Biology subjects

Eggebrecht, A. T.

Publications and source records attributed to Eggebrecht, A. T..

18 recordsLinked to original sources

bioRxiv2026

Unconstrained naturalistic human brain imaging and decoding with a fully wearable high-density optical system

Understanding how the brain supports complex cognition in real-world environments requires neuroimaging systems that impose minimal constraints on natural behavior. Existing high-fidelity modalities, such as functional magnetic resonance imaging (fMRI), confine participants to the scanner, while wearable alternatives sacrifice spatial resolution or cortical coverage. Here, we developed a fully untethered whole-head optical neuroimaging system that achieves high-fidelity tomographic reconstruction through dense spatial sampling, configurable source multiplexing, and high dynamic range detection. This wearable high-density diffuse optical tomography (WHD-DOT) system achieves 151 dB effective dynamic range and nearly 3000 source-detector measurements, comparable to the highest-performing fiber-based DOT systems, while maintaining wireless, battery-powered mobility. We validate WHD-DOT across three paradigms of increasing ecological complexity, ranging from standard functional localizers to naturalistic movie viewing and live piano performance. Across all paradigms, WHD-DOT produces robust, well-localized encoding, repeatable single-trial responses, and above-chance decoding of stimulus-specific dynamics. Piano performance, which requires continuous bimanual movement and an unconstrained posture, is a rigorous real-world test for a wearable neuroimaging system. By decoding song-segment identity of free piano performance at 71.1% accuracy (chance 12.5%), WHD-DOT shows that brain activity from unconstrained, real-world behavior, previously beyond reach of high-fidelity imaging, is now both measurable and decodable.

neuroscience↗

bioRxiv2026

Using HD-DOT in precision accuracy microgenetic research designs to measure change over time in speech, language, and hearing clinical populations

Documenting change is fundamental to understanding the process of intervention among individuals with communication disorders. This technical report demonstrates the clinical applicability of wearable fNIRS systems and the NeuroDOT processing pipelines for examining within-person cortical dynamics of learning. Using a microgenetic research design and a dense sampling approach, we examined changes in the prefrontal cortical hemodynamic response in an adult female participant who completed the same spoken sentence repetition and auditory fixation tasks across eight sessions. In addition to behavioral accuracy, hemodynamic data were collected with a continuous-wave, multi-channel fNIRS system (NIRSport2) using a prefrontal 20-channel optode montage. Data were processed using NeuroDOT (https://www.nitrc.org/projects/neurodot) (Eggebrecht & Culver, 2019) to: (i) standardize signal quality across the sessions to quantify motion levels and to ensure standardized brain map specificity, (ii) to examine both channel space fluctuations in the hemodynamic response and map changes in cortical activation patterns over the sessions. The signal quality met the predefined criteria for only the first five sessions. Participants repetition accuracy did not improve over the five sessions. Channel-wise analysis revealed that HbO concentration differs significantly over right and left hemisphere channels over the course of the five sessions for the Sentence Repetition task, but not for the Auditory Fixation condition. Brain maps revealed qualitative differences in the pattern of prefrontal cortical activation across the five sessions. Behavioral assessments do not fully capture what occurs during speech repetition tasks, and leveraging neuroimaging can help identify and discriminate between disordered and neurotypical populations.

neuroscience↗

bioRxiv2025

Functional Connectivity Graph Theory Analysis of Spoken Word Processing Efficiency in Prefrontal Cortical Activation

PurposeNetwork science models of real-time spoken word processing predict that word frequency and sublexical phonotactic probability directly influence verbal working memory. Behavioral accuracy and speed of processing suggest that high frequency words differ in cognitive processing effort as compared to low frequency words. Cognitive processing effort, assessed indirectly through changes in prefrontal oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations, also suggest that high and low frequency words differ in cognitive effort. This novel study examines potential differences in linguistic processing effort and efficiency directly, using measures of functional connectivity (FC) strength, modularity, and global efficiency, derived from the listeners prefrontal cortex hemodynamic responses using a paradigmatic verbal working memory paradigm. MethodA total of 20 neurologically typical participants (age 18-21 years) completed an auditory n-back working memory task comparing performance for words differing in whole-word frequency and sub lexical phonotactic probability. Changes in HbO and HbR hemoglobin concentration were recorded with a continuous-wave, multi-channel fNIRS system (TechEn, Inc., Milford, MA) using a 20-channel optode montage across the prefrontal cortex. Partial correlation coefficients were calculated between each channel pair to produce 20 x 20 FC matrices for each participant. Prefrontal networks were constructed as a graph where nodes in the graph are the fNIRS measurements and edges are partial correlations between pairs of measurements. ResultsWord frequency effects were evident in both in the behavioral and functional connectivity measures. Task performance, regardless of word frequency, was related to brain measures, such that higher prefrontal FC strength was negatively correlated with accuracy (d) and high modularity was negatively correlated with response times for low frequency words. In keeping with the direction of the brain-behavior correlations, the highest performing participant had lower FC strength and higher efficiency whereas the lowest performing participant had higher modularity and lower global efficiency. ConclusionsWe identified network properties that are linked to lexical indices of cognitive processing effort in typical participants. These preliminary results point to strategies that could assess language function in atypical populations in future studies.

neuroscience↗

bioRxiv2025

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each childs networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization--including finer-scale subdivisions and emerging language lateralization--well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

neuroscience↗

bioRxiv2025

Deep-learning based Embedding of Functional Connectivity Profiles for Precision Functional Mapping

Spatial correlation of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial correlation is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial correlation is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.

neuroscience↗

bioRxiv2025

Mapping brain function underlying naturalistic motor observation and imitation using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a condition defined by deficits in social communication, restricted interests, and repetitive behaviors, is associated with early impairments in motor imitation that persist through childhood and into adulthood. Alterations in the mirror neuron system (MNS), crucial for interpreting and imitating actions, may underlie these ASD-associated differences in motor imitation. High-density diffuse optical tomography (HD-DOT) overcomes logistical challenges of functional magnetic resonance imaging to enable identification of neural substrates of naturalistic motor imitation. ObjectiveWe aim to investigate brain function underlying motor observation and imitation in autistic and non-autistic adults. We hypothesize that HD-DOT will reveal greater activation in regions associated with the MNS during motor imitation than motor observation, and that MNS activity will negatively correlate with autistic traits and motor fidelity. MethodsWe imaged brain function using HD-DOT in N = 100 participants as they engaged in observing or imitating a sequence of arm movements. Additionally, during imitation, participant movements were simultaneously recorded with 3D cameras for computer-vision-based assessment of motor imitation (CAMI). Cortical responses were estimated using general linear models, and multiple regression was used to test for associations with autistic traits, assessed via the Social Responsiveness Scale-2 (SRS), and imitation fidelity, assessed via CAMI. ResultsBoth observing and imitating motor movements elicited significant activations in higher-order visual and MNS regions, including the inferior parietal lobule, superior temporal gyrus, and inferior frontal gyrus. Imitation additionally exhibited greater activation in the superior parietal lobule, primary motor cortex, and supplementary motor area. Notably, the right temporal-parietal junction exhibited activation during observation but not during imitation. Higher autistic traits were associated with increased activation during motor observation in the right superior parietal lobule. No significant correlation between brain activation and CAMI scores was observed. ConclusionsOur findings provide robust evidence of shared and task-specific cortical responses underlying motor observation and imitation, emphasizing the differential engagement of MNS regions during motor observation and imitation.

neuroscience↗

bioRxiv2024

Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical regions across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fit the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

neuroscience↗

bioRxiv2024

A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios. HighlightsO_LIPrevious studies primarily investigated age-specific networks in infants, with limited focus on how well adult networks describe infant functional connectivity (FC). C_LIO_LIur analysis identified a subset of areas in infants showing adult-like network organization, where within-network FC shows less age-related variation and higher scan-to-scan reliability. C_LIO_LIThese areas are positioned near locations with low variability in functional network identity in adults, indicating a potential link between developmental sequence and interindividual variability in functional network organization. C_LI

neuroscience↗

bioRxiv2024

Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood

Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the possible subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.

neuroscience↗

bioRxiv2023

Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography

Modern neuroimaging modalities, particularly functional MRI (fMRI), can decode detailed human experiences. Thousands of viewed images can be identified or classified, and sentences can be reconstructed. Decoding paradigms often leverage encoding models that reduce the stimulus space into a smaller yet generalizable feature set. However, the neuroimaging devices used for detailed decoding are non-portable, like fMRI, or invasive, like electrocorticography, excluding application in naturalistic use. Wearable, non-invasive, but lower-resolution devices such as electroencephalography and functional near-infrared spectroscopy (fNIRS) have been limited to decoding between stimuli used during training. Herein we develop and evaluate model-based decoding with high-density diffuse optical tomography (HD-DOT), a higher-resolution expansion of fNIRS with demonstrated promise as a surrogate for fMRI. Using a motion energy model of visual content, we decoded the identities of novel movie clips outside the training set with accuracy far above chance for single-trial decoding. Decoding was robust to modulations of testing time window, different training and test imaging sessions, hemodynamic contrast, and optode array density. Our results suggest that HD-DOT can translate detailed decoding into naturalistic use.

bioengineering↗

bioRxiv2023

A high-density diffuse optical tomography dataset of naturalistic viewing

Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result, many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from 58 healthy adult participants (aged 18-76 years) who viewed 10 minutes of a movie (The Good, the Bad, and the Ugly, 1966). Most (36) participants viewed the clip more than once, resulting in 106 sessions of data. Cortical responses were mapped using high-density diffuse optical tomography (first-through fourth nearest neighbor separations of 1.3, 3.0, 3.9, and 4.7 cm), covering large portions of superficial occipital, temporal, parietal, and frontal lobes. Consistency of measured activity across subjects was quantified using intersubject correlation analysis. Data are provided in both channel format (SNIRF) and projected to standard space (NIfTI) using an atlas-based light model. These data are suitable for methods exploration as well as investigating a wide variety of cognitive phenomena.

neuroscience↗

bioRxiv2023

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels--a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasnt definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruptions severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in A{beta} PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

neuroscience↗

bioRxiv2023

Network level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly used to identify brain connectivity biomarkers linked to behavior and clinical outcomes. However, non-standard methodological choices in neuroimaging datasets, especially those with families or twins, have prevented robust machine learning applications. Additionally, prioritizing prediction accuracy over biological interpretability has made it challenging to understand the biological processes behind psychopathology. In this study, we employed a linear support vector regression model to study the relationship between resting-state functional connectivity networks and chronological age using data from the Human Connectome Project. We examined the effect of shared variance from twins and siblings by using cross-validation, either randomly assigning or keeping family members together. We also compared models with and without a Pearson feature filter and utilized a network enrichment approach to identify predictive brain networks. Results indicated that not accounting for shared family variance inflated prediction performance, and the Pearson filter reduced accuracy and reliability. Enhancing biological interpretability was achieved by inverting the machine learning model and applying network-level enrichment on the connectome, while directly using regression coefficients as feature weights led to misleading interpretations. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for comprehensible biological interpretation.

neuroscience↗

bioRxiv2023

Anatomical Modeling and Optimization of Speckle Contrast Optical Tomography

Traditional methods for mapping cerebral blood flow (CBF), such as positron emission tomography and magnetic resonance imaging, offer only isolated snapshots of CBF due to scanner logistics. Speckle contrast optical tomography (SCOT) is a promising optical technique for mapping CBF. However, while SCOT has been established in mice, the method has not yet been demonstrated in humans - partly due to a lack of anatomical reconstruction methods and uncertainty over the optimal design parameters. Herein we develop SCOT reconstruction methods that leverage MRI-based anatomical head models and finite-element modeling of the SCOT forward problem (NIRFASTer). We then simulate SCOT for CBF perturbations to evaluate sensitivity of imaging performance to exposure time and SD-distances. We find image resolution comparable to intensity-based diffuse optical tomography at superficial cortical tissue depth ([~]1.5 cm). Localization errors can be reduced by including longer SD-measurements. With longer exposure times speckle contrast decreases, however, noise decreases faster, resulting in a net increase in SNR. Specifically, extending exposure time from 10s to 10ms increased SCOT SNR by 1000X. Overall, our modeling methods provide anatomically-based image reconstructions that can be used to evaluate a broad range of tissue conditions, measurement parameters, and noise sources and inform SCOT system design.

neuroscience↗

bioRxiv2023

Ultra-high density imaging arrays for diffuse optical tomography of human brain improve resolution, signal-to-noise, and information decoding

Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing ([~]13 mm) than sparse fNIRS ([~]30 mm) and therefore provide higher image quality, with spatial resolution [~]1/2 that of fMRI. Herein, simulations indicated reducing inter-optode spacing to 6.5 mm would further improve image quality and noise-resolution tradeoff, with diminishing returns below 6.5 mm. We then constructed an ultra-high-density DOT system (6.5-mm spacing) with 140 dB dynamic range that imaged stimulus-evoked activations with 30-50% higher spatial resolution and repeatable multi-focal activity with excellent agreement with participant-matched fMRI. Further, this system decoded visual stimulus position with 19-35% lower error than previous HD-DOT, throughout occipital cortex.

bioengineering↗

bioRxiv2021

Dorsolateral prefrontal cortex supports speech perception in listeners with cochlear implants

Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of left prefrontal cortex.

neuroscience↗

bioRxiv2021

A processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

AimWe demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. ApproachWe synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. ResultsThis pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. ConclusionsThe pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field. SignificanceImage reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present a novel integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.

neuroscience↗

bioRxiv2020

Global Motion Detection and Censoring in High-Density Diffuse Optical Tomography

Motion-induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high-density diffuse optical tomography (HD-DOT) with hundreds to thousands of source-detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near-infrared spectroscopy (fNIRS). This limitation restricts the application of HD-DOT in many challenging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluate a new motion detection method for multichannel optical imaging systems that leverages spatial patterns across channels. Specifically, we introduce a global variance of temporal derivatives (GVTD) metric as a motion detection index. We show that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion - with area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we show that applying GVTD-based motion censoring on both task and resting state HD-DOT data with natural head motion results in an improved spatial similarity to fMRI mapping for the same respective protocols (task or rest). We then compare the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation-based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring outperforms other methods and results in spatial maps more similar to matched fMRI data.

neuroscience↗