Biology subjects

Jacob, L. P. L.

Publications and source records attributed to Jacob, L. P. L..

4 recordsLinked to original sources

bioRxiv2025

Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity

Technological advances have enabled fMRI acquisition with high temporal resolution, enabling brainwide imaging in just a few hundreds of milliseconds. However, the relationship between fast hemodynamic signals and spontaneous neural activity in the resting state is not yet well understood, limiting our ability to infer neural processes from these fast data. We hypothesized that high-frequency fMRI signals are linked to spontaneous neural activity tied to vigilance states, and that these high-frequency signals could be used to infer the dynamic variations in neuronal activity indexed by EEG neural rhythms. Using fast fMRI (TR=378 ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI power increased during NREM sleep (compared to wakefulness) in frequencies up to 1.3 Hz. High-frequency fMRI power was also correlated to canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal cross-correlation patterns reflecting both shared arousal dynamics and rhythm-specific signatures. Using machine learning, we found that EEG alpha and delta power can be decoded from high-frequency fMRI signals in subjects held-out from the training set, showing that the high-frequency components of fMRI signals contain neurally-coupled information robust enough to generalize across individuals. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI allows for temporally precise quantification of spontaneous neural activity. Significance StatementFunctional MRI enables non-invasive brainwide neuroimaging in humans, but classically had limited temporal resolution. Recently, studies demonstrated that fast fMRI sampling can detect stimulus-evoked neural activity at subsecond timescales. However, it was unknown whether spontaneous neural activity is represented in fast fMRI signals. Using fMRI and simultaneous EEG in subjects who fell asleep, we found that EEG neural activity associated with sleep and wakefulness is represented in high-frequency fMRI signals. The results reveal that fast fMRI sampling can be used to investigate the spontaneous neural activity underlying brain states with high temporal precision.

neuroscience↗

bioRxiv2024

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the widespread brain dynamics underlying these oscillations are difficult to investigate. Using simultaneous EEG and fast fMRI in humans who fell asleep inside the scanner, we developed a machine learning approach to investigate which fMRI regions and networks predict fluctuations in neural rhythms. We demonstrated that the rise and fall of alpha (8-12 Hz) and delta (1-4 Hz) power-- two canonical EEG bands critically involved with cognition and vigilance--can be predicted from fMRI data in subjects that were not present in the training set. This approach also identified predictive information in individual brain regions across the cortex and subcortex. Finally, we developed an approach to identify shared and unique predictive information, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale primarily across the cortex. These results demonstrate that EEG rhythms can be predicted from fMRI data, identify large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal brain dynamics. Author summaryNeurons often fire in synchrony, generating rhythms that play major roles in brain functioning. These rhythms are hallmarks of different brain states of vigilance, such as sleep and wakefulness. Sleep disorders are extremely prevalent among adults, and studying neural rhythms associated with vigilance states is a key step towards understanding sleep disorders and how healthy sleep can be restored. Measuring how neural rhythms affect the brain, however, is difficult: the primary method used in humans, electroencephalography (EEG), can only measure neural activity close to the scalp. EEG can be combined with functional magnetic resonance imaging (fMRI), which is capable of measuring activity in deep brain regions, but fMRI data can be difficult to analyze, as it estimates neural activity indirectly by measuring changes in blood oxygenation. We developed an approach to analyze combined EEG-fMRI data using machine learning, and used it to investigate how fluctuations in neural rhythms across sleep and wakefulness are tied to changes in neural activity throughout the whole brain. Our results describe how different brain networks are coupled to alpha and delta rhythms, and provide a new approach for analyzing EEG-fMRI data that can be employed to investigate other neural rhythms necessary for healthy brain functioning.

neuroscience↗

bioRxiv2020

A Neural Habituation Account of the Negative Compatibility Effect

The Negative Compatibility Effect (NCE) is slower reaction times (RTs) to report the direction of a target arrow that follows a matching prime arrow. The cause has been debated, with some studies indicating perception, while others indicate a response effect. We applied the neural habituation model of Huber and OReilly (2003) to the NCE, explaining the varied results as reflecting changes in the timing of events. We developed a novel variant of the NCE task, specifying the perceptual dynamics of orientation priming as measured with threshold accuracy. This revealed a transition from positive to negative priming as a function of prime duration, and a second experiment ruled out response priming. The perceptual dynamics of the neural habituation model were fit to these results and the parameter values were fixed in applying the model to the NCE literature. Application of the model to RTs necessitated a response representation that accumulates response information during the trial. Our results indicate that the NCE reflects rapid perceptual priming and slower response priming. Because the accumulation of response information is slow and does not suffer from habituation, the response factor of the prime is a positive effect (lingering response information). In contrast, because perceptual activation is fast and habituates, the perceptual factor can be positive or negative priming depending on the timing of the display sequence. These factors interact with the post-prime mask, which can prime the alternative direction when the mask is a related mask created by combining arrows pointing in both directions.

neuroscience↗

bioRxiv2019

Neural habituation enhances novelty detection: an EEG study of rapidly presented words

Huber and OReilly (2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, ERP analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50ms) or long (400ms), and long durations decreased P100/N170 responses to the target word, suggesting that this manipulation increased habituation. Following long duration primes, correct "different" judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the models assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.

neuroscience↗