Faculty Bibliography

Suppression of HIV replication by antiretroviral therapy (ART) or host immunity can prevent AIDS but not other HIV-associated conditions including neurocognitive impairment (HIV-NCI). Pathogenesis in HIV-suppressed individuals has been attributed to reservoirs of latent-inducible virus in resting CD4+ T cells. Macrophages are persistently infected with HIV but their role as HIV reservoirs in vivo has not been fully explored. Here we show that infection of conventional mice with chimeric HIV, EcoHIV, reproduces physiological conditions for development of disease in people on ART including immunocompetence, stable suppression of HIV replication, persistence of integrated, replication-competent HIV in T cells and macrophages, and manifestation of learning and memory deficits in behavioral tests, termed here murine HIV-NCI. EcoHIV established latent reservoirs in CD4+ T lymphocytes in chronically-infected mice but could be induced by epigenetic modulators ex vivo and in mice. In contrast, macrophages expressed EcoHIV constitutively in mice for up to 16 months; murine leukemia virus (MLV), the donor of gp80 envelope in EcoHIV, did not infect macrophages. Both EcoHIV and MLV were found in brain tissue of infected mice but only EcoHIV induced NCI. Murine HIV-NCI was prevented by antiretroviral prophylaxis but once established neither persistent EcoHIV infection in mice nor NCI could be reversed by long-acting antiretroviral therapy. EcoHIV-infected, athymic mice were more permissive to virus replication in macrophages than were wild-type mice, suffered cognitive dysfunction, as well as increased numbers of monocytes and macrophages infiltrating the brain. Our results suggest an important role of HIV expressing macrophages in HIV neuropathogenesis in hosts with suppressed HIV replication.

As optical reporters and modulators of cellular activity have become increasingly sophisticated, the amount that can be learned about the brain via high-speed cellular imaging has increased dramatically. However, despite fervent innovation, point-scanning microscopy is facing a fundamental limit in achievable 3D imaging speeds and fields of view. A range of alternative approaches are emerging, some of which are moving away from point-scanning to use axially-extended beams or sheets of light, for example swept confocally aligned planar excitation (SCAPE) microscopy. These methods are proving effective for high-speed volumetric imaging of the nervous system of small organisms such as Drosophila (fruit fly) and D. Rerio (Zebrafish), and are showing promise for imaging activity in the living mammalian brain using both single and two-photon excitation. This article describes these approaches and presents a simple model that demonstrates key advantages of axially-extended illumination over point-scanning strategies for high-speed volumetric imaging, including longer integration times per voxel, improved photon efficiency and reduced photodamage.

Evolutionary development of vision has provided us with the capacity to detect moving objects. Concordant shifts of visual features suggest movements of the observer, whereas discordant changes are more likely to be indicating independently moving objects, such as predators or prey. Such distinction helps us to focus attention, adapt our behavior, and adjust our motor patterns to meet behavioral challenges. However, the neural basis of distinguishing self-induced and self-independent visual motions is not clarified in unrestrained animals yet. In this study, we investigated the presence and origin of motion-related visual information in the striatum of rats, a hub of action selection and procedural memory. We found that while almost half of the neurons in the dorsomedial striatum are sensitive to visual motion congruent with locomotion (and that many of them also code for spatial location), only a small subset of them are composed of fast-firing interneurons that could also perceive self-independent visual stimuli. These latter cells receive their visual input at least partially from the secondary visual cortex (V2). This differential visual sensitivity may be an important support in adjusting behavior to salient environmental events. It emphasizes the importance of investigating visual motion perception in unrestrained animals.

Stimulation pulse rate affects current amplitude discrimination by cochlear implant (CI) users, indicated by the evidence that the JND (just noticeable difference) in current amplitude delivered by a CI electrode becomes larger at higher pulse rates. However, it is not clearly understood whether pulse rate would affect discrimination of speech intensities presented acoustically to CI processors, or what the size of this effect might be. Intensity discrimination depends on two factors: the growth of loudness with increasing sound intensity and the loudness JND (or the just noticeable loudness increment). This study evaluated the hypothesis that stimulation pulse rate affects loudness JND. This was done by measuring current amplitude JNDs in an experiment design based on signal detection theory according to which loudness discrimination is related to internal noise (which is manifested by variability in loudness percept in response to repetitions of the same physical stimulus). Current amplitude JNDs were measured for equally loud pulse trains of 500 and 3000 pps (pulses per second) by increasing the current amplitude of the target pulse train until it was perceived just louder than a same-rate or different-rate reference pulse train. The JND measures were obtained at two presentation levels. At the louder level, the current amplitude JNDs were affected by the rate of the reference pulse train in a way that was consistent with greater noise or variability in loudness perception for the higher pulse rate. The results suggest that increasing pulse rate from 500 to 3000 pps can increase loudness JND by 60 % at the upper portion of the dynamic range. This is equivalent to a 38 % reduction in the number of discriminable steps for acoustic and speech intensities.

Previous studies have associated Attention-Deficit/Hyperactivity Disorder (ADHD) with a maturational lag of brain functional networks. Functional connectivity of the human brain changes from primarily local to more distant connectivity patterns during typical development. Under the maturational lag hypothesis, we expect children with ADHD to exhibit increased local connectivity and decreased distant connectivity compared with neurotypically developing (ND) children. We applied a graph-theory method to compute local and distant connectivity levels and cross-sectionally compared them in a sample of 120 children with ADHD and 120 age-matched ND children (age range = 7-17 years). In addition, we measured if potential group differences in local and distant connectivity were stable across the age range considered. Finally, we assessed the clinical relevance of observed group differences by correlating the connectivity levels and ADHD symptoms severity separately for each group. Children with ADHD exhibited more local connectivity than age-matched ND children in multiple brain regions, mainly overlapping with default mode, fronto-parietal and ventral attentional functional networks (p < .05- threshold free-cluster enhancement-family-wise error). We detected an atypical developmental pattern of local connectivity in somatomotor regions, that is, decreases with age in ND children, and increases with age in children with ADHD. Furthermore, local connectivity within somatomotor areas correlated positively with clinical severity of ADHD symptoms, both in ADHD and ND children. Results suggest an immature functional state of multiple brain networks in children with ADHD. Whereas the ADHD diagnosis is associated with the integrity of the system comprising the fronto-parietal, default mode and ventral attentional networks, the severity of clinical symptoms is related to atypical functional connectivity within somatomotor areas. Additionally, our findings are in line with the view of ADHD as a disorder of deviated maturational trajectories, mainly affecting somatomotor areas, rather than delays that normalize with age.

OBJECTIVE: To use high-permittivity materials (HPM) positioned near radiofrequency (RF) surface coils to manipulate transmit/receive field patterns. MATERIALS AND METHODS: A large HPM pad was placed below the RF coil to extend the field of view (FOV). The resulting signal-to-noise ratio (SNR) was compared with that of other coil configurations covering the same FOV in simulations and experiments at 7 T. Transmit/receive efficiency was evaluated when HPM discs with or without a partial shield were positioned at a distance from the coil. Finally, we evaluated the increase in transmit homogeneity for a four-channel array with HPM discs interposed between adjacent coil elements. RESULTS: Various configurations of HPM increased SNR, transmit/receive efficiency, excitation/reception sensitivity overlap, and FOV when positioned near a surface coil. For a four-channel array driven in quadrature, shielded HPM discs enhanced the field below the discs as well as at the center of the sample as compared with other configurations with or without unshielded HPM discs. CONCLUSION: Strategically positioning HPM at a distance from a surface coil or array can increase the overlap between excitation/reception sensitivities, and extend the FOV of a single coil for reduction of the number of channels in an array while minimally affecting the SNR.

PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med, 2017. (c) 2017 International Society for Magnetic Resonance in Medicine.

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system. Local field potentials (LFPs) represent summed electrical currents from multiple neurons in a defined brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the affective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifier with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. These results suggest the potential to use LFPs to decode acute pain.