustained response in schizophrenia patients was associated with better immediate memory, suggesting habituation may be a broad indicator of immediate memory ability.

4. Discussion

Understanding the neural signature of memory is foundational to explaining deficits in schizophrenia. Here, we examined the neural signature of habituation. In healthy control subjects, the hippocampus, parahippocampal gyrus, amygdala, precuneus, cingulate, and visual cortex habituated similarly to repeated exposures to stimuli. More habituation was associated with greater repetition-related accuracy at test, suggesting neural habituation contributes to learning through repetition. Furthermore, more habituation was associated with better performance on a standardized memory test, suggesting habituation may be a generalized marker of healthy memory function. Together these findings suggest that individual differences in habituation convey information about repetition-based learning and efficiency of neural encoding in healthy individuals.

We next asked whether variation in neural habituation predicted memory in schizophrenia. Schizophrenia patients had poorer average recognition memory and showed fewer accuracy benefits from repeated stimulus exposures compared to healthy control subjects. As we hypothesized, individual differences in the rate of habituation predicted subsequent memory in schizophrenia patients, although in a different pattern from healthy control subjects. In schizophrenia patients, a more sustained signal over repeated exposures was associated with greater repetition-related recognition. This brain-behavior association is consistent with the neural inefficiency model, which theorizes that when cognitive demands are modest, normal cognitive performance may be accomplished through elevated recruitment of the underlying brain regions (Brauns et al., 2011; Micheloyannis et al., 2006; Subramaniam et al., 2014). Prior studies have revealed that schizophrenia patients show a greater magnitude of response in memory regions when task demands are low and memory performance is similar to healthy participants (Karch et al., 2009; Karlsgodt et al., 2007). Using a low-demand memory task, we now extend these prior findings to show that schizophrenia patients show an abnormally sustained response across a broad memory network, and this response predicts memory performance. Investigation of regions that modulate habituation, such as the lateral and medial prefrontal cortex (Murty et al., 2013), may shed further light on functional interactions that contribute to abnormally-elevated activity in this memory network.

While habituation rates predicted subsequent memory deficits in patients, our results revealed overall similar habituation rates between groups. This is in contrast to our prior studies showing reduced habituation in schizophrenia (Avery et al., 2019a; Williams et al., 2013). One explanation may be differences in the tasks used between studies. Here, we used a unique task designed to build familiarity gradually, with a 2–4 s jittered event-related design and intervening stimuli between same-stimulus repetitions. Although ideal for studying recognition, this slow interleaved design is likely to produce less habituation in healthy individuals than a task using rapid, same-stimulus repetitions (Rankin et al., 2009). Our prior studies used rapid, same-stimulus repetitions and found significantly more habituation in healthy participants than in patients with schizophrenia (Avery et al., 2019a; Williams et al., 2013). It is possible that healthy participants successfully modulate their neural resources relative to the rate and familiarity of incoming sensory information, while patients fail to do so, resulting in either apparent deficits or similarities with healthy individuals depending on the task. However, further investigation is needed to disentangle between-group differences in modulation of habituation across different repetition rates.

Neural habituation paradigms offer a unique window into neural function that makes them ideal for studies of cognitive function in schizophrenia. First, they can be collected without requiring an explicit behavioral response, making them ideally suited for use in patients with motivational and attentional deficits. This also enables the extension of the same paradigm to age groups where behavioral responses are difficult to obtain, such as at-risk infants and young children (Nordt et al., 2016). A second advantage is translation to animal models of illness. Habituation is conserved across species at both the behavioral and neural level and an extensive literature exists in model organisms from drosophila to rodents. Translational signatures are particularly imperative in the exploration of novel interventions (Barron et al., 2016), as complex cognitive processes and associated neuropsychiatric pathology is not readily modeled in animals.

These findings should be interpreted in the context of study limitations. We used a region of interest approach to study an a priori set of regions that support memory function and have previously shown a habituation response. We found similar habituation across this network, which may provide a more reliable index of memory deficits than an individual-region approach. However, there may be individual differences within regions that could further inform differences in learning and memory. Future studies may consider a voxelwise approach to examine the spatial specificity of habituation and potential variations in habituation patterns, and inclusion of an expanded set of regions that may further inform memory differences for faces and objects. The majority of patients (67%) were taking antipsychotic medication at the time of the study, which may affect neural signal. However, chlorpromazine equivalent dose was not associated with neural habituation or memory. Participant attention was not directly measured during the passive-viewing exposure phase. Participants were verbally checked for alertness between each exposure run, and response rate in both groups was high (87%) during the immediately-following testing runs. However, future studies may consider collection of eye-movement data to ensure attention during passive viewing tasks.

In conclusion, our study provides novel evidence for the use of a neural signature— habituation—as a marker of memory function in both memory-intact and impaired populations. We found that habituation to repeated information was specifically associated with improvements in memory gained through repeated exposure, suggesting habituation may be a neural marker of efficient repetition-based learning in healthy individuals. Importantly, habituation differences were elicited using a low cognitive demand task designed to lessen potential cognitive and motivational confounds often associated with neuropsychiatric disorders. Habituation is disrupted in a number of neuropsychiatric disorders and has been associated with memory deficits (McDiarmid et al., 2017; Sokhadze et al., 2009; Tam et al., 2017), suggesting habituation may be useful as transdiagnostic signature of memory function.

Supplementary Material

1

Acknowledgments

We would first like to thank the participants who chose to take part in this study. We would also like to thank Margo Menkes and Margaret Quinn for their support in collecting the research data.

Funding

This work was supported by NIMH grant F31-MH (Dr. Avery), the Charlotte and Donald Test fund, the Jack Martin MD Research Professorship in Psychopharmacology (J.U.B.), the Vanderbilt Psychiatric Genotype/Phenotype Project, and the Vanderbilt Institute for Clinical and Translational Research (through grant 1-UL-1-TR000445 from the National Center for Research Resources/NIH).

Footnotes

Conflict of interest

The authors declare no competing financial interests.

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