Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity.

Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.

Improved resting state functional connectivity sensitivity and reproducibility using a multiband multi-echo acquisition.

Recent advances in functional MRI techniques include multiband (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple slices are acquired simultaneously leading to significant increases in temporal and spatial resolution. Multi-echo imaging enables multiple echoes to be acquired in one shot, where the ME images can be used to denoise the BOLD time series and increase BOLD sensitivity. In this study, resting state fMRI (rs-fMRI) data were collected using a combined MBME sequence and compared to an MB single echo sequence. In total, 29 subjects were imaged, and 18 of them returned within two weeks for repeat imaging. Participants underwent one MBME scan with three echoes and one MB scan with one echo. Both datasets were processed using standard denoising and advanced denoising. Advanced denoising included multi-echo independent component analysis (ME-ICA) for the MBME data and ICA-AROMA for the MB data. Resting state functional connectivity (RSFC) was evaluated using both selective seed-based and whole grey matter (GM) region-of-interest (ROI) based approaches. The reproducibility of connectivity metrics was also analyzed in the repeat subjects. In addition, functional connectivity density (FCD), a data-driven approach that counts the number of significant connections, both within a local cluster and globally, with each voxel was analyzed. Regardless of the standard or advanced denoising technique, all seed-based RSFC was significantly higher for MBME compared to MB. Much more GM ROI combinations showed significantly higher RSFC for MBME vs. MB. Reproducibility, evaluated using the dice coefficient was significantly higher for MBME relative to MB data. Finally, FCD was also higher for MBME vs. MB data. This study showed higher RSFC for MBME vs. MB data using selected seed-based, whole GM ROI-based, and data-driven approaches. Reproducibility found also higher for MBME data. Taken together, these results indicate that MBME is a promising technique for rs-fMRI.

Detecting Task Functional MRI Activation Using the Multiband Multiecho (MBME) Echo-Planar Imaging (EPI) Sequence.

BACKGROUND: Blood oxygen level-dependent (BOLD) functional MRI (fMRI) has been widely applied to detect brain activations. Recent advances in multiband (MB) and multiecho (ME) techniques have greatly improved fMRI methods. MB imaging improves temporal and/or spatial resolution, while ME imaging has been shown to improve BOLD sensitivity. This study aimed to evaluate the novel MBME echo planar imaging (EPI) sequence utilizing MB and ME simultaneously to determine if the MBME outperform the MB single echo (MBSE) sequence for task fMRI. PURPOSE: To compare the performance of MBME with MBSE in a task fMRI study. STUDY TYPE: Prospective. POPULATION: A total of 29 healthy volunteers aged 20-46 years (9 male, 20 female). FIELD STRENGTH/SEQUENCE: MBSE and MBME gradient-echo EPI sequences were applied at 3T. Additional T1 -weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) was collected. ASSESSMENT: A checkerboard visual task was presented during the functional MBSE and MBME scans. The MBME or MBSE signal was evaluated using the temporal signal-to-noise ratio (tSNR). Task activation was evaluated using the z-score, volume, sensitivity, and specificity. Test-retest metrics of task activation were examined with the Dice coefficient (DC) and intraclass correlation coefficient (ICC) on subjects with repeated scans. STATISTICAL TESTS: A linear mixed-effects model was used to compared MBME and MBSE activation at the voxel base. The paired t-test was used to compare tSNR, activation z-score, and volume, along with sensitivity, specificity, and DC between MBSE and MBME. RESULTS: While similar task activation was detected in the visual cortex, MBME showed higher activation volume and higher sensitivity compared with MBSE (P < 0.05). ICC was higher for MBME than MBSE, while there was a trend of differences in DC (P = 0.08). DATA CONCLUSION: MBME resulted in higher task fMRI activation volume and sensitivity without losing specificity. Reliability was also higher for MBME scans compared with MBSE. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex.

OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.

Spinal versus general anesthesia for patients undergoing outpatient total knee arthroplasty: a national propensity matched analysis of early postoperative outcomes.

BACKGROUND: A comparison of different anesthetic techniques to evaluate short term outcomes has yet to be performed for patients undergoing outpatient knee replacements. The aim of this investigation was to compare short term outcomes of spinal (SA) versus general anesthesia (GA) in patients undergoing outpatient total knee replacements. METHODS: The ACS NSQIP datasets were queried to extract patients who underwent primary, elective, unilateral total knee arthroplasty (TKA) between 2005 and 2018 performed as an outpatient procedure. The primary outcome was a composite score of serious adverse events (SAE). The primary independent variable was the type of anesthesia (e.g., general vs. spinal). RESULTS: A total of 353,970 patients who underwent TKA procedures were identified comprising of 6,339 primary, elective outpatient TKA procedures. Of these, 2,034 patients received GA and 3,540 received SA. A cohort of 1,962 patients who underwent outpatient TKA under GA were propensity matched for covariates with patients who underwent outpatient TKA under SA. SAE rates at 72 h after surgery were not greater in patients receiving GA compared to SA (0.92%, 0.66%, P = 0.369). In contrast, minor adverse events were greater in the GA group compared to SA (2.09%, 0.51%), P < 0.001. The rate of postoperative transfusion was greater in the patients receiving GA. CONCLUSIONS: The type of anesthetic technique, general or spinal anesthesia does not alter short term SAEs, readmissions and failure to rescue in patients undergoing outpatient TKR surgery. Recognizing the benefits of SA tailored to the anesthetic management may maximize the clinical benefits in this patient population.

Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this prediction. In this study, several regression-based machine-learning approaches were compared, including GLMs, feed-forward neural networks, and random forest bootstrap aggregation (bagging). Resting-state and task data from 350 Human Connectome Project subjects were analyzed. First, the effect of the number of training subjects on the prediction accuracy was evaluated. In addition, the prediction accuracy and Dice coefficient were compared across models. Prediction accuracy increased with the training number up to 200 subjects; however, an elbow in the prediction curve occurred around 30-40 training subjects. All models performed well with correlation matrices, which displayed correlation between actual and predicted task activation for all subjects, exhibiting a strong diagonal trend for all tasks. Overall, the neural network and random forest bagging techniques outperformed the GLM. These approaches, however, require additional computing power and processing time. These results show that, while the GLM performs well, resting-state fMRI prediction of task activation could benefit from more complex machine learning approaches.

Longitudinal Reproducibility of MR Perfusion Using 3D Pseudocontinuous Arterial Spin Labeling With Hadamard-Encoded Multiple Postlabeling Delays.

BACKGROUND: Arterial spin labeling (ASL) can be confounded by varying arterial transit times (ATT) across the brain and with disease. Hadamard encoding schemes can be applied to 3D pseudocontinuous ASL (pCASL) to acquire ASL data with multiple postlabeling delays (PLDs) to estimate ATT and then correct cerebral blood flow (CBF). PURPOSE: To assess the longitudinal reproducibility of 3D pCASL with Hadamard-encoded multiple PLDs. STUDY TYPE: Prospective, longitudinal. POPULATION: Fifty-two healthy, right-handed male subjects who underwent imaging at four timepoints over 45 days. FIELD STRENGTH/SEQUENCE: A Hadamard-encoded 3D pCASL sequence was acquired at 3.0T with seven PLDs from 1.0-3.7 sec. ASSESSMENT: ATT and corrected CBF (cCBF) were computed. Conventional uncorrected CBF (unCBF) was also estimated. Within- and between-subject coefficient of variation (wCV and bCV, respectively) and intraclass correlation coefficient (ICC) were evaluated across four time intervals: 7, 14, 30, and 45 days, in gray matter and 17 independent regions of interest (ROIs). A power analysis was also conducted. STATISTICAL TESTS: A repeated-measures analysis of variance (ANOVA) was used to compare ATT, cCBF, and unCBF across the four scan sessions. A paired two-sample t-test was used to compare cCBF and unCBF. Pearson's correlation was used to examine the relationship between the cCBF and unCBF difference and ATT. Power calculations were completed using both the cCBF and unCBF variances. RESULTS: ATT showed the lowest wCV and bCV (3.3-4.4% and 6.0-6.3%, respectively) compared to both cCBF (10.5-11.7% and 20.6-22.2%, respectively) and unCBF (12.0-13.6% and 22.7-23.7%, respectively). wCV and bCV were lower for cCBF vs. unCBF. A significant difference between cCBF and unCBF was found in most regions (P = 5.5 × 10-5 -3.8 × 10-4 in gray matter) that was highly correlated with ATT (R2 = 0.79-0.86). A power analysis yielded acceptable power at feasible sample sizes using cCBF. DATA CONCLUSION: ATT and ATT-corrected CBF were longitudinally stable, indicating that ATT and CBF changes can be reliably evaluated with Hadamard-encoded 3D pCASL with multiple PLDs. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1846-1853.

The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver.

PURPOSE: To examine the effect of low b-values (0 < b < 50 s/mm(2)) on the calculation of the intravoxel incoherent motion (IVIM) derived pseudodiffusion parameter in the normal liver. METHODS: Simulations were performed to examine the effects of adding low b-values on the pseudodiffusion parameter. Low b-values were cumulatively added to the distribution and the IVIM signal was generated with varying pseudodiffusion values. The signal was fit with the IVIM model after the addition of Gaussian noise, and the simulated values were compared with the true values. In addition, the livers of eight control subjects were imaged using respiratory-triggered DWI. Pseudodiffusion was calculated with and without low b-values and compared. RESULTS: Pseudodiffusion tended to be underestimated when low b-values were not included in the b-value distribution as predicted by simulations and confirmed with in vivo imaging. The number of outlier values was also reduced as more low b-values were added. CONCLUSION: In conclusion, this study showed pseudodiffusion in the liver tended to be underestimated when too few low b-values (0 < b < 50 s/mm(2)) were included in the distribution. Therefore, it is recommended to include at least two low b-values when performing liver IVIM studies.

The heterogeneity of non-motor symptoms of Parkinson's disease.

In addition to motor symptoms, patients with Parkinson's disease (PD) may experience non-motor symptoms (NMS), more often with more advanced disease stage. However, the clinical feature of the NMS and potential risk factors that affect NMS in idiopathic PD patients remain unclear. 493 PD patients diagnosed with PD via the UK. Parkinson's Disease Society Brain Bank Clinical Diagnostic Criteria and 93 healthy control subjects (CN) were recruited in the current study. Questionnaires were used to assess the NMS, motor symptoms, cognitive function, and disease severity in both groups. Levodopa daily dose was calculated in PD patients. Multivariate linear regression analysis was used to identify the risk factors that potentially affect NMS in PD patients. The results showed that, the NMS occurrence and positive endorsement rate of PD patients were significantly higher compared to CN subjects. Multiple stepwise regression analysis found the motor symptom was the only factor that affected NMS in PD patients within five years of the disease course, whereas motor symptoms, cognitive function, disease severity and Levodopa daily dose significantly affected NMS if the disease course was more than five years. These findings demonstrated that NMS are affected by several risk factors at different stages of PD. The distribution of difference NMS is associated with the severity of motor symptoms and the dosage of anti-PD medications in Chinese PD patients.

Effects of perfusion on diffusion changes in human brain tumors.

PURPOSE: To characterize the influence of perfusion on the measurement of diffusion changes over time when ADC is computed using standard two-point methods. MATERIALS AND METHODS: Functional diffusion maps (FDMs), which depict changes in diffusion over time, were compared with rCBV changes in patients with brain tumors. The FDMs were created by coregistering and subtracting ADC maps from two time points and categorizing voxels where ADC significantly increased (iADC), decreased (dADC), or did not change (ncADC). Traditional FDMs (tFDMs) were computed using b = 0,1000 s/mm(2). Flow-compensated FDMs (fcFDMs) were calculated using b = 500,1000 s/mm(2). Perfusion's influence on FDMs was determined by evaluating changes in rCBV in areas where the ADC change significantly differed between the two FDMs. RESULTS: The mean ΔrCBV in voxels that changed from iADC (dADC) on the tFDM to ncADC on the fcFDM was significantly greater (less) than zero. In addition, mean ΔrCBV in iADC (dADC) voxels on the tFDM was significantly higher (lower) than in iADC (dADC) voxels on the fcFDM. CONCLUSION: The ability to accurately identify changes in diffusion on traditional FDMs is confounded in areas where perfusion and diffusion changes are colocalized. Flow-compensated FDMs, which use only non-zero b-values, should therefore be the standard approach.

Decreased Cerebrovascular Reactivity in Mild Cognitive Impairment Phenotypes.

BACKGROUND: Cerebrovascular health plays an important role in cognitive health in older adults. Cerebrovascular reactivity (CVR), a measure of cerebrovascular health, changes in both normal and pathological aging, and is increasingly being conceptualized as contributory to cognitive decline. Interrogation of this process will yield new insights into cerebrovascular correlates of cognition and neurodegeneration. OBJECTIVE: The current study examines CVR using advanced MRI in prodromal dementia states (amnestic and non-amnestic mild cognitive impairment phenotypes; aMCI and naMCI, respectively) and older adult controls. METHODS: CVR was assessed in 41 subjects (20 controls, 11 aMCI, 10 naMCI) using multiband multi-echo breath-holding task functional magnetic resonance imaging. Imaging data were preprocessed and analyzed using AFNI. All participants also completed a battery of neuropsychological tests. T-tests and ANOVA/ANCOVA analyses were conducted to compare controls to MCI groups on CVR and cognitive metrics. Partial correlation analyses between CVR derived from regions-of-interest (ROIs) and different cognitive functions were conducted. RESULTS: CVR was found to be significantly lower in aMCI and naMCI patients compared to controls. naMCI showed intermediate patterns between aMCI and controls (though aMCI and naMCI groups did not significantly differ). CVR of ROIs were positively correlated with neuropsychological measures of processing speed, executive functioning, and memory. CONCLUSION: The findings highlight regional CVR differences in MCI phenotypes compared to controls, where aMCI may have lower CVR than naMCI. Our results suggest possible cerebrovascular abnormalities associated with MCI phenotypes.

Ventral striatal subregional dysfunction in late-life grief: Relationships with yearning and depressive symptoms.

Bereaved older adults experiencing high grief in the first year after an attachment loss is at increased risk for prolonged grief disorder (PGD) via unknown mechanisms. Yearning, a core grief symptom, is linked to the ventral striatal (VS) brain function, but the role of this neuronal system in late-life grief is poorly understood. As a first step, we examined the VS subregional abnormalities associated with multidimensional symptoms in bereaved elders during the first year post-loss. Sixty-five bereaved elders completed clinical assessments within 13 months post-loss. Ventral caudate (VCau) and nucleus accumbens (NAcc) functional connectivity (FC) was assessed using seed-based resting-state functional MRI. VCau and NAcc FC differences between high (inventory of complicated grief [ICG] score≥30; n = 35) and low (ICG score<30; n = 30) grief, and the relationships between ventral striatal subregional FC and clinical measures (yearning and depressive symptoms) were assessed after covariate adjustments (α < 0.05; 3dClustSim corrected). Relative to low grief participants, those with high grief showed higher FC between VCau and the medial prefrontal, orbitofrontal, and subgenual cingulate cortices. VCau FC abnormalities positively correlated with yearning (r2 = 0.24, p < 0.001). In contrast, FC between VCau and temporoparietal junction negatively correlated with depressive symptoms, a commonly co-occurring symptom (r2 = 0.37, p < 0.001). The FC between NAcc and insula/striatum positively correlated with yearning (r2 = 0.35, p < 0.001); no other NAcc FC findings were seen in the full sample. In women, higher FC between the NAcc and bilateral posterior cingulate, precuneus, and visual areas were found in those with high, relative to low grief symptoms. Distinct VS subregional abnormalities associate with yearning and depressive symptoms in bereaved elders. Whether ventral striatal dysfunction correlates with PGD development and/or worsening depression remains to be elucidated.

Modeling motor task activation from resting-state fMRI using machine learning in individual subjects.

Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications.

Improving the Breath-Holding CVR Measurement Using the Multiband Multi-Echo EPI Sequence.

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is commonly used to measure cerebrovascular reactivity (CVR), which can convey insightful information about neurovascular health. Breath-holding (BH) has been shown to be a practical vasodilatory stimulus for measuring CVR in clinical settings. The conventional BOLD fMRI approach has some limitations, however, such as susceptibility-induced signal dropout at air tissue interfaces and low BOLD sensitivity especially in areas of low T 2 * . These drawbacks can potentially be mitigated with multi-echo sequences, which acquire several images at different echo times in one shot. When combined with multiband techniques, high temporal resolution images can be acquired. This study compared an advanced multiband multi-echo (MBME) echo planar imaging (EPI) sequence with an existing multiband single-echo (MB) sequence to evaluate the repeatability and sensitivity of BH activation and CVR mapping. Images were acquired from 28 healthy volunteers, of which 18 returned for repeat imaging. Both MBME and MB data were pre-processed using both standard and advanced denoising techniques. The MBME data was further processed by combining echoes using a T 2 * -weighted approach and denoising using multi-echo independent component analysis. BH activation was calculated using a general linear model and the respiration response function. CVR was computed as the percent change related to the activation. To account for differences in CVR related to TE, relative CVR (rCVR) was computed and normalized to the mean gray matter CVR. Test-retest metrics were assessed with the Dice coefficient, rCVR difference, within subject coefficient of variation, and the intraclass correlation coefficient. Our findings demonstrate that rCVR for MBME scans were significantly higher than for MB scans across most of the gray matter. In areas of high susceptibility-induced signal dropout, however, MBME rCVR was significantly less than MB rCVR due to artifactually high rCVR for MB scans in these regions. MBME rCVR showed improved test-retest metrics compared with MB. Overall, the MBME sequence displayed superior BOLD sensitivity, improved specificity in areas of signal dropout on MBME scans, enhanced reliability, and reduced variability across subjects compared with MB acquisitions. Our results suggest that the MBME EPI sequence is a promising tool for imaging CVR.

Improving the Assessment of Breath-Holding Induced Cerebral Vascular Reactivity Using a Multiband Multi-echo ASL/BOLD Sequence.

Breath holding (BH) is a viable vasodilatory stimulus for calculating functional MRI-derived cerebral vascular reactivity (CVR). The BH technique suffers from reduced repeatability compared with gas inhalation techniques; however, extra equipment is needed to perform gas inhalation techniques, and this equipment is not available at all institutions. This study aimed to determine the sensitivity and repeatability of BH activation and CVR using a multiband multi-echo simultaneous arterial spin labelling/blood oxygenation level dependent (ASL/BOLD) sequence. Whole-brain images were acquired in 14 volunteers. Ten subjects returned for repeat imaging. Each subject performed four cycles of 16 s BH on expiration interleaved with paced breathing. Following standard preprocessing, the echoes were combined using a T2*-weighted approach. BOLD and ASL BH activation was computed, and CVR was then determined as the percent signal change related to the activation. The "M" parameter from the Davis Model was also computed by incorporating the ASL signal. Our results showed higher BH activation strength, volume, and repeatability for the combined multi-echo (MEC) data compared with the single-echo data. MEC CVR also had higher repeatability, sensitivity, specificity, and reliability compared with the single-echo BOLD data. These data support the usefulness of an MBME ASL/BOLD acquisition for BH CVR and M measurements.

Pectoral nerve blocks and postoperative pain outcomes after mastectomy: a meta-analysis of randomized controlled trials.

BACKGROUND AND OBJECTIVES: Several studies have evaluated the effect of pectoral nerve blocks to improve postoperative analgesia following breast cancer surgery resulting in contradictory findings. The aim of this study was to examine the effect of Pecs blocks on postoperative analgesia in women following mastectomies. METHODS: We performed a quantitative systematic review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Articles of randomized controlled trials that compared Pecs block (types I and II) to a control group in patients undergoing mastectomy were included. The primary outcome was total opioid consumption 24 hours after surgery. Secondary outcomes included pain scores and side effects. Meta-analysis was performed using the random effect model. RESULTS: 7 randomized controlled trials with 458 patients were included in the analysis. The effect of pectoral nerve blocks on postoperative opioid consumption compared with control revealed a significant effect, weighted mean difference (WMD) (95% CI) of --4.99 (-7.90 to -2.08) mg intravenous morphine equivalents (p=0.001). In addition, postoperative pain compared with control was reduced at 6 hours after surgery: WMD (95% CI) of -0.72 (-1.37 to -0.07), p=0.03, and at 24 hours after surgery: WMD (95% CI) of -0.91 (-1.81 to -0.02), p=0.04. DISCUSSION: This quantitative analysis of randomized controlled trials demonstrates that the Pecs block is effective for reducing postoperative opioid consumption and pain in patients undergoing mastectomy. The Pecs block should be considered as an effective strategy to improve analgesic outcomes in patients undergoing mastectomies for breast cancer treatment.

Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI.

Typical simultaneous blood oxygenation-level dependent (BOLD) and arterial spin labeling (ASL) sequences acquire two echoes, one perfusion-sensitive and one BOLD-sensitive. However, for ASL, spatial resolution and brain coverage are limited due to the T1 decay of the labeled blood. This study applies a sequence combining a multiband acquisition with four echoes for simultaneous BOLD and pseudo-continuous ASL (pCASL) echo planar imaging (MBME ASL/BOLD) for block-design task-fMRI. A multiband acceleration of four was employed to increase brain coverage and reduce slice-timing effects on the ASL signal. Multi-echo independent component analysis (MEICA) was implemented to automatically denoise the BOLD signal by regressing non-BOLD components. This technique led to increased temporal signal-to-noise ratio (tSNR) and BOLD sensitivity. The MEICA technique was also modified to denoise the ASL signal by regressing artifact and BOLD signals from the first echo time-series. The MBME ASL/BOLD sequence was applied to a finger-tapping task functional MRI (fMRI) experiment. Signal characteristics and activation were evaluated using single echo BOLD, combined ME BOLD, combined ME BOLD after MEICA denoising, perfusion-weighted (PW), and perfusion-weighted after MEICA denoising time-series. The PW data was extracted using both surround subtraction and high-pass filtering followed by demodulation. In addition, the CBF/BOLD response ratio and CBF/BOLD coupling were analyzed. Results showed that the MEICA denoising procedure significantly improved the BOLD signal, leading to increased BOLD sensitivity, tSNR, and activation statistics compared to conventional single echo BOLD data. At the same time, the denoised PW data showed increased tSNR and activation statistics compared to the non-denoised PW data. CBF/BOLD coupling was also increased using the denoised ASL and BOLD data. Our preliminary data suggest that the MBME ASL/BOLD sequence can be employed to collect whole-brain task-fMRI with improved data quality for both BOLD and PW time series, thus improving the results of block-design task fMRI.

Functional connectivity density mapping: comparing multiband and conventional EPI protocols.

Functional connectivity density mapping (FCDM) is a newly developed data-driven technique that quantifies the number of local and global functional connections for each voxel in the brain. In this study, we evaluated reproducibility, sensitivity, and specificity of both local functional connectivity density (lFCD) and global functional connectivity density (gFCD). We compared these metrics using the human connectome project (HCP) compatible high-resolution (2 mm isotropic, TR = 0.8 s) multiband (MB), and more typical, lower resolution (3.5 mm isotropic, TR = 2.0 s) single-band (SB) resting state functional MRI (rs-fMRI) acquisitions. Furthermore, in order to be more clinically feasible, only rs-fMRI scans that lasted seven minutes were tested. Subjects were scanned twice within a two-week span. We found sensitivity and specificity increased and reproducibility either increased or did not change for the MB compared to the SB acquisitions. The MB scans also showed improved gray matter/white matter contrast compared to the SB scans. The lFCD and gFCD patterns were similar across MB and SB scans and confined predominantly to gray matter. We also observed a strong spatial correlation of FCD between MB and SB scans indicating the two acquisitions provide similar information. These findings indicate high-resolution MB acquisitions improve the quality of FCD data, and seven minute rs-fMRI scan can provide robust FCD measurements.