Biology subjects

Lindquist, M.

Publications and source records attributed to Lindquist, M..

12 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

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

Large language models unlock the ecology of species interactions

Species interactions shape population dynamics, geographic distributions, evolutionary trajectories, and responses to environmental change. Yet data on these interactions remain scarce across broad spatial, temporal, and taxonomic scales because they are difficult to collect in the field. One promising source of interaction data is citizen science platforms, which contain billions of biodiversity observations, often accompanied by unstructured text comments that may document interactions among organisms. Advances in large language models (LLMs) make it increasingly feasible to identify, extract, and categorize biotic interactions from these unstructured data at scale. Here, we present an LLM workflow that collects species interaction observations from multilingual citizen science comments. Using two case studies--bird-bird and plant-pollinator interactions--we show that LLMs can rapidly extract interaction types and participating species with high accuracy. These data can greatly expand the spatial, temporal, and taxonomic coverage and resolution of species interactions data, enable new tests of long-standing ecological questions, and improve our ability to track ecological changes. With appropriate validation, expert review, and attention to data privacy for both users and sensitive species, this approach opens new opportunities to characterize, forecast, and conserve biodiversity under global change.

ecology↗

bioRxiv2026

MV-ComBat and MV-CovBat: Multivariate Frameworks for Joint Harmonization of Multi-Metric Neuroimaging Data

Aggregating neuroimaging data across sites and studies is increasingly common, yet site- and scanner-related batch effects can obscure meaningful biological variation and introduce spurious associations. Although ComBat and its extensions are widely used, they are primarily designed for single-metric harmonization. In practice, neuroimaging studies often involve multiple biologically coupled metrics (e.g., cortical thickness, surface area, and gray-matter volume) measured across multiple features (e.g., regional values), with shared covariance structure both within and across metrics. Applying single-metric ComBat independently to each metric ignores these cross-metric dependencies. Using data from the NIH Acute to Chronic Pain Signatures (A2CPS) program, we show that batch effects occur not only in means and variances but also in covariance across cortical regions and metrics--relationships that single-metric ComBat does not fully remove. We propose MM-ComBat, a multivariate extension of ComBat that jointly harmonizes multiple metrics by borrowing strength across them, better capturing cross-metric dependence while also improving covariance estimation across features. Because joint harmonization whitens residual covariance toward a standardized baseline, it risks distorting biologically meaningful cross-metric structure when batch effects are moderate. We therefore introduce two complementary formulations: a baseline formulation suited to batch-dominated settings, and a target-covariance formulation that remaps adjusted covariances toward an estimated shared biological structure rather than fully whitening them. Both empirical Bayes (EB) and Bayesian Markov Chain Monte Carlo (MCMC) implementations of MM-ComBat effectively reduce batch effects. In our experiments, EB is more robust to measurement error, whereas MCMC more accurately recovers cross-metric correlations when priors are well specified. Recognizing that batch effects can also affect feature-level covariance, CovBat was recently introduced as an extension of ComBat that harmonizes both first- and second-order moments across sites. We extend CovBat to the multivariate framework as MM-CovBat, which performs a second-stage latent-space harmonization to directly address covariance-related batch effects across features and metrics. Simulations confirm that MM-ComBat improves correlation recovery and better preserves biological effects in the mean structure relative to single-metric ComBat, particularly for moderate-to-strong effects, and that MM-CovBat further improves separation of true biological variation from batch effects when independence assumptions are violated. Together, these methods provide a flexible and unified framework for harmonizing complex, multi-metric neuroimaging data in large-scale, multi-site studies.

neuroscience↗

bioRxiv2025

Scalable and Adaptive Spatiotemporal Modeling for Task-Based fMRI Analysis

Task-based fMRI is commonly analyzed using voxel-wise general linear models, a non-spatial scalable approach that can yield fragmented activation maps. Spatial alternatives such as kernel smoothing and Bayesian models address this but either blur activation boundaries or are computationally prohibitive at modern spatial resolutions. We introduce SPLASH (Spline-Based Processing for Localized Adaptive Spatial Hemodynamics), a spatially adaptive and scalable framework based on localized thin-plate spline regression within brain parcels. Its spatial flexibility allows SPLASH to adapt to heterogeneous cortical organization and to generalize across diverse spatial domains. Using its hierarchical structure, we introduce a two-stage selective inference procedure that ensures valid false discovery rate control at the parcel and voxel levels. In simulations, SPLASH consistently delivered the best overall performance: its MSE was typically only 20-40% of that of prior spatial models, and both FPR and FNR remained well controlled. SPLASH also remained stable across smoothing choices and required only 2% of the computation time of Bayesian spatial approaches. Applied to Human Connectome Project data, SPLASH produced sharper activation patterns consistent with the motor homunculus and demonstrated higher reproducibility. SPLASH provides a generalizable, spatially adaptive, and scalable framework that strengthens statistical inference and improves neuroscientific interpretability in large-scale fMRI studies.

neuroscience↗

bioRxiv2025

Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth

BackgroundChildhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear if transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. The current study tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children. MethodsParticipants were aged 9-10 years from the Adolescent Brain Cognitive Development (ABCD) Study, who completed resting-state fMRI (N=877). We employed a dynamic connectivity approach leveraging a hidden semi-Markov model (HSMM) to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive timepoints in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, accounting for other commonly co-occurring symptoms. ResultsDynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Replication of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states. ConclusionTransdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement and informing clinical decisions.

neuroscience↗

bioRxiv2023

Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain

Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a lateral aspect of the right dorsolateral prefrontal cortex (latDLPFC) in migraine patients. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this latDLPFC region, and diffusion weighted imaging (DWI) verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.

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

neuroscience↗

bioRxiv2020

Differences in functional connectivity distribution after transcranial direct-current stimulation: a connectivity density point of view

In this manuscript we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting state functional magnetic resonance imaging association with an intervention. Specifically, we consider 47 primary progressive aphasia (PPA) patients with various levels of language abilities. These patients were randomly assigned to two treatment arms, tDCS (transcranial direct-current stimulation and language therapy) vs sham (language therapy only), in a clinical trial. We propose a novel approach to analyze the effect of direct stimulation on functional connectivity. We estimate the density of correlations among the regions of interest (ROIs) and study the difference in the density post-intervention between treatment arms. We discover that it is the tail of the density, rather than the mean or lower order moments of the distribution, that demonstrates a significant impact in the classification. This approach has several benefits. Among them, it drastically reduces the number of multiple comparisons compared to edge-wise analysis. In addition, it allows for the investigation of the impact of functional connectivity on the outcomes where the connectivity is not geometrically localized.

neuroscience↗

bioRxiv2019

Improved state change estimation in dynamic functionalconnectivity using hidden semi-Markov models

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subjects most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

neuroscience↗

bioRxiv2018

On statistical tests of functional connectome fingerprinting

Fingerprinting of functional connectomes is an increasingly standard measure of reproducibility in functional magnetic resonance imaging connectomics. In such studies, one attempts to match a subjects first session image with their second, in a blinded fashion, in a group of subjects measured twice. The number or percentage of correct matches is usually reported as a statistic. In this manuscript, we investigate the statistical tests of matching based on exchangeability assumption in the fingerprinting analysis. We show that a nearly universal Poisson(1) approximation applies for different matching schemes. We theoretically investigate the permutation tests and explore the issue that the test is overly sensitive to uninteresting directions in the alternative hypothesis, such as clustering due to familial status or demographics. We perform a numerical study on two functional magnetic resonance imaging (fMRI) resting state datasets, the Human Connectome Project (HCP) and the Baltimore Longitudinal Study of Aging (BLSA). These datasets are instructive, as the HCP includes techinical replications of long scans and includes monozygotic and dyzogotic twins as well as non-twin siblings. In contrast, the BLSA study incorporates more typical length resting state scans in a longitudinal study. Finally, a study of single regional connections is performed on the HCP data.

neuroscience↗

bioRxiv2018

Modular preprocessing pipelines can reintroduce artifacts into fMRI data

The preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps--including motion regression, scrubbing, component-based correction, global signal regression, and temporal filtering--are performed sequentially. In this work we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.

neuroscience↗