Biology subjects

Woo, C.-W.

Publications and source records attributed to Woo, C.-W..

16 recordsLinked to original sources

bioRxiv2026

Parallel processing chains span cytoarchitectures to organize association cortex

Task fMRI1 and electrophysiology2 have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places)1,3-5, smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus6. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.

neuroscience↗

bioRxiv2025

In vivo cartography of state-dependent signal flow hierarchy in the human cerebral cortex

Understanding the principle of information flow across distributed brain networks is of paramount importance in neuroscience. Here, we introduce a novel neuroimaging framework, leveraging integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven discovery of the human cerebral functional hierarchy. Simulation and empirical validation demonstrated the high fidelity of iEC in recovering connectome directionality and its potential relationship with histologically defined feedforward and feedback pathways. Notably, the iEC-derived hierarchy revealed a monotonically increasing level along the axis where the sensorimotor, association, and paralimbic areas are sequentially ordered - a pattern supported by the Structural Model of laminar connectivity. This hierarchy was further demonstrated to flexibly reorganize across brain states: flattening during an externally oriented condition, evidenced by a reduced slope in the hierarchy, and steepening during an internally focused condition, reflecting heightened engagement of interoceptive regions. Our study highlights the unique role of macroscale directed functional connectivity in uncovering a biologically interpretable state-dependent signal flow hierarchy.

neuroscience↗

bioRxiv2024

Temporal Dynamics of Brain Mediation in Predictive Cue-induced Pain Modulation

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of prior predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. Here, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from transmodal to unimodal brain systems. Our findings advance the understanding of how the brain integrates prior and sensory information into pain experience over time.

neuroscience↗

bioRxiv2023

Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models

AO_SCPLOWBSTRACTC_SCPLOWNeuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents results from a comprehensive literature survey and benchmarking analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables--the levels of data, spatial scales, idiographic vs. population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmarking analyses using a locally collected functional Magnetic Resonance Imaging (fMRI) dataset (N = 124) involving an experimental thermal pain task. The results clearly demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and using less data averaging in training while increasing it in testing enhanced performance. These findings provide a useful reference for decision-making in the development of neuroimaging-based biomarkers, highlighting the importance of the careful selection of modeling strategies and options to build better-performing neuroimaging pain biomarkers. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers.

neuroscience↗

bioRxiv2023

Connectome-Based Attractor Dynamics Guide Brain Activity in Rest, Task, and Disease

Functional brain connectivity has been instrumental in uncovering the large-scale organization of the brain and its relation to various behavioral and clinical phenotypes. Understanding how this functional architecture relates to the brains dynamic activity repertoire is an essential next step towards interpretable generative models of brain function. We propose functional connectivity-based Attractor Neural Networks (fcANNs), a theoretically inspired model of macro-scale brain dynamics, simulating recurrent activity flow among brain regions based on first principles of self-organization. In the fcANN framework, brain dynamics are understood in relation to attractor states; neurobiologically meaningful activity configurations that minimize the free energy of the system. We provide the first evidence that large-scale brain attractors - as reconstructed by fcANNs - exhibit an approximately orthogonal organization, which is a signature of the self-orthogonalization mechanism of the underlying theoretical framework of free-energy-minimizing attractor networks. Analyses of 7 distinct datasets demonstrate that fcANNs can accurately reconstruct and predict brain dynamics under a wide range of conditions, including resting and task states, and brain disorders. By establishing a formal link between connectivity and activity, fcANNs offer a simple and interpretable computational alternative to conventional descriptive analyses. Key PointsO_LIWe present a simple yet powerful generative computational model for large-scale brain dynamics C_LIO_LIBased on the theory of artificial attractor neural networks emerging from first principles of self-organization C_LIO_LIModel dynamics accurately reconstruct several characteristics of resting-state brain dynamics and confirm theoretical predictions of emergent attractor self-orthogonalization C_LIO_LIOur model captures both task-induced and pathological changes in brain activity C_LIO_LIfcANNs offer a simple and interpretable computational alternative to conventional descriptive analyses of brain function C_LI Project website (with interactive manuscript)https://pni-lab.github.io/connattractor

neuroscience↗

bioRxiv2023

Modulating Subjective Pain Perception with Decoded MNI-space Neurofeedback

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e., brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach leveraging real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants in a double-blinded decoded fMRI neurofeedback experiment to up- or down-regulate the SIIPS. Our results indicate that participants can learn to down-regulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes.

neuroscience↗

bioRxiv2023

Interindividual differences in pain can be explained by fMRI, sociodemographic, and psychological factors

In a recent article, Hoeppli et al. (2022) reported that sociodemographic and psychological factors were not associated with interindividual differences in reported pain intensity. In addition, the interindividual differences in pain could not be detected by thermal pain-evoked brain activities measured by functional Magnetic Resonance Imaging (fMRI). Their comprehensive analyses provided convincing evidence for these null findings, but here we provide another look at their conclusions by analyzing their behavioral data and a large-scale fMRI dataset involving thermal pain (N = 124). Our main findings are as follows: First, a multiple regression model incorporating all available sociodemographic and psychological measures could significantly predict the interindividual differences in reported pain intensity. The key to achieving a significant prediction was including multiple individual difference measures in a single model. Second, with fMRI data from a relatively homogeneous group of 124 participants, we could identify brain regions and a multivariate pattern-based predictive model significantly correlated with the interindividual differences in reported pain intensity. Our results, along with the findings of Hoeppli et al., highlight the challenge of predicting interindividual differences in pain, but also suggest that it is not an impossible task.

neuroscience↗

bioRxiv2023

Bodily Maps of Spontaneous Thought

AO_SCPLOWBSTRACTC_SCPLOWThe intricate relationship between the body and the mind has long been recognized, but the specific bodily representations of spontaneous thought remain elusive. Here, we developed and validated predictive models of spontaneous thought based on body maps using the emBODY and Free-Association Semantic tasks. Our valence and self-relevance models demonstrated robust prediction performances across three test datasets, with the valence model accurately decoding the bodily topography of emotions and feelings. Model weight patterns revealed the significance of peripheral limbs and heart area in predicting valence, while the head area played a crucial role in predicting self-relevance. Furthermore, we investigated the neurobiological underpinnings of body map representations using fMRI and ECG data and found evidence for the reflection of body map responses in central and autonomic nervous system activities. Overall, this study provides insights into the bodily representations of spontaneous thought, highlighting the interconnected relationships between the body and the mind.

neuroscience↗

bioRxiv2023

Brain Representations of Affective Valence and Intensity in Sustained Pleasure and Pain

Pleasure and pain are two opposites that compete and influence each other, implying the existence of brain systems that integrate them to generate modality-general affective experiences. Here, we examined the brains general affective codes (i.e., affective valence and intensity) across sustained pleasure and pain through an fMRI experiment (n = 58). We found that the distinct sub-populations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity, which was replicated in an independent test dataset (n = 62). The affective valence and intensity models were connected to distinct large-scale brain networks--the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting the systems-level understanding of human affective experiences.

neuroscience↗

bioRxiv2023

Getting personal: brain decoding of spontaneous thought using personal narratives

The contents of spontaneous thought and their dynamics are important factors for ones personality traits and mental health. However, they are difficult to assess because spontaneous thought occurs voluntarily without conscious constraints. Here, we aimed to decode two important content dimensions of spontaneous thought--self-relevance and valence--directly from functional Magnetic Resonance Imaging (fMRI) signals. To train brain decoders, we induced a wide range of levels of self-relevance and emotional valence using individually generated personal stories as well as stories written by others to mimic narrative-like spontaneous thoughts (n = 49). We then tested the brain decoders on two resting-state fMRI datasets (n = 49 and 90) with and without intermittent thought sampling, achieving significant predictions. The default mode and ventral attention networks were important contributors to the predictions. Overall, this study paves the way for the brain decoding of spontaneous thought and its use for clinical applications.

neuroscience↗

bioRxiv2021

When self comes to a wandering mind: Brain representations and dynamics of self-generated concepts in spontaneous thought

AO_SCPLOWBSTRACTC_SCPLOWSelf-relevant concepts are major building blocks of spontaneous thought, and their dynamics in a natural stream of thought are likely to reveal ones internal states important for mental health. Here we conducted an fMRI experiment (n = 62) to examine brain representations and dynamics of self-generated concepts in the context of spontaneous thought using a newly developed free association-based thought sampling task. The dynamics of conceptual associations were predictive of individual differences in general negative affectivity, replicating across multiple datasets (n = 196). Reflecting on self-generated concepts strongly engaged brain regions linked to autobiographical memory, conceptual processes, emotion, and autonomic regulation, including the medial prefrontal and medial temporal subcortical structures. Multivariate pattern-based predictive modeling revealed that the neural representations of valence became more person-specific as the level of perceived self-relevance increased. Overall, this study provides a hint of how self-generated concepts in spontaneous thought construct inner affective states and idiosyncrasies.

neuroscience↗

bioRxiv2021

Dynamic Functional Brain Reconfiguration During Sustained Pain

AO_SCPLOWBSTRACTC_SCPLOWPain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain network representations are dynamically reconfigured over time while experiencing pain. Here, we investigated the dynamic changes in the functional brain networks during 20-min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrated with subcortical and frontoparietal regions, constituting a brain-wide pain supersystem. As pain decreased over time, the subcortical and frontoparietal regions were separated from this pain supersystem and connected to multiple cerebellar regions. Machine-learning models based on these dynamic network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, potentially advancing our mechanistic understanding of chronic pain.

neuroscience↗

bioRxiv2021

Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent shortterm (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with [≥]60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions. Significance statementCurrent efforts towards translating brain biomarkers require identifying brain measures that can strongly and reliably predict outcomes of interest. We systematically examined the performance of a well-established brain activity pattern, the Neurological Pain Signature (NPS), in a large and diverse sample of participants. The NPS showed excellent reliability, and the reliability scaled with the number of trials within-person. The NPS responses showed strong correlations with pain reports at the within-person level but only modest correlations at the between-person level. The findings suggest that the NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of a pain-related physiological target. Author NoteThis project was supported by grants R01MH076136 (T.D.W.), R01DA046064, R01EB026549, and R01DA035484. Elizabeth A. Reynolds Losin was supported by a Mentored Research Scientist Development award from National Institute On Drug Abuse of the National Institutes of Health (K01DA045735). Lauren Y. Atlas was supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health. Yoni K. Ashar was supported by NCATS Grant # TL1-TR-002386. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Code for all analyses and figures is available at https://github.com/XiaochunHan/NPS_measurement_properties. Data for all analyses and figures is available at https://osf.io/v9px7/.

neuroscience↗

bioRxiv2020

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Information is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature ( multi-system models); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains. Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.

neuroscience↗

bioRxiv2019

False-positive neuroimaging: Undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding

Hypothesis testing in neuroimaging studies relies heavily on treating named anatomical regions (e.g., \"the amygdala\") as unitary entities. Though data collection and analyses are conducted at the voxel level, inferences are often based on anatomical regions. The discrepancy between the unit of analysis and the unit of inference leads to ambiguity and flexibility in analyses that can create a false sense of reproducibility. For example, hypothesizing effects on \"amygdala activity\" does not provide a falsifiable and reproducible definition of precisely which voxels or which patterns of activation should be observed. Rather, it comprises a large number of unspecified sub-hypotheses, leaving room for flexible interpretation of findings, which we refer to as \"model degrees of freedom.\" From a survey of 135 functional Magnetic Resonance Imaging studies in which researchers claimed replications of previous findings, we found that 42.2% of the studies did not report any quantitative evidence for replication such as activation peaks. Only 14.1% of the papers used exact coordinate-based or a priori pattern-based models. Of the studies that reported peak information, 42.9% of the replicated findings had peak coordinates more than 15 mm away from the original findings, suggesting that different brain locations were activated, even when studies claimed to replicate prior results. To reduce the flexible and qualitative region-level tests in neuroimaging studies, we recommend adopting quantitative spatial models and tests to assess the spatial reproducibility of findings. Techniques reviewed here include permutation tests on peak distance, Bayesian MANOVA, and a priori multivariate pattern-based models. These practices will help researchers to establish precise and falsifiable spatial hypotheses, promoting a cumulative science of neuroimaging.

neuroscience↗

bioRxiv2018

Cognitive self-regulation influences pain-related physiology

Cognitive self-regulation can shape pain experience, but its effects on autonomic responses to painful events are unclear. In this study, participants (N = 41) deployed a cognitive strategy based on reappraisal and imagination to regulate pain up or down on different trials while skin conductance responses (SCR) and electrocardiogram (ECG) activity were recorded. Using a machine learning approach, we first developed stimulus-locked SCR and ECG physiological markers predictive of pain ratings. The physiological markers demonstrated high sensitivity and moderate specificity in predicting pain across two datasets, including an independent test dataset (N = 84). When we tested the markers on the cognitive self-regulation data, we found that cognitive self-regulation had significant impacts on both pain ratings and pain-related physiology in accordance with regulatory goals. These findings suggest that self-regulation can impact autonomic nervous system responses to painful stimuli and provide pain-related autonomic profiles for future studies.

neuroscience↗