Showing papers in "Human Brain Mapping in 2022"
TL;DR: Both structural and functional connectivity metrics were found significantly increased in previously SARS‐CoV‐2 infected subjects and could feature a characteristic brain connectivity response in the presence of COVID‐19 related residual hyposmia.
Abstract: To address the impact of COVID‐19 olfactory loss on the brain, we analyzed the neural connectivity of the central olfactory system in recently SARS‐CoV‐2 infected subjects with persisting olfactory impairment (hyposmia). Twenty‐seven previously SARS‐CoV‐2 infected subjects (10 males, mean age ± SD 40.0 ± 7.6 years) with clinically confirmed COVID‐19 related hyposmia, and eighteen healthy, never SARS‐CoV‐2 infected, normosmic subjects (6 males, mean age ± SD 36.0 ± 7.1 years), were recruited in a 3 Tesla MRI study including high angular resolution diffusion and resting‐state functional MRI acquisitions. Specialized metrics of structural and functional connectivity were derived from a standard parcellation of olfactory brain areas and a previously validated graph‐theoretic model of the human olfactory functional network. These metrics were compared between groups and correlated to a clinical index of olfactory impairment. On the scanning day, all subjects were virus‐free and cognitively unimpaired. Compared to control, both structural and functional connectivity metrics were found significantly increased in previously SARS‐CoV‐2 infected subjects. Greater residual olfactory impairment was associated with more segregated processing within regions more functionally connected to the anterior piriform cortex. An increased neural connectivity within the olfactory cortex was associated with a recent SARS‐CoV‐2 infection when the olfactory loss was a residual COVID‐19 symptom. The functional connectivity of the anterior piriform cortex, the largest cortical recipient of afferent fibers from the olfactory bulb, accounted for the inter‐individual variability in the sensory impairment. Albeit preliminary, these findings could feature a characteristic brain connectivity response in the presence of COVID‐19 related residual hyposmia.
30 citations
TL;DR: Performance metrics used for evaluating age prediction models depend on cohort and study‐specific data characteristics, and cannot be directly compared across different studies.
Abstract: Estimating age based on neuroimaging‐derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine‐learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population‐based datasets, and assessed the effects of age range, sample size and age‐bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age‐bias corrected metrics indicate high accuracy—also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study‐specific data characteristics, and cannot be directly compared across different studies. Since age‐bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.
26 citations
TL;DR: The results suggest that CM has a significant effect on the modulation of FC within theory of mind (ToM) network even decades later in adulthood, and inform a new framework to account for how CM results in the development of impulsivity.
Abstract: Childhood maltreatment (CM) confers a great risk of maladaptive development outcomes later in life, however, the neurobiological mechanism underlying this vulnerability is still unclear. The present study aimed to investigate the long‐term consequences of CM on neural connectivity while controlling for psychiatric conditions, medication, and, substance abuse. A sample including adults with (n = 40) and without CM (n = 50) completed Childhood Trauma Questionnaire (CTQ), personality questionnaires, and resting‐state functional magnetic resonance imaging scan were recruited for the current study. The whole‐brain functional connectivity (FC) was evaluated using an unbiased, data‐driven, multivariate pattern analysis method. Relative to controls, adults with CM suffered a higher level of temperament and impulsivity and showed decreased FC between the insula and superior temporal gyrus (STG) and between inferior parietal lobule (IPL) and middle frontal gyrus, STG, and dorsal anterior cingulate cortex (dACC), while increased FC between IPL and cuneus and superior frontal gyrus (SFG) regions. The FCs of IPL with dACC and SFG were correlated with the anxious and cyclothymic temperament and attentional impulsivity. Moreover, these FCs partially mediated the relationship between CM and attentional impulsivity. Our results suggest that CM has a significant effect on the modulation of FC within theory of mind (ToM) network even decades later in adulthood, and inform a new framework to account for how CM results in the development of impulsivity. The novel findings reveal the neurobiological consequences of CM and provide new clues to the prevention and intervention strategy to reduce the risk of the development of psychopathology.
20 citations
TL;DR: The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation and the functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division regions in the Human Connectome Project-MMP1 atlas.
Abstract: The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project‐MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero‐ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward‐related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for “what,” reward and semantic schema‐related information to access the hippocampus. Second, the antero‐dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the “where” component for hippocampal episodic memory and for spatial navigation. The dorsal–transitional–visual (DVT) and ProStriate regions where the retrosplenial scene area is located have connectivity from early visual cortical areas to the parahippocampal scene area, providing a ventromedial route for spatial scene information to reach the hippocampus. These connectivities provide important routes for “what,” reward, and “where” scene‐related information for human hippocampal episodic memory and navigation. The midcingulate cortex provides a route from the anterior dorsal parts of the PCD and the supracallosal part of the anterior cingulate cortex to premotor regions.
19 citations
TL;DR: In this article , the authors present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of relevant regulations and policies in the United States.
Abstract: Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience.
18 citations
TL;DR: The distance-controlled boundary coefficient (DCBC) as discussed by the authors is an unbiased criterion to evaluate discrete parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting-state data.
Abstract: One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within-parcel to between-parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance-controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting-state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task-based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting-state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi-modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.
17 citations
TL;DR: This study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
Abstract: Biological brain age predicted using machine learning models based on high‐resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21–81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1‐weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1‐weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
16 citations
TL;DR: In this article , the authors proposed a data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood, which restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion.
Abstract: Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.
16 citations
TL;DR: In this article , the authors used transcranial magnetic stimulation (TMS) to examine the causal relationship between the left and right ventrolateral prefrontal cortices (VLPFC) and social emotion reappraisal.
Abstract: The ventrolateral prefrontal cortices (VLPFC) are crucial regions involved in voluntary emotion regulation. However, the lateralization of the VLPFC in downregulating negative emotions remains unclear; and whether the causal role of the VLPFC is generalizable to upregulating positive emotions is unexplored. This study used transcranial magnetic stimulation (TMS) to examine the causal relationship between the left/right VLPFC and social emotion reappraisal. One hundred and twenty participants were randomly assigned to either active (left and right VLPFC groups, n = 40/40) or sham (vertex, n = 40) TMS groups. Participants were instructed to passively receive social feedback or use reappraisal strategies to positively regulate their emotions. While the subjective emotional rating showed that the bilateral VLPFC facilitated the reappraisal success, the electrophysiological measure of the late positive potential (LPP) demonstrated a more critical role of the right VLPFC on social pain relief (decreased LPP amplitudes) and social reward magnification (enhanced LPP amplitudes). In addition, the influence of emotion regulation on social evaluation was found to be mediated by the memory of social feedback, indicating the importance of memory in social behavioral shaping. These findings suggest clinical protocols for the rehabilitation of emotion‐regulatory function in patients with affective and social disorders.
16 citations
TL;DR: HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results.
Abstract: The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the individual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.
15 citations
TL;DR: In this article , the authors used a seed-to-voxel functional connectivity analysis on the fluency functional magnetic resonance imaging task to determine the involvement of visual attention during fluent reading in children with dyslexia and typical readers.
Abstract: Poor phonological processing has typically been considered the main cause of dyslexia. However, visuo‐attentional processing abnormalities have been described as well. The goal of the present study was to determine the involvement of visual attention during fluent reading in children with dyslexia and typical readers. Here, 75 children (8–12 years old; 36 typical readers, 39 children with dyslexia) completed cognitive and reading assessments. Neuroimaging data were acquired while children performed a fluent reading task with (a) a condition where the text remained on the screen (Still) versus (b) a condition in which the letters were being deleted (Deleted). Cognitive assessment data analysis revealed that visual attention, executive functions, and phonological awareness significantly contributed to reading comprehension in both groups. A seed‐to‐voxel functional connectivity analysis was performed on the fluency functional magnetic resonance imaging task. Typical readers showed greater functional connectivity between the dorsal attention network and the left angular gyrus while performing the Still and Deleted reading tasks versus children with dyslexia. Higher connectivity values were associated with higher reading comprehension. The control group showed increased functional connectivity between the ventral attention network and the fronto‐parietal network during the Deleted text condition (compared with the Still condition). Children with dyslexia did not display this pattern. The results suggest that the synchronized activity of executive, visual attention, and reading‐related networks is a pattern of functional integration which children with dyslexia fail to achieve. The present evidence points toward a critical role of visual attention in dyslexia.
TL;DR: The findings demonstrate the value and validity of spectrally specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.
Abstract: Originally applied to alpha oscillations in the 1970s, microstate (MS) analysis has since been used to decompose mainly broadband electroencephalogram (EEG) signals (e.g., 1–40 Hz). We hypothesised that MS decomposition within separate, narrow frequency bands could provide more fine‐grained information for capturing the spatio‐temporal complexity of multichannel EEG. In this study, using a large open‐access dataset (n = 203), we first filtered EEG recordings into four classical frequency bands (delta, theta, alpha and beta) and thereafter compared their individual MS segmentations using mutual information as well as traditional MS measures (e.g., mean duration and time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D and C′). Interestingly, however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A and B, while map D was more prevalent in delta/theta bands. Moreover, using a frequency‐specific MS taxonomy (e.g., ϴA and αC), we were able to predict the eyes‐open versus eyes‐closed behavioural state significantly better using alpha‐band MS features compared with broadband ones (80 vs. 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.
TL;DR: White matter microstructure appears more organized in children without persistent symptoms, consistent with their better clinical outcomes, and this prospective, longitudinal cohort study recruited children aged 8–16.
Abstract: In the largest sample studied to date, white matter microstructural trajectories and their relation to persistent symptoms were examined after pediatric mild traumatic brain injury (mTBI). This prospective, longitudinal cohort study recruited children aged 8–16.99 years with mTBI or mild orthopedic injury (OI) from five pediatric emergency departments. Children's pre‐injury and 1‐month post‐injury symptom ratings were used to classify mTBI with or without persistent symptoms. Children completed diffusion‐weighted imaging at post‐acute (2–33 days post‐injury) and chronic (3 or 6 months via random assignment) post‐injury assessments. Mean diffusivity (MD) and fractional anisotropy (FA) were derived for 18 white matter tracts in 560 children (362 mTBI/198 OI), 407 with longitudinal data. Superior longitudinal fasciculus FA was higher in mTBI without persistent symptoms relative to OI, d (95% confidence interval) = 0.31 to 0.37 (0.02, 0.68), across time. In younger children, MD of the anterior thalamic radiations was higher in mTBI with persistent symptoms relative to both mTBI without persistent symptoms, 1.43 (0.59, 2.27), and OI, 1.94 (1.07, 2.81). MD of the arcuate fasciculus, −0.58 (−1.04, −0.11), and superior longitudinal fasciculus, −0.49 (−0.90, −0.09) was lower in mTBI without persistent symptoms relative to OI at 6 months post‐injury. White matter microstructural changes suggesting neuroinflammation and axonal swelling occurred chronically and continued 6 months post injury in children with mTBI, especially in younger children with persistent symptoms, relative to OI. White matter microstructure appears more organized in children without persistent symptoms, consistent with their better clinical outcomes.
TL;DR: The authors used multiverse analyses to examine the robustness of task-based amygdala-mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years-old; N = 98; 183 scans; 1-3 scans/participant).
Abstract: The amygdala and its connections with medial prefrontal cortex (mPFC) play central roles in the development of emotional processes. While several studies have suggested that this circuitry exhibits functional changes across the first two decades of life, findings have been mixed - perhaps resulting from differences in analytic choices across studies. Here we used multiverse analyses to examine the robustness of task-based amygdala-mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years-old; N = 98; 183 scans; 1-3 scans/participant). Participants recruited from the greater Los Angeles area completed an event-related emotional face (fear, neutral) task. Parallel analyses varying in preprocessing and modeling choices found that age-related change estimates for amygdala reactivity were more robust than task-evoked amygdala-mPFC functional connectivity to varied analytical choices. Specification curves indicated evidence for age-related decreases in amygdala reactivity to faces, though within-participant changes in amygdala reactivity could not be differentiated from between-participant differences. In contrast, amygdala-mPFC functional connectivity results varied across methods much more, and evidence for age-related change in amygdala-mPFC connectivity was not consistent. Generalized psychophysiological interaction (gPPI) measurements of connectivity were especially sensitive to whether a deconvolution step was applied. Our findings demonstrate the importance of assessing the robustness of findings to analysis choices, although the age-related changes in our current work cannot be overinterpreted given low test-retest reliability. Together, these findings highlight both the challenges in estimating developmental change in longitudinal cohorts and the value of multiverse approaches in developmental neuroimaging for assessing robustness of results.
TL;DR: The pathway model in which sleep disturbance and mental problems affect each other through two anticorrelated brain networks (DMN and DAN) suggests a common neural mechanism between them that has important implications for the design of prevention and neurofeedback intervention for mental disorders and sleep problems.
Abstract: Sleep disturbance is known to be associated with various mental disorders and often precedes the onset of mental disorders in youth. Given the increasingly acknowledged bidirectional influence between sleep disturbance and mental disorders, we aim to identify a shared neural mechanism that underlies sleep disturbance and mental disorders in preadolescents. We analyzed a dataset of 9,350 9–10 year‐old children, among whom 8,845 had 1‐year follow‐up data, from the Adolescent Brain Cognitive Development (ABCD) study. Linear mixed‐effects models, mediation analysis, and longitudinal mediation analysis were used to investigate the relationship between sleep disturbance, mental disorders, and resting‐state network connectivity. Out of 186 unique connectivities, the effect of total sleep disturbance (TSP, from Sleep Disturbance Scale) and mental problems (MP, from Child Behavior Checklist) converged in the default mode network (DMN) and the dorsal attention network (DAN). Within‐ and between‐network connectivities (DMN‐DAN, DMN‐DMN, DAN‐DAN) mediated the relationship between baseline TSD and MP at 1‐year follow‐up and the relationship between baseline MP and TSD at 1‐year follow‐up. The pathway model in which sleep disturbance and mental problems affect each other through two anticorrelated brain networks (DMN and DAN) suggests a common neural mechanism between them. Longitudinally, a less segregated DMN and DAN is associated with negative outcomes on mental well‐being and sleep disturbance a year later. These findings have important implications for the design of prevention and neurofeedback intervention for mental disorders and sleep problems.
TL;DR: Zapline-plus as mentioned in this paper segments the data into periods (chunks) in which the noise is spatially stable, and for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline.
Abstract: Removing power line noise and other frequency-specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline-plus, which allows adaptive and automatic removal of frequency-specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to-be-removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re-running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise.
TL;DR: It is demonstrated that the beneficial effects of z-shimming are apparent across different echo times, hold true for both the dorsal and ventral horn, and are also apparent in the temporal signal-to-noise ratio (tSNR) of EPI time-series data.
Abstract: Functional magnetic resonance imaging (fMRI) of the human spinal cord faces many challenges, such as signal loss due to local magnetic field inhomogeneities. This issue can be addressed with slice‐specific z‐shimming, which compensates for the dephasing effect of the inhomogeneities using a slice‐specific gradient pulse. Here, we aim to address outstanding issues regarding this technique by evaluating its effects on several aspects that are directly relevant for spinal fMRI and by developing two automated procedures in order to improve upon the time‐consuming and subjective nature of manual selection of z‐shims: one procedure finds the z‐shim that maximizes signal intensity in each slice of an EPI reference‐scan and the other finds the through‐slice field inhomogeneity for each EPI‐slice in field map data and calculates the required compensation gradient moment. We demonstrate that the beneficial effects of z‐shimming are apparent across different echo times, hold true for both the dorsal and ventral horn, and are also apparent in the temporal signal‐to‐noise ratio (tSNR) of EPI time‐series data. Both of our automated approaches were faster than the manual approach, lead to significant improvements in gray matter tSNR compared to no z‐shimming and resulted in beneficial effects that were stable across time. While the field‐map‐based approach performed slightly worse than the manual approach, the EPI‐based approach performed as well as the manual one and was furthermore validated on an external corticospinal data‐set (N > 100). Together, automated z‐shimming may improve the data quality of future spinal fMRI studies and lead to increased reproducibility in longitudinal studies.
TL;DR: Findings show slower white matter development in young children with PAE than in unexposed controls, which is a deviation from typical development trajectories and may reflect altered brain plasticity, which has implications for cognitive and behavioral learning in children withPAE.
Abstract: Prenatal alcohol exposure (PAE) is associated with alterations to brain white matter microstructure. Previous studies of PAE have demonstrated different findings in young children compared to older children and adolescents, suggesting altered developmental trajectories and highlighting the need for longitudinal research. 122 datasets in 54 children with PAE (27 males) and 196 datasets in 89 children without PAE (45 males) were included in this analysis. Children underwent diffusion tensor imaging between 2 and 8 years of age, returning approximately every 6 months. Mean fractional anisotropy (FA) and mean diffusivity (MD) were obtained for 10 major brain white matter tracts and examined for age‐related changes using linear mixed effects models with age, sex, group (PAE vs. control) and an age‐by‐group interaction. Children with PAE had slower decreases of MD over time in the genu of the corpus callosum, inferior fronto‐occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. No significant age‐by‐group interactions were noted for FA. These findings show slower white matter development in young children with PAE than in unexposed controls. This connects previous cross‐sectional findings of lower MD in young children with PAE to findings of higher MD in older children and adolescents with PAE, and further helps to understand brain development in children with PAE. This deviation from typical development trajectories may reflect altered brain plasticity, which has implications for cognitive and behavioral learning in children with PAE.
TL;DR: It is shown that sensory prediction, induced with predictable ISI, has a suppressive effect on vertex N 100-P200, and that combining standard suppression protocols with sensory prediction provides the best N100-P 200 suppression.
Abstract: The sensory experience of transcranial magnetic stimulation (TMS) evokes cortical responses measured in electroencephalography (EEG) that confound interpretation of TMS‐evoked potentials (TEPs). Methods for sensory masking have been proposed to minimize sensory contributions to the TEP, but the most effective combination for suprathreshold TMS to dorsolateral prefrontal cortex (dlPFC) is unknown. We applied sensory suppression techniques and quantified electrophysiology and perception from suprathreshold dlPFC TMS to identify the best combination to minimize the sensory TEP. In 21 healthy adults, we applied single pulse TMS at 120% resting motor threshold (rMT) to the left dlPFC and compared EEG vertex N100‐P200 and perception. Conditions included three protocols: No masking (no auditory masking, no foam, and jittered interstimulus interval [ISI]), Standard masking (auditory noise, foam, and jittered ISI), and our ATTENUATE protocol (auditory noise, foam, over‐the‐ear protection, and unjittered ISI). ATTENUATE reduced vertex N100‐P200 by 56%, “click” loudness perception by 50%, and scalp sensation by 36%. We show that sensory prediction, induced with predictable ISI, has a suppressive effect on vertex N100‐P200, and that combining standard suppression protocols with sensory prediction provides the best N100‐P200 suppression. ATTENUATE was more effective than Standard masking, which only reduced vertex N100‐P200 by 22%, loudness by 27%, and scalp sensation by 24%. We introduce a sensory suppression protocol superior to Standard masking and demonstrate that using an unjittered ISI can contribute to minimizing sensory confounds. ATTENUATE provides superior sensory suppression to increase TEP signal‐to‐noise and contributes to a growing understanding of TMS‐EEG sensory neuroscience.
TL;DR: In this article , the role of structural eigenmodes in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at individual subject level was analyzed.
Abstract: How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting‐state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time‐resolved amplitude connectivity. Time‐resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co‐occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time‐resolved resting‐state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.
TL;DR: Two distinct neuroanatomical subtypes of OCD are identified by adopting heterogeneity through discriminative analysis (HYDRA), providing a possible explanation for conflicting neuroimaging findings, and a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.
Abstract: Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.
TL;DR: An MRI template is used to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence to accurately measure tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.
Abstract: Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.
TL;DR: In this article , the authors examined the dynamic functional connectivity after sleep deprivation in 55 normal young subjects and found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional level variability in several thalamus subregions.
Abstract: Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10−4) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.
TL;DR: In this article , structural connectivity of two networks, the temporal-parietal-occipital network surrounding the left middle temporal gyrus and the frontal networks surrounding the medial orbital superior frontal gyrus hub, accounted for individual differences in dyslexic children's phonological processing accuracy, but not in VAS.
Abstract: It has been suggested that developmental dyslexia may have two dissociable causes—a phonological deficit and a visual attention span (VAS) deficit. Yet, neural evidence for such a dissociation is still lacking. This study adopted a data‐driven approach to white matter network analysis to explore hubs and hub‐related networks corresponding to VAS and phonological accuracy in a group of French dyslexic children aged from 9 to 14 years. A double dissociation in brain‐behavior relations was observed. Structural connectivity of the occipital‐parietal network surrounding the left superior occipital gyrus hub accounted for individual differences in dyslexic children's VAS, but not in phonological processing accuracy. In contrast, structural connectivity of two networks: the temporal–parietal‐occipital network surrounding the left middle temporal gyrus hub and the frontal network surrounding the left medial orbital superior frontal gyrus hub, accounted for individual differences in dyslexic children's phonological processing accuracy, but not in VAS. Our findings provide evidence in favor of distinct neural circuits corresponding to VAS and phonological deficits in developmental dyslexia. The study points to connectivity‐constrained white matter subnetwork dysfunction as a key principle for understanding individual differences of cognitive deficits in developmental dyslexia.
TL;DR: In this paper , the authors investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and apolipoprotein E-ϵ4 status varied between males and females, according to age at menopause in females, and across different age groups.
Abstract: Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.
TL;DR: The results reveal the possible role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, ranging from subcriticality to supercriticality.
Abstract: The critical brain hypothesis suggests that efficient neural computation can be achieved through critical brain dynamics. However, the relationship between human cognitive performance and scale‐free brain dynamics remains unclear. In this study, we investigated the whole‐brain avalanche activity and its individual variability in the human resting‐state functional magnetic resonance imaging (fMRI) data. We showed that though the group‐level analysis was inaccurate because of individual variability, the subject wise scale‐free avalanche activity was significantly associated with maximal synchronization entropy of their brain activity. Meanwhile, the complexity of functional connectivity, as well as structure–function coupling, is maximized in subjects with maximal synchronization entropy. We also observed order–disorder phase transitions in resting‐state brain dynamics and found that there were longer times spent in the subcritical regime. These results imply that large‐scale brain dynamics favor the slightly subcritical regime of phase transition. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance and found that these regions were located in the prefrontal cortex and inferior parietal cortex, which were believed to be important nodes of brain networks underlying human intelligence. Our results reveal the possible role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, ranging from subcriticality to supercriticality.
TL;DR: It is suggested that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline and showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness.
Abstract: Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio‐metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning “BrainAge” index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD− (N = 964), SMI−/CMD+ (N = 3,765), SMI−/CMD− (N = 8,083). SMI (F = 40.47, p = 2.06 × 10−10) and CMD (F = 24.69, p = 6.82 × 10−7) significantly, independently impacted whole‐brain QRI in SMI+. SSD had the largest effect (Cohen’s d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI− (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole‐brain QRI was significantly (p < 10−16) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10−16). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline.
TL;DR: It is demonstrated that both ICV and sex contributed to variation in DTI indices and emphasized the importance of considering ICV as a covariate inDTI analysis.
Abstract: Whether head size and/or biological sex influence proxies of white matter (WM) microstructure such as fractional anisotropy (FA) and mean diffusivity (MD) remains controversial. Diffusion tensor imaging (DTI) indices are also associated with age, but there are large discrepancies in the spatial distribution and timeline of age‐related differences reported. The aim of this study was to evaluate the associations between intracranial volume (ICV), sex, and age and DTI indices from WM in a population‐based study of healthy individuals (n = 812) aged 50–66 in the Nord‐Trøndelag health survey. Semiautomated tractography and tract‐based spatial statistics (TBSS) analyses were performed on the entire sample and in an ICV‐matched sample of men and women. The tractography results showed a similar positive association between ICV and FA in all major WM tracts in men and women. Associations between ICV and MD, radial diffusivity and axial diffusivity were also found, but to a lesser extent than FA. The TBSS results showed that both men and women had areas of higher and lower FA when controlling for age, but after controlling for age and ICV only women had areas with higher FA. The ICV matched analysis also demonstrated that only women had areas of higher FA. Age was negatively associated with FA across the entire WM skeleton in the TBSS analysis, independent of both sex and ICV. Combined, these findings demonstrated that both ICV and sex contributed to variation in DTI indices and emphasized the importance of considering ICV as a covariate in DTI analysis.
TL;DR: The Bayesian U‐Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH showed the best segmentation results across all methods and provided accurate uncertainty maps.
Abstract: Post‐hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two‐dimensional measurements of the ventricles. Automatic and reliable three‐dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U‐Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U‐Net, and ensemble learning using DenseNets and U‐Nets. A total of 41 T2‐weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4‐fold cross‐validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U‐Net, ensemble learning, and Bayesian U‐Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U‐Net were mean recall = 0.953 ± 0.037, mean negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U‐Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions.
TL;DR: An automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients is proposed and shows its potential for automated ASPECTS scoring on NCCT images.
Abstract: Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non‐contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one‐stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.