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. Author manuscript; available in PMC: 2022 Jun 1.
Published in final edited form as: J Neurovirol. 2021 May 13;27(3):463–475. doi: 10.1007/s13365-021-00981-1

Hypoactivation in the precuneus and posterior cingulate cortex during ambiguous decision making in individuals with HIV

Shana A Hall 1, Sheri L Towe 1, M Tauseef Nadeem 1, Andrea L Hobkirk 2,3, Bennett W Hartley 1, Rosa Li 4, Scott A Huettel 5, Christina S Meade 1,6
PMCID: PMC8276275  NIHMSID: NIHMS1717740  PMID: 33983505

Abstract

People with human immunodeficiency virus (HIV) often have neurocognitive impairment. People with HIV make riskier decisions when the outcome probabilities are known, and have abnormal neural architecture underlying risky decision making. However, ambiguous decision making, when the outcome probabilities are unknown, is more common in daily life, but the neural architecture underlying ambiguous decision making in people with HIV is unknown. Eighteen people with HIV and 20 controls completed a decision making task while undergoing functional magnetic resonance imaging scanning. Participants chose between a certain reward and uncertain reward with a known (risky) or unknown (ambiguous) probability of winning. There were three levels of risk: high, medium, and low. Ambiguous > risky brain activity was compared between groups. Ambiguous > risky brain activity was correlated with emotional/psychiatric functioning in people with HIV. Both groups were similarly ambiguity-averse. People with HIV were more risk-averse than controls and chose the high-risk uncertain option less often. People with HIV had hypoactivity in the precuneus, posterior cingulate cortex (PCC), and fusiform gyrus during ambiguous > medium risk decision making. Ambiguous > medium risk brain activity was negatively correlated with emotional/psychiatric functioning in individuals with HIV. To make ambiguous decisions, people with HIV underrecruit key regions of the default mode network, which are thought to integrate internally and externally derived information to come to a decision. These regions and related cognitive processes may be candidates for interventions to improve decision-making outcomes in people with HIV.

Keywords: HIV, Decision making, Ambiguity, Risk, Functional magnetic resonance imaging (fMRI), Default mode network

Introduction

Approximately 40 million people are living with human immunodeficiency virus (HIV) worldwide, with a steady rate of 1.5 million new infections every year (UNAIDS 2019). An estimated 15–50% of these individuals have HIV-associated neurocognitive disorder (McArthur et al. 2010). Even when HIV disease is well-managed, neurocognitive functioning can be impaired in a range of domains (Simioni et al. 2009). Decision making involves cognitive functioning in multiple cognitive domains. Risky decision making always involves uncertainty about whether a desirable outcome will be achieved. However, sometimes the outcome probability is known (e.g., there is an ~ 15% chance of getting lung cancer in smokers) and sometimes the outcome probability is unknown (e.g., the chance of enjoying food from a new culture is unknown; Ellsberg 1961). The former are referred to as risky decisions, and the latter are referred to as ambiguous decisions, with the latter more typical of real-world decisions. It is established that people with HIV make riskier decisions resulting in lower rewards compared to matched controls (Baker et al. 2014; Fujiwara et al. 2015; Martin et al. 2004; Thames et al. 2012; Vassileva et al. 2013), with alterations in neural mechanisms underlying risky decision making (Connolly et al. 2014; Fujiwara et al. 2015; Meade et al. 2016). However, the neural mechanisms underlying ambiguous decision making have not been investigated in people with HIV.

In neurotypical individuals, risky decision making elicits activity in frontal, parietal, and striatal regions thought to be associated with processing explicit win probabilities (Blankenstein et al. 2017, 2018; Huettel et al. 2005). In individuals with HIV compared to controls, risky decision making can elicit compensatory hyperactivation as well as hypoactivation in response to certain outcomes in frontoparietal, frontostriatal, temporal, and parietal regions, which are typically associated with executive control processes (Connolly et al. 2014; Meade et al 2018; Meade et al. 2016). This indicates that many of the regions typically involved in risky decision making are dysfunctional in individuals with HIV across a variety of risky decision making conditions. Consequently, we expect that ambiguous decision making will elicit abnormal activity in individuals with HIV in neural circuitry that overlaps with regions previously shown to be important for ambiguous decision making. These regions include prefrontal cortices, lateral parietal cortices, precuneus, and temporal cortices, which are associated with forming behavioral plans and evaluating task contexts (Bach et al. 2009; Blankenstein et al. 2018; Huettel et al. 2006; Krain et al. 2006), potentially along with orbitofrontal and amygdala regions associated with using emotional information (Hsu et al. 2005). Furthermore, since ambiguous decision making is thought to involve intuitive, emotional processes (Brand et al. 2008), we predicted that hypoactivity related to ambiguous decision making would be associated with diminished emotional functioning.

The current study focuses on the neural systems involved in ambiguous decision making compared to risky decision making. We used functional magnetic resonance imaging (fMRI) to compare ambiguous decision making with risky decision making including high, medium, and low levels of risk during a gambling task. Consistent with the literature, we predicted that persons with HIV would be more risk-seeking than controls and would have greater ambiguity tolerance (Hardy et al. 2006; Martin et al. 2004), as controls are typically ambiguity averse (Tymula et al. 2012). We also hypothesized that individuals with HIV would have decreased activity in regions typically involved in ambiguous decision making and that increased neural activation in these regions during ambiguous decision making would be predictive of better decision making outside of the scanner. Notably, previous work on decision making in individuals with HIV used tasks in which risky choices are confounded with disadvantageous choices, so it is unclear whether people with HIV are risk-seeking or have difficulty making advantageous choices. To address this gap, we used a task in which the risky and ambiguous choices were often advantageous.

Materials and methods

Participants

The study was open to persons with and without HIV infection who were 18–55 years of age. HIV-positive status was verified by medical record review, while HIV-negative status was verified by an OraQuick© oral rapid test. Participants were excluded for lifetime abuse of illicit drugs other than marijuana, defined as lifetime dependence, history of regular drug use, any use in the past 30 days, or a positive urine drug screen. While alcohol and marijuana use were allowed, participants could not meet criteria for current alcohol or marijuana dependence. Additional exclusion criteria were <8th grade education, illiteracy, severe learning disability, English non-fluency, serious neurological disorders, acute opportunistic brain infections, severe head trauma with loss of consciousness ≥ 30 min and persistent functional decline, severe mental illness (e.g., schizophrenia, bipolar I disorder), or MRI safety contraindications.

Procedures

Participants were recruited via advertisements in local newspapers, websites, community-based organizations, and infectious disease clinics. Potential participants were invited to attend an in-person eligibility screening. Eligible participants returned on another day to complete the MRI brain scan. Participants provided written informed consent, and procedures were approved by the Institutional Review Board at Duke University Health System. Participants were compensated $145 for their participation plus an additional $15 bonus if they had minimal movement during the MRI scan.

Screening measures

Demographics, smoking behavior, and medical history were obtained using an audio computer-assisted self-interview. Substance abuse and impairments were assessed with the Structured Clinical Interview for DSM-IV-TR (First et al. 1996) and the Addiction Severity Index-Lite (McLellan et al. 1992), and timeline follow-back was used to assess the frequency of substance use in the past 90 days (Robinson et al. 2014; Sobell and Sobell 1996). Psychiatric history was assessed with the Mini International Neuropsychiatric Interview (Sheehan et al. 1998). Participants also provided a urine sample for drug and pregnancy testing and a release for their medical records. Medical records were used to confirm no exclusionary conditions and for HIV disease indicators.

FMRI gambling task

To assess neural response to ambiguous and risky choices, we adapted a task developed by Huettel et al. (2006). Participants chose between pairs of gambles presented as pie charts (Fig. 1). Certain gambles had a 100% probability of winning. Risky gambles had a known probability of winning (25% for high risk, 50% for medium risk, and 75% for low risk). Ambiguous gambles had an unknown probability of winning, ranging from 1 to 99%. In each trial, there were two possible combinations of gambles: Risky-Certain or Ambiguous-Certain. In risky trials, the expected values of the uncertain gambles (the potential win multiplied by the probability of winning, or how much the gamble would pay out on average) ranged from $1.50 to $21. The certain values ranged from $3 to $6. The gamble–certain expected value differences ranged from −$3 (certain choice higher expected value than gamble choice) to $15 (gamble choice worth more than certain choice). In ambiguous trials, the expected values of the gambles (the potential win value multiplied by a 50% probability of winning, as a uniform distribution of all possible probabilities would be 50% on average) ranged from $2 to $36. The certain values ranged from $3 to $6. The gamble–certain expected value differences ranged from −$3 to $30. Advantageous trials were those in which the expected value of gamble was worth more than the value of the certain gamble. The gamble had a higher expected value than the certain gamble in 83% of non-catch risky trials and 80% of non-catch ambiguous trials.

Fig. 1.

Fig. 1

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Illustration of the fMRI gambling task. Each trial paired a certain option with a risky or ambiguous option. The risky gambles had a 25% (high risk), 50% (medium risk), or 75% (low risk) chance of winning, while ambiguous gambles had an unknown probability of winning, ranging from 1 to 99%. After choosing, a box appeared around participant’s choice. Each trial was presented for 5.75 s followed by a fixation cross that appeared for an interval jittered from 1.97 to 4.89 s (M = 2.7 s). Each run lasted approximately 5.67 min

To eliminate the possibility that outcomes might influence choices on subsequent trials, gambles were not resolved during the MRI scan. To make the task incentive compatible, participants were informed that their choice on one randomly selected trial after the scanning session would be honored as a bonus payment up to $5, scaled to the amount won. Risky and ambiguous gambles were decided using a computer program. Payment for task performance was scaled as follows: total winnings of $31–$100 = $5 bonus, $20–$30 = $4 bonus, $13–$19 = $3 bonus, $8–$12 = $2 bonus, and $1–$7 = $1 bonus. This task was programmed in MATLAB (Mathworks, Natick, MA) using the Psychtoolbox extension (www.psychtoolbox.org). Visual stimuli were projected onto a screen behind the scanner bore, and responses were recorded using a four-button response box held in the right hand.

The scanning session consisted of three runs of 40 trials each presented in random order. There were 96 risky-certain trials (32 each of high, medium, and low risk) and 24 ambiguous-certain trials. Included were 12 catch trials (8 risky, 4 ambiguous) in which the value of the certain choice was equal to or greater than the value of the uncertain choice, and therefore, choosing the uncertain choice was irrational. Catch trials were included to measure comprehension and task engagement.

Symptom checklist-90-R

Psychiatric symptoms were assessed using the Symptom Checklist-90-R (SCL; Derogatis 1983). The scale has 90 items assessing the amount of distress experienced over the past week. Each item is on a 5-point scale. The Global Severity Index (GSI) combines information on the number of symptoms and the intensity of these symptoms to create a summary measure of symptomology.

MRI data acquisition

Functional and structural MRI data were acquired using a 3.0-T GE scanner with an 8-channel head coil. Whole-brain blood-oxygen-level-dependent (BOLD) images were collected using high-throughput T2*-weighted echo-planar imaging with the following parameters: TR = 2000 ms, TE = 25 ms, FOV = 240 mm2, flip angle = 90°, and in-plane matrix = 64 × 64. This resulted in functional data from 35 axial slices with voxels of 3.75 mm × 3.75 mm × 3.80 mm. High-resolution T1-weighted (T1w) structural images were acquired with the following parameters: TR = 8.096 ms, TE = 3.18 ms, FOV = 256 mm2, flip angle = 12°, and in-plane matrix = 256 × 256. This resulted in anatomical data with 166 axial slices of 1-mm3 voxels.

MRI data processing

The data were processed with fMRIprep and FMRIB Software Library (FSL; see Supplemental Materials).

Data quality assurance

The following data quality measures were conducted: (1) all participants had framewise displacement <0.35 mm and were retained, (2) three participants were excluded for incorrect choices on three or more of the catch trials (i.e., choosing the uncertain gamble when the certain choice had a higher outcome), and (3) runs with 10 or more no-response trials were excluded, resulting in the exclusion of one run for one participant and two runs for a second participant.

Data analysis

Demographic data

All demographic and behavioral analyses were implemented in SPSS 26 (IBM SPSS Statistics; Chicago, IL). Demographic data were compared using independent-samples t-tests and chi-square analyses as appropriate.

FMRI gambling task behavioral data

Independent-samples t-tests were used to compare the groups on the proportion of uncertain choices by trial type using SPSS 26. We then modeled the parameters for risk and ambiguity preference as a metric of each individual’s preference (tolerance) for risk and ambiguity, which weights the amount of potential money earned by the individual’s preference for risk and ambiguity. The equations and other details for the risk and ambiguity parameters (β and α, respectively) are in the Supplemental Materials but in brief, when β = 1, the individual is risk neutral; when β >1, risk seeking; and when β <1, risk averse. For ambiguity, we used the risk β, estimated from the non-catch risky-certain trials, to estimate the ambiguity parameter, estimated from non-catch ambiguous-certain trials. When α = 0.5, the individual is ambiguity neutral, such that choices were made as if the ambiguous gamble had a 50% chance of winning or losing; when α >0.5, ambiguity averse; and when α <0.5, ambiguity seeking. We present the ambiguity parameter as the quantity 1 − α so that an increase in the ambiguity parameter indicates an increase in ambiguity preference, like the risk parameter. Risk and ambiguity parameters were cube root transformed to correct for skew in the original data.

To identify risk and ambiguity aversion, we used two-tailed, one-sample t-tests to compare β and 1 − α to their respective neutral points (1 and 0.5, respectively) in each group separately. We then ran independent samples t-tests to compare the risk and ambiguity parameters between groups.

Because a strength of our task was that the risky/ambiguous choice was advantageous on the majority of trials, we also ran all behavioral analyses including only such trials to confirm that our behavioral results were not being driven by the few trials in which it was disadvantageous to choose the risky/ambiguous choice.

FMRI data

All analyses were conducted in FSL (v5.0; Jenkinson et al. 2012). The decision phase of each trial was modeled, which included the time from the presentation of the gambles to choice selection. At the first level, separate regressors were included for trial type (ambiguous, high risk, medium risk, and low risk). No-response trials were excluded from these regressors of interest. We also included three regressors of no interest: choice (mean centered, excluding no-response trials), no-response trials, and the difference in expected value between the two options (mean centered). Theoretically, if expected value is controlled for, then rational decision makers would have similar brain activity for ambiguous and risky decisions. However, there are still differences between risk and ambiguous trials in the subjective value, that is, the value of each choice to each individual. Therefore, by controlling for the difference in expected value, brain activity is reflecting the cognitive processes that differ in evaluating ambiguous and risky decisions as well as the differences in subjective value that are not driven by differences in the objective expected value. All regressors were convolved with a double-gamma HRF function. Three contrasts were examined: ambiguous > high risk, ambiguous > medium risk, and ambiguous > low risk. At the second level, data from individual runs were combined using a fixed-effects analysis (Beckmann et al. 2003). Group effects were tested using an independent samples t-test modeled with mixed-effects (FLAME 1 + 2). Voxels were considered significantly active if they had a minimum Z-score of 2.3. The cluster threshold was p <0.05, corrected within a gray matter mask. For group comparisons, only voxels with a Z-score above 0 in both groups were considered.

Brain/behavior relationship

To examine the relevance of group differences in neural activation during the fMRI Gambling Task to emotional/psychiatric functioning, we used Pearson coefficient to examine the correlation between the percent signal change in clusters showing significant group differences and the SCL GSI in persons with HIV.

Results

Sample characteristics

There were 38 participants, 18 with HIV and 20 without HIV. The mean age was 42.63 (SD = 8.62). The majority was male (65.79%) and African American (71.05%). There were no group differences in sample characteristics (see Table 1).

Table 1.

Sample characteristics

HIV (N = 18) Control (N = 20) Statistic p-value
Demographic characteristics
 Male, %, N 61.11%, 11 70%, 14 χ2(1) = 0.33 0.56
 Age in years, M (SD) 43.06 (8.48) 42.25 (8.94) t(36) = 0.28 0.78
 Race χ2(2) = 1.08 0.58
 African American, % 77.78% 65.00%
 Caucasian, % 16.67% 20.00%
 Other/Mixed, % 5.56% 15.00%
 Education in years, M (SD) 13.78 (2.32) 13.65 (2.56) t(36) = 0.16 0.87
Substance use characteristics
 Alcohol use in past 30 days, % 44.44% 55.00% χ2(1) = 0.42 0.52
 Marijuana use in past 30 days, % 0.00% 5.00% χ2(1) = 0.92 0.34
 Daily cigarette use in past 30 days, % 22.22% 15.00% χ2(1) = 0.33 0.57
Current psychiatric diagnoses
 Major depressive disorder, % 5.56% 0.00% χ2(1) = 1.14 0.29
 Anxiety Disorders, % 5.56% 5.00% χ2(1) = 0.01 0.94
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HIV human immunodeficiency virus, M mean, SD standard deviation

Participants with HIV had been diagnosed with HIV for a mean of 12.72 years (SD = 7.61). All were currently in HIV care and were prescribed antiretroviral medications. At the time of the study, 77.78% had a suppressed viral load at 50 copies/mL, and the median most recent CD4 count was 520.50 (IQR = 520). The median nadir CD4 count was 192.50 (IRQ = 300), with 55.56% having a nadir <200 indicative of AIDS.

Ambiguity task behavior

FMRI gambling task performance

There were no group differences in the proportion of uncertain choices for ambiguous, low risk, or medium risk trials. There was a significant group difference for high risk trials, with the control group picking the uncertain choice more often than the HIV group (see Table 2).

Table 2.

Behavioral task performance

HIV+ (N = 18) HIV− (N = 20) t-test p-value
Quality assurance measures
 In-scanner mean displacement, M (SD) 0.18 (0.07) 0.16 (0.06) t(36) = 0.77 0.45
 Proportion of missed trials, M (SD) 0.01 (0.01) 0.01 (0.01) t(36) = 1.71 0.10
Proportion of uncertain choices
 Ambiguous trials, M (SD) 0.44 (0.36) 0.49 (0.30) t(36) = −0.46 0.65
 Low-risk trials, M (SD) 0.60 (0.32) 0.64 (0.26) t(36) = −0.42 0.68
 Medium-risk trials, M (SD) 0.55 (0.33) 0.66 (0.29) t(36) = −1.26 0.22
 High-risk trials, M (SD) 0.25 (0.33) 0.55 (0.33) t(36) = −2.79 0.008
Proportion of uncertain choices: advantageous trials only
 Ambiguous trials, M (SD) 0.52 (0.42) 0.58 (0.34) t(36) = −0.51 0.61
 Low-risk trials, M (SD) 0.28 (0.36) 0.65 (0.38) t(36) = −1.44 0.43
 Medium-risk trials, M (SD) 0.56 (0.38) 0.72 (0.30) t(36) = −0.79 0.16
 High-risk trials, M (SD) 0.64 (0.33) 0.72 (0.26) t(36) = −3.10 0.004
Risk and ambiguity preference
 Risk parameter (β), Median (SD) 0.49 (1.01) 0.99 (1.56) t(36) = −2.46a 0.02
 Ambiguity parameter (1 − α), Median (SD) 0.30 (1.60) 0.20 (1.17) t(36) = −0.47a 0.64
Risk and ambiguity preference: advantageous trials only
 Risk parameter (β), Median (SD) 0.49 (0.99) 0.93 (1.51) t(36) = −2.61a 0.01
 Ambiguity Parameter (1 − α), Median (SD) 0.36 (1.83) 0.22 (1.70) t(36) = 0.12a 0.90
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a

Parameters were cube transformed to improve normality prior to conducting group comparisons

Risk and ambiguity parameters

Participants with HIV demonstrated significant risk aversion (t(17) = −2.46, p <0.05), while the control group did not (t(19) = 0.88, p >0.05; see Table 2). This same pattern held when only advantageous trials were included (t(17) = −2.57, p <0.05; t(19) = 1.00, p >0.05). Counter to our expectations, the HIV group was more risk-averse compared to the control group. In contrast, participants in both groups demonstrated significant ambiguity aversion (t(17) = −4.75, p <0.001; (t(19) = 6.14, p <0.001), with no difference between groups.

Task-related brain activity

Figure 2 and Tables S1–3 show the activation patterns for the three ambiguous > risk contrasts. For each, there was activity in middle frontal gyrus, inferior frontal gyrus, superior parietal lobule, angular gyrus, precuneus, posterior cingulate cortex (PCC), and regions throughout the occipital lobes.

Fig. 2.

Fig. 2

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Activity associated with ambiguous > risk choices across both the HIV and control groups across each risk category. a Ambiguous > high risk. b Ambiguous > medium risk. c Ambiguous > low risk

Group differences in ambiguous > risk brain activation

Significant group differences were evident in the ambiguous > medium risk contrast only. Participants in the control group demonstrated greater activity than those in the HIV group in the precuneus, PCC, fusiform gyrus, and occipital cortex (see Fig. 3; Table 3). While controls exhibited increased ambiguous > medium risk differences in these regions, this increase was not observed in participants with HIV.

Fig. 3.

Fig. 3

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Ambiguous > medium risk activity that is greater in controls than in individuals with HIV. a Activity in the precuneus, PCC, and fusiform gyrus cortex are highlighted b BOLD % signal change for ambiguous and risk trials in both groups in the precuneus, PCC, and fusiform cortex. Error bars represent standard error

Table 3.

Clusters eliciting ambiguous > medium risk brain activity for controls > individuals with HIV

No. of voxels Z-score x y z Name of region(s) Laterality
865 3.9 0 −68 54 Precuneus cortex Left/Right
Lateral occipital cortex Left
666 3.9 4 −36 28 Posterior cingulate gyrus Left/Right
Precuneus cortex Right
329 3.65 44 −64 −14 Lateral occipital cortex Right
Fusiform gyrus Right
138 3.58 26 −90 30 Occipital pole Right
89 3.45 46 −84 8 Lateral occipital cortex Right
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Correlations between ambiguity > medium risk brain activity and emotional/psychiatric functioning

Data were missing for one participant for the SCL GSI. In participants with HIV, the mean GSI score was 0.373 (0.338). We correlated ambiguous > medium risk brain activity in the precuneus, PCC, and fusiform gyrus with the GSI in the HIV group. There was a significant negative correlation between brain activity in the precuneus and PCC and GSI (Fig. 4), such that a lower ambiguous > risk BOLD signal difference was associated with higher GSI scores/worse psychiatric functioning.

Fig. 4.

Fig. 4

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Correlations between the Symptom Checklist-Revised Global Severity Index and ambiguous > medium risk brain activity in the a precuneus, b PCC, and c fusiform gyrus in the HIV group

Discussion

To our knowledge, this is the first study to investigate neural activity associated with ambiguity processing in individuals with HIV. Counter to our predictions, people with HIV were more risk averse than controls, but both groups demonstrated ambiguity aversion. Across both groups, ambiguous decision making compared to risky decision making elicited greater activity in lateral and medial frontal and parietal regions. Critically, participants with HIV had hypoactivation during ambiguous versus medium-risk decisions in the precuneus, PCC, and fusiform gyrus. Lastly, hypoactivation in the precuneus and PCC was predictive of increased psychiatric/emotional dysfunction in people with HIV.

The activity elicited by ambiguous decisions > risky decisions in both groups was widely distributed across the brain. This is consistent with previous work done on ambiguous decision making (Blankenstein et al. 2018; Huettel et al. 2006; Krain et al. 2006; Poudel et al. 2020) and suggests that the neural architecture supporting ambiguous decision making is largely intact in persons with HIV. This activity spans multiple networks including frontoparietal regions and the default mode network (Greicius et al. 2003; Kim 2012), indicating that ambiguous decision making may employ a variety of cognitive processes.

Though the neural architecture is largely intact in persons with HIV, there were group differences in activity in the precuneus, PCC, and fusiform gyrus, driven primarily by HIV-related hypoactivation during ambiguous decisions. In previous work, we showed similar HIV-related hypoactivation of the precuneus and fusiform gyrus in response to increasing values of monetary loss on a gambling task (Meade et al. 2018). HIV has also been linked to hypoactivation in the PCC during the IGT, which contributes to worse task performance (Smith et al. 2018) and hypoactivation during safe choices on gambling tasks (Connolly et al. 2014). The precuneus and PCC are central nodes in the default mode network (DMN), a network deactivated during task performance and thought to be involved in mind-wandering and self-referential processing (Addis et al. 2004; Buckner and DiNicola 2019; Fransson 2005; Fransson and Marrelec 2008; Raichle et al. 2001; Utevsky et al. 2014). Together, these findings suggest that HIV may be associated with neural hyposensitivity to a growing list of decision-making contexts, and that during ambiguous decision making, the DMN may be especially impaired.

The precuneus and PCC are also connector hubs, meaning that they have strong connectivity across the brain (Cole et al. 2010; Tomasi and Volkow 2011). Though DMN regions are often deactivated during task performance, they are connected with task-related networks and are thought to be engaged in integrating self-referential and externally derived cognitive states (Cavanna and Trimble 2006; Leech et al. 2012; Utevsky et al. 2014). During decision making, they are thought to integrate information from multiple sources to resolve uncertainty (Paulus et al. 2001). They both have enhanced activity when uncertain choices are made (Causse et al. 2013; Krain et al. 2006; Krug et al. 2014; McCoy and Platt 2005), with increased activity during judgments based on approximations rather than exact calculations (Labudda et al. 2008), and increased activity associated with the integration between exploitation (the use of information already possessed by an individual) and exploration (the process of gathering new information; Pearson et al. 2009). In the context of ambiguous decision making, HIV-related functional deficits in these hub regions may relate to difficulty integrating information from internal and external sources to come to a decision. Risky decisions, by contrast, may not necessitate information from such a variety of sources. This is consistent with the idea that ambiguous decision making relies on intuitive decision making strategies (Brand et al. 2008), which may require contribution from multiple cognitive processes, whereas risky decision making relies on calculative strategies (Brand et al. 2008), which may be associated with more narrow set of cognitive processes (Brand et al. 2009).

With regard to the relationship between neural hypoactivation and behavioral outcomes, we did not find a difference in the proportion of uncertain ambiguous choices made during the task between people with HIV and controls. This is consistent with prior research showing similar performance between people with HIV and controls on a task with unknown outcome probabilities (Hardy et al. 2006; Martin et al. 2004) and suggests that hypoactivity may not be related to ambiguous decision making outcomes. However, there was a correlation between hypoactivity in the precuneus/PCC during ambiguous vs. medium-risk trials and psychiatric symptomology in people with HIV. The intuitive decision making strategies thought to underlie ambiguous decision making include emotional processes (Brand et al. 2008), which suggests that people with HIV who have more psychiatric/emotional impairment are less likely to recruit these regions to make decisions under ambiguity. Not only have these regions have been found to be active during affective decision making (Farrell et al. 2014; Northoff et al. 2006), in addition, a body of work shows that ambiguous decision making is impacted in a variety of affective neuropsychiatric conditions like problem gambling, schizophrenia, obsessive–compulsive disorder, pathological gambling, anxiety, and alexithymia (Brevers et al. 2012a; Fond et al. 2013; Kim et al. 2015; Starcke et al. 2010; Trotzke et al. 2015; Zhang et al. 2015, 2017). Though ambiguous decision making behavior was not impacted in people with HIV, this does suggest that emotional impairments that can accompany HIV may alter the cognitive processes underlying ambiguous decision making, which may be reflected in neural hypoactivity. Future work may reveal situations in which these altered cognitive processes may impact ambiguous decision making outcomes, especially in individuals with emotional difficulties.

It is worth noting that group differences were apparent in ambiguous > medium risk decisions but not in ambiguous > high or low risk decisions. We hypothesize that this occurred because controls may treat high- and low-risk trials as similar to ambiguous trials whereas medium-risk decisions require different cognitive processes. Being presented with a low or high chance of winning may elicit an intuitive impulse to reject/take the risky choice, similarly to the intuition that drives the decision to accept or reject an ambiguous decision. However, a medium risk, 50% chance of winning may not elicit a strong emotional response. This would mean that in controls, the ambiguous > high/low risk contrast would reveal little difference, similar to people with HIV. This is particularly notable because rational behavior would dictate that the ambiguous decisions are treated as a 50% chance of winning and, ergo, would be equivalent to the medium-risk decisions. However, as investigating differences in neural activity between levels of risk was beyond the scope of this study, this hypothesis is purely speculative.

Finally, although the primary aim of the current study was to assess HIV-related effects on ambiguity and not risk, the task that we used enabled us to make an important distinction between risky and disadvantageous choice behavior. Previous work has concluded that people with HIV are more risk-preferring than controls (Fujiwara et al. 2015; Hardy et al. 2006; Iudicello et al. 2013; Martin et al. 2004). However, in most decision making tasks, the riskier choices are also the disadvantageous choices, unlike our task. To our knowledge, this is the first study showing risk aversion rather than preference among people with HIV, even when it is wholly advantageous to make risky choices. Poor executive function and numeracy abilities are thought to underlie disadvantageous risk taking behavior (Brand et al. 2008; Dieckmann et al. 2015; Reyna et al. 2009). People with HIV typically have worse cognitive functioning, including executive functioning abilities (Heaton et al. 2010, 2011) and people with HIV who have cognitive impairment have lower numeracy abilities (Waldrop-Valverde et al. 2010). Our results provide support for an alternative explanation that people with HIV are not inherently risk-preferring but rather, have difficulty discerning the advantageous choice. These findings warrant further investigation into the dissociation between the neural mechanisms underlying risky behavior and disadvantageous behavior.

Strengths/limitations

The unique task design makes several important contributions to the literature: (1) the advantageous choice is the uncertain/risky choice, as discussed; (2) participants are told the level of risk in the risky conditions, thus eliminating the need for learning, which may confound results in the literature found using the IGT; (3) unlike many other risky decision making paradigms, participants cannot lose money, which elicits different neural activity than gaining money (Murty et al. 2016) and may be differentially disrupted in HIV; and (4) there is no feedback that would allow participants to learn the probability of obtaining the reward during ambiguous decisions. There are also several limitations. The sample size is relatively small and we may be underpowered to detect ambiguous > risk effects in all risk conditions. The small sample size may be particularly consequential for the HIV group as there is increased variability in neural activity, possibly due to the heterogeneity of the disorder. However, we still observed robust effects consistent with previous literature. Furthermore, differences in brain activity in clinical populations have also been detected during decision making tasks using similar or smaller group sizes (e.g., Brevers et al. 2012b; Meade et al. 2016). Also, though the increased variability in the HIV group could theoretically mask ambiguity > risk activity that could be detectable with a larger sample, we do not believe this is the case in our sample. Whereas the precuneus and PCC had more activity for ambiguous than medium risk trials in controls, this pattern of activity was reversed in people with HIV, with greater activity in medium risk trials than ambiguous trials. Though this difference was not significant, this suggests that the increased variance in the HIV group was not masking an ambiguous > risk effect in these regions. However, future work with bigger samples may reveal ambiguous > risk activity in other regions people with HIV that we could not detect. Last, as this study was cross-sectional, we do not know whether group differences were pre-existing or whether they were caused by HIV. Future longitudinal work must be done to address this question.

Conclusions

HIV-related hypoactivity in the precuneus and PCC hub regions during ambiguous decision making suggests that people with HIV have deficits in their ability to integrate internal and external information while making complex ambiguous decisions. This may impact the downstream ability to learn outcome probabilities and make advantageous decisions accordingly. Interventions aimed at training individuals to incorporate multiple sources of information during ambiguous decision making could help prevent individuals with HIV from developing maladaptive decision making behaviors. Future work identifying the specific cognitive processes involved in ambiguous decision making in controls and in individuals with HIV and their relationship with brain activation would support these conclusions and would help to refine such interventions.

Supplementary Material

Supplementary Materials

Acknowledgements

We would like to thank all participants for agreeing to take part in this research. We would also like to thank Ryan P. Bell for his assistance with data management and processing.

Funding

This work was supported by the National Institute on Drug Abuse of the National Institutes of Health (R21-DA036450, DP2-DA040226).

Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s13365-021-00981-1.

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