Biology subjects

Wager, T.

Publications and source records attributed to Wager, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal Dissociation of Human Amygdala Response to Negative Affect

The amygdala is central to negative affect and a primary target of top-down regulation, yet its temporal dynamics remain poorly characterized. Most fMRI studies model stimulus-evoked amygdala activity with a canonical hemodynamic response function and collapse across subregions, potentially obscuring functional heterogeneity in time and space. We used finite impulse response modeling in two independent datasets (combined N = 358) to characterize blood oxygenation level-dependent responses during negative picture viewing and instructed cognitive reappraisal, complemented by data-driven clustering of voxelwise time courses. Across two probabilistic atlases, six of seven amygdala subregions showed prolonged activation to negative stimuli but differed in temporal profile. The centromedial amygdala showed sustained activation extending into the post-stimulus rating period, whereas laterobasal and superficial subregions peaked earlier during stimulus presentation. Reappraisal did not substantially alter response magnitude or shape, indicating that amygdala downregulation is not a reliable consequence of instructed regulation under these conditions. Data-driven analysis identified four clusters with distinct temporal profiles corresponding to laterobasal, lateral, superficial, and centromedial territories, converging with established anatomical parcellations while crossing some conventional boundaries. FIR modeling thus reveals a temporal architecture of amygdala responses to negative affective stimuli, characterized by early laterobasal and superficial responses followed by sustained centromedial activity.

neuroscience↗

bioRxiv2026

Individualised mapping of living human brain mitochondria by MRI reveals signatures of bioenergetic defects.

Mitochondria support the bioenergetic processes that enable brain function and cognition, but we have lacked a label-free, non-invasive approach to explore how brain mitochondria are linked to ageing, disease, and cognition in humans. A recently introduced MitoBrainMap neuroimaging framework predicts mitochondrial features from magnetic resonance data alone, potentially bridging cellular biology with macroscale brain organization. Here, we tested whether this framework captures meaningful age- and pathology-related mitochondrial variation. Consistent with existing literature, we find that MR-predicted mitochondrial density and tissue respiratory capacity consistently declined with age, whereas mitochondrial respiratory capacity-an index of mitochondrial quality-was relatively preserved across the lifespan. Moreover, the relations among specific mitochondrial features predicted from our algorithm were consistent with their biological organization, supporting preliminary construct validity for MR-predicted mitochondrial features. In patients with rare mitochondrial diseases, predicted maps revealed region-specific alterations in mitochondrial density and respiratory chain components, particularly the expected compensatory upregulation of complex II, but not of other mitochondrial genome-encoded components. Finally, the MR-based mitochondrial features were associated with the energetic stress marker GDF15 measured in blood, as well as with cognitive performance measures, linking the novel predictions of brain mitochondria to systemic stress and behavior. These findings introduce a first-generation, label-free, neuroimaging-based mitochondrial mapping as a non-invasive window into living human brain mitochondria.

neuroscience↗

bioRxiv2026

Intensity-dependent topographical expansion of sensory representations

Neuroimaging studies typically assume that sensory properties are encoded in response magnitude within fixed neural populations. However, this approach does not capture changes in the spatial extent of activation topography, despite growing evidence for its behavioral relevance. Stimulus intensity provides a powerful test case for the role of activation topography as a coding feature because it is a basic, parametrically varying property shared across sensory modalities. Using a Bayes factor-based approach and four functional magnetic resonance imaging datasets (three large-scale datasets [total N = 609] and one precision dataset [>2300 trials]), we tested whether higher-intensity stimulation is associated with expansion of activation topography. Participants received sensory stimuli of varying intensities in somatosensory (heat, laser, tactile), auditory, and visual modalities. High-versus low-intensity painful stimulation consistently produced topographical expansion in areas including the primary somatosensory, posterior midcingulate, primary visual cortices, and cerebellar lobules V and VI. This result replicated across two independent large-scale datasets and within individual participants in the precision dataset. Expansion was also observed for tactile, auditory, and visual stimulation, and its extent correlated with psychophysical discriminability. Topographical expansion involved both the enlargement of already-activated areas and the recruitment of novel regions. These findings establish topographical expansion as a replicable feature of intensity coding, challenging the prevailing assumption of a fixed neural topography.

neuroscience↗

bioRxiv2026

Cortical maps diverge, representations converge along cortical hierarchy

Brain maps (e.g. topographies like retinotopy, somatotopy) vary across individuals. This is thought to reflect computational differences, motivating personalized functional localization. However, artificial neural networks (ANNs) show that similar performance and internal representations can coexist with diverse circuit layouts. Consequently, we tested whether spatial diversity reflects computational diversity in the brain. Using task and resting-state fMRI data, we compared regional functional topographies and representational geometries-the within-individual dissimilarities among activity patterns that capture computable information. Across individuals (n = 414), representations converged in higher-order cortex despite substantial topographic diversity. Thus, different, individual-specific activity patterns encoded similar information. Topographic differences only tracked representational differences in regions under strong architectural constraints, such as highly myelinated sensory-motor cortices. We show this parallels ANNs which begin with random initial layouts but learn convergent representations, if architectures are permissive. This parallel raises a developmental question: do topographies and representations also have different developmental origins? Examining twins (n = 394), we found representations were sensitive to developmental environments and less heritable than topographies. Together, this shows that representational convergence occurs across idiosyncratic layouts in both artificial and biological systems, but is moderated by architectural constraints on circuit implementations. Accordingly, the relevance of localization-and representation-based paradigms of brain function depends on neural architecture.

neuroscience↗

bioRxiv2026

Vicarious trauma primes innate immunity and reconfigures human brain networks

Vicarious trauma--witnessing serious harm to others--produces systemic neuroimmune effects in animal models. However, its brain, autonomic, and immune consequences in humans remain unclear. We scanned 88 participants with fMRI during a naturalistic movie-viewing paradigm depicting either industrial cruelty to farm animals (Vicarious Trauma, VT) or positive human-animal interactions (Vicarious Community, VC) with concurrent autonomic physiology and gene expression in circulating monocytes. VT, compared to VC, evoked sustained sympathetic arousal lasting 40 minutes post-viewing and primed innate immune cells to elicit elevated pro-inflammatory (upregulating IL1B and CXCL8) and reduced anti-inflammatory (TGFB1) responses. VT reduced integration in a frontoparietal control system and increased coupling between a salience/action-mode system with paralimbic, basal ganglia, and sensorimotor regions. Disrupted negative coupling between frontoparietal/default-mode and hippocampal systems predicted VT-related CXCL8 increases. These findings shed light on how the brain translates vicarious trauma into the priming of innate immunity.

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bioRxiv2025

The first comprehensive case study of early-emerging prosopometamorphopsia

Prosopometamorphopsia (PMO) is a rare perceptual disorder characterized by facial distortions. This report presents the first thorough study of a case of PMO that emerged early in life. Zed has experienced lifelong, dynamic facial distortions in which features move, droop, disappear, expand, shrink, and rotate. He also experiences identity misrecognitions, in which he perceives familiar faces as strangers and vice versa (e.g., seeing his grandfather when viewing a young woman). Behavioral assessments indicate that Zeds distortions occur across viewpoints, visual field positions, picture-plane orientations, and visual angles. Neuropsychological testing revealed face identity recognition deficits, yet, despite his facial distortions, Zed can make accurate fine-grained perceptual judgments about facial age and sex. Structural MRI showed no lesions, but face-selectivity in two right posterior face-selective areas was reduced, and diffusion tensor imaging revealed lower white matter integrity in the left inferior fronto-occipital fasciculus. Zeds case offers new insights into early-emerging PMO, its manifestations, co-occurrence with other face-processing disorders, and underlying neural correlates.

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bioRxiv2025

Mitochondrial Energy Transformation Capacity Influences Brain Activation During Sensory, Affective, and Cognitive Tasks

Mitochondria supply the energy required for brain function, but how energetic capacity constrains human brain activity remains unclear. We compared patients with genetically defined mitochondrial disease to matched controls, testing whether bioenergetic stress selectively limits neural processes assessed with functional magnetic resonance imaging during rest and during tasks probing working memory, sensory perception, and affective responses (cold pain). We report impairment in working memory performance and reduced activation of associated brain networks in a subset of patients with the largest clinical and cognitive impairments. A resilient subgroup showed higher working-memory-related BOLD responses, which were associated with preserved task performance. Neural and behavioral effects scaled with the circulating markers of energetic stress growth differentiation factor 15 (GDF15). Brain activity related to sensory and affective responses and resting-state connectivity were comparable for patients and controls. These findings support an energy-hierarchy model in which complex cognition fails first, while sensory processes remain largely preserved under energetic stress.

neuroscience↗

bioRxiv2024

Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale

Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)--an analytic approach for handling functional misalignment--can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.

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

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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

Facial expression is a distinctive behavioural marker of pain processing in the brain

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge on the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a Facial Expression of Pain Signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

neuroscience↗

bioRxiv2023

Cortical and subcortical mapping of the allostatic-interoceptive system in the human brain: replication and extension with 7 Tesla fMRI

The brain continuously anticipates the energetic needs of the body and prepares to meet those needs before they arise, called allostasis. In support of allostasis, the brain continually models the sensory state of the body, called interoception. We replicated and extended a large-scale system supporting allostasis and interoception in the human brain using ultra-high precision 7 Tesla functional magnetic resonance imaging (fMRI) (N = 90), improving the precision of subgenual and pregenual anterior cingulate topography combined with extensive brainstem nuclei mapping. We observed over 90% of the anatomical connections published in tract-tracing studies in non-human animals. The system also included regions of dense intrinsic connectivity broadly throughout the system, some of which were identified previously as part of the backbone of neural communication across the brain. These results strengthen previous evidence for a whole-brain system supporting the modeling and regulation of the internal milieu of the body.

neuroscience↗

bioRxiv2022

A machine learning based approach towards high-dimensional mediation analysis

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome. I. HIGHLIGHTSO_LICurrent methods for assessing mediation do not easily extend to high dimensions C_LIO_LIWe introduce a new approach for performing high-dimensional mediation analysis C_LIO_LILinks high-dimensional mediator to path analysis model via machine learning algorithm C_LIO_LIMethod illustrated using data from two fMRI studies C_LI

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bioRxiv2021

Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats

Escape from threats has paramount importance for survival. However, it is unknown if a single circuit controls escape from innate and conditioned threats. The hypothalamic dorsal premammillary nucleus (PMd) may control escape, as it is activated by escape-inducing threats and projects to the region most implicated in flight, the dorsolateral periaqueductal gray (dlPAG). We show that in mice cholecystokinin (cck)-expressing PMd cells are activated during escape, but not other defensive behaviors. PMd-cck ensemble activity can also predict future escape. Furthermore, PMd inhibition decreases escape speed from both innate and conditioned threats. Inhibition of the PMd-cck projection to the dlPAG also decreased escape speed. Lastly, human fMRI data show that a posterior hypothalamic-to-dlPAG pathway increased activity during exposure to aversive images, indicating that a similar pathway may possibly have a related role in humans. Our data identify the PMd as a central node of the escape network.

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bioRxiv2020

The ABCD Stop Signal Data: Response to Bissett et al.

This paper responds to a recent critique by Bissett and colleagues (Bissett et al., eLife, In Press) of the fMRI Stop task being used in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study(R)). The critique focuses primarily on a design feature of the task that the authors contend lead to a violation of race model assumptions (i.e., that the Go and Stop processes are fully independent) which are relevant to the calculation of the Stop Signal Reaction Time, a measure of the inhibition process. Bissett and colleagues also raise a number of secondary concerns. In this response we note that satisfying race model assumptions is a pernicious challenge for Stop task designs but also that the race model is quite robust against violations of its assumptions. Most importantly, while Bissett et al. raise conceptual concerns with the task we focus here on analyses of both the performance and the neuroimaging data and we conclude that the concerns appear to have minimal impact on the neuroimaging data (the validity of which do not rely on race model assumptions) and have far less of an impact on the performance data than the critique suggests. We note that Bissett et al. did not apply any performance-based exclusions to the data they analyzed, that a number of the trial coding errors that they flagged were already identified and corrected in the ABCD annual data releases, that a number of the secondary concerns reflect sensible design decisions and, indeed, that their own computational modeling of the ABCD Stop task suggests the problems they identify have just a modest impact on the rank ordering of individual differences in subject performance. In this paper, we list some adjustments that have been made to the task and some new flags that are now added to the annual, curated data releases. We stress that the ABCD data are fully available to the scientific community who are empowered to apply whatever inclusion and exclusion criteria they deem appropriate for their analyses and we conclude that the ABCD Stop task yields valuable data that researchers can use to track adolescent neurodevelopment.

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bioRxiv2020

Human and mouse transcriptome profiling identifies cross-species homology in pulmonary and lymph node mononuclear phagocytes

The mononuclear phagocyte (MP) system consists of macrophages, monocytes, and dendritic cells (DCs). MP subtypes play distinct functional roles in steady state and inflammatory conditions. Though murine MPs are well characterized, their pulmonary and lymph node (LN) human homologs remain poorly understood. To address this gap, we created a gene expression compendium across 15 distinct human and 9 distinct murine MPs from lung, LN, blood, and spleen. Human blood MPs and murine spleen MPs served as validation datasets, as the human-mouse MP homologs are relatively well-defined in these tissues. In-depth RNA sequencing identified corresponding human-mouse MP subtypes and determined marker genes shared and divergent across between species counterparts. Unexpectedly, at the gene expression level, only 13-23% of the top 1000 marker genes (i.e., genes not shared across species-specific MP subtypes) overlapped in corresponding human-mouse MP counterparts, indicating a need for caution when translating mouse studies to human gene targets and functions. Lastly, CD88 was useful in both species to distinguish macrophage and tissue monocytes from DCs. Our cross-species gene expression compendium serves as a resource for future translational studies to investigate beforehand whether pursuing specific MP subtypes, or genes will prove fruitful.

immunology↗

bioRxiv2019

A generalizable multivariate brain pattern for interpersonal guilt

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed Guilt-Related Brain Signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social affective processes, further indicating the specificity of the brain state it represents. These findings provide a step towards developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

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↗