Longitudinal Magnetic Resonance Imaging Data Research Articles

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Effects of multiple anesthetic exposures on rhesus macaque brain development: a longitudinal structural MRI analysis.

Concerns about the potential neurotoxic effects of anesthetics on developing brain exist. When making clinical decisions, the timing and dosage of anesthetic exposure are critical factors to consider due to their associated risks. In our study, we investigated the impact of repeated anesthetic exposures on the brain development trajectory of a cohort of rhesus monkeys (n = 26) over their first 2yr of life, utilizing longitudinal magnetic resonance imaging data. We hypothesized that early or high-dose anesthesia exposure could negatively influence structural brain development. By employing the generalized additive mixed model, we traced the longitudinal trajectories of brain volume, cortical thickness, and white matter integrity. The interaction analysis revealed that age and cumulative anesthetic dose were variably linked to white matter integrity but not to morphometric measures. Early high-dose exposure was associated with increased mean, axial, and radial diffusivities across all white matter regions, compared to late-low-dose exposure. Our findings indicate that early or high-dose anesthesia exposure during infancy disrupts structural brain development in rhesus monkeys. Consequently, the timing of elective surgeries and procedures that require anesthesia for children and pregnant women should be strategically planned to account for the cumulative dose of volatile anesthetics, aiming to minimize the potential risks to brain development.

The impact of sex on the progression of AD atrophy‐based subtypes

AbstractBackgroundRecently, longitudinal subtypes in AD have been identified based on their different brain atrophy trajectories (Poulakis et al. 2022 Nat Commun), which follow either mediotemporal or cortical pathways. The mediotemporal pathway, which is the most prevalent, includes three longitudinal subtypes: the limbic predominant plus (LPA+), the limbic predominant (LPA), and the minimal atrophy (MA) subtypes. While the LPA+ is characterised by the fastest rate of atrophy starting in the entorhinal cortex and later spreading to the temporal lobe and the rest of the cortex, the LPA is restrained to atrophy in temporal regions. The MA subtype, on the other side, is characterised by minimal atrophy in mediotemporal areas. Although the discovery of brain atrophy trajectories in AD represents a significant contribution towards precision medicine, the impact of sex – a major risk factor for AD – on these trajectories has yet to be explored.MethodWe used longitudinal magnetic resonance imaging data as well as clinical data (up to 5 timepoints) from 3 international AD cohorts (ADNI, J‐ADNI and AIBL). AD individuals had previously been identified, as one of the following longitudinal subtypes: LPA+ (N = 23, 61% females), LPA (N = 93, 48% females), and MA subtype (N = 189, 48% females). We applied linear mixed effect models (LMMs) on 34 cortical and 7 subcortical regions to assess differences in sex within each AD longitudinal subtype. Results were adjusted for ageing effects, cohort, disease duration and intracranial volume.ResultLPA+ females showed greater atrophy over time compared to LPA+ males in frontal regions as well as in the lingual and the parahippocampal gyri. LPA females tended to show greater atrophy in the medial orbitofrontal cortex compared to LPA males. Finally, within the MA subtype, females showed greater atrophy than males in the hippocampus over time.ConclusionThis study demonstrates that sex‐specific regional vulnerabilities are crucial to consider in the study of AD heterogeneity, particularly within the mediotemporal pathway. Our results reveal that females exhibit distinct atrophy patterns over time compared to males in different AD subtypes, emphasizing the importance of investigating sex‐related differences to develop more targeted and effective interventions for AD.

Testing the brain maintenance and cognitive reserve hypothesis of cognitive aging: Application to vascular and metabolic risk factors

AbstractBackgroundTwo pathways that help explain cognitive health are 1) brain maintenance – referring to the relative absence of brain changes over time; 2) cognitive reserve – a property of the brain that allows for sustained cognitive ability in the presence of brain pathology. Both pathways influence cognitive aging and are best understood using longitudinal data. Our goal was to assess the effect of modifiable vascular/metabolic and lifestyle enrichment factors on these pathways using longitudinal brain changes measured on structural magnetic resonance imaging (MRI) and longitudinal cognitive decline. Lastly, we combined these pathways and their relationships in the same model.MethodWe identified 1250 participants from a well‐characterized sample of older adults from the Mayo Clinic Study of Aging with both longitudinal MRI and cognitive data. Linear mixed models were performed to assess the impact of modifiable factors on brain maintenance and cognitive reserve. Latent growth curve models (SEM) were used to assess the impact of brain structure and cognitive reserve on longitudinal cognition.ResultThe mean age of our sample was 78.8 years, with 45.4% female, and 27.1% APOE4 carriers. Mean follow‐up time was 4.0 years with a mean of 3.2 MRI scans per participant. Linear mixed models showed that poorer vascular/metabolic health (presence of vascular/metabolic conditions) was associated with lower cross‐sectional temporal region cortical thickness (‐0.014 (0.003), p<0.001) and lower global cognition (‐0.042 (0.018), p = 0.024). Higher estimated cognitive reserve at baseline as assessed by education/occupation composite (0.103 (0.01), p<0.001) was associated with higher global cognition. Unexpectedly, late‐life physical activities were related to lower global cognition (‐0.05 (0.007), p = 0.004). Our final latent growth curve SEM fit the data well showing temporal associations between unmodifiable characteristics, cognitive reserve, and brain structural pathways, as well as the influence of modifiable vascular/metabolic factors.ConclusionWe found that brain maintenance and cognitive reserve pathways were related to better cognition over time. We further showed that vascular/metabolic health impacts long‐term cognitive outcomes (i.e. cognitive aging) primarily through the brain maintenance pathway. These data provide a modeling scheme for future studies and can aid in assessing the pathways by which individual factors may influence cognitive aging.

A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data.

Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

Commonalities and differences in ECT-induced gray matter volume change between depression and schizophrenia

BackgroundElectroconvulsive therapy (ECT) is one of the most effective treatments for depression and schizophrenia, particularly in urgent or treatment-resistant cases. After ECT, regional gray matter volume (GMV) increases have been repeatedly reported both in depression and schizophrenia. However, the interpretation of these findings remains entangled because GMV changes do not necessarily correlate with treatment effects and may be influenced by the intervention itself. We hypothesized that the comparison of longitudinal magnetic resonance imaging data between the two diagnostic groups will provide clues to distinguish diagnosis-specific and transdiagnostic changes. MethodTwenty-nine Japanese participants, including 18 inpatients with major depressive disorder and 11 with schizophrenia, underwent longitudinal voxel-based morphometry before and after ECT. We investigated GMV changes common to both diagnostic groups and those specific to each group. Moreover, we also evaluated potential associations between GMV changes and clinical improvement for each group. ResultsIn both diagnostic groups, GMV increased in widespread areas after ECT, sharing common regions including: anterior temporal cortex; medial frontal and anterior cingulate cortex; insula; and caudate nucleus. In addition, we found a schizophrenia-specific GMV increase in a region including the left pregenual anterior cingulate cortex, with volume increase significantly correlating with clinical improvement. ConclusionsTransdiagnostic volume changes may represent the effects of the intervention itself and pathophysiological changes common to both groups. Conversely, diagnosis-specific volume changes are associated with treatment effects and may represent pathophysiology-specific impacts of ECT.

Topological relationships between perivascular spaces and progression of white matter hyperintensities: A pilot study in a sample of the Lothian Birth Cohort 1936.

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves−1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves−1–2: 73%; 2–3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease

Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Longitudinal Structural MRI Data Prediction in Nondemented and Demented Older Adults via Generative Adversarial Convolutional Network

Alzheimer’s disease (AD) is the most common cause of dementia and threatens the health of millions of people. Early stage diagnosis of AD is critical for improving clinical outcomes and longitudinal magnetic resonance imaging (MRI) data collection can be used to monitor the progress of each patient. However, missing data is a common problem in longitudinal AD studies. The main factors come from subject dropouts and failed scans. This hinders the acquisition of longitudinal sequences that consist of multi-time-point magnetic resonance (MR) images at relatively uniform intervals. In this paper, we present a generative adversarial convolutional network to predict missing structural MRI data. In particular, we include multiple MRI scans as a temporal sequence collected at different times and determine the spatio-temporal relationship between the different scans in the proposed network. We adopt residual bottlenecks in the generator to decrease parameter values and deepen the network. In order to make full use of the longitudinal information, our discriminator classifies not only real MR images from generated MR images, but also fake sequences from real sequences in which the longitudinal MR images for all time points come from the dataset, only the last MR image comes from the generator. Results of our experiment show that our method performs more accurately for the longitudinal structural MRI data prediction of a brain afflicted with AD.

Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis.

The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.

Genetic variants associated with longitudinal changes in brain structure across the lifespan.

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Associations of central auditory processing with brain volumes in the aging and cognitive health evaluation in elders and the atherosclerosis risk in communities neurocognitive study

AbstractBackgroundPrior evidence has suggested associations between hearing loss and smaller brain volumes particularly in the temporal lobe where the primary auditory cortex is located, probably due to changes in cortical organization associated with reduced auditory stimulation. Evidence regarding associations of central auditory processing with brain volumes is relatively limited. The study aims to investigate cross‐sectional associations of central auditory processing with brain volumes in older adults.MethodWe studied 587 cognitively normal (Mini‐Mental State Exam ≥23 if high school degree or less and ≥25 if above high school degree) older adults (mean age: 77±4 years, 56% female, 17% black), including 426 with hearing loss from the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) baseline (2018‐19), which is an ancillary randomized controlled trial of the Atherosclerosis Risk in Communities (ARIC) Study and 161 with normal hearing, comparable age and cognitive status from the ARIC Visit 6/7 (2016‐17/2018‐19). Brain volumes were measured by magnetic resonance imaging (MRI) scans and were transformed to z‐scores. Central auditory processing was measured by the Quick Speech‐in‐Noise (QuickSIN) test. Total scores range from 0 to 30 with lower scores indicating worse speech‐in‐noise performance. Multivariable‐adjusted linear regression was used to estimate cross‐sectional differences in standardized brain volumes associated with differences in QuickSIN score. Models adjusted for age, sex, race, education, cohort, body mass index, smoking, hypertension, diabetes, stroke, intracranial volume, and pure‐tone average.ResultEvery 5‐point decrease in QuickSIN score (worse performance) was associated with smaller volumes of the temporal lobe (‐0.07SD, 95% CI: ‐0.13, ‐0.01). QuickSIN score was also associated with subregions of the temporal lobe related to auditory processing, memory and emotion, including amygdala (‐0.13SD, 95% CI: ‐0.21, ‐0.04), middle (‐0.08SD, 95% CI: ‐0.15, ‐0.00) and superior temporal gyri (‐0.08SD, 95% CI: ‐0.15, ‐0.01).ConclusionPoorer central auditory processing was associated with smaller temporal lobe volumes, including but not limited to subregions important for auditory processing. The ongoing collection of ACHIEVE data with longitudinal MRI data will facilitate understanding of association between hearing loss and brain atrophy as well as the potential promising effects of hearing treatment in mitigating brain aging.

EXTH-07. TEMPORAL ASSESSMENT OF ALTERED PERI-ABLATION BLOOD-BRAIN BARRIER PERMEABILITY FOLLOWING TUMOR CYTOREDUCTION BY THERMAL THERAPY IN A RAT ORTHOTOPIC GLIOBLASTOMA MODEL

Abstract Laser interstitial thermal therapy (LITT) is a minimally invasive tumor cytoreductive treatment for recurrent gliomas, brain tumors in eloquent regions and/or otherwise inaccessible. Following reports of persistent peri-ablation blood-brain barrier (BBB) opening in humans, we examined this phenomenon using a rat glioblastoma model. Athymic female rats were implanted with U251 tumor cells in one brain hemisphere. Tumor growth was monitored using magnetic resonance imaging (MRI) and dynamic contrast enhanced (DCE)-MRI. When tumors reached about 4 mm in diameter, they were ablated under supervision of diffusion-weighted MRI using Visualase®, a clinical LITT system. Four rats were used as controls. Longitudinal MRI data were obtained before LITT, and at post-LITT 2 (n=9), 3 (n=3) and 4 (n=9) weeks. After the terminal MRI at each time point, rats were injected intravenously with fluorescent isothiocyanate dextran (FITC-dextran; 2000 kDa) and Evans Blue (68 kDa after binding to plasma albumin) and the brains immersion fixed in 10% paraformaldehyde. The brains were cut into 100 μM thick slices in a vibratome and examined for the distribution of the two fluorophores. All rats survived the LITT procedure. The sham controls showed increased tumor burden by 2 weeks and were sacrificed. DCE-MRI data and fluorescent data showed elevated BBB permeability in peri-ablation regions, with leakage of a gadolinium contrast on DCE-MRI and of Evans Blue, but not of FITC-dextran. Histology showed little tumor tissue at 2 weeks, but evidence of recurrence at ablation margins at later times. These data demonstrate that LITT is adaptable to rat glioma models and can be performed under MRI monitoring. Peri-ablation regions showed selective increase in BBB permeability acutely due to sublethal heating, but later increases in permeability may be due to tumor recurrence. We suggest this model is useful for examining the temporal and spatial development of peri-ablation BBB opening following LITT.

Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Energetic Cost of Walking and Brain Atrophy in Mid-to-Late Life.

Higher energetic costs for mobility are associated with declining gait speed, and slow gait is linked to cognitive decline and Alzheimer's disease. However, the physiological underpinnings of gait and brain health have not been well explored. We examined the associations of the energetic cost of walking with brain volume in cognitively unimpaired adults from the Baltimore Longitudinal Study of Aging. We used brain magnetic resonance imaging (MRI) data from 850 participants (mean baseline age 66.3 ± 14.5 years), of whom 451 had longitudinal MRI data (2.8 ± 1.0 MRI scans over 4.0 ± 2.0 years). The energetic cost of walking was assessed as the average energy expended (V̇O2) during 2.5 minutes of customary-paced overground walking. Multivariable linear mixed-effects models examined the associations between baseline energetic cost of walking and regional brain volumes adjusting for covariates. At baseline, higher energetic cost of walking was cross-sectionally associated with lower gray and white matter volumes within the frontal, parietal, and temporal lobes, as well as hippocampal, total brain, and larger ventricular volumes (all false-discovery rate [FDR] p < .05). A baseline energetic cost of walking × time interaction demonstrated that participants with higher energetic cost of walking had faster annual decline in hippocampal volume (FDR p = .02) and accelerated annual increase in ventricular volumes (FDR p = .02). The energetic cost of walking is associated with gray and white matter volumes and subsequent hippocampal atrophy and ventricular enlargement. Collectively, these data suggest the energetic cost of walking may be an early marker of neurodegeneration that contributes to the gait brain connection.

Toward MR-integrated proton therapy: modeling the potential benefits for liver tumors

Magnetic resonance imaging (MRI)-integrated proton therapy (MRiPT) is envisioned to improve treatment quality for many cancer patients. However, given the availability of alternative image-guided strategies, its clinical need is yet to be justified. This study aims to compare the expected clinical outcomes of MRiPT with standard of practice cone-beam CT (CBCT)-guided PT, and other MR-guided methods, i.e. offline MR-guided PT and MR-linac, for treatment of liver tumors. Clinical outcomes were assessed by quantifying the dosimetric and biological impact of target margin reduction enabled by each image-guided approach. Planning target volume (PTV) margins were calculated using random and systematic setup, delineation and motion uncertainties, which were quantified by analyzing longitudinal MRI data for 10 patients with liver tumors. Proton treatment plans were created using appropriate PTV margins for each image-guided PT method. Photon plans with margins equivalent to MRiPT were generated to represent MR-linac. Normal tissue complication probabilities (NTCP) of the uninvolved liver were compared. We found that PTV margin can be reduced by 20% and 40% for offline MR-guided PT and MRiPT, respectively, compared with CBCT-guided PT. Furthermore, clinical target volume expansion could be largely alleviated when delineating on MRI rather than CT. Dosimetric implications included decreased equivalent mean dose of the uninvolved liver, i.e. up to 24.4 Gy and 27.3 Gy for offline MR-guided PT and MRiPT compared to CBCT-guided PT, respectively. Considering Child–Pugh score increase as endpoint, NTCP of the uninvolved liver was significantly decreased for MRiPT compared to CBCT-guided PT (up to 48.4%, p < 0.01), offline MR-guided PT (up to 12.9%, p < 0.01) and MR-linac (up to 30.8%, p < 0.05). Target underdose was possible in the absence of MRI-guidance (D90 reduction up to 4.2 Gy in 20% of cases). In conclusion, MRiPT has the potential to significantly reduce healthy liver toxicities in patients with liver tumors. It is superior to other image-guided techniques currently available.

Intracranial arteriosclerosis is related to cerebral small vessel disease: a prospective cohort study

Intracranial arteriosclerosis has been increasingly recognized as a risk factor for cognitive impairment and even dementia. A possible mechanism linking intracranial arteriosclerosis to cognitive impairment and dementia involves structural brain changes including cerebral small vessel disease (CSVD). To assess whether intracranial carotid artery calcification (ICAC) and vertebrobasilar artery calcification (VBAC), as proxies for intracranial arteriosclerosis, are related to CSVD.Within the population-based Rotterdam Study, between 2003 and 2006 a computed tomography (CT)-based measurement of ICAC and VBAC and at least one magnetic resonance imaging (MRI) measurement of structural brain changes were performed from 2005 onwards in 1,489 participants. To estimate the burden of calcification independent of age, we computed age-adjusted percentile curves for ICAC and VBAC separately, based on the calcification volumes. Using the longitudinal MRI data, we assessed whether a larger calcification burden accelerates structural brain changes using appropriate statistical models for repeated outcome measures. A larger burden of ICAC and VBAC was associated with an increase of CSVD markers accelerating over time. A larger burden of ICAC and VBAC was not significantly (p > 0.05) associated with accelerated brain atrophy. Arteriosclerosis is related to accelerating structural brain changes over time.

Longitudinal MEMRI analysis of brain phenotypes in a mouse model of Niemann-Pick Type C disease

Niemann-Pick Type C (NPC) is a rare genetic disorder characterized by progressive cell death in various tissues, particularly in the cerebellar Purkinje cells, with no known cure. Mouse models for human NPC have been generated and characterized histologically, behaviorally, and using longitudinal magnetic resonance imaging (MRI). Previous imaging studies revealed significant brain volume differences between mutant and wild-type animals, but stopped short of making volumetric comparisons of the cerebellar sub-regions. In this study, we present longitudinal manganese-enhanced MRI (MEMRI) data from cohorts of wild-type, heterozygote carrier, and homozygote mutant NPC mice, as well as deformation-based morphometry (DBM) driven brain volume comparisons across genotypes, including the cerebellar cortex, white matter, and nuclei. We also present the first comparisons of MEMRI signal intensities, reflecting brain and cerebellum sub-regional Mn2+-uptake over time and across genotypes.

Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis

Background Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD. Objective This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF). Methods In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD. Results The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P&lt;.01). Conclusions A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.