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Topological organization of whole-brain white matter in HIV infection
*Laurie M Baker1, *Sarah A Cooley2 (co-first author), Ryan P Cabeen3, David H Laidlaw4, John
A Joska5, Jacqueline Hoare5, Dan J Stein5,6, Jodi M Heaps-Woodruff7, Lauren E Salminen3,
Robert H Paul1,7
1University of Missouri – Saint Louis, Department of Psychology, One University Boulevard,
Stadler Hall 327, Saint Louis, MO 63121, 314-566-3761, lauriebaker@umsl.edu
2Washington University in Saint Louis, School of Medicine, Department of Neurology, Saint
Louis, MO 63110
3University of Southern California, Keck School of Medicine, Los Angeles, CA 90032
4Brown University, Computer Science Department, Providence, RI 02912
5University of Cape Town, Department of Psychiatry and Mental Health, Cape Town,
South Africa
6 MRC Unit on Anxiety & Stress Disorders, Cape Town, South Africa
7Missouri Institute of Mental Health, St. Louis, MO 63134
*= contributed equally
Brain Connectivity
© Mary Ann Liebert, Inc.
DOI: 10.1089/brain.2016.0457
Page 1 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
2
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
Abstract
Infection with human immunodeficiency virus (HIV) is associated with neuroimaging
alterations. However, little is known about the topological organization of whole-brain networks
and the corresponding association with cognition. As such, we examined structural whole-brain
white matter connectivity patterns and cognitive performance in 29 HIV+ young adults (mean
age = 25.9) with limited or no HIV treatment history. HIV+ participants and demographically
similar HIV– controls (n = 16) residing in South Africa underwent magnetic resonance imaging
(MRI) and neuropsychological testing. Structural network models were constructed using
diffusion MRI-based multi-fiber tractography and T1-weighted MRI-based regional gray matter
segmentation. Global network measures included whole-brain structural integration, connection
strength, and structural segregation. Cognition was measured using a neuropsychological global
deficit score (GDS) as well as individual cognitive domains. Results revealed that HIV+
participants exhibited significant disruptions to whole-brain networks, characterized by weaker
structural integration (characteristic path length and efficiency), connection strength, and
structural segregation (clustering coefficient) compared to HIV– controls (p values < 0.05). GDS
scores and performance on learning/recall tasks were negatively correlated with the clustering
coefficient (p < 0.05) in HIV+ participants. Results from the present study indicate disruption to
brain network integrity in treatment limited HIV+ young adults with corresponding
abnormalities in cognitive performance.
Keywords: HIV; cognition; whole-brain connectivity; network analysis
1.0 Introduction
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
The human immunodeficiency virus (HIV) crosses the blood brain barrier shortly after
seroconversion (~8 days) and prior to marked immune suppression and overt cognitive
dysfunction (Valcour et al., 2012). Despite the efficacy of combination antiretroviral therapy
(cART) in reducing viral load, current treatments do not appear to prevent or reverse existing
brain damage (Ances, Ortega, Vaida, Heaps, & Paul 2013; Harezlak et al., 2011; Heaton et al.,
2011). Importantly, research shows axonal disruption and synaptic injury following HIV
infection (Avdoshina, Bachis, & Mocchetti, 2013; Ellis, Langford, & Masliah, 2007; Everall et
al., 1999; Everall et al., 2010; Masliah et al., 1997). Although specific brain regions appear
uniquely vulnerable, HIV-mediated neuronal damage is present throughout the brain (Ellis,
Langford, & Masliah, 2007; Ragin et al., 2004) and corresponds to neuropsychological
dysfunction (Masliah et al., 1997).
Diffusion tensor imaging (DTI) provides a robust method for identifying disruptions to
the structural connections throughout the brain. Multiple studies utilizing DTI reveal
abnormalities in brain white matter capable of disrupting connectivity across brain regions in
HIV+ individuals (Filippi et al., 2001, Ragin et al., 2004; Thurnher et al., 2005; Gongvatana et
al., 2009; Hoare et al., 2011). Further, using complex network analysis, structural changes in
white matter connections can be effectively modeled by combining diffusion magnetic resonance
imaging (MRI)-based tractography and T1-weighted MRI–based regional gray matter
segmentation. This network-based approach is highly sensitive to alterations in brain integrity
across multiple disease pathologies including schizophrenia, Alzheimer’s disease, and major
depressive disorder (Bullmore and Sporns 2009; Bassett et al., 2010; He et al., 2008; Lo et al.,
2010; Zhang et al., 2011; Bassett et al., 2008; Yu et al., 2011).
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
Complex network analysis has been recently applied to investigate signatures of HIV
neuropathogenesis (Jahanshad et al., 2012). In this study, significant disruptions to brain
connectivity were identified in older HIV+ adults on cART. However, the relationship between
the topological organization of white matter and cognitive function in HIV remains unclear.
Further, no studies have examined connectivity metrics (e.g., structural segregation, structural
integration, and connection strength) in younger HIV+ individuals. It is necessary to fill this gap
in the literature in order to determine the functional relevance of white matter connectivity in
HIV+ individuals, independent of advanced age.
We used diffusion MRI-based tractography and graph-theoretic approaches to investigate
the topological organization of white matter in 29 HIV+ young adults and 16 HIV–
demographically similar controls utilizing fiber-bundle length (FBL)-defined whole brain
connectivity metrics (structural segregation, structural integration, and connection strength).
These metrics provide insight into communication between regions of the brain. We also
examined the relationship between whole-brain topological organization and cognitive
performance using a global deficit score (GDS) and individual cognitive domain deficit scores
(learning/recall, psychomotor/processing speed, executive function, fine motor skills and
dexterity, and visuospatial skills). We hypothesized that whole-brain topological organization
would be diminished in HIV+ individuals compared to HIV-controls, and the degree of
abnormalities in the three connectivity metrics would significantly correlate with poorer
cognitive performance in young HIV+ individuals.
2.0 Methods
2.1 Participants
HIV+ participants were recruited from primary care HIV clinics in Cape Town, South
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
Africa. Patients who were in the pretreatment counseling phase were identified from clinic
records. Interested participants completed a comprehensive consent process followed by a
detailed medical and demographic history. All participants were either treatment naïve at
enrollment (83%), or had initiated cART within three months of enrollment (17%). All but five
participants began treatment within one month of enrollment. HIV– participants were recruited
from regional Voluntary Counseling and Testing Clinics in Cape Town, South Africa. Table 1
provides demographic information for the 29 HIV+ and 16 HIV– participants.
Inclusion criteria for HIV+ participants included: (1) age between the years of 18 and 45;
(2) Xhosa as the primary language; (3) HIV serostatus documented by ELISA and confirmed by
Western blot, plasma HIV RNA, or a second antibody test for the HIV+ group; and (4) at least 7
years of formal education (all but one participant reported at least 10 years of education).
Exclusion criteria for all participants included (1) any major psychiatric condition that could
significantly affect cognitive status (e.g., schizophrenia or bipolar disorder); (2) confounding
neurological disorders including multiple sclerosis and other central nervous system (CNS)
conditions; (3) head injury with loss of consciousness greater than 30 min; (4) clinical evidence
of opportunistic CNS infections (toxoplasmosis, progressive multifocal leukoencephalopathy,
neoplasms); and (5) current substance use disorder determined by the Mini-International
Neuropsychiatric Interview Plus (MINIPlus) (Sheehan et al., 1998). All participants provided
signed informed consent. Study procedures were approved by local university IRB committees.
2.2 HIV viral load and CD4 T-cell counts
EDTA blood samples were collected at the time of study visit and plasma and cell
aliquots were stored at −70 °C. RNA was isolated from patient samples using the Abbott
RealTime HIV-1 amplification reagent kit, according to the manufacturer’s instructions. Viral
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
load was determined using the Abbott m2000sp and the Abbott m2000rt analyzers (Abbott
laboratories, Abbott Park, IL, USA). All HIV+ participants had a detectable viral load (range
183-1,759,510 copies/ml). Analyses of cells from fresh blood samples were completed on the
FACSCalibur flow cytometer in conjunction with the MultiSET V1.1.2 software (BD
Biosciences, San Jose, CA, USA) for CD4 T-cell counts.
2.3 Neuroimaging Acquisition
Neuroimaging was acquired on a 3T Siemens Allegra scanner (Siemens AG, Erlangen
Germany), with a 4-channel phased-array head coil. Thirty unique diffusion gradient directions
at b=1000 s/mm2 were repeated to give a total of 60 diffusion weighted volumes using a
customized single-shot multi-slice echo-planar tensor-encoded imaging sequence. Six baseline
images were acquired and interleaved in the diffusion-weighted scans to improve motion-
correction. Seventy contiguous slices were obtained per contrast with a 128 x 128 matrix and
field of view of 218 x 218 mm (isotropic 1.7 x 1.7 x 1.7 mm3 voxels); TR: 10s, TE: 103 ms using
a full-Fourier transform. We also acquired a T1-weighted 3-dimensional magnetization-prepared
rapid acquisition gradient echo (MP-RAGE) sequence [time of repetition (TR) = 2400 ms, echo
time (TE = 2.38 ms), inversion time (TI) = 1000 ms flip angle = 8 degrees, 162 slices, and voxel
size = 1 x 1 x 1 mm3 for volumetric analyses.
2.4 Neuroimaging analysis
The T1-weighted MR images were processed with Freesurfer version 5.1.0 (Fischl et al.,
2012) to obtain a high-resolution gray matter parcellation. The diffusion-weighted MR images
were processed with a pipeline including FSL 5.0 (Jenkinson et al., 2012) and custom software,
described as follows. First, FSL eddy correct was used to correct for motion and eddy currents by
registering each diffusion-weighted volume to the first baseline with an affine
Page 6 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
7
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
transformation. The gradient-encoding vectors were also rotated to account for the spatial
transformation of each volume (Leemans et al., 2009). Then, FSL BET was run for brain
extraction, and XFIBRES was run to obtain ball-and-sticks diffusion models in each voxel
(Behrens et al., 2007). Model fitting was performed with two stick compartments to improve
tractography in areas with complex anatomy, such as crossing fibers. Whole-brain deterministic
streamline tractography was performed to obtain geometric models of white matter pathways.
Tractography was executed utilizing an extension of the standard streamline approach to
use multiple fibers per voxel with the following parameters: four seeds per voxel, an angle
threshold of 50 degrees, a minimum length of 10 mm, and a minimum volume fraction of
0.1. During tracking, a kernel regression estimation framework (Cabeen et al., 2016) was used
for smooth interpolation of the multi-fiber ball-and-sticks models with a Gaussian kernel using a
spatial bandwidth of 1.5 mm and voxel neighborhood of 7x7x7. Then a subject-specific
structural network model was constructed from the combination of diffusion MR tractography
and T1-weighted MRI gray matter labels from the Desikan-Killiany atlas (Desikan et al., 2006)
and subcortical segmentations obtained from Freesurfer. For each pair of regions, a structural
connection was defined by first selecting fibers with endpoints in pairs of gray matter areas and
then computing the average FBL of the selected fibers to represent connection strength (Correia
et al. 2008). To avoid resampling artifacts, the tractography was performed in native space and
then the curve data were transformed to T1-space to test for intersection with gray matter
regions. This step registered the T1-weighted MRI to the average baseline diffusion scan using
FSL FLIRT with the mutual information criteria and an affine transformation. The resulting
weighted undirected connectivity matrix was analyzed with the Brain Connectivity Toolbox
(http://https://sites.google.com/site/bctnet/) to obtain global network measures of connection
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
8
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
strength, structural segregation (clustering coefficient), and structural integration (characteristic
path length and global efficiency) (Figure 1; Rubinov & Sporns 2010).
2.5 Neuropsychological evaluation
The neuropsychological battery included tests of the following domains: Learning/recall-
(1) Hopkins Verbal Learning Test-Revised (HVLT-R; Brandt & Benedict 2001), and (2) Brief
Visuospatial Memory Test-Revised (BVMT-R; Benedict et al., 1996). Total correct on the
immediate and delayed recall trials were defined as the dependent variables for the HVLT-R and
BVMT-R. Psychomotor/processing speed- (1) Color Trails 1 (D’Elia et al., 1996), (2) Trail
Making Test A (Reitan, 1955), and (3) Digit Symbol (Wechsler, 2008). Time to completion was
the dependent variable for Color Trails 1 and Trail Making Test A. Total correct was the
dependent variable for Digit Symbol. Executive function- (1) Color Trails 2 (D’Elia et al., 1996),
and (2) Wisconsin Card Sorting Test (WCST; Grant & Berg, 1993). Time to completion was the
dependent variable for Color Trails 2, and total perseveration errors served as the dependent
variable for the WCST. Visuospatial skills- Block Design from the WAIS-IV (Wechsler 1997).
Total correct was the dependent variable. Fine motor skills and dexterity- Grooved Pegboard
Test (GPT; Kløve, 1963) non-dominant hand. Time to completion was the dependent variable.
2.6 Determination of domain specific and global neuropsychological function
For data reduction purposes, raw data from the neuropsychological test battery were
converted to T scores using mean and standard deviations from a sample of 52 HIV– individuals
recruited from South Africa. A deficit score (ranging from 0-5 with a score of 0 indicating
normal range and greater scores indicating greater impairment) for each test was determined
using the methods previously reported by Carey and colleagues (2004). This approach provides a
more sensitive method for generating a summary neuropsychological score than averaging
Page 8 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
9
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
neuropsychological scores (Carey et al., 2004; Heaton et al. 2004). A GDS was then obtained
for each participant, with higher scores indicative of greater impairment. A GDS provides a
continuous measure of impairment with scores > 0.5 providing high rates of specificity (0.89)
and positive predictive value (0.83) in establishing HIV-associated impairment (Carey et al.,
2004; Heaton et al., 2004).
Domain specific deficit scores were calculated using similar methods as the calculation
for the GDS. Specifically, standardized T scores for each neuropsychological test were converted
to a deficit score between 0-5. The deficit scores were averaged to determine domain specific
deficit scores (i.e., learning/recall, psychomotor/processing speed, executive function, fine motor
skills and dexterity, and visuospatial skills).
2.7 Statistical Analysis
All statistical analyses were conducted utilizing SPSS, version 24. Differences in age,
sex, and education between HIV+ and HIV– participants were examined using independent
sample t-tests (age and education) and chi-squared analyses (sex) to determine potential
covariates for the primary analyses. Differences in whole-brain topological organization between
groups were examined using three separate analyses of covariance or multivariate analyses of
covariance (ANCOVA/MANCOVA) models, depending on the number of metrics in each
category. HIV serostatus served as the independent variable and individual measures of
topological organization served as dependent variables in each analysis, with intracranial volume
(ICV) as a covariate. The measures of topological organization included structural segregation
(clustering coefficient), structural integration (characteristic path length and global efficiency),
and connection strength. Viral load was natural log transformed to achieve a normal distribution
for correlation analyses. Pearson’s correlations were used to determine if individual measures of
Page 9 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
10
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
connectivity were significantly related to HIV clinical variables (CD4 T-cell count and log
transformed viral load).
With respect to the distribution of GDS and domain specific deficit scores, the
standardized skewness coefficients and the standardized kurtosis coefficients revealed significant
departures from normality in the entire sample and within the HIV+ group. Therefore, a
nonparametric procedure, the Spearman’s rank order correlation (i.e., Spearman's rho), was
performed to address all correlations that included the GDS or domain scores. These analyses
were performed within the HIV+ sample as well as collapsed across the HIV+ and HIV– groups.
3.0 Results
Subject characteristics are listed in Table 1. There were no statistically significant
differences in demographic factors (age, education, and sex) between HIV+ and HIV–
participants. The ANCOVA/MANCOVA models revealed significantly weaker structural
segregation in HIV+ participants, defined by a lower clustering coefficient (F(1,42) = 11.20, p =
0.002 ), Cohen’s d = 1.06), as well as weaker structural integration defined by higher
characteristic path length and lower global efficiency (Wilks’ Λ = 0.77, F(2,41)= 6.10 , p =
0.005, d = 0.79), with characteristic path length F(1,42)= 12.23, p = 0.001, d = 1.12) and global
efficiency F(1,41)= 12.33 , p = 0.001, d = 1.12) both significantly contributing to the model.
Lastly, HIV+ participants showed weaker connection strength (F(1,42) = 8.29, p = 0.006, d =
0.92) (Table 2). Pearson’s correlational analyses revealed that CD4 T-cell count and viral load
were not significantly associated with any individual measures of connectivity (r values < |0.30|;
p values > 0.05).
Relationships Between GDS Scores and Connectivity Metrics
Collapsed across HIV+ and HIV– participants, Spearman’s rho revealed statistically
Page 10 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
11
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
significant correlations between GDS scores with global characteristic path length (r = 0.34, p =
0.027) and mean connection strength (r = –0.31, p = 0.046). Trend level relationships were also
observed with global efficiency (r = –0.30, p = 0.057) and clustering coefficient (r = –0.30, p =
0.055). Together, these results indicate that poorer cognitive performance is associated with
abnormal network indices. When examined specifically within the HIV+ sample, Spearman’s
rho showed statistically significant negative relationships between the GDS and the clustering
coefficient (r = –0.40, p = 0.042), and trend level negative associations with global efficiency (r
= –0.38, p = 0.056) and connection strength (r = –0.37, p = 0.062). A trend level positive
relationship was observed between the GDS and characteristic path length (r = 0.37, p = 0.060).
Relationships Between Domain-specific Deficit Scores and Connectivity Metrics
Collapsed across HIV+ and HIV– participants, poorer learning/recall was significantly
associated with higher global characteristic path length (r = 0.36, p = 0.010), lower mean
connection strength (r = –0.37, p = 0.012), lower global efficiency (r = –0.36, p = 0.016) and
lower clustering coefficient (r = –0.39, p = 0.009). No other significant relationships were
observed between the brain connectivity metrics and psychomotor/processing speed, executive
function, fine motor skills and dexterity, or visuospatial skills (r values < |0.30|, p values > 0.05).
When examined specifically within the HIV+ sample, learning/recall deficit scores were
significantly negatively associated with the clustering coefficient (r = –0.40, p = 0.037).
Negative trend level relationships were observed between learning/recall deficit scores and mean
connection strength (r = –0.36, p = 0.064) and global efficiency (r = –0.36, p = 0.058), whereas a
trend level positive relationship was observed with global characteristic length (r = 0.36, p =
0.052). No significant relationships were observed between the connectivity metrics and
psychomotor/processing speed, executive function, fine motor skills and dexterity, or
Page 11 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
12
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
visuospatial skills (r values < |0.30|, p values > 0.05) in the HIV+ sample.
4.0 Discussion
The current study revealed topological disorganization of brain white matter in HIV,
including abnormalities in structural segregation, structural integration, and connection strength.
Further, these abnormalities in network connectivity metrics were significantly associated with
cognitive dysfunction both across the entire sample and specifically within the HIV+ group.
These abnormalities were not significantly related to HIV clinical status (CD4 T-cell count and
viral load). Findings indicate that younger HIV+ participants with limited or no antiretroviral
treatment history exhibit significantly altered measures of whole-brain connectivity relative to
demographically similar HIV– controls. These data suggest that alterations in whole-brain
network disruption are behaviorally relevant in the context of HIV.
Structural segregation refers to neural processing within interconnected regions of the
brain, whereas structural integration refers to the potential to rapidly combine specialized
information from distributed brain networks. The interplay of segregation and integration in
brain networks generates information that is simultaneously diversified and synthesized,
resulting in patterns of high complexity. Extensive research indicates that the dynamic patterns
generated by these networks provide the basis for cognition and perception (Bressler & Kelso,
2001; Frackowiak, 2004; McIntosh, 1999; Varela, Lachaux, Rodriguez, & Martinerie, 2001).
Underlying these global properties is a measure of connectivity between brain regions, which we
examined with the average FBL of tractography curves. Overall lower structural segregation
(clustering coefficient), structural organization (characteristic path length and global efficiency),
and connection strength were observed, indicating that HIV is associated with abnormal whole-
brain network connectivity.
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Neuroimaging studies have revealed consistent disruptions to subcortical and cortical
brain structures among individuals infected with HIV (Archibald et al., 2004; Stout et al., 1998;
Berger and Arendt 2000; Ances, Ortega, Vaida, Heaps, & Paul, 2012; Becker et al., 2011; Cohen
et al., 2010; Ragin et al., 2012; Heaps et al., 2012). Specifically, reduced volumes have been
observed within the thalamus, caudate, putamen, hippocampus, cortical white matter, and gray
matter (Ances, Ortega, Vaida, Heaps, & Paul, 2012; Holt et al., 2012; Ortega et al., 2013; Paul et
al., 2008; Paul et al., 2016; Thompson et al., 2005). Individuals with more advanced disease
exhibit reduced cortical thickness in primary sensory and motor areas (Thompson et al., 2005),
possibly reflecting distal effects of basal ganglia damage. Results from Jahanshad et al. (2012)
revealed pronounced white matter network disruption in primary motor and sensory areas of the
parietal and frontal lobes of older HIV individuals on stable treatment. Our study extends
previous work by revealing global network disruption in younger HIV+ individuals with immune
suppression and limited or no treatment history.
DTI abnormalities observed using scalar metrics in frontal, callosal, and deep white matter
regions in HIV+ individuals have been associated with poor cognitive performance (Chang et al.,
2008; Chen et al., 2009; Müller-Oehring et al., 2010; Pomara et al., 2001; Thurnher et al., 2005;
reviewed in Hardy and Hinkin, 2002; Hoare et al., 2015; Jernigan et al., 1993; Ortega et al.,
2013; Stout et al., 1998). Our results reveal a strong association between cognitive dysfunction
and diffuse brain network disruption in HIV+ young adults. Collapsed across HIV+ and HIV–
participants, we observed significant associations between both GDS and learning/recall with
structural integration (characteristic path length) and connection strength, indicative of reduced
information transfer across networks (Latora and Marchiori, 2001) and reduced FBL.
Conversely, the most prominent relationships in the HIV+ group were observed between
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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structural segregation (clustering coefficient) and both global neuropsychological impairment as
well as learning/recall. This pattern of structural abnormalities provides evidence of cognitive
impairment related to a measure of neural processing within densely interconnected networks.
Inflammation is hypothesized to be one of many important drivers of neuronal injury and
loss in HIV. Inflammation occurs soon after viral entry into the central nervous system (CNS)
and is associated with the release of proinflammatory cytokines, chemokines, and neurotoxic
viral proteins in response to HIV-infected macrophages and microglia (Anthony et al., 2005;
Lentz et al., 2011; Sailasuta et al., 2012; Harezlak et al., 2011; Valcour et al., 2012; Vera et al.,
2016). In turn, these activate uninfected macrophages and microglia to further release neurotoxic
substances that lead to compromised synaptodendritic connections, damage to axonal and myelin
integrity, and potentially neuronal death (Conant et al., 1998; Raja et al., 1997). These injuries
are distributed widely throughout the brain and correspond to white matter damage (Ellis,
Langford, & Masliah, 2007) as well as cognitive impairment (Everall et al., 1999).
An advantage of our study is the tractography method employed to quantify structural
connectivity. Typically, a major challenge of estimating whole-brain connectivity metrics is the
presence of complex configurations of fiber bundle anatomy such as fiber crossings. The
diffusion tensor model does not accurately represent voxels consisting of multiple fiber
populations, which limits the anatomical validity of network models derived using single tensor
models. More sophisticated techniques that represent multiple fibers, such as multi-compartment
and high angular resolution diffusion imaging, offer greater anatomical accuracy and improved
sensitivity in detecting complex anatomical features related to white matter changes due to
disease (Tuch et al., 1999; Tuch et al., 2002). We used the ball-and-sticks multi-compartment
model (Behrens et al., 2007) and a model-based estimation framework (Cabeen et al., 2016) to
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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improve the accuracy of connectivity mapping. Importantly, while this approach is ideal for the
single shell data, more sophisticated microstructure models that utilize multi-shell acquisitions
may provide improved anatomical accuracy and sensitivity to detect white matter changes.
Future studies may benefit by using neurite orientation dispersion and density imaging (NODDI)
to characterize changes in neurite density and orientation dispersion (Zhang et al., 2012).
Several limitations are important to address. First, we did not have sufficient numbers of
male HIV+ participants to examine sex differences in brain network topology. Previous research
conducted in HIV– populations reveals sex differences in brain topology (Gong et al., 2009; Yan
et al., 2010), emphasizing the importance of examining sex differences in future studies.
Additionally, future research is needed to determine whether treatment improves whole-brain
connectivity abnormalities. Lastly, we excluded participants with substance use disorder due to
evidence that structural connectivity is disrupted in substance users independent of HIV (Bava et
al., 2009; Kim et al., 2014). Our approach ensured that the observed effects were not
confounded by substance use. However, our results may not generalize to the population of
HIV+ substance users. Despite these limitations, our findings provide strong evidence for
functionally relevant disruptions to network organization in HIV.
5.0 Conclusions
The current manuscript extends the literature in three novel ways. First, our cohort was
comprised of young HIV+ adults. Second, our sample was predominantly free of treatment
confounds on brain connectivity. Lastly, the present study included measures of cognition that
inform the functional relevance of the connectivity measures. Collectively, the results support a
model of diffuse network changes in young HIV+ individuals with limited or no treatment
history and corresponding cognitive dysfunction. The results provide further evidence of the
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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utility of anatomical brain connectivity as a noninvasive biomarker of white matter disruption in
HIV infection.
Acknowledgments
There are no actual or potential conflicts of interest for any of the authors on this manuscript.
Funding was supported by the National Institute of Mental Health (MH085604). Dr. Stein is
supported by the Medical Research Council of South Africa.
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Page 27 of 33
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Page 28 of 33
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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29
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Table 2. Differences in network connectivity between HIV+ and HIV– participants
HIV+ (n =29)
HIV– (n=16)
p value
Global Clustering Coefficient (mm)
53.18 (4.47)
57.67 (3.37)
0.002
Global Characteristic Path Length (1/mm)
0.015 (0.0012)
0.013 (0.0009)
0.001
Global Efficiency (mm)
77.86 (7.17)
85.62 (6.54)
0.001
Global Connection Strength (mm)
2939.75 (385.99)
3271.76 (312.58)
0.006
Note: Mean (SD)
Table 1. Subject Characteristics
HIV+ (n=29)
HIV– (n=16)
p value
Mean Age ± SD (range)
25.89 ± 2.12 (22-29)
24.69 ± 4.53 (20-32)
0.55
Mean Education ± SD (range)
10.76 ± 0.69 (10-12)
10.94 ± 1.29 (7-12)
0.31
Sex (% Male)
17%
31%
0.46
Mean recent CD4 (cells/mm3) ± SD (range)
249.79 ±164.23 (35-799)
Mean plasma VL (copies/ml)a ± SD (range)
4.21 ± 1.06 (2.26-6.25)
Mean months of infection± SD (range)
9.33 ± 19.56 (0-97)
% Prescribed Antiretroviral Therapy
17%
Mean Intracranial Volume (cm3) ± SD (range)
1,307.15 ± 246.69 (1027.86-1728.54)
1,345.50 ± 211.64 (961.03-2033.13)
0.60
Mean Global Deficit Score ± SD (range)
0.34 ± 0.32 (0-1)
0.18 ± 0.22 (0-0.82)
0.06
Note: aViral load log10 transformed
Page 30 of 33
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Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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Brain Connectivity
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Table 1. Subject Characteristics
Note: aViral load log10 transformed
Table 2. Differences in network connectivity between HIV+ and HIV– individuals
Note: Mean (SD)
Page 31 of 33
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Figure 1. Structural Network Analysis Visualizations
Left: A visualization of imaging-based reconstructions of anatomy, showing diffusion MRI-based
tractography and T1-weighted MRI-based gray matter segmentations. The left hemisphere shows
Desikan-Killiany regions-of-interest, and the right hemisphere shows streamline tractography
curves used to define connectivity between regions.
Right: A visualization of a structural network model derived from neuroimaging data. The left
hemisphere shows Desikan-Killiany regions-of-interest, and the right hemisphere shows a node-
link diagram representing the topological organization of white matter. Nodes are placed at the
centroid of each region and the links are derived from the average fiber bundle length between the
pairs of regions with structural connections.
Figure 1. Structural Network Analysis Visualizations
Figure 1: Left: A visualization of imaging-based reconstructions of anatomy, showing diffusion
MRI-based tractography and T1-weighted MRI-based gray matter segmentations. The left
Page 32 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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hemisphere shows Desikan-Killiany regions-of-interest, and the right hemisphere shows
streamline tractography curves used to define connectivity between regions.
Right: A visualization of a structural network model derived from neuroimaging data. The left
hemisphere shows Desikan-Killiany regions-of-interest, and the right hemisphere shows a node-
link diagram representing the topological organization of white matter. Nodes are placed at the
centroid of each region and the links are derived from the average fiber bundle length between
the pairs of regions with structural connections.
Page 33 of 33
Brain Connectivity
Topological organization of whole-brain white matter in HIV infection (doi: 10.1089/brain.2016.0457)
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