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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 score30; 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 (r

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 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.

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.

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.

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.

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 T2*. 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 T2*-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.

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

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). 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. 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. 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. • 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.

Study objective To review research highlights of manuscripts published in 2016 that pertain to all aspects of the clinical practice of anesthesiology. Design Narrative review. Setting N/A. Materials The major themes address broad categories of general anesthesia including airway management, abdominal surgery, and obstetrical and gynaecological anesthesia. In addition, recent advancements in specialties of anesthesiology including regional anesthesia are reviewed. Interventions N/A. Main results N/A. Conclusions This recent body of evidence will both help inform anesthesiologists of the developing trends in anesthesiology and will also pose new challenges for further studies.

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.

Background and objectivesSeveral 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.MethodsWe 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.Results7 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.DiscussionThis 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.

Study objective To review research highlights of manuscripts published in 2017 that pertain to all aspects of the clinical practice of anesthesiology. Design Narrative review. Setting N/A. Materials The major themes addressed in this review include recent studies examining airway management, obstetrical and gynecological anesthesia, pediatric anesthesia, cardiac anesthesia, regional analgesia and pain management. Interventions N/A. Main results N/A. Conclusions This review will highlight and inform anesthesiologists of the developing trends in clinical anesthesia and will also pose new challenges for further studies.

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.

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.

Purpose To examine the effect of low b-values (0

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/mm2. Flow-compensated FDMs (fcFDMs) were calculated using b = 500,1000 s/mm2. 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. J. Magn. Reson. Imaging 2013;38:868–875. © 2013 Wiley Periodicals, Inc.

A novel sequence has been introduced that combines multiband imaging with a multi-echo acquisition for simultaneous high spatial resolution pseudo-continuous arterial spin labeling (ASL) and blood-oxygenation-level dependent (BOLD) echo-planar imaging (MBME ASL/BOLD). Resting-state connectivity in healthy adult subjects was assessed using this sequence. Four echoes were acquired with a multiband acceleration of four, in order to increase spatial resolution, shorten repetition time, and reduce slice-timing effects on the ASL signal. In addition, by acquiring four echoes, advanced multi-echo independent component analysis (ME-ICA) denoising could be employed to increase the signal-to-noise ratio (SNR) and BOLD sensitivity. Seed-based and dual-regression approaches were utilized to analyze functional connectivity. Cerebral blood flow (CBF) and BOLD coupling was also evaluated by correlating the perfusion-weighted timeseries with the BOLD timeseries. These metrics were compared between single echo (E2), multi-echo combined (MEC), multi-echo combined and denoised (MECDN), and perfusion-weighted (PW) timeseries. Temporal SNR increased for the MECDN data compared to the MEC and E2 data. Connectivity also increased, in terms of correlation strength and network size, for the MECDN compared to the MEC and E2 datasets. CBF and BOLD coupling was increased in major resting-state networks, and that correlation was strongest for the MECDN datasets. These results indicate our novel MBME ASL/BOLD sequence, which collects simultaneous high-resolution ASL/BOLD data, could be a powerful tool for detecting functional connectivity and dynamic neurovascular coupling during the resting state. The collection of more than two echoes facilitates the use of ME-ICA denoising to greatly improve the quality of resting state functional connectivity MRI.

Impulsivity is a pathological hallmark of drug addiction. However, little is known about the neuropsychological underpinnings of this impaired impulsive control network on drug addiction. Twenty two abstinent heroin dependent (HD) subjects and fifteen cognitively normal (CN) subjects participated in this study. Resting-state functional connectivity MRI was employed to measure abnormalities in the intrinsic amygdala functional connectivity (iAFC) network activity and the Barratt Impulsive Scale, 11th version was used to measure impulsivity. Linear regression analysis was applied to detect the neural constructs underlying impulsivity by correlating iAFC network activity with impulsive scores. In the HD group, higher impulsivity scores and significantly enhanced iAFC network activity were found, especially in bilateral thalamus, right insula, and inferior frontal gyrus. Markedly decreased anticorrelated iAFC network activity was seen in the left precuneus, and even switched to positive correlation pattern in right precuneus, relative to the CN group. The iAFC network strengths in the HD group were positively correlated with impulsivity in the right subcallosal gyrus, insula, thalamus and posterior cingulate cortex, and negatively correlated in left fusiform area. In the CN group, the left pre-somamotor area-amygdala connectivity was positively correlated, and right orbital frontal cortex-amygdala and precuneus-amygdala connectivity were negatively correlated with impulsivity scores. Our study demonstrates different constructs of the impulsive network in HD and CN subjects. Altered iAFC network connectivity in HD subjects may contribute to the loss of impulsive control. This further facilitates our understanding of the neural underpinnings of behavior dysfunction in addiction.

The cerebellum is known to be a relatively well preserved structure, but subtle alterations may occur early in the evolution of Alzheimer's disease (AD). Amnestic mild cognitive impairment (aMCI) patients appear to be particularly vulnerable to AD. However, little is currently known whether altered patterns of cerebellar function occur in aMCI patients. 26 aMCI patients and 18 well-matched healthy controls underwent a baseline resting-state functional magnetic resonance imaging (fMRI) scan. After a mean follow-up period of 20 months, the subjects who successfully completed baseline fMRI scans underwent a further follow-up scan, while spontaneous activation and functional connectivity of the cerebellum were explored by using resting-state fMRI. Compared to controls, increased amplitude of low frequency fluctuation of the posterior cerebellar lobe may contribute to the underlying mechanisms affected, while greater decreased functional connections to the posterior cerebellar lobe were identified in the longitudinal study of aMCI patients. This suggests that abnormal functional connectivity of the cerebellum may offer a more sensitive and possibly preferred index of functional disturbance than regional activity measures in aMCI patients. The cerebellum may be partly related to the underlying mechanisms of aMCI, and it could help guide subsequent investigations designed to specify the precise functional role of cerebellum in aMCI patients.

BACKGROUND: Diffusion-weighted MR (DWI) images are collected at different diffusion weightings (b-values) and the corresponding signal decreases as b-value increases. Typically, images are collected at two b-values to calculate an apparent diffusion coefficient (ADC). Calculating ADC in this way involves the underlying assumption that the diffusion-related signal decay is monoexponential. However, studies have shown this is often not the case at higher b-values (b > 1000 s/mm2). In this study, a biexponential model is used to model the DWI signal in a rat U87 brain tumor model. The inflection b-value (binf) is also introduced. This parameter is the b-value where the signal begins to deviate from monoexponential. METHODS: Eight male Athymic nude rats were inoculated with U87 brain tumor cells and imaged on day 18 post-inoculation without any treatment. Pre and post contrast T1-weighted images were collected. DWI images were acquired prior to contrast injection with b = 0,50,100,150,200,400,600,800,1000,2000,3000 s/mm2. Tumor ROIs were determined from the contrast-enhancing region on the T1 + C images. Contralateral normal ROIs were also drawn. The DWI signal was fit with a biexponential model: S/S0 = ffast*exp(-b*Dfast) + (1-ffast)*exp(-b*Dslow). The inflection b-value was computed as the minimum of the third derivative of the natural log of the biexponential fit. Biexponential parameters and binf were compared between tumor and normal tissue with a paired two-sample t-test. RESULTS: No differences were seen between tumor and normal tissue for any of the biexponential parameters. The inflection b-value (binf) was significantly lower in tumor compared to normal tissue (1920(160) vs. 3280(340) respectively, P = 0.008). CONCLUSIONS: The inflection b-value (i.e. the b-value where the DWI signal deviates from monoexponential) was lower in tumor tissue compared to normal tissue. Deviation from monoexponential behavior is thought to occur due to the presence of multiple diffusion pools and restrictions to water movement. Therefore, binf is a potential marker of tumor heterogeneity and/or invasion.