Abstract
Background
Little is known regarding effects of perinatally-acquired HIV infection (PHIV) on longitudinal change in memory and executive functioning (EF) during adolescence, despite the importance of these skills for independence in adulthood.
Methods
PHIV (n=144) and perinatally HIV-exposed uninfected youth (PHEU, n=79), ages 12–17, completed standardized tests of memory and EF at baseline and two years later. Changes from baseline for each memory and EF outcome were compared between PHEU and PHIV youth with (PHIV/C, n=39) and without (PHIV/non-C, n=105) history of CDC Class C (AIDS-defining) diagnoses. Among PHIV youth, associations of baseline and past disease severity with memory and EF performance at follow-up were evaluated using adjusted linear regression models.
Results
Participants were primarily black (79%); 16% were Hispanic; 55% were female. Mean memory and EF scores at follow-up generally fell in the low-average to average range. Pairwise comparison of adjusted mean change from baseline to follow-up revealed significantly greater change for PHIV/non-C compared to PHEU youth in only one verbal recognition task, with a difference in mean changes for PHIV/non-C versus PHEU of −0.99 (95%CI: −1.80, −0.19; p=0.02). Among youth with PHIV, better immunologic status at baseline was positively associated with follow-up measures of verbal recall and recognition and cognitive inhibition/flexibility. Past AIDS-defining diagnoses and higher peak viral load were associated with lower performance across multiple EF tasks at follow-up.
Conclusions
Youth with PHIV demonstrated stable memory and EF during a two-year period of adolescence, allowing cautious optimism regarding long-term outcomes.
Keywords: perinatal HIV infection, adolescents, memory, executive functioning, longitudinal
INTRODUCTION
The development of higher-order cognitive functions, including retrospective memory and executive functioning (EF), among youth with perinatally acquired human immunodeficiency virus infection (PHIV) is of significant concern due to their influence on academic learning, medication adherence and successful transition to adulthood.1–3 Memory and EF are known areas of vulnerability among adults with HIV infection yet their integrity and development in youth with PHIV are not fully understood.4–6 PHIV may affect children’s neural networks early and during sensitive periods of development, with potential for ongoing or intermittent damage, particularly in the absence of viral suppression.7 Global and selective cognitive risks associated with PHIV and its medical treatments may be amplified by children’s concomitant exposure to psychosocial and environmental stressors often present among families living with PHIV, including poverty, violence, and stigma. 8–12
Inconsistencies in the engagement of higher-order skills are often observed during the adolescent period and are related to hormonal and physical changes, substantial structural changes in the brain, and variability in genetics and in affective and social contexts.13–16 As the brain matures, with increasing myelination and synaptic pruning in prefrontal regions and activation in parietal and motor regions and subcortical structures, executive networks consolidate and become more refined, and behavior becomes more modulated.17–20 This developmental process may be delayed or altered in the context of PHIV, with variable effects related to timing and/or severity of HIV disease and efficacy of medical treatment with combination antiretroviral therapy (ART). Furthermore, variable penetrance of the blood-brain barrier by ART may allow a reservoir of latent HIV to persist in the brain, increasing risk for cognitive deficits even with peripheral viral suppression.21–25
Recent neuroimaging studies of youth with PHIV revealed associations among historical markers of HIV disease severity, combination antiretroviral therapy and alterations in overall and regional white matter microstructure, functional connectivity, and/or subcortical deformation. These, in turn, have been associated with risk for deficits in memory, EF, attention or information processing. Atypical neuroimaging results have been observed among those who remain immunologically stable during childhood as well as those with more significant HIV related disease, including encephalopathy.26–31
Results of cross-sectional studies comparing memory and EF among children with PHIV with children with HIV exposure who are uninfected (PHEU) and children without HIV exposure have been mixed, depending in part upon measures used to evaluate memory and EF, health status of PHIV participants, sample sizes, and comparison groups.32–34 A systematic review highlighted greater impairment among PHIV children compared to HIV unexposed and uninfected children in working memory, executive function and processing speed.35 Memory and EF appear to vary among youth based on individual histories of disease severity and viral suppression, yet other factors, including socio-demographic characteristics, are also implicated.36–37 Associations of Centers for Disease Control and Prevention (CDC)38 Class C (AIDS-defining) diagnosis with poorer visual recognition memory among youth with PHIV have been observed, as well as associations between current immunologic status and poorer verbal learning.39 Youth with Class C diagnoses also performed more poorly on some EF measures compared to PHEU youth and demonstrated poorer performance relative to youth with PHIV without class C diagnoses, particularly on timed measures of cognitive flexibility and inhibitory interference and control.40
While it appears clear that children and adolescents with PHIV are at risk for divergent development of higher-order cognitive functioning, little is known about the stability of their functioning as they age into adulthood. Few studies have addressed longitudinal changes in higher-order cognitive functioning in youth with PHIV and their relationship with disease severity and viral suppression in the era of effective ART. Early identification of persistent or newly developing problems would provide guidance regarding ART efficacy and offer opportunities for interventions to alter long-term cognitive outcomes and their impact on skills for successful adulthood. The purpose of this investigation was to examine associations of HIV infection and socio-demographic factors with change or stability of memory and EF outcomes during two years of follow-up among PHIV youth and a comparison group of PHEU youth; among PHIV youth, we evaluated the role of HIV disease characteristics in stability of such outcomes.
METHODS
Participants
Participants for this study were enrolled in the Memory and Executive Functioning Substudy (Memory/EF) of the Adolescent Master Protocol (AMP) of the Pediatric HIV/AIDS Cohort Study (PHACS).41 Participants in the Memory/EF substudy, including PHIV and PHEU youth, were enrolled between 2010 and 2012 at 8 of the 15 AMP sites in the United States.40 This paper presents analyses of change in memory and EF from baseline to follow-up two years later (herein referred to as “follow-up”). Eligibility criteria included enrollment in AMP, age 9 to <19 years at Memory/EF enrollment, ability to participate in testing procedures, and fluency in English (because some study measures were available only in English). PHIV youth were enrolled regardless of immune status or adherence history.
The Institutional Review Boards (IRBs) at each site and at the Harvard University T. H. Chan School of Public Health approved PHACS AMP and Memory/EF. Informed consent and assent were obtained from all participants and parents or legal guardians of participants younger than 18, according to local IRB guidelines. Data collection for AMP occurred at 6-month (2007–2010) and annual (2010–2014) visits and included a physical exam, medical record review, structured interviews to obtain demographic and psychosocial information, and neurodevelopmental evaluations. The baseline assessment for the Memory/EF substudy was timed to coincide with collection of medical, demographic, and neurodevelopmental data in AMP. The follow-up evaluation occurred two years (±3 months) after baseline.
Measures
Retrospective memory and EF were evaluated using standardized measures with nationally representative normative samples for the age range of study participants and with established reliability and validity.42–44 Invalid data were excluded from analysis based on data reviews by study psychologists. Details about the measures are presented below and in Supplemental Table 1.
Memory
Two subtests of the Wide Range Assessment of Memory and Learning, Second Edition (WRAML2) were administered to assess learning and retrospective memory.42 The Verbal Learning subtest, a measure of verbal memory, is a list-learning task that measures immediate and delayed recall, delayed recognition, and intrusion errors. The Design Memory subtest, measuring visual memory, requires examinees to draw geometric designs from memory and assesses delayed recall (10 sec.) and delayed recognition (10–15 mins.). The analyses included age-referenced scaled scores (Mean = 10; standard deviation [SD] = 3) and error scores.
Executive Function
Four subtests of the Delis-Kaplan Executive Function System (D-KEFS) were administered: Verbal Fluency, Design Fluency, Color-Word Interference (CWI), and Twenty Questions.43 These tasks assess verbal and nonverbal fluency, cognitive flexibility, inhibitory control, problem-solving, and concept formation. Alternate versions of the D-KEFS Verbal Fluency and Twenty Questions subtests were used at follow-up to reduce likelihood of practice effects. The analysis included age-referenced scaled scores for the four subtests (Mean = 10, SD = 3) and error scores for all but Twenty Questions. For all D-KEFS subtests, higher scaled scores reflected better performance. For all measures of task errors, higher scaled scores indicated fewer errors.
Covariates and Potential Confounding Variables
Demographic information was obtained through parent and youth interviews conducted within AMP. Child information included age at study entry, sex, race/ethnicity, and primary language. Caregiver information included biological relationship to child, household income, and education. For youth with PHIV, historical and current health indicators were collected via chart abstraction and laboratory tests. The following variables were collected at entry into AMP and at the AMP visit closest in time to Memory/EF visits: current, nadir, and age at nadir CD4+ T-lymphocyte count and percent (CD4%); current HIV RNA viral load (VL), peak VL, and age at peak VL; CDC classification of HIV disease and age at first Class C classification; diagnosis of encephalopathy and age at diagnosis; and ART regimen.
Statistical Analysis
Three groups were defined: perinatally HIV-exposed, uninfected (PHEU); perinatally HIV-infected, no Class C diagnosis (PHIV/non-C); and perinatally HIV-infected with Class C diagnosis (PHIV/C). Only participants who completed both entry and follow-up Memory/EF evaluations were included in the analysis. Child and caregiver characteristics for the three groups were compared, using a Chi-Square test for categorical measures and Kruskal-Wallis test for continuous measures. The primary outcomes (memory and EF mean scores at baseline and follow-up) were summarized, and the proportions of the sample with scaled scores more than two SDs below the mean at each of the two time points were computed.
Our analysis included three different components: (1) comparison of changes in memory and EF across HIV status cohorts, 2) for PHIV, evaluation of baseline HIV disease measures as predictors of follow-up memory and EF, and (3) evaluation of changes in HIV disease severity measures as predictors of follow-up memory and EF. More specifically, changes from baseline (follow-up value minus baseline value) for each memory and EF outcome were computed and compared among the HIV-status cohorts, both unadjusted and adjusted for covariates. Adjusted linear models included HIV cohort and all potential confounders with unadjusted p < 0.20, with backward steps requiring p < 0.15 for inclusion in the final multivariable models. Generalized estimating equations (GEE) and robust error variances were used in all regression analyses. Least squares means and pairwise contrasts between cohorts were estimated along with their 95% confidence intervals.
For analyses of the associations of markers of baseline HIV disease severity and changes in HIV disease severity with follow-up memory and EF outcomes, unadjusted GEE linear regression models were used to estimate the association of child/caregiver characteristics and HIV disease severity measures with each memory and EF outcome. Based on these results, a core multivariable model was built for each memory and EF outcome, initially including all personal and family covariates with p < 0.20 in the univariable models and retaining only those with p < 0.15 in the core multivariable model. To each core model, we added each HIV disease severity marker (or change in HIV disease severity) and performed an adjusted regression analysis. Observations with missing values on any covariate were excluded from the analysis. Results with p-values < 0.05 were considered statistically significant. Given the exploratory nature of these analyses, no corrections were made for multiple comparisons, and observed findings warrant confirmation in future studies. Analyses were based on Memory/EF data submitted as of April 2015 and relevant AMP data submitted as of January 2015. SAS v9.2 or v9.4 (SAS Institute, Cary, NC) were used for all analyses.
RESULTS
Participant Characteristics
Table 1 presents relevant demographic and HIV-related characteristics of the study sample at follow-up (N = 223; PHEU = 79, PHIV/non-C = 105, PHIV/C = 39). The majority of youth were black (78%); 16% were Hispanic, and 55% were female. Youth with PHIV were significantly older than PHEU at follow-up (PHIV/non-C= 16.5 years, PHIV/C=17.2 years, PHEU=14.9 years). Compared to the PHIV groups, PHEU youth were more likely to live in homes with a biological parent, a caregiver with HIV, and lower annual household income.
Table 1.
Personal and Family Characteristics of 223 Perinatally HIV-Infected and Perinatally HIV-Exposed Uninfected Participants of the PHACS Memory and Executive Functioning Study
| Study Group | |||||
|---|---|---|---|---|---|
| Characteristic | Total (N=223) |
PHEU (n=79) |
PHIV/ non-C (n=105) |
PHIV/C (n=39) |
P-Value |
| Male sex | 101 (45%) | 39 (49%) | 49 (47%) | 13 (33%) | 0.24 |
| Age at entry, years, mean (SD) | 14.0 (2.8) | 12.8 (2.6) | 14.4 (2.8) | 15.2 (2.3) | <0.001 |
| Age at follow-up, years, mean (SD) | 16.1 (2.8) | 14.9 (2.6) | 16.5 (2.8) | 17.2 (2.3) | <0.001 |
| Race | 0.46 | ||||
| White | 43 (19%) | 15 (19%) | 22 (21%) | 6 (15%) | |
| Black | 173 (78%) | 62 (79%) | 81 (77%) | 30 (77%) | |
| Other/Unknown | 7 (3.1%) | 2 (2.5%) | 2 (1.9%) | 3 (7.7%) | |
| Hispanic ethnicity | 36 (16%) | 16 (20%) | 13 (13%) | 7 (18%) | 0.35 |
| Child’s primary language is English | 207 (93%) | 70 (89%) | 101 (96%) | 36 (92%) | 0.14 |
| Primary caregiver is biological parent | 123 (55%) | 60 (76%) | 45 (43%) | 18 (46%) | <0.001 |
| Primary caregiver is high school graduate | 172 (77%) | 60 (76%) | 84 (80%) | 28 (72%) | 0.55 |
| Annual household income > $20,000 | 108 (48%) | 26 (33%) | 63 (60%) | 19 (49%) | <.001 |
| Caregiver HIV status | |||||
| HIV-uninfected | 94 (42%) | 18 (23%) | 58 (55%) | 18 (46%) | <.001 |
| HIV-infected | 110 (49%) | 56 (71%) | 40 (38%) | 14 (36%) | |
| HIV status unknown | 19 (8.5%) | 5 (6.3%) | 7 (6.7%) | 7 (18%) | |
| Caregiver Race | |||||
| White | 68 (32%) | 21 (28%) | 36 (35%) | 11 (31%) | 0.17 |
| Black | 136 (64%) | 52 (69%) | 64 (62%) | 20 (57%) | |
| Other/Unknown | 9 (4.2%) | 2 (2.7%) | 3 (2.9%) | 4 (11%) | |
| Missing | 10 | 4 | 2 | 4 | |
Note. P-value by Chi-Square Test for categorical measures and by Kruskal-Wallis Test for continuous measures. PHEU: perinatally HIV-exposed, uninfected; PHIV/non-C: perinatally acquired HIV, without CDC Class C diagnosis; PHIV/C: perinatally acquired HIV, with CDC Class C diagnosis.
Compared with PHIV/non-C youth, those with PHIV/C diagnoses were significantly younger at the time of their most severe CDC staging and more likely to have nadir CD4% <15% and an encephalopathy diagnosis prior to or at entry (Table 2). Other disease severity markers among youth with PHIV did not differ at entry or follow-up and did not change significantly from baseline to follow-up. Approximately 75% entered the substudy with suppressed VL (< 400 copies/mL) and with CD4% ≥ 25%; there were no significant changes at follow-up. There were no differences in ARV regimens between PHV/C and PHIV/non-C at entry or follow-up.
Table 2.
Measures of HIV Disease Severity and Antiretroviral Regimens for 144 Perinatally HIV-Infected Participants of the PHACS Memory and Executive Functioning Study
| Study group | ||||
|---|---|---|---|---|
| Characteristic | Total (N=144) |
PHIV/non-C (N=105) |
PHIV/C (N=39) |
P-Value |
| HIV disease history, n (%) | ||||
|
| ||||
| Nadir CD4% < 15% | 58 (40%) | 33 (31%) | 25 (64%) | <.001 |
| Age at nadir CD4%, years | 0.66 | |||
| 0 – 3 | 59 (41%) | 43 (41%) | 16 (41%) | |
| > 3 to 5 | 20 (14%) | 13 (12%) | 7 (18%) | |
| > 5 | 65 (45%) | 49 (47%) | 16 (41%) | |
| Log peak HIV VL, median (IQR) | 5.73 (5.34, 5.88) | 5.73 (5.20, 5.88) | 5.84 (5.43, 5.89) | 0.17 |
| Peak HIV VL > 100,000 copies/mL | 121 (84%) | 85 (81%) | 36 (92%) | 0.10 |
| Age at peak viral load, years | 0.45 | |||
| 0 – 3 | 97 (67%) | 73 (70%) | 24 (62%) | |
| > 3 to 5 | 12 (8%) | 7 (7%) | 5 (13%) | |
| > 5 | 35 (24%) | 25 (24%) | 10 (26%) | |
| Prior encephalopathy | 15 (10%) | 1 (1%) | 14 (36%) | <.001 |
| Age at first CDC Class C diagnosis prior to entry | 0.025 | |||
| 0 – 3 | 83 (58%) | 55 (52%) | 28 (72%) | |
| > 3 to 5 | 18 (13%) | 12 (11%) | 6 (15%) | |
| > 5 | 43 (30%) | 38 (36%) | 5 (13%) | |
|
| ||||
| HIV disease severity at study entry | ||||
|
| ||||
| CD4 count (cells/mm3), median (IQR) | 655 (489, 882) | 660 (495, 884) | 620 (450, 861) | 0.48 |
| CD4 percent ≥ 25%, n (%) | 112 (78%) | 82 (78%) | 30 (77%) | 0.88 |
| Log HIV-1 RNA VL, median (IQR) | 1.68 (1.67, 2.57) | 1.68 (1.68, 2.48) | 1.68 (1.60, 2.96) | 0.99 |
| Unsuppressed VL (>400 copies/mL, n (%) | 36 (25%) | 25 (24%) | 11 (28%) | 0.59 |
|
| ||||
| HIV disease severity at follow-up, median (IQR) | ||||
|
| ||||
| CD4 count (cells/mm3) | 639 (461, 880) | 660 (487, 868) | 573 (368, 885) | 0.31 |
| CD4 percent | 35 (27, 41) | 36 (29, 41) | 31.4 (22, 40) | 0.15 |
| Log HIV-1 RNA VL | 1.60 (1.30, 2.69) | 1.60 (1.30, 2.50) | 1.60 (1.30, 3.15) | 0.16 |
|
| ||||
| Changes in HIV disease severity (follow-up – entry), median (IQR) | ||||
|
| ||||
| CD4 count (cells/mm3) | −34 (−164, 83) | −30 (−162, 73) | −78 (−193, 83) | 0.38 |
| CD4 percent | 0.9 (−3.1, 3.2) | 1.0 (−3.0, 3.1) | 0.0 (−4.0, 3.2) | 0.52 |
| Log HIV-1 RNA VL | 0.00 (−0.38, 0.19) | −0.08 (−0.38, 0.00) | 0.00 (−0.38, 0.68) | 0.12 |
|
| ||||
| Antiretroviral (ARV) Regimens | ||||
|
| ||||
| ARV regimen at entry | 0.86 | |||
| HAART with PI | 96 (67%) | 69 (66%) | 27 (69%) | |
| HAART without PI | 32 (22%) | 23 (22%) | 9 (23%) | |
| Non-HAART ARV | 7 (4.9%) | 6 (5.7%) | 1 (2.6%) | |
| Not on ARV | 9 (6.3%) | 7 (6.7%) | 2 (5.1%) | |
| ARV regimen at follow-up | 0.35 | |||
| HAART with PI | 66 (52%) | 48 (53%) | 18 (51%) | |
| HAART without PI | 44 (35%) | 31 (34%) | 13 (37%) | |
| Non-HAART ARV | 6 (4.8%) | 4 (4.4%) | 2 (5.7%) | |
| Not on ARV | 10 (7.9%) | 8 (8.8%) | 2 (5.7%) | |
| ARV regimen change in 4 months before study entry | 15 (10%) | 11 (11%) | 4 (10%) | 0.97 |
Note. P-value by Chi-Square Test for categorical measures and by Kruskal-Wallis test for continuous measures. The following characteristics were unavailable or not reported for some participants: follow-up CD4 count and CD4% (n=5), follow-up VL (n=4), change in CD4 count and CD4% (n=5), change in VL (n=4). PHEU: perinatally HIV-exposed, uninfected; PHIV/non-C: perinatally acquired HIV, without CDC Class C diagnosis; PHIV/C: perinatally acquired HIV, with CDC Class C diagnosis; VL: viral load; IQR=interquartile range; ARV=antiretroviral; HAART=highly active antiretroviral treatment (at least 3 drugs from at least 2 drug classes); PI=protease inhibitor.
Thirty-five (14%) of the original 258 study participants were unable to complete follow-up evaluations due to relocation (n = 14), unwillingness to participate (n = 13), severe debilitation or death (n = 5), or inability to contact (n = 3). Compared to participants with baseline but no follow-up data, those who were included in the longitudinal analysis were younger (p = 0.04), more often black (p < 0.05), and living with an HIV-uninfected caregiver (p < 0.001); fewer of their caregivers had unknown HIV status (p < 0.001).
Memory
Comparisons by HIV status and prior AIDS diagnosis
In unadjusted analyses, group means on the WRAML2 memory subtests were generally in the low-average to average range at both time points relative to the U.S. general population (Supplemental Table 2). Comparisons among the three cohorts revealed minimal, nonsignificant changes from baseline to follow-up in most memory measures (Supplemental Figure 1). Only a single pairwise comparison of adjusted mean change from baseline to follow-up in the Verbal Recognition task revealed significantly greater change; PHIV/non-C participants declined from baseline to follow-up by 0.79 points on average, whereas the PHEU group improved by 0.21 points (respective difference in mean changes for PHIV/non-C versus PHEU = −0.99; 95%CI: −1.80, −0.19; p = 0.02). No other significant differences in change in memory functioning across the three groups were observed.
Prospective associations with HIV disease markers
Among the two groups with PHIV, performance on follow-up measures of retrospective memory was associated with markers of HIV disease, adjusted for significant sociodemographic factors (Table 3). Participants with CD4% ≥ 25% at baseline achieved higher scores at follow-up than those with baseline CD4% <25% on tasks assessing Verbal Learning Delay Recall and Verbal Recognition, yet improvement in CD4% from baseline to follow-up was associated with poorer Verbal Learning scores at follow-up. Higher HIV VL at baseline was associated with lower performance on Verbal Recognition at follow-up. Participants in the PHIV/C group who were diagnosed before age 3 had lower Design Memory scores at follow-up than those diagnosed at older ages and those in the PHIV/non-C group.
Table 3.
Adjusted Means and Adjusted Mean Changes in Follow-up Measures of Retrospective Memory (WRAML2) for Specific HIV Disease Severity Measures* in Youth with PHIV
| Adjusted Results
|
|||
|---|---|---|---|
| Disease Severity Measure | Covariate/Level | Mean (95% CI) or Estimate (SE) |
P-Value |
| Verbal Learning 1,2,3 | |||
|
| |||
| Positive Change in CD4% | (continuous) | −0.08 (0.03) | 0.01 |
|
| |||
| Verbal Delay Recall 1,2,4 | |||
|
| |||
| CD4% ≥ 25% at entry | ≥ 25% | 9.45 (8.94,9.96) | 0.05 |
| < 25% | 8.60 (7.80,9.40) | ||
|
| |||
| Verbal Recognition 2,5 | |||
|
| |||
| CD4% ≥ 25% at entry | ≥ 25% | 9.94 (9.45, 10.44) | 0.001 |
| < 25% | 8.45 (7.65, 9.24) | ||
| Log HIV RNA VL at entry | (continuous) | −0.50 (0.21) | 0.02 |
|
| |||
| Design Memory 2,6 | |||
|
| |||
| Age at CDC Class C | 0 – 3 years | 7.63 (6.71, 8.56) | 0.01 |
| > 3 years | 9.83 (8.06, 11.60) | ||
| PHIV/non-C status | 9.14 (8.60, 9.69) | ||
Note. *Table includes only those HIV disease severity measures with significant differences in adjusted means (categorical covariates) or significant regression coefficients (continuous measures). The adjusted mean and 95% CI are provided for each level of categorical covariates. For continuous covariates, the adjusted mean change (and standard error) in outcome for each one-unit change in predictor is presented. Adjusted models included the following covariates: 1-Black race; 2-age at Memory/EF entry; 3-annual household income > $20,000; 4-male sex; 5-caregiver is high school graduate; 6-caregiver is biological parent. PHIV/non-C: perinatally acquired HIV, without CDC Class C diagnosis; VL: viral load.
Executive Function
Comparisons by HIV status and prior AIDS diagnosis
In unadjusted analyses at baseline and follow-up, group mean scores were generally in the low-average to average range among youth with PHIV/non-C and PHEU youth and in the borderline to low-average range among youth with PHIV/C (Supplemental Table 2). There were no significant group differences in change scores for PHIV and PHEU youth (Supplemental Figure 2); however, all three groups showed significant improvement from baseline to follow-up on the Verbal Category Switching Accuracy and CWI Inhibition tasks and fewer errors on both CWI Inhibition and CWI Inhibition/Switching. Participants performed more poorly on Verbal Category Fluency and Verbal Category Switching at follow-up.
Prospective associations with HIV disease markers
Among the two groups with PHIV, historical markers of HIV disease severity were associated with specific aspects of EF at follow-up, after adjusting for significant sociodemographic factors (Table 4). Youth with CDC Class C diagnoses at baseline and those with PHIV/C before age 3 achieved lower scores at follow-up on measures of Verbal Category Switching, Verbal Category Switching Accuracy, Design Fluency, and CWI Primary Combined. Youth with encephalopathy diagnoses similarly achieved lower scores on Verbal Category Switching, Verbal Category Switching Accuracy, and CWI Primary Combined. At follow-up, peak VL > 100,000 copies/mL was associated with lower scaled scores on CWI Inhibition/Switching, and more frequent errors on CWI Inhibition and CWI Inhibition/Switching.
Table 4.
Adjusted Means and Adjusted Mean Changes in Follow-up Measures of Executive Function (D-KEFS) for Specific HIV Disease Severity Measures in Youth with PHIV*
| Adjusted Results
|
|||
|---|---|---|---|
| Disease Severity Measure | Covariate/Level | Mean (95% CI) or Estimate (SE) |
P-Value |
| Verbal Letter Fluency7 | |||
|
| |||
| Age at peak HIV VL, years | 0 – 3 | 8.33 (7.66, 9.00) | 0.04 |
| > 3 – 5 | 6.90 (5.59, 8.21) | ||
| > 5 | 8.77 (7.59, 9.96) | ||
|
| |||
| Verbal Category Switching 2,7 | |||
|
| |||
| CDC Class C | PHIV/C | 6.31 (5.32, 7.31) | 0.05 |
| PHIV/non-C | 7.39 (6.61, 8.16) | ||
| Age at CDC Class C, years | 0 – 3 | 5.85 (4.76, 6.93) | 0.01 |
| > 3 | 8.00 (6.19, 9.82) | ||
| PHIV/non-C | 7.41 (6.63, 8.19) | ||
| Encephalopathy diagnosis | Encephalopathy | 5.25 (3.91, 6.59) | 0.003 |
| No encephalopathy | 7.33 (6.63, 8.02) | ||
|
| |||
| Verbal Category Switching Accuracy 2,4 | |||
|
| |||
| CDC Class C | PHIV/C | 9.19 (8.75, 9.63) | 0.04 |
| PHIV/non-C | 9.78 (9.43, 10.13) | ||
| Age at CDC Class C, years | 0 – 3 | 8.94 (8.49, 9.38) | 0.01 |
| > 3 | 10.08 (8.98, 11.18) | ||
| PHIV/non-C | 9.78 (9.43, 10.12) | ||
| Encephalopathy diagnosis | Encephalopathy | 8.73 (8.13, 9.34) | 0.004 |
| No encephalopathy | 9.74 (9.43, 10.04) | ||
|
| |||
| Design Fluency Composite 3,6 | |||
|
| |||
| CDC Class C | PHIV/C | 7.97 (7.27, 8.67) | 0.01 |
| PHIV/non-C | 9.05 (8.60, 9.50) | ||
| Age at CDC Class C, years | 0 – 3 | 7.93 (7.14, 8.73) | 0.04 |
| > 3 | 8.09 (6.63, 9.54) | ||
| PHIV/non-C | 9.05 (8.60, 9.50) | ||
|
| |||
| Design Fluency Set Loss Errors1 | |||
|
| |||
| CD4% ≥ 25% at entry | ≥ 25% | 12.39 (12.04, 12.74) | 0.05 |
| < 25% | 11.24 (10.15, 12.32) | ||
|
| |||
| Color-Word Interference (CWI)Primary Combined 1,2,3,5,7 | |||
|
| |||
| CDC Class C | PHIV/C | 7.33 (6.22, 8.44) | 0.02 |
| PHIV/non-C | 8.61 (7.93, 9.29) | ||
| Age at CDC Class C, years) | 0 – 3 | 6.97 (5.84, 8.10) | 0.02 |
| > 3 | 8.48 (6.37, 10.58) | ||
| PHIV/non-C | 8.63 (7.95, 9.31) | ||
| Encephalopathy diagnosis | Encephalopathy | 6.31 (4.82, 7.79) | 0.003 |
| No encephalopathy | 8.50 (7.86, 9.15) | ||
| Positive Change in CD4% | (continuous) | 0.05 (0.02) | 0.02 |
| Change in log HIV-1 RNA VL | (continuous) | −0.67 (0.26) | 0.01 |
|
| |||
| Color-Word Interference (CWI) Inhibition 1,2 | |||
|
| |||
| Age at nadir CD4% (years) | 0 – 3 | 8.87 (7.98, 9.77) | 0.01 |
| > 3 – 5 | 10.38 (9.19, 11.57 | ||
| > 5 | 8.02 (7.05, 9.00)) | ||
|
| |||
| Color-Word Interference (CWI) Inhibition/Switching 1,8 | |||
|
| |||
| CD4% ≥ 25% at entry | ≥ 25% | 10.03 (9.30, 10.76) | 0.05 |
| < 25% | 8.68 (7.26, 10.10) | ||
| Age at nadir CD4%, years | 0 – 3 | 10.05 (9.06, 11.04) | 0.03 |
| > 3 – 5 | 11.18 (9.98, 12.38) | ||
| > 5 | 9.50 (8.49, 10.51) | ||
| Change in log HIV-1 RNA VL | (continuous) | −0.54 (0.26) | 0.04 |
| Peak HIV VL > 100,000 copies/mL | > 100,000 | 9.51 (8.89, 10.12) | 0.01 |
| 0 – 100,000 | 10.99 (10.00, 11.98) | ||
| Age at HIV peak VL, years | 0 – 3 | 10.23 (9.30, 11.15) | 0.04 |
| > 3 – 5 | 11.33 (9.87, 12.79) | ||
| > 5 | 9.11 (7.93, 10.29) | ||
|
| |||
| Color-Word Interference (CWI) Inhibition Total Errors 8 | |||
|
| |||
| Log HIV-1 RNA VL at entry | ≤ 400 | 10.13 (9.04, 11.21) | 0.04 |
| > 400 | 11.26 (10.11, 12.42) | ||
| Peak HIV VL > 100,000 copies/mL | > 100,000 | 10.02 (9.00, 11.04) | < 0.001 |
| 0 – 100,000 | 11.87 (10.82, 12.93) | ||
| Change in log HIV-1 RNA VL | (continuous) | −0.76 (0.31) | 0.01 |
|
| |||
| Color-Word Interference (CWI) Inhibition/Switching Total Errors 1,2 | |||
|
| |||
| Peak HIV VL > 100,000 copies/mL | > 100,000 | 9.53 (8.95, 10.11) | 0.01 |
| 0 – 100,000 | 10.89 (10.03, 11.75) | ||
|
| |||
| 20 Questions Weighted Achievement 1,3 | |||
|
| |||
| Age at nadir CD4%, years | 0 – 3 | 9.72 (9.20, 10.23) | 0.01 |
| > 3 – 5 | 10.29 (9.38, 11.21) | ||
| > 5 | 8.64 (7.85, 9.43) | ||
Note. *Table includes only those HIV disease severity measures with significant differences in adjusted means (categorical covariates) or significant regression coefficients (continuous measures). The adjusted mean and 95% CI are provided for each level of categorical covariates; for continuous covariates, the adjusted mean change (and standard error) in outcome for each one-unit change in predictor is presented. Adjusted models included the following covariates: 1-Black race, 2-age at entry, 3-annual household income > $20,000, 4-male sex, 5-caregiver is high school graduate, 6-caregiver is biological parent, 7-Hispanic ethnicity, and 8-primary language of child is English. PHIV/non-C: perinatally acquired HIV, without CDC Class C diagnosis; PHIV/C: perinatally acquired HIV, with CDC Class C diagnosis; VL: viral load.
Youth with nadir CD4% after age 5 performed more poorly at follow-up on CWI Inhibition, CWI Inhibition/Switching, and Twenty Questions; peak VL after age 5 was also associated with lower CWI Inhibition/Switching scores. Peak VL between ages 3 and 5 was associated with poorer performance on Verbal Letter Fluency.
Additional HIV disease markers evaluated during the two-year study interval were significantly associated with performance on EF tasks at follow-up. CD4% > 25% at baseline was associated with higher CWI Inhibition/Switching scores and fewer Design Fluency set loss errors. Higher CD4% at follow-up compared to baseline was associated with better performance on the CWI Primary Combined measure while higher VL at follow-up compared to baseline was associated with lower CWI Primary Combined scores and more frequent errors (lower scores) on CWI Inhibition at follow-up. Suppressed VL at baseline (VL < 400 copies/mL) was also associated with a greater number of CWI Inhibition errors, contrary to expectations.
Prevalence of Impairment in Memory and EF
Impairment, defined as performance > 2.0 SDs below the population mean (scaled scores ≤ 4, 2nd percentile), was observed more frequently than expected among study participants compared to the general population (Supplemental Table 3). The proportion of participants with scores in the impaired range at baseline or follow-up varied across measures and ranged from 0–29%. With rare exceptions, prevalence of impairment was generally <6 % for WRAML2 memory subtests and D-KEFS Design Fluency subtests. Impaired performance was observed more frequently than expected for D-KEFS Verbal Category Fluency (0–29 %), Verbal Category Switching (5–14 %), and CWI tasks (2–23 %).
DISCUSSION
The development and integrity of memory and executive functions among youth with PHIV warrant careful consideration given their influence on learning and potential role in risk behaviors, adult health care management, and activities of daily living.2 Our results are similar to other studies of youth with PHIV and PHEU youth in that they reveal generally low-average to average functioning across multiple memory and EF tasks, with increased risk observed among youth with histories of CDC Class C diagnoses.37, 39–40 The results are encouraging in that they reveal no significant group differences or declines in performance among youth with PHIV and PHEU during two years of follow-up. The noted slight improvement in multiple EF and memory tasks could be a result of age-appropriate development, non-specific support services and/or to familiarity with test activities; the longitudinal reliability of each measure may be implicated as well. There was a very modest diminution among youth with PHIV/non-C compared to PHEU youth in only one task, a measure of verbal recognition memory, despite participants’ potential risks for medication nonadherence and immune compromise during adolescence. Adequate immune health, which was observed among the majority of participants with PHIV, is likely protective and was positively associated with performance at follow-up, particularly on verbal memory tasks and several EF tasks.
Our results support the UNAIDS goals of early HIV diagnosis and early initiation of ART to prevent severe HIV-related complications and associated impact on higher-order cognitive functioning. In this study, youth with histories of severe HIV disease, as indicated by classification of CDC Class C diagnoses (including encephalopathy), achieved lower, clinically relevant scores on a number of memory and EF tasks. In addition, rates of impairment in EF tasks related to cognitive inhibition and interference control were higher at baseline and follow-up than rates observed in the US general population, particularly among those with PHIV/C. This pattern of impairment among those with PHIV/C has been observed in studies of global cognitive function and other cognitive domains among children with PHIV 45 and may be related to the early impact of HIV and its treatments on neural circuits supporting EF and early or intermittent inflammation of the central nervous system.
Deficits or impairment in specific memory or EF tasks, if present and especially if persistent, may have implications for youth with PHIV during transition to adulthood, when they are expected to simultaneously avoid risk, manage multiple demands, understand information in varied formats, and organize and communicate complex information regarding their HIV status, medication adherence, side effects, and other health needs. Youth with PHIV and memory or EF difficulties may require extensive preparation, practice, and collaboration as they assume responsibility for such tasks in adulthood, particularly if they are at greater sociodemographic risk. At the same time, the absence of significant impairment in memory and some EF tasks as well as relative memory and EF stability among many youth with PHIV in our study suggests the possibility of successful self-management of health care and other adult responsibilities if other complications are avoided and adequate partnership and preparation for transition are provided.
Assumption of responsibilities for independent adulthood is challenging for most young people, with incremental change occurring across a number of years. If risk is apparent, our results may guide youth affected by HIV, including PHEU youth, their families and clinicians to identify strategies and implement interventions to support memory and EF prior to and during youth transition to adulthood. Educational or targeted training programs that recognize relative strengths and address specific weaknesses in memory or EF may be beneficial, especially among youth with global cognitive deficits.46–48 Strategy-based interventions that have clear links to functional outcomes of interest may be appropriate for youth with PHIV who are assuming specific and critical adult responsibilities, such as health care self-management.49 Cognitive behavioral therapy to address motivational factors, stress, and mental health may also have direct and indirect effects on new learning and development of adaptive skills, regardless of the presence or absence of memory and/or EF difficulties, particularly in light of increased risk for mood disorders and anxiety among adolescents and young adults with perinatal HIV exposure.50–51 Further study is needed to delineate the efficacy and ecological validity of available approaches.
Our work is not without limitations. Participants who were lost to follow-up may have been at higher risk for memory and EF difficulties or more significant changes in physical health over time than those who remained enrolled. We did not enroll youth who were not fluent in English or those with significant impairment that precluded participation in testing procedures, limiting the generalizability of findings; we also were unable to include an HIV-unexposed comparison group. The two year interval between memory and EF evaluations and use of published alternative forms for two EF subtests, particularly the D-KEFS verbal fluency measure, likely minimized potential practice effects; however, differences in the alternative test norms may account in part for observed scaled score decline in several conditions. It is also possible that our measures of memory and EF were not sufficiently sensitive to subtle or non-linear memory or EF development or regression during the developmental period of adolescence.
Despite limitations, this study is one of the first to describe stability in memory and EF among youth with PHIV during adolescence, when risks to adherence, physical and mental health and behavioral functioning sometimes accelerate. Although HIV is among the diseases with dynamic effects upon the brain and cognitive development throughout life, our results, which warrant confirmation in future studies, allow cautious optimism regarding the cognitive developmental trajectory of children and adolescents with PHIV and their potential readiness to assume age-appropriate responsibilities as they become young adults.
Supplementary Material
Acknowledgments
We thank the children and families for their participation in the Memory/EF Substudy and PHACS and the individuals and institutions involved in the conduct of these studies. The Memory and Executive Functioning Substudy of the Pediatric HIV/AIDS Cohort Study (PHACS) was supported by the National Institute of Mental Health (MH084794; Principal Investigator (PI): Sharon Nichols). Data analysis services were provided by the Center for Biostatistics in AIDS Research, Harvard University T.H. Chan School of Public Health (PI: Paige Williams); data management services were provided by Frontier Science and Technology Research Foundation (PI: Suzanne Siminski); and regulatory services and logistical support were provided by Westat, Inc. (PI: Julie Davidson). Additional support was provided by PHACS, which was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development with co-funding from the National Institute on Drug Abuse, the National Institute of Allergy and Infectious Diseases, the Office of AIDS Research, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, the National Institute on Deafness and Other Communication Disorders, the National Heart Lung and Blood Institute, the National Institute of Dental and Craniofacial Research, and the National Institute on Alcohol Abuse and Alcoholism, through cooperative agreements with the Harvard University T.H. Chan School of Public Health (HD052102) (Principal Investigator: George Seage; Project Director: Julie Alperen) and the Tulane University School of Medicine (HD052104) (Principal Investigator: Russell Van Dyke; Co-Principal Investigators: Kenneth Rich, Ellen Chadwick; Project Director: Patrick Davis).
Appendix
The following institutions, clinical site investigators and staff participated in conducting PHACS AMP; sites participating in the Memory Study and the site PI are marked with an asterisk. In alphabetical order: Ann & Robert H. Lurie Children’s Hospital of Chicago*: Ram Yogev, Margaret Ann Sanders, Kathleen Malee*, Scott Hunter; Baylor College of Medicine*: William Shearer, Mary Paul, Norma Cooper, Lynnette Harris*; Bronx Lebanon Hospital Center: Murli Purswani, Mahboobullah Baig, Anna Cintron; Children’s Diagnostic & Treatment Center*: Ana Puga, Sandra Navarro, Patricia Garvie*, James Blood; Children’s Hospital, Boston*: Sandra Burchett, Nancy Karthas, Betsy Kammerer*; Jacobi Medical Center*: Andrew Wiznia, Marlene Burey, Molly Nozyce*; Rutgers - New Jersey Medical School: Arry Dieudonne, Linda Bettica, Susan Adubato; St. Christopher’s Hospital for Children: Janet Chen, Maria Garcia Bulkley, Latreaca Ivey, Mitzie Grant; St. Jude Children’s Research Hospital*: Katherine Knapp, Kim Allison, Megan Wilkins*; San Juan Hospital/Department of Pediatrics: Midnela Acevedo-Flores, Heida Rios, Vivian Olivera; Tulane University Health Sciences Center*: Margarita Silio, Medea Jones, Patricia Sirois*; University of California, San Diego*: Stephen Spector, Kim Norris, Sharon Nichols*; University of Colorado Denver Health Sciences Center: Elizabeth McFarland, Alisa Katai, Jennifer Dunn, Suzanne Paul; University of Miami: Gwendolyn Scott, Patricia Bryan, Elizabeth Willen.
Footnotes
The conclusions and opinions expressed in this article are those of the authors and do not necessarily reflect those of the National Institutes of Health or U.S. Department of Health and Human Services.
References
- 1.Jacobson LA, Mahone EM. Educational implications of executive dysfunction. In: Hunter SJ, Sparrow EP, editors. Executive function and dysfunction. New York, N.Y.: Cambridge University Press; 2012. pp. 232–246. [Google Scholar]
- 2.Sirois PA, Chernoff MC, Malee KM, et al. Associations of memory and executive functioning with academic and adaptive functioning among youth with perinatal HIV exposure and/or infection. J Pediatric Infect Dis Soc. 2016;5(suppl 1):S24–S32. doi: 10.1093/jpids/piw046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Thames AD, Arentoft A, Rivera-Mindt M, et al. Functional disability in medication management and driving among individuals with HIV: a 1-year follow-up study. J Clin Exp Neuropsychol. 2013;35(1):49–58. doi: 10.1080/13803395.2012.747596. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Heaton RK, Franklin DR, Ellis RJ, et al. HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. J Neurovirol. 2011 Feb;17(1):3–16. doi: 10.1007/s13365-010-0006-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hinkin CH, Castellon SA, Durvasula RS, et al. Mecication adherence among HIV+ adults. Neurology. 2002;59(12):1944–1950. doi: 10.1212/01.wnl.0000038347.48137.67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Woods SP, Moore DJ, Weber E, et al. Cognitive neuropsychology of HIV-associated neurocognitive disorders. Neuropsychol Rev. 2009;19(2):152–68. doi: 10.1007/s11065-009-9102-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Crowell C, Huo Y, Tassiopoulos K, et al. Early viral suppression improves neurocognitive outcomes in HIV-infected children. AIDS. 2015;29:295–304. doi: 10.1097/QAD.0000000000000528. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hermetet-Lindsay K, Correia K, Williams P, et al. Contributions of disease severity, psychosocial factors and cognition to behavioral functioning in US youth perinatally exposed to HIV. AIDS Behav. 2016:1–13. doi: 10.1007/s10461-016-1508-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hochhauser CJ, Gaur S, Marone R, et al. The impact of environmental risk factors on HIV-associated cognitive decline in children. AIDS Care. 2008;20(6):692–9. doi: 10.1080/09540120701693982. [DOI] [PubMed] [Google Scholar]
- 10.Kishiyama MM, Boyce WT, Jimenez AM, et al. Socieoeconomic disparities affect prefrontal function in children. J Cogn Neurosci. 2009;21(6):1106–15. doi: 10.1162/jocn.2009.21101. [DOI] [PubMed] [Google Scholar]
- 11.McEwen BS, Gianaros PJ. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann NY Acad Sci. 2010;1186:190–222. doi: 10.1111/j.1749-6632.2009.05331.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Noble KG, Houston SM, Brito NH, et al. Family income, parental education and brain structure in children and adolescents. Nat Neurosci. 2015;18:773–778. doi: 10.1038/nn.3983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Casey BJ, Jones RM, Hare TA. The adolescent brain. Ann NY Acad Sci. 2008;1124:111–126. doi: 10.1196/annals.1440.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Crone EA. Executive functions in adolescence: Inferences from brain and behavior. Developmental Science. 2009;12(6):825–30. doi: 10.1111/j.1467-7687.2009.00918.x. [DOI] [PubMed] [Google Scholar]
- 15.Giedd JN, Blumenthal J, Jeffries N, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2:861–863. doi: 10.1038/13158. [DOI] [PubMed] [Google Scholar]
- 16.Best JR, Miller PH. A developmental perspective on executive function. Child Dev. 2010;81(6):1641–1660. doi: 10.1111/j.1467-8624.2010.01499.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Giedd JN. Structural magnetic resonance imaging of the adolescent brain. Ann NY Acad Sci. 2004;1021:77–85. doi: 10.1196/annals.1308.009. [DOI] [PubMed] [Google Scholar]
- 18.Luna B, Sweeney JA. The emergence of collaborative brain function: fMRI studies of the development of response inhibition. In: Dahire, Spear LP, editors. Adolescent Brain Development: Vulnerabilities and Opportunities. New York, NY: New York Academy of Sciences; 2004. pp. 296–309. [DOI] [PubMed] [Google Scholar]
- 19.Paus T. Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci. 2005;9:60–68. doi: 10.1016/j.tics.2004.12.008. 2005. [DOI] [PubMed] [Google Scholar]
- 20.Sowell ER, Thompson PM, Toga AW. Mapping changes in the human cortex throughout the span of life. Neurosci. 2004;10:372–392. doi: 10.1177/1073858404263960. [DOI] [PubMed] [Google Scholar]
- 21.Churchill MJ, Deeks SG, Margolis DM, et al. HIV reservoirs: what, where and how to target them. Nat Rev Microbiol. 2015;14:1–6. doi: 10.1038/nrmicro.2015.5. [DOI] [PubMed] [Google Scholar]
- 22.Ene L, Duiculescu D, Ruta S. How much do antiretroviral drugs penetrate into the central nervous system? J. Med Life. 2011;4:432–439. [PMC free article] [PubMed] [Google Scholar]
- 23.Spudich SS. Immune activation in the central nervous system throughout the course of HIV infection. Curr Opin HIV AIDS. 2016;11(2):226–2233. doi: 10.1097/COH.0000000000000243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sturdevant CB, Dow A, Jabara CB, et al. Central nervous system compartmentalization of HIV-1 subtype C variants early and late in infection in young children. PLoS Pathog. 2012;8(12):e1003094. doi: 10.1371/journal.ppat.1003094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Thompson KA, Cherry CL, Bell JE, McLean CA. Brain cell reservoirs of latent virus in presymptomatic HIV-infected individuals. Am J Pathol. 2011;179:1623–1629. doi: 10.1016/j.ajpath.2011.06.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Herting MM, Uban KA, Williams PL, et al. Default mode connectivity in youth with perinatally acquired HIV. Medicine (Baltimore) 2015 Sep;94(37):e1417. doi: 10.1097/MD.0000000000001417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Lewis-de Los Angeles CP, Alpert KI, Williams PL, et al. Deformed subcortical structures are related to past HIV disease severity in youth with perinatally-acquired HIV infection. J Pediatric Infect Dis Soc. 2016;5(suppl 1):S6–S14. doi: 10.1093/jpids/piw051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Uban KA, Herting MM, Williams PL, et al. White matter microstructure among youth with perinatally acquired HIV is associated with disease severity. AIDS. 2015;29:1035–1044. doi: 10.1097/QAD.0000000000000648. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hoare J, Fouche JP, Spottiswoode B, et al. A diffusion tensor imaging and neurocognitive study of HIV-positive children who are HAART-naïve “slow progressors”. J Neurovirol. 2012;18:205–212. doi: 10.1007/s13365-012-0099-9. [DOI] [PubMed] [Google Scholar]
- 30.Hoare J, Fouche J-P, Phillips N, et al. Clinical associations of white matter damage in cART-treated HIV-positive children in South Africa. J Neurovirol. 2015;21:120–128. doi: 10.1007/s13365-014-0311-1. [DOI] [PubMed] [Google Scholar]
- 31.Hoare J, Fouche J-P, Phillips N, et al. White matter micro-structural chaanges in ART-naïve and ART-treated children and adolescents infected with HIV in Sourh Africa. AIDS. 2015;29:1793–1801. doi: 10.1097/QAD.0000000000000766. [DOI] [PubMed] [Google Scholar]
- 32.Bisiacchi PS, Suppiej A, Laverda A. Neuropsychological evaluation of neurologically asymptomatic HIV-infected children. Brain Cogn. 2000 Jun-Aug;43(1):49–52. [PubMed] [Google Scholar]
- 33.Koekkoek S, de Sonneville LM, Wolfs TF, et al. Neurocognitive function profile in HIV-infected school-age children. Eur J Paediatr Neurol. 2008;12(4):290–97. doi: 10.1016/j.ejpn.2007.09.002. [DOI] [PubMed] [Google Scholar]
- 34.Linn K, Fay A, Meddles K, et al. HIV-related cognitive impairment of orphans in Myanmar with vertically transmitted HIV taking antiretroviral therapy. Pediatr Neurol. 2015;53(6):485–90. e1. doi: 10.1016/j.pediatrneurol.2015.08.004. [DOI] [PubMed] [Google Scholar]
- 35.Phillips N, Amos T, Kuo C, et al. HIV-associated cognitive impairment in perinatally infected children: A meta-analysis. Pediatrics. 2016;138(5):e20160893. doi: 10.1542/peds.2016-0893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Llorente AM, Brouwers P, Leighty R, et al. An analysis of select emerging executive skills in perinatally HIV-1-infected children. Appl Neuropsychol Child. 2014;3(1):10–25. doi: 10.1080/21622965.2012.686853. [DOI] [PubMed] [Google Scholar]
- 37.Nichols S, Brummel SS, Smith RA, et al. Executive functioning in children and adolescents with perinatal HIV infection. Pediatr Infect Dis J. 2015;34(9):969–75. doi: 10.1097/INF.0000000000000809. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Centers for Disease Control. Revised classification system for human immunodeficiency virus infection in children less than 13 years of age. MMWR Morb Mort Wkly Rep. 1994;43(1–12) [Google Scholar]
- 39.Nichols SL, Chernoff MC, Malee K, et al. Learning and memory in children and adolescents with perinatal HIV infection and perinatal HIV exposure. Pediatr Infect Dis J. 2016;35(6):549–54. doi: 10.1097/INF.0000000000001131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Nichols SL, Chernoff MC, Malee KM, et al. Executive functioning in children and adolescents with perinatal HIV infection and perinatal HIV exposure. J Pediatric Infect Dis Soc. 2016;5(suppl 1):S15–S23. doi: 10.1093/jpids/piw049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.https://phacsstudy.org
- 42.Shesow D, Adams W. Wide Range Assessment of Memory and Learning. Second. Wilmington, DE: Wide Range, Inc; 2003. [Google Scholar]
- 43.Delis DC, Kaplan E, Kramer JH. Delis-Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation; 2001. [Google Scholar]
- 44.Delis DC, Kramer JH, Kaplan E, et al. Reliability and validity of the Delis-Kaplan Executive Function System: An update. J Int Neuropsychol Soc. 2004;10:301–313. doi: 10.1017/S1355617704102191. [DOI] [PubMed] [Google Scholar]
- 45.Smith R, Wilkins M. Perinatally acquired HIV infection: Long-term neuropsychological consequences and challenges ahead. Child Neuropsychol. 2014;21:1–35. doi: 10.1080/09297049.2014.898744. [DOI] [PubMed] [Google Scholar]
- 46.Kirk HE, Gray K, Riby DM, et al. Cognitive training as a resolution for early executive function difficulties in children with intellectual disabilities. Res Dev Disabil. 2015;38:145–160. doi: 10.1016/j.ridd.2014.12.026. [DOI] [PubMed] [Google Scholar]
- 47.Karbach J, Unger K. Executive control training from middle childhood to adolescence. Front Psychol. 2014;5:1–14. doi: 10.3389/fpsyg.2014.00390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Slomine B, Locascio G, Kramer M. Empirical status regarding remediation of executive skills. In: Hunter SJ, Sparrow EP, editors. Executive function and dysfunction. New York, N.Y.: Cambridge University Press; 2012. pp. 208–230. [Google Scholar]
- 49.McCabe JA, Redick TS, Engle RW. Brain-training pessimism, but applied memory optimism. Psychol Sci Public Interest. 2016;17(3):187–191. doi: 10.1177/1529100616664716. [DOI] [PubMed] [Google Scholar]
- 50.Mellins CA, Elkington KS, Leu S-L, et al. Prevalence and change in psychiatric disorders among perinatally HIV-infected and HIV-exposed youth. AIDS Care. 2012;24(8):953–62. doi: 10.1080/09540121.2012.668174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Diamond A, Ling DS. Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Dev Cognit Neurosci. 2016;18:34–48. doi: 10.1016/j.dcn.2015.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
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