Structural MRI-based measures of neuroplasticity in an extended amygdala network as a target for attention bias modification treatment outcome

Accepted Manuscript Structural MRI-based measures of neuroplasticity in an extended amygdala network as a target for attention bias modification treatment outcome Jacob Aday, Joshua M. Carlson PII: DOI: Reference:

S0306-9877(17)30680-1 http://dx.doi.org/10.1016/j.mehy.2017.09.002 YMEHY 8668

To appear in:

Medical Hypotheses

Received Date: Revised Date: Accepted Date:

29 June 2017 17 August 2017 3 September 2017

Please cite this article as: J. Aday, J.M. Carlson, Structural MRI-based measures of neuroplasticity in an extended amygdala network as a target for attention bias modification treatment outcome, Medical Hypotheses (2017), doi: http://dx.doi.org/10.1016/j.mehy.2017.09.002

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Structural MRI-based measures of neuroplasticity in an extended amygdala network as a target for attention bias modification treatment outcome Jacob Aday, M.S. & Joshua M. Carlson, Ph.D. Department of Psychology, Northern Michigan University, Marquette, MI, USA Correspondence should be addressed to: Joshua M. Carlson, Ph.D. Department of Psychology Northern Michigan University 1401 Presque Isle Avenue Marquette, MI 49855 [email protected]

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ABSTRACT Increased attentional bias to threat has been identified as a causal mechanism in the development of anxiety. As such, attention bias modification (ABM) was conceived as a treatment option where anxiety is alleviated through a computerized cognitive training regimen that reduces an individual’s attentional bias to threat. Although ABM appears to be a promising treatment option for anxiety, the mechanism of action by which the treatment is effective is unknown. We hypothesize that effective ABM treatment is associated with neuroplasticityrelated structural changes in an extended amygdala – prefrontal cortex network that can be detected with standard T1-weighted magnetic resonance imaging (MRI). Literature regarding (1) effects of brain damage on attention bias, (2) functional neuroimaging of attention bias, (3) structural neuroimaging of attention bias, and (4) functional neuroimaging of ABM training all support the role of this network as the underlying mechanism of attention bias behavior and neuroplasticity-related changes in attentional bias. Additionally, we provide proof of principle pilot data that ABM training reduces MRI measures of gray matter volume in the basal forebrain/extended amygdala and medial prefrontal cortex. Greater reduction in gray matter volume corresponds to greater reduction in attentional bias. In addition, level of pre-training bias appears to be a strong indicator of treatment outcome. In short, we provide converging evidence for the hypothesis that the mechanism underlying effective ABM training is reduced gray matter volume in an extended amygdala network. MRI-based measures of neuroplasticity in this network could be an important target outcome for the treatment of anxiety with ABM. Keywords: Attention bias modification; Attention bias; Cognitive training; Cognitive bias modification; Anxiety; Neuroplasticity

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INTRODUCTION Anxiety disorders are among the most prevalent psychological disorders in the United States with 30% of Americans being diagnosed with an anxiety disorder at some point in their lifetime (1). These disorders can have massive societal and economic impacts; in the United States alone, anxiety disorders are estimated to cost as much as $42.3 billion a year (2). Current treatments for anxiety disorders are limited by their high costs, low efficacy, and undesired side effects. A relatively new treatment, attention bias modification (ABM), is a computerized cognitive training regimen inspired by empirical evidence indicating that many anxious individuals show a heightened attentional bias towards threatening stimuli in laboratory settings (3). This heightened attentional bias to threat has been shown to play a casual role in the development of anxious symptoms (4). These empirical findings corroborate what many consider to be the cardinal symptom of anxiety—hypervigilance to threat. ABM attempts to alter these biased attention patterns by continuously directing participants’ focus away from threatening stimuli and towards emotionally neutral stimuli. After more than a decade of research, there is now meta-analytic support for ABM in reducing anxious symptoms (5-7). ABM has been shown to be effective for both generalized and social anxiety (3), suggesting that there may be a shared mechanism by which these anxiety disorders develop and can be treated. ABM has also been shown to reduce anxiety in non-clinical individuals when under stress (4). Lastly, ABM and attentional control training have been shown to have modest effects on depression (8) and PTSD (9). Together, it seems that ABM can be broadly applied to reduce anxiety by way of attenuating attentional resources afforded towards threat. According to recent cognitive theories, schemas largely influence how information is attended to, interpreted, and remembered (3). Schemas are thought to be biased towards 3

threat in anxious individuals and thus their attention is inclined to focus on threatening information—leading to the development and maintenance of anxious symptoms. This tendency to preferentially direct attention towards threatening stimuli constitutes an attentional bias. Attentional biases are most commonly measured in laboratory settings with the dot-probe task (10, 11). In this task, two stimuli are briefly presented on a screen, with one on each side of a fixation point. Typically, one is a threat-related stimulus and the other is nonthreatening. After the stimuli disappear, a target (e.g., a dot) is presented on one side of the screen and the participant must respond to the target. Half of the time the target appears behind the threatening stimulus (referred to as congruent trials) and half the time the target occurs behind the non-threatening/neutral stimulus (referred to as incongruent trials). Individuals with high attentional biases tend to respond faster to the target on congruent compared to incongruent trials. This facilitated response is presumably due to the threatrelated stimulus preferentially capturing attentional resources. Evidence suggests that attentional bias to threat is elevated in individuals vulnerable to anxiety (12-14). Most ABM studies utilize a modified variant of the dot-probe task where the target always occurs (or occurs more often) in the location previously held by the neutral stimulus (i.e., incongruent trials). The stimuli used in the treatment are typically differentially valenced images or words. However, because ABM is still relatively new, there has yet to be a standard established for what type(s) of stimuli are most effective or what time-course is best suited for successful treatment. The rationale behind the treatment is that participants will practice directing their attention towards neutral stimuli (i.e., at the location in which the target will occur) to successfully find the target and after repeated training, will begin to bias their 4

attention away from threatening stimuli. For the treatment to be effective, these effects must generalize outside of the laboratory and the participants should attenuate their bias towards threatening stimuli in their daily lives. Critically, given that attentional bias to threat has been found to cause heightened levels of anxiety, the ABM rationale expects that decreased attentional biases following training should consequently result in lower anxiety (4). A number of recent reviews (3, 14-19) and meta-analyses (5-8)indicate that ABM is successful in reducing attentional bias to threat and anxious symptoms (d = 0.61) (5). In randomized control trials with anxious patients, the efficacy of ABM treatment is comparable to antidepressant and cognitive-behavioral therapy (CBT) treatment options (5). Additional evidence suggests that ABM can be successfully self-administered at home (20) or over the Internet (21, 22). ABM’s anxiolytic effects also appear to be maintained long-term. In a study conducted by Schmidt et al. (23), researchers found that their treatment group scored significantly lower on social anxiety measures compared to a control group four months after the training had ceased. Thus, ABM appears to be a very promising treatment option, which is noninvasive, practical, and could be easily disseminated to a large patient base. It should be noted that a recent review has reported that in many cases, both ABM and attention-control training result in comparable reductions in anxiety—somewhat muddling the cognitive mechanism by which the training is effective (24). While ABM has been shown to be an effective treatment for some anxious individuals, the underlying neural mechanisms facilitating effective treatment have received relatively little attention (25-27). It is critical to understand the neurobiological changes that accompany successful ABM in order to improve the validity of measuring training-relating changes and 5

outcomes. Indeed, recent research suggests that neural measures of attentional bias are more reliable than traditional behavioral measures (28) and accordingly may be more suitable as target outcome measures for ABM. As will be explored in detail below, fronto-limbic structures have been implicated in numerous studies of attention bias and therefore, alterations in attention bias behavior should be linked to changes in these structures. In addition to the need to better understand the neural mechanisms underlying ABM, there is a growing understanding that the consideration of individual-level factors is needed for the treatment of anxiety with ABM and other treatment options. Anxiety is a complex, multifaceted construct and although increased attentional bias to threat has been shown to be a causal factor in the development of anxiety (4), it is unlikely to be the only mechanism by which anxiety develops. Identifying which individuals are likely to benefit from ABM is an important factor for clinicians to consider when determining treatment options. Given that ABM is thought to reduce anxiety by reducing attentional bias, it would seem that a preexisting attentional bias is necessary for ABM to be effective. Although a majority of ABM studies have yet to consider the impact of pre-training bias, a number of recent studies have reported that higher levels of pre-training attentional bias predict greater reduction in attentional bias and in anxiety symptoms (6, 29-31). THE HYPOTHESES Primary Hypothesis: Underlying the rational for ABM is a learning-related reduction in attentional bias. Changes in behavior following ABM treatment are ostensibly linked to change in brain structure and function. This ability of the brain to change based on past experiences is known as neuroplasticity. The neurobiological locus and characteristics of this learning-related 6

neuroplasticity are currently unknown. We hypothesize that ABM training-related reductions in attentional bias and anxious symptoms are mediated by neuroplasticity in a basal forebrain and extended amygdala network, which includes the prefrontal cortex (PFC). It is further hypothesized that structural changes within this network can be effectively measured with structural MRI and used as a target for ABM outcome. In support of this hypothesis we provide the following forms of evidence 1: 1. Damage to the human amygdala and prefrontal cortex critically disrupts the preferential processing of emotional stimuli. 2. Functional neuroimaging dot-probe studies indicate that attentional bias to threat is linked to increased activation in the amygdala and prefrontal cortex. 3. Structural neuroimaging studies suggest that individual differences in attentional bias behavior are related to structural differences in the amygdala – prefrontal cortex system. 4. ABM neuroimaging studies indicate that activity in the amygdala and/or prefrontal cortex changes following ABM training. 5. We provide proof of principle pilot data that the structure of the brain is changed following ABM. Portions of the extended amygdala, basal forebrain, and medial prefrontal cortex show reductions in gray matter (measured by T1weighted MRI) following a 6-week ABM training program and the degree of change in these areas predicts treatment success.

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Another line of evidence regarding the genetics of attention bias and neuroplasticity is expanded upon in the Supplementary Material.

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Together these sources of converging evidence provide support for the hypothesis that fronto-limbic structures underlie attention bias behavior and ABM-related changes in attention bias behavior. In particular, neuroplastic changes in the brain following ABM are accompanied by structural-level decreases in gray matter within this amygdala – prefrontal cortex network, which can be noninvasively measures with structural MRI. Secondary Hypothesis: Given that ABM is thought to reduce anxiety by reducing attentional bias, it would seem that a preexisting attentional bias is necessary for ABM to be effective. We hypothesize that the success of ABM treatments—measured across both behavioral and neural measures—is most pronounced in those demonstrating a pre-training bias. EVALUATION OF THE HYPOTHESES Effects of brain damage on attention bias Studies of human brain damage implicate the amygdala in a number of processes related to attentional bias. For example, research on human subjects with amygdala damage point to a critical role of the amygdala in the recognition of emotional expression (especially fear) (32, 33), associative fear learning (34), as well as the experience of fear (35). Given this robust role in emotional learning and appraisal, it would follow that the amygdala is critical in the formation, elicitation, and perhaps modification of threat biases. A small number of human lesion studies have directly measured the extent to which the amygdala is necessary for threat-elicited attention and enhanced sensory processing (36-38). Across a number of different operationalizations of attentional bias, the amygdala has consistently been shown to play a critical role in this behavior. For example, emotional stimuli 8

typically receive enhanced sensory processing as a result of their evolutionary salience; however one study reported that individuals with amygdala damage do not display increased visual cortical processing of emotional images (36). Thus, the typical attention-related enhancement of sensory processing afforded to threatening stimuli is dependent on the amygdala. Further support for the amygdala’s requisite role in threat-enhanced perceptual processing comes from a study using the attentional blink phenomenon. In this task, a rapid sequence of emotional words (or other stimuli) are presented and participants are asked to respond to target words (usually presented in a different colored font). When a target follows in close temporal proximity to an earlier target, it is often undetected—this phenomenon is referred to as the “attentional blink.” However, when the second target is a highly emotional stimulus, the attentional blink is overcome. The ability for emotionally relevant stimuli to grab our attention during the attentional blink has been found to be dependent upon an intact amygdala (37). Another study found that when completing a visual search (face-in-the-crowd) task, healthy controls prioritized threat-related faces while amygdala damaged patients showed the opposite trend (38). Collectively, data gathered from these lesion studies suggests a critical role for the amygdala in threat-related processing and prioritizing attention towards threat. Even early research indicating that the amygdala is necessary for the recognition of fearful expressions has more recently been linked to the amygdala’s role in directing attention to emotionally salient locations in visual space (39). In particular, eye-tracking research suggests that individuals with amygdala damage do not naturally direct their gaze towards the eyeregion of fearful faces and under these circumstances are unable to recognize fearful facial expressions. However, when instructed to direct their gaze to the eye-region, amygdala 9

damage no longer impairs the ability to recognize fear (39). Thus, the amygdala appears to be critical for the automatic directing of attention to fearful eyes. This finding is complemented by behavioral research indicating that the eyes are sufficient for eliciting attentional biases to fearful faces in the dot-probe task (40, 41). Beyond the amygdala, a similar deficit in directing attention to fearful eyes has been found in patients with damage to the ventral medial prefrontal cortex (vmPFC) (42). Given that the vmPFC contains both gray matter as well as the white matter fibers connecting the PFC and amygdala (43), it is unclear which of these components is the critical factor affecting attention following damage. In sum, evidence from human lesion studies indicates that the amygdala – prefrontal cortex network is necessary for automatic shifts in attention to emotionally and socially salient stimuli including those indicative of potential threat. Functional neuroimaging of attentional bias to threat in the dot-probe task Human neuroimaging research using the dot-probe task suggests that attention to threat is mediated by a broad network including the amygdala, prefrontal cortex, sensory cortex, and other regions (44). To best understand the dot-probe neuroimaging literature, it is important to revisit the rational of the dot-probe task. As discussed in detail above, in the dotprobe task, two stimuli are briefly presented prior to the presentation of a target stimulus at the location previously occupied by of one of the two initial stimuli. Attention bias is measured by reaction time differences between trials in which the targets are spatially congruent vs. incongruent with the threatening/emotional stimulus. The rationale being that if attention is automatically captured by the threatening stimulus, reaction times will be faster for congruent relative to incongruent trials because participants’ attention is already directed to this location 10

prior to the presentation of the target. Incongruent trials require participants to disengage from the threat stimulus and reorient towards the target stimulus. Thus, the reaction time difference between congruent and incongruent trials is due to both the benefits and costs of attentional bias. The overall attentional bias can be subdivided into facilitated orienting and delayed disengagement by the inclusion of trials in which two stimuli of the same type (e.g., two neutral stimuli) are presented and attention is not differentially biased to one side of the screen or the other (45, 46). Given that the same processes—and therefore the same underlying neural correlates—are involved in directing attention to the location of the threat image during both congruent and incongruent trials, a direct comparison of these two trial types will not be useful for identifying the initial orienting of attention to threat. In order to capture this shared process, it is necessary to compare congruent and incongruent trials to another condition— namely one in which attention is not preferentially directed to one location or another (i.e., trials in which the same stimulus type is presented to each side of the screen). The amygdala is thought to appraise a stimulus for its threat potential (32, 39, 47) as well as code its spatial location (48). Based on this understanding of the amygdala, as well as the research of individuals with amygdala damage reviewed above, it would be expected that the amygdala is involved in the initial orienting of attention to threatening and other types of salient stimuli. Indeed, dot-probe research, including the necessary comparison conditions (44, 49, 50), has found that amygdala activity is elevated during trials in which attention is biased towards threat (i.e., congruent and incongruent trials) and does not habituate (51). Furthermore, this elevated amygdala activation has been found to correlate with activity in prefrontal regions including both ventral and dorsal portions of the anterior cingulate cortex 11

(ACC) as well as visual cortical regions (44). Direct projections from the amygdala back to spatially specific locations in the visual cortex facilitate visual perceptual processing at the location of potential threat (36, 47, 52-54). In addition, the amygdala projects to the nucleus basalis of Meynert (nbM), which contains acetylcholine (ACh) producing cells that project diffusely throughout the cortex—including visual and other sensory cortex—to facilitate perceptual processing (55-58). Attention-related facilitation of visual perceptual processing has been captured in dot-probe tasks when threat location has been included as a condition of interest. Due to the fact that each visual field is represented in the contralateral hemisphere, it is not surprising that visual threat signals in the dot-probe task have been found to enhance visual cortical activity in the hemisphere contralateral to their presentation (52-54). Although comparisons between congruent and incongruent trials do not associate unique neural activation with the initial orienting of attention to threat, greater activation on incongruent relative to congruent trials is indicative of processing related to attentional disengagement from threat and reorienting to the task-relevant target. Dot-probe studies utilizing this contrast have generally found that portions of the ACC and other prefrontal regions are more activated for incongruent trials (59, 60). Thus, these prefrontal regions appear to resolve conflict between stimuli competing for attention (61-63) and regulate the duration of attentional engagement by threat. Given their role in regulating emotional attention, it should come as no surprise that the amygdala and prefrontal cortex are commonly implicated in neuroimaging dot-probe studies of attentional bias. In addition, activation within this network is higher among anxious individuals during attentional bias to threat (49, 59, 60). Thus, there is strong functional neuroimaging 12

evidence implicating this amygdala-based system in attentional bias to threat and therefore this system represents a likely locus of ABM-related neuroplasticity. The relationship between brain structure and attention bias behavior Greater structural connectivity between the amygdala and prefrontal cortex is regulated by the BDNF genotype and in turn linked to greater levels of attentional bias to threat (64). Furthermore, the strength of the structural connectivity between the amygdala and prefrontal cortex—particularly the amygdala and ACC—is linked to the degree of functional coupling between these two structures. Individual differences in functional and structural amygdalaprefrontal coupling relate to individual differences in attention bias behavior where in both instances greater connectivity between the two structures is related to greater levels of attention bias (65). Furthermore, across two experiments, it was found that greater ACC gray matter volume is associated with greater attentional bias to threat (66). In this same study, greater gray matter volume in a cluster of voxels within the basal forebrain was found to be associated with greater attentional bias to threat. This basal forebrain cluster appears to include the nbM as well as nuclei of the extended amygdala (BF/eAm)—both of which are associated with affective attention and/or sustained threat responsivity (58, 67-77) and are densely connected to the amygdala and medial PFC (mPFC, including the ACC) (43). As mentioned above, the nbM contains acetylcholine producing cells which project diffusely throughout the cortex to increase arousal and attention. Projections from the amygdala to these cholinergic nbM cells play an essential role in animal models of affective attention (57, 58, 77), which appears to be complemented by this structural relationship observed in humans (66). Additional nuclei within the basal forebrain are thought to be part of the extended 13

amygdala, which can be considered a functional extension of the amygdala. The stria terminalis is a white matter pathway connecting the amygdala to the extended amygdala including the bed nucleus of the stria terminalis (BNST). The BNST has been linked to anxiety-like behaviors in animal models (67-69) and more recently has been linked to similar behaviors in human experiments (70-76). In sum, structural variability in the prefrontal cortex, BF/eAm, and the white matter pathway connecting the amygdala and prefrontal cortex are linked to variability in attention bias behavior. Given this relationship between brain structure and attention bias behavior, it is reasonable to expect that changes in attention bias behavior following ABM are linked to neuroplastic changes in these structures. Functional neuroimaging research of ABM training Functionally, many neuroimaging studies involving attentional bias and ABM commonly implicate the PFC and amygdala as regions with altered activity following training. However, evidence for localization of that activity within the PFC has been inconsistent as some studies have noted alterations in medial prefrontal cortex activity (mPFC) (27) while others observed changes in the lateral prefrontal cortex (lPFC) (26, 78, 79). There has also been mixed evidence regarding the direction of the change in activity (some studies report increases in activity (26, 78) while others report decreases (79)). Discrepancies in localization of activity may have to do with the fact that different types of tasks (as well as different variants of tasks) have been used to elicit activation following ABM. The lateral prefrontal cortices (lPFC) are thought to support the deployment and maintenance of executive attention through sustained representation of current goals and task rules (80, 81). Previous studies examining ABM have noted increases in lPFC activation when participants’ attention was oriented in the direction opposite to that 14

found in training—ostensibly, because conflict demands resulted in the need to exert greater attentional control (26). Functional parcellation of the mPFC has been less clear, but there is research noting its involvement in ABM and particularly in the appraisal and expression of negative emotion (82). Taylor et al. (27) found that ABM resulted in increases in activation across several prefrontal regions (i.e., left anterior dorsal ACC, left dorsolateral PFC, vmPFC, and right ventrolateral PFC). Of note, the researchers found that changes in vmPFC activation independently predicted participants’ change in bias scores as well as change in anxiety from before to after in a task designed to induce anxiety (i.e., speech). Activity in the mPFC has been negatively correlated with amygdala activity in paradigms where reappraisal resulted in downregulation of amygdala activity in response to negative pictures (83), however this effect was not found in depressed individuals (84). This data suggests that controlled top-down regulations, like emotional conflict regulation, utilize mPFC areas to inhibit negative emotional processing in the amygdala, thus dampening task interference. These results led Etkins et al. (82) to hypothesize that the ventral ACC and mPFC may perform a generic negative emotion inhibitory function that can be recruited by other regions (e.g., dorsal ACC and lPFC) when there is a need to suppress limbic reactivity. This claim is supported by rodent research demonstrating altered fear expression following mPFC lesions (85). Thus, the mPFC seems to function generally in the appraisal and expression of fear and overall, there is extensive research supporting the PFC as a region of high interest in understanding the neurobiological changes accompanying ABM. Like the PFC, the amygdala is a region that has been implicated repeatedly in studies of attention bias and ABM (86), which is unsurprising given its importance in threat appraisal and 15

emotional attention. Taylor et al. (27) found decreased bilateral amygdala activation after just a single session of ABM training. The amygdala’s exact role in ABM remains somewhat ambiguous however as Mansson et al. demonstrated increased amygdala activation after training (87). Researchers have also found that reduced ACC-amygdala coupling is predictive of improved long-term treatment response (87). Lastly, greater pre-training amygdala activation has been shown to predict social anxiety symptom reductions (24). Overall, there is strong evidence implicating the amygdala in ABM training albeit the exact nature of that relationship is not fully clear. Given that functional neuroimaging measures may be influenced by task conditions and transient affective states, it would seem that identifying structural changes associated with successful ABM would be a more reliable outcome measure. Empirical proof of principle data assessing changes in brain structure following ABM As already briefly noted, genes related to neuroplasticity are associated with attention bias behavior (88-94) and the degree of change in attention bias behavior following ABM (95). These findings generally support the hypothesis that plasticity-related changes in the brain underlie ABM outcome. Additional evidence from patients with localized brain damage, functional neuroimaging dot-probe studies, structural neuroimaging dot-probe studies, and functional neuroimaging studies of ABM all implicate an amygdala – prefrontal cortex network involved in attentional bias as well as change in attentional bias following ABM. Yet, the existing evidence does not directly address the degree to which ABM is followed by structural-level neuroplastic changes and the degree to which these changes are detectible with structural brain imaging measures. Furthermore, the degree to which such structural-level changes are proportionally related to ABM-related behavioral outcomes is unclear. We provide initial proof 16

of principle evidence that gray matter volume within a basal forebrain/extended amygdala region and a medial prefrontal cortex region decrease following ABM training and that the degree of structural change in these regions correlates with behavioral changes in attentional bias and anxiety. A small group of participants (N = 6, Female = 4; Mean Age = 20.5) were recruited to undergo 6 weeks of ABM training, separated by a pre- and post-training testing session. The ABM training was completed at home using a cellphone app. The app is a variant of ABM training utilizing fearful and neutral faces. Participant were asked to indicate which side of the screen a dot appeared on (it always occurred behind the neutral face) and were instructed to complete 6 (200 trial) training sessions per week totaling an approximate hour of training per week and 7200 total training trials across the six-week training period. Pre and post-training testing sessions consisted of behavioral and physiological measures. Participants completed the dot-probe task to measure attentional biases, the Speilberger State Trait Anxiety Inventory (STAI-T) (96) to measure self-reported anxiety, and T1-weighted structural MRI scans were collected to measure brain volumes in regions of interest2. To test the extent to which training reduces BF/eAM and ACC gray matter volume (GMV), a paired samples t-test was conducted in SPM12 comparing changes in gray matter volume across time (pre-training vs. post-training)3. At the group-level, ABM training reduced

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To assess changes in gray matter volume, structural T1–weighted images were acquired with the following parameters: TR = 5300 ms, TE = 1.92, flip angle = 9°, FOV = 246 × 246 × 149 mm, matrix = 512 × 512 × 248, and voxel size = 0.48 × 0.48 × 0.60 mm. Regions of interest were based on our previous work implicating variability in BF/eAm and mPFC/ACC gray matter volume with individual differences in attentional bias (91). MRI data was analyzed using a voxel-based morphometry (VBM) methodology in SPM12 (115). The VBM preprocessing procedure was identical to that described in our previous work (91, 116, 117). 3 Given that our sample size was too small to formally test our primary hypothesis and given that the following analyses are only meant to provide initial proof of principle evidence to support the rationale of our hypothesis,

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gray matter volume in the basal forebrain/extended amygdala region (BF/eAm) (MNIxyz = -12, 2, -2; t = 5.57, M = 5%, p < 0.05) and mPFC (MNIxyz = -10, 52, 12; t = 2.80, M = -5.9%, p < 0.05) (see Figure 1). At the individual level, greater gray matter reductions in the BF/eAm (r = -0.78, p = 0.04, see left scatterplot in Figure 1) and mPFC (r = -0.79, p = 0.03, see right scatterplot in Figure 1) were linked to greater reductions in attentional bias. In contrast to gray matter reduction, a number of regions—including ventrolateral and dorsolateral prefrontal cortex—displayed an increase in gray matter (see Table 1). However, these gray matter changes were not tightly linked to behavioral change. Although not as promising as the BF/eAm and ACC, these regions represent additional candidate structures for the underlying mechanism of successful ABM. As mentioned in the hypothesis section, our secondary hypothesis states that preexisting attentional bias predicts ABM outcome. Additional evidence from our pilot study found that reductions in attentional bias (r = -0.96, p = 0.001, see Figure 2), anxiety symptoms (r = 0.67, p = 0.07), as well as BF/eAm (r = 0.71, p = 0.06) and mPFC (r = 0.86, p = 0.02) gray matter volume were predicted by pre-training attentional bias, such that those with the highest level of bias benefited the most from ABM training. It should be noted that post-training bias did not correlate with change in attentional bias (r = -0.06). Thus, the efficacy of ABM seems to be specifically linked to an individual’s initial bias score. CONSEQUENCES AND IMPLICATIONS OF THE HYPOTHESIS The reviewed literature suggests the following: (1) Damage to the amygdala or PFC critically impairs attention to threat. (2) Functional neuroimaging dot-probe studies implicate a we used a very liberal statistical threshold of p < 0.05 uncorrected for the BF/eAm and mPFC regions of interest identified in our earlier work linking attention bias with gray matter volume (91).

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broad network of regions in attentional bias to threat—including the amygdala and PFC. (3) Structural variability in gray matter within the mPFC and BF/eAm as well as the white matter pathway connecting the amygdala and PFC are correlated to individual differences in attentional bias behavior. (4) Functional changes in the amygdala and PFC have been found following ABM treatment. In addition, our pilot data provide proof of principle evidence that ABM training results in structural changes in the BF/eAm and mPFC and these structural changes are related to the relative degree to which the ABM intervention was successful. Thus, there is convergence across these multiple lines of evidence, which support our hypothesis that ABM training-related reductions in attentional bias are mediated by neuroplasticity in a BF/eAm and mPFC network, which can be measured with structural MRI. Significance of structural changes in this network What is the significance of the hypothesized reductions in gray matter within the BF/eAm and mPFC? For several decades now, a portion of the basal forebrain has been conceptualized as an extension of the amygdala that includes subnuclei involved in various aspects of threat processing (70). In particular, rodent research suggests that although the amygdala is linked to immediate fear-relevant behaviors like attention, the BNST, part of the extended amygdala, is more involved in long-term threat-reactivity reminiscent of anxiety (6769). In recent years, there has been a revitalization of the extended amygdala and its relationship to anxiety disorders. Indeed, recent research suggests that the primate and human BNST are also linked to anxiety-related behaviors (70-76). Thus, decreases in BNST gray matter volume following ABM could be particularly important in the effectiveness of ABM in reducing anxiety. Also within the basal forebrain is the nbM, which contains acetylcholine producing 19

neurons. Projections from the amygdala to these cholinergic basal forebrain cells play an essential role in animal models of affective attention (58, 77), where cholinergic efferents diffusely modulate neural activity throughout the cortex as a mechanism of increasing attention (55-57). Thus, although the amygdala appears to be a critical structure underling the immediate allocation of attentional resources to environmental threat, it may be through its connections with the BNST and other BF/eAM structures that attentional bias to threat leads to anxiety. In the same vein, it may be through these same connections that ABM reduces anxiety. It has become clear that, in human participants, experience-dependent neuroplasticity is visible in the MR signal (97) following motor (98-102), working memory (103), and other types of training/learning. However, it is unclear what learning-related structural changes the MR signal is actually sensitive to. It is most commonly assumed that MRI measures of human gray matter are reflective of changes in axonal and/or dendritic morphology, the creation or elimination of new synapses and/or neurons, as well as changes in the properties of glial cells (97). In animal models, fear learning elicits changes in dendritic spine density in the amygdala (104, 105), mPFC (106), and a number of additional regions. Although dendritic spine density cannot be directly measured in human participants, recent rodent research using MRI measures of gray matter volume in conjunction with confocal microscopy of ex vivo brain sections indicates that fear learning-related increases in dendritic spine density (rather than a number of other potential mechanisms) account for increases in gray matter volume (107). Thus, ABMrelated changes in MRI measures of gray matter volume are most likely reflective of changes in dendritic spine density. Our preliminary findings, supporting the hypothesis that ABM reduces gray matter in the BF/eAm and mPFC, likely reflect decreases in dendritic spine density in these 20

regions. Given that change in dendritic spine density is a known cellular mechanism underling neuroplasticity, MRI-based measures of gray matter volume are an ideal method of measuring neuroplasticity following ABM in humans. This notion is in line with a growing interest in using MRI to measure neuroplasticity in both animal and human models (108, 109). As mentioned above, basal forebrain structures such as the BNST and nbM are densely connected to the amygdala and mPFC (70, 110). In addition, the amygdala and mPFC themselves are densely connected (43). Given that MRI measures of gray matter volume are related to dendritic spine density (107) and given that dendritic spine morphology is reflective of the relative strength of afferent connectivity, an intriguing interpretation of our pilot data and hypothesis is that input(s) into the BF/eAm and mPFC following ABM are weakened (i.e., lower numbers of dendritic spines). Given the circuitry discussed above, these inputs likely originate within the amygdala. This interpretation may explain why gray matter changes are not observed in the amygdala after ABM and why amygdala gray matter volume is not associated with individual differences in attentional bias (66). That is, ABM might not affect bottom-up or top-down inputs into the amygdala or the inter-connectivity between amygdala subnuclei (at least to the degree that they are detectable by MRI), but rather outputs of the amygdala to the BF/eAm and mPFC. Neuroplasticity as an outcome measure of ABM intervention What are the clinical implications of the hypothesized reductions in gray matter within the BF/eAm and mPFC? There is a growing understanding that affective disorders and other neuropsychiatric disorders are on one hand heterogeneous while on the other hand include overlapping symptoms. As such, there has been a recent focus on targeting the underlying 21

mechanisms of particular symptoms or behaviors that may span across traditional diagnostic categories. Indeed, the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC) project has called for a focus on outcome targets based on genetics, neurobiology, or behavioral observations rather than traditional diagnostic categories (for review see (111-113). Within the NIMH’s strategic plan there is a focus on developing treatments based on empirical evidence from the areas of genomics, neuroscience, and behavioral science. In particular, the NIMH plans to support research to both identify and validate novel targets for treatment outcomes based on the underlying mechanisms of the disorder. Traditionally, measures of anxiety and behavioral metrics of attentional bias have been the target of ABM treatment. Our hypothesis would change the focus from these behavioral measures to the underlying mechanism of change following ABM. That is, neuroplasticity in the BF/eAM and mPFC may represent a promising a new target for ABM treatments. Establishing the precise neurobiological changes that accompany successful ABM training is critical to understanding how ABM works and how it can be improved. Furthermore, establishing the mechanism of action for ABM could serve as a target to objectively track training-related changes and outcomes. As mentioned above, behavioral metrics of attentional bias have traditionally been utilized as the outcome measure of ABM treatment (3, 5-8, 14-19). However, these bias metrics have recently been criticized for having low reliability (114, 115) and therefore represent a very poor outcome measure. Indeed, recent attempts have been made to create new behavioral metrics such as attention bias variability measures (116-118). Although, even the validity of these new measures have recently been called into question (119). Research suggests that functional neuroimaging measures of attentional bias obtained in 22

the dot-probe task are more reliable than behavioral measures (28). Yet, structural MRI measures are even more reliable (120, 121), which is likely due to the fact that structural measures are robust to variability and noise related to experimental design (122), participant mood (123), and alertness (124), which can confound functional measures. Although a strong functional-structural relationship has been found between amygdala – PFC connectivity and attentional bias (65), other functional neuroimaging measures of the PFC related to anxiety have been found to be linked to more distant and distributed underlying structural abnormalities. In particular, the vmPFC has been found to track the generalization of conditioned fear in healthy individuals (125) and is linked to the overgeneralization of conditioned fear in anxiety disorders (126, 127). This abnormal vmPFC reactivity in anxious patients has been found to arise from a multitude of structural abnormalities (128). In other words, functional changes resulting from ABM may reflect downstream effects rather than the actual locus of learning-related neuroplasticity associated with ABM. There is thus a need for more definitive and stable measurements to track the recovery of anxiety following ABM treatment. Given that changes in gray matter volume are (at least partially) explained by changes in dendritic spine density (107)—a known mechanism for learning—it appears to be an ideal target measure of ABM training-related learning. If our hypothesis is correct, structural changes in the BF/eAM and mPFC should prove to be reliable target measures of treatment efficacy and will therefore have the potential to limit premature treatment termination and anxiety relapse. Another advantage of structural MRI-based target measures is their comparatively short duration of acquisition (5-10min) and their ubiquity in most hospitals. If our hypothesis is 23

correct, the use of MRI-based measures of gray matter volume as a metric for target engagement in ABM training would be a relatively easy measure to obtain on a large scale basis. However, an even more feasible option would be to identify a highly correlated metabolite (e.g., salivary or serum BDNF) of the hypothesized structural-level neuroplastic changes. Of course, establishing the validity of MRI-based measures of ABM-related structural plasticity is a necessary prerequisite for this option. Nevertheless, if our hypothesis is correct, structural changes in the BF/eAM and mPFC should provide a stable target measurement to track the recovery of anxiety following ABM treatment. Pre-training attentional bias as a predictor of treatment outcome As mentioned above, there is a growing understanding that the consideration of individual-level factors is needed for the treatment of anxiety with ABM and other treatment options. Anxiety is a complex multifaceted construct and although increased attentional bias to threat has been shown to be a causal factor in the development of anxiety (129), it is unlikely to be the only mechanism by which anxiety develops. Identifying which individuals are likely to benefit from ABM is an important factor for clinicians to consider when determining treatment options. Given that ABM is thought to reduce anxiety by reducing attentional bias, it is intuitive to assume that a preexisting attentional bias is necessary for ABM to be effective. Our pilot data indicate an individual’s pre-training attention bias is an incredibly strong predictor of treatment outcomes. Those with the greatest pre-training bias saw the greatest reductions in anxious symptoms, attention bias, and gray matter in the mPFC and BF/eAm. Although a majority of ABM studies have yet to consider the impact of pre-training bias, a number of recent studies have reported that higher levels of pre-training attentional bias predict greater reduction in 24

attentional bias and in anxiety symptoms (6, 29, 31, 130). Thus, our pilot results add to mounting evidence that a preexisting attention bias is necessary for effective treatment with ABM. Of note, our pilot data suggest that in addition to predicting greater reduction in attentional bias and anxiety symptoms, level of pre-training attentional bias predicts the level of gray matter volume reduction in the BF/eAM and mPFC. This observation has important clinical implications. In particular, the NIMH is interested in identifying methods of adapting interventions to maximize outcome efficacy and develop effective methods of identifying individual- or subgroup-level etiologies that predict outcome efficacy. As mentioned above, anxiety is a diverse set of symptoms—some of which are common to both anxiety and other mood disorders (131). Those lacking an initial bias towards threat would not be expected to benefit from ABM—given that the reduction in attention bias is thought to mediate reductions in anxiety. Beyond the relationship between pre-training bias and reduced behavioral measures of anxiety and attention bias behavior following ABM, we further hypothesize (and provide preliminary support) that pre-existing bias predicts the ABMrelated neuroplasticity within the BF/eAM and mPFC. Thus, in line with the objectives of NIMH, identifying individuals with heightened levels of pre-existing attention bias appears to be a valid method of predicting ABM outcome efficacy. If our hypothesis is supported, clinicians could consider an individual’s pre-training attention bias prior to prescribing ABM as a viable treatment option. Directions for future research to test the hypothesis Although our pilot data provide some promising results, our small study is not sufficiently designed to formally test our hypothesis. Rather, our data only provide proof of 25

principle that gray matter changes following ABM can be observed and that these changes underlie the therapeutic effects of ABM. Future research using a similar methodological approach, but utilizing a larger sample size and a control group of participants not completing ABM, will need to be conducted. Furthermore, this future research should screen participating individuals for both heightened levels of anxiety and a pre-existing attention bias. It is possible, and perhaps inevitable, that anxious populations will react somewhat differently to training than healthy populations. However, the underlying cognitive processes being examined remain constant across populations and therefore our nonclinical sample was appropriate as a pilot study. Our lab has obtained funding to carry out a large-scale study of this nature. However, this general research protocol should be explored in independent lab sites to determine the reliability and generalizability of these findings. If the general hypothesis is supported in a larger sample, the next step would be to examine the timeframe in which these neuroplastic changes occur. That is, longitudinal studies tracking changes in BF/eAm and mPFC gray matter volume across an extended timeframe (e.g., 2-3 months or more) will be needed to determine the optimal length of ABM training to induce maximal neuroplastic changes. Similarly, the extent to which these neuroplastic changes remain after ABM treatment has ended should be assessed across multiple post-treatment scanning sessions. Based on the outcomes of these proposed studies, the duration of effective ABM treatment could be established as well as the extent to which “booster” ABM sessions may, or may not, be needed. Additional future lines of research should aim to establish animal models of attentional bias and bias modification training to establish the cellular level changes following ABM training 26

that are detectable in the MR signal. That is, similar to animal research in which fear learningrelated changes in MRI measures of gray matter volume were linked to dendritic spine density (107), a similar approach is needed following the specific type of learning that accompanies ABM. The dot-probe task has been successfully used in nonhuman primate studies (132) and has received interest as method of measuring emotional processing across species (133). Furthermore, MRI is a promising tool for cross-species studies of neuroplasticity (108, 109) and the usage of this tool in potential animal models of attentional bias and ABM could help inform the cellular-level neuroplastic changes that are detectable in MRI measures of gray matter volume. Furthermore, research should aim to detect easily obtainable correlates of the gray matter-related neuroplasticity that follows ABM. For example, metabolites for BDNF, 5-HT, and other neuroplasticity-related molecules should be identified. Of course, the identification of such measures might be facilitated by the initial identification of these molecules in animal models and later verification in human participants. That is, although the use of MRI is ubiquitous in hospital settings, reliable peripheral biomarkers—highly related to MRI measures of neuroplasticity—would be even more easily obtainable and potentially incorporated into clinical practice. In short, future research should first aim to reproduce our promising preliminary findings that ABM results in neuroplastic changes in BF/eAm and mPFC MRI-based measures of gray matter volume and the degree of change in these measures correlates with reductions in attention bias. Going forward, the timeframe of these changes and the cellular-level mechanisms responsible for them should be explored. Throughout the sections detailing the significance of our hypotheses, we have linked the implications of our hypotheses to NIMH’s 27

RDoC to demonstrate ABM’s therapeutic potential to the mental health community. We are optimistic that scientists in the field will embrace our enthusiasm to further test the possibility that structural change in this extended amygdala network underlies successful ABM outcome. CONCLUSION To conclude, there is convergence across multiple lines of evidence to support the hypothesis that ABM training-related reductions in attentional bias and anxious symptoms are mediated by neuroplastic changes in MRI measures of gray matter in an extended amygdala – PFC network. Neuroplasticity in this network, in the form gray matter reduction, is thought to mediate a reduction in attention bias and in turn, reduce anxiety (see Figure 3). Furthermore, ABM treatment outcome appears to be highly dependent on an individual’s pre-training attention bias, such that those with the greatest initial attention bias will see the greatest changes in attention bias, anxious symptoms, and gray matter volume. Future research is needed to test our hypotheses. Yet, if the hypotheses are correct, these MRI-based neuroplasticity measures could provide a new mechanism-based target measure for ABM outcome in the treatment of anxiety disorders.

28

FIGURES

1. 2.

3.

29

30

Tables Table 1. Table 1. Noteworthy Gray Matter Changes Following ABM Region Increases Anterior Cingulate Cortex Medial Prefrontal Cortex Insula Insula Ventrolateral Prefrontal Cortex Ventrolateral Prefrontal Cortex Dorsolateral Prefrontal Cortex Dorsolateral Prefrontal Cortex Decreases Basal Forebrain Anterior Cingulate Cortex

MNI Coordinates y z

Hemisphere

Voxels

x

t-value

L R L R L R L R

120 139 66 24 54 83 389 59

-8 24 -28 28 -34 48 -38 40

36 60 24 22 52 42 36 36

16 10 2 0 2 0 28 24

3.06 2.66 2.2 2.19 2.55 2.31 3.56 2.42

L-R L

1140 68

-12 -10

2 52

-2 12

5.57 2.8

Table 2. Evidence for Neuroplasticity in an Extended Amygdala-Prefrontal Cortex Network Underlying Attention Bias Modification Training Evidence

Details

Sources

1. Effects of Brain Damage on Attention Bias

Attention-related enhancement of sensory processing afforded to threatening stimuli is dependent on the amygdala.

2. Functional Neuroimaging of Attention Bias

The amygdala, prefrontal cortex, and visual cortex are active during different contrasts in the dot-probe task.

3. Structural Neuroimaging of Attention Bias

Greater connectivity between the amygdala and PFC is related to (64-66) greater levels of attention bias as is ACC gray matter volume.

4. Functional Neuroimaging of Attention Bias Modification

Multiple studies report altered PFC and amygdala activity following ABM training.

(26, 27, 78, 79, 87)

5. Structural Neuroimaging of Attention Bias Modification

ABM training reduced gray matter volume in the PFC and amygdala.

Pilot Data

(36-39)

(44, 49-54, 59, 60)

31

CAPTIONS TO ILLUSTRATIONS Figure 1. Following 6 weeks of attention bias modification (ABM) training, gray matter volume within the basal forebrain/extended amygdala (BF/eAm) & anterior cingulate cortex (ACC) decreased by 5-6%. The reduction in gray matter volume in the BF/eAm & ACC correlates with reduced attentional bias to threat (r = -0.78 & r = -0.79, respectively) Figure 2. Scatterplot showing the relationship between participants’ initial bias scores and change in attention bias following attention bias modification (ABM) training. Greater pretraining bias predicts a greater reduction in attentional bias following training (r = -0.96, r < 0.001). Figure 3. Model illustrating how attention bias modification (ABM) reduces anxiety by weakening attention biases (AB) toward threat, which is mediated by a reduction of gray matter volume in the basal forebrain/extended amygdala (BF/eAm) & anterior cingulate cortex (ACC).

Table 1. Notable evidence implicating an amygdala-prefrontal network underlying ABM. Table 2. Notable changes in gray matter volume following ABM training.

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