Biology subjects

Grafton, S. T.

Publications and source records attributed to Grafton, S. T..

At least 19 recordsLinked to original sources

bioRxiv2026

Neural Control of Autonomic Arousal During Threat Anticipation Revealed by High-Resolution Cardiac Contractility

The sympathetic nervous system prepares the organism for adaptive action by shaping physiological, affective, and behavioral responses to environmental demands. Yet, how sympathetic signals dynamically couple with neural systems supporting emotional experience and behavior remains poorly understood, in part because common indices such as skin conductance responses lack sufficient temporal resolution to track these dynamics. Here, we evaluated trans-radial electrical bioimpedance velocimetry (TREV), a non-invasive measure of beat-to-beat cardiac contractility, and compared it with skin conductance responses during threat anticipation and simultaneous fMRI. Participants (n=60) completed a threat-of-shock paradigm requiring goal-directed action. Cardiac contractility increased during threat anticipation, covaried with skin conductance responses, and independently predicted self-reported emotional intensity. Critically, threat-related increases in contractility--but not skin conductance--tracked threat-related modulation of activation in dorsomedial prefrontal cortex, posterior parietal cortex, and cerebellum, with contractility-modulated cerebellar activation predicting faster motor responses under threat. These findings establish TREV-derived cardiac contractility as a physiological signal linking sympathetic drive with neural responding, emotional experience, and adaptive behavioral mobilization during emotion-guided action.

neuroscience↗

bioRxiv2026

Distinct amygdalar pathways to frontopolar cortex and medial temporal lobe support temporal memory for emotional events

Growing evidence suggests that emotion shapes temporal aspects of memory--such as remembering when an event occurred--yet the neural bases of these effects remain unclear. Prior work indicates that temporal-context representations in memory are supported by the function of medial temporal lobe (MTL) and prefrontal regions with privileged access to amygdala-originated affective signals, which may mediate emotional influences on temporal memory--including hippocampus, perirhinal cortex, and lateral frontopolar cortex (FPl). To test whether these candidate pathways shape temporal memory for emotional events, participants encoded trial-unique negative and positive images in the scanner, followed by a surprise temporal memory task. We found that more negative stimuli were associated with greater amygdala activation and lower temporal-memory error for when emotional images occurred. Replicating and extending prior work, higher stimulus-evoked multivoxel pattern similarity in the hippocampus (between trials) and FPl (within and between trials) was associated with higher temporal memory errors. Notably, greater amygdala responding to emotional images predicted higher between-trial pattern similarity in the hippocampus, but lower within- and between-trial similarity in FPl. Amygdala engagement was also correlated with greater perirhinal activation, which in turn predicted stronger item memory and a recency bias in temporal memory estimates. Collectively, these findings reveal distinct--and opposing--modulatory effects of amygdala-FPl and amygdala-MTL pathways on temporal memory, with the potential to sharpen and blur memory for when emotional events occurred. Significance statementEmotional events influence how we remember time. Prior work implicates function of the lateral frontal pole (FPl) and medial temporal lobe (MTL) regions--including hippocampus and perirhinal cortex--in temporal memory. Yet, whether and how these areas support temporal memory for emotional events remains unclear. Using trial-unique emotional images, we found that negative-emotional valence was associated with stronger amygdala activation and temporal-memory accuracy. Higher pattern similarity in hippocampus and FPl predicted worse temporal memory. Notably, amygdala activation predicted higher between-trial hippocampal pattern similarity but lower within-trial FPl similarity. Perirhinal engagement, which correlated with amygdala activation and item memory, predicted a recency bias in temporal-memory judgments. Together, these findings reveal distinct amygdala-MTL and amygdala-prefrontal pathways through which emotion shapes temporal memory.

neuroscience↗

bioRxiv2025

Cardiac-sympathetic state predicts action restraint, gated by demonstrated agency

Withholding action until the appropriate moment is a core challenge of motivated behavior. Using beat-to-beat cardiac contractility during an incentivized reaching task, we show that cardiac-sympathetic outflow predicts action restraint. Under high-reward conditions that induce a speed-accuracy tradeoff, reduced contractility at the time of instruction preceded premature responses (false starts). Under high-loss-avoidance conditions, elevated pre-movement contractility predicted slower, more controlled initiation, but only among participants with above-median task success. These findings suggest cardiac-sympathetic engagement does not simply serve mobilization but flexibly supports context-appropriate action regulation, with recruitment for restraint gated by demonstrated agency.

physiology↗

bioRxiv2025

Incentive valence differentially engages open- and closed-loop basal ganglia circuits during movement initiation

Incentives modulate voluntary movement, yet the circuitry channeling these signals into motor output remains unclear. Classical models emphasize a closed-loop circuit (CLC) linking dorsal putamen (PUTd) with motor cortex, but this pathway is anatomically segregated from affective processing regions. Anatomical and clinical evidence point to an alternative: an open-loop circuit (OLC) from ventral putamen (PUTv) that may route affective signals to motor cortex. Here, we conducted two experiments to test whether a functional OLC exists in humans and whether it is differentially engaged by incentive conditions. First, in 7 T resting-state fMRI (multi-echo), PUTv showed robust functional connectivity with both affective and motor regions, including the cingulate motor area (CMA), even after accounting for PUTd variance. This connectivity pattern supports the plausibility of an independent pathway linking affective basal ganglia regions to the motor cortex. Second, in 3 T task fMRI (incentivized reaching), jackpot (high-reward) and robber (high-loss avoidance) incentive conditions produced distinct behavioral and neural signatures. Jackpot produced a speed-accuracy trade-off, with faster movement initiation but more false starts. Neurally, this coincided with engagement (BOLD responses relevant for initiation speed) being reduced in CLC nodes but not in OLC. Robber, in contrast, eliminated engagement in both OLC and CLC nodes, instead recruiting stopping-related regions (e.g., STN), consistent with an avoidance phenomenology. Together, these findings support a versatile architecture for movement initiation that flexibly engages distinct cortico-subcortical circuits depending on incentive phenomenology, and offer a candidate mechanism through which affective salience and valence modulate voluntary movement. Significance StatementAffective signals profoundly influence movement, yet the mechanisms linking motivationally relevant contexts with motor behavior remain unclear. Combining ultra-high field (7 T) connectomics with task-based (3 T) neuroimaging, we provide the first systems-level evidence in humans for such a mechanism: a ventral putamen-centered open-loop circuit (OLC) connecting affective and motor areas, operating alongside the canonical dorsal putamen-centered closed-loop sensorimotor circuit (CLC). Critically, the phenomenological quality of incentive (how it is construed as reward versus threat) rather than magnitude alone, likely determines which circuit dominates during movement initiation. These findings help to explain paradoxical kinesia in Parkinsons disease, where affective contexts can bypass degraded sensorimotor circuits, and establish foundations for context-based therapeutic interventions.

neuroscience↗

bioRxiv2025

Integrated Representations of Threat and Controllability in the Lateral Frontal Pole

Emotional processing is ubiquitous in everyday life, informing goal pursuit not only in response to current demands, but also in anticipation of future outcomes. Lateral prefrontal (LPFC) function supports cognitive control, and emerging evidence suggests a unique role for its anterior-most region--the lateral frontal pole (FPl)--in integrating putatively amygdala-originated emotion signals with goal information. However, whether these organizational properties of LPFC are expressed during the anticipation of future threat remains unknown. Here, we used FIR modeling and pattern similarity analysis to examine dynamic engagement and representational properties of distinct LPFC regions during threat anticipation requiring goal-directed action. Healthy participants (n=67) were scanned during a threat-of-shock paradigm consisting of a prolonged (18s) countdown to possible shock administration. Threat unpleasantness and controllability were manipulated orthogonally: in controllable trials, participants could avoid an unpleasant or mild shock by making a successful time-sensitive response; in uncontrollable trials, shocks were administered regardless of performance. LPFC robustly coded for anticipated threat unpleasantness, with FPl showing the strongest modulation by threat unpleasantness and controllability relative to caudal and mid-LPFC regions. Caudal and mid-LPFC maintained independent representations of threat unpleasantness and controllability. In contrast, FPl held conjunctive threat-and-controllability representations, which were associated with successful motor performance following anticipation of unpleasant shocks. Stronger conjunctive FPl representations were also associated with greater inverse amygdala-FPl coupling. Together, these findings provide insight into LPFC organization under naturalistic emotional challenges and highlight a key role for FPl in integrating affective and control-related information during threat anticipation to support goal-directed action. Significance StatementAnticipating emotionally-charged events--such as a painful outcome we may or may not be able to avoid--requires integrating emotion and control to guide behavior. However, the neural mechanisms through which emotional states influence goal-directed behavior in naturalistic, anticipatory emotional contexts remain unclear. Using a threat-of-shock paradigm and multivariate analyses we show that the anterior-most region of the lateral prefrontal cortex (LPFC)--the lateral frontal pole (FPl)--uniquely integrates information about the emotional unpleasantness and controllability of a future event, and that the strength of this integrated signal predicts better behavioral performance. These findings extend models of LPFC function to naturalistic, emotional contexts, and highlight the FPl as a key node for translating emotional and behavioral-control information into adaptive action.

neuroscience↗

bioRxiv2024

Dissociation of putative open loop circuit from ventral putamen to motor cortical areas in humans I: high-resolution connectomics

Human movement is partly organized and executed by cortico-basal ganglia-thalamic closed-loop circuits (CLCs), wherein motor cortical areas both send inputs to and receive feedback from the basal ganglia, particularly the dorsal putamen (PUTd). These networks are compromised in Parkinsons disease (PD) due to neurodegeneration of dopaminergic inputs primarily to PUTd. Yet, fluid movement in PD can sporadically occur, especially when induced by emotionally arousing events. Rabies virus tracing in non-human primates has identified a potential alternative motor pathway, wherein the ventral putamen (PUTv) receives inputs from subcortical limbic areas (such as amygdala nuclei) and sends outputs to motor cortical areas putatively via the nucleus basalis of Meynert (NBM). We hypothesize that this separable open loop circuit (OLC) may exist in humans and explain the preservation of movement after CLC degradation. Here, we provide evidence for the normal human OLC with ultra-high field (7T), multi-echo functional magnetic resonance imaging. We acquired resting-state functional connectivity (FC) scans from 21 healthy adults (avg. age = 29, 12M/9F, all right-handed) and mapped left-hemisphere seed-to-voxel connectivity to assess PUTv FC with putative subcortical nodes and motor cortical areas. We found that putative OLC node (basolateral amygdala, NBM) FC was greater with PUTv (p < 0.05), while CLC subcortical seed (ventrolateral nucleus of thalamus) FC was greater with PUTd (p<0.01). Striatal FC patterns varied across cortical motor areas, with cingulate (p < 0.0001) and supplementary (p < 0.0001) motor areas showing greater FC with PUTv vs. nucleus accumbens. SMA had greater FC with PUTd vs. PUTv (p < 0.0001), while cingulate and primary motor areas showed no significant differences in FC between PUTd and PUTv (p > 0.1). Collectively, these results suggest that PUTv is functionally connected to motor cortical areas and may be integrated into a separable motor OLC with subcortical limbic inputs.

neuroscience↗

bioRxiv2024

Dissociation of novel open loop from ventral putamen to motor areas from classic closed loop in humans II: task-based function

Humans ubiquitously increase the speed of their movements when motivated by incentives (i.e., capturing reward or avoiding loss). The complex interplay between incentivization and motor output is pertinent for unpacking the functional profiles of different circuits that link the basal ganglia with motor cortical areas. Here, we analyzed the functional profile of nodes forming two circuits involving putamen and motor cortical areas: the traditional "closed-loop circuit" (CLC) from sensorimotor dorsal putamen (PUTd) and a putative "open-loop circuit" (OLC) from ventral putamen (PUTv). Establishing differential function between CLC and OLC is particularly relevant for therapeutic approaches to Parkinsons disease, where OLC function is hypothesized to be relatively spared by the disease process. In a large sample fMRI study, 68 healthy controls executed speeded reaches with a joystick under different levels of incentivization to accurately hit precision targets. We dissociated effects of "incentive per se" (i.e., changes in brain activity when an upcoming movement obtains a reward or avoids a loss) from "RT effects" (i.e., brain activity that directly scales with adjustments to movement initiation time). Incentive per se was observed across sites in both CLC and OLC. However, RT effects were primarily in nodes of the OLC and motor sites, consistent with the hypothesized anatomy and function of OLC. Our findings additionally suggest valence might mediate when incentives recruit OLC to more prominent control of motor behavior.

neuroscience↗

bioRxiv2023

Cardiac-sympathetic contractility and neural alpha-band power: cross-modal collaboration during approach-avoidance conflict

As evidence mounts that the cardiac-sympathetic system reacts to challenging cognitive settings, we ask if these responses are passive companions or if they are instead fundamentally intertwined with cognitive function. Healthy human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous neural and cardiac signals. Participants were reward-sensitive, but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters revealed that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics suggested that widening decision boundaries served to combat reward-sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically-mediated positive inotropy). We also saw evidence of conflict-specific collaboration between the neural and cardiac-sympathetic signals. Specific to states of conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signals singular influence. Cross-trial coherence analyses provided additional support for a direct role of cardiac-sympathetics in nurturing fair assessment of information streams during conflict by disrupting the prepotent reward signals. We conclude that cardiac-sympathetic activity is not a mere companion, rather it is a critical component collaborating with cognitive processes to combat reward-sensitivity during the approach-avoidance conflict.

physiology↗

bioRxiv2023

Menstrual cycle-driven hormone concentrations co-fluctuate with white and grey matter architecture changes across the whole brain

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17{beta}-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (FA), while follicle-stimulating hormone (FSH) was directly proportional to cortical thickness. Within several individual regions, FSH and progesterone demonstrated opposing associations with mean diffusivity and cortical thickness. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Lastly, progesterone was associated with increased tissue and decreased CSF volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and cortical thickness coinciding with menstrual cycle-driven hormone rhythms. Strong brain-hormone interaction effects may not be limited to classically known HPG-axis receptor-dense regions.

neuroscience↗

bioRxiv2023

ReTrace: Topological evaluation of white matter tractography algorithms using Reeb graphs

We present ReTrace, a novel graph matching-based topological evaluation and validation method for tractography algorithms. ReTrace uses a Reeb graph whose nodes and edges capture the topology of white matter fiber bundles. We evaluate the performance of 96 algorithms from the ISMRM Tractography Challenge and the the standard algorithms implemented in DSI Studio for the population-averaged Human Connectome Project (HCP) dataset. The existing evaluation metrics such as the f-score, bundle overlap, and bundle overreach fail to account for fiber continuity resulting in high scores even for broken fibers, branching artifacts, and mis-tracked fiber crossing. In contrast, we show that ReTrace effectively penalizes the incorrect tracking of fibers within bundles while concurrently pinpointing positions with significant deviation from the ground truth. Based on our analysis of ISMRM challenge data, we find that no single algorithm consistently outperforms others across all known white matter fiber bundles, highlighting the limitations of the current tractography methods. We also observe that deterministic tractography algorithms perform better in tracking the fundamental properties of fiber bundles, specifically merging and splitting, compared to probabilistic tractography. We compare different algorithmic approaches for a given bundle to highlight the specific characteristics that contribute to successful tracking, thus providing topological insights into the development of advanced tractography algorithms.

neuroscience↗

bioRxiv2022

Diffusion MRI Head Motion Correction Methods are Highly Accurate but Impacted by Denoising and Sampling Scheme

Correcting head motion artifacts in diffusion-weighted MRI (dMRI) scans is particularly challenging due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSLs Eddy. Recently, the 3dSHORE-based SHORELine method was introduced to correct any dMRI sequence that has more than one shell. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme, the pervasiveness of head motion, and the denoising strategy applied before head motion correction. Our study also provides an open and fully-reproducible workflow that could be used to accelerate evaluation studies of other dMRI processing methods in the future. HIGHLIGHTSO_LIBoth Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data C_LIO_LIDenoising with MP-PCA can improve head motion correction performance when Eddy is used C_LIO_LISHORELine effectively corrects motion in non-shelled acquisitions C_LI

neuroscience↗

bioRxiv2022

An Accelerometer Based Heart Monitor to Measure Changes of the Autonomic Nervous System

The electrocardiogram (ECG) and impedance cardiography (ICG) are typically combined to estimate electromechanical features such as the pre-ejection period (PEP) and left ventricular ejection time (LVET); indicators of changes in the cardiac specific drive of the autonomic nervous system (ANS). Current methods of ICG are time intensive in subject preparation and the measurements are vulnerable to non-reproducible subject-specific electrode configuration. Furthermore, analysis of impedance waveforms can be time consuming and labeling of key time points can suffer from experimenter bias. Here we present a wearable heart monitor that includes ECG, but replaces the commonly used 8 ICG electrodes with a single accelerometer (ACC) placed at the suprasternal notch. The ACC indirectly measures movement of the arterial pulse wave as blood is ejected into the aorta and great vessels. The resulting ACC waveform is processed into two smooth and readily identified waves, corresponding to the timing of the opening and closing of the aortic valve. We tested the ACCs utility and reliability for tracking cardiac ANS tone by comparing PEP and LVET measurements obtained simultaneously with conventional ICG and the ACC. Participants were recorded in the sitting and supine position with ECG, ICG, and ACC. While seated, they engaged in a classic physical stress task known to modulate ANS activity. There were obvious and significant associations between ICG and ACC estimates of PEP and LVET derivatives with respect to time. These findings support ACC as a complementary method for tracking ANS that is robust, time efficient, and readily accessible to researchers.

physiology↗

bioRxiv2022

Within- and between-subject reproducibility and variability in multi-modal, longitudinal brain networks

Network analysis provides new and important insights into the function of complex systems such as the brain by examining structural and functional networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. Although network models can shed light on cognition and pathology, questions remain regarding the importance of these findings, due in part to the reproducibility of the core measurements and subsequent modeling strategies. In order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first investigate the reproducibility of individual brain connections and network measures and find that across all modalities, within-subject reproducibility is higher than between-subject reproducibility, reaffirming the ability to detect individual differences in network structure in both structural and functional human brain networks. We see high variability in the reproducibility of pairwise connections between brain regions, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than networks derived from other frequency bands. Further, reproducible connections correspond to strong connections. Structural networks show a higher reliability in network statistics than functional networks, and certain measures such as synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the analysis of either structural or functional networks to study individual differences should depend on whether or not one wants to take into account state dependencies of the observed networks.

neuroscience↗

bioRxiv2022

Decision heuristics in contexts exploiting intrinsic skill

Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards. The requisite movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are nontrivial. We developed a novel action selection-execution task requiring joint optimisation of action selection and spatio-temporal skillful execution. Optimal choices could be determined by a simple spatial heuristic, or by more complex planning. Computational models of action selection parsimoniously distinguished human participants who adopted the heuristic from those using a more complex planning strategy. Broader comparative analyses then revealed that participants using the heuristic showed combined decisional (selection) and skill (execution) advantages, consistent with a less-is-more framework. In addition, the skill advantage of the heuristic group was predominantly in the core spatial features that also shaped their decision policy, evidence that the dimensions of information guiding action selection might be yoked to salient features in skill learning. Author SummaryWe often must choose between actions and then execute them, e.g., a tennis player chooses between a forehand and backhand and then skilfully executes the shot. To select actions, the player might plan their action with either shot, and select whichever simulated outcome is more advantageous. However, a more efficient approach might instead be to use a "heuristic", i.e., a simpler rule, such as, forehand always on one side of the court, and backhand on the other. In this work, we look at whether styles of planning are related to physical skill performing actions, e.g., would a more skillful tennis player be more likely to use planning or a heuristic? We use a new task that requires people to choose and execute complex actions. Regarding choices, we use computational modeling to identify which people use some degree of planning, and which people use a simpler heuristic. Then, regarding action execution, we reveal that heuristic decision makers are in fact more skilled. However, they are not superiorly skilled in all aspects of performance, showing an advantage solely in the aspect of skill most closely linked to the information (spatial) they use for their heuristic. We therefore reveal the first ever evidence that a relation exists between the complexity of our action-related decisions and how broadly we learn associated motor behaviour.

neuroscience↗

bioRxiv2022

A robust Reeb graph model of white matter fibers with application to Alzheimer's disease progression

Tractography generates billions of complex curvilinear fibers (streamlines) in 3D that exhibit the geometry of white matter pathways. Analysis of raw streamlines on such a large scale is time-consuming and intractable. Further, it is well known that tractography computations produce noisy streamlines, and this in turn severely affect their use in structural brain connectivity analysis. Prompted by these challenges, we propose a novel method to model the bundling structures of streamlines using the construct of a Reeb graph. Three key parameters in our method capture the geometry and topology of the streamlines: (i){epsilon} - distance between a pair of streamlines in a bundle that defines its sparsity; (ii) - spatial length of the bundle that introduces persistence; and (iii){delta} - the bundle thickness. Together, these parameters control the robustness and granularity of the model to provide a compact signature of the streamlines and their underlying anatomic fiber structure. We validate the robustness of the bundling structure using synthetic and ISMRM datasets. Next, we demonstrate the potential of this approach as a tool for efficient tractogram comparison by quantifying the fiber densities in the progression of Alzheimers disease. Our results on ADNI data localize the maximal bundles of various brain regions and show a significant depletion in the fiber density as Alzheimers disease progresses. The source code for the implementation is available on GitHub.

neuroscience↗

bioRxiv2020

QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as "glass-box" methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

bioinformatics↗

bioRxiv2020

The role of primary motor cortex in sequence learning: resolving conflicting fMRI evidence from repetition suppression and pattern analysis

How does the brain change during learning? In functional magnetic resonance imaging studies, both multivariate pattern analysis and repetition suppression (RS) have been used to detect changes in neuronal representations. In the context of motor sequence learning, the two techniques have provided discrepant findings: pattern analysis showed that only premotor and parietal regions, but not primary motor cortex (M1), develop a representation of trained sequences. In contrast, RS suggested trained sequence representations in all these regions. Here we applied both analysis techniques to a 5-week finger sequence training study, in which participants executed each sequence twice before switching to a different sequence. Both RS and pattern analysis indicated learning-related changes for parietal areas, but only RS showed a difference between trained and untrained sequences in M1. A more fine-grained analysis, however, revealed that the RS effect in M1 reflects a fundamentally different process than in parietal areas. On the first execution, M1 represents especially the first finger of each sequence, likely reflecting preparatory processes. This effect dramatically reduces during the second execution. In contrast, parietal areas represent the identity of a sequence, and this representation stays relatively stable on the second execution. These results suggest that the RS effect does not reflect a trained sequence representation in M1, but rather a preparatory signal for movement initiation. More generally, our study demonstrates that across regions RS can reflect different representational changes in the neuronal population code, emphasizing the importance of combining pattern analysis and RS techniques. Significance statementPrevious studies using pattern analysis have suggested that primary motor cortex (M1) does not represent learnt sequential actions. However, a study using repetition suppression (RS) has reported M1 changes during motor sequence learning. Combining both techniques, we first replicate the discrepancy between them - with learning-related changes in M1 in RS, but not pattern dissimilarities. We further analysed the representational changes with repetition, and found that the RS effects differ across regions. M1s activity represents the starting finger of the sequence, an effect that vanishes with repetition. In contrast, activity patterns in parietal areas exhibit sequence dependency, which persists with repetition. These results demonstrate the importance of combining RS and pattern analysis to understand the function of brain regions.

neuroscience↗

bioRxiv2020

Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intra-network increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain-hormone interactions with dynamic community detection suggests that ovulation results in a temporary, localized patterns of brain network reorganization. Author SummarySex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood how day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network activity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the menstrual cycle, there is temporary reorganization of several largescale functional brain networks during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with regions from temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.

neuroscience↗