Noise contributions to the fMRI signal: An overview

The effect of acquisition duration on cerebral blood flow-based resting-state functional connectivity

Resting-state functional connectivity (rs-FC) is widely used to examine the functional architecture of the brain, and the blood-oxygenation-level-dependent (BOLD) signal is often utilized for determining rs-FC. However, the BOLD signal is susceptible to various factors that have less influence on the cerebral blood flow (CBF). Therefore, CBF could comprise an alternative for determining rs-FC. Since acquisition duration is one of the essential parameters for obtaining reliable rs-FC, we investigated the effect of acquisition duration on CBF-based rs-FC to examine the reliability of CBF-based rs-FC. Nineteen participants underwent CBF scanning for a total duration of 50 min. Variance of CBF-based rs-FC within the whole brain and 13 large-scale brain networks at various acquisition durations was compared to that with a 50-min duration using the Levene's test. Variance of CBF-based rs-FC at any durations did not differ from that at a 50-min duration (p > .05). Regarding variance of rs-FC within each large-scale brain network, the acquisition duration required to obtain reliable estimates of CBF-based rs-FC was shorter than 10 min and varied across large-scale brain networks. Altogether, an acquisition duration of at least 10 min is required to obtain reliable CBF-based rs-FC. These results indicate that CBF-based resting-state functional magnetic resonance imaging (rs-fMRI) with more than 10 min of total acquisition duration could be an alternative method to BOLD-based rs-fMRI to obtain reliable rs-FC.

A comprehensive investigation of physiologic noise modeling in resting state fMRI; time shifted cardiac noise in EPI and its removal without external physiologic signal measures

Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.

A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Covariance and Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data Acquired in a Clinical Trial of Mindfulness-Based Stress Reduction and Exercise in Older Individuals

We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Correlated functional connectivity and glucose metabolism in brain white matter revealed by simultaneous MRI/positron emission tomography

PURPOSE

There has been converging evidence of reliable detections of blood oxygenation level dependent (BOLD) signals evoked by neural stimulation and in a resting state in white matter (WM), within which few studies examined the relationship between BOLD functional signals and tissue metabolism. The purpose of the present study was to explore whether such relationship exists using combined functional MRI and positron emission tomography (PET) measurements of glucose uptake.

METHODS

Functional and metabolic imaging data from 25 right-handed healthy human adults (aged 18-23 years, 18 females) were analyzed. Measures, including average resting state functional connectivity (FC) with respect to 82 Brodmann areas, fractional amplitude of low-frequency fluctuations (FALFF), and average fluorodeoxyglucose (FDG) uptake by PET, were computed for 48 predefined WM bundles. Pearson correlations across the bundles and 25 subjects studied were calculated among these measures. Linear mixed effects models were used to estimate the variance explainable by a predictor variable in the absence of inter-subject variations.

RESULTS

Analysis of six separate imaging intervals found that average FC the bundles was significantly correlated with local FDG uptake (r = 0.25, p < 0.001), and the FC also covaried significantly with FALFF (r = 0.41, p < 0.001). When random effects from inter-subject variations were controlled, these correlations appeared to be medium to strong (r = 0.41 for FC vs. FDG uptake, and r = 0.65 for FALFF vs. FC).

CONCLUSION

This study indicates that BOLD signals in WM are directly related to variations in metabolic demand and engagement with cortical processing and suggests they should be incorporated into more complete models of brain function.

Correlated noise in brain magnetic resonance elastography

PURPOSE

Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined.

METHODS

To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations.

RESULTS

We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise.

CONCLUSION

Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity

The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability

A notorious issue of task-based functional magnetic resonance imaging (fMRI) is its large cross-trial variability. To quantitatively characterize this variability, the blood oxygenation level-dependent (BOLD) signal can be modeled as a linear summation of a stimulation-relevant and an ongoing (i.e. stimulation-irrelevant) component. However, systematic investigation on the spatiotemporal features of the ongoing BOLD component and how these features affect the BOLD response is still lacking. Here we measured fMRI responses to light onsets and light offsets in awake rats. The neuronal response was simultaneously recorded with calcium-based fiber photometry. We established that between-region BOLD signals were highly correlated brain-wide at zero time lag, including regions that did not respond to visual stimulation, suggesting that the ongoing activity co-fluctuates across the brain. Removing this ongoing activity reduced cross-trial variability of the BOLD response by ~30% and increased its coherence with the Ca2+ signal. Additionally, the negative ongoing BOLD activity sometimes dominated over the stimulation-driven response and contributed to the post-stimulation BOLD undershoot. These results suggest that brain-wide ongoing activity is responsible for significant cross-trial BOLD variability, and this component can be reliably quantified and removed to improve the reliability of fMRI response. Importantly, this method can be generalized to virtually all fMRI experiments without changing stimulation paradigms.

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

Recordings from resting state functional Magnetic Resonance Imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of “ground truth” has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. When we applied dCov to rs-fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose dCov-FCs were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration significantly correlated with behavior.

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

Intrinsic dynamics and topography of sensory input systems

The brain is continuously bombarded by external stimuli, which are processed in different input systems. The intrinsic features of these sensory input systems remain yet unclear. Investigating topography and dynamics of input systems is the goal of our study in order to better understand the intrinsic features that shape their neural processing. Using a functional magnetic resonance imaging dataset, we measured neural topography and dynamics of the input systems during rest and task states. Neural dynamics were probed by scale-free activity, measured with the power-law exponent (PLE), as well as by order/disorder as measured with sample entropy (SampEn). Our main findings during both rest and task states are: 1) differences in neural dynamics (PLE, SampEn) between regions within each of the three sensory input systems 2) differences in topography and dynamics among the three input systems; 3) PLE and SampEn correlate and, as demonstrated in simulation, show non-linear relationship in the critical range of PLE; 4) scale-free activity during rest mediates the transition of SampEn from rest to task as probed in a mediation model. We conclude that the sensory input systems are characterized by their intrinsic topographic and dynamic organization which, through scale-free activity, modulates their input processing.

Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal

BACKGROUND

Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala).

NEW METHOD

We explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region.

RESULTS

SDR allowed detection of FC with small brain regions.

COMPARISON WITH EXISTING METHOD

For both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA.

CONCLUSION

The improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.

Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review

For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.

Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of 'weak classifiers' (i.e., single voxels) in higher dimensions. We propose instead that 'real' multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.

NeuroGen: Activation optimized image synthesis for discovery neuroscience

Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network’s ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system.

Even a little sleepiness influences neural activation and clinical reasoning in novices

BACKGROUND AND AIMS

Sleepiness influences alertness and cognitive functioning and impacts many aspects of medical care, including clinical reasoning. However, dual processing theory suggests that sleepiness will impact clinical reasoning differently in different individual, depending on their level of experience with the given condition. Our aim, therefore, was to examine the association between clinical reasoning, neuroanatomical activation, and sleepiness in senior medical students.

METHODS

Our methodology replicated an earlier study but with novices rather than board-certified physicians. Eighteen final-year medical students answered validated multiple-choice questions (MCQs) during an fMRI scan. Each MCQ was projected in three phases: reading, answering, and reflection (modified think aloud). Echo-planar imaging (EPI) scans gave a time series that reflected blood oxygenation level dependent (BOLD) signal in each location (voxel) within the brain. Sleep data were collected via self-report (Epworth Sleepiness Scale) and actigraphy. These data were correlated with answer accuracy using Pearson correlation.

RESULTS

Analysis revealed an increased BOLD signal in the right dorsomedial prefrontal cortex (P < .05) during reflection (Phase 3) associated with increased self-reported sleepiness (ESS) immediately before scanning. Covariate analysis also revealed that increased BOLD signal in the right supramarginal gyrus (P < .05) when reflecting (Phase 3) was associated with increased correct answer response time. Both patterns indicate effortful analytic (System 2) reasoning.

CONCLUSION

Our findings that novices use System 2 thinking for clinical reasoning and even a little (perceived) sleepiness influences their clinical reasoning ability to suggest that the parameters for safe working may be different for novices (eg, junior doctors) and experienced physicians.

Recommendations of Choice of Head Coil and Prescan Normalize Filter Depend on Region of Interest and Task

The performance of MRI head coils together with the influence of the prescan normalize filter in different brain regions was evaluated. Functional and structural data were recorded from 26 participants performing motor, auditory, and visual tasks in different conditions: with the 20- and 64-channel Siemens head/neck coil and the prescan normalize filter turned ON or OFF. Data were analyzed with the MRIQC tool to evaluate data quality differences. The functional data were statistically evaluated by comparison of the β estimates and the time-course signal-to-noise ratio (tSNR) in four regions of interest, i.e., the auditory, visual, and motor cortices and the thalamus. The MRIQC tool indicated a better data quality for both functional and structural data with the prescan normalize filter, with an advantage for the 20-channel head coil in functional data and an advantage for the 64-channel head coil in structural measurements. Nevertheless, recommendations for the functional data regarding choice of head coils and prescan normalize filter depend on the brain regions of interest. Higher β estimates and tSNR values occurred in the auditory cortex and thalamus with the prescan normalize filter, whereas the contrary was true for the visual and motor cortices. Due to higher β estimates in the visual cortex in the 64-channel head coil, this head coil is recommended for studies investigating the visual cortex. For most of the research questions, the 20-channel head coil is better suited for functional experiments, with the prescan normalize filter, especially when investigating deep brain areas. For anatomical studies, the 64-channel head coil seemed to be the better choice.

Comparing Autoregressive and Network Features for Classification of Depression and Anxiety

Autocorrelation in functional MRI (fMRI) time series has been studied for decades, mostly considered as noise in the time series which is removed via prewhitening with an autoregressive model. Recent results suggest that the coefficients of an autoregressive model t to fMRI data may provide an indicator of underlying brain activity, suggesting that prewhitening could be removing important diagnostic information. This paper explores the explanatory value of these autoregressive features extracted from fMRI by considering the use of these features in a classification task. As a point of comparison, functional network based features are extracted from the same data and used in the same classification task. We find that in most cases, network based features provide better classification accuracy. However, using principal component analysis to combine network based features and autoregressive features for classification based on a support vector machine provides improved classification accuracy compared to single features or network features, suggesting that when properly combined there may be additional information to be gained from autoregressive features.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.

Correcting for Superficial Bias in 7T Gradient Echo fMRI

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.

Striving toward translation: strategies for reliable fMRI measurement

fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting-state fMRI

Individualized treatment of acute stroke depends on the timely detection of ischemia and potentially salvageable tissue in the brain. Using functional MRI (fMRI), it is possible to characterize cerebral blood flow from blood‐oxygen‐level‐dependent (BOLD) signals without the administration of exogenous contrast agents. In this study, we applied spatial independent component analysis to resting‐state fMRI data of 37 stroke patients scanned within 24 hr of symptom onset, 17 of whom received follow‐up scans the next day. Our analysis revealed “Hypoperfusion spatially‐Independent Components” (HICs) whose spatial patterns of BOLD signal resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user‐independent manner (area under the curve = 0.93, balanced accuracy = 0.90, sensitivity = 1.00, and specificity = 0.85). Our study therefore presents a new, noninvasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion.

A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO₂ pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO₂ pressure: a breath-hold task to induce hypercapnia (CO₂ increase) and a cued deep breathing task to induce hypocapnia (CO₂ decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO₂ by systematically shifting the CO₂ regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO₂ and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.

Correcting physiological noise in whole-head functional near-infrared spectroscopy

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage.

NEW METHOD

We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated.

RESULTS

After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas.

COMPARISON WITH EXISTING METHODS

Analysis showed that our method improves upon the four established preprocessing methods in the literature.

CONCLUSIONS

An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.

Altered Relationship Between Heart Rate Variability and fMRI-Based Functional Connectivity in People With Epilepsy

Background: Disruptions in central autonomic processes in people with epilepsy have been studied through evaluation of heart rate variability (HRV). Decreased HRV appears in epilepsy compared to healthy controls, suggesting a shift in autonomic balance toward sympathetic dominance; recent studies have associated HRV changes with seizure severity and outcome of interventions. However, the processes underlying these autonomic changes remain unclear. We examined the nature of these changes by assessing alterations in whole-brain functional connectivity, and relating those alterations to HRV. Methods: We examined regional brain activity and functional organization in 28 drug-resistant epilepsy patients and 16 healthy controls using resting-state functional magnetic resonance imaging (fMRI). We employed an HRV state-dependent functional connectivity (FC) framework with low and high HRV states derived from the following four cardiac-related variables: 1. RR interval, 2. root mean square of successive differences (RMSSD), 4. low-frequency HRV (0.04-0.15 Hz; LF-HRV) and high-frequency HRV (0.15-0.40 Hz; HF-HRV). The effect of group (epilepsy vs. controls), HRV state (low vs. high) and the interactions of group and state were assessed using a mixed analysis of variance (ANOVA). We assessed FC within and between 7 large-scale functional networks consisting of cortical regions and 4 subcortical networks, the amygdala, hippocampus, basal ganglia and thalamus networks. Results: Consistent with previous studies, decreased RR interval (increased heart rate) and decreased HF-HRV appeared in people with epilepsy compared to healthy controls. For both groups, fluctuations in heart rate were positively correlated with BOLD activity in bilateral thalamus and regions of the cerebellum, and negatively correlated with BOLD activity in the insula, putamen, superior temporal gyrus and inferior frontal gyrus. Connectivity strength in patients between right thalamus and ventral attention network (mainly insula) increased in the high LF-HRV state compared to low LF-HRV; the opposite trend appeared in healthy controls. A similar pattern emerged for connectivity between the thalamus and basal ganglia. Conclusion: The findings suggest that resting connectivity patterns between the thalamus and other structures underlying HRV expression are modified in people with drug-resistant epilepsy compared to healthy controls.

Feature selection framework for functional connectome fingerprinting

The ability to uniquely characterize individual subjects based on their functional connectome (FC) is a key requirement for progress toward precision psychiatry. FC fingerprinting is increasingly studied in the neuroimaging community for this purpose, where a variety of approaches have been developed for effective FC fingerprinting. Recent independent studies showed that fingerprinting accuracy suffers at large sample sizes and when coarser parcellations are used for computing the FC. Quantifying this problem and understanding the reasons these factors impact fingerprinting accuracy is crucial to develop more accurate fingerprinting methods for large sample sizes. Part of the challenge in fingerprinting is that FC captures both generic and subject‐specific information. A systematic approach for identifying subject‐specific FC information is crucial for making progress in addressing the fingerprinting problem. In this study, we addressed three gaps in our understanding of the FC fingerprinting problem. First, we studied the joint effect of sample size and parcellation granularity. Second, we explained the reason for reduced fingerprinting accuracy with increased sample size and reduced parcellation granularity. To this end, we used a clustering quality metric from the data mining community. Third, we developed a general feature selection framework for systematically identifying resting‐state functional connectivity (RSFC) elements that capture information to uniquely identify subjects. In sum, we evaluated six different approaches from this framework by quantifying both subject‐specific fingerprinting accuracy and the decrease in accuracy with an increase in sample size to identify which approach improved quality metrics the most.

fMRI-based detection of alertness predicts behavioral response variability

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.

Resample aggregating improves the generalizability of connectome predictive modeling

It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.

Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest

Attention-Deficit/Hyperactivity Disorder (ADHD) comprises disturbances in attention, emotional regulation, and reward-related processes. In spite of the active efforts in researching neurofunctional correlates of these symptoms, how the activity of subcortical regions-such as basal ganglia-is related to ADHD has yet to be clarified. More specifically, how age may influence the critical changes observed in functional dynamics from childhood to adulthood remains relatively unexplored. We hence selected five core subcortical regions (amygdala, caudate, putamen, pallidum and hippocampus) as regions of interest from the previous literature, measuring their whole-brain voxel-wise rsFC in a sample of 95 ADHD and 90 neurotypical children and adolescents aged from 7 to 18. The only subcortical structure showing significant differences in rsFC was the caudate nucleus. Specifically, we measured increased rsFC with anterior cingulate and right insula, two mesolimbic regions pertaining to the Salience Network. The degree of hyper-rsFC positively correlated with ADHD symptomatology, and showed different patterns of evolution in ADHD vs neurotypical subjects. Finally, the rsFC scores allowed a fair discrimination of the ADHD group (Area Under the Curve ≥ 0.7). These findings shed further light on the fundamental role covered by subcortical structures in ADHD pathogenesis and neurodevelopment, providing new evidence to fill the gap between neurofunctional and clinical expressions of ADHD.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Reliability modelling of resting-state functional connectivity

Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.

An MRI protocol for anatomical and functional evaluation of the California sea lion brain

BACKGROUND

Domoic acid (DOM) is a neurotoxin produced by some harmful algae blooms in coastal waters. California sea lions (Zalophus californianus) exposed to DOM often strand on beaches where they exhibit a variety of symptoms, including seizures. These animals typically show hippocampal atrophy on MRI scans.

NEW METHOD

We describe an MRI protocol for comprehensive evaluation of DOM toxicosis in the sea lion brain. We intend to study brain development in pups exposed in utero. The protocol depicts the hippocampal formation as the primary region of interest. We include scans for quantitative morphometry, functional and structural connectivity, and a cerebral blood flow map.

RESULTS

High-resolution 3D anatomical scans facilitate post hoc slicing in arbitrary planes and accurate morphometry. We demonstrate the first cerebral blood flow map using MRI, and the first structural tractography from a live sea lion brain.

COMPARISON WITH EXISTING METHODS

Scans were compared to prior anatomical and functional studies in live sea lions, and structural connectivity in post mortem specimens. Hippocampal volumes were broadly in line with prior studies, with differences likely attributable to the 3D approach used here. Functional connectivity of the dorsal left hippocampus matched that found in a prior study conducted at a lower magnetic field, while structural connectivity in the live brain agreed with findings observed in post mortem studies.

CONCLUSIONS

Our protocol provides a comprehensive, longitudinal view of the functional and anatomical changes expected to result from DOM toxicosis. It can also screen for other common neurological pathologies and is suitable for any pinniped that can fit inside an MRI scanner.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.

FMRI based on transition-band balanced SSFP in comparison with EPI on a high-performance 0.55 T scanner

PURPOSE

Low-field (<1 tesla) MRI scanners allow more widespread diagnostic use for a range of cardiac, musculoskeletal, and neurological applications. However, the feasibility of performing robust fMRI at low field has yet to be fully demonstrated. To address this gap, we investigated task-based fMRI using a highly sensitive transition-band balanced steady-state free precession approach and standard EPI on a 0.55 tesla scanner equipped with modern high-performance gradient coils and a receive array.

METHODS

TR and flip-angle of transition-band steady-state free precession were optimized for 0.55 tesla by simulations. Static shimming was employed to compensate for concomitant field effects. Visual task-based fMRI data were acquired from 8 healthy volunteers. For comparison, standard EPI data were also acquired with TE = T 2 ∗ . Retrospective image-based correction for physiological effects (RETROICOR) was used to quantify physiological noise effects.

RESULTS

Activation was robustly detected using both methods in a 4-min scan time. Transition-band steady-state free precession was found to be sensitive to interference from subtle spatial and temporal (field drift, respiration) variations in the magnetic field, counteracting potential advantages of the reduced magnetic susceptibility effects compared to its utilization at high field. These adverse effects could be partially remedied with static shimming and postprocessing approaches. Standard EPI proved more robust against the sources of interference.

CONCLUSION

BOLD contrast is sufficiently large at 0.55 tesla for robust detection of brain activation and may be employed to broaden the spectrum of applications of low-field MRI. Standard EPI outperforms transition-band steady-state free precession in terms of signal stability.

The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer's disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

Ground-truth "resting-state" signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T₂* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four "best-case" scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6-18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8-19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40-70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T₂* decay, and S₀-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations.

Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.

Revisiting correlation-based functional connectivity and its relationship with structural connectivity

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations or their unnormalized version encoded in the precision matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture ‘direct’ statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.

Task-related activity in human visual cortex

The brain exhibits widespread endogenous responses in the absence of visual stimuli, even at the earliest stages of visual cortical processing. Such responses have been studied in monkeys using optical imaging with a limited field of view over visual cortex. Here, we used functional MRI (fMRI) in human participants to study the link between arousal and endogenous responses in visual cortex. The response that we observed was tightly entrained to task timing, was spatially extensive, and was independent of visual stimulation. We found that this response follows dynamics similar to that of pupil size and heart rate, suggesting that task-related activity is related to arousal. Finally, we found that higher reward increased response amplitude while decreasing its trial-to-trial variability (i.e., the noise). Computational simulations suggest that increased temporal precision underlies both of these observations. Our findings are consistent with optical imaging studies in monkeys and support the notion that arousal increases precision of neural activity.

The brain exhibits widespread endogenous responses in the absence of visual stimuli, even at the earliest stages of visual cortical processing. This fMRI study characterizes a widespread hemodynamic response in early visual cortex that is not related to visual input but instead reflects a participant’s engagement in a task, is modulated by expected monetary reward, and may reflect neural quenching.

Neurovascular coupling and bilateral connectivity during NREM and REM sleep

To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.

Alterations of Functional Connectivity in Stroke Patients With Basal Ganglia Damage and Cognitive Impairment

Background: Stroke with basal ganglia damage (SBG) is a neurological disorder characterized by cognitive impairment. The neurobiological mechanism of cognitive impairment in stroke patients with basal ganglia damage (SBG patients) remains unclear. This study aimed to explore the underlying neurobiological mechanism of cognitive impairment in SBG patients using resting-state functional magnetic resonance imaging (rs-fMRI).

Methods: The differences in functional connectivity (FC) between 14 SBG patients (average age: 61.00 ± 7.45 years) and 21 healthy controls (HC) (average age: 60.67 ± 6.95 years) were examined using voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC). Moreover, we compared the cognitive functions of SBG patients with HC using the Chinese Revised Wechsler Adult Intelligence Scale (WAIS-RC) and Wechsler Memory Scale (WMS).

Results: Full-scale intelligence quotient (FIQ) (t = 2.810, p < 0.010) and memory quotient (MQ) (t = 2.920, p < 0.010) scores of SBG patients were significantly lower than those of HC. Compared with HC, significantly decreased VMHC values in the bilateral angular gyrus, supramarginal gyrus, inferior frontal gyrus, middle temporal gyrus, hippocampus, precuneus, precentral gyrus, and middle occipital gyrus and decreased DC values in the right supramarginal gyrus, bilateral angular gyrus, and right postcentral gyrus were observed in SBG patients. Moreover, the VMHC values in the angular gyrus, inferior frontal gyrus, supramarginal gyrus, and middle temporal gyrus and the DC values in the right supramarginal gyrus were significantly correlated with cognitive functions in all participants.

Conclusion: Our findings may provide a neural basis for cognitive impairments in SBG patients. Furthermore, local abnormalities of functional networks and interhemispheric interaction deficits may provide new ideas and insights for understanding and treating SBG patients' cognitive impairments.