Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

Developing both graphical and commandline user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The technological The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software - BioImage Suite (bioimagesuite.org)

Enhancement attacks in biomedical machine learning

The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features: 1) general enhancement of prediction performance, and 2) enhancement of a particular method over another. Our enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's r's>0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed logistic regression by 17% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar (r=0.99). Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance, which presents an interesting ethical challenge for the future of biomedical machine learning. These findings emphasize the need for more robust data provenance tracking and other precautionary measures to ensure the integrity of biomedical machine learning research.Comment: 12 pages, 3 figure

Neural correlates of success and failure signals during neurofeedback learning

Feedback-driven learning, observed across phylogeny and of clear adaptive value, is frequently operationalized in simple operant conditioning paradigms, but it can be much more complex, driven by abstract representations of success and failure. This study investigates the neural processes involved in processing success and failure during feedback learning, which are not well understood. Data analyzed was acquired during a multisession neurofeedback experiment in which ten participants were presented with, and instructed to modulate, the activity of their orbitofrontal cortex with the aim of decreasing their anxiety. We assessed the regional blood-oxygenation-level-dependent response to the individualized neurofeedback signals of success and failure across twelve functional runs acquired in two different magnetic resonance sessions in each of ten individuals. Neurofeedback signals of failure correlated early during learning with deactivation in the precuneus/posterior cingulate and neurofeedback signals of success correlated later during learning with deactivation in the medial prefrontal/anterior cingulate cortex. The intensity of the latter deactivations predicted the efficacy of the neurofeedback intervention in the reduction of anxiety. These findings indicate a role for regulation of the default mode network during feedback learning, and suggest a higher sensitivity to signals of failure during the early feedback learning and to signals of success subsequently

Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases

Effective adaptation to distribution shifts in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated information, a process known as machine unlearning. Traditional approaches typically assume that data variations are random, which makes it difficult to adjust the model parameters accurately to remove patterns and characteristics from unlearned data. In this work, we present Unlearning Information Bottleneck (UIB), a novel information-theoretic framework designed to enhance the process of machine unlearning that effectively leverages the influence of systematic patterns and biases for parameter adjustment. By proposing a variational upper bound, we recalibrate the model parameters through a dynamic prior that integrates changes in data distribution with an affordable computational cost, allowing efficient and accurate removal of outdated or unwanted data patterns and biases. Our experiments across various datasets, models, and unlearning methods demonstrate that our approach effectively removes systematic patterns and biases while maintaining the performance of models post-unlearning

An Opportunity to Increase Collaborative Science in Fetal, Infant, and Toddler Neuroimaging

The field of fetal, infant, and toddler (FIT) neuroimaging research—including magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography, and functional near-infrared spectroscopy, among others—offers pioneering insights into early brain development and has grown in popularity over the past 2 decades. In broader neuroimaging research, multisite collaborative projects, data sharing, and open-source code have increasingly become the norm, fostering big data, consensus standards, and rapid knowledge transfer and development. Given the aforementioned benefits, along with recent initiatives from funding agencies to support multisite and multimodal FIT neuroimaging studies, the FIT field now has the opportunity to establish sustainable, collaborative, and open science practices. By combining data and resources, we can tackle the most pressing issues of the FIT field, including small effect sizes, replicability problems, generalizability issues, and the lack of field standards for data collection, processing, and analysis—together. Thus, the goals of this commentary are to highlight some of the potential barriers that have waylaid these efforts and to discuss the emerging solutions that have the potential to revolutionize how we work together to study the developing brain early in life

Initial validation of a novel method of presurgical language localization through functional connectivity (fcMRI)

OBJECTIVE: Neurosurgery is potentially curative in chronic epilepsy but can only be offered to patients if the surgical risk to language is known. Clinical functional magnetic resonance imaging (fMRI) is an ideal, noninvasive method for localizing language cortex yet remains to be validated for this purpose. We have recently presented a novel method for localizing language cortex. Here we present a preliminary evaluation of this method’s validity. We hypothesized language regions identified using this novel method would demonstrate stronger functional connectivity than randomly generated set of proximal networks. METHOD: fMRI data were collected from sixteen temporal lobe patients (12 left) being evaluated for epilepsy surgery at UCLA (mean age 38.9 [sd 11.4]; 6 female; per Wada 14 left language dominant, 1 right, 1 mixed). Language maps were generated using a recently standardized method relying on a conjunction of language tasks (e.g., visual object naming; auditory naming; reading) to identify known language regions (Broca’s area; inferior and superior Wernicke’s Areas; Angular gyrus; Basal Temporal Language Area; Exner’s Area; and Supplementary Speech Area). With activations defined as network nodes, mean network connectivity was compared via permutation tests with alternate (i) fully random and (ii) proximal random networks. Mean network connectivity was determined in independently-acquired motor fMRI datasets (9 foot, 16 hand, 14 tongue). FINDINGS: 77% (30/39) of clinician-derived language networks exhibited mean connectivity greater than fully random networks (p\u3c0.05). Similarly, 69% (27/39) of clinician-derived language networks exhibited mean connectivity greater than proximal random networks (p\u3c0.05). Further analysis of networks not passing the permutation test suggests that low connectivity of non-valid networks may be driven not by low connectivity across all nodes, but by individual nodes that may not actually possess membership within the network. CONCLUSIONS: This study provides preliminary validity for a novel, clinician-based approach to mapping language cortex pre-surgery. This complements our recent work showing this method is reliable, and supports a proposed study comparing fMRI language maps using this technique with the results of direct stimulation mapping

Impact of postnatal weight gain on brain white matter maturation in very preterm infants.

BACKGROUND AND PURPOSE: Very preterm infants (VPIs, <32 weeks gestational age at birth) are prone to long-term neurological deficits. While the effects of birth weight and postnatal growth on VPIs neurological outcome are well established, the neurobiological mechanism behind these associations remains elusive. In this study, we utilized diffusion tensor imaging (DTI) to characterize how birth weight and postnatal weight gain influence VPIs white matter (WM) maturation. METHODS: We included VPIs with complete birth and postnatal weight data in their health record, and DTI scan as part of their predischarge Magnetic Resonance Imaging (MRI). We conducted voxel-wise general linear model and tract-based regression analyses to explore the impact of birth weight and postnatal weight gain on WM maturation. RESULTS: We included 91 VPIs in our analysis. After controlling for gestational age at birth and time between birth and scan, higher birth weight Z-scores were associated with DTI markers of more mature WM tracts, most prominently in the corpus callosum and sagittal striatum. The postnatal weight Z-score changes over the first 4 weeks of life were also associated with increased maturity in these WM tracts, when controlling for gestational age at birth, birth weight Z-score, and time between birth and scan. CONCLUSIONS: In VPIs, birth weight and post-natal weight gain are associated with markers of brain WM maturation, particularly in the corpus callosum, which can be captured on discharge MRI. These neuroimaging metrics can serve as potential biomarkers for the early effects of nutritional interventions on VPIs brain development

Identification of a brain fingerprint for overweight and obesity

The brain plays a central role in the pathophysiology of overweight and obesity. Connectome-based Predictive Modeling (CPM) is a newly developed, data-driven approach that exploits whole-brain functional connectivity to predict a behavior or trait that varies across individuals. We used CPM to determine whether brain “fingerprints” evoked during milkshake consumption could be isolated for common measures of adiposity in 67 adults with overweight and obesity. We found that CPM captures more variance in waist circumference than either percent body fat or BMI, the most frequently used measures to assess brain correlates of obesity. In a post-hoc analysis, we were also able to derive a largely separable functional connectivity network predicting fasting blood insulin. These findings suggest that, in individuals with overweight and obesity, brain network patterns may be more tightly coupled to waist circumference than BMI or percent body fat and that adiposity and glucose tolerance are associated with distinct maps, pointing to dissociable central pathophysiological phenotypes for obesity and diabetes.</p

Automated brain masking of fetal functional MRI with open data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing