Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by difficulties in social communication and having repetitive behaviors or interests. Sensory processing disorder (SPD) is a term used to describe a collection of symptoms stemming from atypical neural integration of sensory information. Evidence from animal models suggests a significant role of GABAergic signaling in the amygdala in regulating behaviors related to anxiety. Previous research suggests that an imbalance in inhibitory and excitatory signaling in neural connections is present in ASD, a neurodevelopmental disorder that is associated with high rates of co-morbid anxiety (up to 40%). This current study investigated the relationship between anxiety and neurochemical profiles in high-functioning children between 8 and 12 years of age with ASD and a clinical comparison group, children with SPD. Our hypothesis is that there are underlying biological differences in amygdalar functioning between children with ASD and those with SPD, despite these two groups sharing similarities in sensory sensitivity and comorbid anxiety. To explore this hypothesis, a composite of anxiety symptoms was created (α = 0.790) using questions from the Child Sensory Profile-2, Vineland Adaptive Behavior Scales, and the DSM-5 Cross-Cutting Symptom Measure. Presence of anxiety was also investigated using the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS). Using a 3T MR system, brain metabolite concentrations (i.e., γ-Amino Butyric Acid (GABA) levels) in the left amygdala-hippocampal region were measured using single-voxel proton magnetic resonance imaging (1H-MRS). We ran a regression analysis looking at the interaction between anxiety symptoms and amygdalar GABA levels in the ASD and SPD groups while controlling for autism symptoms.

Parkinson's disease (PD) is degenerative disorder of the nervous system that primarily impairs motor function along with other cognitive functions due to a specific loss of striatal dopamine neurons. The most challenging aspect of treating PD is that there is no explicit cause or any definitive long-term treatment. Current therapies restore dopamine levels in brain to alleviate the symptoms of PD. In this study, our goal is to address and identify possible markers of PD by studying the action of dopamine using functional MRI (fMRI) data. Resting-state fMRI allows us to examine the patterns of neuronal firing in vivo. We performed fMRI in 26 PD patients (age = 68 +/- 8 years) before (OFF group) and after (ON group) administration of dopaminergic medication to understand which brain regions showed altered neuronal firing with dopamine therapy. We applied the Integrated Local Coherence (ILC) approach to study temporal coherence of neuronal fluctuations within regions. High ILC corresponds to coherent neuronal fluctuations or recruitment of larger number of neurons as baseline. Low ILC corresponds to reduced temporal coherence in neuronal fluctuations due to neurodegeneration. ILC was computed in the cortex and compared between the ON and OFF groups using a paired t-test. Significance was considered for a voxel-wise threshold of p=0.001 and a Family-Wise Error correction for multiple comparisons at p=0.05. ILC measures were higher bilaterally in the superior parietal lobe, angular gyrus, and precuneal regions in the OFF compared to ON group. These results support another study where increased temporal coherence was observed in the same regions in PD patients compared to healthy controls. These regions are also part of the PD-related disease pattern network involved in motor dysfunction. With this work we believe that administration of levodopa reduces the excessive recruitment of neuronal regions during rest in PD patients.

ImageJ allows users to create plugins that work with other imported packages and the ImageJ libraries. Using the ImageJ API (application program interface) and the macros recorder built into ImageJ, each image analysis performed within the Lee laboratory at the Department of Radiology was programmed into individual plugins by both Vineeth Narajala and Selina Liu, to be used by research scientists. Before writing each data analysis plugin, the standard operating procedure (SOP) of image analysis created by the Lee lab was practiced manually by both the programmers so we understood the process researchers perform on each data set. After understanding the SOP, we commented a simple outline in Java to start writing the plugin code. After the code was outlined with comments, Selina wrote algorithms for portions of the analysis that required numeric patterns and searched through the ImageJ API to manipulate the image using built-in commands. Methods were created to perform calculations, scale the images, and close extra images that were generated during the analysis. These methods helped generate the final image map used for measurements by the researchers. Plugins were tested for usage and updated when a researcher required other procedures that were not specified in the SOP. We also created a python program in Python 2.7 to automate the data analysis of the dataset. The program can also plot rudimentary graphs of the analyzed results. The python program used pandas to analyze the data and matplotlibs to create the graphs. Overall, each plugin and the automation program save researchers hours of time from importing, calculating, and archiving the images when following a single SOP. The research scientists typically used Microsoft Excel to analyze the data after the images were created and regions of interest were identified. This method was very time consuming and could not be scaled to different input sizes.

The goal of this research is to improve breast cancer treatment by identifying novel imaging markers of tumor immune response. New immunotherapy treatment approaches are emerging that harness the body’s own immune system to fight cancer. These exciting advances increase the need for new tools to quantify response and assess treatment effectiveness in vivo.  We are investigating the potential role of magnetic resonance imaging (MRI) in monitoring chemotherapy-induced immune response in breast tumors. HER2 (human epidermal growth factor receptor 2) is a protein overexpressed in up to 30% of breast cancers that can increase cell proliferation and result in more aggressive cancers with worse outcomes. Trastuzumab is a drug that has been shown to not only amplify, but also induce immune response in HER2+ tumors. This induced immune response contributes to the effectiveness of trastuzumab in fighting HER2+ breast tumors and improving overall survival.  We hypothesize that functional MRI measures can identify changes associated with tumors’ immune responses. We are testing this hypothesis preoperatively in a group of patients with HER2+ breast cancer. Each patient has an MRI image taken and a tissue sampling of their tumor before and after a dose of trastuzumab. Quantitative MRI metrics we are measuring include contrast enhancement kinetics reflecting vascular permeability and diffusion coefficients reflecting tumor cellularity. Changes reflected on MRI are being compared with changes in histologic measures of immune response (including tumor infiltrating lymphocytes, TILs, and inflammation). Objectives are to identify quantitative MRI markers that are best suited to track breast tumor’s responses to trastuzumab. These findings are contributing to developing a novel method of observing immune response in tumors treated with a variety of emerging therapies.

The default mode network (DMN) changes with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal aging, but these changes are not well characterized. The posterior cingulate cortex (PCC) is a critical hub in the DMN. Using data from the Alzheimer’s Disease Neuroimaging Initiative database, we modeled longitudinal change in PCC connectivity in three subject groups: healthy controls, MCI, and AD, and tested whether connectivity changes were linear or quadratic with time. The HC group had no evidence of Aβ accumulation (N=56, 25 male, 75.7±6.23 years). The MCI group pooled all MCI and significant memory concern subjects with no signs of dementia (N=127, 59 male, 72.6±7.14 years). The AD group fulfilled the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) and the Alzheimer's Disease and Related Disorders Association (ADRDA, now known as the Alzheimer’s Association) criteria for probable AD (N=34, 16 male, 74.2±7.47 years). We processed resting state fMRI images using a workflow that represents best practices for removal of noise, censoring motion outliers. We measured individual connectivity to a 10mm sphere in the PCC (2,-52,28) and transformed the Pearson correlations using a Fisher Z-transformation. We used a software developed in our lab called neuropointillist to fit a mixed effects growth model in R to each voxel, comparing linear and quadratic forms of longitudinal change. Data were cluster corrected for multiple comparisons at p=.05, α=.05. A linear model was superior to a quadratic model for all groups across the brain. All three groups showed decreased connectivity with age of the PCC to the medial frontal wall. However, we found increased connectivity to the dorsal attention network only in the MCI group. We identified distinct regions of the brain that increased and decreased in connectivity to the PCC with age in the three subgroups. These differences likely represent the different pathophysiological processes occurring in normal aging, MCI, and AD.

Fluorescent nanocrystal quantum dots (QDs) show promise for biomedical application, but are often negatively associated with cellular toxicity. To investigate the potential of QDs as a biomarker in the brain, a systematic evaluation of potential toxicity is necessary. In this study, we evaluated QDs with various surface functionalities and assessed toxicity as a function of concentration and exposure time. We utilized organotypic brain slices obtained from healthy postnatal day 14 (P14) rat pups. Four cadmium selenide (CdSe)-core QD conjugates were evaluated: mercaptopropionic acid (MPA), hydroxyl-polyethylene glycol (PEG-OH), amine-PEG (PEG-NH2), and methoxy-PEG (PEG-MeO) at 0.01, 0.1, and 1.0 μM concentration. Cell toxicity was primarily quantified by measuring lactate-dehydrogenase (LDH) production, which is an indicator of cell death, over a five-day period. Results were supplemented with confocal microscopy analysis of two imaging-based assays: propidium iodide, a stain of the nuclei of dying cells, and fluoro-jade C, a stain of degenerating neurons. QD-MPA treated slices had 7-8% greater toxicity than the non-treated (NT) control. All other functionalities were comparable to the NT control except QD-PEG-OMe, which had 3% lower cytotoxicity suggesting a possible neuroprotective effect. Alternatively, certain functionalities (MPA, NH2) may show lower than expected toxicity due to aggregation before cellular uptake. Compared to 0.1 μM concentrations, 0.01 μM QD treated groups had around 4% lower toxicity. Similarly, 1-3% greater toxicity was observed in extended QD exposure conditions (24h) versus shorter exposures (1h). Results show that toxicity is dependent on surface chemistry, concentration, and exposure time. This is useful in identifying QD conjugates with low cytotoxicity in the developing brain. Understanding QD toxicity can lead to rational design of QDs for site and cell-specific uptake in the brain as a biomarker of neurological disease severity, improving the selectivity of current imaging techniques and providing a powerful diagnostic with regards to diseased cell fate.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, as well as restrictive or repetitive behaviors and interests. Some individuals with autism demonstrate deficits in face memory and recognition, which may contribute to overall social impairments. Increasing evidence also suggests that ASD is associated with abnormal sensory processing, including within the olfaction pathway, although results vary as to which components of olfaction, including odor identification, might be affected. Both odor identification and face recognition have been associated with activity in the orbitofrontal cortex (OFC), a brain region possibly implicated in ASD. The current study aims to measure odor identification and face memory performance in children with ASD compared to children who are typically developing (TD) and children with sensory processing difficulties (SPD), and assess whether there is a relationship between performance on face memory and odor identification tasks. Odor identification will be measured using University of Pennsylvania Smell Identification Test (UPSIT), a 40 item “scratch n’ sniff” activity, and face recognition will be assessed using the Wechsler Memory Scale III (WMS III); children will complete both the Faces I and Faces II subtests, which assess immediate recognition of faces and delayed recognition of faces respectively. Data collection for this study is ongoing; final analyses will consist of a Student’s t-test to assess for group differences on the odor identification and face recognition tasks, and Pearson’s correlation analyses to detect any significant associations between these tasks, using a significance threshold of α=.05. We hypothesize that children with ASD will show reduced performance on both the odor identification task and face recognition tasks compared to the TD and SPD groups, and that performance on the UPSIT will be strongly correlated to WMS III scores, reflecting the shared underlying role of the OFC.

Drug delivery to the brain is difficult due to the strictly regulated barriers and a complex microenvironment. Nanoparticles can help overcome these barriers to delivery by providing highly stable and tailorable platforms. Charged nanoparticles have an electrostatic double layer that ensures colloidal stability, while sterically stabilized particles have ligands (e.g. polymers, surfactants) that improve colloidal stability. In the brain, changes in the local pH, temperature, and ionic composition of the surrounding media, either in normal physiological function or in the case of injury, can change the stability of suspended nanoparticles. However, there are limited systematic studies of nanoparticle stability in representative brain microenvironments. This research focuses on how calcium concentration, a key ion in the brain microenvironment, influences aggregation kinetics in artificial cerebrospinal fluid (aCSF) and how other physiological factors (temperature, pH) impact nanoparticle aggregation kinetics. We designed an experiment to examine how divalent cations (calcium, magnesium) alter the aggregation kinetics of both carboxylated polystyrene (PS-COOH) and poly(ethylene glycol)-coated polystyrene (PS-PEG) nanoparticles in physiologically relevant conditions. We hypothesized that the PS-PEG nanoparticles would remain stable at physiological conditions, while the PS-COOH nanoparticles would experience aggregation. Dynamic light scattering (DLS) was used to characterize the nanoparticle hydrodynamic diameters. Each experiment was performed in aCSF at a range of pHs (5.5, 7.2, 8.5) and temperatures (23, 37°C). We demonstrate that PS-PEG nanoparticles have higher aggregation thresholds than PS-COOH nanoparticles. We also show that higher temperatures accelerate aggregation kinetics of both particle types. Acidic environments seem to provide a protective effect for PS-PEG nanoparticles, while basic environments can cause catastrophic aggregation even when the particles are stabilized with PEG. Future research will examine the effect of PEG chain length (2k, 5k, 10k) as well as PEG coverage density on aggregation kinetics in the brain.

Metabotropic glutamate receptor 5 is a receptor for glutamate, one of the major excitatory neurotransmitters. Glutamate serves many functions throughout the nervous system, such as activating microglial cells, the immune cells of the nervous system. Microglial activation then initiates the release of cytokines. Cytokines are responsible for triggering signaling cascades that lead to pro- and anti-inflammatory immune responses. Although cytokines play an important role in mitigating disease and injury states in the nervous system, excessive cytokine release may cause ongoing neuroinflammation and neurotoxicity through a complex series of pathways. mGluR5 deficiency has been linked to neurotoxic cytokine release as well as neurological disorders such as Fragile X Syndrome, autism spectrum disorders, and schizophrenia. The Nance lab established the first mGluR5 rat knockout (KO) colony to investigate the links between glutamate dysfunction and neuroinflammation. This project quantitatively analyzes the levels of pro- and anti-inflammatory cytokines in mGluR5 KO pups, across gender and genotype. RNA was isolated from mGluR5 postnatal day 12 (P12) to P14 KO, wild-type (WT), and heterozygous (HET) pups and converted to cDNA. RT-PCR was performed to analyze mRNA levels of TNF-α, IL-1-β, IL-6, TGF-β, IL-10, GCP-II, and IL-12-β cytokines. 2-ΔΔCt values demonstrated 2 to 4-fold changes in these cytokines due to differences in genotype and gender, relative to the housekeeping gene GAPDH. Characterizing cytokine profiles leads to greater understanding of the interplay between inflammation and ongoing injury in the developing brain, and may elucidate pathways for therapeutic intervention. Identifying specific cytokines linked to increases in neuroinflammation will also provide further insight into the roles that glutamate, and glutamate receptor deficiency, play in the development of neurological disease.

Quantum dots (QDs), fluorescent semiconductor nanocrystals, can be used as a biomarker and diagnostic tool for central nervous system (CNS) diseases because of their unique physical properties. To serve as an effective diagnostic platform, QDs must diffuse from the point of access to a desired target within the brain. To maximize diffusion within the CNS, QDs need to remain monodisperse, and avoid any adhesive interactions or steric limitations imposed by the brain microenvironment. Therefore, characterization of physicochemical properties and overall colloidal stability in physiologically relevant solvents is required to understand QDs diffusivity within the CNS. A methodology based on dynamic light scattering (DLS) was employed to assay QD hydrodynamic diameter, where increase in particle size served as an indicator of QD aggregation and instability. Time dependent stability was investigated by incubating QDs in 1xPBS, a low concentration salt solution, and artificial cerebrospinal fluid (aCSF), a more complex medium that mimics in vivo interstitial fluid. Measurements were recorded for 24 h at both room and physiological temperatures. Compared to 1xPBS, QDs without poly(ethylene glycol) (PEG) ligands showed more rapid and significant aggregation in aCSF while PEGylated QDs remains relatively stable in both mediums. To explore the rate at which QDs initially aggregate, particle size was measured for the first 200 seconds post-incubation in solutions of varying concentrations of calcium ion, an ion which plays an important role in many neuronal processes and is often found in abnormal amounts in the brain in diseased states. The results showed that the rate of initial aggregation is concentration dependent and is diminished by PEGylation. With further analysis of QD stability in CNS relevant mediums, this methodology will aid in establishing key properties that govern QD behavior in the brain microenvironment, which can be used to employ QDs as an effective biomarker for brain diseases.

We are developing a program that allows us to thank research participants who are involved in Magnetic Resonance Imaging (MRI) studies in a unique and inexpensive way. Our program takes a participant’s structural brain scan and turns it into a personalized birthday card. This project involves testing the program to ensure that it is portable to different scanner sites and subject populations (children through adults), and modifying it as necessary. Upon completion we want to document and share this program using GitHub, a code repository. This program is a Bash shell script that has been developed to be run from the command line in Linux, using only additional programs from a popular neuroimaging package called FSL. The program is designed to use input brain images in Neuroimaging Informatics Technology Initiative (NiFTI) format. Our program takes the brain images, identifies an attractive sagittal slice, overlays a party hat image, text that states “Happy Birthday” and “We Lobe You” on this slice, and creates an output image that can be used to create a birthday card. We will test NiFTI files from our own scanner and from other sites to ensure portability across multiple sites, and modify the hat positioning algorithm as necessary to make it work across different head sizes and image dimensions. We will document not just program use, but a system for creating cards in advance using photo frame holders. We will evaluate ease of use by asking target potential users to install the program and generate cards. In the end we will have a completely portable program ready for distribution and published on GitHub. This project is a good example of how neuroimaging data can be used in a non-scientific way to thank participants for their contribution, conveying to them that their time is important and appreciated.