Longitudinal Imaging Research Articles

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

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

The choroid plexus synergizes with immune cells during neuroinflammation

The choroid plexus (ChP) is a vital brain barrier and source of cerebrospinal fluid (CSF). Here, we use longitudinal two-photon imaging in awake mice and single-cell transcriptomics to elucidate the mechanisms of ChP regulation of brain inflammation. We used intracerebroventricular injections of lipopolysaccharides (LPS) to model meningitis in mice and observed that neutrophils and monocytes accumulated in the ChP stroma and surged across the epithelial barrier into the CSF. Bi-directional recruitment of monocytes from the periphery and, unexpectedly, macrophages from the CSF to the ChP helped eliminate neutrophils and repair the barrier. Transcriptomic analyses detailed the molecular steps accompanying this process and revealed that ChP epithelial cells transiently specialize to nurture immune cells, coordinating their recruitment, survival, and differentiation as well as regulation of the tight junctions that control the permeability of the ChP brain barrier. Collectively, we provide a mechanistic understanding and a comprehensive roadmap of neuroinflammation at the ChP brain barrier.

Investigation of a chronic single-stage sheep Fontan model

ObjectivesOur goal was to conduct a hemodynamic analysis of a novel animal model of Fontan physiology. Poor late-term outcomes in Fontan patients are believed to arise from Fontan-induced hemodynamics, but the mechanisms remain poorly understood. Recent advances in surgical experimentation have resulted in the development of a chronic sheep model of Fontan physiology; however, detailed analysis of this model is lacking. MethodsWe created a single-stage Fontan model in juvenile sheep with normal biventricular circulation. The superior vena cava was anastomosed to the main pulmonary artery, and the inferior vena cava was connected to the main pulmonary artery using an expanded polytetrafluoroethylene conduit. Longitudinal hemodynamics, including catheterization and magnetic resonance imaging were evaluated. ResultsFour out of 12 animals survived, with the longest surviving animal living 3 years after single-stage Fontan. We showed a significant era effect regarding survival (1 out of 8 and subsequently 3 out of 4 animals surviving beyond 2 months) attributed in large part to the procedural learning curve. Key characteristics of Fontan hemodynamics, namely systemic venous hypertension and low normal cardiac output, were observed. However, recapitulation of passive human Fontan hemodynamics is affected by volume loading of the right ventricle given an anatomic difference in sheep azygous venous anatomy draining to the coronary sinus. ConclusionsA significant learning curve exists to ensure long-term survival and future surgical modifications, including banding of the main pulmonary artery and ligation of the azygous to coronary sinus connection are promising strategies to improve the fidelity of model hemodynamics.

Longitudinal imaging of 8-year progression in a teenager with Stargardt disease

Longitudinal imaging of 8-year progression in a teenager with Stargardt disease

Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

Tuning the photophysical properties of cyanine by barbiturate functionalization and nanoformulation for efficient optoacoustics- guided phototherapy

Cyanine derivatives are organic dyes widely used for optical imaging. However, their potential in longitudinal optoacoustic imaging and photothermal therapy remains limited due to challenges such as poor chemical stability, poor photostability, and low photothermal conversion. In this study, we present a new structural modification for cyanine dyes by introducing a strongly electron-withdrawing group (barbiturate), resulting in a new series of barbiturate-cyanine dyes (BC810, BC885, and BC1010) with suppressed fluorescence and enhanced stability. Furthermore, the introduction of BC1010 into block copolymers (PEG114-b-PCL60) induces aggregation-caused quenching, further boosting the photothermal performance. The photophysical properties of nanoparticles (BC1010-NPs) include their remarkably broad absorption range from 900 to 1200 nm for optoacoustic imaging, allowing imaging applications in NIR-I and NIR-II windows. The combined effect of these strategies, including improved photostability, enhanced nonradiative relaxation, and aggregation-caused quenching, enables the detection of optoacoustic signals with high sensitivity and effective photothermal treatment of in vivo tumor models when BC1010-NPs are administered before irradiation with a 1064 nm laser. This research introduces a barbiturate-functionalized cyanine derivative with optimal properties for efficient optoacoustics-guided theranostic applications. This new compound holds significant potential for biomedical use, facilitating advancements in optoacoustic-guided diagnostic and therapeutic approaches.

Classifying and quantifying changes in papilloedema using machine learning

BackgroundMachine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify...

Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies.

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy

Background and purposeVolume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methodsPre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. ResultsSignificant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. ConclusionsMultiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Ischemia-reperfusion injury in a salvaged penumbra: Longitudinal high-tesla perfusion magnetic resonance imaging in a rat model

IntroductionAlthough ischemia-reperfusion (I/R) injury varies between cortical and subcortical regions, its effects on specific regions remain unclear. In this study, we used various magnetic resonance imaging (MRI) techniques to examine the spatiotemporal dynamics of I/R injury within the salvaged ischemic penumbra (IP) and reperfused ischemic core (IC) of a rodent model, with the aim of enhancing therapeutic strategies by elucidating these dynamics. Materials and methodsA total of 17 Sprague–Dawley rats were subjected to 1 h of transient middle cerebral artery occlusion with a suture model. MRI, including diffusion tensor imaging (DTI), T2-weighted imaging, perfusion-weighted imaging, and T1 mapping, was conducted at multiple time points for up to 5 days during the I/R phases. The spatiotemporal dynamics of blood–brain barrier (BBB) modifications were characterized through changes in T1 within the IP and IC regions and compared with mean diffusivity (MD), T2, and cerebral blood flow. ResultsDuring the I/R phases, the MD of the IC initially decreased, normalized after recanalization, decreased again at 24 h, and peaked on day 5. By contrast, the IP remained relatively stable. Both the IP and IC exhibited hyperperfusion, with the IP reaching its peak at 24 h, followed by resolution, whereas hyperperfusion was maintained in the IC until day 5. Despite hyperperfusion, the IP maintained an intact BBB, whereas the IC experienced persistent BBB leakage. At 24 h, the IC exhibited an increase in the T2 signal, corresponding to regions exhibiting BBB disruption at 5 days. ConclusionsHyperperfusion and BBB impairment have distinct patterns in the IP and IC. Quantitative T1 mapping may serve as a supplementary tool for the early detection of malignant hyperemia accompanied by BBB leakage, aiding in precise interventions after recanalization. These findings underscore the value of MRI markers in monitoring ischemia-specific regions and customizing therapeutic strategies to improve patient outcomes.

Variability of scan quality and perfusion density in longitudinal optical coherence tomography angiography imaging

Background/aimsOptical coherence tomography angiography (OCT-A) images are subject to variability, but the extent to which learning impacts OCT-A measurements is unknown. We determined whether there is a learning effect in...

A simple MATLAB toolbox for analyzing calcium imaging data in vitro and in vivo

BackgroundFluorescence imaging of calcium dynamics in neuronal populations is powerful because it offers a way of relating the activity of individual cells to the broader population of nearby cells. The method’s growth across neuroscience has particularly been driven by the introduction of sophisticated mathematical techniques related to motion correction, image registration, cell detection, spike estimation, and population characterization. However, for many researchers, making good use of these techniques has been difficult because they have been devised by different workers and impose differing – and sometimes stringent – technical requirements on those who seek to use them. New methodWe have built a simple toolbox of analysis routines that encompass the complete workflow for analyzing calcium imaging data. The workflow begins with preprocessing of data, includes motion correction and longitudinal image registration, detects active cells using constrained non-negative matrix factorization, and offers multiple options for estimating spike times and characterizing population activity. The routines can be navigated through a simple graphical user interface. Although written in MATLAB, a standalone version for researchers who do not have access to MATLAB is included. ResultsWe have used the toolbox on two very different preparations: spontaneously active brain slices and microendoscopic imaging from deep structures in awake behaving mice. In both cases, the toolbox offered a seamless flow from raw data all the way through to prepared graphs. ConclusionThe field of calcium imaging has benefited from the development of numerous innovative mathematical techniques. Here we offer a simple toolbox that allows ordinary researchers to fully exploit these techniques.

LGG-15. DEEP LEARNING ENABLES LONGITUDINAL RISK PREDICTION FOR PEDIATRIC LOW-GRADE GLIOMAS AFTER SURGERY

Abstract BACKGROUND Disease progression is challenging to predict following surgery for pediatric low-grade glioma (pLGG), and early progression indicators on MRI surveillance imaging would help guide management. Longitudinal surveillance imaging data may capture subtle temporal tumor changes and patterns that could inform recurrence risk but are difficult to synthesize clinically. We applied deep, self-supervised learning to longitudinal surveillance MRI to predict short-interval event-free-survival (EFS) risk from time-of-scan. METHODS We retrospectively collected data from 278 patients (2,174 scans) who underwent surgical resection for pLGG at Dana-Farber Cancer Institute/Boston Children’s Hospital (DFCI/BCH) from 1990-2022. A deep-learning model was pretrained with T2-FLAIR sequences to determine the chronological scan order for each patient (temporal pretraining). The model was fine-tuned to predict 1-year EFS from point-of-scan using a subset of scans from DFCI/BCH and then validated on a hold-out dataset (511 scans, 67 patients). Separately, the model was fine-tuned on an external dataset from the Children’s Brain Tumor Network (CBTN) and validated on a hold-out set (204 scans, 61 patients). Results were compared to a model trained without temporal pretraining. RESULTS Median follow-up and interval between scans were 73 and 6.3 months for DFCI/BCH and 20 and 5.8 months for CBTN. Temporal pretraining improved performance for 1-year EFS, for DFCI/BCH (AUC: 0.83 [0.71-0.90] vs. 0.78 [0.62-0.91]; F1-score: 0.80 vs. 0.67), and CBTN hold-out sets (AUC: 0.75 [0.58-0.90] vs. 0.53 [0.35-0.70], p=0.01; F1-score: 0.65 vs. 0.55). Increasing the number of longitudinal scans available to the model incrementally improved performance up to 7 scans/patient for DFCI/BCH and 10 scans/patient for CBTN. CONCLUSIONS Deep-learning with temporal pretraining improves the predictive analysis of tumor growth patterns in postoperative pLGG surveillance MRI, enabling accurate short-interval EFS prediction. This model could help inform patients and clinicians regarding surveillance regimen and initiation of adjuvant therapy.

IMG-25. PREDICTING ANATOMICAL TUMOR GROWTH IN PEDIATRIC HIGH-GRADE GLIOMAS VIA DENOISING DIFFUSION MODELS

Abstract BACKGROUND Pediatric diffuse midline glioma (DMG) has a poor prognosis with radiotherapy as the standard of palliative care. Radiation strategies using individual geometric doseshaping have the potential to maximize patient benefit. This study aimed to establish a proof-of-principle to generate clinically relevant anatomical representations of the progression of pediatric DMG magnetic resonance image (MRI) slices through denoising diffusion implicit models (DDIM). METHODS Based on the BraTS23 data, we analyzed the generation of MRIs with enlarged tumor sizes starting from a baseline scan. Models were pre-trained on adult glioblastoma (GBM) prior to transfer learning on pediatric high-grade glioma. Test set performance was evaluated using the Frechet Inception Distance (FID), and Structural Similarity Index (SSIM). Qualitative evaluation involved visual classification of real vs generated images by two independent radiology trainees. The optimized network was tested to forecast anatomical tumor growth in five DMG patients from an independent dataset comprising longitudinal T2-FLAIR images. RESULTS We obtain high-fidelity generated images, supported by an FID of 12.4 and 0.8 SSIM on the adult test set. Despite the smaller pediatric BraTS dataset yielding a 34.8 FID and 0.84 SSIM, human observers failed to distinguish generated from real MRIs (mean recall and precision of 0.51). The generated anatomical predictions also closely resemble the observed longitudinal tumor progression in the pediatric DMG subset. Notably, the direction and extent of tumor growth align while the surrounding brain anatomy is preserved. Quantitatively, the mean DICE score between segmentations of generated and real tumors was 0.8. CONCLUSIONS Our model produces high-quality images that conform to growth patterns of pediatric DMGs. Further quantitative validation on larger longitudinal datasets is required, to ensure the method’s robustness. The proposed method enables a personalized approach for defining target regions in radiation therapy which could translate to improved clinical outcomes.

Racial Discrimination and Risk for Internalizing and Externalizing Symptoms Among Black Youths

Racial discrimination is a psychosocial stressor associated with youths' risk for psychiatric symptoms. Scarce data exist on the moderating role of amygdalar activation patterns among Black youths in the US. To investigate the association between racial discrimination and risk for psychopathology moderated by neuroaffective processing. This cohort study used longitudinal self-report and functional magnetic resonance imaging (fMRI) data from Black youth participants in the US from the Adolescent Brain Cognitive Development (ABCD) study. Data were analyzed from January 2023 to May 2024. At time 1 of the current study (12 months after baseline), youths self-reported on their experiences of interpersonal racial discrimination and their feelings of marginalization. Amygdalar response was measured during an emotionally valenced task that included blocks of faces expressing either neutral or negative emotion. At 24 and 36 months after baseline, youths reported their internalizing (anxiety and depressive symptoms) and externalizing symptoms (aggression and rule-breaking symptoms). A total of 1596 youths were a mean (SD) age of 10.92 (0.63) years, and 803 were female (50.3%). Families in the study had a mean annual income range of $25 000 to $34 999. Two factors were derived from factor analysis: interpersonal racial discrimination and feelings of marginalization (FoM). Using structural equation modeling in a linear regression, standardized β coefficients were obtained. Neural response to faces expressing negative emotion within the right amygdala significantly moderated the association between FoM and changes in internalizing symptoms (β = -0.20; 95% CI, -0.32 to -0.07; P < .001). The response to negative facial emotion within the right amygdala significantly moderated the association between FoM and changes in externalizing symptoms (β = 0.24; 95% CI, 0.04 to 0.43; P = .02). Left amygdala response to negative emotion significantly moderated the association between FoM and changes in externalizing symptoms (β = -0.16; 95% CI, -0.32 to -0.01; P = .04). In this cohort study of Black adolescents in the US, findings suggest that amygdala function in response to emotional stimuli can both protect and intensify the affective outcomes of feeling marginalized on risk for psychopathology, informing preventive interventions aimed at reducing the adverse effects of racism on internalizing and externalizing symptoms among Black youths.

Patterns of longitudinal subcortical atrophy over one year in amnestic mild cognitive impairment and its impact on cognitive performance: a preliminary study.

Amnestic mild cognitive impairment (aMCI) is a risk factor for dementia, and thus, it is of interest to enlighten specific brain atrophy patterns in aMCI patients. We aim to define the longitudinal atrophy pattern in subcortical structures and its effect on cognition in patients with aMCI. Twenty patients with aMCI and 20 demographically matched healthy controls with baseline and longitudinal structural magnetic resonance imaging scans and neuropsychological assessments were studied. The algorithm FIRST (FMRIB's integrated registration and segmentation tool) was used to obtain volumes of subcortical structures (thalamus, putamen, caudate nucleus, nucleus accumbens, globus pallidus, hippocampus, and amygdala). Correlations between volumes and cognitive performance were assessed. Compared with healthy controls, aMCI demonstrated subcortical atrophies in the hippocampus (p = 0.001), nucleus accumbens (p = 0.003), and thalamus (p = 0.003) at baseline. Significant associations were found for the baseline volumes of the thalamus, nucleus accumbens, and hippocampus with memory, the thalamus with visuospatial skills. aMCI demonstrated subcortical atrophies associated with cognitive deficits. The thalamus, nucleus accumbens, and hippocampus may provide additional diagnostic information for aMCI.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. Methods The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. Results For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214–0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). Conclusion We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Longitudinal associations of reproductive factors and exogeneous estrogens with neuroimaging biomarkers of Alzheimer's disease and cerebrovascular disease.

Female-specific reproductive factors and exogeneous estrogen use are associated with cognition in later life. However, the underlying mechanisms are not understood. The present study aimed to investigate the effect of reproductive factors on neuroimaging biomarkers of Alzheimer's disease (AD) and cerebrovascular pathologies. We evaluated 389 females (median age of 71.7 years) enrolled in the Mayo Clinic Study of Aging with reproductive history data and longitudinal magnetic resonance imaging (MRI) scans. We used linear mixed effect models to examine the associations between reproductive factors and changes in neuroimaging measures. Ever hormonal contraception (HC) use was longitudinally associated with higher fractional anisotropy across the corpus callosum, lower white matter hyperintensity (WMH) volume, and greater cortical thickness in an AD meta-region of interest (ROI). The initiation of menopausal hormone therapy (MHT)>5 years post menopause was associated with higher WMH volume. HC use and initiation of MHT>5 years post menopause were generally associated with neuroimaging biomarkers of cerebrovascular pathologies. Hormonal contraception use was associated with better brain white matter (WM) integrity. Initiation of menopausal hormone therapy>5 years post menopause was associated with worsening brain WM integrity. Hormonal contraception use was associated with greater cortical thickness. Ages at menarche and menopause and number of pregnancies were not associated with imaging measures. There were few associations between reproductive factors or exogenous estrogens and amyloid or tau PET.

Longitudinal Magnetic Resonance Imaging Tracking of Transplanted Neural Progenitor Cells in the Spinal Cord Utilizing the Bright-Ferritin Mechanism.

Human neural progenitor cells (hNPCs) hold promise for treating spinal cord injury. Studies to date have focused on improving their regenerative potential and therapeutic effect. Equally important is ensuring successful delivery and engraftment of hNPCs at the injury site. Unfortunately, no current imaging solution for cell tracking is compatible with long-term monitoring in vivo. The objective of this study was to apply a novel bright-ferritin magnetic resonance imaging (MRI) mechanism to track hNPC transplants longitudinally and on demand in the rat spinal cord. We genetically modified hNPCs to stably overexpress human ferritin. Ferritin-overexpressing (FT) hNPCs labeled with 0.2 mM manganese provided significant T1-induced bright contrast on in vitro MRI, with no adverse effect on cell viability, morphology, proliferation, and differentiation. In vivo, 2 M cells were injected into the cervical spinal cord of Rowett nude rats. MRI employed T1-weighted acquisitions and T1 mapping on a 3 T scanner. Conventional short-term cell tracking was performed using exogenous Mn labeling prior to cell transplantation, which displayed transient bright contrast on MRI 1 day after cell transplantation and disappeared after 1 week. In contrast, long-term cell tracking using bright-ferritin allowed on-demand signal recall upon Mn supplementation and precise visualization of the surviving hNPC graft. In fact, this new cell tracking technology identified 7 weeks post-transplantation as the timepoint by which substantial hNPC integration occurred. Spatial distribution of hNPCs on MRI matched that on histology. In summary, bright-ferritin provides the first demonstration of long-term, on-demand, high-resolution, and specific tracking of hNPCs in the rat spinal cord.

Specific changes in contractive functions and skeletal muscle architecture in humans in response to the use of two protocols of unmodulated neuro-muscular electrostimulation

The purpose of this study was to study the effect of unmodulated low-frequency superficial of neuromuscular electrical stimulation (NMES) of 30 and 60 min/day for 7 weeks on the force, velocity-strength properties of the triceps surae muscle (TS) and architecture (lengths and angles of fascicles) of human the medial gastrocnemius muscle (MG). Many studies have examined the effect of training intensity (percentage of maximal voluntary isometric contraction — MVC) during NMES on muscle force response. However, no study has examined the effect of the number of NMES sessions per day over 7 weeks on changes in the TS strength. Ten healthy volunteers (23.2 ± 3.2 years; age range 18–28 years) volunteered for the study and were randomly assigned to group 1 (30 min NMES) and group 2 (60 min NMES) 5 times a day. NMES for a 7-week period, a total of 35 NMES workouts Isometric triceps calf strength was recorded with a Biodex isokinetic dynamometer. The longitudinal ultrasonic images of the MG was measured in vivo using the B-mode Edge ultrasound system. After a 7-week training period, MVC and voluntary maximal “explosive” strength differed significantly between groups. Based on electrical stimulation parameters and healthy subjects in this study, electrical training caused an increase in foot extensor muscle strength and a gradient in voluntary explosive strength when used for 5 training per week for 30 min for 7 weeks.