Biology subjects

Handwerker, D. A.

Publications and source records attributed to Handwerker, D. A..

13 recordsLinked to original sources

bioRxiv2026

A new fMRI quality metric using multi-echo information: Theory, validation and implications

Consistent, high-quality data is key to the success of fMRI studies given the many confounding factors and undesired signals that contaminate these data. Several quality assurance (QA) metrics exist for fMRI (e.g., temporal signal-to-noise ratio (TSNR), percent ghosting, motion estimates), but none of them leverage relationships between echoes that are part of multi-echo (ME) fMRI acquisitions. Here, we fill this gap by proposing a new QA metric for for ME-fMRI that quantifies the likelihood a given ME scan is dominated by BOLD (Blood Oxygenation Level-Dependent) fluctuations. We refer to this metric as pBOLD; the probability of the signal change being primarily BOLD contrast-dominated. Having an estimate of overall BOLD weighting - both before and after preprocessing - is meaningful because BOLD is the intrinsic contrast mechanism used in fMRI to infer neural activity. We introduce pBOLD to the neuroimaging community by first describing the theoretical principles supporting the metric. Next, we validate pBOLD efficacy using a small dataset (N=7 scans) of constant- and cardiac-gated scans that have distinct levels of contributing BOLD fluctuations. Third, we apply pBOLD to a larger publicly available ME dataset (N=439 scans), to evaluate six different pre-processing pipelines, and show how pBOLD provides complementary information to TSNR. Our results show that ME-based denoising increases both pBOLD and TSNR relative to basic denoising; however, including the global signal (GS) as a regressor only improves TSNR, but worsens pBOLD. Further analyses looking at the BOLD-like characteristics of the GS and its relationship to cardiac and respiratory traces suggest that the observed decrease in pBOLD is likely due to a decrease in BOLD fluctuations of neural origin contributing to the GS, and not due to contributions from other physiological BOLD fluctuations (i.e., respiratory and cardiac function). Finally, we also demonstrate how pBOLD can be applied as a data quality metric, by showing how higher pBOLD results in better ability to predict phenotypes based on whole-brain functional connectivity matrices.

neuroscience↗

bioRxiv2025

The human brain mechanisms of afterimages: From networks to cortical layers

Afterimages are common visual illusions that have long attracted popular and scientific interest, yet their neural mechanisms remain little understood. We used high-spatial-resolution fMRI to investigate human whole brain and cortical layer activity in primary visual cortex (V1) linked with afterimages and perceptually similar animated images - stimuli designed to imitate the appearance of afterimages. Both afterimages and perceptually similar images engaged overlapping, widespread brain activity, particularly in visual sensory regions that follow the contralateral circuitry of the primary visual pathway. However, afterimages elicited weaker fMRI signals across the brain compared to images, except in salience network regions, where activity was enhanced for afterimages. Salience network activity could reflect conflict or perceptual reality monitoring mechanisms that detect the illusory nature of afterimages. Cortical layer-specific analyses in V1 revealed afterimages selectively engaged middle and deep cortical layers, while perceptually similar images activated middle and superficial layers. In agreement, layer-specific decoding of afterimage and mock afterimage fMRI signals was present only in deep layers. One interpretation of these V1 cortical layer findings is that afterimage perception involves feedback or top-down neural input akin to self-generated or endogenous conscious perception (e.g., visual imagery). In addition, baseline eye behavior (pupil size and blinking) and fMRI signals in arousal, salience, and visual networks differed depending on whether afterimages were perceived or not perceived. These results challenge the prevailing framing on the neurophysiological origins of afterimages as arising from either retinal or central neural processes. As with image perception, typical afterimages emerge by the interaction between retinal and central neural mechanisms. These findings on the neural processing underlying afterimages may be broadly informative of the neural mechanisms of visual consciousness.

neuroscience↗

bioRxiv2025

Rapid decoding of neural information representation from ultra-fast functional magnetic resonance imaging signals

High spatio-temporal resolution is crucial for neuroimaging techniques to improve our understanding of human brain function. While the fMRI signal is slow and shows a spread in latencies over space, the precision of hemodynamic response latency for each voxel is preserved and has been shown to be able to detect oscillatory hemodynamic changes approaching 1 Hz, suggesting its potential to reveal rapid neural dynamics. To examine how fast neural information can be derived from fMRI signals, we performed experiments that acquire high-field (7T) fMRI signals at an ultra-fast sampling rate (TR = 125 ms) from the visual cortex while participants observed naturalistic object stimuli. We applied multivariate pattern decoders to extract presented object-category information from the acquired signals at each sampling time after stimulus onset. Results showed that decoding accuracy rose above statistical significance less than 2 s after signal onset, faster than the peak latency of the hemodynamic response. The peak latency of the decoding accuracy was independent of variations in the hemodynamic latency of voxels used for decoding. The application of sparse decoders further revealed that rapid and accurate decoding was possible by pruning vein-rich voxels off from the multivariate voxel input to the decoders. These results suggest that a combination of ultra-fast sampling and multivariate decoding allows fast and temporally precise analysis of neural activity using fMRI signals. Significance statementFunctional magnetic resonance imaging (fMRI) is the most successful method to evaluate human brain function at fine spatial scales but is thought to lack temporal resolution because of slow hemodynamics. We challenge this conventional notion by fast sampling of fMRI signals, combined with multivariate decoding to extract information content represented in the fMRI signals. Results showed that the information content can be extracted faster than the magnitude of hemodynamic response rises, independent of the large spatial variation of hemodynamic latencies across individual voxels, and even after removing venous voxels from decoders. Our method is thus effective at filtering out slow and variable hemodynamic components, leading to the extraction of rapid and temporally precise components reflecting neuronal activity in human fMRI signals.

neuroscience↗

bioRxiv2025

Robust-tedana: An automated denoising pipeline for multi-echo fMRI data

Multi-echo functional Magnetic Resonance Imaging (fMRI) data are acquired by recording image volumes at multiple echo times and can be used to improve the separation of neural activity from noise. TE-Dependent ANAlysis (tedana) is an open-source software tailored to denoising of multi-echo fMRI data. The efficacy of denoising can however be inconsistent, often necessitating manual inspection that precludes its application in large-scale studies where processing is ideally fully automated. Here, we introduce Robusttedana, an optimised denoising pipeline that achieves adequate results at both single-subject and group level. Robust-tedana incorporates Marchenko-Pastur Principal Component Analysis (MPPCA) for effective thermal noise reduction, robust independent component analysis for stabilised signal decomposition, and a modified component classification process. We evaluated its performance on Multi-Band Multi-Echo (MBME) language-task fMRI data from the Australian Epilepsy Project (AEP) using objective measures, comparing to conventional fMRI analysis with and without multi-echo-based denoising. Experts manual evaluation was undertaken on a subset of these data to validate the objective measures. The proposed pipeline both mitigates the prevalence of erroneous attenuation of genuine task activation due to instability of single-subject analysis, and increases the magnitude of group-wise effects. Robust-tedana therefore facilitates advanced analysis of MBME fMRI data in an automated pipeline, including for clinical research assessment of individuals.

bioengineering↗

bioRxiv2024

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants rest matters.

Resting-state fMRI (rs-fMRI) scans--namely those lacking experimentally-controlled stimuli or cognitive demands--are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

neuroscience↗

bioRxiv2023

Visual imagery vividness correlates with afterimage brightness and sharpness

Afterimages are illusory, visual conscious perceptions. A widely accepted theory is that afterimages are caused by retinal signaling that continues after the physical disappearance of a light stimulus. However, afterimages have been reported without preceding visual, sensory stimulation (e.g., conditioned afterimages and afterimages induced by illusory vision). These observations suggest the role of top-down, brain mechanisms in afterimage conscious perception. Therefore, some afterimages may share perceptual features with sensory-independent conscious perceptions (e.g., imagery, hallucinations, and dreams) that occur without bottom-up, sensory input. In the current investigation, we tested for a link between the vividness of visual imagery and afterimage conscious perception. Participants reported their vividness of visual imagery and perceived sharpness, contrast, and duration of negative afterimages. The afterimage perceptual features were acquired using perception matching paradigms that were validated on image stimuli. Relating these perceptual reports revealed that the vividness of visual imagery positively correlated with afterimage contrast and sharpness. These behavioral results support shared neural mechanisms between visual imagery and afterimages. This study encourages future research combining neurophysiology recording methods and afterimage paradigms to directly examine the neural mechanisms of afterimage conscious perception.

neuroscience↗

bioRxiv2023

Neuroanatomical changes observed over the course of a human pregnancy

Pregnancy is a period of profound hormonal and physiological change experienced by millions of women annually, yet the neural changes unfolding in the maternal brain throughout gestation have not been studied in humans. Leveraging precision imaging, we mapped neuroanatomical changes in an individual from preconception through two years postpartum. Pronounced decreases in gray matter volume and cortical thickness were evident across the brain, which stand in contrast to increases in white matter microstructural integrity, ventricle volume, and cerebrospinal fluid, with few regions untouched by the transition to motherhood. This dataset serves as the first comprehensive map of the human brain across gestation, providing an open-access resource for the brain imaging community to stimulate further exploration and discovery.

neuroscience↗

bioRxiv2023

Manifold Learning for fMRI time-varying FC

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolve over time in meaningful ways at temporal scales going from years (e.g., development) to seconds (e.g., within-scan time-varying FC (tvFC)). Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) expected to retain its most informative aspects (e.g., relationships to behavior, disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)--namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies--are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions; ID) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LE), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

neuroscience↗

bioRxiv2022

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice---focusing on highlighting results, rather than hiding all but the strongest ones---can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses. HighlightsO_LIMost studies do not present all results of their analysis, hiding subthreshold ones. C_LIO_LIHiding results negatively affects the interpretation and understanding of the study. C_LIO_LINeuroimagers should present all results of their study, highlighting key ones. C_LIO_LIUsing the public NARPS data, we show several benefits of the "highlighting" approach. C_LIO_LIThe highlighting approach improves individual studies and meta-analyses. C_LI

neuroscience↗

bioRxiv2021

The Ubiquitous Vigilance Signal in fMRI Time Series Data

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations ([~]0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

neuroscience↗

bioRxiv2020

Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration

Haptic object perception begins with continuous exploratory contacts, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration across time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimation. For this experiment, we designed sixteen haptic stimuli (4 kinds of curve x 4 kinds of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along with the surface twice and to estimate one of the two features--roughness or curvature--dependent on the task instruction. We found that the brain activity in several higher-level regions (e.g., bilateral posterior parietal cortex) linearly increased with curvature through the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase, but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns for haptic roughness estimation in either the contralateral PSC or in the higher-level regions. Together, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, while the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon object features. HighlightsO_LIWe observed the brain activity of haptic object perception using parametric stimuli. C_LIO_LIHaptic curve estimation showed parametric modulation across the cortical hierarchy. C_LIO_LICurve parametric modulation in the sensorimotor cortex showed time dependency. C_LIO_LIRoughness parametric modulation showed very little dependency in any regions of the brain. C_LIO_LIThese findings reflect the nature of time-dependent haptic object processing in the brain. C_LI

neuroscience↗

bioRxiv2019

Thetaburst TMS to the posterior superior temporal sulcus decreases resting-state fMRI connectivity across the face processing network

Humans process faces using a network of face-selective regions distributed across the brain. Neuropsychological patient studies demonstrate that focal damage to nodes in this network can impair face recognition, but such patients are rare. We approximated the effects of damage to the face network in neurologically normal human participants using thetaburst transcranial magnetic stimulation (TBS). Multi-echo functional magnetic resonance imaging (fMRI) resting-state data were collected pre- and post-TBS delivery over the face-selective right superior temporal sulcus (rpSTS), or a control site in the right motor cortex. Results showed that TBS delivered over the rpSTS reduced resting-state connectivity across the extended face-processing network. This connectivity reduction was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas across the ventral, medial and lateral brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). TBS delivered over the motor cortex did not produce significant changes in resting-state connectivity across the face-processing network. These results demonstrate that, even without task-induced fMRI signal changes, disrupting a single node in a brain network can decrease the functional connectivity between nodes in that network that have not been directly stimulated. Author SummaryHuman behavior is dependent on brain networks that perform different cognitive functions. We combined thetaburst transcranial magnetic stimulation (TBS) with resting-state fMRI to study the face processing network. Disruption of the face-selective right posterior superior temporal sulcus (rpSTS) reduced fMRI connectivity across the face network. This impairment in connectivity was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas on the ventral and medial brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). Thus, combined TBS/fMRI can be used to approximate and measure the effects of focal brain damage on brain networks, and suggests such an approach may be useful for mapping intrinsic network organization. Technical TermsO_ST_ABSTBS vs TMSC_ST_ABSTranscranial magnetic stimulation (TMS) is a method that induces current in neural tissue by using a rapidly changing magnetic field. The pattern of magnetic field changes can vary. Thetaburst TMS (TBS) is a type of TMS where the same stimulation pattern fluctuates at around a 5Hz cycle. Multi-echo fMRIDuring typical fMRI, protons are excited and there is a delay, the echo time, before data are collected. That delay is typically designed to result in a high contrast for blood oxygenation differences. In multi-echo fMRI, data are collected at several echo times each time protons are excited. This results in data that have different levels of contrast for blood oxygenation differences. This added information can be used to empirically decrease noise. Face networkA group of brain regions that show significant activity changes in response to visual face stimuli. While these regions have been defined using univariate analyses with task-based fMRI, they often significantly correlate with each other at rest. In this manuscript, the following regions were a priori defined as part of the face network: posterior superior temporal sulcus (pSTS), amygdala, fusiform face area (FFA), and occipital face area (OFA). Matrix based analysis (MBA)A recent approach that uses a Bayesian multilevel modeling framework to identify pairs of ROIs where a decrease in correlation magnitude was larger than expected along with a measure of statistical evidence. With this approach, correlations between all pairs of ROIs are assessed as part of a single model rather than many independent statistical tests.

neuroscience↗

bioRxiv2018

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits muscles are used for different motor actions such as flexion movements like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for noninvasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

neuroscience↗