Session N18: Physics of Disease, Diagnosis and Treatment

Restoring Cerebrospinal Fluid Drainage Following Traumatic Brain Injury: Insights from Computational Modeling

Cerebrospinal fluid (CSF) envelops the brain and spinal cord, providing physical protection and essential nutrients. Recent studies have expanded our understanding of CSF's function, highlighting its role in removing metabolic wastes from the brain. This waste clearance includes drainage of CSF through multiple pathways, some of which empty into the cervical lymphatic vessels (CLVs) in the neck. We have recently showed that following traumatic brain injury (TBI), drainage through this pathway is severely impaired. We attributed this decrease in CLV drainage to impairment of CLV contractions (which drive fluid through CLVs), likely due to increased norepinephrine following TBI. To delve deeper into the relation of vessel contractility and CSF drainage, we use a lumped parameter model to simulate the CSF efflux from the brain to CLVs. Our objective is to gain insights into how altered contractility impacts CSF drainage. Additionally, we examine the evolutionary aspects of CSF drainage from the skull. Our results suggest the routes draining CSF to CLVs serve to dampen the oscillations in intracranial pressure. This analysis will provide avenues for investigating potential strategies to enhance CSF transport and for developing treatments for post-TBI conditions.   

Longitudinal Analysis of Brain Images for the Study of Cognitive Decline and Alzheimer's Disease Response Assessment

Treatment response assessment remains a significant challenge in the study of Alzheimer’s disease (AD). Rates of cognitive decline are heterogeneous across patients and difficult to separate from that of normal aging. It is imperative to improve the quantification of longitudinal changes in the brain, particularly considering recent breakthroughs in AD therapies. Neuroimaging is a powerful tool for quantifying brain change over time. The goal of this work was to establish quantitative, longitudinal patterns of cognitive decline in both AD and normal aging using neuroimaging. Several interpretable, increasingly complex approaches were applied including voxel-wise calculations, region-based metric extraction, Independent Component Analysis (ICA) feature tracking, and a Scaled Subprofile Modeling-based brain pattern analysis. We created extensive longitudinal imaging cohorts for development and validation using various dataset sources and explored multiple image modalities including ¹⁸F-FDG PET, T1-MRI, and ¹¹C-PiB (amyloid) PET. Performance was evaluated by applying the identified patterns to a classification task for separating cohorts of AD and normally aging subjects and implementing ROC analysis. The preliminary longitudinal brain patterns quantified in this study hold great potential for both clinical diagnostics of AD and treatment response assessment in clinical trials of AD therapies.   

Astrocytes Mediate Hyperexcitability in Normal Neuronal Function During Tauopathy Disease Progression

During early stages of tauopathy disease progression, tau hyperphosphorylates and then mediates an increase in the release of the neurotransmitter glutamate. This increased glutamate release causes excessive neuronal activity called hyperexcitability. Hyperexcitability in turn induces the extracellular release of hyperphosphorylated tau causing it to spread to other neurons. Over time, the extracellular spread of hyperphosphorylated tau results in a buildup leading to neurodegeneration. However, neurons aren't the only cells in the brain that mediate the exctracellular spread of tau. Recently, neighboring cells, called astrocytes, have been shown to support extracellular tau spread by releasing the same tau to nearby neurons exacerbating disease progression. Understanding how astrocytes mediate extracellular tau spread is thus an important open question to address. We present our recent experimental results showing how astrocytes mediate extracellular spread of tau and supporting hyperexcitability in neuronal networks. We use normal hippocampal primary mouse neurons grown with primary astrocytes from a P301L mouse model. We show how P301L astrocytes mediate increased glutamate release in normal neurons equivalent to P301L neurons. This increase in glutamate release occurs across different P301L tau concentrations. Finally, we show how increased glutamate can be rescued by targeting presynaptic mechanisms of glutamate recycling.   

Detangling neuronal networks in epilepsy for better interventions

Epilepsy is a condition of recurrent spontaneous seizures.  Fundamental questions remain in epilepsy, limiting effective intervention strategies.  Temporal lobe epilepsy is the most common form of epilepsy, with seizures typically arising in a brain region known as the hippocampal formation.  However, the neural circuits at play in epilepsy, including temporal lobe epilepsy, and the circuits capable of stopping seizures, are not fully elucidated.  By using modern techniques, including on-demand optogenetics, closed-loop optimization strategies, and in vivo imaging methods, we work to disentangle neuronal networks and their relationship to seizures to identify much needed novel therapeutic approaches.   

A gas sensor array for the selective detection of cancer related volatile organic compounds

In recent years, there has been growing interest in utilizing gas sensors for an enhanced perception of the surrounding environment in various domains. Gas sensors find widespread applications, from automating industrial processes, operating household appliances, automobiles, and robots, monitoring environmental pollution, and so on. Among the diverse types of gas sensors, the semiconducting metal oxides (SMOX) based sensors stands out due to its ability to respond rapidly to the environment by changing its electrical resistance in response to an analyte and to make the detection device cost effective. Notably, recent efforts are oriented toward employing SMOX gas sensors in the realm of medical diagnosis. This endeavor is grounded on the understanding that the volatile organic compounds (VOCs) present in exhaled breath may bear information about the health condition of an individual. Human breath contains a multitude of VOCs, spanning concentrations from parts per million (ppm) to trillion (ppt), thereby posing a significant challenge in precisely detecting select VOCs that convey crucial information about a specific medical condition. We have addressed this challenge by developing a new nanostructured material and engineering it to be sensitive to low concentration VOCs. In this presentation, we showcase the performance of a four-sensor array developed using this material in detecting VOC biomarkers for cancer detection.   

Positron Emission Tomography with Liquid Argon Detection

Advances in utilizing Liquid Argon Time Projection Chamber (LArTPC) for low-energy particle detection create an opportunity for their application in the medical physics community. Positron Emission Tomography (PET) scanners are a critical tool for doctors when diagnosing a patient and creating a treatment plan for a variety of harmful diseases like cancer and cardiovascular disease. The low-energy particle identification capabilities and scalability of LArTPCs, now demonstrated by several neutrino physics experiments, could provide greater accuracy and resolution when detecting 511 keV gamma rays utilized in PET scans. This detector technology can also be scaled up to sizes that would enable full-body PET scans to be performed. This presentation will describe our efforts to construct a prototype LArTPC featuring a pixelated anode and VUV light sensors and expose it to a radioactive source (Na-22), allowing the response of the detector to 511 keV gammas to be studied. Preliminary results from this prototype will be presented, and future directions involving larger-scale detectors will be discussed.   

Brain synchronization as a predictive factor in depression treatment given EEG-synchronized rTMS

Repetitive Transcranial Magnetic Stimulation (rTMS) is an FDA-approved therapy recognized for its effectiveness in treating major depressive disorder, particularly in cases of treatment-resistant depression (TRD). Although substantial clinical data supports its efficacy, the precise mechanism of action and the optimal combination of stimulation parameters remain elusive. This lack of clarity results in a significant portion of patients (around 50%) not achieving desired treatment outcomes. This variability in treatment response may be attributed to the absence of robust biomarkers or measures of target engagement.

Since recent evidence emphasizes the crucial role of alpha oscillations in network connectivity, especially in mediating top-down influences within the human brain, including responses to neurostimulation methods like rTMS, in response to the challenge, we introduce an innovative approach—a closed-loop, phase-locked repetitive TMS (rTMS) treatment. This approach synchronizes the delivery of rTMS pulses with an individual patient's electroencephalographic (EEG) prefrontal alpha oscillations, as validated by functional magnetic resonance imaging (fMRI).

Our results reveal that, in responders, synchronized rTMS significantly enhances brain synchronization within the target region (near the dorsolateral prefrontal cortex). Notably, it emerges as a strong predictor of clinical responses within the synchronized treatment group (N=12) but not in an active-treatment unsynchronized control group (N=12). More importantly, in addition to a retrospective assessment of the correlation between measurements and clinical improvement in the end, we further demonstrate the potential utility of this biomarker in predicting the likelihood of a favorable treatment outcome before the treatment concludes. Our study underscores the potential of weekly tracking of brain synchronization changes, which enables the prediction of treatment efficacy and the opportunity for dynamic adjustments throughout the treatment course. This innovative approach substantially improves overall response rates, offering a promising direction for personalized and adaptive depression treatment strategies.   

Enhancing the MRI Signals of Metabolic Biosensors by >10,000-fold via Dissolution Dynamic Nuclear Polarization

Dynamic nuclear polarization (DNP) via the dissolution method is a technique that allows the production of highly-amplified or "hyperpolarized" liquid-state nuclear magnetic resonance (NMR) or magnetic resonance imaging (MRI) signals with >10,000-fold enhancement at physiologically tolerable temperatures. The NMR amplification process occurs at cryogenic temperature and high magnetic field where the electron spin polarization is almost 100%, in which slightly off-resonance microwave irradiation is employed to tranfer the high electron polarization to the nuclear spins. A dissolution device is then inserted into the cryostat to rapidly dissolved the frozen polarized solid into hyperpolarized liquid. With the relatively long liquid-state spin-lattice relaxation time T1 of the chosen target nuclei, most of the enhanced or hyperpolarized NMR signal of the nuclei is preserved in room temperature. Armed with >10,000-fold MRI signal enhancement, this allows for highly sensitive and real-time MRI tracking of the metabolic fates of MRI biosensors for diagnostic biochemical imaging of a number of pathologies including cancer. Details of the instrumental process and biomedical examples will be discussed.   

Dynamic Nuclear Polarization of 23Na and 13C Nuclear Spins at 4.6T and 1.25 K

Dynamic Nuclear Polarization (DNP) is a hyperpolarization technique used to amplify the inherently weak magnetization of nuclei by transferring the near unity polarization of a free electron to the nuclei using an external microwave source at low temperatures and high magnetic fields. The strength of this transfer is dependent on many factors including free radicals and solvents. In this work we attempt to maximize the polarization of the spin active 13C and 23Na nuclei in Sodium acetate at 4.6T and 1.25K. We accomplish this by varying combinations of the solvent, the free radical, and the labeling of the nuclei. We expect this system will fall under the thermal mixing regime of DNP where the physics is described thermodynamically by coupled thermal reservoirs representing both nuclear and electron spin systems. We expect that removing the carbon labeling will improve sodium DNP due to the removal of a coupled reservoir of carbon nuclei from the system. The results will be discussed.   

Hyperspectral Imaging with Active Illumination using Incandescent Lamp and Variable Filament Temperature

Presented novel hyperspectral imaging technique use variable filament temperature incandescent lamp and multi-channel image acquisition. This novel hyperspectral imaging approach is characterized with the simulation.

We simulated an illumination of ColorChecker chart with a black body spectra at varying temperatures and multi-channel image capture in spectral range 400 to 700 nm. We present an algorithm for spectrum reconstruction, addressing the ill-posedness of inverse problem. We assessed the impact of various acquisition parameters on the accuracy of reconstructed spectra, including noise levels, temperature steps, filament temperature range, illumination spectral uncertainties, spectral step sizes in reconstructed spectra, and the number of detected spectral channels.

We successfully reconstructed all spectra, with Root Mean Squared Errors below 5%, reaching as low as 0.1% for specific cases such as Black color. Flat spectra exhibit higher accuracy than complex ones. The illumination spectrum accuracy emerges as a critical factor influencing spectrum reconstruction quality.

Ultimately, our study establishes the feasibility of this innovative hyperspectral imaging approach and identifies optimal acquisition parameters, setting the stage for future practical implementations.