Individual Modalities Research Articles

Quantitative MRI Assessment of Post-Surgical Spinal Cord Injury Through Radiomic Analysis

Purpose: This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Background: Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization, which are needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. Methods: A total of 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Results: Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98), and localized lesions (MCC: 0.85; ACC: 0.90). Combined T1 and T2 features outperformed individual modalities across tasks with gradient boosting models showing the highest efficacy. Conclusions: The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration.

Causal Inference for Modality Debiasing in Multimodal Emotion Recognition

Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose Causal Inference in Multimodal Emotion Recognition (CausalMER), which leverages counterfactual reasoning and causal graphs to capture relationships between modalities and reduce direct modality effects contributing to bias. This allows CausalMER to make unbiased predictions while being easily applied to existing MER methods in a model-agnostic manner, without requiring any architectural modifications. We evaluate CausalMER on the IEMOCAP and CMU-MOSEI datasets, widely used benchmarks in MER, and compare it with existing methods. On the IEMOCAP dataset with the MulT backbone, CausalMER achieves an average accuracy of 83.4%. On the CMU-MOSEI dataset, the average accuracies with MulT, PMR, and DMD backbones are 50.1%, 48.8%, and 48.8%, respectively. Experimental results demonstrate that CausalMER is robust in missing modality scenarios, as shown by its low standard deviation in performance drop gaps. Additionally, we evaluate modality contributions and show that CausalMER achieves balanced contributions from each modality, effectively mitigating direct biases from individual modalities.

Equity-Focused Interventions Improve Interpreter Use in the Pediatric Intensive Care Unit.

Federal guidelines and equitable care mandate that patients who use a language other than English receive interpretation in their preferred language. Substantial variability exists in interpreter use in intensive care settings. We aimed to increase the rate of interpretations in our pediatric intensive care unit (PICU) through a series of targeted interventions. A multidisciplinary team developed a key driver diagram to identify areas for focused intervention. Each plan-do-study-act cycle informed the next cycle of interventions, targeting increasing interpreter (video, phone, and in-person) use. Interventions included standardizing technology, standardizing placement of interpretation devices in patient rooms, provider education, and creating accountability systems of interpreter use by care providers. We reviewed data from PICU encounters between January 2018 and January 2022 and used summary statistics and statistical process control methods to measure the impact of our interventions. We analyzed 882 patient encounters over the 4-year study period. Demographic characteristics were similar in the preintervention and postintervention periods. The total interpretation rate increased to 2.7 interpretations per patient per day from a baseline rate of 1.4. Each individual interpretation modality demonstrated increases in use. Average time spent interpreting via phone increased from 8 to 10.5 minutes per patient per day, and average time spent interpreting via video went from 9.5 to 22 minutes per patient per day. Iterative quality improvement methodology effectively identified barriers to equitable care, guided development of focused interventions, and improved interpreter use among pediatric patients who were critically ill.

Lipid-based nanoparticles for drug delivery in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder that predominantly affects dopaminergic neurons in the substantia nigra and ventral tegmental area, resulting in symptoms such as tremors, muscle rigidity, bradykinesia, and potential cognitive and affective disturbances. The effective delivery of pharmacological agents to the central nervous system is hindered by various factors, including the restrictive properties of the blood‒brain barrier and blood‒spinal cord barrier, as well as the physicochemical characteristics of the drugs. Traditional drug delivery methods may not provide the therapeutic concentrations necessary for functional restoration in PD patients. However, lipid-based nanoparticles (NPs) offer new possibilities for enhancing the bioavailability of established treatment regimens and developing innovative therapies that can modify the course of the disease. This review provides a concise overview of recent advances in lipid-based NP strategies aimed at mitigating specific pathological mechanisms relevant to PD progression. This study also explores the potential applications of nanotechnological innovations in the development of advanced treatment modalities for individuals with PD.

Predicting Mental Health Treatment Outcomes using Latent Growth Mixture Models and Machine Learning in a Real-World Clinical Setting

Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population, subsequently developing machine learning models to predict clinical outcomes based on baseline characteristics and symptom measures. The study analyzed approximately 15,000 clients aged 18-89 in a real-world clinical setting, treated for up to 180-days between 2015 and 2020. Three distinct groups were identified: Low-Low (stable low scores), High-Low (improving scores), and High-High (stable high scores), representing 56%, 18%, and 26% of the cohort, respectively. Significant differences were observed in baseline factors and outcome assessments across these groups. The machine learning model demonstrated balanced accuracies of 0.67-0.93, and ROC-AUC values between 0.87-0.97 in predicting group membership from baseline data. Key predictors included baseline PHQ-9 scores, sex, age, and PTSD diagnosis. Prospective application of the model accurately categorized new client data, aligning predictions with observed outcomes. The study highlights the potential of machine learning in clinical settings to predict client outcomes, offering valuable insights based on initial data, potentially enhancing treatment personalization and efficacy. Further research should explore the impact of individual treatment modalities to validate the model’s clinical utility.

Tuned Manganese-Impregnated Mesoporous Silica Nanoparticles as a pH-Responsive Dual Imaging Probe.

The limitations of individual imaging modalities have led to significant interest in hybrid imaging methods that combine the advantages of multiple techniques. The development of diverse dual imaging agents, which offer the exceptional sensitivity of single-photon emission computed tomography (SPECT) and the high spatial resolution of magnetic resonance imaging (MRI), has been addressing the demand for more advanced diagnostic pharmaceuticals. In this study, 99mTc-labeled manganese oxide-loaded mesoporous silica nanoparticles (MSNs), conjugated with folic acid as the targeting moiety and the chelating agent H2pentapa-en-NH2 (99mTc-MnOx-MSN-FA-pa), were developed for targeted SPECT-MRI dual imaging. The toxicity of the nanoparticles was confirmed through an MTT assay, showing >90% viability in HEK-293 and MDA-MB-231 cells at concentrations up to 200 μg/mL, indicating nonsignificant toxicity. Cellular uptake studies showed that folic acid functionalization effectively accentuated tumor-specific intracellular uptake of nanoparticles in MDA-MB-231 cells through folate receptor-mediated endocytosis. Additionally, the radiolabeling yield of 99mTc-MnOx-MSN-FA-pa was found to be 99.6 ± 0.8% (n = 3), and the pH-responsive release of paramagnetic manganese ions increased the r1 relaxivity of the nanoprobe to 11.37 mM-1 s-1. In vivo SPECT imaging demonstrated rapid tracer accumulation in MDA-MB-231 xenografts, with a tumor-to-muscle ratio of 6.01 ± 0.51 at 2 h, and minimal uptake in nontargeted organs. In vivo MRI studies indicated the strongest tumor contrast at 2 h postinjection. Given its desirable contrast enhancement in T1 MRI and SPECT imaging, along with low toxicity, MnOx-MSN-FA-pa shows potential as an effective multifunctional nanoprobe for precise tumor imaging.

Abstract A016: Targeting metabolic vulnerability of non-small cell lung cancer with annonacin and 2-deoxy-d-glucose in individual and combination modality

Abstract Non-small cell lung cancer (NSCLC) is a primary type of lung cancer and a leading cause of cancer-related mortality worldwide. The Warburg effect, characterized by increased glycolysis and reduced oxidative phosphorylation, is a hallmark of NSCLC and presents a therapeutic target. Annonacin, a natural compound found in fruits from the Annonaceae family, interferes with oxidative phosphorylation by inhibiting mitochondrial complex I. 2-deoxy-d-glucose (2DG), a glucose analog, disrupts glycolysis. This study examines the effects of Annonacin and 2DG on NSCLC A549 cells, both individually and in combination, with the hypothesis that inhibiting both metabolic pathways simultaneously will lead to more effective cancer treatment. In addition, we explored how these treatments might alter the tumor microenvironment, oxidative stress, and effects on normal epithelial bronchial cells. A549 NSCLC cells were exposed to different concentrations of Annonacin (0, 1, 2, 4, 6 µM) and 2DG (0,1.25, 2.5, 5, 10 mM), separately and together. Cell viability was measured using the MTT assay. Genotoxicity was assessed through the Comet Assay, and oxidative stress was evaluated by measuring superoxide dismutase and glutathione peroxidase activities. The long-term ability of the cells to proliferate was tested using colony-forming assays. The NL20 bronchial epithelial cell line was tested as a parallel control. Both Annonacin and 2DG showed a dose-dependent ability to kill A549 cells. However, when used together, these substances had a stronger effect, significantly lowering the number of surviving cells compared to when each was used alone. The response of this combination treatment on genotoxicity using Comet Assay is being pursued. Changes in oxidative stress responses were found, as shown by the altered superoxide dismutase and glutathione peroxidase enzyme activities. The ability of A549 cells to form colonies was greatly reduced following combination treatment, indicating a reduction in their long-term proliferative capacity. The combination of Annonacin and 2DG effectively inhibits the growth of NSCLC A549 cells by targeting their metabolic weaknesses, which may lead to increased DNA damage and oxidative stress. These results suggest simultaneous targeting glycolysis and oxidative phosphorylation could be a promising new approach for treating NSCLC. Future in vivo studies will evaluate this combination in live models and can offer insights into efficacy and possible translation to clinical application. Citation Format: Bhoj Raj Bhattarai, Cora Teets, Avinash Tope. Targeting metabolic vulnerability of non-small cell lung cancer with annonacin and 2-deoxy-d-glucose in individual and combination modality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A016.

Federated Learning Using Multi-Modal Sensors with Heterogeneous Privacy Sensitivity Levels

Data from multi-modal sensors, such as Red-Green-Blue (RGB) cameras, thermal cameras, microphones, and mmWave radars, have gradually been adopted in various classification problems for better accuracy. Some sensors, like RGB cameras and microphones, however, capture privacy-invasive data, which are less likely to be used in centralized learning. Although the Federated Learning (FL) paradigm frees clients from sharing their sensor data, doing so results in reduced classification accuracy and increased training time. In this article, we introduce a novel Heterogeneous Privacy Federated Learning (HPFL) paradigm to better capitalize on the less privacy-invasive sensor data, such as thermal images and mmWave point clouds, by uploading them to the server for closing the performance gap between FL and centralized learning. HPFL not only allows clients to keep the more privacy-invasive sensor data private, such as RGB images and human voices, but also gives each client total freedom to define the levels of their privacy concern on individual sensor modalities. For example, more sensitive users may prefer to keep their thermal images private, while others do not mind sharing these images. We carry out extensive experiments to evaluate the HPFL paradigm using two representative classification problems: semantic segmentation and emotion recognition. Several key findings demonstrate the merits of HPFL: (i) compared to FedAvg, it improves foreground accuracy by 18.20% in semantic segmentation and boosts the F1-score by 4.20% in emotion recognition, (ii) with heterogeneous privacy concern levels, it achieves an even larger F1-score improvement of 6.17–16.05% in emotion recognition, and (iii) it also outperforms the state-of-the-art FL approaches by 12.04–17.70% in foreground accuracy and 2.54–4.10% in F1-score.

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

BackgroundA multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.Methods78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners’ Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.ResultsThe ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.ConclusionsThis study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with computers. Previous studies have achieved significant breakthroughs and extensive applications in the past decade, such as public opinion analysis and intelligent voice service. With the rapid development of deep learning, SA based on various modalities has become a research hotspot. However, only individual modality has been analyzed separately, lacking a systematic carding of comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal SA (MSA) have been explored yet. In this article, we first take the modality as the thread to design a novel framework of SA tasks to provide researchers with a comprehensive understanding of relevant advances in SA. Then, we introduce the general workflows and recent advances of single-modal in detail, discuss the similarities and differences of single-modal SA in data processing and modeling to guide MSA, and summarize the commonly used datasets to provide guidance on data and methods for researchers according to different task types. Next, a new taxonomy is proposed to fill the research gaps in MSA, which is divided into multimodal representation learning and multimodal data fusion. The similarities and differences between these two methods and the latest advances are described in detail, such as dynamic interaction between multimodalities, and the multimodal fusion technologies are further expanded. Moreover, we explore the advanced studies on multimodal alignment, chatbots, and Chat Generative Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research challenges of MSA and provide four potential aspects to improve future works, such as cross-modal contrastive learning and multimodal pretraining models.

Plasmonic Supramolecular Nanozyme-Based Bio-Cockleburs for Synergistic Therapy of Infected Diabetic Wounds.

Diabetic wounds are a major devastating complication of diabetes due to hyperglycemia, bacterial invasion, and persistent inflammation, and the current antibiotic treatments can lead to the emergence of multidrug-resistant bacteria. Herein, a bimetallic nanozyme-based biomimetic bio-cocklebur (GNR@CeO2@GNPs) is designed and synthesized for diabetic wound management by depositing spiky ceria (CeO2) shells and gold nanoparticles (GNPs) on a gold nanorod (GNR) nanoantenna. The plasmonic-enhanced nanozyme catalysis and self-cascade reaction properties simultaneously boost the two-step enzyme-mimicking catalytic activity of GNR@CeO2@GNPs, leading to a significant improvement in overall therapeutic efficacy rather than mere additive effects. Under the glucose activation and 808nm laser irradiation, GNR@CeO2@GNPs material captures photons and promotes the transfer of hot electrons from GNR and GNPs into CeO2, realizing a "butterfly effect" of consuming local glucose, overcoming the limited antibacterial efficiency of an individual PTT modality, and providing substantial reactive oxygen species. In vitro and in vivo experiments demonstrate the material's exceptional antibacterial and antibiofilm properties against Gram-negative and Gram-positive bacteria, which can reduce inflammation, promote collagen deposition, and facilitate angiogenesis, thereby accelerating wound healing. This study provides a promising new strategy to develop plasmonic-enhanced nanozymes with a catalytic cascade mode for the antibiotic-free synergistic treatment of infected diabetic wounds.

Combining CEUS and CT/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC.

To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS. A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations. Reference standards included biopsy, explant histology, and follow-up CT/MRI. The diagnostic performance of the combinations of LI-RADS major features for definitive diagnosis of HCC was calculated. A reverse, stepwise logistical regression sub-analysis was also performed. This study included 114 observations indeterminate on both CT/MRI and CEUS. These observations were categorized as LR-3 (n = 37), LR-4 (n = 41), and LR-M (n = 36) on CT/MRI and LR-3 (n = 48), LR-4 (n = 36), LR-M (n = 29), and LR-TIV (n = 1) on CEUS. Of them, 43.0% (49/114) were confirmed as HCC, 37.3% (43/114) non-malignant, and 19.3% (22/114) non-hepatocellular malignancies. The highest diagnostic accuracy among the combinations of imaging features was achieved in CT/MRI LR-3 observations, where the combination of CEUS arterial phase hyper-enhancement (APHE) + CT/MRI APHE had 96.7% specificity, 75.0% positive predictive value (PPV), and 86.5% accuracy for HCC. The combination of LI-RADS major features on CT/MRI and CEUS showed higher specificity, PPV, and accuracy compared to individual modalities' assessments, particularly for CT/MRI LR-3 observations.

Decoding Silent Speech Cues From Muscular Biopotential Signals for Efficient Human‐Robot Collaborations

AbstractSilent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators.

Cross-Modal Clustering With Deep Correlated Information Bottleneck Method.

Cross-modal clustering (CMC) intends to improve the clustering accuracy (ACC) by exploiting the correlations across modalities. Although recent research has made impressive advances, it remains a challenge to sufficiently capture the correlations across modalities due to the high-dimensional nonlinear characteristics of individual modalities and the conflicts in heterogeneous modalities. In addition, the meaningless modality-private information in each modality might become dominant in the process of correlation mining, which also interferes with the clustering performance. To tackle these challenges, we devise a novel deep correlated information bottleneck (DCIB) method, which aims at exploring the correlation information between multiple modalities while eliminating the modality-private information in each modality in an end-to-end manner. Specifically, DCIB treats the CMC task as a two-stage data compression procedure, in which the modality-private information in each modality is eliminated under the guidance of the shared representation of multiple modalities. Meanwhile, the correlations between multiple modalities are preserved from the aspects of feature distributions and clustering assignments simultaneously. Finally, the objective of DCIB is formulated as an objective function based on a mutual information measurement, in which a variational optimization approach is proposed to ensure its convergence. Experimental results on four cross-modal datasets validate the superiority of the DCIB. Code is released at https://github.com/Xiaoqiang-Yan/DCIB.

A case study on reducing language anxiety and enhancing speaking skills through online conversation lessons

Second language learners, particularly those with lower proficiency, often struggle with insufficient practice and heightened anxiety when speaking. This case study investigates the impact of one-onone online conversation lessons on a university student’s speaking skills and language anxiety. The participant, a university student with low proficiency in the target language, engaged in eight online lessons. Quantitative measurements of speaking skills and anxiety levels were taken using pre- and post-tests. Results indicated that the online lessons effectively improved the participant’s speaking abilities and reduced their anxiety. Qualitative analysis of lesson and interview transcripts highlighted specific factors contributing to these changes, such as the role of feedback. This study adds to the field of technology in language learning by demonstrating the potential of online conversation lessons with corrective feedback as an effective individual learning modality for lower-level second language learners.

Effects of group-based versus individual physical exercise programs for knee osteoarthritis: A systematic review and meta-analysis

Background: Knee osteoarthritis (OA) is the most common joint disease worldwide and is associated with a high rate of disability and poor quality of life. However, little is known about the therapeutic effects of group-based versus individual-based physical exercise protocols. Objective: To investigate the effects of individual versus group-based physical exercises on pain intensity and functional outcomes in people with knee OA. Methods: MEDLINE/PubMed, Cochrane, EMBASE, and PEDro databases were searched from the earliest date available to July 2023. Study quality was evaluated using the PEDro scale. Mean difference (MD), standardized mean difference (SMD), and 95% confidence interval (CI) were calculated using a random effect model. Results: Six studies, with 763 patients, met the study criteria. Group-based physical exercises improved pain intensity (VAS 0-100) MD -17.2 (95% CI: -22.2 to -12.3), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale MD -0.54 (95% CI: -1.0 to -0.08), WOMAC function subscale MD -2.1 (95% CI: -4.1 to -0.08) compared to individual modality. No significant difference regarding muscle strength and exercise tolerance was found for participants in the group-based physical exercises compared with individual physical ones. Conclusion: Group-based physical exercise was more successful in reducing pain intensity and functional impairment in patients with knee OA than individual exercise programs. Both group-based and individual physical exercise programs enhanced muscle strength and 6-minute walk distance.

GEXMERT: Geometrically enhanced cross-modality encoder representations from transformers inspired by higher-order visual percepts

Owing to the rapid progress made in transformer-based models and multi-modal fusion strategies, the performance of multi-modal networks has remarkably improved in recent years. However, intrinsic connections among individual modalities are crucial in multi-modal fusion strategies, presenting substantial opportunities for further enhancement of multi-modal perception in this field. Taking inspiration from the biological mechanisms of the human multi-modal cognitive system, we proposed a novel cross-modality fusion network. This network leverages the lateral occipital complex, which is a region responsible for object-shape perception in human vision and integrates an auxiliary modality derived using the conventional multi-stream model. This integration for learning complementary information across image and language modalities effectively captures the biological mechanisms of human processing of higher-order visual percepts through parameter updates of the intermediate modality to optimize the suitability for further fusion. To evaluate the effectiveness of our approach, we conducted experiments under various settings. Our statistical analyses demonstrate a considerable enhancement in the performance of vision-language tasks, thereby validating biological plausibility of our model.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study’s core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis.

Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.

A - 27 Case Study Approach to Initial Exploration of Adaptations of Cognitive Behavioral Therapy (CBT) for Non-Epileptic Seizures (NES) in the Context of Cognitive Impairment

Abstract Objective The following case study seeks to review implementation of CBT for NES, discuss psychotherapeutic adaptations and outcomes while balancing treatment fidelity, and offer preliminary recommendations for expansion of psychotherapeutic modalities for individuals with cognitive impairment. Methods 49-year-old Hispanic male with a history of mixed epileptic (non-lesional frontal lobe medically refractory) and non-epileptic events. Neurological workup included video EEG, which captured seven events without EEG correlate and an unremarkable brain MRI. Neuropsychological evaluation indicated mild cognitive impairment due to epilepsy with deficits in processing speed, executive functioning, and confrontation naming with borderline to low average core visual and verbal abilities. The patient participated in manualized CBT for PNES. Results Treatment adaptations included extending treatment sessions from 12–19 sessions and including the patient’s sister. They read the chapters together and practiced strategies outside of session. She participated in helping him notice patterns of manifestations of fear and anger and their influence on somatic symptoms. Omission of treatment elements with higher cognitive demand (i.e., thought chart). Outcomes included increased awareness of emotions, triggers, and warnings but not episode reduction in the context of increased epileptic seizures. Conclusions Feasibility of treatment may be limited for individuals with mixed epileptic and NES episodes, particularly if there is cognitive impairment and difficulty differentiating between the two. Psychotherapeutic techniques that are concrete and behaviorally based will = be beneficial for individuals with cognitive impairment. Episode reduction in isolation is likely reductionistic when considering treatment effectiveness. Ability to self-manage episodes, quality of life, functional status, and self-efficacy are valuable markers of improvement.