Biology subjects

Chen, J. E.

Publications and source records attributed to Chen, J. E..

12 recordsLinked to original sources

bioRxiv2026

Human Cerebral Cortex Organization Characterized by Functional PET-FDG "Metabolic Connectivity"

PurposeIn this study, we characterize the spatiotemporal organization of resting-state metabolic connectivity (RSMC) in the human brain, as measured by [18F]- fluorodeoxyglucose (FDG) functional PET (fPET-FDG). We examine the relationship between RSMC organization and resting-state functional connectivity (RSFC) derived from functional magnetic resonance imaging and other known cortical organizational principles. MethodsResting-state fPET-FDG data from 24 individuals were obtained from a publicly available repository. We characterized local metabolic organization using connectivity-based boundary mapping, with adaptations to account for the low signal-to-noise ratio of fPET-FDG data. We then estimated global metabolic organization through community detection-based network and principal gradient analyses. Furthermore, we examined how metabolic connectivity is shaped by temporal-frequency-specific components of fPET-FDG signal. Finally, we contextualized metabolic organization by relating metabolic gradients to anatomical, functional, and energetic reference measures. ResultsAt the local scale, boundary mapping results indicated structured transitions shaped by a combination of both fast and slow fPET-FDG signals, partly overlapping with RSFC boundary maps. Globally, RSMC analyses revealed a robust metabolic structure organized along a superior-inferior cortical gradient. This pattern remained consistent across network community detection and principal gradient analyses and was primarily driven by low-frequency, minute-scale fPET-FDG dynamics. The identified large-scale metabolic profile aligns closely with several known anatomical and energetic constraints. ConclusionThis study characterizes the spatiotemporal organizational principles of RSMC, deepening insight into the brains energetic framework and providing a basis for future cognitive and clinical investigations of metabolic connectivity organization.

neuroscience↗

bioRxiv2026

Energetic implications of fMRI-based nodal complex network metrics: a complex picture unfolds across diverse brain states

Functional MRI-based graph theory has provided profound insights into the brains functional organization, yet the neuroenergetic meaning of widely used graph-theoretical metrics remains poorly understood. Although resting-state research suggests a positive coupling between network topology and glucose metabolism, it remains unclear whether this relationship reflects a general principle of brain organization or a state-specific phenomenon. Here, we test the neuroenergetic interpretability of nodal graph-theoretical metrics by linking complex network topology to cerebral glucose consumption across diverse brain states. Leveraging simultaneous functional PET-MRI, we directly compare state-dependent fluctuations in glucose consumption and network topology during sensory, cognitive, and arousal conditions. We further assess metabolic-topological couplings in disease through a meta-analysis of resting-state FDG-PET and fMRI studies involving Alzheimers disease, Parkinsons disease, major depressive disorder, and schizophrenia. Our results show that nodal graph-theoretical metrics exhibit state- and network-dependent metabolic associations, with coupling patterns diverging across experimental and disease contexts. Notably, frontoparietal and attentional networks show more conserved metabolic-topological coupling than other large-scale networks across states. These findings underscore a dynamic, complex interplay between metabolic demand and complex network organization, highlighting the need for a nuanced interpretation of the energetic underpinnings of nodal graph-theoretical metrics in health and disease.

neuroscience↗

bioRxiv2025

Distributed fMRI patterns coupled to low-frequency cardiorespiratory dynamics provide markers of aging

How aging affects brain-body connections can be investigated through changes in the coupling between functional magnetic resonance imaging (fMRI) signals and bodily autonomic processes across the adult lifespan. Recent studies using univariate approaches have identified age-related changes in the association between fMRI signals from multiple individual brain regions and low-frequency respiratory and cardiac activity. Here, we investigate if whole-brain spatial fMRI patterns associated with low-frequency physiological processes (heart rate and respiratory volume fluctuations) present generalizable changes with age. Data from human participants of both sexes are included in the analysis. We find that chronological age can be predicted statistically beyond chance from patterns of low-frequency fMRI-physiology coupling, even after accounting for individual differences in physiological signal characteristics and brain anatomy. Notably, brain areas implicated in central autonomic regulation, including nodes within salience and ventral attention networks (e.g., insula and middle cingulate cortex), are amongst the strongest contributors to age prediction. Further, we observe that after removing physiological effects from fMRI data, the residual blood oxygen level-dependent (BOLD) signal variability is still a reliable indicator of age. Together, these findings underscore the close integration between brain and body physiology, and highlight this interaction as a potential biomarker of the aging process. Significance StatementThe association between brain activity and respiratory or cardiac activity is often dismissed as "noise" in functional magnetic resonance imaging (fMRI) studies. However, emerging evidence suggests that coupling between fMRI and peripheral physiological signals can provide valuable insight into the brain-body connection. In this study, we show that whole brain patterns of coupling between fMRI signals and low-frequency respiratory and cardiac processes can reliably predict age across the adult lifespan. Brain regions involved in autonomic regulation, such as insula and cingulate cortex, were among the most informative predictors of age. These findings suggest that fMRI-physiology coupling may capture aging-related changes in brain vascular health and autonomic function and may have broader relevance for tracking disease-related disruptions in brain-body interaction.

neuroscience↗

bioRxiv2025

Visual stimulus-evoked blood velocity responses in individual human posterior cerebral arteries measured with dynamic phase-contrast functional MR angiography

Functional MRI (fMRI) tracks brain activity through the associated hemodynamic changes via neurovascular coupling. Neurons communicate with the microvessels of the parenchyma to initiate a hemodynamic response, and these microvessels then communicate with upstream arterioles and arteries. The role of the larger feeding arteries--far upstream from the site of neuronal activity--in coordinating this response is incompletely understood, yet is important for the interpretation of fMRI. Functional Transcranial Doppler (fTCD) can noninvasively measure blood velocity changes in a subset of the largest macrovessels, albeit with poor spatial resolution, whereas existing functional MR angiography (fMRA) methods can assess blood velocity in mid-sized macrovessels but still lack the temporal resolution required to capture dynamic responses. This study aims to propose a new, quantitative fMRA method for measuring blood velocity responses in individual vessels in humans at high spatiotemporal resolution. A dynamic functional phase-contrast MRA approach was developed to quantify responses evoked by visual stimuli in the "P2" segment of the posterior cerebral artery (PCA), located [~]6 cm away from primary visual cortex. The achieved temporal resolution is comparable to that of conventional blood-oxygenation-level-dependent (BOLD) fMRI, enabling block-design stimulation paradigms similar to those used in conventional fMRI studies. The temporal and spatial properties of the blood velocity responses were evaluated using both long- and short-duration visual stimuli presented to either the full visual field or a single hemifield. Robust responses were measured on both 3T and 7T clinical MRI scanners, and an approximately 3.3 {+/-} 1.2 cm/s increase in the blood velocity in the targeted segment was observed, which is roughly a 10% increase from baseline. Visual hemifield stimulation generated a measurable blood velocity response only in the contralateral cerebral hemisphere, indicating that systemic physiological changes occurring with stimulation cannot account for the observed response, suggesting that they instead reflect neurovascular coupling initiated in the visual cortex. The observed arterial blood velocity response is consistent with a downstream reduction in microvascular resistance and may represent a passive response rather than an active vessel dilation at the targeted arterial segment. The proposed method has the potential to extend the capability of commonly used approaches, such as fTCD, in clinical applications.

neuroscience↗

bioRxiv2025

Functional MRI Signatures of Autonomic Physiology in Aging

While traditionally regarded as "noise", blood-oxygenation-level-dependent (BOLD) fMRI fluctuations coupled to systemic physiology--such as heart rate and respiratory changes--also hold valuable information about brain vascular properties and autonomic function. In this study, we leverage these physiological BOLD signals to characterize age-related changes in brain physiology. Using a large dataset from the Lifespan Human Connectome Project Aging study, we investigated how the spatiotemporal BOLD-fMRI signatures of autonomic physiology, specifically heart rate and respiratory variation, change with advancing age. Our findings reveal that aging is associated with globally slower respiratory fMRI responses, alongside faster cardiac fMRI responses and enhanced brain-cardiac signal coupling. Moreover, we show that the impact of age on physiological fMRI signals exhibits a notable turning point after age 60, suggesting a critical role of declining vascular health and autonomic function in aging. The potential impact of age-related changes in brain structure, tissue perfusion, and in-scan arousal states on the identified physiological fMRI patterns is also tested and discussed. Altogether, our results underscore significant age effects in the fMRI signatures of systemic physiology, emphasizing the pivotal role of altered vascular properties and autonomic function in aging. Methodologically, this study also demonstrates the utility of resting-state fMRI for extracting multi-parametric information about brain physiology, offering new biomarker opportunities that complement established functional connectivity metrics.

physiology↗

bioRxiv2025

The Physiological Component of the BOLD Signal: Impact of Age and Heart Rate Variability Biofeedback Training

Aging is associated with declines in autonomic nervous system (ANS) function, including reduced heart rate variability (HRV), impaired neurovascular coupling, and diminished cerebrovascular responsiveness--factors that may contribute to cognitive decline and neurodegenerative diseases. Understanding how aging alters physiological signal integration in the brain is crucial for identifying potential interventions to promote brain health. This study examines age-related differences in how cardiac and respiratory fluctuations influence the blood oxygenation level-dependent (BOLD) signal, using two independent resting-state fMRI datasets with concurrent physiological recordings from younger and older adults. Our findings reveal significant age-related reductions in the percent variance of the BOLD signal explained by heart rate (HR), respiratory variation (RV), and end-tidal CO2, particularly in regions involved in autonomic regulation, including the orbitofrontal cortex, anterior cingulate cortex, insula, basal ganglia, and white matter. Cross-correlation analysis also revealed that younger adults exhibited stronger HR-BOLD coupling in white matter, as well as a more rapid BOLD response to RV and CO2 in gray matter. Additionally, we investigated the effects of heart rate variability biofeedback (HRV-BF) training, a non-invasive intervention designed to modulate heart rate oscillations. The intervention altered physiological-BOLD coupling in an age- and training-dependent manner: older adults who underwent HRV-BF to enhance HR oscillations exhibited a shift toward younger-like HR-BOLD coupling patterns, while younger adults who trained to suppress HR oscillations showed increased CO2-BOLD coupling. These findings suggest that HRV-BF may help mitigate age-related declines in autonomic or cerebrovascular function. Overall, this study underscores the role of physiological dynamics in brain aging and highlights the importance of considering autonomic function when interpreting BOLD signals. By demonstrating that HRV-BF can modulate physiological-BOLD interactions, our findings suggest a potential pathway for enhancing cerebrovascular function and preserving brain health across the lifespan.

neuroscience↗

bioRxiv2025

Simultaneous EEG-PET-MRI identifies temporally coupled, spatially structured hemodynamic and metabolic dynamics across wakefulness and NREM sleep

Sleep entails significant changes in cerebral hemodynamics and metabolism. Yet, the way these processes evolve throughout wakefulness and sleep and their spatiotemporal dependence remain largely unknown. Here, by integrating a novel functional PET technique with simultaneous EEG-fMRI, we reveal a tightly coupled temporal progression of global hemodynamics and metabolism during the descent into NREM sleep, with large hemodynamic fluctuations emerging as global glucose metabolism declines, both of which track EEG arousal dynamics. Furthermore, we identify two distinct network patterns that emerge during NREM sleep: an oscillating, high-metabolism sensorimotor network remains active and dynamic, whereas hemodynamic and metabolic activity in the default-mode network is suppressed. These results elucidate how sleep diminishes awareness while preserving sensory responses, and uncover a complex, alternating balance of neuronal, hemodynamic, and metabolic dynamics in the sleeping brain. This work also demonstrates the potential of EEG-PET-MRI to explore neuro-hemo-metabolic dynamics underlying cognition and arousal in humans.

neuroscience↗

bioRxiv2024

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal lag (CortiLag) patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed CortiLag-ICA framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.

neuroscience↗

bioRxiv2024

On the analysis of functional PET (fPET)-FDG: baseline mischaracterization can introduce artifactual metabolic (de)activations

Functional Positron Emission Tomography (fPET) with (bolus plus) constant infusion of [18F]-fluorodeoxyglucose FDG), known as fPET-FDG, is a recently introduced technique in human neuroimaging, enabling the detection of dynamic glucose metabolism changes within a single scan. However, the statistical analysis of fPET-FDG data remains challenging because its signal and noise characteristics differ from both classic bolus-administration FDG PET and from functional Magnetic Resonance Imaging (fMRI), which together compose the primary sources of inspiration for analytical methods used by fPET-FDG researchers. In this study, we present an investigate of how inaccuracies in modeling baseline FDG uptake can introduce artifactual patterns to detrended TAC residuals, potentially introducing spurious (de)activations to general linear model (GLM) analyses. By combining simulations and empirical data from both constant infusion and bolus-plus-constant infusion protocols, we evaluate the effects of various baseline modeling methods, including polynomial detrending, regression against the global mean time-activity curve, and two analytical methods based on tissue compartment model kinetics. Our findings indicate that improper baseline removal can introduce statistically significant artifactual effects, although these effects characterized in this study ([~]2-8%) are generally smaller than those reported by previous literature employing robust sensory stimulation ([~]10-30%). We discuss potential strategies to mitigate this issue, including informed baseline modeling, optimized tracer administration protocols, and careful experimental design. These insights aim to enhance the reliability of fPET-FDG in capturing true metabolic dynamics in neuroimaging research.

neuroscience↗

bioRxiv2019

Decoupling of Dopamine Release and Neural Activity in Major Depressive Disorder during Reward Processing Assessed by Simultaneous fPET-fMRI

The interaction of the midbrain dopaminergic system and the striatum is implicated in reward processing; it is still unknown, however, how this interaction is altered in Major Depressive Disorder (MDD). In the current study, we related the dopamine release/binding inferred by [11C] Raclopride functional Positron Emission Tomography (fPET) to neural activity monitored by blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in adults diagnosed with MDD and healthy controls (CTL). Participants completed a monetary incentive delay (MID) task during simultaneous [11C] Raclopride fPET and fMRI. Instead of the usual kinetic modeling method for analyzing dynamic PET time activity curves (TACs), we used a simpler general linear model (GLM) approach, which includes introducing a fPET dopamine activation response function to model changes in the TAC associated with the MID task. In addition, using simulations, we show that the GLM approach has several advantages over kinetic modeling. This is achieved without invoking erroneous steady-state assumptions or selecting a suitable reference region. Our results include the observation of both decreased fMRI activation and dopamine release/binding in the striatum in the MDD cohort, implying a reduced reward processing capacity in MDD. Furthermore, in the MDD group, individuals with lower fMRI activations in the right middle putamen and ventral medial prefrontal cortex (vmPFC) had higher reflection rumination scores, and individuals with lower dopamine release/binding in the left putamen and the right nucleus accumbens (NAcc) also had higher reflection rumination scores. Significant cross-modal inter-subject and intra-subject correlations of dopamine release/binding and fMRI activation were observed in the CTL group, but not in the MDD group. The intra-subject correlation of the two modalities was negatively associated with reflection rumination scores in the CTL group, indicating that decoupling of the dopaminergic system and striatum may be important in the pathophysiology of MDD.

neuroscience↗

bioRxiv2018

Genetic transformation of the dinoflagellate chloroplast

Coral reefs are some of the most important and ecologically diverse marine environments. At the base of the reef ecosystem are dinoflagellate algae, living in symbiosis with coral. Efforts to understand the relationship between alga and coral have been greatly hampered by the lack of an appropriate dinoflagellate genetic transformation technology. By making use of the plasmid-like fragmented chloroplast genome, we have introduced novel genetic material into the dinoflagellate chloroplast genome. We have shown that the introduced genes are expressed and confer the expected phenotypes. Genetically modified cultures have been grown for five months with subculturing, maintaining the introduced genes and phenotypes. This indicates that cells continue to divide after transformation and that the transformation is stable.

genetics↗

bioRxiv2017

The Genetic Intractability Of Symbiodinium microadriaticum To Standard Algal Transformation Methods

Modern transformation and genome editing techniques have shown great success across a broad variety of organisms. However, no study of successfully applied genome editing has been reported in a dinoflagellate despite the first genetic transformation of Symbiodinium being published about 20 years ago. Using an array of different available transformation techniques, we attempted to transform Symbiodinium microadriaticum (CCMP2467), a dinoflagellate symbiont of reef-building corals, with the view to performing subsequent CRISPR-Cas9 mediated genome editing. Plasmid vectors designed for nuclear transformation containing the chloramphenicol resistance gene under the control of the CaMV p35S promoter as well as several putative endogenous promoters were used to test a variety of transformation techniques including biolistics, electroporation and silicon carbide whiskers. Chloroplast-targeted transformation were attempted using an engineered Symbiodinium chloroplast minicircle encoding a modified PsbA protein that confers atrazine resistance. We report that we have been unable to confer chloramphenicol or atrazine resistance to Symbiodinium microadriaticum strain CCMP2467.

molecular biology↗