Browsing by Subject "neuroscience"

Now showing 1 - 18 of 18

Ali Preston
(The Texas Scientist, 2019) The Texas Scientist
An Automated MRI Analysis Tool to Measure the Tumor Volume and Assess the Treatment Response for Glioblastoma
(2022-09-29) Kabir, Tanjida; Hsieh, Kang-Lin; Nunez-Rubiano, Luis; Cai, Yu; Hsu, Yu-Chun; Quintero, Juan C. Rodriguez; Arevalo, Octavio; Zhao, Kangyi; Zhang, Jackie Jiaqi; Zhu, Jay-Jiguang; Riascos, Roy F.; Jiang, Xiaoqian; Shams, Shayan
Glioblastoma is the most common and aggressive grade IV glioma tumor. During surgical resection, complete tumor removal is impossible due to irregular structure and can be infiltrative into the adjacent brain tissue. Therefore, measuring residual tumor volumes and their detailed location becomes a vital predictor of patients’ survival. Furthermore, radiologists manually segment tumor regions from normal brain tissue and compare the pre-surgery and follow-up MRIs to conduct post-surgery evaluations. This process is time-consuming and operator-dependent due to postoperative changes and tumors' irregularity. Therefore, the overarching aim of this work is to design an artificial intelligence (AI) framework to estimate the residual tumor volume considering the brain structural variations and assess the efficacy of the therapy utilizing imaging and clinical features. We used 419 pre-surgical and 310 follow-up segmented MRIs. All cases include four MRI modalities- T1, T1+Gd, T2, T2-FLAIR. Our study demonstrates that because of the morphological changes in the post-surgical brain, the state-of-the-art AI segmentation models trained on pre-surgery MRIs suffer from a 3-20% performance drop on follow-up MRIs. Besides, these models show a significant drop in generalizability and consistency on independent MRIs (p-value<0.05), indicating the necessity of training follow-up MRI-based segmentation models to assess the treatment response. We propose an encoder-decoder-based segmentation model where encoders utilize contrastive learning schemes to identify tumor location and shape by consolidating domain-specific and problem-specific features to reduce variation of the tumor’s irregularity. We replace the decoder’s deterministic layers with Bayesian layers to quantify the uncertainty in the model's prediction. The proposed model achieved >0.85 dice score on average for segmenting and measuring the volume of different tumor sub-regions- edema, enhancing, and non-enhancing regions. These areas’ prompt and accurate identification is crucial to measure residual disease, detect tumor recurrence, and identify treatment-associated side effects.
Brain Beacons
(The Texas Scientist, 2016) The Texas Scientist
Developing and Using Reference Datasets to Support Reproducible, Big Data Neuroscience
(2022-09-29) Caron, B.; Hayashi, S.; Pestilli, F.
"Advancements in data collection in neuroimaging have ushered in an “Age of Big Data” in neuroscience(Fair et al., 2021; Poldrack & Gorgolewski, 2014; Webb-Vargas et al., 2017). With the growing size of neuroimaging datasets(Alexander et al., 2017; Casey et al., 2018; Sudlow et al., 2015), and the continued persistence of the Replicability Crisis in neuroscience(Tackett et al., 2019), data quality assurance becomes a challenge requiring new approaches for quality assurance at scale. The traditional methods for QA do not scale well. More specifically, the gold standard for QA requires a combination of visual inspection of each individual data derivative, and complex reports that require expertise and time (fMRIPrep, Freesurfer, QSIPrep, Fibr)(Cieslak et al., 2021; Dale et al., 1999; Esteban et al., 2019; Jenkinson et al., 2012; Richie-Halford et al., 2022). Some attempts have been made to approach this at scale(Richie-Halford et al., 2022), however few approaches exist to bridge the gap between community-based visual inspection and expertise-required technical reports. To address this gap, we propose a data-driven approach that uses the natural statistics and variability of large datasets and provides a reference whose variability in value can be compared against. To do this, we processed over 2,000 individual brains from 3 large-scale, open datasets using TACC supercomputers (i.e. PING(Jernigan et al., 2016), HCP(Van Essen et al., 2012), CAMCAN(Shafto et al., 2014)), across multiple imaging modalities and statistical brain properties. For each brain property and dataset, distributions were computed, statistical outliers were removed, and the cleaned distributions were released via brainlife.io(Avesani et al., 2019). The goal of this work is to provide the greater community with tools to perform efficient, automated, data-drive quality assurance, ultimately allowing for the scaling up and increasing of value of large scale datasets processed on supercomputers."
Discovering Pathophysiologic Networks of Temporal Lobe Epilepsy Using the BrainMap Community Portal through the Texas Advanced Computing Center
(2022-09-29) Towne, Jonathan M.; Eslami, Vahid; Fox, P. Mickle; Cavazos, José E.; Fox, Peter T.
Temporal lobe epilepsy (TLE) seizures cause regional damage, detectable on imaging of brain structure (VBM) and function (VBM). Damage is mediated by aberrant neuronal activity propagating along existing network architecture. TLE networks remain ill-defined and are of great interest to diagnostic and therapeutic development. Independent component analysis (ICA) can detect neural-networks by computing multi-variate co-occurrence patterns across a volume and is validated for coordinate-based meta-analysis (CBMA/Meta-ICA). Meta-ICA is methodologically distinct from mass-univariate meta-analytics (ALE) that simply detect robust regions/hubs of pathology. Although meta-ICA is typically used to extract canonical/healthy networks, we applied meta-ICA to VBM/VBP reports of TLE-pathology, to infer TLE-specific network anomalies. To identify TLE-networks, BrainMap Community Portal applications were used to access coordinate-results (Sleuth) of 74 experiments (n=1599), model coordinates as spatial probability distributions (weighted by sample-size) and apply ICA (Meta-ICA) at dimensions:d=1&2 (per sample-size restrictions), computing coordinate co-occurrence within and across experiments. TLE pathology-hubs were computed (GingerALE) separately as spatial convergence across studies (ALE), agnostic to within-study co-occurrence. Two anatomically distinct TLE-networks were identified (i.e. no overlap, excepting ALE hubs). Network-IC1 included brain-regions involved in language processing (speech-execution:Z=6.95; speech-cognition:Z=4.10) at d=2, with similar results (spatial-correlation:R=0.89) at d=1. Network-IC2 included brain-regions involved in emotion (reward:Z=4.76) and cognition (attention:Z=4.02; memory:Z=3.80). Meta-ICA implicated a superset of hub regions from GingerALE (IC1-tonsil; IC2-pulvinar, caudate, superior-temporal; Both-hippocampus, MDN-thalamus), uniquely identifying anterior nucleus, insula, supramarginal, and pre/paracentral gyri. Neither network matched canonical networks. VBM/VBP distribution to ICs was homogenous (χ2:p=0.07). Meta-ICA networks align with TLE symptom profiles. IC1 (verbal/visual) disruption can impair communication and cause visual hallucinosis. IC2 (limbic) aligns with social-emotional deficits; dyscognitive seizures disrupt cognition (attention/memory), impair awareness, and induce postictal amnesia. These findings reveal two novel TLE-networks, debut the first low-d meta-ICA detection of disease-networks, and highlight a BrainMap Community Portal use-case with implications in biomarker development within/beyond epilepsy pathologies.
Examining the role of Dif B in the learning and memory of Drosophila melanogaster using a simplified aversive phototaxis suppression assay
(2023) Gedamu, Hanna; Atkinson, Nigel
Drosophila melanogaster, the common fruit fly, is a great model for investigating learning and memory. As such, they are used to model human disorders involving neurological deficits, including Alzheimer’s disease and Fragile-X syndrome (1). This is made possible by the existence of various analog proteins and processes between Drosophila and humans. For example, the Toll-like receptor (TLR) pathway is a signaling pathway involved in human innate immunity and was initially discovered in D. melanogaster as the Toll pathway, hence the nomenclature. One of the actors downstream of the Toll signaling pathway of the fly is a nuclear factor kappa B (NF-κB) called Dorsal-like-immunity factor (Dif). Dif mRNA undergoes differential splicing to produce two protein isoforms, DifA and DifB. Between these two splice isoforms, the absence of the DifB variant has been seen to dramatically alter various fly behaviors and is thus the focus of this paper. Neurological deficits like those presumably caused by DifB mutations can be assayed using an existing experiment that allows researchers to leverage an organism’s natural affinity for light. It involves aversive phototaxic suppression and has been found to be effective with D. melanogaster. The version of the assay used in this paper has been modified from previously published methods to be a more efficient and accessible way to test learning. In general, the assay utilizes operant conditioning to train flies before they are tested for memory. By using an aversive chemical stimulus, flies can be trained to exhibit a photophobic response and avoid light, which is contrary to their natural behavior of being attracted to light. Flies that are mutant for different genes can be scored on their ability to learn and recall the punishment to help identify genes or proteins that are involved in learning processes. To explore this and test the efficacy of the newly developed methods, flies mutant for DifB expression were tested for learning and memory deficits using the modified procedure. It was ultimately found that the data trended as hypothesized and will likely be ideal with the collection of more data. With recent studies suggesting that NF-κBs play a role in adult mammalian memory and brain plasticity (2), the expectancy is that the revealed importance of NF-κBs in the learning and memory of arguably the most foundational model organism will support human learning and memory disorder research, especially upon discovery of a Dif or DifB mammalian analog.
Growing Neurons: How to Boost Neurogenesis
(The Texas Scientist, 2015) Drew, Michael
High resolution fMRI reveals distinct forms of associative novelty in the medial temporal lobe
(2012) Manthuruthil, Christine; Preston, Alison
Both Alzheimer’s Disease and Parkinson’s Disease involve alterations to the structure of the medial temporal lobe (MTL). Varying patterns of neuronal connectivity, however, suggest that not only does the MTL support learning and memory, but that its subregions play distinct roles in these processes as well. The exact nature of these contributions remains an area of active investigation. Examinations of associative novelty may offer an important tool for characterizing the processes carried out by different subregions. Associative novelty can be further broken down into associative novelty per se, which are simply novel stimulus configurations, and associative mismatch novelty, which are novel stimulus configurations that violate existing expectations. In this study, we used high resolution fMRI to characterize different associative novelty signals across the MTL; specifically, we were interested in whether there was a dissociation of associative novelty signal types between MTL subregions, or instead, a functional specialization for associative novelty signal types distributed across these subregions. Establishing subregional function could help elucidate the spectrum of cognitive deficits manifest in both Parkinson’s and Alzheimer’s patients.
Improving Structural Brain Connectomes through Statistical Evaluation via Model Optimization
(2022-09-29) Heinsfeld, Anibal Sólon; McDonald, Daniel J.; Pestilli, Franco
Accurate mapping of the structural brain connectomes is fundamental to understanding the role of white matter in health and disease. Diffusion-weighted magnetic resonance imaging (dMRI) and fiber tractography provide the only way to map brain connectomes in living human brains. Several studies have shown technical gaps in robustly mapping brain connectomes. The lack of connectome evaluation methods is evident from the recent findings. The present work focuses on developing methods for the statistical evaluation of brain connectomes. We present a new method that builds on LiFE and COMMIT2 methods to reduce a candidate tractography to an optimized one by identifying the brain connections that best model the dMRI signal. We used sparse group regularization, which requires finding a parameter (λ) for the trade-off between better fitting the signal with individual streamlines while maintaining the bundle's cohesion. Previous methods using regularizations to evaluate connectomes set fixed λs, refitting the model for several values of λ. We propose an efficient approach to selecting the optimal λ value. We performed experiments to test the complexity and efficacy of the approach using two datasets: simulated and real datasets. The simulated data were generated using Phantomas, with simple bundles and tissue factors. In addition, we used diffusion data from the Human Connectome Project (HCP). Results show that our approach can identify the optimal λ in a reliable amount of time. The full λ optimization process for 100 different λ took 17 min on a standard desktop computer, while it takes 4x more time than COMMIT 2 to select the optimal λ. In addition, the model's mean squared error is 0.0036 for the HCP dataset and 3.89e-5 for the simulated dataset. This is 14.78x less than COMMIT 2 (0.0544). The reduction in error is due precisely to the optimized selection of λ.
Investigating the Permissive Environment of Perisynaptic Astroglia for Information Storage in the Dentate Gyrus
(2022-09-29) Nam, A.J.; Kuwajima, M.; Mendenhall, J.M.; Hubbard, D.D.; Hanka, D.C.; Parker, P.H.; Wetzel, A.; Bartol, T.M.; Sejnowski, T.J.; Abraham, W.C.; Harris, K.M.
Perisynaptic astroglial processes (PAPs), are active modulators of neuronal activity and directly contribute to information processing in the brain. Both in vivo and in vitro experiments have demonstrated that PAPs undergo activity-dependent structural changes. Thus, here we employ cutting-edge resources at the Texas Advanced Computing Center (TACC) to explore PAP structural remodeling associated with long-term potentiation (LTP) and long-term depression (LTD) that may help support local changes in information processing. Long-term potentiation (LTP) and long-term depression (LTD), widely accepted cellular mechanisms of learning and memory, were induced in vivo in the awake adult rat hippocampal dentate gyrus. LTP induction in the middle molecular layer (MML) was achieved by delta-burst stimulation in the medial perforant pathway, a procedure that produced concurrent long-term depression (cLTD) in the outer molecular layer (OML). The contralateral control hemisphere received only baseline stimulation to the medial perforant path. Three-dimensional electron microscopy (3DEM) offers significant advantages over two-dimensional approaches including a more complete view of ultrastructure in all X-Y-Z planes. AlignEM Swift, the state-of-the-art interactive application available at TACC, is integral for achieving the standard of perfect serial section image alignment needed for 3DEM analysis. Furthermore, Blender at TACC, equipped with the computing power of TACC’s supercomputers, similarly facilitates large-scale and realistic PAP reconstructions for visualization and quantitative mesh analysis. Changes to PAP ultrastructure have important implications on the spatiotemporal dynamics of astrocyte calcium signaling. Thus, TACC resources will further enable computational modeling to investigate the functional consequences of PAP morphological changes. Preliminary analysis suggests that more than 80% of all dentate gyrus synapses exhibit some degree of PAP apposition at the axon-spine interface (ASI). Results from this study made possible using TACC systems will contribute to our overall understanding of the cellular mechanism of information processing and the role of specifically astrocytes in this process.
Manipulating Neurons
(The Texas Scientist, 2018) The Texas Scientist
Pharmacological Cognitive Enhancement: The Science And Ethics Of Enhancing The Mind
(2018-05) Liu, Charles
This thesis explores the controversial nature of pharmacological cognitive enhancement (PCE), defined as the consumption of pharmaceutical drugs to improve cognitive faculties. To the general public and outspoken critics of PCE, the use of cognitive enhancers is considered immoral because it is cheating, unfair, and inauthentic. However, I contend that the consumption of cognitive enhancers can be ethical, and that most worries about PCE’s societal effects may be rooted in misconceptions about cognitive enhancers. To clarify what present-day cognitive enhancers are capable of, I will examine the history and science behind their use and illustrate their benefits and downsides. Then I will synthesize textual sources with original arguments to defend PCE against its critics and show why we they can be tools for social justice and self-actualization.
Science Outreach
(2013-07-26) Bogucka, Roxanne
Science Visualized
(The Texas Scientist, 2021) The Texas Scientist
Undergraduate Research Journal, Volume 20, No.1
(University of Texas at Austin, 2021) Yang, Yunhao; Pridgen, Jacey; Rubenzer, Kaelin; Copenhaver, Kaleigh; Harrington, Madeline; Kosted, Raquel
Unlocking the Mind's Mysteries
(The Texas Scientist, 2016) The Texas Scientist
When Fear Floods Back
(The Texas Scientist, 2020) The Texas Scientist
Wired to Wander
(The Texas Scientist, 2017) The Texas Scientist