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1. NORDIC denoising on VASO data

Author: Lasse Knudsen, Luca Vizioli, Federico De Martino, Lonike K. Faes, Daniel A. Handwerker, Steen Moeller, Peter A. Bandettini, and Laurentius Huber
Subjects: NORDIC, denoising, VASO, laminar fMRI, submillimeter resolution, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: The use of submillimeter resolution functional magnetic resonance imaging (fMRI) is increasing in popularity due to the prospect of studying human brain activation non-invasively at the scale of cortical layers and columns. This method, known as laminar fMRI, is inherently signal-to-noise ratio (SNR)-limited, especially at lower field strengths, with the dominant noise source being of thermal origin. Furthermore, laminar fMRI is challenged with signal displacements due to draining vein effects in conventional gradient-echo blood oxygen level-dependent (BOLD) imaging contrasts. fMRI contrasts such as cerebral blood volume (CBV)-sensitive vascular space occupancy (VASO) sequences have the potential to mitigate draining vein effects. However, VASO comes along with another reduction in detection sensitivity. NOise Reduction with DIstribution Corrected (NORDIC) PCA (principal component analysis) is a denoising technique specifically aimed at suppressing thermal noise, which has proven useful for increasing the SNR of high-resolution functional data. While NORDIC has been examined for BOLD acquisitions, its application to VASO data has been limited, which was the focus of the present study. We present a preliminary analysis to evaluate NORDIC’s capability to suppress thermal noise while preserving the VASO signal across a wide parameter space at 3T. For the data presented here, with a proper set of parameters, NORDIC reduced thermal noise with minimal bias on the underlying signal and preserved spatial resolution. Denoising performance was found to vary with different implementation strategies and parameter choices, for which we provide recommendations. We conclude that when applied properly, NORDIC has the potential to overcome the sensitivity limitations of laminar-specific VASO fMRI. Since very few groups currently have 3T VASO data, by sharing our analysis and code, we can compile and compare the effects of NORDIC across a broader range of acquisition parameters and study designs. Such a communal effort will help develop robust recommendations that will increase the utility of laminar fMRI at lower field strengths.
Published: 2025
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2. Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility

Author: Paul A. Taylor, Richard C. Reynolds, Vince Calhoun, Javier Gonzalez-Castillo, Daniel A. Handwerker, Peter A. Bandettini, Amanda F. Mejia, and Gang Chen
Subjects: FMRI, Results, Visualization, Reproducibility, Highlight, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: 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, 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 and extensions of this approach 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.
Published: 2023
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3. Manifold learning for fMRI time-varying functional connectivity

Author: Javier Gonzalez-Castillo, Isabel S. Fernandez, Ka Chun Lam, Daniel A. Handwerker, Francisco Pereira, and Peter A. Bandettini
Subjects: manifold learning, fMRI, time-varying functional connectivity, data visualization, T-SNE, Uniform Manifold Approximation and Projection (UMAP), Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves 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 often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and 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 (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), 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.
Published: 2023
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4. The art and science of using quality control to understand and improve fMRI data

Author: Joshua B. Teves, Javier Gonzalez-Castillo, Micah Holness, Megan Spurney, Peter A. Bandettini, and Daniel A. Handwerker
Subjects: fMRI, quality control, neuroimaging, reproducibility, resting state, GLM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions.
Published: 2023
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5. Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Author: Javier Gonzalez-Castillo, Isabel S. Fernandez, Daniel A. Handwerker, and Peter A. Bandettini
Subjects: Functional MRI, Wakefulness, Sleep, CSF, Resting-state, Fourth ventricle, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: 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.
Published: 2022
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6. Theta-burst TMS to the posterior superior temporal sulcus decreases resting-state fMRI connectivity across the face processing network

Author: Daniel A. Handwerker, Geena Ianni, Benjamin Gutierrez, Vinai Roopchansingh, Javier Gonzalez-Castillo, Gang Chen, Peter A. Bandettini, Leslie G. Ungerleider, and David Pitcher
Subjects: Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: AbstractHumans process faces by 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 by using theta burst 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 nonstimulated 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.
Published: 2020
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7. Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing

Author: Yinghua Yu, Laurentius Huber, Jiajia Yang, Masaki Fukunaga, Yuhui Chai, David C. Jangraw, Gang Chen, Daniel A. Handwerker, Peter J. Molfese, Yoshimichi Ejima, Norihiro Sadato, Jinglong Wu, and Peter A. Bandettini
Subjects: Layer-specific fMRI, Tactile prediction, Primary somatosensory cortex, Temporal prediction error, High-resolution CBV–fMRI, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer-specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short-delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.
Published: 2022
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8. Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration

Author: Jiajia Yang, Peter J. Molfese, Yinghua Yu, Daniel A. Handwerker, Gang Chen, Paul A. Taylor, Yoshimichi Ejima, Jinglong Wu, and Peter A. Bandettini
Subjects: Haptic object perception, Primary somatosensory cortex, Primary motor cortex, fMRI, Parametric modulation, Cortical hierarchy, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Haptic object perception begins with continuous exploratory contact, 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 over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves × 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features—roughness or curvature—depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during 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 during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.
Published: 2021
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9. Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

Author: Emily S. Finn, Enrico Glerean, Arman Y. Khojandi, Dylan Nielson, Peter J. Molfese, Daniel A. Handwerker, and Peter A. Bandettini
Subjects: Individual differences, Naturalistic, Inter-subject correlation, fMRI, Behavior, Representational similarity analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or “idiosynchrony”. Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.
Published: 2020
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10. Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex

Author: Laurentius Huber, Emily S. Finn, Daniel A. Handwerker, Marlene Bönstrup, Daniel R. Glen, Sriranga Kashyap, Dimo Ivanov, Natalia Petridou, Sean Marrett, Jozien Goense, Benedikt A. Poser, and Peter A. Bandettini
Subjects: fMRI, Motor cortex, Cortical layers, Cortical columns, VASO, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: 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. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. 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 non-invasive 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.
Published: 2020
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11. Time-varying whole-brain functional network connectivity coupled to task engagement

Author: Hua Xie, Javier Gonzalez-Castillo, Daniel A. Handwerker, Peter A. Bandettini, Vince D. Calhoun, Gang Chen, Eswar Damaraju, Xiangyu Liu, and Sunanda Mitra
Subjects: Whole-brain connectivity pattern, Cognitive marker, Task-evoked connectivity dynamics, Cognitive dynamics, Brainwide integration, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels. In this study, we hypothesized that whole-brain dynamic functional connectivity (FC) patterns carry fine-grained information that allows for tracking short-term task engagement levels. We derived three task engagement markers from whole-brain dynamic FC pattern, that is, dissimilarity between dynamic FC patterns and high/low-engagement FC patterns, as well as brainwide integration level. We employed a linear mixed effects model to relate those task engagement markers with short-term task performance, and confirmed our hypothesis with the working memory task.
Published: 2018
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12. Visual temporal frequency preference shows a distinct cortical architecture using fMRI.

Author: Yuhui Chai, Daniel A. Handwerker, Sean Marrett, Javier Gonzalez-Castillo, Elisha P. Merriam, Andrew Hall, Peter J. Molfese, and Peter A. Bandettini
Published: 2019
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13. Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information.

Author: Hua Xie, Charles Y. Zheng, Daniel A. Handwerker, Peter A. Bandettini, Vince D. Calhoun, Sunanda Mitra, and Javier Gonzalez-Castillo
Published: 2019
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14. Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications.

Author: Laurentius Huber, Dimo Ivanov, Daniel A. Handwerker, Sean Marrett, Maria Guidi, Kâmil Uludag, Peter A. Bandettini, and Benedikt A. Poser
Published: 2018
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15. A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.

Author: David C. Jangraw, Javier Gonzalez-Castillo, Daniel A. Handwerker, Merage Ghane, Monica D. Rosenberg, Puja Panwar, and Peter A. Bandettini
Published: 2018
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16. TE-dependent analysis of multi-echo fMRI with tedana.

Author: Elizabeth Dupre, Taylor Salo, Zaki Ahmed, Peter A. Bandettini, Katherine L. Bottenhorn, César Caballero-Gaudes, Logan Dowdle, Javier Gonzalez-Castillo, Stephan Heunis, Prantik Kundu, Angela R. Laird, Ross D. Markello, Christopher J. Markiewicz, Stefano Moia, Isla Staden, Joshua Teves, Eneko Uruñuela, Maryam Vaziri-Pashkam, Kirstie J. Whitaker, and Daniel A. Handwerker
Published: 2021
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17. Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest.

Author: Javier Gonzalez-Castillo, César Caballero-Gaudes, Natasha Topolski, Daniel A. Handwerker, Francisco Pereira, and Peter A. Bandettini
Published: 2019
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18. Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI.

Author: Javier Gonzalez-Castillo, Puja Panwar, Laura C. Buchanan, César Caballero-Gaudes, Daniel A. Handwerker, David C. Jangraw, Valentinos Zachariou, Souheil J. Inati, Vinai Roopchansingh, John A. Derbyshire, and Peter A. Bandettini
Published: 2016
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19. Manifold Learning for fMRI time-varying FC

Author: Javier Gonzalez-Castillo, Isabel Fernandez, Ka Chun Lam, Daniel A Handwerker, Francisco Pereira, and Peter A Bandettini
Subjects: Article
Abstract: 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-varyingFC(tvFC)). Yet, our ability to exploretvFCis severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g.,2Dand3Dscatter 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 tvFCdata to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions;ID) oftvFCdata manifolds. Third, we describe the inner workings of three state-of-the-artMLTs: 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 oftvFCdata, as well as their robustness against hyper-parameter selection. Our results show thattvFCdata has anIDthat ranges between 4 and 26, and thatIDvaries significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed:UMAPandT-SNEcan capture these two levels of detail concurrently, but LEcould only capture one at a time. We observed substantial variability in embedding quality acrossMLTs, and within-MLTas 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 ofMLTstotvFCdata. Overall, we conclude that whileMLTscan be useful to generate summary views of labeledtvFCdata, their application to unlabeled data such as resting-state remains challenging.
Published: 2023
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20. Measuring the Consistency of Global Functional Connectivity Using Kernel Regression Methods.

Author: Carlton Chu, Daniel A. Handwerker, Peter A. Bandettini, and John Ashburner
Published: 2011
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21. Fast detection and reduction of local transient artifacts in resting-state fMRI.

Author: Hang Joon Jo, Richard C. Reynolds, Stephen J. Gotts, Daniel A. Handwerker, Irena Balzekas, Alex Martin, Robert W. Cox, and Peter A. Bandettini
Published: 2020
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22. Highlight Results, Don’t Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Author: Paul A. Taylor, Richard C. Reynolds, Vince Calhoun, Javier Gonzalez-Castillo, Daniel A. Handwerker, Peter A. Bandettini, Amanda F. Mejia, and Gang Chen
Abstract: 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.HighlightsMost studies do not present all results of their analysis, hiding subthreshold ones.Hiding results negatively affects the interpretation and understanding of the study.Neuroimagers should present all results of their study, highlighting key ones.Using the public NARPS data, we show several benefits of the “highlighting” approach.The highlighting approach improves individual studies and meta-analyses.
Published: 2022
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23. Connectivity trajectory across lifespan differentiates the precuneus from the default network.

Author: Zhi Yang, Catie Chang, Ting Xu 0001, Lili Jiang, Daniel A. Handwerker, F. Xavier Castellanos, Michael P. Milham, Peter A. Bandettini, and Xi-Nian Zuo
Published: 2014
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24. Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

Author: Somayeh Shahsavarani, David N. Thibodeaux, Weihao Xu, Sharon H. Kim, Fatema Lodgher, Chinwendu Nwokeabia, Morgan Cambareri, Alexis J. Yagielski, Hanzhi T. Zhao, Daniel A. Handwerker, Javier Gonzalez-Castillo, Peter A. Bandettini, and Elizabeth M.C. Hillman
Subjects: General Biochemistry, Genetics and Molecular Biology
Published: 2023
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25. Dynamic functional connectivity: Promise, issues, and interpretations.

Author: R. Matthew Hutchison, Thilo Womelsdorf, Elena A. Allen, Peter A. Bandettini, Vince D. Calhoun, Maurizio Corbetta, Stefania Della Penna, Jeff H. Duyn, Gary H. Glover, Javier Gonzalez-Castillo, Daniel A. Handwerker, Shella D. Keilholz, Vesa Kiviniemi, David A. Leopold, Francesco de Pasquale, Olaf Sporns, Martin Walter, and Catie Chang
Published: 2013
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26. The continuing challenge of understanding and modeling hemodynamic variation in fMRI.

Author: Daniel A. Handwerker, Javier Gonzalez-Castillo, Mark D'Esposito, and Peter A. Bandettini
Published: 2012
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27. Periodic changes in fMRI connectivity.

Author: Daniel A. Handwerker, Vinai Roopchansingh, Javier Gonzalez-Castillo, and Peter A. Bandettini
Published: 2012
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28. Simple explanations before complex theories: Alternative interpretations of Sirotin and Das' observations.

Author: Daniel A. Handwerker and Peter A. Bandettini
Published: 2011
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29. Hemodynamic signals not predicted? Not so: A comment on Sirotin and Das (2009).

Author: Daniel A. Handwerker and Peter A. Bandettini
Published: 2011
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30. fMRI in the presence of task-correlated breathing variations.

Author: Rasmus M. Birn, Kevin Murphy 0001, Daniel A. Handwerker, and Peter A. Bandettini
Published: 2009
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31. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

Author: Kevin Murphy 0001, Rasmus M. Birn, Daniel A. Handwerker, Tyler B. Jones, and Peter A. Bandettini
Published: 2009
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32. Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Author: Isabel Fernandez, Javier Gonzalez-Castillo, Daniel A. Handwerker, and Peter A. Bandettini
Subjects: Computer science, media_common.quotation_subject, Cognitive Neuroscience, Electroencephalography, Signal, Arousal, medicine, Humans, Wakefulness, media_common, Fourth Ventricle, Human Connectome Project, medicine.diagnostic_test, business.industry, Brain, Pattern recognition, Magnetic Resonance Imaging, Weighting, Neurology, Eye tracking, Artificial intelligence, business, Sleep, Vigilance (psychology)
Abstract: 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.
Published: 2022

33. Spatio-temporal information analysis of event-related BOLD responses.

Author: Galit Fuhrmann Alpert, Felice T. Sun, Daniel A. Handwerker, Mark D'Esposito, and Robert T. Knight
Published: 2007
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34. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses.

Author: Daniel A. Handwerker, John M. Ollinger, and Mark D'Esposito
Published: 2004
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35. Centering inclusivity in the design of online conferences - An OHBM - Open Science perspective

Author: Elizabeth Levitis, Cassandra Doris Gould van Praag, Remi Gau, Stephan Heunis, Elizabeth DuPre, Greg Kiar, Katherine L Bottenhorn, Tristan Glatard, Aki Nikolaidis, Kirstie Jane Whitaker, Matteo Mancini, Guiomar Niso, Soroosh Afyouni, Eva Alonso Ortiz, Stefan Appelhoff, Aurina Arnatkeviciute, Melvin Selim ATAY, Tibor Auer, Giulia Baracchini, Johanna Margarete Marianne Bayer, Michael J. S. Beauvais, Janine Diane Bijsterbosch, Isil Poyraz Bilgin, Saskia Bollmann, Steffen Bollmann, Rotem Botvinik-Nezer, Molly G Bright, Vince D Calhoun, Xiao Chen, Sidhant Chopra, Hu Chuan-Peng, Thomas Close, Savannah Cookson, Cameron Craddock, Alejandro De La Vega, Benjamin De Leener, Damion Demeter, Paola Di Maio, Erin W Dickie, Simon B Eickhoff, Oscar Esteban, Karolina Finc, Matteo Frigo, Saampras Ganesan, Melanie Ganz, Kelly Garner, Eduardo A. Garza-Villarreal, Gabriel Gonzalez-Escamilla, Rohit Goswami, John David Griffiths, Tijl Grootswagers, Samuel Guay, Olivia Guest, Daniel A. Handwerker, Peer Herholz, Katja Heuer, Dorien Huijser, Vittorio Iacovella, Michael Joseph, Agah Karakuzu, David Keator, Xenia Kobeleva, Manoj Kumar, Angie Laird, Linda J. Larson-Prior, Alexandra Lautarescu, Alberto Lazari, Jon Haitz Legarreta Gorroño, Jeff jeffers, Jinglei Lv, Sina Mansour L., David Meunier, Dustin Moraczewski, Tulika Nandi, Samuel A. Nastase, Matthias Nau, Stephanie Noble, Martin Norgaard, Johnes Obungoloch, Robert Oostenveld, Edwina R Orchard, Ana Luísa Pinho, Russell Poldrack, Anqi Qiu, Pradeep Reddy Raamana, Ariel Rokem, Saige Rutherford, Malvika Sharan, Thomas Shaw, Warda T Syeda, Meghan M. Testerman, Roberto Toro, Sofie L. Valk, Sofie Van Den Bossche, Gael P. Varoquaux, Frantisek Vasa, Michele Veldsman, Jakub Vohryzek, Adina Svenja Wagner, Reubs J Walsh, Tonya White, null zuxfoucault, Xihe Xie, Chao-Gan Yan, Yu-Fang Yang, Yohan Yee, Gaston E Zanitti, Ana Van Gulick, Eugene Duff, Camille MAUMET, National Institute of Mental Health (NIMH), University College of London [London] (UCL), University of Oxford, Université Catholique de Louvain = Catholic University of Louvain (UCL), Eindhoven University of Technology [Eindhoven] (TU/e), McGill University = Université McGill [Montréal, Canada], Florida International University [Miami] (FIU), Concordia University [Montreal], Child Mind Institute, The Alan Turing Institute, University of Sussex, Cardiff University, École Polytechnique de Montréal (EPM), Indiana University [Bloomington], Indiana University System, Universidad Politécnica de Madrid (UPM), Max Planck Institute for Human Development, Max-Planck-Gesellschaft, Monash University [Melbourne], Middle East Technical University [Ankara] (METU), University of Surrey (UNIS), Montreal Neurological Institute and Hospital, University of Melbourne, Orygen Youth Health Research Centre [Melbourne], Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), University of Reading (UOR), University of Queensland [Brisbane], Dartmouth College [Hanover], Northwestern University [Evanston], Georgia State University, University System of Georgia (USG), Institute of Psychology [Beijing], Chinese Academy of Sciences [Changchun Branch] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), Nanjing Normal University (NNU), The University of Sydney, University of California (UC), University of Texas at Austin [Austin], CHU Sainte Justine [Montréal], Institute for Globally Distributed Open Research and Education (IGDORE ), Centre for Addiction and Mental Health [Toronto] (CAMH), University of Toronto, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, Université de Lausanne = University of Lausanne (UNIL), Nicolaus Copernicus University [Toruń], Université Côte d'Azur (UCA), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Rigshospitalet [Copenhagen], Copenhagen University Hospital, University of Copenhagen = Københavns Universitet (UCPH), University of Birmingham [Birmingham], Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), University of Iceland [Reykjavik], Indian Institute of Technology Kanpur (IIT Kanpur), Western Sydney University, Université de Montréal (UdeM), Radboud University [Nijmegen], Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Erasmus University Rotterdam, Universiteit Leiden, University of Trento [Trento], Université du Québec à Montréal = University of Québec in Montréal (UQAM), University Hospital Bonn, German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Princeton University, University of Arkansas for Medical Sciences (UAMS), Arkansas Children's Research Institute, King‘s College London, Université de Sherbrooke (UdeS), Aarhus University [Aarhus], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Yale University [New Haven], Stanford University, Mbarara University of Science and Technology [Mbarara] (MUST), Karolinska Institutet [Stockholm], Université Paris-Saclay, National University of Singapore (NUS), Johns Hopkins University (JHU), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Washington [Seattle], Radboud University Medical Center [Nijmegen], University of Michigan [Ann Arbor], University of Michigan System, Institut Pasteur [Paris] (IP), Universiteit Gent = Ghent University (UGENT), Vrije Universiteit Amsterdam [Amsterdam] (VU), Department of Public Health, Erasmus University Medical Centre, Rotterdam, Erasmus University Medical Centre, Rotterdam, Academia Sinica, Weill Medical College of Cornell University [New York], Chinese Academy of Sciences [Beijing] (CAS), University of Würzburg, The Hospital for sick children [Toronto] (SickKids), Carnegie Mellon University [Pittsburgh] (CMU), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), R25-DA051675, National Institutes of Health, 1631325, National Science Foundation, 5R21MH118556-02, National Institute of Mental Health, BB/S008314/1, Biotechnology and Biological Sciences Research Council, G036716N, Fonds Wetenschappelijk Onderzoek, University of Oxford [Oxford], University of California, University of Lausanne (UNIL), University of Copenhagen = Københavns Universitet (KU), Universidad Nacional Autónoma de México (UNAM), Johannes Gutenberg - Universität Mainz (JGU), Radboud university [Nijmegen], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Leiden University, Institut Pasteur [Paris], Universiteit Gent = Ghent University [Belgium] (UGENT), Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Maumet, Camille
Subjects: SDG 16 - Peace, collaborative events, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, SDG 16 - Peace, Justice and Strong Institutions, Review, Justice and Strong Institutions, online conferences, diversity, World Wide Web, inclusivity, open science, ddc:610, Sociology
Abstract: As the global health crisis unfolded throughout the world, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers and effectively enabled many individuals who have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g. caregiving responsibilities. Yet, the mere existence of online conferences is unfortunately no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the setup of the conference can provide more visibility to well-established researchers and reduce opportunities for early career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to: attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
Published: 2021
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36. Topographical and laminar distribution of audiovisual processing within human planum temporale

Author: Sean Marrett, Arman Y. Khojandi, Tina T. Liu, Peter A. Bandettini, Lars Muckli, Linqing Li, Arjen Alink, Yuhui Chai, and Daniel A. Handwerker
Subjects: Brain Mapping, genetic structures, Computer science, General Neuroscience, Planum temporale, media_common.quotation_subject, Feed forward, Multisensory integration, Brain, Laminar flow, Signal, Magnetic Resonance Imaging, Article, Stimulus modality, Visual cortex, medicine.anatomical_structure, Acoustic Stimulation, medicine, Auditory Perception, Contrast (vision), Humans, Neuroscience, media_common
Abstract: The brain is capable of integrating signals from multiple sensory modalities. Such multisensory integration can occur in areas that are commonly considered unisensory, such as planum temporale (PT) representing the auditory association cortex. However, the roles of different afferents (feedforward vs. feedback) to PT in multisensory processing are not well understood. Our study aims to understand that by examining laminar activity patterns in different topographical subfields of human PT under unimodal and multisensory stimuli. To this end, we adopted an advanced mesoscopic (sub-millimeter) fMRI methodology at 7 T by acquiring BOLD (blood-oxygen-level-dependent contrast, which has higher sensitivity) and VAPER (integrated blood volume and perfusion contrast, which has superior laminar specificity) signal concurrently, and performed all analyses in native fMRI space benefiting from an identical acquisition between functional and anatomical images. We found a division of function between visual and auditory processing in PT and distinct feedback mechanisms in different subareas. Specifically, anterior PT was activated more by auditory inputs and received feedback modulation in superficial layers. This feedback depended on task performance and likely arose from top-down influences from higher-order multimodal areas. In contrast, posterior PT was preferentially activated by visual inputs and received visual feedback in both superficial and deep layers, which is likely projected directly from the early visual cortex. Together, these findings provide novel insights into the mechanism of multisensory interaction in human PT at the mesoscopic spatial scale.
Published: 2021

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

Author: Jinglong Wu, Paul A. Taylor, Daniel A. Handwerker, Gang Chen, Yinghua Yu, Jiajia Yang, Peter J. Molfese, Yoshimichi Ejima, and Peter A. Bandettini
Subjects: medicine.diagnostic_test, business.industry, Computer science, media_common.quotation_subject, Posterior parietal cortex, Sensory system, Human brain, Stimulus (physiology), medicine.anatomical_structure, medicine, Contrast (vision), Computer vision, Artificial intelligence, business, Functional magnetic resonance imaging, Object perception, Haptic technology, media_common
Abstract: 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 × 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.HighlightsWe observed the brain activity of haptic object perception using parametric stimuli.Haptic curve estimation showed parametric modulation across the cortical hierarchy.Curve parametric modulation in the sensorimotor cortex showed time dependency.Roughness parametric modulation showed very little dependency in any regions of the brain.These findings reflect the nature of time-dependent haptic object processing in the brain.
Published: 2020
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38. Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex

Author: Yinghua Yu, Jiajia Yang, Laurentius Huber, Peter J. Molfese, Yoshimichi Ejima, Daniel A. Handwerker, Jinglong Wu, Gang Chen, David C. Jangraw, and Peter A. Bandettini
Subjects: Adult, Male, genetic structures, Computer science, Middle layer, Cognitive Neuroscience, brain, fmri, Sensory system, Somatosensory system, Brain mapping, Motion, Young Adult, 03 medical and health sciences, visual-cortex, 0302 clinical medicine, Sensation, medicine, Humans, Layer (object-oriented design), Research Articles, EXPECTATIONS, 030304 developmental biology, Brain Mapping, 0303 health sciences, Multidisciplinary, medicine.diagnostic_test, ATTENTION, SciAdv r-articles, Somatosensory Cortex, VASO, Middle Aged, Magnetic Resonance Imaging, connections, Sensory input, nervous system, SINGLE, Touch, Linear Models, Female, Functional magnetic resonance imaging, Neuroscience, 3B, 030217 neurology & neurosurgery, Research Article
Abstract: Using laminar fMRI, we show that prediction and sensory inputs activate specific layers in human primary somatosensory cortex., When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex.
Published: 2019

39. Visual temporal frequency preference shows a distinct cortical architecture using fMRI

Author: Sean Marrett, Peter A. Bandettini, Javier Gonzalez-Castillo, Peter J. Molfese, Elisha P. Merriam, Daniel A. Handwerker, Andrew Hall, and Yuhui Chai
Subjects: Adult, Male, Visual perception, Time Factors, genetic structures, Adolescent, Computer science, Cognitive Neuroscience, Stimulus (physiology), 050105 experimental psychology, Article, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Thalamus, Modulation (music), medicine, Humans, 0501 psychology and cognitive sciences, Visual Pathways, Visual Cortex, Brain Mapping, medicine.diagnostic_test, Flicker, 05 social sciences, Middle Aged, Magnetic Resonance Imaging, Visual cortex, medicine.anatomical_structure, Neurology, Human visual system model, Visual Perception, Female, Functional magnetic resonance imaging, Neuroscience, 030217 neurology & neurosurgery, Photic Stimulation
Abstract: Studies of visual temporal frequency preference typically examine frequencies under 20 Hz and measure local activity to evaluate the sensitivity of different cortical areas to variations in temporal frequencies. Most of these studies have not attempted to map preferred temporal frequency within and across visual areas, nor have they explored in detail, stimuli at gamma frequency, which recent research suggests may have potential clinical utility. In this study, we address this gap by using functional magnetic resonance imaging (fMRI) to measure response to flickering visual stimuli varying in frequency from 1 to 40 Hz. We apply stimulation in both a block design to examine task response and a steady-state design to examine functional connectivity. We observed distinct activation patterns between 1 Hz and 40 Hz stimuli. We also found that the correlation between medial thalamus and visual cortex was modulated by the temporal frequency. The modulation functions and tuned frequencies are different for the visual activity and thalamo-visual correlations. Using both fMRI activity and connectivity measurements, we show evidence for a temporal frequency specific organization across the human visual system.
Published: 2019

40. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author: Selim Melvin Atay, Iska Moxon-Emre, Anders Eklund, Paula P. Brooks, Nandita Vijayakumar, Anibal Sólon Heinsfeld, Jean-Baptiste Poline, Yaroslav O. Halchenko, David Meunier, Daniela M. Hohmann, Martin Szinte, Arshitha Basavaraj, Andrija Štajduhar, Link Tejavibulya, Michael Dayan, Anisha Keshavan, Chao Jiang, Felix Hoffstaedter, Michael P. Milham, Davide Momi, Hua Xie, Ai Wern Chung, Georg Langs, Hao-Ting Wang, Xenia Kobeleva, Robert Oostenveld, Thomas G. Close, Lena Dorfschmidt, Jesse A. Brown, Serge Koudoro, J.P. Manzano-Patron, Isil P. Bilgin, Shreyas Fadnavis, Sylvain Takerkart, R. Cameron Craddock, Stephan Heunis, Scott Peltier, Kathryn Berluti, Hayli Spence, Pablo F. Damasceno, David H. O’Connor, Eleftherios Garyfallidis, Eugene P. Duff, Rudolph Pienaar, Nicolas Traut, AmanPreet Badhwar, Ali R. Khan, Marissa Laws, Abigail S. Greene, Richard A. I. Bethlehem, Angela R. Laird, Krista DeStasio, Ina Thome, Camille Maumet, Alexandru D. Iordan, P. Christiaan Klink, Damion V. Demeter, John A. Onofrey, Cassandra D. Gould van Praag, Jakub Vohryzek, Suzanne T. Witt, Aysha Motala, Valerie Hayot-Sasson, Geetika Gupta, Alexandre Rosa Franco, John W. VanMeter, Michel Thiebaut de Schotten, James M. Shine, Lucina Q. Uddin, Thomas S. Hartmann, Guillaume Flandin, Clara Moreau, Tomislav Lipic, Claire Bradley, Fernando A. Barrios, Daniel S. Margulies, R. Austin Benn, Sofie Van Den Bossche, Lydia Riedl, Xihe Xie, Christopher R. Madan, Roberto Toro, Viviana Siless, Fabrizio De Vico Fallani, Nikoloz Sirmpilatze, Emily Olafson, Anqi Qiu, Theresa W. Cheng, Valentina Borghesani, Sidhant Chopra, Claire Cury, Giorgio Marinato, Horea-Ioan Ioanas, Giorgia Cona, Michael Joseph, Angela Tam, Mathias Scharinger, Daniel A. Handwerker, Katherine L. Bottenhorn, Sina Mansour L, Kathryn L. Mills, Christopher J. Markiewicz, Elizabeth Levitis, Cyril Pernet, Stephanie J. Forkel, Agah Karakuzu, Edwina R Orchard, Sarah L. Dziura, Saige Rutherford, Kamalaker Dadi, Enrico Glerean, Desiree Lussier, Davide Poggiali, Molly Simmonite, Jason Kai, Jessica Flannery, Ting Xu, Jon Haitz Legarreta, Nasim Anousheh, Marco Bedini, Tristan Glatard, Thomas E. Nichols, Joscelin Rocha-Hidalgo, Erin W. Dickie, Dipankar Bachar, Malin Sandström, Xi-Nian Zuo, Pedro Pinheiro-Chagas, Laura C. Rice, Jakša Vukojević, Javier Gonzalez-Castillo, José C. García Alanis, Lorenzo Pasquini, Yu-Fang Yang, Matteo Mancini, Deena Shariq, Chao-Gan Yan, Laura Tomaz da Silva, César Caballero-Gaudes, Yasmine Bassil, Aurina Arnatkeviciute, Dustin Scheinost, Satrajit S. Ghosh, Gaël Varoquaux, Etienne Combrisson, Bramsh Qamar Chandio, Kelly Garner, Tiago Quendera, Patrick Friedrich, Shawn A. Rhoads, Roxane Licandro, Elizabeth DuPre, Aki Nikolaidis, Simon Schwab, Stephanie Noble, Guillaume Auzias, Daniel J. Lurie, Mahboobeh Parsapoor, Eneko Uruñuela, Andrew Doyle, Peer Herholz, Saampras Ganesan, Vincent Koppelmans, Corey Horien, Samir Das, Junaid S. Merchant, Siyuan Gao, Matheus Marcon, Nathalia Bianchini Esper, B.T. Thomas Yeo, Katja Heuer, Caroline O’Brien, Micaela Y. Chan, Sook-Lei Liew, Lindsay D. Oliver, Kirstie Whitaker, Christoph Vogelbacher, Dylan M. Nielson, Krisanne Litinas, Dorien C. Huijser, Pierre Bellec, R. Todd Constable, David N. Kennedy, Julia Sprenger, Lea-Theresa Mais, Oscar Esteban, Patrick J. Park, Patrick Callahan, Christopher R. Nolan, Johanna Bayer, Guillaume Dumas, Elise Bannier, Elizabeth A. McDevitt, Ariel Rokem, Samuel A. Nastase, Olivia W. Stanley, Ruggero Basanisi, Daniele Marinazzo, Gregory Kiar, Lisa Novello, Samuel Guay, John C. Flournoy, Stefano Moia, Kendra Oudyk, Fang-Cheng Yeh, Gustav Nilsonne, Thomas B. Shaw, Steve Wideman, Saskia Bollmann, Steffen Bollmann, Julia M. Huntenburg, Augusto Buchweitz, Matteo Visconti di Oleggio Castello, Dimitra Maoutsa, Lucy B. Whitmore, Catherine Alice Hahn, Antonino Vallesi, Remi Gau, Felipe Meneguzzi, Université Catholique de Louvain = Catholic University of Louvain (UCL), Yale University [New Haven], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Florida International University [Miami] (FIU), University of Reading (UOR), University of Würzburg, Champalimaud Centre for the Unknown [Lisbon], University of Melbourne, University of Cambridge [UK] (CAM), Georgetown University [Washington] (GU), Philipps Universität Marburg = Philipps University of Marburg, Université de Montréal (UdeM), University College of London [London] (UCL), University of Sussex, Universiteit Gent = Ghent University (UGENT), German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Université de Sherbrooke (UdeS), Middle East Technical University [Ankara] (METU), McGill University = Université McGill [Montréal, Canada], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universiteit Leiden, Karolinska Institutet [Stockholm], Princeton University, Universität Zürich [Zürich] = University of Zurich (UZH), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), University medical center and campus biotech Geneva, Emory University [Atlanta, GA], Cardiff University's Brain Research Imaging Centre [Cardiff] (CUBRIC), School of Psychology [Cardiff University], Cardiff University-Cardiff University, The University of Sydney, Weill Cornell Medicine [Cornell University], Cornell University [New York], Università degli Studi di Padova = University of Padua (Unipd), Jülich Research Centre, University of Texas at Austin [Austin], Institut Pasteur [Paris] (IP), Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Basque Center on Cognition Brain and Language [Gipuzkoa, Espagne] (BCBL), Linköping University (LIU), University of Queensland [Brisbane], University of New South Wales [Sydney] (UNSW), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), University of Maryland [College Park], University of Maryland System, University of Washington [Seattle], Harvard Medical School [Boston] (HMS), Child Mind Institute, University of Western Ontario (UWO), Indiana University [Bloomington], Indiana University System, Monash university, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), National Institute of Mental Health (NIMH), Università degli Studi di Trento (UNITN), Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), Queensland University of Technology [Brisbane] (QUT), University of California [San Francisco] (UC San Francisco), University of California (UC), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), University of Texas at Dallas [Richardson] (UT Dallas), University of Oregon [Eugene], Monash University [Melbourne], Boston Children's Hospital, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (EMPENN), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Chinese Academy of Sciences [Beijing] (CAS), Beijing Normal University (BNU), This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg, from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut, and from NIH K00MH122372 for Stephanie Noble., Brainhack Community: Nasim Anousheh, Aurina Arnatkeviciute, Guillaume Auzias, Dipankar Bachar, Elise Bannier, Ruggero Basanisi, Arshitha Basavaraj, Marco Bedini, Pierre Bellec, R Austin Benn, Kathryn Berluti, Steffen Bollmann, Saskia Bollmann, Claire Bradley, Jesse Brown, Augusto Buchweitz, Patrick Callahan, Micaela Y Chan, Bramsh Q Chandio, Theresa Cheng, Sidhant Chopra, Ai Wern Chung, Thomas G Close, Etienne Combrisson, Giorgia Cona, R Todd Constable, Claire Cury, Kamalaker Dadi, Pablo F Damasceno, Samir Das, Fabrizio De Vico Fallani, Krista DeStasio, Erin W Dickie, Lena Dorfschmidt, Eugene P Duff, Elizabeth DuPre, Sarah Dziura, Nathalia B Esper, Oscar Esteban, Shreyas Fadnavis, Guillaume Flandin, Jessica E Flannery, John Flournoy, Stephanie J Forkel, Alexandre R Franco, Saampras Ganesan, Siyuan Gao, José C García Alanis, Eleftherios Garyfallidis, Tristan Glatard, Enrico Glerean, Javier Gonzalez-Castillo, Cassandra D Gould van Praag, Abigail S Greene, Geetika Gupta, Catherine Alice Hahn, Yaroslav O Halchenko, Daniel Handwerker, Thomas S Hartmann, Valérie Hayot-Sasson, Stephan Heunis, Felix Hoffstaedter, Daniela M Hohmann, Corey Horien, Horea-Ioan Ioanas, Alexandru Iordan, Chao Jiang, Michael Joseph, Jason Kai, Agah Karakuzu, David N Kennedy, Anisha Keshavan, Ali R Khan, Gregory Kiar, P Christiaan Klink, Vincent Koppelmans, Serge Koudoro, Angela R Laird, Georg Langs, Marissa Laws, Roxane Licandro, Sook-Lei Liew, Tomislav Lipic, Krisanne Litinas, Daniel J Lurie, Désirée Lussier, Christopher R Madan, Lea-Theresa Mais, Sina Mansour L, J P Manzano-Patron, Dimitra Maoutsa, Matheus Marcon, Daniel S Margulies, Giorgio Marinato, Daniele Marinazzo, Christopher J Markiewicz, Camille Maumet, Felipe Meneguzzi, David Meunier, Michael P Milham, Kathryn L Mills, Davide Momi, Clara A Moreau, Aysha Motala, Iska Moxon-Emre, Thomas E Nichols, Dylan M Nielson, Gustav Nilsonne, Lisa Novello, Caroline O'Brien, Emily Olafson, Lindsay D Oliver, John A Onofrey, Edwina R Orchard, Kendra Oudyk, Patrick J Park, Mahboobeh Parsapoor, Lorenzo Pasquini, Scott Peltier, Cyril R Pernet, Rudolph Pienaar, Pedro Pinheiro-Chagas, Jean-Baptiste Poline, Anqi Qiu, Tiago Quendera, Laura C Rice, Joscelin Rocha-Hidalgo, Saige Rutherford, Mathias Scharinger, Dustin Scheinost, Deena Shariq, Thomas B Shaw, Viviana Siless, Molly Simmonite, Nikoloz Sirmpilatze, Hayli Spence, Julia Sprenger, Andrija Stajduhar, Martin Szinte, Sylvain Takerkart, Angela Tam, Link Tejavibulya, Michel Thiebaut de Schotten, Ina Thome, Laura Tomaz da Silva, Nicolas Traut, Lucina Q Uddin, Antonino Vallesi, John W VanMeter, Nandita Vijayakumar, Matteo Visconti di Oleggio Castello, Jakub Vohryzek, Jakša Vukojević, Kirstie Jane Whitaker, Lucy Whitmore, Steve Wideman, Suzanne T Witt, Hua Xie, Ting Xu, Chao-Gan Yan, Fang-Cheng Yeh, B T Thomas Yeo, Xi-Nian Zuo, ANR-19-DATA-0025,NeuroWebLab,Un laboratoire de neuroscience collectif: Au delà de FAIR(2019), European Project: 867458,LC-FMR, Maumet, Camille, Un laboratoire de neuroscience collectif: Au delà de FAIR - - NeuroWebLab2019 - ANR-19-DATA-0025 - DONNEES - VALID, Brainhack: Developing a culture of open, inclusive, community-driven neuroscience - LC-FMR - 867458 - INCOMING, Leiden University, Weill Cornell Medicine [New York], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), Universiteit Gent = Ghent University [Belgium] (UGENT), University Hospital Bonn, Sherbrooke Connectivity Imaging Lab [Sherbrooke] (SCIL), Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS)-Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Videos and Images Theory and Analytics Laboratory (VITAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Erasmus University Rotterdam, Origami (Origami), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Princeton Neuroscience Institute [Princeton], Human Neuroscience Platform, Brighton and Sussex Medical School (BSMS), Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Institute of Neuroscience and Medicine [Jülich] (INM-1), Département de Neuroscience - Department of Neuroscience, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Queensland Brain Institute, Universidad Nacional Autónoma de México (UNAM), Radboud university [Nijmegen], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), The Brainhack Community - https://pubmed.ncbi.nlm.nih.gov/33932337, Philipps University of Marburg, University of the Basque Country [Bizkaia] (UPV/EHU), Universita degli Studi di Padova, Philipps Universität Marburg, Institut Pasteur [Paris], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), University of California [San Francisco] (UCSF), University of California, Geochemistry, and University of Padova [Padova, Italy]
Subjects: 0301 basic medicine, Open science, Community building, Best practice, Neuroscience(all), Brainhack, Article, neuroscience, 03 medical and health sciences, 0302 clinical medicine, best practices, collaboration, community building, hackathon, inclusivity, open science, reproducibility, training, Congresses as Topic, Neurosciences, Practice Guidelines as Topic, Communication, Internet, Research community, Medical Bioscience, Psychology, ddc:610, Sociology, Brainhack Community, Training, Neurology & Neurosurgery, Scientific progress, organization & administration [Neurosciences], General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Medicinsk biovetenskap, 030104 developmental biology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Cognitive Sciences, Engineering ethics, 030217 neurology & neurosurgery
Abstract: Available online 30 April 2021. Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress. The present manuscript is part of a growing community effort to collate Brainhack-related insights and expertise into a Jupyter Book (http://brainhack.org/brainhack_jupyter_book/) that will serve as a centralized set of resources for the community; we acknowledge all the individuals who contributed and will make ongoing contributions to these resources. A pre-print version of the present manuscript is available as part of the Jupyter Book. Moreover, we would like to acknowledge all Brainhack organizers, supporters, presenters, and participants for their contribution to growing and maintaining this community. The benefits described in this manuscript would not be possible without them. We also thank all institutions, labs, and organizations who have helped this community grow, meet in stimulating environments, and add an excellent educational resource pool and agenda. With an expanding community, Brainhack’s support network keeps growing, and we thank all labs and individual researchers for their dedication and expertise offered to this community (see http://brainhack.org/brainhack_jupyter_book/acknowledgments.html for a full list of individual acknowledgments; an updated list will be maintained in the Jupyter Book). Grants and funding bodies: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg; from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut; and from NIH K00MH122372 for Stephanie Noble. The Brainhack Community member list and contributions of the different authors are detailed at http://brainhack.org/brainhack_jupyter_book/contributors.html. Our crediting system is described here: http://brainhack.org/brainhack_jupyter_book/neuroview_authorship-agreement.html.
Published: 2021
Full Text: View/download PDF

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

Author: Paul A. Taylor, Yinghua Yu, Jinglong Wu, Peter A. Bandettini, Yoshimichi Ejima, Daniel A. Handwerker, Jiajia Yang, Gang Chen, and Peter J. Molfese
Subjects: Adult, Male, Computer science, Cognitive Neuroscience, media_common.quotation_subject, Primary motor cortex, Posterior parietal cortex, Neurosciences. Biological psychiatry. Neuropsychiatry, Sensory system, Stimulus (physiology), Somatosensory system, behavioral disciplines and activities, 050105 experimental psychology, Young Adult, 03 medical and health sciences, Cortical hierarchy, 0302 clinical medicine, Primary somatosensory cortex, Haptic object perception, Physical Stimulation, medicine, Humans, Contrast (vision), 0501 psychology and cognitive sciences, Computer vision, Object perception, Haptic technology, media_common, medicine.diagnostic_test, business.industry, 05 social sciences, fMRI, Magnetic Resonance Imaging, Form Perception, body regions, Touch Perception, Neurology, Visual Perception, Female, Parametric modulation, Sensorimotor Cortex, Artificial intelligence, business, Functional magnetic resonance imaging, Photic Stimulation, 030217 neurology & neurosurgery, psychological phenomena and processes, RC321-571
Abstract: Haptic object perception begins with continuous exploratory contact, 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 over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves x 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features -roughness or curvature -depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during 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 during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.
Published: 2021

42. Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

Author: Natasha Topolski, Javier Gonzalez-Castillo, Francisco Pereira, César Caballero-Gaudes, Peter A. Bandettini, and Daniel A. Handwerker
Subjects: Adult, Male, Computer science, Cognitive Neuroscience, Rest, Flow (psychology), 050105 experimental psychology, Article, Motion (physics), Task (project management), Thinking, Young Adult, 03 medical and health sciences, Cognition, 0302 clinical medicine, Connectome, Humans, 0501 psychology and cognitive sciences, 030304 developmental biology, Dynamic functional connectivity, Rest (physics), 0303 health sciences, Functional connectivity, 05 social sciences, Magnetic Resonance Imaging, Neurology, Female, Neuroscience, 030217 neurology & neurosurgery
Abstract: Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states—commonly used to summarize and interpret resting dFC—can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.
Published: 2019

43. Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information

Author: Hua Xie, Javier Gonzalez-Castillo, Peter A. Bandettini, Vince D. Calhoun, Sunanda Mitra, Daniel A. Handwerker, and Charles Y. Zheng
Subjects: Adult, Computer science, Cognitive Neuroscience, 050105 experimental psychology, Article, 03 medical and health sciences, 0302 clinical medicine, Cognition, medicine, Connectome, Humans, 0501 psychology and cognitive sciences, Dynamic functional connectivity, business.industry, Functional connectivity, 05 social sciences, Brain, Pattern recognition, Human brain, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Metric (mathematics), Artificial intelligence, business, Jackknife resampling, 030217 neurology & neurosurgery
Abstract: Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.
Published: 2018

44. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Author: Eugene P. Duff, Gunnar Schaefer, R. Cameron Craddock, Xiangrui Li, Vince D. Calhoun, Tibor Auer, Michael Hanke, David Keator, Camille Maumet, Ariel Rokem, Thomas E. Nichols, Jean-Baptiste Poline, Daniel A. Handwerker, Guillaume Flandin, Yaroslav O. Halchenko, Vanessa Sochat, Samir Das, Satrajit S. Ghosh, Gaël Varoquaux, Zachary Michael, Russell A. Poldrack, Tristan Glatard, B. Nolan Nichols, John Pellman, William Triplett, Jessica A. Turner, Krzysztof J. Gorgolewski, Stanford University, University of Cambridge [UK] (CAM), Department of Electrical and Computer Engineering [Albuquerque] (ECE Department), The University of New Mexico [Albuquerque], The Mind Research Network, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, McGill University = Université McGill [Montréal, Canada], Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Psychological and Brain Sciences, Dartmouth College [Hanover], National Institute of Mental Health (NIMH), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UC Irvine), University of California (UC), Shandong University, Squishymedia [Portland], Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council (BBSRC), Department of Statistics [Warwick], Child Mind Institute, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), University of Washington [Seattle], Georgia State University, University System of Georgia (USG), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Maumet, Camille, University of Oxford [Oxford], Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UCI), University of California, Biotechnology and Biological Sciences Research Council, University of California [Berkeley], University of California-University of California, Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Statistics and Probability, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Datasets as Topic, Neuroimaging, Library and Information Sciences, computer.software_genre, Data publication and archiving, Field (computer science), Article, Education, 03 medical and health sciences, 0302 clinical medicine, Software, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data processing, business.industry, Data Collection, Neurosciences, Data structure, File format, Magnetic Resonance Imaging, R1, Brain Disorders, Research data, Computer Science Applications, Metadata, 030104 developmental biology, Neurological, Biomedical Imaging, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Statistics, Probability and Uncertainty, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract: The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Published: 2018
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45. Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex

Author: Daniel R. Glen, Daniel A. Handwerker, Marlene Bönstrup, Jozien Goense, Emily S. Finn, Benedikt A. Poser, Laurentius Huber, Sean Marrett, Sriranga Kashyap, Dimo Ivanov, Peter A. Bandettini, Natalia Petridou, MRI, and RS: FPN CN 5
Subjects: Adult, Computer science, Cognitive Neuroscience, SOMATOTOPY, ORGANIZATION, Motor Activity, HAND, 050105 experimental psychology, lcsh:RC321-571, Fingers, 03 medical and health sciences, 0302 clinical medicine, Cortical columns, Neuroimaging, AREAS, Motor system, medicine, Humans, 0501 psychology and cognitive sciences, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, 030304 developmental biology, Brain Mapping, 0303 health sciences, MOVEMENTS, Cortical layers, 05 social sciences, fMRI, VASO, Human brain, HUMAN BRAIN, Magnetic Resonance Imaging, Central sulcus, Cortex (botany), OUTPUT, Electrophysiology, medicine.anatomical_structure, Neurology, Motor cortex, PRIMATE, Primary motor cortex, BOLD RESPONSES, Neuroscience, 030217 neurology & neurosurgery
Abstract: 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. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. 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 non-invasive 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.
Published: 2018
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46. Effects of Thoracic Pressure Changes on MRI Signals in the Brain

Author: Paula Wu, Daniel A. Handwerker, Peter A. Bandettini, and Ronald M. Harper
Subjects: Adult, Male, medicine.medical_specialty, Valsalva Maneuver, medicine.medical_treatment, cerebral blood flow, Clinical Sciences, Cardiorespiratory Medicine and Haematology, Signal, Breath Holding, Hypercapnia, breath hold, Valsalva, Young Adult, Clinical Research, Heart Rate, Internal medicine, Heart rate, Pressure, medicine, Valsalva maneuver, Humans, Neurology & Neurosurgery, medicine.diagnostic_test, Inhalation, business.industry, Respiration, Neurosciences, Brain, Exhalation, hypercapnia, Magnetic resonance imaging, Carbon Dioxide, Magnetic Resonance Imaging, Neurology, Cerebral blood flow, Cerebrovascular Circulation, Anesthesia, Cardiology, Biomedical Imaging, functional MRI, Original Article, Neurology (clinical), medicine.symptom, Cardiology and Cardiovascular Medicine, business
Abstract: Cerebrovascular stressors, such as breath holding or CO2 inhalation, cause global magnetic resonance imaging (MRI) signal changes. In this study, we show that intrathoracic pressure changes cause rapid MRI signal alterations that have similar spatial patterns to the changes associated with breath holding or CO2 inhalation. Nine subjects performed the Valsalva maneuver during functional MRI data collection. Expiratory pressures ranged from 10 to 40 mm Hg. Breath holds ending on either inhalation or exhalation were also collected. The maximal and minimal functional MRI (fMRI) signal scaled with thoracic pressure load, and the overall amplitude of responses to the Valsalva varied, depending on brain tissue. Additionally, a Valsalva effort as short as 5 seconds yielded signal changes similar in spatial distribution and magnitude to a 20-second breath hold, suggesting potential applications of the Valsalva maneuver for calibrated fMRI experiments.
Published: 2015
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47. High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1

Author: Javier Gonzalez-Castillo, Sean Marrett, Benedikt A. Poser, Gang Chen, Andrew Hall, Peter A. Bandettini, Maria Guidi, Jozien Goense, Laurentius Huber, David C. Jangraw, Daniel A. Handwerker, Carsten Stüber, Dimo Ivanov, MRI, and RS: FPN CN 5
Subjects: 0301 basic medicine, genetic structures, Computer science, Rest, Efferent, High resolution, HUMAN-BRAIN, Somatosensory system, behavioral disciplines and activities, Article, ACTIVATION, 03 medical and health sciences, 0302 clinical medicine, Afferent, Image Processing, Computer-Assisted, medicine, Humans, VISUAL-CORTEX, MOTOR CORTEX, Brain Mapping, General Neuroscience, 7 T, Neurosciences, Brain, Laminar flow, Human brain, FUNCTIONAL MRI, Magnetic Resonance Imaging, SIGNAL, Oxygen, 030104 developmental biology, medicine.anatomical_structure, Visual cortex, nervous system, Cerebrovascular Circulation, SPIN-ECHO, Neuroscience, 030217 neurology & neurosurgery, psychological phenomena and processes, Motor cortex, BOLD, RESPONSES
Abstract: Layer-dependent fMRI allows measurements of information flow in cortical circuits, as afferent and efferent connections terminate in different cortical layers. However, it is unknown to what level human fMRI is specific and sensitive enough to reveal directional functional activity across layers. To answer this question, we developed acquisition and analysis methods for blood-oxygen-level-dependent (BOLD) and cerebral-blood-volume (CBV)-based laminar fMRI and used these to discriminate four different tasks in the human motor cortex (M1). In agreement with anatomical data from animal studies, we found evidence for somatosensory and premotor input in superficial layers of M1 and for cortico-spinal motor output in deep layers. Laminar resting-state fMRI showed directional functional connectivity of M1 with somatosensory and premotor areas. Our findings demonstrate that CBV-fMRI can be used to investigate cortical activity in humans with unprecedented detail, allowing investigations of information flow between brain regions and outperforming conventional BOLD results that are often buried under vascular biases.
Published: 2017
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48. Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications

Author: Dimo Ivanov, Maria Guidi, Sean Marrett, Benedikt A. Poser, Daniel A. Handwerker, Kâmil Uludağ, Peter A. Bandettini, Laurentius Huber, MRI, RS: FPN CN 5, and RS: FPN MaCSBio
Subjects: Male, 7 TESLA, Computer science, Blood volume, Brain mapping, 030218 nuclear medicine & medical imaging, Vascular space occupancy, 0302 clinical medicine, NEGATIVE BOLD RESPONSES, TO-NOISE RATIO, LAMINAR DIFFERENCES, Image Processing, Computer-Assisted, Contrast (vision), 3D-EPI, Image resolution, media_common, Brain Mapping, Blood Volume, medicine.diagnostic_test, SINGLE-SHOT, GRADIENT-ECHO FMRI, Brain, Simultaneous multi-slice, Middle Aged, FUNCTIONAL MRI, Magnetic Resonance Imaging, Neurology, Female, SPIN-ECHO, Algorithms, Adult, Cognitive Neuroscience, media_common.quotation_subject, Article, 03 medical and health sciences, VASCULAR-SPACE-OCCUPANCY, medicine, Humans, WHOLE-BRAIN, Cortical surface, Sensitivity (control systems), SS-SI VASO, business.industry, Cerebral blood volume, Magnetic resonance imaging, Pattern recognition, Layer-dependent fMRI, Weighting, Temporal resolution, 7 T MRI, Spin echo, Millimeter, Artificial intelligence, Nuclear medicine, business, 030217 neurology & neurosurgery
Abstract: Quantitative cerebral blood volume (CBV) fMRI has the potential to overcome several specific limitations of BOLD fMRI. It provides direct physiological interpretability and promises superior localization specificity in applications of sub-millimeter resolution fMRI applications at ultra-high magnetic fields (7 T and higher). Non-invasive CBV fMRI using VASO (vascular space occupancy), however, is inherently limited with respect to its data acquisition efficiency, restricting its imaging coverage and achievable spatial and temporal resolution. This limitation may be reduced with recent advanced acceleration and reconstruction strategies that allow two-dimensional acceleration, such as in simultaneous multi-slice (SMS) 2D-EPI or 3D-segmented-EPI in combination with CAIPIRINHA field-of-view shifting. In this study, we sought to determine the functional sensitivity and specificity of these readout strategies with VASO over a broad range of spatial resolutions; spanning from low spatial resolution (3 mm) whole-cortex to sub-millimeter (0.75 mm) slab of cortex (for cortical layer-dependent applications). In the thermal-noise-dominated regime of sub-millimeter resolutions, 3D-segmented-EPI-VASO provides higher temporal stability and sensitivity to detect changes in CBV compared to 2D-EPI-VASO. In this regime, 3D-segmented-EPI-VASO unveils task activation located in the cortical laminae with little contamination from surface veins, in contrast to the cortical surface weighting of GE-BOLD fMRI. In the physiological-noise-dominated regime of lower resolutions, however, 2D-SMS-VASO shows superior performance compared to 3D-segmented-EPI-VASO. Due to its superior sensitivity at a layer-dependent level, 3D-segmented-EPI VASO promises to play an important role in future neuroscientific applications of layer-dependent fMRI.
Published: 2016

49. Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI

Author: Valentinos Zachariou, Laura C. Buchanan, Javier Gonzalez-Castillo, Puja Panwar, Vinai Roopchansingh, Daniel A. Handwerker, David C. Jangraw, John Andrew Derbyshire, César Caballero-Gaudes, Souheil Inati, and Peter A. Bandettini
Subjects: Adult, Male, Computer science, Cardiac-Gated Imaging Techniques, COGNITIVE NEUROSCIENCE, Signal-To-Noise Ratio, computer.software_genre, Sensitivity and Specificity, Article, 030218 nuclear medicine & medical imaging, Task (project management), 03 medical and health sciences, 0302 clinical medicine, Sensitivity, Image Interpretation, Computer-Assisted, Task Performance and Analysis, Humans, Sensitivity (control systems), Set (psychology), Multi-echo fMRI, NEUROLOGY, Block (data storage), Brain Mapping, Principal Component Analysis, Block design, Resting state fMRI, business.industry, ME-ICA, Brain, Reproducibility of Results, Pattern recognition, Rapid event related, Image Enhancement, Independent component analysis, Magnetic Resonance Imaging, Repetition Time, Female, Data mining, Artificial intelligence, business, Artifacts, computer, 030217 neurology & neurosurgery, Algorithms
Abstract: Epub ahead of print 27/07/2016 Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2⁎ weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data. This research was possible thanks to the support of the National Institute of Mental Health Intramural Research Program. Portions of this study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD (biowulf.nih.gov). This study is part of NIH clinical protocol number NCT00001360, protocol ID 93-M-0170 and annual report ZIAMH002783-14. Dr. Caballero-Gaudes was supported by the Spanish Ministry of Economy and Competitiveness, through grant PSI 2013-42343 Neuroimagen Multimodal and the Severo Ochoa Programme for Centres/Units of Excellence in R&D (SEV-2015-490).
Published: 2016

50. Long-term neural and physiological phenotyping of a single human

Author: Joanne E. Curran, Russell A. Poldrack, Laurie Frick, James M. Shine, Jeffrey J. Luci, Zack B. Simpson, Sung Jun Joo, Alexander Leemans, Brenda Gregory, Babatunde Adeyemo, Jeanette A. Mumford, Abraham Z. Snyder, Harald H H Göring, Timothy O. Laumann, Krzysztof J. Gorgolewski, Ashleigh M. Hover, Melanie A. Carless, Mei Yen Chen, Evan M. Gordon, John Blangero, Scott Patrick Hunicke-Smith, Steven E. Petersen, Edward M. Marcotte, D. Reese McKay, W Thomas Caven, Oluwasanmi Koyejo, Robert F. Dougherty, Daniel A. Handwerker, David C. Glahn, Vanessa Sochat, and Ryan L. Boyd
Subjects: Male, Chemistry(all), Non-P.H.S, Gene regulatory network, Gene Expression, General Physics and Astronomy, Biology, Physics and Astronomy(all), Research Support, Bioinformatics, Biochemistry, Article, General Biochemistry, Genetics and Molecular Biology, N.I.H, Metabolomics, Research Support, N.I.H., Extramural, Neural Pathways, medicine, Journal Article, Humans, Gene Regulatory Networks, Non-U.S. Gov't, Brain function, Multidisciplinary, medicine.diagnostic_test, Psychological function, Biochemistry, Genetics and Molecular Biology(all), Research Support, Non-U.S. Gov't, Brain, Extramural, Magnetic resonance imaging, General Chemistry, Human brain, Middle Aged, Precision medicine, Magnetic Resonance Imaging, 3. Good health, Term (time), Radiography, Phenotype, medicine.anatomical_structure, U.S. Gov't, Neuroscience, Research Support, U.S. Gov't, Non-P.H.S, Follow-Up Studies, Genetics and Molecular Biology(all)
Abstract: Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders., Large-scale, multimodal phenotypic characterisation is a valuable tool to explore brain function. Poldrack et al. collect and relate MRI, psychological, physiological, metabolic and gene expression data from a single human over an 18 month period, providing a rich resource for future studies.
Published: 2015

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