Analysis Of Magnetic Resonance Imaging Data Research Articles

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81–0.87), sensitivity of 77% (95% CI 71–82), and specificity of 79% (95% CI 73–84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs.

The Enhancing NeuroImaging Genetics through Meta‐Analysis copy number variant (ENIGMA‐CNV) and 22q11.2 Deletion Syndrome Working Groups (22q‐ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA‐CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q‐ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest‐ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi‐site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene‐dosage effects on distinct brain regions also emerged, providing further insight into genotype–phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This “genotype‐first” approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

Correlation of Electrodiagnostic Findings in C8 Radiculopathies With Radiographic Data: A Retrospective Chart Review.

Signs and symptoms of a C8 radiculopathy could mimic common comorbidities such as entrapment and peripheral neuropathies. These conditions and a C8 radiculopathy both can result in abnormal findings on needle examination of intrinsic hand muscles. It was hypothesized that needle examination of C8-innervated muscles in the forearm might improve concordance with magnetic resonance imaging (MRI) in the presence of underlying comorbidities. A retrospective analysis of electromyogram and C-spine MRI data in 80 patients with negative MRI of C-spine for C8-T1 neuroforaminal stenosis was performed. The percentage of false-positive results in the MRI-negative group undergoing electromyogram testing for hand and forearm muscles (MRI-NH + F) was 3% compared with 18% (P = 0.06) in the group with electromyogram of the hand intrinsic muscles only (MRI-NH). The false-positive result tends to be lower in the MRI-NH + F group in comparison with the MRI-NH group especially in the presence of underlying peripheral and entrapment neuropathies.

Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data.

ObjectiveTo compare the location of suspect lesions detected by computational analysis of multimodal magnetic resonance imaging data with areas of seizure onset, early propagation, and interictal epileptiform discharges (IEDs) identified with stereoelectroencephalography (SEEG) in a cohort of patients with medically refractory focal epilepsy and radiologically normal magnetic resonance imaging (MRI) scans.MethodsWe developed a method of lesion detection using computational analysis of multimodal MRI data in a cohort of 62 control subjects, and 42 patients with focal epilepsy and MRI‐visible lesions. We then applied it to detect covert lesions in 27 focal epilepsy patients with radiologically normal MRI scans, comparing our findings with the areas of seizure onset, early propagation, and IEDs identified at SEEG.ResultsSeizure‐onset zones (SoZs) were identified at SEEG in 18 of the 27 patients (67%) with radiologically normal MRI scans. In 11 of these 18 cases (61%), concordant abnormalities were detected by our method. In the remaining seven cases, either early seizure propagation or IEDs were observed within the abnormalities detected, or there were additional areas of imaging abnormalities found by our method that were not sampled at SEEG. In one of the nine patients (11%) in whom SEEG was inconclusive, an abnormality, which may have been involved in seizures, was identified by our method and was not sampled at SEEG.SignificanceComputational analysis of multimodal MRI data revealed covert abnormalities in the majority of patients with refractory focal epilepsy and radiologically normal MRI that co‐located with SEEG defined zones of seizure onset. The method could help identify areas that should be targeted with SEEG when considering epilepsy surgery.

Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time-consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.

Fractal Analysis of MRI Data at 7 T: How Much Complex Is the Cerebral Cortex?

The human brain is a highly complex structure, which can be only partially described by conventional metrics derived from magnetic resonance imaging (MRI), such as volume, cortical thickness, and gyrification index. In the last years, the fractal dimension (FD) - a useful quantitative index of fractal geometry - has proven to well express the morphological complexity of the cerebral cortex. However, this complexity is likely higher than that we can observe using MRI scanners with 1.5 T or 3 T field strength. Ultrahigh-field MRI (UHF-MRI) improves imaging of smaller anatomical brain structures by exploring down to a submillimetric spatial resolution with higher signal-to-noise and contrast-to-noise ratios. Accordingly, we hypothesized that UHF-MRI might reveal a higher level of the structural complexity of the cerebral cortex. In this study, using an improved box-counting algorithm, we estimated the FD of the cerebral cortex in six public or private T1-weighted MRI datasets of young healthy subjects (for a total of 87 subjects), acquired at different field strengths (1.5 T, 3 T, and 7 T). Our results showed, for the first time, that MRI-derived FD values of the cerebral cortex imaged at 7 T were significantly higher than those observed at lower field strengths. UHF-MRI provides an anatomical definition not achievable at lower field strengths and can improve unveiling the real structural complexity of the human brain.

Quantitative MRI of the liver: Evaluation of extracellular volume fraction and other quantitative parameters in comparison to MR elastography for the assessment of hepatopathy

BackgroundChronic liver diseases pose a major health problem worldwide, while common tests for diagnosis and monitoring of diffuse hepatopathy have considerable limitations. Preliminary data on the quantification of hepatic extracellular volume fraction (ECV) with magnetic resonance imaging (MRI) for non-invasive assessment of liver fibrosis are encouraging, with ECV having the potential to overcome several of these constraints. PurposeTo clinically evaluate ECV provided by quantitative MRI for assessing the severity of liver disease. Materials and methodsIn this prospective study, multiparametric liver MRI, including T1 mapping and magnetic resonance elastography (MRE), was performed in subjects with and without hepatopathy between November 2018 and October 2019. T1, T2, T2*, proton density fat fraction and stiffness were extracted from parametric maps by regions of interest and ECV was calculated from T1 relaxometries. Serum markers of liver disease were obtained by clinical database research. For correlation analysis, Spearman rank correlation was used. ROC analysis of serum markers and quantitative MRI data for discrimination of liver cirrhosis was performed with MRE as reference standard. Results109 participants were enrolled (50.7 ± 16.1 years, 61 men). ECV, T1 and MRE correlated significantly with almost all serum markers of liver disease, with ECV showing the strongest associations (up to r = 0.67 with MELD, p < 0.01). ECV and T1 correlated with MRE (0.75 and 0.73, p < 0.01 each). ECV (AUC 0.89, cutoff 32.2%, sensitivity 85%, specificity 87%) and T1 mapping (AUC 0.85, cutoff 592.5 ms, sensitivity 83%, specificity 75%) featured good performances in detection of liver cirrhosis with only ECV performing significantly superior to model of end stage liver disease (MELD), AST/ALT ratio and international normalized ratio (p < 0.01, respectively). ConclusionQuantification of hepatic extracellular volume fraction with MRI is suitable for estimating the severity of liver disease when using MRE as the standard of reference. It represents a promising tool for non-invasive assessment of liver fibrosis and cirrhosis.

Association of cardiovascular risk burden with cognitive trajectories and structural brain differences

AbstractBackgroundThe impact of cardiovascular risk burden on cognitive trajectories and brain structure changes remains unclear.MethodWithin the Rush Memory and Aging Project, 1,588 dementia‐free participants (mean age=79.5 years) were followed for up to 21 years. FGCRS was assessed at baseline and categorized into tertiles (the lowest, middle, and the highest). Episodic memory, semantic memory, working memory, visuospatial ability, and perceptual speed were assessed annually with a battery of 19 tests, from which composite scores were derived. A subsample (n=378) of the participants underwent magnetic resonance imaging (MRI). Structural total and regional brain volumes were estimated. Data were analyzed using linear mixed‐effects models and linear regression models.ResultIn all participants, FGCRS ranged from 4 to 28 (mean score=15.6±3.7). Compared with the lowest tertile of FGCRS, the highest was associated with faster decline in global cognition (β=‐0.019, 95% confidence interval [CI] ‐0.035 to ‐0.003), episodic memory (β=‐0.023, 95% CI ‐0.041 to ‐0.004), working memory (β=‐0.021, 95% CI ‐0.035 to ‐0.007), and perceptual speed (β=‐0.027, 95% CI ‐0.042 to ‐0.011) over the follow‐up. In MRI data analyses, higher FGCRS was related to smaller volumes of the hippocampus (β=‐0.021, 95% CI ‐0.042 to ‐0.000), total gray matter (β=‐1.569 95% CI ‐2.757 to ‐0.382), and total brain (β=‐1.588, 95% CI ‐2.832 to ‐0.344), and greater volume of white matter hyperintensities (β=0.035, 95% CI 0.001 to 0.069).ConclusionHigher cardiovascular risk burden may predict decline in episodic memory, working memory and perceptual speed, and is associated with neurodegeneration and vascular lesions in the brain.

Magnetic resonance imaging subtypes in subjective cognitive decline

AbstractBackgroundOlder adults who do not meet clinical criteria for mild cognitive impairment (MCI) or dementia, but who report progressive change in their cognitive abilities (i.e. subjective cognitive decline, SCD) are at increased risk for developing Alzheimer’s disease (AD). However, SCD can be associated to other conditions and its heterogeneity needs to be investigated further. In patients with AD dementia, data‐driven approaches to biological information have been useful in disentangling heterogeneity. We therefore utilized a data‐driven clustering method on regional structural magnetic resonance imaging (MRI) data to identify subtypes of SCD.MethodCognitively normal adults with SCD (n=86) and without SCD (n=68) from the AIBL cohort were studied. Clusters of SCD were determined based on 82 cortical and subcortical grey matter volumes using random forest pairwise similarity, multidimensional scaling, and distance‐based hierarchical clustering. The number of clusters was decided according to Carlinski‐Harabasz index. We also studied neuroanatomical, clinical, cognitive, and genetic characteristics at baseline, and clinical and cognitive progression over 7.5 years of follow‐up.ResultsThe data driven analyses yielded four SCD subtypes. The largest proportion (49%) showed regional volumes equivalent to those without SCD. Nonetheless, this group could be divided into two subtypes depending on the volume of the thalamus (no atrophy + low thalamic volume, 35%; and no atrophy + high thalamic volume, 15%). We also identified a Hippocampal‐sparing SCD subtype (32%), which displayed reduced cortical volumes but normal volume of the hippocampus; and a Diffuse SCD subtype (16%), which displayed reduced volume in both the hippocampus and cortical regions. Diffuse, Hippocampal‐sparing, and no atrophy + low Thalamic volume SCD subtypes were each associated with increased risk for MCI or dementia over 7.5 years, and showed faster decline in MMSE. The Diffuse SCD subtype was also characterized by an increased amyloid‐PET burden, small vessel disease (white matter hypointensities), and worse CDR scores at baseline.ConclusionsData‐driven analyses of structural MRI data revealed that SCD characterized by a Diffuse, Hippocampal‐sparing, or no atrophy with a low Thalamic volume pattern were at increased risk for MCI or dementia. Thus, these data‐driven methods show great potential for disentangling biological heterogeneity within SCD.

Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma ‘ground truth’ simulation on MRI

Purpose/objective(s)Gliomas are uniformly fatal brain tumours with significant neurological and quality of life detriment to patients. Improvement in outcomes has remained largely unchanged in nearly 20 years. MRI (magnetic resonance imaging) is often used in diagnosis and management. Machine learning analyses of large-scale MRI data are pivotal in advancing the diagnosis, management and improve outcomes in neuro-oncology. A common challenge to robust machine learning approaches is the lack of large ‘ground truth’ datasets in supervised learning for building classification and prediction models. The creation of these datasets relies on human-expert input and is time-consuming and subjective error-prone, limiting effective machine learning applications. Simulation of mechanistic aspects such as geometry, location and physical properties of brain tumours can generate large-scale ground-truth datasets allowing for comparison of analysis techniques in clinical applications. We aimed to develop a transparent and convenient method for building ‘ground truth’ presentations of simulated glioma lesions on anatomical MRI. Materials/methodsThe simulation workflow was created using the Feature Manipulation Engine (FME®), a data integration platform specializing in the spatial data processing. By compiling and integrating FME’s functions to read, integrate, transform, validate, save, and display MRI data, and experimenting with ways to manipulate the parameters concerning location, size, shape, and signal intensity with the presentations of glioma, we were able to generate simulated appearances of high-grade gliomas on gadolinium-based high-resolution 3D T1-weighted MRI (1 mm3). Data of patients with canonical high-grade tumours were used as real-world tumours for validating the accuracy of the simulation. Twenty raters who are experienced with brain tumour interpretation on MRI independently completed a survey, designed to distinguish simulated and real-world brain tumours. Sensitivity and specificity were calculated for assessing the performance of the approach with the binary classification of simulated vs real-world tumours. Correlation and regression were used in run time analysis, assessing the software toolset’s efficiency in producing different numbers of simulated lesions. Differences in the group means were examined using the non-parametric Kruskal-Wallis test. ResultsThe simulation method was developed as an interpretable and useful workflow for the easy creation of tumour simulations and incorporation into 3D MRI. A linear increase in the running time and memory usage was observed with an increasing number of generated lesions. The respondents' accuracy rate ranged between 33.3 and 83.3 %. The sensitivity and specificity were low for a human expert to differentiate simulated lesions from real gliomas (0.43 and 0.58) or vice versa (0.65 and 0.62). The mean scores ranking the real-world gliomas did not differ between the simulated and real tumours. ConclusionThe reliable and user-friendly software method can allow for robust simulation of high-grade glioma on MRI. Ongoing research efforts include optimizing the workflow for generating glioma datasets as well as adapting it to simulating additional MRI brain changes.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study.

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

Cortical and subcortical changes following sphenopalatine ganglion blocks in chronic migraine with medication overuse headache: a preliminary longitudinal study

ObjectiveThe purpose of this pilot study was to investigate potential changes in brain morphology (cortical thickness and cortical/subcortical volume) accompanying a series of sphenopalatine ganglion (SPG) blockade treatments in chronic migraine with medication overuse headaches (CMw/MOH).BackgroundLocal anesthetization of the SPG via intranasal application is used for the treatment for multiple types of headache disorders, including CM. Our previous longitudinal fMRI study revealed improved network connectivity after such treatment. However, the impact of SPG blocks on cortical, subcortical gray matter volume and cortical thickness has yet to be assessed.MethodsUsing magnetic resonance imaging (MRI), cortical/subcortical volume were measured in 12 chronic migraine patients before and after a series of 12 SPG blocks administered over a 6-week period (2 per week). The average time between MRI assessments was 6 weeks. Targeted, within-subjects t-tests comparing pre-treatment and post-treatment values in specific apriori brain regions of interest, including the hippocampus, amygdala, basal ganglia, somatosensory cortex, temporal cortex and occipital cortex, were used to estimate the impact of repetitive SPG blocks treatment on brain morphology in CMw/MOH.ResultsCompared to baseline values, the number of moderate/severe headache days per month, HIT-6, PHQ-9 scores and allodynia scores were all significantly improved at the end of treatment. Analysis of MRI data revealed that the volume of the right hippocampus and the right palladium significantly decreased following SPG block treatment, while the volume of the left nucleus accumbens significantly increased following treatment. Cortical thickness in the left temporal pole and left lateral occipito-temporal gyrus significantly decreased following SPG block treatment.ConclusionOur results suggest SPG block treatment is associated with significant symptom improvement as well as significant structural brain changes in regions known to be associated with migraine and chronic pain processing in CMw/MOH.

Planning principles for radical surgical intervention for liver alveococcosis based on computed and magnetic resonance imaging

Aim. To develop the planning principles for radical surgical intervention for liver alveococcosis based on the computed and magnetic resonance imaging data.Materials and methods. The data of computed tomography and magnetic resonance imaging, as well as the results of treatment of 189 patients with liver alveococcosis in 2011–2020 were analyzed.Results. A sequence of data analysis of CT-scan and magnetic resonance imaging, consisting of several stages, is proposed. CT-scan and magnetic resonance imaging data of all patients were analyzed according to the developed algorithm, which was adapted to the planning stages of surgical treatment. The accuracy of CT-scan and magnetic resonance imaging in determining the localization of the parasitic process by sections and predicting the need for a two-stage resection of the liver, the use of total vascular isolation technique was 100%. The accuracy of CT-scan in the defining of anatomical variants of extrahepatic arteries and the presence of a significant accessory right hepatic vein of the liver was 98.6%, type of division of the portal vein – 97.2%. Radiation semiotics of the relationship of parasitic masses with the main vessels of the liver and the inferior vena cava were determined. The accuracy of CT-scan in the diagnosis of involvement of the hepatic arteries, portal and hepatic veins was 96.9%, 91.2% and 91.4%, respectively. In predicting circular resection of the inferior vena cava we recommend to use the cut-off of contact of parasitic masses around it more than 180 ° (AUC = 0.950). With borderline volumes of liver remnant (30–35%), prolonged hyperbilirubinemia, suspected development of secondary biliary cirrhosis, magnetic resonance imaging with gadoxetic acid makes it possible to assess the functional state of the parenchyma. This may require additional preoperative therapy, planning a two-stage resection or the process may be considered unresectable in the diagnosis of severe fibrosis.Conclusion. The developed CT-scan/ magnetic resonance imaging data analysis algorithm allows to determine the clinical and surgical group of patients with liver alveococcosis and to plan the type of surgical intervention. Additional option of magnetic resonance imaging with gadoxetic acid in evaluating of functional state of liver remnant allows you to adjust surgical tactics in a number of clinical situations.

Effect of the dynamic curve type from the perifocal infiltration zone obtained by magnetic resonance imaging on prognosis in pelvic pathologies in women

The aim of the study is to evaluate the role and prognostic significance of magnetic resonance imaging (MRI) perfusion methods in prognosis of disease course and outcome based on evaluation of the perifocal infiltration zone in pelvic disorders in women.Materials and methods. Retrospective analysis of pelvic MRI data obtained using MRI system with induced magnetic field of 1.5 T was performed. The study included 530 protocols of pelvic scans in women. Two equal groups (n = 265) of patients with oncological and nononcological pathologies of pelvic organs were formed; after application of propensity score matching, each group contained 165 patients. All examination protocols included dynamic contrast enhancement. The obtained data were statistically analyzed using the Stata 13 software.Results. In the study, types of dynamic curves obtained using dynamic contrast enhancement from the perifocal infiltration zone were analyzed. There was no data showing a correlation between the type of dynamic curve from the zone of perifocal changes and disease prognosis or possibility of differential diagnosis.Conclusions. Evaluation of the perifocal infiltration zone per MRI results with intravenous contrast does not affect prognostic accuracy of the method (p &gt;0.05); contrast can be used only for differential diagnosis or evaluation of local advancement of the process.

Associations Between Cardiovascular Risk, Structural Brain Changes, and Cognitive Decline

BackgroundThe impact of cardiovascular risk burden on cognitive trajectories and brain structure changes remains unclear. ObjectivesThis study aimed to examine whether cardiovascular risk burden assessed by the Framingham General Cardiovascular Risk Score (FGCRS) is associated with cognitive decline and structural brain differences. MethodsWithin the Rush Memory and Aging Project, 1,588 dementia-free participants (mean age: 79.5 years) were followed for up to 21 years. FGCRS was assessed at baseline and categorized into tertiles (lowest, middle, and highest). Episodic memory, semantic memory, working memory, visuospatial ability, and perceptual speed were assessed annually with a battery of 19 tests, from which composite scores were derived. A subsample (n = 378) of participants underwent magnetic resonance imaging. Structural total and regional brain volumes were estimated. Data were analyzed using linear mixed-effects models and linear regression models. ResultsIn all participants, FGCRS ranged from 4 to 28 (mean score: 15.6 ± 3.7). Compared with the lowest tertile of FGCRS, the highest tertile was associated with faster decline in global cognition (β = −0.019; 95% confidence interval [CI]: −0.035 to −0.003), episodic memory (β = −0.023; 95% CI: −0.041 to −0.004), working memory (β = −0.021; 95% CI: −0.035 to −0.007), and perceptual speed (β = −0.027; 95% CI: −0.042 to −0.011) over the follow-up. In magnetic resonance imaging data analyses, higher FGCRS was related to smaller volumes of the hippocampus (β = −0.021; 95% CI: −0.042 to −0.000), gray matter (β = −1.569; 95% CI: −2.757 to −0.382), and total brain (β = −1.588; 95% CI: −2.832 to −0.344), and greater volume of white matter hyperintensities (β = 0.035; 95% CI: 0.001 to 0.069). ConclusionsHigher cardiovascular risk burden may predict decline in episodic memory, working memory, and perceptual speed and is associated with neurodegeneration and vascular lesions in the brain.

Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results

Abstract Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p &amp;lt; 0.01) for total cellularity, total volume, and longest axis, respectively. The model predicts that for the alternative dosing regimens assessed, individual patients could have achieved an additional 0-43% reduction in total cellularity compared to the therapeutic regimens patients actually received. This indicates that standard regimens may not be the most effective for every patient. Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-16-17.

Correlation of Cervical Spinal Degeneration with Rise in Smartphone Usage Time in Young Adults.

The study aimed to define the association between spinal degeneration parameters and the rise in smartphone usage time. This was a cross-sectional study. Young adults aged 20-35 years, who presented to our outpatient clinic due to neck pain between 2016 and 2018, were examined. Cervical disc degeneration, disc placement, Modic changes, and sagittal balance were retrospectively measured using magnetic resonance imaging (MRI) in 107 relatively patients. Data about daily phone usage times of the participants were obtained by a questionnaire filled in at the time of admission. The total number of disc distances analyzed was 535 (Group 1; n = 200, Group 2; 335). In Group 1, the disc displacement was present in 30%, and in Group 2, the disc displacement was present in 35%. In terms of DD severity, the total DD score was >10 in 18 (18/40; 45%) patients in Group 1, and in 39 patients (39/67; 58%) in Group 2. The mean Cobb angle of Group 1 was 10.3° ± 6.57° (range, 2° to 34°), and that of Group 2 was 7.6° ± 5.14° (range, 1° to 26°) (Pcobb = 0.048). Modic changes were detected in 17 of the 107 patients (15.8%). Of the 17 patients, 3 (3/40, %7.5) were Group 1 and 14 (14/67, %20.9) were Group 2. MC was detected in 4 vertebrae in Group 1, and 24 vertebrae in Group 2 (P = 0.001). The analysis of cervical spine MRI data of young adult patients with neck pain shows that the smartphone usage time is effective in cervical sagittal balance disruption, disc degeneration, and development of Modic changes.

Music Therapy Enhances Executive Functions and Prefrontal Structural Neuroplasticity after Traumatic Brain Injury: Evidence from a Randomized Controlled Trial.

Traumatic brain injury (TBI) causes lifelong cognitive deficits, particularly impairments of executive functioning (EF). Musical training and music-based rehabilitation have been shown to enhance cognitive functioning and neuroplasticity, but the potential rehabilitative effects of music in TBI are still largely unknown. The aim of the present crossover randomized controlled trial (RCT) was to determine the clinical efficacy of music therapy on cognitive functioning in TBI and to explore its neural basis. Using an AB/BA design, 40 patients with moderate or severe TBI were randomized to receive a 3-month neurological music therapy intervention either during the first (AB, n = 20) or second (BA, n = 20) half of a 6-month follow-up period. Neuropsychological and motor testing and magnetic resonance imaging (MRI) were performed at baseline and at the 3-month and 6-month stage. Thirty-nine subjects who participated in baseline measurement were included in an intention-to-treat analysis using multiple imputation. Results showed that general EF (as indicated by the Frontal Assessment Battery [FAB]) and set shifting improved more in the AB group than in the BA group over the first 3-month period and the effect on general EF was maintained in the 6-month follow-up. Voxel-based morphometry (VBM) analysis of the structural MRI data indicated that gray matter volume (GMV) in the right inferior frontal gyrus (IFG) increased significantly in both groups during the intervention versus control period, which also correlated with cognitive improvement in set shifting. These findings suggest that neurological music therapy enhances EF and induces fine-grained neuroanatomical changes in prefrontal areas.

Investigating imaging network markers of cognitive dysfunction and pharmacoresistance in newly diagnosed epilepsy: a protocol for an observational cohort study in the UK

IntroductionEpilepsy is one of the most common serious brain disorders, characterised by seizures that severely affect a person’s quality of life and, frequently, their cognitive and mental health. Although most...

Application of Magnetic Resonance to Assess Lyophilized Drug Product Reconstitution

PurposeDynamic in-situ proton (1H) magnetic resonance imaging (MRI) and 1H T2-relaxometry experiments are described in an attempt to: (i) understand the physical processes, that occur during the reconstitution of lyophilized bovine serum albumin (BSA) and monoclonal antibody (mAb) proteins; and (ii) objectify the reconstitution time.MethodsRapid two-dimensional 1H MRI and diffusion weighted MRI were used to study the temporal changes in solids dissolution and characterise water mass transport characteristics. One-shot T2 relaxation time measurements were also acquired in an attempt to quantify the reconstitution time. Both MRI data and T2-relaxation data were compared to standard visual observations currently adopted by industry. The 1H images were further referenced to MRI calibration data to give quantitative values of protein concentration and, percentage of remaining undissolved solids.ResultsAn algorithmic analysis of the 1H T2-relaxation data shows it is possible to classify the reconstitution event into three regimes (undissolved, transitional and dissolved). Moreover, a combined analysis of the 2D 1H MRI and 1H T2-relaxation data gives a unique time point that characterises the onset of a reconstituted protein solution within well-defined error bars. These values compared favourably with those from visual observations. Diffusion weighted MRI showed that low concentration BSA and mAb samples showed distinct liquid-liquid phase separation attributed to two liquid layers with significant density differences.ConclusionsT2 relaxation time distributions (whose interpretation is validated from the 2D 1H MR images) provides a quick and effective framework to build objective, quantitative descriptors of the reconstitution process that facilitate the interpretation of subjective visual observations currently adopted as the standard practice industry.