Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models

Uterine Leiomyoma in Kinshasa, the Capital of the Democratic Republic of Congo

The aim of the present study was to determine the particularities of Uterine Leiomyomas among Congolese in Kinshasa the capital of the Democratic Republic of Congo (DRC) in the present conditions of medical practices. A sample of 644 patients with uterine leiomyoma were selected from 6440 cases of uterine leiomyoma among 30395 patients treated in gynecology units of three medical institutions of Kinshasa (University hospital of Kinshasa, Saint joseph hospital and Edith medical center) from January Ist ,2003 to December 31,2012. The study is a descriptive one. The following variables were taken account: medical history [age, age at menarche, parity, education, civil state, history of UL, symptoms and body mass index (BMI)]; lifestyle (smoking, alcohol intake); ultrasounds characteristics; hysteroslpingographies characteristics, treatment, and direct cost of treatment. Statistical analysis were performed using Excel 12.0 software. Demographic, clinical, ultrasound, hysterosalpingography and treatment data were evaluated using descriptive statistics: mean, standard deviation (SD), and percentage (%) as appropriate. The frequency of uterine leiomyoma was 21, 18%. That one concern mainly patients at 35 years old or more [49, 6% (35-44years), ?45years (20, 6%)], singles (70, 4%), null parous (59,4%), having a high level of study (university: 54, 6%), history of UL (56, 7%), and alcohol intake (75, 5%). Hemorrhage (33, 2%) and pelvic pain (31, 6%) are the most frequent expression of those tumors. The most of those patients have excess weight (43, 1%) or obesity (46, 5%). The majority of uterine leiomyoma was corporeal (82, 9%) intramuscular (42, 4%) and their number didn’t overtake five by patient (70, 8%) in majority of cases. Majoration of the uterine cavity (46, 5%) and Fallopian tubes obstructions (30, 6%) are the most frequent abnormalities in hysterosalpingography. Myomectomy is the main treatment (65, 2%). The mean of direct cost were 803$USA and 884$ USA for myomectomy and hysterectomy respectively

Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multi-rater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency

The value of subtraction MRI in detection of amyloid-related imaging abnormalities with oedema or effusion in Alzheimer's patients: An interobserver study

BACKGROUND: Immunotherapeutic treatments targeting amyloid-β plaques in Alzheimer's disease (AD) are associated with the presence of amyloid-related imaging abnormalities with oedema or effusion (ARIA-E), whose detection and classification is crucial to evaluate subjects enrolled in clinical trials. PURPOSE: To investigate the applicability of subtraction MRI in the ARIA-E detection using an established ARIA-E-rating scale. METHODS: We included 75 AD patients receiving bapineuzumab treatment, including 29 ARIA-E cases. Five neuroradiologists rated their brain MRI-scans with and without subtraction images. The accuracy of evaluating the presence of ARIA-E, intraclass correlation coefficient (ICC) and specific agreement was calculated. RESULTS: Subtraction resulted in higher sensitivity (0.966) and lower specificity (0.970) than native images (0.959, 0.991, respectively). Individual rater detection was excellent. ICC scores ranged from excellent to good, except for gyral swelling (moderate). Excellent negative and good positive specific agreement among all ARIA-E imaging features was reported in both groups. Combining sulcal hyperintensity and gyral swelling significantly increased positive agreement for subtraction images. CONCLUSION: Subtraction MRI has potential as a visual aid increasing the sensitivity of ARIA-E assessment. However, in order to improve its usefulness isotropic acquisition and enhanced training are required. The ARIA-E rating scale may benefit from combining sulcal hyperintensity and swelling. KEY POINTS: • Subtraction technique can improve detection amyloid-related imaging-abnormalities with edema/effusion in Alzheimer's patients. • The value of ARIA-E detection, classification and monitoring using subtraction was assessed. • Validation of an established ARIA-E rating scale, recommendations for improvement are reported. • Complementary statistical methods were employed to measure accuracy, inter-rater-reliability and specific agreement

The relation between APOE genotype and cerebral microbleeds in cognitively unimpaired middle- and old-aged individuals

Positive associations between cerebral microbleeds (CMBs) and APOE-ε4 (apolipoprotein E) genotype have been reported in Alzheimer's disease, but show conflicting results. We investigated the effect of APOE genotype on CMBs in a cohort of cognitively unimpaired middle- and old-aged individuals enriched for APOE-ε4 genotype. Participants from ALFA (Alzheimer and Families) cohort were included and their magnetic resonance scans assessed (n = 564, 50% APOE-ε4 carriers). Quantitative magnetic resonance analyses included visual ratings, atrophy measures, and white matter hyperintensity (WMH) segmentations. The prevalence of CMBs was 17%, increased with age (p < 0.05), and followed an increasing trend paralleling APOE-ε4 dose. The number of CMBs was significantly higher in APOE-ε4 homozygotes compared to heterozygotes and non-carriers (p < 0.05). This association was driven by lobar CMBs (p < 0.05). CMBs co-localized with WMH (p < 0.05). No associations between CMBs and APOE-ε2, gray matter volumes, and cognitive performance were found. Our results suggest that cerebral vessels of APOE-ε4 homozygous are more fragile, especially in lobar locations. Co-occurrence of CMBs and WMH suggests that such changes localize in areas with increased vascular vulnerability

White matter microstructure disruption in early stage amyloid pathology.

Introduction: Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. Methods: We included 179 individuals from the European Medical Information Framework for AD (EMIF‐AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract‐level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. Results: Regression analyses showed a non‐linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. Discussion: Early amyloid deposition is associated with changes in WM microstructure. The non‐linear relationship might reflect multiple stages of axonal damage

Amyloid-driven disruption of default mode network connectivity in cognitively healthy individuals

Cortical accumulation of amyloid beta is one of the first events of Alzheimer's disease pathophysiology, and has been suggested to follow a consistent spatiotemporal ordering, starting in the posterior cingulate cortex, precuneus and medio-orbitofrontal cortex. These regions overlap with those of the default mode network, a brain network also involved in memory functions. Aberrant default mode network functional connectivity and higher network sparsity have been reported in prodromal and clinical Alzheimer's disease. We investigated the association between amyloid burden and default mode network connectivity in the preclinical stage of Alzheimer's disease and its association with longitudinal memory decline. We included 173 participants, in which amyloid burden was assessed both in CSF by the amyloid beta 42/40 ratio, capturing the soluble part of amyloid pathology, and in dynamic PET scans calculating the non-displaceable binding potential in early-stage regions. The default mode network was identified with resting-state functional MRI. Then, we calculated functional connectivity in the default mode network, derived from independent component analysis, and eigenvector centrality, a graph measure recursively defining important nodes on the base of their connection with other important nodes. Memory was tested at baseline, 2- and 4-year follow-up. We demonstrated that higher amyloid burden as measured by both CSF amyloid beta 42/40 ratio and non-displaceable binding potential in the posterior cingulate cortex was associated with lower functional connectivity in the default mode network. The association between amyloid burden (CSF and non-displaceable binding potential in the posterior cingulate cortex) and aberrant default mode network connectivity was confirmed at the voxel level with both functional connectivity and eigenvector centrality measures, and it was driven by voxel clusters localized in the precuneus, cingulate, angular and left middle temporal gyri. Moreover, we demonstrated that functional connectivity in the default mode network predicts longitudinal memory decline synergistically with regional amyloid burden, as measured by non-displaceable binding potential in the posterior cingulate cortex. Taken together, these results suggest that early amyloid beta deposition is associated with aberrant default mode network connectivity in cognitively healthy individuals and that default mode network connectivity markers can be used to identify subjects at risk of memory decline

Medial temporal lobe atrophy and posterior atrophy scales normative values

OBJECTIVES: The medial temporal lobe atrophy (MTA) and the posterior atrophy (PA) scales allow to assess the degree hippocampal and parietal atrophy from magnetic resonance imaging (MRI) scans. Despite reliable, easy and widespread employment, appropriate normative values are still missing. We aim to provide norms for the Italian population. // METHODS: Two independent raters assigned the highest MTA and PA score between hemispheres, based on 3D T1-weighted MRI of 936 Italian Brain Normative Archive subjects (age: mean ± SD: 50.2 ± 14.7, range: 20-84; MMSE>26 or CDR = 0). The inter-rater agreement was assessed with the absolute intraclass correlation coefficient (aICC). We assessed the association between MTA and PA scores and sociodemographic features and APOE status, and normative data were established by age decade based on percentile distributions. // RESULTS: Raters agreed in 90% of cases for MTA (aICC = 0.86; 95% CI = 0.69-0.98) and in 86% for PA (aICC = 0.82; 95% CI = 0.58-0.98). For both rating scales, score distribution was skewed, with MTA = 0 in 38% of the population and PA = 0 in 52%, while a score ≥ 2 was only observed in 12% for MTA and in 10% for PA. Median denoted overall hippocampal (MTA: median = 1, IQR = 0-1) and parietal (PA: median = 0, IQR = 0-1) integrity. The 90th percentile of the age-specific distributions increased from 1 (at age 20-59) for both scales, to 2 for PA over age 60, and up to 4 for MTA over age 80. Gender, education and APOE status did not significantly affect the percentile distributions in the whole sample, nor in the subset over age 60. // CONCLUSIONS: Our normative data for the MTA and PA scales are consistent with previous studies and overcome their main limitations (in particular uneven representation of ages and missing percentile distributions), defining the age-specific norms to be considered for proper brain atrophy assessment

Multi-tracer model for staging cortical amyloid deposition using PET imaging

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: 3027 subjects (1763 Cognitively Unimpaired (CU), 658 Impaired, 467 Alzheimer's disease (AD) dementia, 111 non-AD dementia, and 28 with missing diagnosis) from six cohorts (EMIF-AD, ALFA, ABIDE, ADC, OASIS-3, ADNI) who underwent amyloid PET were retrospectively included; 1049 subjects had follow-up scans. Applying dataset-specific cut-offs to global Standard Uptake Value ratio (SUVr) values from 27 regions, single-tracer and pooled multi-tracer regional rankings were constructed from the frequency of abnormality across 400 CU subjects (100 per tracer). The pooled multi-tracer ranking was used to create a staging model consisting of four clusters of regions as it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices, then the associative, temporal and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of stage 0-4 subjects, respectively. Baseline stage predicted decline in MMSE (ADNI: N=867, F=67.37, p3000 subjects across cohorts and radiotracers, and detects pre-global amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive subjects