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. Author manuscript; available in PMC: 2024 Oct 31.
Published in final edited form as: Neurobiol Dis. 2024 Sep 24;201:106680. doi: 10.1016/j.nbd.2024.106680

People with HIV exhibit spectrally distinct patterns of rhythmic cortical activity serving cognitive flexibility

Katherine K Landler a,1, Mikki Schantell a,b,1, Ryan Glesinger a, Lucy K Horne a, Christine M Embury a, Jake J Son a,b, Yasra Arif a, Anna T Coutant a, Grant M Garrison a, Kellen M McDonald a,c, Jason A John a, Hannah J Okelberry a, Thomas W Ward a,c, Abraham D Killanin a,b, Maureen Kubat d, Renae A Furl d, Jennifer O’Neill d, Sara H Bares d, Pamela E May-Weeks e, James T Becker f, Tony W Wilson a,b,c,*
PMCID: PMC11525061  NIHMSID: NIHMS2031286  PMID: 39326464

Abstract

Despite effective antiretroviral therapy, cognitive impairment remains prevalent among people with HIV (PWH) and decrements in executive function are particularly prominent. One component of executive function is cognitive flexibility, which integrates a variety of executive functions to dynamically adapt one’s behavior in response to changing contextual demands. Though substantial work has illuminated HIV-related aberrations in brain function, it remains unclear how the neural oscillatory dynamics serving cognitive flexibility are affected by HIV-related alterations in neural functioning. Herein, 149 participants (PWH: 74; seronegative controls: 75) between the ages of 29–76 years completed a perceptual feature matching task that probes cognitive flexibility during high-density magnetoencephalography (MEG). Neural responses were decomposed into the time-frequency domain and significant oscillatory responses in the theta (4–8 Hz), alpha (10–16 Hz), and gamma (74–98 Hz) spectral windows were imaged using a beamforming approach. Whole-brain voxel-wise comparisons were then conducted on these dynamic functional maps to identify HIV-related differences in the neural oscillatory dynamics supporting cognitive flexibility. Our findings indicated group differences in alpha oscillatory activity in the cingulo-opercular cortices, and differences in gamma activity were found in the cerebellum. Across all participants, alpha and gamma activity in these regions were associated with performance on the cognitive flexibility task. Further, PWH who had been treated with antiretroviral therapy for a longer duration and those with higher current CD4 counts had alpha responses that more closely resembled those of seronegative controls, suggesting that optimal clinical management of HIV infection is associated with preserved neural dynamics supporting cognitive flexibility.

Keywords: Hierarchical cognitive control, Perceptual feature matching, Higher-order cognition, Oscillations, Magnetoencephalography

1. Introduction

HIV is known to cross the blood-brain barrier and affect neural processes early in the course of infection, with a large literature supporting structural and functional changes in the brain that are detrimental to cognition (Alagaratnam and Winston, 2022; González-Scarano and Martín-García, 2005; Manji and Miller, 2004). Viral suppression of HIV is associated with significantly improved health, including performance on cognitive tasks and activities of daily living (Anderson et al., 2019). Since the advent of effective antiretroviral therapies (ART), the prevalence of the most severe forms of HIV-associated neurocognitive disorders (HAND) has substantially diminished (Cysique and Brew, 2009; Heaton et al., 2015). Despite this clinical success, people with HIV (PWH) remain at a much higher risk of developing deficits in cognitive function relative to seronegative individuals (Antinori et al., 2007; Cysique and Brew, 2009; Dastgheyb et al., 2019; Gannon et al., 2011; Grant et al., 2014; Heaton et al., 2010, 2011; Moschopoulos et al., 2024; Robertson et al., 2007; Rubin and Maki, 2019; Simioni et al., 2010; Sundermann et al., 2018; Trunfio et al., 2024; Winston and Spudich, 2020). These deficits span across multiple cognitive domains, with phenotypes of cognitive impairment among virally suppressed PWH being largely driven by deficits in attention and executive functions (Dastgheyb et al., 2019; Wang et al., 2017; Winston and Spudich, 2020), which can have substantial ramifications given the importance of intact executive functions for day-to-day behavior, including managing one’s healthcare-related needs and adhering to medications such as ART (Ettenhofer et al., 2010).

Broadly, executive functions support goal-directed behaviors via top-down effects on posterior and subcortical regions (Diamond, 2013; Miller, 2000; Norman and Shallice, 1986; Yuan and Raz, 2014), which are implemented through distributed network-level interactions involving the prefrontal, cingulate, and parietal cortices (Cabeza and Nyberg, 1997; Cavanagh and Frank, 2014; Pastötter et al., 2013; Yuan and Raz, 2014). In particular, oscillations in the theta, alpha, and gamma bands in association cortices are critical for supporting executive functions (Arif et al., 2021; Busch et al., 2009; Cavanagh and Frank, 2014; Dietz et al., 2023; Dijk et al., 2008; Forte et al., 1999; Gupta et al., 2012; Händel et al., 2011; Jensen et al., 2012; Klimesch, 2012; Klimesch et al., 1998; Landau and Fries, 2012; McDonald et al., 2024; Rajan et al., 2019; Sauseng et al., 2010; Schantell et al., 2024b, 2024a, 2024c, 2022a; Spaak et al., 2014; Wiesman et al., 2017). Furthermore, task difficulty may modulate these oscillatory responses during task performance (Gevins et al., 1997; Sauseng et al., 2002).

One component of executive function is cognitive flexibility, which refers to the ability to dynamically adapt one’s behavior in response to changing environmental demands (Armbruster et al., 2012; Dajani and Uddin, 2015; Scott, 1962). The architecture and dynamic coordination of neural oscillatory responses subserving cognitive flexibility are likely to be particularly vulnerable to HIV-related neurological changes, as prior studies have shown widespread reductions in gray matter, in conjunction with differences in functional connectivity in the temporo-parietal regions relative to seronegative individuals (Becker et al., 2012a; Lew et al., 2021; O’Connor et al., 2018; Sanford et al., 2017; Schantell et al., 2021). Many studies have shown that PWH exhibit aberrations in oscillatory theta, alpha, beta, and gamma dynamics serving basic sensory processing across a range of modalities, as well as higher-order attention and other cognitive processes (Arif et al., 2020; Becker et al., 2012b; Casagrande et al., 2021; Lew et al., 2018; Schantell et al., 2022b; Spooner et al., 2022, 2020, 2018; Wiesman et al., 2018; Wilson et al., 2013a, 2013b). Such oscillatory aberrations scale with key clinical indices of HIV health and behavioral performance on cognitive tasks. These data suggest that PWH who exhibit altered CNS structure and function may use compensatory processes to meet task demands (Casagrande et al., 2021; Chang et al., 2008; Ernst et al., 2009; Lew et al., 2018; Schantell et al., 2022b; Spooner et al., 2022; Wiesman et al., 2018); however, whether such findings relate to cognitive flexibility remains unknown.

In the current study, we leveraged a novel perceptual feature matching task and the excellent spatiotemporal precision of magnetoencephalography (MEG) to investigate the neural oscillatory dynamics serving cognitive flexibility in a large sample of PWH and demographically similar seronegative controls. In addition to quantifying the neural dynamics, we examined the relationship between oscillatory signatures of HIV-related neural alterations, behavioral metrics, and clinical indices of HIV. We hypothesized that PWH would exhibit aberrant oscillatory activity during the cognitive flexibility task and that these differences would scale with clinical measures of HIV disease and behavioral indices.

2. Materials and methods

2.1. Participants

A total of 149 adults (74 PWH, 75 seronegative controls) were enrolled as part of a study examining the Research Domain Criteria (RDoC) framework in the context of neuroHIV (R01-MH118013; see Table 1). PWH were required to be on an effective ART regimen and have an HIV-1 RNA viral load of less than 50 copies/mL within three months of participation in the study. All controls were confirmed seronegative using the OraQuick ADVANCE® Rapid HIV-1/2 Antibody Test at the time of neuropsychological testing. Exclusion criteria for the study included any major diagnosed neurological or psychiatric disorder (except HAND), any medical illness associated with CNS dysfunction (other than HIV), history of head trauma, current substance use disorder, the presence of metallic implants that could affect MEG signal quality or be an MRI safety hazard, and pregnancy. Additionally, we excluded participants who had artifactual or missing MEG data, or had incidental findings (e.g., tumor).

Table 1.

Participant demographics and neuropsychological profiles.

HIV
(n = 64)		 Controls
(n = 73)		 Effect
size		 P
value
Demographics
 Age (years) 51.28 (11.80) 51.36 (10.01) d = −0.007 .968
 Sex at Birth (n) 59 Male / 5 Female 64 Male / 9 Female V = 0.074 .384
 Race (n) 49 White / 10 Black / 5 Other 63 White / 6 Black / 4 Other V = 0.160 .477
 Ethnicity (n) 57 Not Hispanic / 7 Hispanic / 0 Unknown 68 Not Hispanic / 4 Hispanic / 1 Unknown V = 0.127 .332
 Smoking Status (n) 21 Current Smokers / 16 Past Smokers / 26 Nonsmokers / 1 Unknown 3 Current Smokers / 25 Past Smokers / 42 Nonsmokers / 3 Unknown V = 0.380 < .001
HIV Clinical Metrics
 CD4 Nadir (cells/μL) 268.88 (169.51) – – –
 Current CD4 (cells/μL) 800.31 (351.01) – – –
 Years Since HIV Diagnosis 16.93 (10.20) – – –
 Years on ART 14.31 (8.54) – – –
Neuropsychological Performance
 Learning T-Score 48.53 (8.79) 50.31 (7.50) d = −0.218 .206
 Memory T-Score 48.53 (8.18) 49.88 (7.60) d = −0.171 .327
 Executive Function T-Score 52.11 (7.03) 55.25 (5.96) d = −0.484 .006
 Processing Speed T-Score 47.45 (7.03) 49.15 (8.01) d = −0.225 .194
 Attention T-Score 48.47 (7.10) 54.61 (8.79) d = −0.764 < .001
 Language T-Score 52.42 (7.74) 50.41 (9.74) d = 0.227 .192
 Motor Dexterity T-Score 48.05 (8.97) 51.20 (9.97) d = −0.331 .058
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Note. Means and standard deviations for the final sample are displayed for each continuous variable. Cohen’s d is the effect size reported for continuous variables, and Cramér’s V is reported for categorical variables. Domain T-scores were calculated by averaging the T-scores from the individual assessments comprising each cognitive domain. ART: Antiretroviral therapy.

2.2. Standard Protocol Approvals and Participant Consents

The Institutional Review Board at Boys Town National Research Hospital reviewed and approved this investigation. Participants provided written informed consent following a detailed description of the study, which was conducted in accordance with the Declaration of Helsinki.

2.3. Neuropsychological Assessment

Cognitive function was measured using a neuropsychological battery that assessed premorbid function and functionality across seven domains (i.e., learning, memory, executive function, attention, processing speed, language, and motor dexterity). The battery included the following tests for each domain: learning (Wechsler Memory Scale [WMS-IV] Logical Memory I (Wechsler, 2009), Brief Visuospatial Memory Test – Revised [BVMT-R] Total Recall (Benedict, 1997), Hopkins Verbal Learning Test – Revised [HVLT-R] Total Recall (Brandt and Benedict, 2001)); memory (WMS-IV Logical Memory II (Wechsler, 2009), HVLT-R Delayed Recall (Brandt and Benedict, 2001), and BVMT-R Delayed Recall (Benedict, 1997)); executive function (Stroop Color Word Test Interference Trial (Golden and Freshwater, 2002) and Trail Making Test Part B (Heaton et al., 2004)); processing speed (Stroop Color Word Test, Color and Word Trials (Golden and Freshwater, 2002), Wechsler Adult Intelligence Scale [WAIS-IV] Coding (Wechsler, 2008), and Trail Making Test Part A (Heaton et al., 2004)); attention (WAIS-IV Letter-Number Sequencing (Wechsler, 2008), WAIS-IV Digit Span (Wechsler, 2008)); language (Boston Naming Test (Heaton et al., 2004; Kaplan et al., 2001), phonemic (FAS) and semantic (Animals) verbal fluencies (Heaton et al., 2004); and motor dexterity (Grooved Pegboard, dominant and non-dominant hands (Heaton et al., 2004; Matthews and Klove, 1964)). Further, premorbid function was assessed using the Wide Range Achievement Test, Fourth Edition (WRAT-4) Word Reading (Wilkinson and Robertson, 2006). Finally, perceived functional impairment was quantified using a modified version of the Lawton and Brody Activities of Daily Living Scale (Lawton and Brody, 1969).

Demographically corrected scores (T-scores) for each assessment were obtained using published normative data (Benedict, 1997; Brandt and Benedict, 2001; Golden and Freshwater, 2002; Heaton et al., 2004; Kaplan et al., 2001; Matthews and Klove, 1964; Wechsler, 2009, 2008; Wilkinson and Robertson, 2006). Domain composite scores were calculated by averaging the T-scores of assessments that comprised each respective cognitive domain (Table 1). Cognitive impairment was assigned per the Frascati criteria (Antinori et al., 2007) by a board-certified clinical neuropsychologist using the composite domain T-scores.

2.4. Experimental Paradigm

Participants completed a 15-min executive function task aimed at assessing cognitive flexibility by matching stimuli based on perceptual feature cues (Fig. 1). Participants were seated in a magnetically-shielded room and were instructed to fixate on a centrally located crosshair with a variable ISI (range: 1500–1900 ms) followed by the presentation of a set of three images for 2700 ms, one of which was presented centrally above the crosshair and surrounded by either a square or a diamond. The instructions pertaining to the meaning of the surrounding squares versus diamonds were counterbalanced across conditions and groups (χ2 = 0.02, p = .900) to eliminate potential confounding effects. For one set of instructions, the squares indicated that participants should select which of the bottom two images matched the top image based on its shape, while the diamonds indicated that participants should select which of the bottom two images matched the top image based on its pattern. In the other set of instructions, the meaning of diamonds and squares was reversed. Participants selected the bottom image on the left by clicking a button with their right index finger and that on the right using their right middle finger. Trials were equally divided between rule sets and their presentation was pseudorandomized. Reaction time and accuracy data were collected and used for behavioral analysis. Independent samples t-tests were used to assess differences in reaction time and accuracy between seronegative controls and PWH, and Pearson correlations were used to examine possible associations between reaction time, accuracy, and the duration in which participants had been diagnosed with HIV and other clinical metrics.

Fig. 1. Illustration of the perceptual feature matching task.

Fig. 1.

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Trials had a fixation period lasting an average of 1700 ms and a stimulus presentation period of 2700 ms, during which, a set of three images were displayed. Participants matched the top image to one of the two bottom images based on either its pattern or shape, which was dictated by whether the top image was surrounded by a diamond or square. The meaning of diamond versus square was counterbalanced within each participant group. Participants indicated their choice via button press.

2.5. MEG Data Acquisition

Our MEG data acquisition, structural coregistration, preprocessing, and sensor–/source-level analyses closely followed previously reported pipelines (Wiesman and Wilson, 2020). Functional MEG data were collected using a MEGIN TRIUX Neo MEG system (Helsinki, Finland) equipped with 306 sensors (204 planar gradiometers, 102 magnetometers) using a 1 kHz sampling rate and an acquisition bandwidth of 0.1–330 Hz in a two-layer magnetically shielded VACOSHIELD room (Vacuumschmelze, Hanau, Germany). Participants were monitored during data acquisition via real-time audio-video feeds from inside the shielded room. MEG data were subjected to environmental noise reduction and corrected for head motion using the signal space separation method with a temporal extension (Taulu and Simola, 2006). Only data from the 204 planar gradiometers were used for further analysis.

2.6. Structural MRI Processing and MEG Coregistration

Prior to MEG acquisition, five coils were attached to the participant’s head and localized along with fiducial and scalp surface points using a three-dimensional (3D) digitizer (FASTRAK, Polhemus Navigator Sciences, Colchester, Vermont). Once the participants were positioned for MEG recording, an electric current with a unique frequency label (e.g., 322 Hz) was fed to each of the five coils, thus inducing a measurable magnetic field and thereby allowing each coil to be localized in reference to the MEG sensor array throughout the recording session. MEG data were coregistered with structural T1-weighted MRI data using BESA MRI (Version 3.0) prior to source-space analysis. These structural T1-weighted images were acquired using a Siemens Prisma 3T MRI scanner with a 32-channel head coil and a 3D MP-RAGE sequence with the following parameters: TR = 2400 ms; TE = 2.05 ms; flip angle = 8°; FOV = 256 mm; slice thickness = 1 mm; voxel size = 1 mm3. The structural MRI data were aligned parallel to the anterior and posterior commissures and transformed into standardized space.

2.7. MEG Preprocessing

Cardiac and ocular artifacts were removed from the MEG data using signal space projection (SSP) (Uusitalo and Ilmoniemi, 1997). The continuous magnetic time series was then filtered with a 60 Hz notch filter and divided into 4200 ms epochs (−1500 to 2700 ms), with 0.0 ms defined as stimulus onset. Only trials with correct responses were considered for further analysis. Epochs containing artifacts were rejected using a fixed threshold method that was set per participant and supplemented with visual inspection. Briefly, in MEG, the raw signal amplitude is strongly affected by the distance between the brain and the MEG sensor array, as the magnetic field strength falls off sharply as the distance from the current source (i.e., brain) increases. To account for this source of variance across participants, as well as other sources of variance (e.g., head size), we used an individualized threshold based on the signal distribution for both amplitude and gradient to reject artifacts. The average amplitude threshold across all participants was 1064.36 (SD = 547.72) fT/cm, the average gradient threshold was 245.39 (SD = 195.34) fT/(cm*ms), and an average of 162.71 (SD = 13.84) trials out of the original 200 were used for further analysis. The number of trials included in the final MEG analyses did not statistically differ by group (t = 1.12, p = .263).

2.8. MEG Time-Frequency Transformation and Sensor-Level Statistics

Artifact-free epochs were transformed into the time-frequency domain (resolution: 2 Hz, 25 ms) using complex demodulation (Kovach and Gander, 2016; Papp and Ktonas, 1977), and the resulting spectral power estimations per sensor were averaged across trials to generate time-frequency plots of mean spectral density. These sensor-level data were then normalized with respect to the mean baseline power (i.e., −700 to 0 ms). The time-frequency windows used for subsequent source imaging were identified using a stringent two-stage statistical approach that utilized paired-sample t-tests against baseline on each pixel in the spectrogram (per sensor) at the first stage, followed up with cluster-based nonparametric permutation testing at the second level. This testing was conducted across all participants and the entire frequency range (4–100 Hz) and used an initial cluster threshold of p < .05 and 5000 permutations. These methods are described in depth in our recent publications (Wiesman et al., 2021; Wiesman and Wilson, 2020). All MEG and MRI data were processed in BESA (Research: V7.1; MRI: V3.0; Statistics: V2.1).

2.9. MEG Source Analysis

Time-frequency resolved source images were computed using the dynamic imaging of coherent sources (DICS) beamformer to image oscillatory activity within the time-frequency windows of interest per participant (Dalal et al., 2006; Gross et al., 2001; Van Veen et al., 1997). We used task and baseline periods of equal duration and bandwidth (Hillebrand et al., 2005) to compute noise-normalized source power per voxel, with the resulting pseudo-t units reflecting power differences (i.e., active versus baseline) per voxel (resolution: 4 × 4 × 4 mm). The resulting beamformer maps were then transformed into standardized space and spatially resampled by applying the same transform that was applied to the native space structural images per participant.

2.10. Statistical Analyses

To assess for differences in neural oscillatory activity serving task performance among PWH and seronegative controls, the spectrally specific whole-brain maps per participant and time-frequency component were used for whole-brain, voxel-wise independent samples t-tests. A significance threshold of p < .005 was used for the identification of significant clusters in all whole-brain statistical maps, accompanied by a cluster (k) threshold of at least 10 contiguous voxels (i.e., 640 mm3 of brain tissue) based on the theory of Gaussian random fields (Poline et al., 1995; Worsley et al., 1999, 1996). All whole-brain statistical analyses were computed using a custom function in MATLAB (MathWorks; Natick, Massachusetts) and other statistical analyses were conducted in IBM SPSS v.29. Of note, sensitivity analyses were conducted to evaluate whether the main findings held after adjusting for tobacco dependence as well as after removing females from statistical analyses.

3. Results

3.1. Participant Characteristics and Behavioral Results

Of the 149 participants enrolled, six participants were excluded from further analysis due to poor task performance (accuracy <60 % correct; 5 PWH, 1 seronegative control), four PWH were excluded for HIV viral loads >50 copies/mL, one PWH was excluded due to excessive noise during MEG recording, and one control was withdrawn from the study resulting in a final sample of 137 participants (64 PWH, 73 seronegative controls). The groups were similar in demographic characteristics such as age, sex, race, and ethnicity (Table 1).

In terms of neuropsychological performance, PWH performed more poorly on the attention (t = −4.50 [95 % CI: −8.84, −3.41], Cohen’s d = −0.76 [95 % CI: −1.11, −0.41], p < .001) and executive function (t = −2.81 [95 % CI: −5.36, −0.93], Cohen’s d = −0.48 [95 % CI: −0.83, −0.14], p = .006) domains relative to seronegative controls, though both groups performed similarly on the learning, memory, processing speed, language, and motor domains (all p-values > .05; Table 1). Based on the Frascati criteria (Antinori et al., 2007), 13 PWH (20.3 %) met criteria for HAND (asymptomatic neurocognitive impairment (ANI): 11; mild neurocognitive disorder (MND): 1; HIV-associated dementia (HAD): 1). Further, behavioral cognitive flexibility performance on the MEG perceptual feature matching task was worse among PWH, with PWH performing less accurately (PWH: M = 91.73 %, SD = 7.51 %; Controls: M = 96.05 %, SD = 3.83 %; t = 4.16 [95 % CI: −6.39, −2.26], Cohen’s d = −0.74 [95 % CI: −1.09, −0.39], p < .001; Fig. 2A) and responding more slowly (M = 1642.56 ms, SD = 293.65) than controls (M = 1514.40 ms, SD = 245.33; t = −2.78 [95 % CI: 35.92, 220.42], Cohen’s d = 0.48 [95 % CI: 0.14, 0.82], p = .006; Fig. 2B). Further, those with longer HIV disease durations tended to have slower reaction times, above and beyond the effects of age (r = 0.30 [95 % CI: 0.04, 0.51], p = .023; Fig. 2C). Next, we probed whether these group differences in task behavior held after adjusting for scores on the attention domain and HAND classifications, which revealed that group differences in accuracy held after adjusting for attention scores (F(2,135) = 11.38, p < .001) and HAND classifications (F(2,135) = 15.54, p < .001), while group differences in reaction time held after adjusting for HAND classifications (F(2,135) = 5.46, p = .021), but not after adjusting for attention domain scores (F(2,135) = 1.02, p = .314). Of note, there were no significant group-by-condition interactions in terms of reaction time (F(1,135) = 0.09, p = .770) or accuracy (F(1,135) = 0.83, p = .363).

Fig. 2. Behavioral performance on the MEG perceptual feature matching task.

Fig. 2.

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People with HIV (PWH) were less accurate (A) and responded slower (B) on the perceptual feature matching task compared to controls. Accuracy, expressed as the percent correct, appears on the y-axis of (A) and reaction time, in ms, is displayed on the y-axis of (B), with group on the x-axes of both panels. Violin plots show the probability density per group, with the black bars indicating the group means. (C) Reaction time (y-axis) was also significantly correlated with HIV disease duration in PWH, with longer HIV disease durations (in years, x-axis) being associated with slower reaction times, controlling for the effect of age. The gray shading surrounding the trendline represents the standard error of the mean. **p < .01, *p < .05.

Finally, to help validate the task’s assessment of cognitive flexibility, we evaluated relationships between raw scores of individual tests comprising the executive function and attention domains. As expected, faster reaction time and higher accuracy were most strongly associated with a faster completion time during the Trail Making Test Part B (Reaction time: r = 0.51 [95 % CI: 0.37, 0.62], Pcorrected < .001; Accuracy: r = −0.34 [95 % CI: −0.48, −0.19], pcorrected < −001), and were also associated with more optimal performance on the WAIS-IV Digit Span Test (Reaction time: r = −0.46 [95 % CI: −0.58, −0.32], Pcorrected < −001; Accuracy: r = 0.37 [95 % CI: 0.21, 0.50], Pcorrected = .007), WAIS-IV Letter-Number Sequencing Test (Reaction time: r = −0.39 [95 % CI: −0.53, −0.24], Pcorrected < .001; Accuracy: r = 0.22 [95 % CI: 0.06, 0.38], Pcorrected = .072), and the Stroop Interference Trial (Reaction time: r = −0.25 [95 % CI: −0.40, −0.08], pcorrected = .032; Accuracy: r = 0.11 [95 % CI: −0.06, 0.28], Pcorrected > .99) after adjusting for multiple comparisons using a Bonferroni correction. These findings provide compelling evidence that our MEG task is assessing cognitive flexibility.

3.2. Oscillatory Neural Responses

We observed robust neural oscillatory responses in four temporally and spectrally defined windows serving cognitive flexibility during the perceptual feature matching task (Fig. 3). These included statistically significant increases in power relative to the baseline period in the theta band (50–450 ms; 4–8 Hz), a decrease in power in the alpha band (10–16 Hz) from 200 to 500 ms and from 650 to 1350 ms, and an increase in power in the gamma band (100–250 ms; 74–98 Hz). All responses were significant at p < .05 following multiple comparisons correction using nonparametric cluster-based permutation testing. At the cortex level, the theta, alpha, and gamma responses originated from the bilateral occipital regions and extended into the higher-order association cortices.

Fig. 3. Neural oscillatory responses during the perceptual feature matching task.

Fig. 3.

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Grand-averaged time-frequency spectrograms of MEG sensors exhibiting one or more significant oscillatory responses. Shown from top to bottom: gamma, early and late alpha, and theta activity. In each spectrogram, frequency (Hz) is shown on the y-axis, and time (ms) is shown on the x-axis. Signal power data are expressed as a percent difference from the baseline period, with color legends shown to the right of each spectrogram.

3.3. Group Differences in Oscillatory Activity

To address our primary hypotheses, we assessed the effects of HIV status on these neural responses by conducting whole-brain independent samples t-tests separately for the theta, early alpha, late alpha, and gamma responses. There were no significant differences in oscillatory theta activity among PWH and controls. However, in the early alpha band, PWH had stronger alpha oscillations in the right PFC (i.e., more negative relative to baseline; t = −3.19 [95 % CI: −3.79, −0.89], Cohen’s d = −0.55 [95 % CI: −0.90, −0.21], p < .005; Fig. 4A). Likewise, in the late alpha band, several peaks were found in the left insula (t = −2.74 [95 % CI: −6.70, −1.08], Cohen’s d = −0.48 [95 % CI: −0.83, −0.13], p < .005; Fig. 4B), left anterior cingulate cortex (ACC; t = −2.99 [95 % CI: −5.74, −1.17], Cohen’s d = −0.53 [95 % CI: −0.88, −0.18], p < .005; Fig. 4C), and the left ventral temporal cortex (t = −2.85 [95 % CI: −5.00, −0.91], Cohen’s d = −0.50 [95 % CI: −0.85, −0.15], p < .005; Fig. 4D), with PWH exhibiting stronger oscillatory alpha activity relative to controls in all of these regions. In contrast, PWH exhibited reduced oscillatory gamma responses in the right cerebellum relative to controls (t = −3.02 [95 % CI: −1.97, −0.41], Cohen’s d = −0.53 [95 % CI: −0.88, −0.18], p < .005; Fig. 4E).

Fig. 4. People with HIV exhibit altered oscillatory alpha and gamma activity during performance of the MEG task.

Fig. 4.

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(A) The brain image on the left represents the voxel-wise group difference in oscillatory alpha activity during the early time window, with stronger alpha responses (i.e., more negative relative to baseline) in the right prefrontal cortex (PFC) of PWH compared to controls. (B—D) Similarly, voxel-wise group differences in oscillatory alpha activity during the late window were observed in the (B) left insula, (C) left anterior cingulate cortex (ACC), and in the (D) left ventral temporal cortex, with PWH having stronger oscillatory alpha activity relative to controls in all regions. (E) Group differences in gamma oscillations were also identified in the right cerebellum, with controls exhibiting stronger responses than PWH. In each panel, the peak voxel power values were extracted from the cluster depicted in the 3D rendition and are plotted to the right in the accompanying violin plots. *p < .005.

We then probed how these distinct neural oscillatory responses were related to clinical indices of HIV (i.e., CD4 nadir, current CD4 count, duration on ART, disease duration) and behavioral performance on the MEG task (i.e., reaction time, accuracy). Pseudo-t values were extracted from peak voxels identified in the whole-brain analyses and analyzed with Pearson correlations. We found that those who had been treated with ART for a longer duration of time had weaker (i.e., more optimal) oscillatory alpha responses in the left ventral temporal cortex, after adjusting for age (r = 0.29 [95 % CI: 0.001, 0.55], p = .035; Fig. 5A). Further, higher current CD4 counts (cells/μL) were associated with weaker oscillatory alpha responses (i.e., more optimal) in the left anterior cingulate (r = 0.30 [95 % CI: 0.01, 0.54], p = .042; Fig. 5B). Additionally, across all participants and independent of HIV-status, stronger gamma power (i.e., more optimal) in the right cerebellum was associated with faster reaction times during the MEG perceptual feature matching task (r = −0.36 [95 % CI: −0.50, −0.20], p < .001; Fig. 6A), while stronger late alpha responses (i.e., less optimal) in the left ACC (r = −0.21 [95 % CI: −0.36, −0.03], p = .019; Fig. 6B) and left ventral temporal cortex (r = −0.24 [95 % CI: −0.39, −0.07], p = .007; Fig. 6C) were associated with slower reaction times. Further, stronger alpha oscillations in the left insula (r = 0.21 [95 % CI: 0.04, 0.37], p = .015; Fig. 6D) and left ventral temporal cortex (r = 0.21 [95 % CI: 0.04, 0.37], p = .017; Fig. 6E) were associated with poorer task accuracy. Finally, to assess whether our primary findings were biased by the sex of participants and/or were sensitive to group differences in tobacco use, we conducted sensitivity analyses which revealed that all main findings remained statistically significant after adjusting for tobacco dependence and after removing females from the statistical analyses.

Fig. 5. Clinical HIV metrics are related to alpha oscillations.

Fig. 5.

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(A) People with HIV (PWH) who had been treated with ART for a longer duration had weaker (i.e., less negative; more optimal) oscillatory alpha responses in the left ventral temporal cortex, above and beyond the effects of age. (B) Higher current CD4 counts in PWH were associated with weaker alpha oscillations (i.e., more optimal) in the anterior cingulate cortex (ACC). The gray shading surrounding the trendline represents the standard error of the mean. *p < .05.

Fig. 6. Oscillatory gamma and alpha power scale with behavioral measures.

Fig. 6.

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(A) Regardless of HIV status, stronger gamma oscillations in the right cerebellum were associated with faster responses during the MEG task. (B-E) In contrast, stronger (i.e., more negative) alpha responses in the (B) left anterior cingulate and (C) left ventral temporal cortex were associated with slower reaction times, and stronger alpha oscillations in the (D) left insula and (E) left ventral temporal cortex were associated with lower accuracy on the task. Note that controls exhibited weaker alpha activity compared to PWH, and thus in all cases, poorer performance was associated with the neural response patterns observed in PWH. The gray shading surrounding the trendline represents the standard error of the mean. *p < .05, **p < .01.

4. Discussion

Previous research has established that PWH experience higher rates of neurocognitive impairment compared to seronegative controls, with deficits in executive function being especially common (Dastgheyb et al., 2019; Heaton et al., 2011; Winston and Spudich, 2020). Unfortunately, the neurobiological underpinnings of these impairments remain incompletely understood, especially among virally suppressed individuals. In the current study, we exploited the spatiotemporal precision of MEG and a novel cognitive flexibility task to identify the neural oscillatory dynamics underlying such executive dysfunction in PWH. Our findings indicated the expected pattern of impaired behavioral performance on the perceptual feature matching MEG task in PWH, as well as altered alpha and gamma oscillations within higher-order cortices that are known to play a critical role in executive function. Additionally, we found that alterations in these oscillatory responses scaled with key behavioral metrics and clinical indices of HIV health.

In terms of task behavior, PWH performed less accurately and responded more slowly compared to controls, which corroborates many studies of higher-order cognitive function in PWH (Lew et al., 2018; Schantell et al., 2022b, 2022a; Wiesman et al., 2018). Interestingly, PWH who had been diagnosed with HIV for a longer duration had slower reaction times during the task, even after controlling for age. This finding is of particular interest, as our sample of PWH was virally suppressed, and all had been receiving ART for the vast majority of time since acquiring HIV. Therefore, these results suggest that the neurocognitive impacts of HIV persist despite viral suppression (Schantell et al., 2022b).

Beyond these behavioral findings, we demonstrated that PWH have alterations in the neural dynamics serving cognitive flexibility. In the high-frequency gamma range, PWH had reduced oscillatory power in the right cerebellum relative to controls. Across both groups, stronger oscillatory gamma activity in this region was associated with faster reaction times during task performance, indicating that gamma oscillations in the right cerebellar cortices are a critical component of the neurophysiological network serving cognitive flexibility and may scale with the efficiency of task performance (Bellebaum and Daum, 2007; Kirschen et al., 2005). Gamma oscillations are tightly coupled to local GABA activity (Edden et al., 2009; Gaetz et al., 2011; Kujala et al., 2015; Muthukumaraswamy et al., 2009), which suggests that reduced neuronal inhibition in PWH contributes to their altered performance on this task and, more broadly, in tasks and environments that require cognitive flexibility.

In addition, we found alterations in oscillatory alpha activity in PWH. Specifically, we found that PWH had stronger (i.e., more negative relative to baseline) alpha oscillations in the right PFC during the early time window, as well as stronger alpha responses in the left insula, ACC, and ventral temporal regions during the late window. These regions have been linked with hierarchical cognitive control and cognitive flexibility (Barredo et al., 2015; Brockett et al., 2020; Miller, 2000; Varjacic et al., 2018), which helps highlight their importance and likely role in the current context. Further, we identified several relationships between oscillatory alpha activity in these regions and behavioral performance. These included stronger alpha oscillations (i.e., more negative) in the left ACC, left insula, and left ventral temporal cortex being associated with poorer behavioral performance in terms of accuracy and reaction time across all participants, which would be expected based on the direction of the neural findings. Further supporting these findings, we found that weaker alpha oscillations in the left ventral temporal cortex (i.e., more similar to controls) were associated with receiving ART for a longer duration of time, and the same oscillatory pattern in the left ACC was associated with higher current CD4 T-cell counts. Overall, these findings suggest that alpha oscillations in these regions are directly tied to clinical indices of HIV health and are critical for task performance in our study and, by extension, cognitive flexibility (Schantell et al., 2022b).

Previous work has suggested that PWH may exhaust their cognitive resources during performance of higher-order cognitive tasks, especially when task demands are high (Chang et al., 2008; Ernst et al., 2009). The compensation-related utilization of neural circuits hypothesis, or CRUNCH (Reuter-Lorenz and Cappell, 2008), postulates that people recruit more neural resources as task demands increase, and those experiencing cognitive decline may recruit greater resources at lower task demands to compensate for this decline. In this context, compensation refers to the recruitment of additional neural resources to address higher cognitive load (Schantell et al., 2022b; Schneider-Garces et al., 2010). As described above, across multiple regions that play a putative role in executive function processes, PWH exhibited stronger alpha oscillations during task performance relative to controls. This suggests that PWH may differentially engage neural resources as an attempt to compensate for processing deficits and/or inefficiencies in order to meet the demands of the task at hand. While this strategy may work when task demands are relatively lower, as demands rise, these compensatory processes eventually become exhausted and performance declines (Arif et al., 2020; Schantell et al., 2022b; Schneider-Garces et al., 2010). Given the relationships we observed between alpha oscillations in several brain regions and behavioral performance, we believe such a breaking point may have been reached in the current study, as the stronger alpha activity observed in PWH was associated with lower accuracy and slower reaction times, indicating an exhausted attempt to compensate.

Before closing, it is important to note the limitations of this study. First, due to the variety of ART regimens used across our sample and potential medication changes among our patients during the course of their treatment, we were not able to analyze the impact of using different ART regimens on task performance or neural oscillatory dynamics. Several ART treatments are known to cross the blood-brain barrier and are associated with neurocognitive impairment (Robertson et al., 2012), and thus, future work should consider calculating a CNS Penetration Effectiveness score to provide further insight into the mechanisms of HIV-related CNS dysfunction (Letendre et al., 2008). Second, this study did not investigate persistent inflammation. Despite viral suppression during ART, PWH continue to experience damaging levels of chronic systemic inflammation (French et al., 2009). This is a relevant avenue to explore in the future to fully understand the neuro-biology of cognitive decrements observed in PWH. Third, we focused on one facet of executive function (i.e., cognitive flexibility), and future studies should extend our findings to the other core functions (Diamond, 2013). Next, substance use among participants included in our sample was low, and participants had relatively minimal psychiatric symptomatology, which is important to consider as our findings may not generalize to populations of PWH at-large as substance use disorders and other psychiatric conditions are more common in PWH. Note that this was by design in the current study, as these comorbidities are known to independently affect neural dynamics and brain function more generally. Finally, our sample reflected the demographics of the local HIV population, and therefore, we were underpowered to investigate the influence of sex on our findings. Future work should consider evaluating the interaction between sex and HIV status on the neural responses serving cognitive flexibility.

To close, this study provides clear evidence that virally suppressed PWH exhibit robust deficits in cognitive flexibility compared to seronegative controls. These deficits appear to be related to altered alpha and gamma oscillations in higher-order association cortices, with such neurophysiological markers tightly coupled to behavioral performance on the task, as well as to clinical indices of HIV health. Although future work is clearly needed, this study provides novel insight on the neural oscillatory dynamics and brain regions that may be of critical importance to understanding the origins of deficits in cognitive flexibility observed in virally suppressed PWH.

Acknowledgments

The authors thank the participants for generously volunteering their time to participate in the study. The authors also wish to specifically thank Dr. Susan Swindells for her extensive help with the clinical aspects of the study and Nichole Knott for helping with the MEG recordings.

Funding

This work was supported by the National Institutes of Health [R01-MH116782 (TWW), R01-MH118013 (TWW and JTB), S10-OD028751 (TWW), P20-GM144641 (TWW), F31-DA056296 (MS), F30-MH134713 (JJS), F30-MH130150 (ADK)]. The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

Footnotes

CRediT authorship contribution statement

Katherine K. Landler: Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization. Mikki Schantell: Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Ryan Glesinger: Formal analysis, Data curation. Lucy K. Home: Formal analysis, Data curation. Christine M. Embury: Writing – review & editing, Validation, Investigation, Formal analysis. Jake J. Son: Writing – review & editing, Validation, Investigation, Formal analysis. Yasra Arif: Writing – review & editing, Validation, Investigation, Formal analysis. Anna T. Coutant: Formal analysis, Data curation. Grant M. Garrison: Formal analysis, Data curation. Kellen M. McDonald: Writing – review & editing, Validation, Investigation, Formal analysis. Jason A. John: Formal analysis, Data curation. Hannah J. Okelberry: Formal analysis, Data curation. Thomas W. Ward: Writing – review & editing, Validation, Investigation, Formal analysis. Abraham D. Killanin: Writing – review & editing, Validation, Investigation, Formal analysis. Maureen Kubat: Formal analysis, Data curation. Renae A. Furl: Formal analysis, Data curation. Jennifer O’Neill: Formal analysis, Data curation. Sara H. Bares: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Pamela E. May-Weeks: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis. James T. Becker: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Tony W. Wilson: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

Declaration of competing interest

SHB reports scientific advisory to Gilead Sciences and research grants to her institution from ViiV Healthcare and Janssen. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

De-identified data have been made available to the public through the Collaborative Informatics and Neuroimaging Suite (COINS) database. Data requests can also be fulfilled via the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

De-identified data have been made available to the public through the Collaborative Informatics and Neuroimaging Suite (COINS) database. Data requests can also be fulfilled via the corresponding author.

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