Biology subjects

Ogg, M.

Publications and source records attributed to Ogg, M..

5 recordsLinked to original sources

bioRxiv2025

Optimizing Artificial Neural Network Models to Predict Brain-Age from Functional MRI

Large MRI datasets combined with deep learning methods have realized a new state of the art for brain-age prediction. Age prediction may serve as a valuable biomarker for brain health and disease given that over-estimated age based on MRI (usually as predicted by a machine learning model; sometimes called a "brain-age gap") has been associated with neurological and psychiatric disorders. However, most of these results have been achieved via the use of high-resolution structural (T1w) MRI scans. Brain-age prediction via deep learning over large volumes of functional MRI (fMRI) data is less well studied, but could help form a bridge between neural health biomarkers observed in MRI and more portable platforms like functional near infrared spectroscopy (fNIRS), which measure a hemodynamic signal similar to fMRI. In this work, we studied how to optimize deep learning model architectures and training pipelines to predict brain-age from resting state fMRI connectivity data. A wide set of pre-processing and model hyperparameters was explored that included varying the number of nodes and the composition of the input functional connectivity matrices, the size, depth and objective functions of the neural network models, and a time series sub-sampling method as a data augmentation strategy. Model performance was evaluated on both an internal validation set of held-out participants (from the multi-study corpus compiled for training), as well as numerous external corpora not seen during training, which comprised healthy controls and clinical participants. Neural network models with a variety of hyperparameter configurations supported accurate brain-age prediction using fMRI and many models generalized effectively to predict the age of healthy individuals among data sets not seen during training (< 8 years mean absolute error on the external validation dataset). However, we report mixed results regarding a brain-age gap for held out clinical populations using these methods, with a gap observed only among neurodegenerative disorders (here, Alzheimers disease), and not among psychiatric disorders or patients with traumatic brain injury. This work constitutes a valuable step towards scalable, portable brain-age prediction but highlights a number of areas where additional work and improvements are needed.

neuroscience↗

bioRxiv2025

Behavioral and neural sound classification: Insights from natural and synthetic sounds

Throughout the course of a day, human listeners encounter an immense variety of sounds in their environment. These are quickly transformed into mental representations of objects and events that guide subsequent cognition and behavior in the world. Previous studies using behavioral and temporally resolved neuroimaging methods have demonstrated the importance of certain acoustic qualities for distinguishing among different classes of sounds during the early time period following sound onset (noisiness, spectral envelope, spectrotemporal change over time, and change in fundamental frequency over time). However, this evidence is largely based on correlational studies of natural sounds. Thus, two additional behavioral (Experiment 1) and EEG (Experiment 2) studies further tested these results using a set of synthesized stimuli (interspersed among a new set of natural sounds) that explicitly manipulated previously identified acoustic dimensions. In addition to finding similar correlational results as previous work (using new natural stimuli and tasks), classification results for the synthesized acoustic feature manipulations reinforced the importance of aperiodicity, spectral envelope, spectral variability and fundamental frequency change for representations of superordinate sound-categories. Analyses of the synthesized stimuli suggest that aperiodicity is a particularly robust cue in distinguishing some categories and that speech is difficult to characterize within this framework (i.e., using these acoustic dimensions and synthetic feature manipulations). These results provide a deeper understanding of the neural and perceptual dynamics that support the recognition of behaviorally important categories of sound in the time windows soon after sound onset.

neuroscience↗

bioRxiv2024

Large-Scale Functional Connectome Fingerprinting for Generalization and Transfer Learning in Neuroimaging

Functional MRI currently supports a limited application space stemming from modest dataset sizes, large interindividual variability and heterogeneity among scanning protocols. These constraints have made it difficult for fMRI researchers to take advantage of modern deep-learning tools that have revolutionized other fields such as NLP, speech transcription, and image recognition. To address these issues, we scaled up functional connectome fingerprinting as a neural network pre-training task, drawing inspiration from speaker recognition research, to learn a generalizable representation of brain function. This approach sets a new high-water mark for neural fingerprinting on a previously unseen scale, across many popular public fMRI datasets (individual recognition over held out scan sessions: 94% on MPI-Leipzig, 94% on NKI-Rockland, 73% on OASIS-3, and 99% on HCP). Near-ceiling performance is maintained even when the duration of the evaluation scan is truncated to less than two minutes. We show that this representation can also generalize to support accurate neural fingerprinting for completely new datasets and participants not used in training. Finally, we demonstrate that the representation learned by the network encodes features related to individual variability that supports some transfer learning to new tasks. These results open the door for a new generation of clinical applications based on functional imaging data. SIGNIFICANCE STATEMENTDeep learning models that leverage the increasing scale of available fMRI data could address fundamental generalization challenges. We drew inspiration from other domains that have successfully used AI to address these problems, namely human language technology, to guide our exploration of the potential for this approach in neuroimaging. Our pre-training approach sets a new high-watermark for functional connectome fingerprinting, achieving very high recognition accuracy across different tasks, scanning sessions, and acquisition parameters, even when the duration of a scan is limited to less than two minutes. We showed that we could re-purpose the representation learned by our model to recognize new individuals from new datasets and to predict new participants cognitive performance and traits.

neuroscience↗

bioRxiv2024

Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model

The American Academy of Sleep Medicine (AASM) recognizes five sleep/wake states (Wake, N1, N2, N3, REM), yet this classification schema provides only a high-level summary of sleep and likely overlooks important neurological or health information. New, data-driven approaches are needed to more deeply probe the information content of sleep signals. Here we present a self-supervised approach that learns the structure embedded in large quantities of neurophysiological sleep data. This masked transformer training procedure is inspired by high performing self-supervised methods developed for speech transcription. We show that self-supervised pre-training matches or outperforms supervised sleep stage classification, especially when labeled data or compute-power is limited. Perhaps more importantly, we also show that our pre-trained model is flexible and can be fine-tuned to perform well on new EEG recording montages not seen in training, and for new tasks including distinguishing individuals or quantifying "brain age" (a potential health biomarker). This suggests that modern methods can automatically learn information that is potentially overlooked by the 5-class sleep staging schema, laying the groundwork for new sleep scoring schemas and further data-driven exploration of sleep.

neuroscience↗

bioRxiv2024

Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis

Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are flexible neural networks that not only excel at classification tasks, but also can enable data-driven discovery through un-or self-supervised learning, which requires no human annotations to the input data. While transformers have been extensively used in supervised learning scenarios for sleep stage classification, they have not been fully explored or optimized in forms designed from the ground up for use in un-or self-supervised learning tasks in sleep. A necessary first step will be to study these models on a canonical benchmark supervised learning task (5-class sleep stage classification). Hence, to lay the groundwork for future data-driven discovery efforts, we evaluated optimizations of a transformer-based architecture that has already demonstrated substantial success in self-supervised learning in another domain (audio speech recognition), and trained it to perform the canonical 5-class sleep stage classification task, to establish foundational baselines in the sleep domain. We found that small transformer models designed from the start for (later) self-supervised learning can match other state-of-the-art automated sleep scoring techniques, while also providing the basis for future data-driven discovery efforts using large sleep data sets.

neuroscience↗