Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort.

Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.

Amyloid Deposition Is Greater in Cerebral Gyri than in Cerebral Sulci with Worsening Clinical Diagnosis Across the Alzheimer's Disease Spectrum.

BACKGROUND: Histopathologic studies have demonstrated differential amyloid-ß (Aß) burden between cortical sulci and gyri in Alzheimer's disease (AD), with sulci having a greater Aß burden. OBJECTIVE: To characterize Aß deposition in the sulci and gyri of the cerebral cortex in vivo among subjects with normal cognition (NC), mild cognitive impairment (MCI), and AD, and to evaluate if these differences could improve discrimination between diagnostic groups. METHODS: T1-weighted 3T MR and florbetapir (amyloid) positron emission tomography (PET) data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). T1 images were segmented and the cortex was separated into sulci/gyri based on pial surface curvature measurements. T1 images were registered to PET images and regional standardized uptake value ratios (SUVr) were calculated. A linear mixed effects model was used to analyze the relationship between clinical variables and amyloid PET SUVr measurements in the sulci/gyri. Receiver operating characteristic (ROC) analysis was performed to define amyloid positivity. Logistic models were used to evaluate predictive performance of clinical diagnosis using amyloid PET SUVr measurements in sulci/gyri. RESULTS: 719 subjects were included: 272 NC, 315 MCI, and 132 AD. Gyral and sulcal Aß increased with worsening cognition, however there was a greater increase in gyral Aß. Females had a greater gyral and sulcal Aß burden. Focusing on sulcal and gyral Aß did not improve predictive power for diagnostic groups. CONCLUSION: While there were significant differences in Aß deposition in cerebral sulci and gyri across the AD spectrum, these differences did not translate into improved prediction of diagnosis. Females were found to have greater gyral and sulcal Aß burden.

Baseline Brain Segmental Volumes in Responders and Nonresponders to Anticholinergic Therapy for Overactive Bladder Syndrome.

OBJECTIVE: Evaluate structural differences in brains of responders (R) and nonresponders (NR) to anticholinergic (AC) therapy for overactive bladder (OAB). MATERIALS AND METHODS: This was a retrospective cohort study of age matched women treated with an AC medication for OAB and underwent magnetic resonance imaging within 12 months before treatment. Data on pretreatment demographic and clinical variables and symptom severity was also collected.T1-weighted magnetic resonance images of the brain for each subject were segmented using FreeSurfer software. Structures included for analysis were cerebral cortex, white matter, subcortical gray matter, cerebellum, and brain stem.Nonresponders were defined as patients who reported less than 50% improvement after a minimum of 4 weeks on the maximum dose of the prescribed medication. Pairwise analysis between groups was performed using the Wilcoxon-Rank Sum test and Fisher exact test where appropriate. Spearman ρ was used to evaluate for correlations between neurologic structures and symptom severity. RESULTS: There were no differences in pretreatment characteristics or symptom severity between the 21 R and 18 NR. Nonresponders had lower volumes of the right caudal anterior cingulate gyrus white matter (1919 mm3 vs 2416 mm3, P = 0.008) and right parahippocampal gyrus white matter (1008 mm3 vs 1469 mm3, P = 0.001). Incontinence episode frequency showed a negative moderate correlation with the anterior cingulate gyrus white matter volume (ρ = -0.4228, P = 0.007). The right and left cerebellar cortices showed weak and moderate negative correlations to frequency of nocturia (ρ = -0.384, P = 0.02 and ρ -0.443, P = 0.005, respectively). CONCLUSION: There are measurable volumetric differences in brain structures in R and NR to AC therapy.

The Alzheimer structural connectome: changes in cortical network topology with increased amyloid plaque burden.

PURPOSE: To evaluate differences in the structural connectome among patients with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer disease (AD) and to determine associations between the structural connectome and cortical amyloid deposition. MATERIALS AND METHODS: Patients enrolled in a multicenter biomarker study (Alzheimer's Disease Neuroimaging Initiative [ADNI] 2) who had both baseline diffusion-tensor (DT) and florbetapir positron emission tomography (PET) data at the time of data analyses in November 2012 were studied. All institutions received institutional review board approval. There were 102 patients in ADNI 2 who met criteria for analysis. Patients' T1-weighted images were automatically parcellated into cortical regions of interest. Standardized uptake value ratio (SUVr) was calculated from florbetapir PET images for composite cortical regions (frontal, cingulate, parietal, and temporal). Structural connectome graphs were created from DT images, and connectome topology was analyzed in each region by using graph theoretical metrics. Analysis of variance of structural connectome metrics and florbetapir SUVr across diagnostic group was performed. Linear mixed-effects models were fit to analyze the effect of florbetapir SUVr on structural connectome metrics. RESULTS: Diagnostic group (NC, MCI, or AD) was associated with changes in weighted structural connectome metrics, with decreases from the NC group to the MCI group to the AD group shown for (a) strength in the bilateral frontal, right parietal, and bilateral temporal regions (P < .05); (b) weighted local efficiency in the left temporal region (P < .05); and (c) weighted clustering coefficient in the bilateral frontal and left temporal regions (P < .05). Increased cortical florbetapir SUVr was associated with decreases in weighted structural connectome metrics; namely, strength (P = .00001), weighted local efficiency (P = .00001), and weighted clustering coefficient (P = .0006), independent of brain region. For every 0.1-unit increase in florbetapir SUVr, there was a 14% decrease in strength, an 11% decrease in weighted local efficiency, and a 9% decrease in weighted clustering coefficient, regardless of the analyzed cortical region or, in the case of weighted local efficiency and clustering coefficient, diagnostic group. CONCLUSION: Increased amyloid burden, as measured with florbetapir PET imaging, is related to changes in the topology of the large-scale cortical network architecture of the brain, as measured with graph theoretical metrics of DTI tractography, even in the preclinical stages of AD. Online supplemental material is available for this article.

The aging athlete: part 1, "boomeritis" of the lower extremity.

OBJECTIVE: The purpose of this review is to describe the physiologic changes that occur in the musculoskeletal system during aging and the common injuries that occur in the lower extremity as a consequence of these changes. Several clinical presentations are addressed, and their differential diagnoses are discussed with an emphasis on the most likely injury for each presentation. CONCLUSION: A unique quality of the newly aging group of people referred to as baby boomers is their expectation to continue exercising as they grow older, thus the incidence of exercise-induced injuries among older people is increasing. The concepts behind factors that predispose older athletes to certain pathologic conditions that affect the muscles, tendons, and bones of the lower extremity must be understood.

Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative.

In this paper, we present a semi-automated segmentation method for magnetic resonance images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely, sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were as follows: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; and vastus medialis, 0.69 ± 0.16.

Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation.

In this paper, we present a method of quantifying the heterogeneity of cervical cancer tumors for use in radiation treatment outcome prediction. Features based on the distribution of masked wavelet decomposition coefficients in the tumor region of interest (ROI) of temporal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies were used along with the imaged tumor volume to assess the response of the tumors to treatment. The wavelet decomposition combined with ROI masking was used to extract local intensity variations in the tumor. The developed method was tested on a data set consisting of 23 patients with advanced cervical cancer who underwent radiation therapy; 18 of these patients had local control of the tumor, and five had local recurrence. Each patient participated in two DCE-MRI studies: one prior to treatment and another early into treatment (2-4 weeks). An outcome of local control or local recurrence of the tumor was assigned to each patient based on a posttherapy follow-up at least 2 years after the end of treatment. Three different supervised classifiers were trained on combinational subsets of the full wavelet and volume feature set. The best-performing linear discriminant analysis (LDA) and support vector machine (SVM) classifiers each had mean prediction accuracies of 95.7%, with the LDA classifier being more sensitive (100% vs. 80%) and the SVM classifier being more specific (100% vs. 94.4%) in those cases. The K-nearest neighbor classifier performed the best out of all three classifiers, having multiple feature sets that were used to achieve 100% prediction accuracy. The use of distribution measures of the masked wavelet coefficients as features resulted in much better predictive performance than those of previous approaches based on tumor intensity values and their distributions or tumor volume alone.

An automated method to detect interstitial adipose tissue in thigh muscles for patients with osteoarthritis.

In this paper we explore a method of segmentation of muscle interstitial adipose tissue (IAT) in MR images of the thigh. The objective is to apply the method towards research into biomarkers of osteoarthritis (OA). T1-weighted images of the thigh are intensity standardized through bias field correction and intensity normalization. IAT within the thigh muscles is then segmented using a threshold combined with morphological constraints applied on connected regions in the thresholded image. The morphological constraints can be adjusted to allow for highly sensitive or highly specific IAT segmentation. The use of the morphological constraints improved the specificity of IAT segmentation over a threshold segmentation method from 0.54 to 0.67, while retaining a nearly equivalent sensitivity of 0.82 compared to 0.84. We then present a preliminary statistical analysis to demonstrate the application of the automated IAT segmentation. Finally, we specify a protocol for further exploration of IAT by leveraging the massive imaging dataset of the Osteoarthritis Initiative (OAI).

An automated method to segment the femur for osteoarthritis research.

In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation algorithm with ground truth manual segmentations was 0.94 +/- 0.03 calculated using the Zijdenbos similarity index (ZSI). A pooled variance t-test analysis found significant associations between the KL grade, a clinical measure of OA severity, and both the cross-sectional area (CSA) of the femur medulla (p = 0.02) and the ratio of the femur medulla CSA to the femur cortex CSA (p = 0.04) for women. No significant association between femur measurements and KL grade was found for men.