Quality assurance of human functional magnetic resonance imaging: a literature review

Fast 3D fMRI acquisition with high spatial resolutions over a reduced FOV

PURPOSE

To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k-t 3D reduced FOV imaging (3D-rFOVI).

METHODS

Based on 3D gradient-echo EPI, k-t 3D-rFOVI used a 2D RF pulse to reduce the FOV in the in-plane phase-encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k-space region along the through-slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k-t 3D-rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k-t 3D-rFOVI and compare it with a commercial multiband EPI sequence.

RESULTS

The simulation data showed that k-t 3D-rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5-mm spatial resolution and 800-ms volume TR (TRvol), k-t 3D-rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k-t 3D-rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation.

CONCLUSIONS

k-t 3D-rFOVI can simultaneously achieve a high spatial resolution (1.5-mm isotropically) and short TRvol (480-ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume.

The German research consortium for the study of bipolar disorder (BipoLife): a quality assurance protocol for MR neuroimaging data

BACKGROUND

The German multicenter research consortium BipoLife aims to investigate the mechanisms underlying bipolar disorders. It focuses in particular on people at high risk of developing the disorder and young patients in the early stages of the disease. Functional and structural magnetic resonance imaging (MRI) data was collected in all participating centers. The collection of neuroimaging data in a longitudinal, multicenter study requires the implementation of a comprehensive quality assurance (QA) protocol. Here, we outline this protocol and illustrate its application within the BipoLife consortium.

METHODS

The QA protocol consisted of (1) a training of participating research staff, (2) regular phantom measurements to evaluate the MR scanner performance and its temporal stability across the course of the study, and (3) the assessment of the quality of human MRI data by evaluating a variety of image metrics (e.g., signal-to-noise ratio, ghosting level). In this article, we will provide an overview on these QA procedures and show exemplarily the influence of its application on the results of standard neuroimaging analysis pipelines.

DISCUSSION

The QA protocol helped to characterize the various MR scanners, to record their performance over the course of the study and to detect possible malfunctions at an early stage. It also assessed the quality of the human MRI data systematically to characterize its influence on various analyses. Furthermore, by setting up and publishing this protocol, we define standards that must be considered when analyzing data from the BipoLife consortium. It further promotes a systematic evaluation of data quality and a definition of subject inclusion criteria. In the long term, it will help to increase the chance of achieving clinically relevant results.

A Temporal Instability Measure for fMRI Quality Assurance

BACKGROUND

There exist several fMRI quality assurance measures to assess scanner stability. Because they have practical and/or theoretical limitations, a different and more practical measure for instability would be desirable.

PURPOSE

To develop and test a sensitive, reliable and widely applicable temporal instability measure (TIM) for fMRI quality assurance.

STUDY TYPE

Technical development.

PHANTOM

Spherical gel phantom.

POPULATION

A total of 120 datasets from a local Philips scanner with two different receive-only head coils (32ch and 8ch, 60 datasets per coil) were collected as well as 29 additional datasets with three different receive-only head coils (20ch, 32ch, and 64ch) from two additional sites with GE (seven runs with 32ch) and Siemens scanners (seven runs with 32ch and Multiband imaging, five runs with 20ch, 32ch, and 64ch) were borrowed.

FIELD STRENGTH/SEQUENCE

2D Echo-planar-imaging (EPI).

ASSESSMENT

A new TIM was proposed that is based on the eigenratio of the correlation coefficient matrix, where each entry of the matrix is a correlation coefficient between two time-points of the time-series.

STATISTICAL TESTS

Nonparametric bootstrap resampling was used twice to estimate confidence intervals (CI) of the TIM values and to assess the improved sensitivity of this measure. Differences in coil performance were assessed via a nonparametric bootstrap two-sample t-test. P-values <0.05 were considered significant.

RESULTS

The TIM values ranged between 60 parts-per-million and 10,780 parts-per-million across all 149 experiments. The mean CI was 2.96% and 2.16% for the 120 and 29 fMRI datasets, respectively (the repeated bootstrap analysis gave 2.9% and 2.19%, respectively). The 32ch coils of the local Philips data provided more stable measurements than the 8ch coil (observed two-sample t-values = 26.36, -0.2 and -6.2 for TIM, tSNR, and RDC, respectively. PtSNR  = 0.58).

DATA CONCLUSION

The proposed TIM is particularly useful for multichannel coils with spatially nonuniform receive sensitivity and overcomes several limitations of other measures. As such, it provides a reliable test for ascertaining scanner stability for fMRI experiments.

EVIDENCE LEVEL

5.

TECHNICAL EFFICACY

Stage 1.

An implementation of integrated information theory in resting-state fMRI

Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φ^max, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ^max to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φ^max presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ^max closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.

Classification of Migraine Using Static Functional Connectivity Strength and Dynamic Functional Connectome Patterns: A Resting-State fMRI Study

Migraine is a common, chronic dysfunctional disease with recurrent headaches. Its etiology and pathogenesis have not been fully understood and there is a lack of objective diagnostic criteria and biomarkers. Meanwhile, resting-state functional magnetic resonance imaging (RS-fMRI) is increasingly being used in migraine research to classify and diagnose brain disorders. However, the RS-fMRI data is characterized by a large amount of data information and the difficulty of extracting high-dimensional features, which brings great challenges to relevant studies. In this paper, we proposed an automatic recognition framework based on static functional connectivity (sFC) strength features and dynamic functional connectome pattern (DFCP) features of migraine sufferers and normal control subjects, in which we firstly extracted sFC strength and DFCP features and then selected the optimal features using the recursive feature elimination based on the support vector machine (SVM−RFE) algorithm and, finally, trained and tested a classifier with the support vector machine (SVM) algorithm. In addition, we compared the classification performance of only using sFC strength features and DFCP features, respectively. The results showed that the DFCP features significantly outperformed sFC strength features in performance, which indicated that DFCP features had a significant advantage over sFC strength features in classification. In addition, the combination of sFC strength and DFCP features had the optimal performance, which demonstrated that the combination of both features could make full use of their advantage. The experimental results suggested the method had good performance in differentiating migraineurs and our proposed classification framework might be applicable for other mental disorders.

Functional connectivity MRI quality control procedures in CONN

Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (N = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor - a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant's head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.

Assessing MR-compatibility of somatosensory stimulation devices: A systematic review on testing methodologies

Functional magnetic resonance imaging (fMRI) has been extensively used as a tool to map the brain processes related to somatosensory stimulation. This mapping includes the localization of task-related brain activation and the characterization of brain activity dynamics and neural circuitries related to the processing of somatosensory information. However, the magnetic resonance (MR) environment presents unique challenges regarding participant and equipment safety and compatibility. This study aims to systematically review and analyze the state-of-the-art methodologies to assess the safety and compatibility of somatosensory stimulation devices in the MR environment. A literature search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines, was performed in PubMed, Scopus, and Web of Science to find original research on the development and testing of devices for somatosensory stimulation in the MR environment. Nineteen records that complied with the inclusion and eligibility criteria were considered. The findings are discussed in the context of the existing international standards available for the safety and compatibility assessment of devices intended to be used in the MR environment. In sum, the results provided evidence for a lack of uniformity in the applied testing methodologies, as well as an in-depth presentation of the testing methodologies and results. Lastly, we suggest an assessment methodology (safety, compatibility, performance, and user acceptability) that can be applied to devices intended to be used in the MR environment.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42021257838.

A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB

Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.

Modulating swallowing-related functional connectivity and behavior via modified pharyngeal electrical stimulation: A functional near-infrared spectroscopy evidence

Introduction

Modified pharyngeal electrical stimulation (mPES) is a novel therapeutic modality for patients with neurogenic dysphagia. However, the underlying neural mechanism remains poorly understood. This study aimed to use functional near-infrared spectroscopy (fNIRS) to explore the influence of mPES on swallowing-related frequency-specific neural networks and ethology.

Methods

Twenty-two healthy right-handed volunteers participated in the study. Each participant was randomly assigned to either the sham or the mPES group and provided a 10-min intervention program every day for 5 days. Oxyhemoglobin and deoxyhemoglobin concentration changes verified by fNIRS were recorded on days 1, 3, and 5. Five characteristic frequency signals (0.0095-2 Hz) were identified using the wavelet transform method. To calculate frequency-specific functional connectivity, wavelet phase coherence (WPCO) was adopted. Furthermore, behavioral performance was assessed pre- and post-mPES using a 150 ml-water swallowing stress test.

Results

Compared with sham stimulation on day 1, the significantly decreased WPCO values were mainly associated with the dorsolateral prefrontal lobe, Broca's area, and middle temporal lobe. Compared with the sham mPES on day 1, the mPES showed a noticeable effect on the total swallow duration. Compared with the baseline, the WPCO values on days 3 and 5 showed a stepwise decrease in connectivity with the application of mPES. Furthermore, the decreased WPCO was associated with a shortened time per swallow after mPES.

Conclusions

The mPES could modulate swallowing-related frequency-specific neural networks and evoke swallowing cortical processing more efficiently. This was associated with improved performance in a water swallowing stress test in healthy participants.

Real-time and Recursive Estimators for Functional MRI Quality Assessment

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.

The Function of the Autonomic Nervous System in Asian Patients With Chronic Migraine

Background

The pathogenic mechanisms underlying the autonomic nervous system (ANS) dysfunction in patients with chronic migraine (CM) remain unclear. This study investigated the pathogenesis of ANS dysfunction in this population.

Methods

A total of 60 patients diagnosed with CM and 60 healthy subjects were recruited to participate in this study. The pupil diameter, pupil contraction velocity, latency, amplitude, and the maximum gradient recovery time were examined before, at 2 min and at 5 min after the cold pressor test, which was combined with the pupillary light reflex method. A brain 3D T1-weighted structural imaging scan, resting-state functional magnetic resonance imaging scan, and diffusion tensor imaging (DTI) scan were also acquired.

Results

Patients with CM exhibited a longer recovery time to the maximum gradient at 2 min and at 5 min after cold pressing compared with the control group (P < 0.01 and P < 0.05, respectively). There was no significant difference in the pupil diameter, pupillary contraction velocity, latency, amplitude, blood pressure, or heart rate between the two groups (all P > 0.05). In the CM group, the regional homogeneity (ReHo) values of the left amygdala and left lateral hypothalamic area were significantly higher than those of other brain areas (P < 0.001, Alphasim corrected). The DTI scan of the whole brain area showed a lack of significant difference in DTI indices, including FA, MD, AD, and RD values between the two groups (P > 0.05, Alphasim corrected).

Conclusion

The dysfunction of the left amygdala and left lateral hypothalamic area may be related to ANS dysfunction in patients with CM.

Development of a novel task-based functional magnetic resonance imaging phantom based on a bubble-compression approach

PURPOSE

Phantoms used in previous functional magnetic resonance imaging (fMRI) studies have drawbacks, such as a complicated circuit and equipment use, a single signal-change rate, and T₂ * values that do not correspond to those of living human brains. We aimed to develop a phantom for use in task-based fMRI studies (gradient-echo echo-planar imaging; GRE-EPI) with bioequivalent T₁ and T₂ * values, using an innovative method to control the rate of signal change.

METHODS

A gel phantom with T₁ and T₂ * values equivalent to that of the living brain gray matter was fixed in a 150 mm diameter container, with five holes, each of which could hold a 30-mL syringe. The gel phantom contained microscopic air bubbles; this made it possible to control the percent signal change by injector-induced water pressure changes. Using this phantom, we investigated the percent signal change, derived an equation that can approximately reproduce an arbitrary percent signal change, compared different gel phantom samples, investigated the change in relaxation time and bubble size during signal change, and assessed the change in values in each sample over time.

RESULTS

The relaxation time of the gel phantom was similar to the literature values for gray matter. The percent signal change achieved was approximately 0-13.51% and was dependent on the water pressure change. The derived equation was y = 0.000008x3 - 0.000771x2 + 0.034222x - 0.026054, with y being the percent signal change and x being the pressure in kPa; the reproducibility was high. No significant difference was detected among samples of gray matter gel phantoms (P > 0.05). The change in the rate of signal change with the change in water pressure was due to the change in T₂ * value with the change in bubble size. With pressure increasing from 0 to 151.7 kPa, the T₂ * value increased from 52 ms to 85 ms. The newly developed gel phantom was stable for 60 days, but its bubble size changed after 21 days.

CONCLUSION

We developed a novel phantom for use in fMRI, which could reproduce minute signal changes similar to the blood-oxygen-level-dependent effect and with bioequivalent T₁ and T₂ * values, and used an innovative method to control the percent signal change by compressing the air contained in the phantom for validation of fMRI using GRE-EPI. This phantom reproduced the percent signal change due to changes in T₂ * values, which is very similar to scanning a human body. This phantom is expected to be a powerful tool for advancing the study of task-based fMRI. This article is protected by copyright. All rights reserved.

Immediate acupuncture with GB34 for biliary colic: protocol for a randomised controlled neuroimaging trial

INTRODUCTION

As the main manifestation of gallstone disease, biliary colic (BC) is an episodic attack that brings patients severe pain in the right upper abdominal quadrant. Although acupuncture has been documented with significance to lead to pain relief, the immediate analgesia of acupuncture for BC still needs to be verified, and the underlying mechanism has yet to be covered. Therefore, this trial aims first to verify the immediate pain-alleviation characteristic of acupuncture for BC, then to explore its influence on the peripheral sensitised acupoint and central brain activity.

METHODS AND ANALYSIS

This is a randomised controlled, paralleled clinical trial, with patients and outcome assessors blinded. Seventy-two patients with gallbladder stone disease presenting with BC will be randomised into a verum acupuncture group and the sham acupuncture group. Both groups will receive one session of immediate acupuncture treatment. Improvements in patients' BC will be evaluated by the Numeric Rating Scale, and the pain threshold of acupoints will also be detected before and after treatment. During treatment, brain neural activity will be monitored with functional near-infrared spectroscopy (fNIRS), and the needle sensation will be rated. Clinical and fNIRS data will be analysed, respectively, to validate the acupuncture effect, and correlation analysis will be conducted to investigate the relationship between pain relief and peripheral-cerebral functional changes.

ETHICS AND DISSEMINATION

This trial has been approved by the institutional review boards and ethics committees of the First Teaching Hospital of Chengdu University of Traditional Chinese Medicine, with the ethical approval identifier 2019 KL-029, and the institutional review boards and ethics committees of the First People's Hospital of Longquanyi District, with the ethical approval identifier AF-KY-2020071. The results of this trial will be disseminated through peer-reviewed publications and conference abstracts or posters.

TRIAL REGISTRATION NUMBER

CTR2000034432.

Functional magnetic resonance imaging progressive deformable registration based on a cascaded convolutional neural network

Background

Intersubject registration of functional magnetic resonance imaging (fMRI) is necessary for group analysis. Accurate image registration can significantly improve the results of statistical analysis. Traditional methods are achieved by using high-resolution structural images or manually extracting functional information. However, structural alignment does not necessarily lead to functional alignment, and manually extracting functional features is complicated and time-consuming. Recent studies have shown that deep learning-based methods can be used for deformable image registration.

Methods

We proposed a deep learning framework with a three-cascaded multi-resolution network (MR-Net) to achieve deformable image registration. MR-Net separately extracts the features of moving and fixed images via a two-stream path, predicts a sub-deformation field, and is cascaded three times. The moving and fixed images' deformation field is composed of all sub-deformation fields predicted by the MR-Net. We imposed large smoothness constraints on all sub-deformation fields to ensure their smoothness. Our proposed architecture can complete the progressive registration process to ensure the topology of the deformation field.

Results

We implemented our method on the 1000 Functional Connectomes Project (FCP) and Eyes Open Eyes Closed fMRI datasets. Our method increased the peak t values in six brain functional networks to 19.8, 17.8, 15.0, 16.4, 17.0, and 13.2. Compared with traditional methods [i.e., FMRIB Software Library (FSL) and Statistical Parametric Mapping (SPM)] and deep learning networks [i.e., VoxelMorph (VM) and Volume Tweening Network (VTN)], our method improved 47.58%, 11.88%, 18.60%, and 15.16%, respectively.

Conclusions

Our three-cascaded MR-Net can achieve statistically significant improvement in functional consistency across subjects.

Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine

Background

Chronic migraine (CM) is a common and disabling neurological disorder that affects 1-2% of the global population. The aim of the present study was to identify the functional characteristics of the CM brain using static functional connectivity (s-FC), static functional network connectivity (s-FNC), and dynamic functional network connectivity (d-FNC) analyses.

Methods

In the present study, 17 CM patients and 20 sex- and age-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We utilized independent component (IC) analysis to identify 13 ICs. These 13 ICs were then classified into the following 6 resting-state networks (RSNs): the default mode network (DMN), executive control network (ECN), dorsal attention network, auditory network (AN), visual network (VN), and cerebellum network. Subsequently, s-FC, s-FNC, and d-FNC analyses of 13 ICs were employed for between-group comparisons. Three temporal metrics (fraction of time spent, mean dwell time, and number of transitions), which were derived from the state-transition vector, were calculated for group comparisons. In addition, correlation analyses were performed between these dynamic metrics and clinical characteristics [mean visual analog scale (VAS) scores, days with headache per month, days with migraine pain feature per month, and disease duration].

Results

In the comparison of s-FC of 13 ICs within RSNs between the CM and HC groups, increased connectivity was observed in the left angular gyrus (Angular_L) of the ECN (IC 2) and the right superior parietal gyrus (Parietal_Sup_R) of the AN (IC 5), and reduced connectivity was found in the left superior frontal gyrus (Frontal_Sup_2_L) of the AN (IC 5) and DMN (IC 19), the right calcarine sulcus (Calcarine_R) of the VN (IC 7), and the left precuneus (Precuneus_L) of the DMN (IC 17) in CM patients. In the comparison of the d-FNC of 13 IC pairs within RSNs between the two groups, the CM group exhibited significantly decreased connections between the DMN (IC 11) and AN (IC 5), and increased connections between the ECN (IC 2, IC 4) and DMN (IC 19), ECN (IC 4) and AN (IC 5), and ECN (IC 4) and VN (IC 13) in state 1. However, no significant differences in s-FNC were observed between the two groups during the s-FNC analysis. Between-group comparisons of three dynamic metrics between the CM and HC groups showed a longer fraction of time spent and mean dwell time in state 2 for CM patients. Furthermore, from the correlation analyses between these metrics and clinical characteristics, we observed a significant positive correlation between the number of transitions and mean VAS scores.

Conclusions

Our findings suggest that functional features of the CM brain may fluctuate over time instead of remaining static, and provide further evidence that migraine chronification may be related to abnormal pattern connectivity between sensory and cognitive brain networks.

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

The value of a shorter-delay arterial spin labeling protocol for detecting cerebrovascular impairment

Background

The aim of this study was to determine the relationship between blood oxygen level dependent (BOLD) cerebrovascular reactivity (CVR) and cerebral blood flow (CBF) obtained from arterial spin labeling (ASL) using different post labeling delays (PLD).

Methods

Forty-two patients with steno-occlusive diseases and impaired CVR were divided into two groups, one scanned with a 1.5-second (1.5-s) and the other with a 2.5-second (2.5-s) PLD ASL protocol. For all patients, a region of interest (ROI) was drawn around the CVR impairment. This affected ROI was then left-right flipped across the brain midline to obtain the control ROI. For both groups, the difference in grey matter CVR between affected and control ROI was first tested to confirm significance. The average grey matter CBF of affected and control ROIs were then compared. The same analysis method was used to compare affected and control hemispheres.

Results

In both groups of 1.5-s and 2.5-s PLD, CVR values in the affected ROI (-0.049±0.055 and -0.042±0.074%/mmHg, respectively) were significantly lower compared to that in the control ROI (0.152±0.054 and 0.152±0.053%/mmHg, respectively, P<0.0001). In the group with the 1.5-s PLD, CBF in the affected ROI (37.62±11.37 mL/100 g/min) was significantly lower compared to CBF in the control ROI (44.13±11.58 mL/100 g/min, P<0.05). However, in the group with the 2.5-s PLD, no significant differences could be seen between CBF in the affected ROI (40.50±14.82 mL/100 g/min) and CBF in the control ROI (39.68±12.49 mL/100 g/min, P=0.73). In the hemisphere-based analysis, CBF was significantly lower in the affected side than in the control side for the group with the 1.5-s PLD (P<0.05) when CVR was impaired (P<0.0001), but not for the group with the 2.5-s PLD (P=0.49).

Conclusions

In conclusion, our study reveals and highlights the value of a shorter-PLD ASL protocol, which is able to reflect CVR impairment. At the same time, we offer a better understanding of the relationship between BOLD CVR and CBF obtained from ASL.

Neural responses to food cues in middle to older aged adults: a scoping review of fMRI studies

AIM

Understanding neural responses through functional magnetic resonance imaging (fMRI) to food and food cues in middle-older adults may lead to better treatment options to address the growing issue of malnutrition. This scoping review aimed to determine the extent, range and nature of research using fMRI, related to reward-based regions, in response to food cues in middle to older aged adults (50 years and over).

METHODS

The following databases were systematically searched in July 2019: CINAHL, CENTRAL, Embase, Dissertations and Theses, Ovid Medline, PsycINFO, PsycEXTRA, Scopus and Web of Science. Studies were eligible for inclusion if participants had a mean or median age ≥50 years, utilised and reported outcomes of either a food cue task-related fMRI methodology or resting-state fMRI. Data from included studies were charted, and synthesised narratively.

RESULTS

Twenty-two studies were included. Eighteen studies utilised a task-related design to measure neural activation, two studies measured resting state neural connectivity only and an additional two studies measured both. The fMRI scanning paradigms, food cue tools and procedure of presentation varied markedly. Four studies compared the neural responses to food between younger and older adults, providing no consensus on neural age-related changes to food cues; two studies utilised longitudinal scans.

CONCLUSION

This review identified significant extent, range and nature in the approaches used to assess neuronal activity in response to food cues in adults aged 50 years and over. Future studies are needed to understand the age-related appetite changes whilst considering personal preferences for food cues.

The Status of the Quality Control in Neuroimaging Studies of Acupuncture Analgesia

Neuroimaging technology is an important technology used to explore the neural mechanisms of acupuncture analgesia. In this study, we extracted original studies published in Chinese and English focusing on the use of neuroimaging technology to explore the mechanisms of acupuncture analgesia from PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, Web of Science, and CNKI databases from January 1999 to August 2020. The extracted data were statistically analyzed in terms of year of publication, country, experimental design, and quality control approaches used, sample size, characteristics of participants, acupuncture operation, and other information. Analysis of the literature revealed that international cooperation promotes scientific research. Flexible experimental design can better explain the mechanism of acupuncture analgesia. Reasonable sample size, strict participant inclusion criteria, and standard acupuncture practices are essential for repeatability of conclusions. These findings show that attention should be paid to quality control in future research to improve the reliability of research on acupuncture analgesia.

A new virtue of phantom MRI data: explaining variance in human participant data

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to "noise". Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

Improvement in MR quality control workflow and outcomes with a web-based database

PURPOSE

To describe a custom-built, web-based MR Quality Control (QC) database, and to assess its impact on the QC workflow and outcomes in a large U.S. academic medical center.

METHODS

The MR QC database was built with Microsoft Access 2010 and published on a Microsoft Sharepoint website owned and maintained by the authors' institution. Authorized users can access the database remotely with mainstream web browsers on any institutional computers. QC technologists were granted access to add, review, and print daily and weekly QC records. Qualified medical physicists (QMPs) were granted additional access to edit, review, and approve existing QC records and to change tolerance limits. A macro was utilized to conduct an automatic weekly review of QC status and to email the results to a QMP. This web-based QC database was implemented on 17 clinical MRIs at the authors' institution. Weekly ACR QC findings within one year before and after implementation were compared.

RESULTS

We analyzed 158 QC issues detected by the web-based database and 127 QC issues identified in conventional paper records before we implemented the database. The web-based database significantly reduced the number of QC issues due to technologist error (before/after: 59/24 cases, P < 0.0001) but did not affect the number of QC issues related to scanner performance (before/after: 49/46 cases, P = 1). Further analysis revealed that the web-based database significantly reduced the average time for the QMPs to identify a QC issue (before/after: 177 ± 110/2 ± 2 days, P < 0.0001) and time to correction (before/after: 81 ± 102/7 ± 8 days, P < 0.0001). The correction rate also significantly increased (before/after: 22%/99%, P < 0.0001).

CONCLUSION

The web-based QC database provides a positive impact on our MR QC workflow and outcomes. It simplifies QC workflow, enables early detection of quality issues, and facilitates quick resolution of problems that may affect the quality of clinical MRI studies.