Resting State fMRI: Going Through the Motions

The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain activity for classifying individuals with ADHD, focusing on a 3D convolutional neural network (CNN) architecture to facilitate the design of decision support systems. We developed a novel deep learning model based on 3D CNNs using the ADHD-200 database, which comprises datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU). We used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data in three dimensions and performed a fivefold cross-validation to address the dataset imbalance. We aimed to verify the efficacy of our proposed 3D CNN by contrasting it with a fully connected neural network (FCNN) architecture. The 3D CNN achieved accuracy rates of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data. The FCNN model yielded lower accuracy rates across all datasets. For generalizability, we trained on NI and NYU datasets and tested on PU. The 3D CNN achieved 69.48% accuracy on fALFF outperforming the FCNN. Our results demonstrate that using 3D CNNs for classifying fALFF data is an effective approach for diagnosing ADHD. Also, FCNN confirmed the efficiency of the designed model.

Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data

Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.

Resting-state MRI reveals spontaneous physiological fluctuations in the kidney and tracks diabetic nephropathy in rats

The kidneys maintain fluid-electrolyte balance and excrete waste in the presence of constant fluctuations in plasma volume and systemic blood pressure. The kidneys perform these functions to control capillary perfusion and glomerular filtration by modulating the mechanisms of autoregulation. An effect of these modulations are spontaneous, natural fluctuations in glomerular perfusion. Numerous other mechanisms can lead to fluctuations in perfusion and flow. The ability to monitor these spontaneous physiological fluctuations in vivo could facilitate the early detection of kidney disease. The goal of this work was to investigate the use of resting-state magnetic resonance imaging (rsMRI) to detect spontaneous physiological fluctuations in the kidney. We performed rsMRI of rat kidneys in vivo over 10 min, applying motion correction to resolve time series in each voxel. We observed spatially variable, spontaneous fluctuations in rsMRI signal between 0 and 0.3 Hz, in frequency bands associated with autoregulatory mechanisms. We further applied rsMRI to investigate changes in these fluctuations in a rat model of diabetic nephropathy. Spectral analysis was performed on time series of rsMRI signals in the kidney cortex and medulla. The power from spectra in specific frequency bands from the cortex correlated with severity of glomerular pathology caused by diabetic nephropathy. Finally, we investigated the feasibility of using rsMRI of the human kidney in two participants, observing the presence of similar, spatially variable fluctuations. This approach may enable a range of preclinical and clinical investigations of kidney function and facilitate the development of new therapies to improve outcomes in patients with kidney disease.NEW & NOTEWORTHY This work demonstrates the development and use of resting-state MRI to detect low-frequency, spontaneous physiological fluctuations in the kidney consistent with previously observed fluctuations in perfusion and potentially due to autoregulatory function. These fluctuations are detectable in rat and human kidneys, and the power of these fluctuations is affected by diabetic nephropathy in rats.

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Background and purpose

There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.

Methods

In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.

Results

To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.

Conclusion

Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Prenatal Magnesium Sulfate and Functional Connectivity in Offspring at Term-Equivalent Age

Importance

Understanding the effect of antenatal magnesium sulfate (MgSO4) treatment on functional connectivity will help elucidate the mechanism by which it reduces the risk of cerebral palsy and death.

Objective

To determine whether MgSO4 administered to women at risk of imminent preterm birth at a gestational age between 30 and 34 weeks is associated with increased functional connectivity and measures of functional segregation and integration in infants at term-equivalent age, possibly reflecting a protective mechanism of MgSO4.

Design, Setting, and Participants

This cohort study was nested within a randomized placebo-controlled trial performed across 24 tertiary maternity hospitals. Participants included infants born to women at risk of imminent preterm birth at a gestational age between 30 and 34 weeks who participated in the MAGENTA (Magnesium Sulphate at 30 to 34 Weeks' Gestational Age) trial and underwent magnetic resonance imaging (MRI) at term-equivalent age. Ineligibility criteria included illness precluding MRI, congenital or genetic disorders likely to affect brain structure, and living more than 1 hour from the MRI center. One hundred and fourteen of 159 eligible infants were excluded due to incomplete or motion-corrupted MRI. Recruitment occurred between October 22, 2014, and October 25, 2017. Participants were followed up to 2 years of age. Analysis was performed from February 1, 2021, to February 27, 2024. Observers were blind to patient groupings during data collection and processing.

Exposures

Women received 4 g of MgSO4 or isotonic sodium chloride solution given intravenously over 30 minutes.

Main Outcomes and Measures

Prior to data collection, it was hypothesized that infants who were exposed to MgSO4 would show enhanced functional connectivity compared with infants who were not exposed.

Results

A total of 45 infants were included in the analysis: 24 receiving MgSO4 treatment and 21 receiving placebo; 23 (51.1%) were female and 22 (48.9%) were male; and the median gestational age at scan was 40.0 (IQR, 39.1-41.1) weeks. Treatment with MgSO4 was associated with greater voxelwise functional connectivity in the temporal and occipital lobes and deep gray matter structures and with significantly greater clustering coefficients (Hedge g, 0.47 [95% CI, -0.13 to 1.07]), transitivity (Hedge g, 0.51 [95% CI, -0.10 to 1.11]), local efficiency (Hedge g, 0.40 [95% CI, -0.20 to 0.99]), and global efficiency (Hedge g, 0.31 [95% CI, -0.29 to 0.90]), representing enhanced functional segregation and integration.

Conclusions and Relevance

In this cohort study, infants exposed to MgSO4 had greater voxelwise functional connectivity and functional segregation, consistent with increased brain maturation. Enhanced functional connectivity is a possible mechanism by which MgSO4 protects against cerebral palsy and death.

Similar imaging changes and their relations to genetic profiles in bipolar disorder across different clinical stages

Bipolar disorder (BD) across different clinical stages may present shared and distinct changes in brain activity. We aimed to reveal the neuroimaging homogeneity and heterogeneity of BD and its relationship with clinical variables and genetic variations. In present study, we conducted fractional amplitude of low-frequency fluctuations (fALFF), functional connectivity (FC) and genetic neuroimaging association analyses with 32 depressed, 26 manic, 35 euthymic BD patients and 87 healthy controls (HCs). Significant differences were found in the bilateral pre/subgenual anterior cingulate cortex (ACC) across the four groups, and all bipolar patients exhibited decreased fALFF values in the ACC when compared to HCs. Furthermore, positive associations were significantly observed between fALFF values in the pre/subgenual ACC and participants' cognitive functioning. No significant changes were found in ACC-based FC. We identified fALFF-alteration-related genes in BD, with enrichment in biological progress including synaptic and ion transmission. Taken together, abnormal activity in ACC is a characteristic change associated with BD, regardless of specific mood stages, serving as a potential neuroimaging feature in BD patients. Our genetic neuroimaging association analysis highlights possible heterogeneity in biological processes that could be responsible for different clinical stages in BD.

Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Higher body weight-dependent neural activation during reward processing

Obesity is associated with alterations in brain structure and function, particularly in areas related to reward processing. Although brain structural investigations have demonstrated a continuous association between higher body weight and reduced gray matter in well-powered samples, functional neuroimaging studies have typically only contrasted individuals from the normal weight and obese body mass index (BMI) ranges with modest sample sizes. It remains unclear, whether the commonly found hyperresponsiveness of the reward circuit can (a) be replicated in well-powered studies and (b) be found as a function of higher body weight even below the threshold of clinical obesity. 383 adults across the weight spectrum underwent functional magnetic resonance imaging during a common card-guessing paradigm simulating monetary reward. Multiple regression was used to investigate the association of BMI and neural activation in the reward circuit. In addition, a one-way ANOVA model comparing three weight groups (normal weight, overweight, obese) was calculated. Higher BMI was associated with higher reward response in the bilateral insula. This association could no longer be found when participants with obesity were excluded from the analysis. The ANOVA revealed higher activation in obese vs. lean, but no difference between lean and overweight participants. The overactivation of reward-related brain areas in obesity is a consistent finding that can be replicated in large samples. In contrast to brain structural aberrations associated with higher body weight, the neurofunctional underpinnings of reward processing in the insula appear to be more pronounced in the higher body weight range.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

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.

External Hardware and Sensors, for Improved MRI

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Brain motion networks predict head motion during rest- and task-fMRI

Introduction

The capacity to stay still during scanning, which is necessary to avoid motion confounds while imaging, varies markedly between people.

Methods

Here we investigated the effect of head motion on functional connectivity using connectome-based predictive modeling (CPM) and publicly available brain functional magnetic resonance imaging (fMRI) data from 414 individuals with low frame-to-frame motion (Δd < 0.18 mm). Leave-one-out was used for internal cross-validation of head motion prediction in 207 participants, and twofold cross-validation was used in an independent sample (n = 207).

Results and Discussion

Parametric testing, as well as CPM-based permutations for null hypothesis testing, revealed strong linear associations between observed and predicted values of head motion. Motion prediction accuracy was higher for task- than for rest-fMRI, and for absolute head motion (d) than for Δd. Denoising attenuated the predictability of head motion, but stricter framewise displacement threshold (FD = 0.2 mm) for motion censoring did not alter the accuracy of the predictions obtained with lenient censoring (FD = 0.5 mm). For rest-fMRI, prediction accuracy was lower for individuals with low motion (mean Δd < 0.02 mm; n = 200) than for those with moderate motion (Δd < 0.04 mm; n = 414). The cerebellum and default-mode network (DMN) regions that forecasted individual differences in d and Δd during six different tasks- and two rest-fMRI sessions were consistently prone to the deleterious effect of head motion. However, these findings generalized to a novel group of 1,422 individuals but not to simulated datasets without neurobiological contributions, suggesting that cerebellar and DMN connectivity could partially reflect functional signals pertaining to inhibitory motor control during fMRI.

Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis

Introduction

Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes.

Methods

We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS.

Results

No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation.

Discussion

Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.

Motion-corrected 4D-Flow MRI for neurovascular applications

Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experiments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize performance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative measures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P<0.001 Lt ICA, P=0.002 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; Area: P<0.001 Lt ICA, P<0.001 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; flow pulsatility index: P=0.042 Rt ICA, P=0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measurement bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.

Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

Sleep to Internalizing Pathway in Young Adolescents (SIPYA): A proposed neurodevelopmental model

The prevalence of internalizing disorders, i.e., anxiety and depressive disorders, spikes in adolescence and has been increasing amongst adolescents despite the existence of evidence-based treatments, highlighting the need for advancing theories on how internalizing disorders emerge. The current review presents a theoretical model, called the Sleep to Internalizing Pathway in Young Adolescents (SIPYA) Model, to explain how risk factors, namely sleep-related problems (SRPs), are prospectively associated with internalizing disorders in adolescence. Specifically, SRPs during late childhood and early adolescence, around the initiation of pubertal development, contribute to the interruption of intrinsic brain networks dynamics, both within the default mode network and between the default mode network and other networks in the brain. This interruption leaves adolescents vulnerable to repetitive negative thought, such as worry or rumination, which then increases vulnerability to internalizing symptoms and disorders later in adolescence. Sleep-related behaviors are observable, modifiable, low-stigma, and beneficial beyond treating internalizing psychopathology, highlighting the intervention potential associated with understanding the neurodevelopmental impact of SRPs around the transition to adolescence. This review details support for the SIPYA Model, as well as gaps in the literature and future directions.

Children with strabismus and amblyopia presented abnormal spontaneous brain activities measured through fractional amplitude of low-frequency fluctuation (fALFF)

Purpose

Based on fMRI technology, we explored whether children with strabismus and amblyopia (SA) showed significant change in fractional amplitude of low-frequency fluctuation (fALFF) values in specific brain regions compared with healthy controls and whether this change could point to the clinical manifestations and pathogenesis of children with strabismus to a certain extent.

Methods

We enrolled 23 children with SA and the same number matched healthy controls in the ophthalmology department of the First Affiliated Hospital of Nanchang University, and the whole brain was scanned by rs-fMRI. The fALFF value of each brain area was derived to examine whether there is a statistical difference between the two groups. Meanwhile, the ROC curve was made in a view to evaluate whether this difference proves useful as a diagnostic index. Finally, we analyzed whether changes in the fALFF value of some specific brain regions are related to clinical manifestations.

Results

Compared with HCs, children with SA presented decreased fALFF values in the left temporal pole: the superior temporal gyrus, right middle temporal gyrus, right superior frontal gyrus, and right supplementary motor area. Meanwhile, they also showed higher fALFF values in specific brain areas, which included the left precentral gyrus, left inferior parietal, and left precuneus.

Conclusion

Children with SA showed abnormal fALFF values in different brain regions. Most of these regions were allocated to the visual formation pathway, the eye movement-related pathway, or other visual-related pathways, suggesting the pathological mechanism of the patient.

Performance of Temporal and Spatial Independent Component Analysis in Identifying and Removing Low-Frequency Physiological and Motion Effects in Resting-State fMRI

Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach.

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

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

Functional alterations in large-scale resting-state networks of amyotrophic lateral sclerosis: A multi-site study across Canada and the United States

Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disorder characterized by progressive degeneration of upper motor neurons and lower motor neurons, and frontotemporal regions resulting in impaired bulbar, limb, and cognitive function. Magnetic resonance imaging studies have reported cortical and subcortical brain involvement in the pathophysiology of ALS. The present study investigates the functional integrity of resting-state networks (RSNs) and their importance in ALS. Intra- and inter-network resting-state functional connectivity (Rs-FC) was examined using an independent component analysis approach in a large multi-center cohort. A total of 235 subjects (120 ALS patients; 115 healthy controls (HC) were recruited across North America through the Canadian ALS Neuroimaging Consortium (CALSNIC). Intra-network and inter-network Rs-FC was evaluated by the FSL-MELODIC and FSLNets software packages. As compared to HC, ALS patients displayed higher intra-network Rs-FC in the sensorimotor, default mode, right and left fronto-parietal, and orbitofrontal RSNs, and in previously undescribed networks including auditory, dorsal attention, basal ganglia, medial temporal, ventral streams, and cerebellum which negatively correlated with disease severity. Furthermore, ALS patients displayed higher inter-network Rs-FC between the orbitofrontal and basal ganglia RSNs which negatively correlated with cognitive impairment. In summary, in ALS there is an increase in intra- and inter-network functional connectivity of RSNs underpinning both motor and cognitive impairment. Moreover, the large multi-center CALSNIC dataset permitted the exploration of RSNs in unprecedented detail, revealing previously undescribed network involvement in ALS.

Cerebral Small Vessel Disease: Neuroimaging Features, Biochemical Markers, Influencing Factors, Pathological Mechanism and Treatment

Cerebral small vessel disease (CSVD) is the most common chronic vascular disease involving the whole brain. Great progress has been made in clinical imaging, pathological mechanism, and treatment of CSVD, but many problems remain. Clarifying the current research dilemmas and future development direction of CSVD can provide new ideas for both basic and clinical research. In this review, the risk factors, biological markers, pathological mechanisms, and the treatment of CSVD will be systematically illustrated to provide the current research status of CSVD. The future development direction of CSVD will be elucidated by summarizing the research difficulties.

The association between head motion during functional magnetic resonance imaging and executive functioning in older adults

Minimizing head motion during functional magnetic resonance imaging (fMRI) is important for maintaining the integrity of neuroimaging data. While there are a variety of techniques to control for head motion, oftentimes, individuals with excessive in-scanner motion are removed from analyses. Movement in the scanner tends to increase with age; however, the cognitive profile of these "high-movers" in older adults has yet to be explored. This study aimed to assess the association between in-scanner head motion (i.e., number of "invalid scans" flagged as motion outliers) and cognitive functioning (e.g., executive functioning, processing speed, and verbal memory performance) in a sample of 282 healthy older adults. Spearman's Rank-Order correlations showed that a higher number of invalid scans was significantly associated with poorer performance on tasks of inhibition and cognitive flexibility and with older age. Since performance in these domains tend to decline as a part of the non-pathological aging process, these findings raise concerns regarding the potential systematic exclusion due to motion of older adults with lower executive functioning in neuroimaging samples. Future research should continue to explore prospective motion correction techniques to better ensure the collection of quality neuroimaging data without excluding informative participants from the sample.

Applications of Advanced MRI to Disorders of Consciousness

Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.

Sexually divergent development of depression-related brain networks during healthy human adolescence

Sexual differences in human brain development could be relevant to sex differences in the incidence of depression during adolescence. We tested for sex differences in parameters of normative brain network development using fMRI data on N = 298 healthy adolescents, aged 14 to 26 years, each scanned one to three times. Sexually divergent development of functional connectivity was located in the default mode network, limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of sexually divergent brain network development was robustly colocated with i prior loci of reward-related brain activation ii a map of functional dysconnectivity in major depressive disorder (MDD), and iii an adult brain gene transcriptional pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. We found normative sexual divergence in adolescent development of a cortico-subcortical brain functional network that is relevant to depression.

A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

Characteristics of the Fractional Amplitude of Low-Frequency Fluctuation in Ocular Hypertension Patients: A Resting-State fMRI Study

Background

The fractional amplitude of low-frequency fluctuation (fALFF) method has been underutilized in research on the pathogenesis and clinical manifestations of ocular hypertension (OH).

Purpose

This study uses resting state functional magnetic resonance imaging (rs-fMRI) and fALFF to investigate the nature of spontaneous brain activity in OH patients and the relationship, if any, between changes in activity and clinical features.

Materials and Methods

A total of 18 subjects (9 females and 9 males) with ocular hypertension (OH) and 18 healthy controls (HCs) matched for gender, age, and educational level were recruited to this study. All participants underwent an rs-fMRI scan, and spontaneous brain activity was assessed using the fALFF method. Receiver operating characteristic curves were plotted to investigate differences between OH and HC groups.

Results

The fALFF values of OH patients were significantly higher in the left precuneus lobe (LP), compared with the same region in controls (P < 0.05). Conversely, values in the left anterior cingulate lobe (LAC), were significantly lower (P < 0.05) in OH than in controls. However, no significant association was found between the mean fALFF values and clinical characteristics in either brain area.

Conclusion

High spontaneous activity in two brain areas may reflect neuropathological mechanisms underpinning visual impairment in OH patients.

Probing the association between resting-state brain network dynamics and psychological resilience

This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.

The "Neural Shift" of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully understood. We tested the hypothesis that a shift in brain networks dynamics relates to sleep quality and cognitive performance across the lifespan. Network dynamics were assessed using Hidden Markov Models (HMMs) in resting-state MEG data from a large cohort of population-based adults (N = 564, aged 18-88). Using multivariate analyses of brain-sleep profiles and brain-cognition profiles, we found an age-related "neural shift," expressed as decreased occurrence of "lower-order" brain networks coupled with increased occurrence of "higher-order" networks. This "neural shift" was associated with both increased sleep dysfunction and decreased fluid intelligence, and this relationship was not explained by age, sex or other covariates. These results establish the link between poor sleep quality, as evident in aging, and a behavior-related shift in neural dynamics.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus - alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Test-retest stability of spontaneous brain activity and functional connectivity in the core resting-state networks assessed with ultrahigh field 7-Tesla resting-state functional magnetic resonance imaging

The growing demand for precise and reliable biomarkers in psychiatry is fueling research interest in the hope that identifying quantifiable indicators will improve diagnoses and treatment planning across a range of mental health conditions. The individual properties of brain networks at rest have been highlighted as a possible source for such biomarkers, with the added advantage that they are relatively straightforward to obtain. However, an important prerequisite for their consideration is their reproducibility. While the reliability of resting‐state (RS) measurements has often been studied at standard field strengths, they have rarely been investigated using ultrahigh‐field (UHF) magnetic resonance imaging (MRI) systems. We investigated the intersession stability of four functional MRI RS parameters—amplitude of low‐frequency fluctuations (ALFF) and fractional ALFF (fALFF; representing the spontaneous brain activity), regional homogeneity (ReHo; measure of local connectivity), and degree centrality (DC; measure of long‐range connectivity)—in three RS networks, previously shown to play an important role in several psychiatric diseases—the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Our investigation at individual subject space revealed a strong stability for ALFF, ReHo, and DC in all three networks, and a moderate level of stability in fALFF. Furthermore, the internetwork connectivity between each network pair was strongly stable between CEN/SN and moderately stable between DMN/SN and DMN/SN. The high degree of reliability and reproducibility in capturing the properties of the three major RS networks by means of UHF‐MRI points to its applicability as a potentially useful tool in the search for disease‐relevant biomarkers.

Robust Correlation for Link Definition in Resting-State fMRI Brain Networks Can Reduce Motion-Related Artifacts

Introduction: It is well known that even small head movements introduce artifacts in resting-state functional magnetic resonance imaging data, and over the years, numerous methods were introduced to correct for this issue. The field of robust statistics, however, has not yet received much attention in this regard. In this article, we tested a recently developed statistical method called wrapping and compared it with two already established methods: data scrubbing and an independent component analysis-based approach for the automatic removal of motion artifacts (ICA-AROMA). Methods: A group of N = 120 healthy adult subjects were divided into high and low movement cohorts. The functional connectomes following wrapping, data scrubbing, and ICA-AROMA of the high movement cohort were compared with the mean functional connectome of the low movement cohort. Results and Discussion: Our results showed that wrapping could significantly decrease the Euclidean distance between connectomes of the two cohorts. Furthermore, wrapping was able to compensate the systematic effect of increased short distance correlations and reduced long distance correlations in functional connectomes, which often result from high subject motion. Our findings suggest that wrapping constitutes a valuable approach to correct for movement-related artifacts when estimating functional connectivity in the brain. Impact statement The influence of subject motion on functional magnetic resonance imaging (fMRI) data is still an actively discussed topic. However, to handle this problem, the field of robust statistics has not been given much attention yet. We want to fill this void by introducing and validating a recently developed method for calculating robust correlations. Our study shows that estimating robust correlations can improve fMRI preprocessing, and documents for a wider readership that fMRI analyses can benefit from new methods in the field of robust statistics.

Transient neural network dynamics in cognitive ageing

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganize and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=595) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Photoacoustic Neuroimaging - Perspectives on a Maturing Imaging Technique and its Applications in Neuroscience

A prominent goal of neuroscience is to improve our understanding of how brain structure and activity interact to produce perception, emotion, behavior, and cognition. The brain's network activity is inherently organized in distinct spatiotemporal patterns that span scales from nanometer-sized synapses to meter-long nerve fibers and millisecond intervals between electrical signals to decades of memory storage. There is currently no single imaging method that alone can provide all the relevant information, but intelligent combinations of complementary techniques can be effective. Here, we thus present the latest advances in biomedical and biological engineering on photoacoustic neuroimaging in the context of complementary imaging techniques. A particular focus is placed on recent advances in whole-brain photoacoustic imaging in rodent models and its influential role in bridging the gap between fluorescence microscopy and more non-invasive techniques such as magnetic resonance imaging (MRI). We consider current strategies to address persistent challenges, particularly in developing molecular contrast agents, and conclude with an overview of potential future directions for photoacoustic neuroimaging to provide deeper insights into healthy and pathological brain processes.

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity (FC) and brain disorders. However, FC analysis can be seriously affected by random and structured noise from non-neural sources, such as physiology. Thus, it is essential to first reduce thermal noise and then correctly identify and remove non-neural artifacts from rs-fMRI signals through optimized data processing methods. However, existing tools that correct for these effects have been developed for human brain and are not readily transposable to rat data. Therefore, the aim of the present study was to establish a data processing pipeline that can robustly remove random and structured noise from rat rs-fMRI data. It includes a novel denoising approach based on the Marchenko-Pastur Principal Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artifact classification and cleaning, and global signal regression (GSR). Our results show that: (I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio, (II) the pre-trained FIX classifier achieves a high accuracy in artifact classification, and (III) both independent component analysis (ICA) cleaning and GSR are essential steps in correcting for possible artifacts and minimizing the within-group variability in control animals while maintaining typical connectivity patterns. Reduced within-group variability also facilitates the exploration of potential between-group FC changes, as illustrated here in a rat model of sporadic Alzheimer's disease.

A review of resting-state fMRI and its use to examine psychiatric disorders

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.

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

BACKGROUND

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

NEW METHOD

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

RESULTS

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

COMPARISON TO EXISTING METHODS

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

CONCLUSIONS

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

Brain reactivity using fMRI to insomnia stimuli in insomnia patients with discrepancy between subjective and objective sleep

Subjective–objective discrepancy of sleep (SODS) might be related to the distorted perception of sleep deficit and hypersensitivity to insomnia-related stimuli. We investigated differences in brain activation to insomnia-related stimuli among insomnia patients with SODS (SODS group), insomnia patients without SODS (NOSODS group), and healthy controls (HC). Participants were evaluated for subjective and objective sleep using sleep diary and polysomnography. Functional magnetic resonance imaging was conducted during the presentation of insomnia-related (Ins), general anxiety-inducing (Gen), and neutral (Neu) stimuli. Brain reactivity to the contrast of Ins vs. Neu and Gen vs. Neu was compared among the SODS (n = 13), NOSODS (n = 15), and HC (n = 16) groups. In the SODS group compared to other groups, brain areas including the left fusiform, bilateral precuneus, right superior frontal gyrus, genu of corpus callosum, and bilateral anterior corona radiata showed significantly increased blood oxygen level dependent (BOLD) signal in the contrast of Ins vs. Neu. There was no brain region with significantly increased BOLD signal in the Gen vs. Neu contrast in the group comparisons. Increased brain activity to insomnia-related stimuli in several brain regions of the SODS group is likely due to these individuals being more sensitive to sleep-related threat and negative cognitive distortion toward insomnia.

Abnormal Functional Connectivity of the Amygdala in Mild Cognitive Impairment Patients With Depression Symptoms Revealed by Resting-State fMRI

Convergent evidence indicates that individuals with symptoms of depression exhibit altered functional connectivity (FC) of the amygdala, which is a key brain region in processing emotions. At present, the characteristics of amygdala functional circuits in patients with mild cognitive impairment (MCI) with and without depression are not clear. The current study examined the features of amygdala FC in patients with MCI with depression symptoms (D-MCI) using resting-state functional magnetic resonance imaging. We acquired resting-state functional magnetic resonance imaging data from 16 patients with D-MCI, 18 patients with MCI with no depression (nD-MCI), and 20 healthy controls (HCs) using a 3T scanner and compared the strength of amygdala FC between the three groups. Patients with D-MCI exhibited significant FC differences in the amygdala-medial prefrontal cortex and amygdala-sensorimotor networks. These results suggest that the dysfunction of the amygdala-medial prefrontal cortex network and the amygdala-sensorimotor network might be involved in the neural mechanism underlying depression in MCI.

Involvement of the dentate nucleus in the pathophysiology of amyotrophic lateral sclerosis: A multi-center and multi-modal neuroimaging study

•

This original research article highlights cerebellar structural and functional connectivity abnormalities implicated in the pathophysiology of ALS.

•

In this study, resting-state functional MRI (rs-FMRI), diffusion tensor imaging (DTI), and 3D T1W structural images were examined.

•

Functional connectivity was investigated between the cerebral cortex and cerebellum targeting the dentate nucleus (DN).

•

Microstructural white matter diffusivity was examined along the cerebellar peduncles connecting the DN with the cerebral cortex and brain stem.

•

Grey matter volumes of the cerebellar lobules and DN were determined.

•

Overall, we provide evidence supporting involvement of the DN and associated cerebellar white matter tracts in the pathophysiology of ALS.

Amyotrophic lateral sclerosis (ALS) is characterized primarily by motor neuron but also frontotemporal lobar degeneration. Although the cerebellum is involved in both motor and cognitive functions, little is known of its role in ALS. We targeted the dentate nucleus (DN) in the cerebellum and the associated white matter fibers tracts connecting the DN to the rest of the brain using multimodal imaging techniques to examine the cerebellar structural and functional connectivity patterns in ALS patients and hypothesized that the DN is implicated in the pathophysiology of ALS. A cohort of 127 participants (56 healthy subjects (HS); 71 ALS patients) were recruited across Canada through the Canadian ALS Neuroimaging Consortium (CALSNIC). Resting state functional MRI, diffusion tensor imaging (DTI), and 3D weighted T1 structural images were acquired on a 3-tesla scanner. The DN in the cerebellum was used as a seed to evaluate the whole brain cerebral resting-state functional connectivity (rsFC). The superior cerebellar peduncle (SCP), middle cerebellar peduncle (MCP) and inferior cerebellar peduncle (ICP) were used as a region of interest in DTI to evaluate the structural integrity of the DN with the cortex and brain stem. Cerebellar volumetric analysis was done to examine the lobular and DN grey matter (GM) changes in ALS patients. Lastly, an association between DN rsFC and structural alterations were explored. DN rsFC was reduced with cerebrum (supplementary motor area, precentral gyrus, frontal, posterior parietal, temporal), lobule IV, and brain stem, and increased with parieto-occipital region. DN rsFC and white matter (WM) diffusivity alterations at SCP, MCP, and ICP were accompanied by correlations with ALSFRS-R. There were no DN volumetric changes. Notably, DN rsFC correlated with WM abnormalities at superior cerebellar peduncle. The DN plays a pathophysiological role in ALS. Impaired rsFC is likely due to the observed cerebellar peduncular WM damage given the lack of GM atrophy of the DN. This study demonstrates altered cerebellar rsFC connectivity with motor and extra-motor regions in ALS, and impaired rsFC is likely due to the observed cerebellar peduncular WM damage given the lack of GM atrophy of the DN. The correlation between the altered DN connectivity, and the behavioral data support the hypothesis that the DN plays a pathophysiological role in ALS.

Abnormal Fractional Amplitude of Low-Frequency Fluctuation Changes in Patients with Monocular Blindness: A Functional Magnetic Resonance Imaging (MRI) Study

BACKGROUND We used fractional amplitude of low-frequency fluctuation (fALFF) technology to investigate spontaneous cerebral activity in patients with monocular blindness (MB) and in healthy controls (HCs). MATERIAL AND METHODS Thirty MB patient and 15 HCs were included in this study. All subjects were scanned by resting-state functional magnetic resonance imaging (rs-fMRI). The independent sample t test and chi-squared test were applied to analyze demographics of MB patients and HCs. The 2-sample t test and receiver operating characteristic (ROC) curves were applied to identify the difference in average fALFF values between MB patients and HCs. Pearson's correlation analysis was applied to explore the relationship between the average fALFF values of brain areas and clinical behavior in the MB group. RESULTS MB patients had lower fALFF values in the left anterior cingulate and higher fALFF values in the left precuneus and right and left inferior parietal lobes than in HCs. Moreover, the mean fALFF values of MB patients in the left anterior cingulate had negative correlations with the anxiety scale score (r=-0.825, P<0.001) and the depression scale score (r=-0.871, P<0.001). CONCLUSIONS Our study found that MB patients had abnormal spontaneous activities in the visual and vision-related regions. The finding of abnormal neuronal activity helps to reveal the underlying neuropathologic mechanisms of vision loss.

Evaluation of distraction techniques for patients aged 4-10 years undergoing magnetic resonance imaging examinations

OBJECTIVES

The main aim of the review is to identify potentially effective distraction techniques for the 4 to 10 age range whilst reducing the need for sedation. Objectives also included assessment of the applicability of distraction for the 4-10 age range and, where appropriate, to identify potential cost implications and assess the interventions' impact on image quality.

KEY FINDINGS

A priori search terms, inclusion and exclusion criteria were developed and two independent reviewers were employed to assess study quality. Five studies fitted the criteria of the systematic search strategy. The studies implemented a range of distraction and preparatory techniques resulting in paediatric patients being able to complete an MRI scan to a diagnostic level in the 4 to 10-year-old age category with a sedation rate of 5-20%. All interventions included in the review required time with the patient prior to the scan.

CONCLUSION

There are a range of efficacious techniques that can be employed to reduce the sedation rates in children aged 4-10 years, whilst allowing diagnostic images to be acquired. The introduction of play and the engagement with the patient prior to the scan appear to be indicators of intervention effectiveness. The efficacy of these interventions does not appear to be linked with proprietary equipment.

IMPLICATIONS FOR PRACTICE

Age appropriate interventions are necessary for children of different ages and these distraction interventions may be implemented within departments, for little cost, with notable benefits in terms of sedation.

Graphical interface for automated management of motion artifact within fMRI acquisitions: INFOBAR

Independent Component Analysis-based Automatic Removal of Motion Artifacts (ICA-AROMA; Pruim et al., 2015) is a robust approach to remove brain activity related to head motion within functional magnetic resonance imaging (fMRI) datasets. However, ICA-AROMA requires command line implementation and customized code to batch process large datasets. We developed a cross-platform, open-source graphical user Interface for Batch processing fMRI datasets using ICA-AROMA (INFOBAR). INFOBAR allows a user to search directories, identify appropriate datasets, and batch execute ICA-AROMA. INFOBAR also has additional data processing options and visualization features to support all researchers interested in mitigating head motion artifact in post-processing using ICA-AROMA.

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood‐oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo‐controlled, three‐way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting‐state networks. Independent components analysis (ICA)‐denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross‐modal comparisons of pharmacologically‐modulated functional connectivity.