Skyra Scanner Research Articles

Systematic Comparison of Reproducibility of Diffusion MRI‐Derived Free‐Water Measures Across Two Multi‐Shell Protocols

AbstractBackgroundFree‐water (FW) measures have been widely used to understand and predict the progression of various neurodegenerative disorders1,2. However, the estimation of FW in a brain voxel needs a multi‐shell acquisition to ensure reproducible fit3,4. With the availability of multi‐shell diffusion MRI (dMRI) protocol using simultaneous multislice technique from the Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI‐3), such protocols can now be harmonized across various centers and MRI vendors. However, limited knowledge exists on whether the results obtained for FW measures across HCP protocols are generalizable across ADNI‐3 protocols along with which protocol is more reproducible that will assist us in understanding the variability of FW measure in an individual participant longitudinally. Hence, in this study, we compared the reproducibility of FW across major white matter (WM) tracts across both HCP and ADNI‐3 protocols.MethodA 32‐year‐old healthy male participant was scanned over five weeks, both with Basic (2mm3, b‐value = 1000s/mm2) and Advanced ADNI‐3 protocol and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a clinical 3T Skyra scanner. FW dMRI measures were obtained using the original Matlab implementation (Matlab)5 and DiPY implementation6 of multi‐shell dMRI acquisition (DiPY). WM atlas from Johns Hopkins University (JHU) of 20 major WM tracts pre‐registered to MNI152 was then used as a mask to obtain FW at each WM tract, and the coefficient of variance (CoV) was computed across each protocol. The total acquisition scan time was 75 minutes.ResultFW estimated using Matlab had a mean CoV = 2.78±1.09 across all the protocols with the least CoV for Basic ADNI‐3 (CoV = 1.31±0.54) and the maximum CoV for 2mm3 HCP sequence (CoV = 4.98±1.83) (Fig.1A) while FW estimated using DiPY had a mean CoV = 6.71±2.86 across all the protocols with the least CoV for Advanced ADNI‐3 (CoV = 4.31±1.85) and the maximum CoV for 2mm3 HCP sequence (CoV = 9.66±4.15) (Fig.1A).ConclusionBoth acquisition parameters and processing techniques significantly affect the absolute quantification of FW measures. Furthermore, our data suggest that the pooling of dMRI data across various protocols should be performed with caution.

Systematic Comparison of Reproducibility of Diffusion MRI‐Derived Free‐Water Measures Across Two Multi‐Shell Protocols

AbstractBackgroundFree‐water (FW) measures have been widely used to understand and predict the progression of various neurodegenerative disorders1,2. However, the estimation of FW in a brain voxel needs a multi‐shell acquisition to ensure reproducible fit3,4. With the availability of multi‐shell diffusion MRI (dMRI) protocol using simultaneous multislice technique from the Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI‐3), such protocols can now be harmonized across various centers and MRI vendors. However, limited knowledge exists on whether the results obtained for FW measures across HCP protocols are generalizable across ADNI‐3 protocols along with which protocol is more reproducible that will assist us in understanding the variability of FW measure in an individual participant longitudinally. Hence, in this study, we compared the reproducibility of FW across major white matter (WM) tracts across both HCP and ADNI‐3 protocols.MethodsA 32‐year‐old healthy male participant was scanned over five weeks, both with Basic (2mm3, b‐value = 1000s/mm2) and Advanced ADNI‐3 protocol and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a clinical 3T Skyra scanner. FW dMRI measures were obtained using the original Matlab implementation (Matlab)5 and DiPY implementation6 of multi‐shell dMRI acquisition (DiPY). WM atlas from Johns Hopkins University (JHU) of 20 major WM tracts pre‐registered to MNI152 was then used as a mask to obtain FW at each WM tract, and the coefficient of variance (CoV) was computed across each protocol. The total acquisition scan time was 75 minutes.ResultFW estimated using Matlab had a mean CoV = 2.78±1.09 across all the protocols with the least CoV for Basic ADNI‐3 (CoV = 1.31±0.54) and the maximum CoV for 2mm3 HCP sequence (CoV = 4.98±1.83) (Fig.1A) while FW estimated using DiPY had a mean CoV = 6.71±2.86 across all the protocols with the least CoV for Advanced ADNI‐3 (CoV = 4.31±1.85) and the maximum CoV for 2mm3 HCP sequence (CoV = 9.66±4.15) (Fig.1A).ConclusionBoth acquisition parameters and processing techniques significantly affect the absolute quantification of FW measures. Furthermore, our data suggest that the pooling of dMRI data across various protocols should be performed with caution.

Effects of acquisition protocol on advanced diffusion MRI measures

AbstractBackgroundSeveral studies have suggested that advanced diffusion MRI (dMRI) measures derived through the analytic techniques of diffusion kurtosis imaging (DKI) and neurite orientation density and dispersion imaging (NODDI) might provide better sensitivity to predict the progression of Alzheimer’s disease (AD) when compared to conventional single tensor diffusion tensor imaging. Two popular simultaneous multislice (SMS) sequences, namely Alzheimer’s Disease Neuroimaging Initiative (ADNI)‐3 and Human Connectome Protocol (HCP) are currently widely used in the acquisition of such advanced dMRI data. Since HCP sequences require a custom connectome scanner, several variants of HCP protocol compatible with clinical MRI are in wide use. Before such techniques are used widely across the globe to understand AD, it is imperative to understand whether the absolute WM measures derived through such advanced dMRI measures are reproducible and whether harmonization of dMRI across sites employing different acquisition protocols is applicable. In this study, we hypothesize that higher spatial and angular resolution dMRI data will result in higher quantification of DKI and NODDI metrics, as compared with comparatively lower spatial resolution advanced ADNI‐3 sequences.MethodA 32‐year‐old healthy male participant was scanned over five weeks, both with Advanced ADNI‐3 protocol (114 directions, 2mm3 isotropic, b‐values = 500s/mm2, 1000s/mm2, and 2000s/mm2) and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a 3T Skyra scanner. Total scan time for the ADNI‐3 sequence was 11:37 minutes, 2mm3 CCF sequence was 18:41 minutes, and 1.5mm3 CCF sequence was 23:13 minutes. Various DKI and NODDI measures were computed on all repetitions in‐house. JHU atlas of WM tracts was used as a mask to extract values along each tract at each run separately.ResultAs shown in Figs 1‐4, dMRI‐derived measures were underestimated for most of the WM tracts for the ADNI‐3 sequence, although they had less variance across different runs. Second, NODDI measures were more reproducible across most WM tracts when compared to DKI measures across protocols.ConclusionAcquisition parameters affect the absolute quantification of advanced dMRI measures. Our data suggest that the pooling of dMRI data across various protocols should be performed with caution

P.114 When functional neuroimaging is ambiguous for language localization: a case for Wada testing

Background: To localize cortical speech areas, methods such as fMRI are commonly used, but the Wada test can also determine whether a region is critical to the particular task. We report a case of a left-handed patient with a left frontal tumour in whom fMRI language paradigms produced both left and right Broca’s and Wernicke’s areas. Methods: All imaging used a 3 Tesla Siemens Skyra scanner. The patient performed five speech tasks: word reading, picture naming, semantic questions, pseudohomophone reading, and word generation. All preprocessing and statistical analyses for functional images were performed using Brain Voyager QX. Results: The fMRI results revealed right hemisphere dominance for language processing. A Wada test was performed in order to confirm whether the regions in the left hemisphere were critical to speech. The patient experienced speech arrest during the Wada test, thus confirming that despite bilateral speech activation, the left hemisphere speech regions are required for speech production. Conclusions: This case emphasizes the importance of preoperative fMRI in assessing the location of eloquent cortices adjacent to a tumour and the Wada test is still warranted for examining necessity of left hemisphere language regions when fMRI fails to show clear left-lateralization.

Magnetic resonance imaging based neurosurgical planning on hololens 2: A feasibility study in a paediatric hospital.

The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design. Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler. The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive. We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.

Effects of Acquisition Protocol on Advanced Diffusion MRI Measures

AbstractBackgroundSeveral studies have suggested that advanced diffusion MRI (dMRI) measures derived through the analytic techniques of diffusion kurtosis imaging (DKI) and neurite orientation density and dispersion imaging (NODDI) might provide better sensitivity to predict the progression of Alzheimer’s disease (AD) when compared to conventional single tensor diffusion tensor imaging. Two popular simultaneous multislice (SMS) sequences, namely Alzheimer's Disease Neuroimaging Initiative (ADNI)‐3 and Human Connectome Protocol (HCP) are currently widely used in the acquisition of such advanced dMRI data. Since HCP sequences require a custom connectome scanner, several variants of HCP protocol compatible with clinical MRI are in wide use. Before such techniques are used widely across the globe to understand AD, it is imperative to understand whether the absolute WM measures derived through such advanced dMRI measures are reproducible and whether harmonization of dMRI across sites employing different acquisition protocols is applicable. In this study, we hypothesize that higher spatial and angular resolution dMRI data will result in higher quantification of DKI and NODDI metrics, as compared with comparatively lower spatial resolution advanced ADNI‐3 sequences.MethodA 32‐year‐old healthy male participant was scanned over five weeks, both with Advanced ADNI‐3 protocol (114 directions, 2mm3 isotropic, b‐values=500s/mm2, 1000s/mm2, and 2000s/mm2) and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a 3T Skyra scanner. Total scan time for the ADNI‐3 sequence was 11:37 minutes, 2mm3 CCF sequence was 18:41 minutes, and 1.5mm3 CCF sequence was 23:13 minutes. Various DKI and NODDI measures were computed on all repetitions in‐house. JHU atlas of WM tracts was used as a mask to extract values along each tract at each run separately.ResultAs shown in Figs 1‐4, dMRI‐derived measures were underestimated for most of the WM tracts for the ADNI‐3 sequence, although they had less variance across different runs. Second, NODDI measures were more reproducible across most WM tracts when compared to DKI measures across protocols.ConclusionAcquisition parameters affect the absolute quantification of advanced dMRI measures. Our data suggest that the pooling of dMRI data across various protocols should be performed with caution

Asynchronous magnetic resonance elastography: Shear wave speed reconstruction using noise correlation ofincoherent waves.

The noninvasive measurement of biological tissue elasticity is an evolving technology that enables the robust characterization of soft tissue mechanics for a wide array of biomedical engineering and clinical applications. We propose, design, and implement here a new MRI technique termed asynchronous magnetic resonance elastography (aMRE) that pushes the measurement technology toward a driverless implementation. This technique can be added to clinical MRI scanners without any additional specialized hardware. Asynchronous MRE is founded on the theory of diffuse wavefields and noise correlation previously developed in ultrasound to reconstruct shear wave speeds using seemingly incoherent wavefields. Unlike conventional elastography methods that solve an inverse problem, aMRE directly reconstructs a pixel-wise mapping of wave speed using the spatial-temporal statistics of the measured wavefield. Incoherent finger tapping served as the wave-generating source for all aMRE measurements. Asynchronous MRE was performed on a phantom using a Siemens Prismafit as an experimental validation of the theory. It was further performed on thigh muscles as a proof-of-concept implementation of in vivo imaging using a Siemens Skyra scanner. Numerical and phantom experiments show an accurate reconstruction of wave speeds from seemingly noisy wavefields. The proof-of-concept thigh experiments also show that the aMRE protocol can reconstruct a pixel-wise mapping of wave speeds. Asynchronous MRE is shown to accurately reconstruct shear wave speeds in phantom experiments and remains at the proof-of-concept stage for in vivo imaging. After further validation and improvements, it has the potential to lower both the technical and monetary barriers of entry to measuring tissue elasticity.

Comparing cardiac MRI and lung PET/CT after completion of tuberculosis treatment – preliminary findings of participants co-enrolled into the StatinTB trial and Cardiac Imaging After TB (CIA-TB) study

Abstract Introduction Mortality rates after successful tuberculosis (TB) treatment remain elevated and healthy survival is impaired by chronic pulmonary disease and persistent inflammation. Inflammation leads to a complex cascade of events and has been associated with cardiovascular disease (CVD). Recent epidemiological work described an increased risk of CVD in persons with history of TB. Aims and objectives In our ongoing StatinTB trial (NCT04147286), we evaluate safety and efficacy of 40 mg atorvastatin to reduce persistent lung inflammation (PLI) after TB treatment in HIV−/HIV+ adults measured by 18F-FDG-PET/CT. Participants are co-enrolled into the Cardiac Imaging After Tuberculosis (CIA-TB) study. Here we report preliminary findings of participants enrolled into the StatinTB trial and CIA-TB study with the aim to correlate PLI with cardiac magnetic resonance imaging (CMR) parameters. Methods Participants with clinical response to TB treatment and a negative sputum culture at 16 weeks were screened at the end of a 24-week course of treatment for drug-sensitive TB. PET/CT was performed on a Philips Gemini TF scanner and analysed using MIM®Software. PLI was defined as total lung glycolysis (TLG) ≥50 SUV·mL (Figure 1). CMR was performed using a 3T Siemens Magnetom Skyra scanner and parameters were derived from the following acquisitions: SSFP cine imaging of the short and long axes, T1 mapping (MOLLI, 5 (3) 3), T2 mapping and late gadolinium enhancement (LGE) imaging. StatinTB trial is conducted according to ICH-GCP. Results 26 participants (mean age 37.8±13.8 years, 38.5% women) underwent PET/CT and CMR. 23.1% were infected with HIV, 34.6% smokers, and 26.9% had a previous history of TB. Median BMI was 21.3 (IQR 19.7; 24.3). One participant reported hypertension. Common symptoms at enrollment were cough (26.9%), shortness of breath (11.5%), and chest pain (3.9%). On CT scan, 73.1% had pulmonary nodules, 57.7% scarring, 50.0% cavities, 42.3% bronchiectasis, 19.2% centrilobular disease, 19.2% distorted lung architecture, and 11.5% consolidations. On PET/CT, PLI was detected among 11 (42.3%) participants with a mean TLG of 216.0 (IQR 78.2; 384.0). In a multivariable regression analysis, left ventricular end systolic volume (LVESV) (−1.46; 95% CI: −2.89, −0.23; p=0.047) and distorted lung architecture (2.84; 95% CI: 1.17, 4.52; p=0.003) were significantly associated with TLG. Native T1 relaxation time showed a strong, but not significant association with TLG (15.01; 95% CI: −8.08, 38.10; p=184). Conclusion We present data of a young cohort at the end of TB treatment without significant cardiac risk factors. Structural lung disease is affecting at least half of participants, and many remain symptomatic. PLI is present in more than one-third of participants and our preliminary findings suggest an association with cardiac impairment. Further enrolment and ongoing laboratory studies will shed more light into post-TB cardio-pulmonary sequelae. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): EDCTP

An evolutionary gap in primate default mode network organization

SUMMARYThe human default mode network (DMN) is engaged at rest and in cognitive states such as self-directed thoughts. Interconnected homologous cortical areas in primates constitute a network considered as the equivalent. Here, based on a cross-species comparison of the DMN between humans and non-hominoid primates (macaques, marmosets, and mouse lemurs), we report major dissimilarities in connectivity profiles. Most importantly, the medial prefrontal cortex (mPFC) of non-hominoid primates is poorly engaged with the posterior cingulate cortex (PCC), though strong correlated activity between the human PCC and the mPFC is a key feature of the human DMN. Instead, a fronto-temporal resting-state network involving the mPFC was detected consistently across non-hominoid primate species. These common functional features shared between non-hominoid primates but not with humans suggest a substantial gap in the organization of the primate’s DMN and its associated cognitive functions.

Abstract P3-03-03: Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Abstract Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p < 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p < 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.

Abstract P3-02-09: Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams

Abstract Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-02-09.

Advanced diffusion‐MRI models reveals sex‐specific differences in white matter organization in patients with mild cognitive impairment

AbstractBackgroundRecent literature suggests that free‐water corrected (fiso) diffusion MRI (dMRI) measures provide higher sensitivity as compared to conventional single‐tensor (ST) dMRI measures to understand white‐matter (WM) changes due to neurodegeneration. However, fiso‐corrected dMRI measures are still biased at crossing‐fiber regions (CFR) of WM which are shown to be around 90% of the total WM voxels with conventional dMRI data acquisition. In this study, we systematically compared dMRI measures from ST‐dMRI, fiso‐corrected ST‐dMRI measures, and measures from advanced dMRI models such as Diffusion Kurtosis Imaging (DKI) and Neurite Orientation Distribution and Density Imaging (NODDI) which are sensitive to CFRs. We hypothesize that these advanced dMRI models will be sensitive in understanding sex‐specific WM organizational changes at regional level in patients with Alzheimer’s disease (AD)‐related mild cognitive impairment (MCI).Method40 healthy controls [HC; 21 Females (F), 19 Males (M)] and 38 MCI participants [13F, 25 M] were recruited at our center. MCI diagnoses were based on expert consensus between a neurologist (AR) and a neuropsychologist (JC) using results of a neuropsychological test battery and the Clinical Dementia Rating (CDR) interview. Isotropic 1.5mm3 multi‐shell dMRI (b‐values=500s/mm2, 1000s/mm2, and 2500s/mm2) each with identical 71 diffusion‐encoding directions was acquired on a 3T Siemens Skyra scanner for every participant using the CMRR sequence. 8 uniformly interspersed non‐diffusion‐weighted (b0) images were also acquired in the same run. ST, fiso‐corrected, DKI, and NODDI dMRI measures were estimated for every brain voxel in‐house using the standard analytic tools. WM was skeletonized using FSL tools and statistical comparisons were done non‐parametrically using PALM tools. GLM was utilized to test for sex‐specific group effect. Significance was established at family‐wise error correction (FWE) pcorr<0.05.ResultThalamocortical tracts of males in both HC and MCI showed a significantly higher axonal kurtosis (AK) and orientation density (ODI) derived from DKI and NODDI when compared to within‐group females. DKI also revealed presence of higher axonal water fraction (AWF) along the entire WM‐tracts between HC‐female and HC‐male, and lower mean kurtosis (MK) along the thalamic‐cortical tracts in HC‐females as compared to MCI‐females.ConclusionAdvanced dMRI‐models are sensitive to understand sex‐specific WM disorganization due to healthy ageing and MCI.

Influence of Prolapse Volume in Myxomatous Mitral Valve Prolapse

Myxomatous mitral valve prolapse (MMVP) is characterized by excessive leaflet tissue leading to a wide spectrum of mitral regurgitation (MR) ranging from trivial to complex severe incompetence. The prolapse volume (PV) below the prolapsing leaflets in end-systole was recently suspected to impact both chamber remodeling and MR grading in Barlow's disease. The aims of the current study using Cardiac magnetic resonance (CMR) was (i) to assess the interaction between PV and cardiac chambers size and function (ii) to study the impact of PV on MR grading in Barlow's disease. Over a 60-months period, 157 patients (45 women; mean age 62 ± 15) consecutively referred at the Monaco Heart center, Monaco for CMR assessment of primary MR secondary to MVP either from MMVP ( n = 91; 58%) or fibroelastic disease (FED) ( n = 66; 42%)were eligible for inclusion into the study. Over the same period, we enrolled 52 age and sex-matched controls. Patients were imaged with a 3-Tesla Siemens Skyra scanner. Prolapse volume (PV) was calculated on the end systolic frame in each of the 4-, 3- and 2-chamber views. Despite similar left ventricular (LV) size, PV was larger in MMVP (11 ± 9 ml) than in FED (2 ± 2 ml). PV progressively increased with the severity of MR in MMVP but not in FED. PV correlated significantly ( r = 0.52) with the LV dilatation in severe MR but also in less than moderate MR. Despite a low RegV by CMR (32 ± 18 ml), some MMVP patients with less than moderate MR exhibit significant cardiac chambers remodeling compared to the 52 controls. In MMVP, PV > 14 ml was associated with a significant underestimation (Bias = −26 ± 32 ml) of RegV by PISA compared to CMR ( Fig. 1 ). In conclusion, in MMVP, PV may play a role in left cardiac chambers remodeling, even in patients without severe MR, and in discordant grading of MR between echocardiography and CMR.

Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer – ASEG and SAMSEG

Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation (ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes.

Functional Brain Networks of Trait and State Anxiety in Late-Life Depression

<h3>Introduction</h3> Late-life depression (LLD) is associated with psychiatric comorbidities that may worsen mood outcomes in older adults, yet the neural mechanisms that underlie these comorbid conditions remain understudied. Anxiety and neuroticism have been independently associated with LLD, and its mood and cognitive outcomes. In a preliminary behavioral data analysis we found that anxiety measures correlated with depression measures in older adults. We also found that several baseline neuroticism and anxiety traits predicted depression scores over time. In this study, we analyzed neuroimaging data in the setting of anxiety and neuroticism measures in LLD and hypothesized that there would be differences in resting state activity patterns in the Salience network (SN), Default-Mode network (DMN) and Executive Control network (ECN) related to severity of: (1) generalized anxiety, (2) state and trait anxiety, and (3) neuroticism. <h3>Methods</h3> This was a cross-sectional secondary data analysis on clinical and MRI data collected from our NBOLD project. LLD patients without dementia were included. Generalized anxiety disorder (GAD) symptoms, state and trait anxiety, neuroticism and depression were assessed using a GAD questionnaire, State-Trait Anxiety Inventory (STAI), NEO Personality Inventory (NEO PI), and Montgomery-Asberg Depression Rating Scale (MADRS), respectively. A study psychiatrist confirmed or ruled out diagnosis of depression. There were 102 older adults with LLD, and among them, 87 subjects who had measures of STAI-trait, STAI-state anxiety, GAD symptoms and neuroticism as well as usable neuroimaging data were included into our final analyses. The neuroimaging data was collected from 3T Siemens Skyra scanner. The imaging parameters for T1-weighted images were: TR/TE=2200/2.8ms, flip angle=13^o, matrix = 256 × 256 × 169, Voxelsize=1×1×1mm³. The 7-min resting-state fMRI data was collected using EPI sequence with FOV=240mm, flip angle=90^o, TR/TE=2000/31ms, matrix=64×64×34, voxelsize = 3.8×3.8×3.75mm³. The fMRI data was preprocessed using the default protocol setting recommend by DPABI software. The Amplitude of Low-Frequency Fluctuations (ALFF) was calculated using DPABI. Multiple regression model was used to examine the association between ALFF and GAD, state and trait anxiety as well as neuroticism controlled for age, gender and depression severity (MADRS) using SPM12. Significance was set at p<0.05 with FDR correction for multiple comparisons. <h3>Results</h3> Data analyses revealed that trait anxiety was positively correlated with greater ALFF in the ECN (left dorsolateral prefrontal cortex - dlPFC, bilateral inferior parietal cortex - IPS, and precuneus) and lateral orbitofrontal cortex (OFC). Higher state anxiety was correlated with lower ALFF in bilateral OFC and frontal pole. GAD symptom severity was positively correlated with ALFF in the right superior temporal cortex (STG), Heschl's gyrus, and dorsomedial frontal cortex (dMFC). The GAD symptom severity was also negatively correlated with ALFF in the right hippocampus, parahippocampus and temporal pole (TP), whereas there was no significant clusters found between ALFF and neuroticism after regressing out the covariates. <h3>Conclusions</h3> This is one the few studies that examined resting state neural oscillation pattern (ALFF) specific to state and trait anxiety and GAD symptoms in LLD. Higher trait anxiety was associated with greater ALFF in the ECN and OFC, all of which are related to emotion regulation, whereas higher state anxiety was correlated with lower ALFF in regions related to emotion regulation. Higher GAD was specifically correlated with higher ALFF in brain regions related to sound and social information processing, but with lower ALFF in regions related to memory. No significant clusters were found related to neuroticism suggesting that neuroticism might be able explained by trait and state anxiety as well as GAD. These results suggests a distinct neural construct that may distinguish state and trait anxiety in LLD. Uncovering the brain connectivity changes related to state and trait anxiety would deepen our understanding in the neural mechanisms and mood outcomes of LLD. Future studies are needed to determine differences in these networks between depressed and non-depressed individuals. <h3>Funding</h3> The Leo and Anne Albert Charitable Trust, National Institute of Mental Health Grant R01 MH108578, The University of Connecticut Department of Psychiatry

Resting-State Functional Connectivity and Anxiety Predict Relapse in Remitted Late-Life Depression

<h3>Introduction</h3> Late-life depression (LLD) refers to depression occurring in older age and is associated with a multitude of psychosocial and biological factors, including conditions related to pathological aging, including cardiovascular and cerebrovascular diseases, and dementia. Relevant psychosocial factors comprise personality disorders, neuroticism, trauma and stress. Depending on the relative contribution of conditions related to pathological aging versus psychosocial factors, some patients may not respond to antidepressants and others may be prone to relapse even when they achieved initial remission on an antidepressant. The neural mechanisms related to recurrence of LLD are not clear. About 50% of individuals who have recovered from a first episode of depression will suffer from at least one additional episode. Therefore, identifying patients prone to relapse is important for developing tailored treatment regimens, preventing suicide, and reducing economic burdens and other morbidities. To understand the neural mechanisms related to relapse of depression, and identify neuroimaging markers to predict individuals who tend to relapse, we conducted a secondary data analysis on clinical and fMRI data collected from our NBOLD project. We hypothesized that, compared with individuals who did not relapse during a two-year clinical follow-up period, those who relapsed would show significantly altered resting-state activity patterns in core neural networks related to LLD including the default mode, executive control and salience networks. <h3>Methods</h3> The sample included 102 adults with LLD who were free of medication for at least two weeks before study enrollment. Depression severity was measured by MADRS and was assessed by a study psychiatrist at each study visit, which occurred frequently while the subject remained depressed and quarterly when remission was achieved. Among the enrolled participants, 52 (age range of 60 – 89) achieved depression remission after antidepressant treatment (MADRS< 8 for at least 2 months). We defined relapse as having a MADRS > 15 for at least two weeks. Individuals were classified as non-relapsed if they maintained a MADRS <13 after remission during the two follow-up years. Among the 52 LLD patients, 27 of them relapsed and 25 did not. We also measured trait and state anxiety and neuroticism using State-Trait Anxiety Inventory (STAI), NEO Personality Inventory (NEO PI). The neuroimaging data was collected from 3T Siemens Skyra scanner. The imaging parameters for T1-weighted images were: TR/TE=2200/2.8ms, flip angle=13^o, matrix = 256 × 256 × 169, voxelsize=1 × 1 × 1mm³. The 7-min resting-state fMRI data was collected using EPI sequence with FOV=240mm, flip angle=90^o, TR/TE=2000/31ms, matrix=64 × 64 × 34, voxelsize=3.8 × 3.8 × 3.75mm³. The resting-state fMRI data was preprocessed using the default protocol settings in Conn Toolbox version17f. The preprocessed data was entered to DPABI software for calculating the Amplitude of Low-Frequency Fluctuations (ALFF). Functional connectivity (FC) was done using Conn Toolbox version17f. Two sample t-tests were conducted to compare resting-state activity, ALFF, and FC to examine differences in ALFF and FC between relapsed and non-relapsed LLD groups (age and gender were controlled). Significance was set at p<0.05 with FDR correction for multiple comparisons. <h3>Results</h3> There were no significant differences between relapsed and non-relapsed groups in age, gender, state anxiety or neuroticism. Those who relapsed during the two-year follow-up had less severe depressive symptoms at baseline (mean (SD) MADRS=18.40(4.06) than those non-depressed group (mean (SD) MADRS=20.81(6.23) with p=0.001), whereas the relapsed group had higher STAI-trait anxiety than the non-relapsed group (p=0.04). Two-sample t-tests on FC showed that the relapsed group had a lower FC between the right parahippocampus and the superior and inferior parietal cortex. The relapsed group also had a reduced FC between the right globus pallidum and the right superior frontal gyrus. In addition, the relapsed group had significantly greater ALFF in the left amygdala and globus pallidum compared with the non-relapsed group; however, the significance was lost after controlling for depression severity and STAI-trait anxiety. <h3>Conclusions</h3> This is one of few studies that have examined clinical and neural characters in relapsed LLD. Lower FC between the right parahippocampus and the superior and inferior parietal cortex suggests a potential reduction in attention for memory. Low FC between the right globus pallidum and the right superior frontal gyrus may indicate reduced executive function in relapsed individuals. Importantly, the relapsed group also showed lower depressive symptoms yet higher trait anxiety. These results suggest that depression comorbid with anxiety might be an essential factor of relapse tendency. Future studies will examine whether the relapsed group also has lower executive function.

A Pilot Study of Perspective Taking and Emotional Contagion in Mental Health Professionals: Glass Brain View of Empathy.

Background:Empathy plays a fundamental role in the context of psychotherapy. Mental health professionals (MHP) are required to express empathy on a daily basis. “Perspective taking” (cognitive empathy) and “emotional contagion” (affective empathy) are elements of empathy that are both innate and acquired. This study aimed to explore the underlying neural correlates of empathy using functional magnetic resonance imaging (fMRI).Method:A total of six healthy subjects from MHP and other professionals (OP) were recruited in a single-assessment study design. Subjects were screened using the Mini-International Neuropsychiatric Interview and the Standard Progressive Matrices. Behavioral measures such as cognitive and affective empathy, interpersonal reactivity, and emotional and social quotient were assessed. Perspective taking was examined with the fMRI face recognition task and the reading mind through eyes task. Emotional contagion was examined by the negative, positive, and pain emotions task. The fMRI was conducted in a 3T Siemens Magnetom Skyra scanner, using a block design paradigm.Results:Activation was noted in the following areas: cingulate and thalamus for positive and negative emotions, precuneus for negative emotion and pain, inferior parietal lobe for reading mind task and negative emotion, declive for reading mind and pain, and precuneus and frontal gyrus for reading mind task and facial recognition.Conclusions:There was no significant difference between MHP and OP groups on the behavioral measures. However, there were variations in cerebral and cerebellar activation in the functional imaging parameters.

Carbon fiber\u2013reinforced PEEK versus titanium implants: an in vitro comparison of susceptibility artifacts in CT and MR imaging

Artifacts in computed tomography (CT) and magnetic resonance imaging (MRI) due to titanium implants in spine surgery are known to cause difficulties in follow-up imaging, radiation planning, and precise dose delivery in patients with spinal tumors. Carbon fiber–reinforced polyetheretherketon (CFRP) implants aim to reduce these artifacts. Our aim was to analyze susceptibility artifacts of these implants using a standardized in vitro model. Titanium and CFRP screw-rod phantoms were embedded in 3% agarose gel. Phantoms were scanned with Siemens Somatom AS Open and 3.0-T Siemens Skyra scanners. Regions of interest (ROIs) were plotted and analyzed for CT and MRI at clinically relevant localizations. CT voxel–based imaging analysis showed a significant difference of artifact intensity and central overlay between titanium and CFRP phantoms. For the virtual regions of the spinal canal, titanium implants (ti) presented − 30.7 HU vs. 33.4 HU mean for CFRP (p < 0.001), at the posterior margin of the vertebral body 68.9 HU (ti) vs. 59.8 HU (CFRP) (p < 0.001) and at the anterior part of the vertebral body 201.2 HU (ti) vs. 70.4 HU (CFRP) (p < 0.001), respectively. MRI data was only visually interpreted due to the low sample size and lack of an objective measuring system as Hounsfield units in CT. CT imaging of the phantom with typical implant configuration for thoracic stabilization could demonstrate a significant artifact reduction in CFRP implants compared with titanium implants for evaluation of index structures. Radiolucency with less artifacts provides a better interpretation of follow-up imaging, radiation planning, and more precise dose delivery.

Comparison between 1.5- and 3-T Magnetic Resonance Acquisitions for Direct Targeting Stereotactic Procedures for Deep Brain Stimulation: A Phantom Study

Introduction: Deep brain stimulation (DBS) is a well-established treatment for movement disorders. High magnetic fields could have an impact on distortion. We evaluated 1.5- and 3-T magnetic resonance imaging (MRI) sequences for accuracy, precision, and trueness of our MRI-guided direct targeting protocol. Methods: Effects of distortion on MR sequences (T1- and T2-weighted sequences) can be evaluated using a dedicated phantom (Elekta). Field strength capabilities were assessed on Siemens Avanto (1.5 T) and Skyra (3 T) scanners. We assessed the precision of our stereotactic MRI-guided procedure. Results: We focused on the risk of error due to a high field strength. Error values on the localizer box were between 0.4 and 0.7 mm at 1.5 T and between 0.6 and 2 mm at 3 T. The most accurate 1.5-T sequence is the 3D FLASH T1-weighted sequence, which had an accuracy value of 0.6 mm. At 3 T, the accuracy value of the isotropic 3D FLASH T1-weighted sequence was 1.6 mm. Conclusion: Given the millimetric size of stereotactic targets and electrodes, lead implantation for neuromodulation therapy needs to be accurate. We demonstrate that 3-T imaging could not be used for stereotaxy in our MRI-guided direct targeting protocol because of a risk of error induced by distortion.

T33. NEUROIMMUNE MECHANISMS OF PSYCHIATRIC DISORDERS: LONGITUDINAL EVALUATION OF DIFFUSION MEASURES OF EXTRACELLULAR FREE WATER IN A NON-HUMAN PRIMATE MODEL OF MATERNAL IMMUNE ACTIVATION

BackgroundA key finding in developmental neurobiology is a widespread role for immune molecules in normal brain development and synaptic function and evidence has been accumulating for an immune-based developmental pathophysiology in psychiatric disorders, particularly in schizophrenia. Epidemiological studies have revealed an increased incidence of schizophrenia in offspring of mothers who had an infection during pregnancy while GWAS studies have identified genetic links to the major histocompatibility complex and peripheral changes in immune markers are widely reported in the illness. The murine maternal immune activation model system is widely used to investigate the effects of immune activation during pregnancy on brain development in behavior in offspring. Here we report findings from an ongoing study of a unique cohort on non-human primates (NHP) who underwent MIA (compared to controls) on a promising biomarker of neuroimmune perturbation in vivo--extracellular free water--a diffusion magnetic resonance imaging measure obtained with a multi-shell acquisition, which we have shown in multiple studies to be increased in young people with early psychosis.MethodsFourteen pregnant rhesus monkeys (Macaca mulatta) received polyICLC and 14 control animals have been scanned prospectively from both to their current age of 3.5 years. The offspring from both groups underwent a diffusion MRI scan on a 3 Tesla Siemens Skyra scanner in which multiple b-value shells were acquired to improve estimation of extracellular free water. Data were collected when the offspring were 1, 6, 12, 24 and 36 months to date. Diffusion images were nonlinearly aligned to individual subject MPRAGE scans, which were segmented and parcellated into regions of interest using multi-atlas techniques. For this preliminary analysis, frontal, cingulate, and temporo-limbic regions were selected as a priori ROIs in addition to whole-brain gray and white matter masks. Group differences were assessed using repeated measures ANOVA and independent samples t-tests.ResultsResults from birth to age 2 years showed a significant main effect of group in both white (p<.05) and gray (p<.001) cingulate cortex free water, with MIA-exposed offspring showing higher free water. Similar trends were also identified in prefrontal white matter free water (p=.07) and whole-brain white (p=.11) and gray matter free water (p=.07). No significant group by time interactions were identified. Data analysis is currently underway including the 3-year time point.DiscussionDespite the lack of gross behavioral abnormalities at age 2, extracellular free water values are increased in MIA-exposed offspring, particularly in the cingulate cortex. More global whole-brain free water group differences, however, did not reach statistical significance, which may indicate some regional specificity to these changes early in development. These NHP MIA model complement the human schizophrenia literature in which extracellular free water increases have been repeatedly identified. And show that changes in the brain occur early in life, well before the emergence of atypical behaviors in the NHP model.