Multimodal Investigation of Brain Network Systems: From Brain Structure and Function to Connectivity and Neuromodulation

The field of cognitive neuroscience has benefited greatly from multimodal investigations of the human brain, which integrate various tools and neuroimaging data to understand brain functions and guide treatments for brain disorders. In this dissertation, we present a series of studies that illustrate the use of multimodal approaches to investigate brain structure and function, brain connectivity, and neuromodulation effects. Firstly, we propose a novel landmark-guided region-based spatial normalization technique to accurately quantify brain morphology, which can improve the sensitivity and specificity of functional imaging studies. Subsequently, we shift the investigation to the characteristics of functional brain activity due to visual stimulations. Our findings reveal that the task-evoked positive blood-oxygen-level dependent (BOLD) response is accompanied by sustained negative BOLD responses in the visual cortex. These negative BOLD responses are likely generated through subcortical neuromodulatory systems with distributed ascending projections to the cortex. To further explore the cortico-subcortical relationship, we conduct a multimodal investigation that involves simultaneous data acquisition of pupillometry, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). This investigation aims to examine the connectivity of brain circuits involved in the cognitive processes of salient stimuli. Using pupillary response as a surrogate measure of activity in the locus coeruleus-norepinephrine system, we find that the pupillary response is associated with the reorganization of functional brain networks during salience processing. In addition, we propose a cortico-subcortical integrated network reorganization model with potential implications for understanding attentional processing and network switching. Lastly, we employ a multimodal investigation that involves concurrent transcranial magnetic stimulation (TMS), EEG, and fMRI to explore network perturbations and measurements of the propagation effects. In a preliminary exploration on brain-state dependency of TMS-induced effects, we find that the propagation of left dorsolateral prefrontal cortex TMS to regions in the lateral frontoparietal network might depend on the brain-state, as indexed by the EEG prefrontal alpha phase. Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease

Plasma Amyloid and in vivo Brain Amyloid in Late Middle-Aged Hispanics

BACKGROUND: Determining amyloid positivity is possible with cerebrospinal fluid and brain imaging of amyloid, but these methods are invasive and expensive. OBJECTIVE: To relate plasma amyloid-β (Aβ), measured using Single-molecule array (Simoatrademark) assays, to in vivo brain Aβ, measured using positron emission tomography (PET), examine the accuracy of plasma Aβ to predict brain Aβ positivity, and the relation of APOE ɛ4 with plasma Aβ. METHODS: We performed a cross-sectional analysis in a cohort of 345 late middle-aged Hispanic men and women (age 64 years, 72% women). Our primary plasma variable was Aβ 42/Aβ 40 ratio measured with Simoa. Brain Aβ burden was measured as global SUVR with 18F-Florbetaben PET examined continuously and categorically. RESULTS: Plasma Aβ 42/Aβ 40 ratio was inversely associated with global Aβ SUVR (β= -0.13, 95% Confidence Interval (CI): -0.23, -0.03; p = 0.013) and Aβ positivity (Odds Ratio: 0.59, 95% CI: 0.38, 0.91; p = 0.016), independent of demographics and APOE ɛ4. ROC curves (AUC = 0.73, 95% CI: 0.64, 0.82; p <  0.0001) showed that the optimal threshold for plasma Aβ 42/Aβ 40 ratio in relation to brain Aβ positivity was 0.060 with a sensitivity of 82.4% and specificity of 62.8% . APOE ɛ4 carriers had lower Aβ 42/Aβ 40 ratio and a higher Aβ positivity determined with the Aβ 42/Aβ 40 ratio threshold of 0.060. CONCLUSION: Plasma Aβ 42/Aβ 40 ratio assayed using Simoa is weakly correlated with in vivo brain amyloid and has limited accuracy in screening for amyloid positivity and for studying risk factors of brain amyloid burden when in vivo imaging is not feasible

Pupillary response is associated with the reset and switching of functional brain networks during salience processing.

The interface between processing internal goals and salient events in the environment involves various top-down processes. Previous studies have identified multiple brain areas for salience processing, including the salience network (SN), dorsal attention network, and the locus coeruleus-norepinephrine (LC-NE) system. However, interactions among these systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, EEG, and fMRI during an auditory oddball paradigm. The analyses of EEG and fMRI data uncovered spatiotemporally organized target-associated neural correlates. By modeling the target-modulated effective connectivity, we found that the target-evoked pupillary response is associated with the network directional couplings from late to early subsystems in the trial, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with the pupil-indexed LC-NE system in the reset and switching of cortical networks, and shed light on their implications in various cognitive processes and neurological diseases

Enhanced Oil Production Through a Combined Application of Gel Treatment and Surfactant Huff\u27n\u27puff Technology

Surfactant huff-puff for production wells has become a recent interest, especially for extremely heterogeneous reservoirs. However, injected surfactant will preferentially move into fractures in fractured reservoirs or higher permeability zones in unfractured reservoirs and will bypass much of the reservoir. In addition, the surfactant will be produced immediately after the production well resumes to production, so the soaking time is limited. This paper introduces a novel process which couples gel treatment and surfactant huff-puff in one EOR process in which surfactant solution is first injected into the production well, then followed by gel treatment. The proposed method has been applied for 10 production wells in a polymer flooding unit in Daqing oilfield. The best compatible surfactant and gel were screened out for the given reservoir conditions before the field application. The mechanisms of the proposed method were studied using core flooding experiments. The field application design process is presented and the application results have been analyzed. Application results show pressure drawdown test can be an important criterion to select candidate for the combined method

Flowchart of organ deformation and radiotherapy simulation.

<p>Flowchart of organ deformation and radiotherapy simulation.</p

Ensemble learning-based approach for residential building heating energy prediction and optimization

Accurate building energy consumption prediction is critical for engineers to design optimized operational strategies for building heating, ventilation, and air-conditioning systems. In this paper, an stacking ensemble learning-based model is established based on the operational data of a district resident buildings heating station for building heating system energy consumption prediction. The ensemble model is optimized by outlier processing, feature selection, parameter optimization based on grid search. A new feature based on Exponentially Weighted Moving Average (EWMA) algorithm was proposed to take historical energy feature into consideration. The performance of the ensemble model and four base machine learning methods, including multiple linear regression, extreme learning machine, extreme gradient boosting and support vector regression, are evaluated. Compared with the four base models, the Mean Absolute Error (MAE) of the ensemble model decreases by 4.36%–71.70%, and the Root Mean Squared Error (RMSE) by 3.80%–49.73%. Using the new feature based on EWMA can further reduce the MAE and RMSE of the ensemble model by 10.36% and 19.89%, respectively. The result proves that the proposed ensemble model with the added historical feature effectively improves the prediction model's accuracy for building heating energy consumption

Properties of the organs in the pelvic region.

<p>Properties of the organs in the pelvic region.</p

Monte Carlo Simulations for Dosimetry in Prostate Radiotherapy with Different Intravesical Volumes and Planning Target Volume Margins - Fig 6

<p>(A) Intestines volume that received more than 50 Gy for different PTV margins and (B) an intuitive representation of the distribution of the dose (Gy) to the intestines when intravesical volume varied from 100 ml to 700 ml with 5 (a–g) and 10 mm (h–n) PTV margins.</p

Ten pelvic models with different bladder volumes.

<p>Only the bladder and bone are shown for easier comparison.</p

FE models constructed in Hypermesh.

<p>Right views of (a) intestines, (b) prostate, (c) bladder, and (d) seminal vesicles. (e) Anterior view and (f) posterior view of the VCH pelvic part.</p