Topological organization of whole-brain white matter in HIV infection

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 multifiber tractography and T(1)-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) than HIV− controls (p < 0.05). GDSs and performance on learning/recall tasks were negatively correlated with the clustering coefficient (p < 0.05) in HIV+ participants. Results from this study indicate disruption to brain network integrity in treatment-limited HIV+ young adults with corresponding abnormalities in cognitive performance

Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective

HIV is capable of invading the brain soon after seroconversion. This ultimately can lead to deficits in multiple cognitive domains commonly referred to as HIV-associated neurocognitive disorders (HAND). Clinical diagnosis of such deficits requires detailed neuropsychological assessment but clinical signs may be difficult to detect during asymptomatic injury of the central nervous system (CNS). Therefore neuroimaging biomarkers are of particular interest in HAND. In this study, we constructed brain connectivity profiles of 40 subjects (20 HIV positive subjects and 20 age-matched seronegative controls) using two different methods: a non-linear mutual connectivity analysis approach and a conventional method based on Pearson's correlation. These profiles were then summarized using graph-theoretic methods characterizing their topological network properties. Standard clinical and laboratory assessments were performed and a battery of neuropsychological (NP) tests was administered for all participating subjects. Based on NP testing, 14 of the seropositive subjects exhibited mild neurologic impairment. Subsequently, we analyzed associations between the network derived measures and neuropsychological assessment scores as well as common clinical laboratory plasma markers (CD4 cell count, HIV RNA) after adjusting for age and gender. Mutual connectivity analysis derived graph-theoretic measures, Modularity and Small Worldness, were significantly (p < 0.05, FDR adjusted) associated with the Executive as well as Overall z-score of NP performance. In contrast, network measures derived from conventional correlation-based connectivity did not yield any significant results. Thus, changes in connectivity can be captured using advanced time-series analysis techniques. The demonstrated associations between imaging-derived graph-theoretic properties of brain networks with neuropsychological performance, provides opportunities to further investigate the evolution of HAND in larger, longitudinal studies. Our analysis approach, involving non-linear time-series analysis in conjunction with graph theory, is promising and it may prove to be useful not only in HAND but also in other neurodegenerative disorders

MR Imaging Biomarkers in HIV associated Neurocognitive Impairment in the Era of cART

HIV associated neurocognitive disorder (HAND) continues to occur despite virally suppressive combination of antiretroviral therapy. The viral toxins, neuroinflammation secondary to host factor (ARV toxicity, immune reconstitution are additional factors) and the comorbidities in combination or individually appear to drive the ongoing HAND. Although in the pre-cART era the biomarkers of HIV dementia were clearly laid out in terms of clinical, biochemical and imaging criteria, in the cART era this has become more blurred. Some of the observations drawn from the imaging studies to identify the pathological underpinnings have shown conflicting results by different authors. The cause of these contradictory imaging observations are multifocal but principally linked to the observation that “HIV neural injury is not a one-time event”. Therefore, the paradigm of imaging should be tailored to the diversity of the disease spectrum. I have used the advanced imaging techniques to identify if there are any imaging techniques which can demonstrate the ongoing neural injury as well as monitor the response to the therapy in this research using both cross sectional and longitudinal experiments. I have also explored if there is any imaging equivalent to identify the neuroinflammation

The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models

EEG and ERP biomarkers of Alzheimer's disease: a critical review.

Here we critically review studies that used electroencephalography (EEG) or event-related potential (ERP) indices as a biomarker of Alzheimer's disease. In the first part we overview studies that relied on visual inspection of EEG traces and spectral characteristics of EEG. Second, we survey analysis methods motivated by dynamical systems theory (DST) as well as more recent network connectivity approaches. In the third part we review studies of sleep.  Next, we compare the utility of early and late ERP components in dementia research. In the section on mismatch negativity (MMN) studies we summarize their results and limitations and outline the emerging field of computational neurology. In the following we overview the use of EEG in the differential diagnosis of the most common neurocognitive disorders. Finally, we provide a summary of the state of the field and conclude that several promising EEG/ERP indices of synaptic neurotransmission are worth considering as potential biomarkers. Furthermore, we highlight some practical issues and discuss future challenges as well

EEG and ERP biomarkers of Alzheimer's disease: a critical review

Here we critically review studies that used electroencephalography (EEG) or event-related potential (ERP) indices as a biomarker of Alzheimer's disease. In the first part we overview studies that relied on visual inspection of EEG traces and spectral characteristics of EEG. Second, we survey analysis methods motivated by dynamical systems theory (DST) as well as more recent network connectivity approaches. In the third part we review studies of sleep. Next, we compare the utility of early and late ERP components in dementia research. In the section on mismatch negativity (MMN) studies we summarize their results and limitations and outline the emerging field of computational neurology. In the following we overview the use of EEG in the differential diagnosis of the most common neurocognitive disorders. Finally, we provide a summary of the state of the field and conclude that several promising EEG/ERP indices of synaptic neurotransmission are worth considering as potential biomarkers. Furthermore, we highlight some practical issues and discuss future challenges as well

HIV-associated structural brain changes as related to cognition

Nearly half of all HIV-positive individuals present with some form of HIV-associated neurocognitive disorder (HAND). The experiments described in this thesis examined the structural changes that occur in the brain as a result of HIV infection. While previous work has established that HIV targets the basal ganglia and fronto-striatal systems and impacts cortical and white matter pathways, it was unknown whether these changes occur in the absence of HAND. The studies described here focused on cognitively asymptomatic HIV+ individuals (CAHIV+) without HAND as determined by widely accepted neuropsychological performance guidelines. Experiment 1 utilized diffusion tensor imaging (DTI) to examine HIV-associated alterations in white matter (WM) fractional anisotropy (FA) in the absence of HAND in 23 HIV+ individuals and 17 control participants (HIV-) matched for age, education, and verbal IQ. The hypothesis was that CAHIV+ participants would show lower FA values than HIV- in the corpus callosum, frontotemporal, and parietal regions of interest (ROIs). CAHIV+ individuals demonstrated higher FA in the frontotemporal region and posterior corpus callosum, but lower FA in parietal WM relative to HIV- individuals. Experiment 2 utilized structural MRI to compare cortical thickness in 22 CAHIV+ individuals and 19 control participants (HIV-) matched for age, education, and verbal IQ. The hypothesis was that CAHIV+ participants would have thinner frontal, temporal, and parietal regions than HIV- participants. Reduced cortical thickness measures were identified in the cingulate and superior temporal gyri, with increased cortical thickness measures in the inferior occipital gyrus, for HIV+ participants compared to HIV-. Experiment 3 examined the relationship between the structural alterations identified in Experiments 1 and 2, neuropsychological performance on tests sensitive to HAND identification, and immunological characteristics in 30 HIV+ participants and 28 HIV- control participants. As hypothesized, regional FA values, cortical thickness, and viral load were related to neuropsychological composite scores for CAHIV+, but not HIV-. Together, results from these three studies suggest that regional FA and cortical alterations identified in CAHIV+ patients may contribute to the cognitive deficits often seen in later stages of HIV disease