Potential pitfalls when denoising resting state fMRI data using nuisance regression

Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI

Introduction

Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.

Methods

In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets.

Results

Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively.

Discussion

This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

Spontaneous cerebrovascular reactivity at rest in older adults with and without mild cognitive impairment and memory deficits

Background

Older adults with mild cognitive impairment (MCI) exhibit deficits in cerebrovascular reactivity (CVR), suggesting CVR is a biomarker for vascular contributions to MCI. This study examined if spontaneous CVR is associated with MCI and memory impairment.

Methods

161 older adults free of dementia or major neurological/psychiatric disorders were recruited. Participants underwent clinical interviews, cognitive testing, venipuncture for Alzheimer's biomarkers, and brain MRI. Spontaneous CVR was quantified during 5 minutes of rest.

Results

Whole brain CVR was negatively associated with age, but not MCI. Lower CVR in the parahippocampal gyrus (PHG) was found in participants with MCI and was linked to worse memory performance on memory tests. Results remained significant after adjusting for Alzheimer's biomarkers and vascular risk factors.

Conclusion

Spontaneous CVR deficits in the PHG are observed in older adults with MCI and memory impairment, indicating medial temporal microvascular dysfunction's role in cognitive decline.

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling

Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A β ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A β biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A β detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A β status, and offer key insights into dynamic functional connectivity analysis in AD.

Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data

Resting-state fMRI (rs-fMRI) data is used to study the intrinsic functional connectivity (FC) in the human brain. In the past decade, interest has focused on studying the temporal dynamics of FC on short timescales, ranging from seconds to minutes. These studies of time-varying FC (TVFC) have enabled the classification of whole-brain dynamic FC profiles into distinct "brain states", defined as recurring whole-brain connectivity profiles reliably observed across subjects and sessions. The analysis of rs-fMRI data is complicated by the fact that the measured BOLD signal consists of changes induced by neuronal activation, as well as non-neuronal nuisance fluctuations that should be removed prior to further analysis. Thus, the data undergoes significant preprocesing prior to analysis. In previous work [24], we illustrated the potential pitfalls involved with using modular preprocessing pipelines, showing how later preprocessing steps can reintroduce correlation with signal previously removed from the data. Here we show that the problem runs deeper, and that certain statistical analysis techniques can potentially interact with preprocessing and reintroduce correlations with previously removed signal. One such technique is the popular sliding window analysis, used to compute TVFC. In this paper, we discuss the problem both theoretically and empirically in application to test-retest rs-fMRI data. Importantly, we show that we are able to obtain essentially the same brain states and state transitions when analyzing motion induced signal as we do when analyzing the preprocessed but windowed data. Our results cast doubt on whether the estimated brain states obtained using sliding window analysis are neuronal in nature, or simply reflect non-neuronal nuisance signal variation (e.g., motion).

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study

Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.

Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study

INTRODUCTION

Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes.

METHODS

We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults.

RESULTS

Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes.

CONCLUSIONS

Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.

Plasma Neurofilament Light Relates to Divergent Default and Salience Network Connectivity in Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Background

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) show differential vulnerability to large-scale brain functional networks. Plasma neurofilament light (NfL), a promising biomarker of neurodegeneration, has been linked in AD patients to glucose metabolism changes in AD-related regions. However, it is unknown whether plasma NfL would be similarly associated with disease-specific functional connectivity changes in AD and bvFTD.

Objective

Our study examined the associations between plasma NfL and functional connectivity of the default mode and salience networks in patients with AD and bvFTD.

Methods

Plasma NfL and neuroimaging data from patients with bvFTD (n = 16) and AD or mild cognitive impairment (n = 38; AD + MCI) were analyzed. Seed-based functional connectivity maps of key regions within the default mode and salience networks were obtained and associated with plasma NfL in these patients.

RESULTS

We demonstrated divergent associations between NfL and functional connectivity in AD + MCI and bvFTD patients. Specifically, AD + MCI patients showed lower default mode network functional connectivity with higher plasma NfL, while bvFTD patients showed lower salience network functional connectivity with higher plasma NfL. Further, lower NfL-related default mode network connectivity in AD + MCI patients was associated with lower Montreal Cognitive Assessment scores and higher Clinical Dementia Rating sum-of-boxes scores, although NfL-related salience network connectivity in bvFTD patients was not associated with Neuropsychiatric Inventory Questionnaire scores.

CONCLUSIONS

Our findings indicate that plasma NfL is differentially associated with brain functional connectivity changes in AD and bvFTD.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.

Hemodynamic timing in resting-state and breathing-task BOLD fMRI

The blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology.

Characterising stationary and dynamic effective connectivity changes in the motor network during and after tDCS

The exact mechanisms behind the effects of transcranial direct current stimulation (tDCS) at a network level are still poorly understood, with most studies to date focusing on local (cortical) effects and changes in motor-evoked potentials or BOLD signal. Here, we explored stationary and dynamic effective connectivity across the motor network at rest in two experiments where we applied tDCS over the primary motor cortex (M1-tDCS) or the cerebellum (cb-tDCS) respectively. Two cohorts of healthy volunteers (n = 21 and n = 22) received anodal, cathodal, and sham tDCS sessions (counterbalanced) during 20 min of resting-state functional magnetic resonance imaging (fMRI). We used spectral Dynamic Causal Modelling (DCM) and hierarchical Parametrical Empirical Bayes (PEB) to analyze data after (compared to a pre-tDCS baseline) and during stimulation. We also implemented a novel dynamic (sliding windows) DCM/PEB approach to model the nature of network reorganisation across time. In both experiments we found widespread effects of tDCS that extended beyond the targeted area and modulated effective connectivity between cortex, thalamus, and cerebellum. These changes were characterised by unique nonlinear temporal fingerprints across connections and polarities. Our results support growing research challenging the classic notion of anodal and cathodal tDCS as excitatory and inhibitory respectively, as well as the idea of a cumulative effect of tDCS over time. Instead, they described a rich set of changes with specific spatial and temporal patterns. Our work provides a starting point for advancing our understanding of network-level tDCS effects and may guide future work to optimise its cognitive and clinical applications.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Can this data be saved? Techniques for high motion in resting state scans of first grade children

Motion remains a significant technical hurdle in fMRI studies of young children. Our aim was to develop a straightforward and effective method for obtaining and preprocessing resting state data from a high-motion pediatric cohort. This approach combines real-time monitoring of head motion with a preprocessing pipeline that uses volume censoring and concatenation alongside independent component analysis based denoising. We evaluated this method using a sample of 108 first grade children (age 6-8) enrolled in a longitudinal study of math development. Data quality was assessed by analyzing the correlation between participant head motion and two key metrics for resting state data, temporal signal-to-noise and functional connectivity. These correlations should be minimal in the absence of noise-related artifacts. We compared these data quality indicators using several censoring thresholds to determine the necessary degree of censoring. Volume censoring was highly effective at removing motion-corrupted volumes and ICA denoising removed much of the remaining motion artifact. With the censoring threshold set to exclude volumes that exceeded a framewise displacement of 0.3 mm, preprocessed data met rigorous standards for data quality while retaining a large majority of subjects (83 % of participants). Overall, results show it is possible to obtain usable resting-state data despite extreme motion in a group of young, untrained subjects.

Knowing what you feel: Inferior frontal gyrus-based structural and functional neural patterns underpinning adaptive body awareness

BACKGROUND

Heightened body awareness (BA) is conducive for increasing understanding of bodily state and improves individuals' health and well-being. Although there has been cumulative research concentrating on the self-perceived tendency to focus on negatively valenced interoceptive sensations, the specific structural and functional neural patterns underlying BA and their role in the relationship between BA and individual well-being remain unclear.

METHODS

Voxel-based morphometry and whole brain functional connectivity analyses were conducted to examine the structural and functional neural patterns, respectively, in 686 healthy subjects. BA and subjective well-being were assessed using questionnaires.

RESULTS

BA was inversely related to gray matter volume of the right inferior frontal gyrus, opercular part (IFGoperc). Higher BA was correlated with enhanced IFGoperc-precuneus and IFGoperc-anterior supramarginal gyrus connectivities, and with decreased IFGoperc-lateral occipital cortex and IFGoperc-medial frontal cortex connectivities. The inferior frontal gyrus, triangular part (in the fronto-parietal task control network) acted as the hub that linked the sensory/somatomotor network, the default mode network, and the dorsal and ventral attention network. The IFGoperc-precuneus connectivity moderated the association between BA and subjective well-being.

LIMITATIONS

We were unable to rank all the networks by their relative importance, because the absolute weighted value in each module was not calculated.

CONCLUSION

Our findings demonstrated that BA was reflected by specific neural patterns mainly involved in cognitive-affective control, attentional and self-referential processing, as well as multisensory integration, which could offer some references for current therapies (e.g., mindfulness, yoga training) that are dedicated to solving health problems and improving individual well-being.

Empirical evaluation of human fetal fMRI preprocessing steps

Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

Estimations of the weather effects on brain functions using functional MRI: A cautionary note

The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross‐sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting‐state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting‐state parameters, including the amplitude of low‐frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross‐validation using the resting‐state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long‐term effects using MRI.

Technical Note: Modulation of fMRI brainstem responses by transcutaneous vagus nerve stimulation

Our increasing knowledge about gut-brain interaction is revolutionising the understanding of the links between digestion, mood, health, and even decision making in our everyday lives. In support of this interaction, the vagus nerve is a crucial pathway transmitting diverse gut-derived signals to the brain to monitor of metabolic status, digestive processes, or immune control to adapt behavioural and autonomic responses. Hence, neuromodulation methods targeting the vagus nerve are currently explored as a treatment option in a number of clinical disorders, including diabetes, chronic pain, and depression. The non-invasive variant of vagus nerve stimulation (VNS), transcutaneous auricular VNS (taVNS), has been implicated in both acute and long-lasting effects by modulating afferent vagus nerve target areas in the brain. The physiology of neither of those effects is, however, well understood, and evidence for neuronal response upon taVNS in vagal afferent projection regions in the brainstem and its downstream targets remain to be established. Therefore, to examine time-dependent effects of taVNS on brainstem neuronal responses in healthy human subjects, we applied taVNS during task-free fMRI in a single-blinded crossover design. During fMRI data acquisition, we either stimulated the left earlobe (sham), or the target zone of the auricular branch of the vagus nerve in the outer ear (cymba conchae, verum) for several minutes, both followed by a short 'stimulation OFF' period. Time-dependent effects were assessed by averaging the BOLD response for consecutive 1-minute periods in an ROI-based analysis of the brainstem. We found a significant response to acute taVNS stimulation, relative to the control condition, in downstream targets of vagal afferents, including the nucleus of the solitary tract, the substantia nigra, and the subthalamic nucleus. Most of these brainstem regions remarkably showed increased activity in response to taVNS, and these effect sustained during the post-stimulation period. These data demonstrate that taVNS activates key brainstem regions, and highlight the potential of this approach to modulate vagal afferent signalling. Furthermore, we show that carry-over effects need to be considered when interpreting fMRI data in the context of general vagal neurophysiology and its modulation by taVNS.

A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18-76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects' age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

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.

Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6–12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following functional scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

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.

Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

A novel method of quantifying hemodynamic delays to improve hemodynamic response, and CVR estimates in CO2 challenge fMRI

Elevated carbon dioxide (CO2) in breathing air is widely used as a vasoactive stimulus to assess cerebrovascular functions under hypercapnia (i.e., "stress test" for the brain). Blood-oxygen-level-dependent (BOLD) is a contrast mechanism used in functional magnetic resonance imaging (fMRI). BOLD is used to study CO2-induced cerebrovascular reactivity (CVR), which is defined as the voxel-wise percentage BOLD signal change per mmHg change in the arterial partial pressure of CO2 (PaCO2). Besides the CVR, two additional important parameters reflecting the cerebrovascular functions are the arrival time of arterial CO2 at each voxel, and the waveform of the local BOLD signal. In this study, we developed a novel analytical method to accurately calculate the arrival time of elevated CO2 at each voxel using the systemic low frequency oscillations (sLFO: 0.01-0.1 Hz) extracted from the CO2 challenge data. In addition, 26 candidate hemodynamic response functions (HRF) were used to quantitatively describe the temporal brain reactions to a CO2 stimulus. We demonstrated that our approach improved the traditional method by allowing us to accurately map three perfusion-related parameters: the relative arrival time of blood, the hemodynamic response function, and CVR during a CO2 challenge.

Pupil-based States of Brain Integration across Cognitive States

Arousal is a potent mechanism that provides the brain with functional flexibility and adaptability to external conditions. Within the wake state, arousal levels driven by activity in the neuromodulatory systems are related to specific signatures of neural activation and brain synchrony. However, direct evidence is still lacking on the varying effects of arousal on macroscopic brain characteristics and across a variety of cognitive states in humans. Using a concurrent fMRI-pupillometry approach, we used pupil size as a proxy for arousal and obtained patterns of brain integration associated with increasing arousal levels. We carried out this analysis on resting-state data and data from two attentional tasks implicating different cognitive processes. We found that an increasing level of arousal was related to a state of increased brain integration. This effect was prominent in the salience, visual and default-mode networks in all conditions, while other regions showed task-specificity. Increased integration in the salience network was also related to faster pupil dilation in the two attentional tasks. Furthermore, task performance was related to arousal level, with lower accuracy at higher level of arousal. Taken together, our study provides evidence in humans for pupil size as an index of brain network state, and supports the role of arousal as a switch that drives brain coordination in specific brain regions according to the cognitive state.

Which multiband factor should you choose for your resting-state fMRI study?

Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

Human larynx motor cortices coordinate respiration for vocal-motor control

Vocal flexibility is a hallmark of the human species, most particularly the capacity to speak and sing. This ability is supported in part by the evolution of a direct neural pathway linking the motor cortex to the brainstem nucleus that controls the larynx the primary sound source for communication. Early brain imaging studies demonstrated that larynx motor cortex at the dorsal end of the orofacial division of motor cortex (dLMC) integrated laryngeal and respiratory control, thereby coordinating two major muscular systems that are necessary for vocalization. Neurosurgical studies have since demonstrated the existence of a second larynx motor area at the ventral extent of the orofacial motor division (vLMC) of motor cortex. The vLMC has been presumed to be less relevant to speech motor control, but its functional role remains unknown. We employed a novel ultra-high field (7T) magnetic resonance imaging paradigm that combined singing and whistling simple melodies to localise the larynx motor cortices and test their involvement in respiratory motor control. Surprisingly, whistling activated both 'larynx areas' more strongly than singing despite the reduced involvement of the larynx during whistling. We provide further evidence for the existence of two larynx motor areas in the human brain, and the first evidence that laryngeal-respiratory integration is a shared property of both larynx motor areas. We outline explicit predictions about the descending motor pathways that give these cortical areas access to both the laryngeal and respiratory systems and discuss the implications for the evolution of speech.

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.

Temporal lobe epilepsy with or without hippocampal sclerosis: Structural and functional connectivity using advanced MRI techniques

BACKGROUND AND PURPOSE

The aim of this study was to investigate the differences in structural connectivity based on diffusion tensor imaging (DTI) and functional connectivity based on arterial spin labeling (ASL) MRI between temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS).

METHODS

We enrolled 50 patients with TLE, including 25 patients with HS and 25 patients without HS, who underwent brain MRI, including DTI and ASL. We calculated the network parameters of structural connectivity based on DTI and functional connectivity based on ASL using a graph theoretical analysis. The parameters included global network measures (radius, diameter, characteristic path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, assortative coefficient, and small-worldness index) and a local network measure (betweenness centrality).

RESULTS

The global and local network measures of structural connectivity were not different between TLE patients with and without HS. However, significant differences in functional connectivity existed between the two groups. The radius and diameter of the global network measures in the TLE patients with HS were significantly increased compared with those without HS (4.140 vs. 3.140, p = 0.045; 6.812 vs. 5.132, p = 0.049; respectively). No differences were detected between other global network measures of functional connectivity and local network measure.

CONCLUSIONS

Significant differences in global network measures of functional connectivity based on ASL existed between TLE patients with and without HS. These findings suggest that TLE patients with HS exhibit a more disconnected functional brain network than those without HS.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Functional connectivity of the anterior cingulate cortex with pain-related regions in children with post-traumatic headache

Introduction:

Post-traumatic headaches (PTH) are common following mild traumatic brain injury (mTBI). There is evidence of altered central pain processing in adult PTH; however, little is known about how children with PTH process pain. The anterior cingulate cortex (ACC) plays a critical role in descending central pain modulation. In this study, we explored whether the functional connectivity (FC) of the ACC is altered in children with PTH.

Methods:

In this case-control study, we investigated resting-state FC of 5 ACC seeds (caudal, dorsal, rostral, perigenual, and subgenual) in children with PTH ( n = 73) and without PTH ( n = 29) following mTBI, and healthy controls ( n = 27). Post-concussion symptoms were assessed using the Post-Concussion Symptom Inventory and the Child Health Questionnaire. Resting-state functional Magnetic Resonance Imaging (fMRI) data were used to generate maps of ACC FC. Group-level comparisons were performed within a target mask comprised of pain-related regions using FSL Randomise.

Results:

We found decreased FC between the right perigenual ACC and the left cerebellum, and increased FC between the right subgenual ACC and the left dorsolateral prefrontal cortex in children with PTH compared to healthy controls. The ACC FC in children without PTH following mTBI did not differ from the group with PTH or healthy controls. FC between rostral and perigenual ACC seeds and the cerebellum was increased in children with PTH with pre-injury headaches compared to those with PTH without pre-injury headaches. There was a positive relationship between PTH severity and rostral ACC FC with the bilateral thalamus, right hippocampus and periaqueductal gray.

Conclusions:

Central pain processing is altered in children with PTH. Pre-existing headaches help to drive this process.

Trial registration:

The PlayGame Trial was registered in ClinicalTrials.gov database ( ClinicalTrials.gov Identifier: NCT01874847).

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.

Aging effect on head motion: A Machine Learning study on resting state fMRI data

BACKGROUND

Resting-state-fMRI is a technique used to explore the functional brain architecture in term of brain networks and their interactions. However, the robustness of Resting-state-fMRI analysis is negatively affected by physiological noise caused by subject head motion. The aim of our study was to provide new knowledge about the effect of normal aging on the head motion signals.

NEW METHOD

For the first time, we proposed a method for evaluating the most sensitive head motion parameters linked to subjects'aging. We enrolled 14-young(9females; mean-age = 28 ± 4.07) and 14-elderly(9females; mean-age = 66 ± 5.19) subjects. Along three axes(X,Y,Z), we extracted six motions parameters which reflected the head's movements to characterize translations(x,y,z) and rotations(angles phi,theta,psi). We performed:1)univariate analysis for comparing the groups and correlation to investigate the relationship between age and movement parameters; 2)Support-Vector-Machine, using bootstrap and calculating the feature importance.

RESULTS

Statistical analyses showed significant association between the aging and some motion's parameters(rotation psi; translations y and z). These results were also confirmed by multivariate analysis with Support-Vector-Machine that presented an AUC of 90 %.

COMPARISON TO EXISTING METHODS

The proposed method shows that normal aging produces significant increase in head motion parameters, highlighting the critical effect of motion on resting data analyses in particular considering psi, y and z movements. To our knowledge and at the present, this represents the first study investigating the accurate characterization of motion parameters in aging.

CONCLUSIONS

Our results have a high impact to improve healthy control recruitment and appropriately decreasing the risk of signal distortion, according to the age of enrolled subjects.

Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide

Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms. Assessment of CVR is usually based on the use of a vasoactive stimulus combined with a CBF measurement technique. Although transcranial Doppler ultrasound has been frequently used to obtain global flow velocity measurements, MRI techniques are being increasingly employed for obtaining CBF maps. For the vasoactive stimulus, vasodilatory hypercapnia is usually induced through the manipulation of respiratory gases, including the inhalation of increased concentrations of carbon dioxide. However, most of these methods require an additional apparatus and complex setups, which not only may not be well-tolerated by some populations but are also not widely available. For these reasons, strategies based on voluntary breathing fluctuations without the need for external gas challenges have been proposed. These include the task-based methodologies of breath holding and paced deep breathing, as well as a new generation of methods based on spontaneous breathing fluctuations during resting-state. Despite the multitude of alternatives to gas challenges, existing literature lacks definitive conclusions regarding the best practices for the vasoactive modulation and associated analysis protocols. In this work, we perform an extensive review of CVR mapping techniques based on MRI and CO2 variations without gas challenges, focusing on the methodological aspects of the breathing protocols and corresponding data analysis. Finally, we outline a set of practical guidelines based on generally accepted practices and available data, extending previous reports and encouraging the wider application of CVR mapping methodologies in both clinical and academic MRI settings.

Integrated and segregated frequency architecture of the human brain network

The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01-0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.

Spatiotemporal functional interactivity among large-scale brain networks

The macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N = 200) we applied prediction correlation techniques to four resting-state fMRI scans (each scan has TRs = 1200). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual, and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in the hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

Altered regional homogeneity and functional brain networks in Type 2 diabetes with and without mild cognitive impairment

Patients with Type-2 Diabetes Mellitus (T2DM) have a considerably higher risk of developing mild cognitive impairment (MCI) and dementia. The initial symptoms are very insidious at onset. We investigated the alterations in spontaneous brain activity and network connectivity through regional homogeneity (ReHo) and graph theoretical network analyses, respectively, of resting-state functional Magnetic Resonance Imaging (rs-fMRI) in T2DM patients with and without MCI, so as to facilitate early diagnose. Twenty-five T2DM patients with MCI (DM-MCI), 25 T2DM patients with normal cognition (DM-NC), 27 healthy controls were enrolled. Whole-brain ReHo values were calculated and topological properties of functional networks were analyzed. The DM-MCI group exhibited decreased ReHo in the left inferior/middle occipital gyrus and right inferior temporal gyrus, and increased ReHo in frontal gyrus compared to the DM-NCs. Significant correlations were found between ReHo values and clinical measurements. The DM-MCI group illustrated greater clustering coefficient/local efficiency and altered nodal characteristics (efficiency, degree and betweenness), which increased in certain occipital, temporal and parietal regions but decreased in the right inferior temporal gyrus, compared to the DM-NCs. The altered ReHo and impaired network organization may underlie the impaired cognitive functions in T2DM and suggesting a compensation mechanism. These rs-fMRI measures have the potential as biomarkers of disease progression in diabetic encephalopathy.

Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders

Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.

Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these "functional connectomes" are reliable over time. In a large public sample of healthy participants (N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6-8%) to excellent (0.08-0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.

Orbitofrontal, dorsal striatum, and habenula functional connectivity in psychiatric patients with substance use problems

Substance abuse is commonly defined as the persistence of drug use despite negative consequences. Recent preclinical work has shown that higher input from the orbitofrontal cortex (OFC) to the dorsal striatum was associated with compulsive reward-seeking behavior despite negative effects. It remains unknown whether drug use is associated with the connectivity between the OFC and dorsal striatum in humans. We studied the resting state functional connectivity (RSFC) between the OFC, dorsal striatum, and habenula (and the whole brain in a separate analysis) in psychiatric inpatients with high (PU, problem users) and low (LU, low users) substance use. We matched PU and LU for psychiatric comorbidities. We found that PU showed higher RSFC between the left OFC and the left dorsal striatum than LU. RSFC between the habenula and both OFC and dorsal striatum was also higher in PU, which suggests the habenula may be a part of the same circuit. Finally, higher RSFC between the OFC and insula was also observed in PU. Our data shows that OFC, habenula, dorsal striatum, and insula may play an important role in PU. Furthermore, we postulate that the habenula may link the mesolimbic and cortico-striatal systems, which are altered in PU.

Noise removal in resting-state and task fMRI: functional connectivity and activation maps

OBJECTIVE

Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI.

APPROACH

Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC.

MAIN RESULTS

Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results.

SIGNIFICANCE

This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.

The Contribution of Functional Magnetic Resonance Imaging to the Understanding of the Effects of Acute Physical Exercise on Cognition

The fact that a single bout of acute physical exercise has a positive impact on cognition is well-established in the literature, but the neural correlates that underlie these cognitive improvements are not well understood. Here, the use of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers great potential, which is just starting to be recognized. This review aims at providing an overview of those studies that used fMRI to investigate the effects of acute physical exercises on cerebral hemodynamics and cognition. To this end, a systematic literature survey was conducted by two independent reviewers across five electronic databases. The search returned 668 studies, of which 14 studies met the inclusion criteria and were analyzed in this systematic review. Although the findings of the reviewed studies suggest that acute physical exercise (e.g., cycling) leads to profound changes in functional brain activation, the small number of available studies and the great variability in the study protocols limits the conclusions that can be drawn with certainty. In order to overcome these limitations, new, more well-designed trials are needed that (i) use a more rigorous study design, (ii) apply more sophisticated filter methods in fMRI data analysis, (iii) describe the applied processing steps of fMRI data analysis in more detail, and (iv) provide a more precise exercise prescription.

Efficient de-noising of high-resolution fMRI using local and sub-band information

BACKGROUND

High-resolution fMRI, useful for accurate brain mapping, suffers from low functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques although reduce the noise and boost the sensitivity, but degrade the spatial resolution of fMRI.

NEW METHODS

We propose a novel spatial de-noising technique to increase sensitivity while preserving the boundaries of active regions in the high-resolution fMRI. A modified version of PCA that utilizes adjacent voxels information (LPCA) is first suggested for de-noising. This technique is then further empowered by its application to wavelet sub-bands (WLPCA).

RESULTS

Proposed techniques were assessed on both simulated and experimental data. Identifiablity index was calculated for evaluation of the denoising on the simulated data. Maximum and mean z-scores along with LAE and SSIM were reported on experimental data for two presented techniques as well as Guassian smoothing. WLPCA outperformed other techniques in Identifiablity index, for simulation, and in preserving maximum z-score, for experimental study.

COMPARISON WITH EXISTING METHODS

The presented technique was developed to simultaneously suppress the noise and preserve the boundaries of active areas against leakage. For first aim, its achievable mean z-score was compared to conventional Gaussian. For second aim, its maximum z-score was compared to that of no-smoothing. While Gaussian and no-smoothing can work fine with only one measure, WLPCA was able to improve both measures concurrently.

CONCLUSIONS

The local PCA based methods, and in particular WLPCA, is an effective noise reduction step that preserves the spatial resolution by preventing activity leakage of high-resolution fMRI data.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

BACKGROUND

Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.

METHODS

Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.

RESULTS

A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.

CONCLUSIONS

Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.