Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses

PURPOSE: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods. METHODS: Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC). RESULTS: For all six software, both between-scanner agreement (ICCs ranging 0.4–1) and within-scanner agreement (ICC range: 0.6–1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus). CONCLUSION: Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements

Converging evidence points towards a role of insulin signaling in regulating compulsive behavior.

Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder with childhood onset, and is characterized by intrusive thoughts and fears (obsessions) that lead to repetitive behaviors (compulsions). Previously, we identified insulin signaling being associated with OCD and here, we aim to further investigate this link in vivo. We studied TALLYHO/JngJ (TH) mice, a model of type 2 diabetes mellitus, to (1) assess compulsive and anxious behaviors, (2) determine neuro-metabolite levels by 1 H magnetic resonance spectroscopy (MRS) and brain structural connectivity by diffusion tensor imaging (DTI), and (3) investigate plasma and brain protein levels for molecules previously associated with OCD (insulin, Igf1, Kcnq1, and Bdnf) in these subjects. TH mice showed increased compulsivity-like behavior (reduced spontaneous alternation in the Y-maze) and more anxiety (less time spent in the open arms of the elevated plus maze). In parallel, their brains differed in the white matter microstructure measures fractional anisotropy (FA) and mean diffusivity (MD) in the midline corpus callosum (increased FA and decreased MD), in myelinated fibers of the dorsomedial striatum (decreased FA and MD), and superior cerebellar peduncles (decreased FA and MD). MRS revealed increased glucose levels in the dorsomedial striatum and increased glutathione levels in the anterior cingulate cortex in the TH mice relative to their controls. Igf1 expression was reduced in the cerebellum of TH mice but increased in the plasma. In conclusion, our data indicates a role of (abnormal) insulin signaling in compulsivity-like behavior

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Novel imaging phantom for accurate and robust measurement of brain atrophy rates using clinical MRI

Brain volume loss, or atrophy, has been proven to be an important characteristic of neurological diseases such as Alzheimer's disease and multiple sclerosis. To use atrophy rate as a reliable clinical biomarker and to increase statistical power in clinical treatment trials, measurement variability needs to be minimized. Among other sources, systematic differences between different MR scanners are suspected to contribute to this variability. In this study we developed and performed initial validation tests of an MR-compatible phantom and analysis software for robust and reliable evaluation of the brain volume loss. The phantom contained three inflatable models of brain structures, i.e. cerebral hemisphere, putamen, and caudate nucleus. Software to reliably quantify volumes form the phantom images was also developed. To validate the method, the phantom was imaged using 3D T1-weighted protocols at three clinical 3T MR scanners from different vendors. Calculated volume change from MRI was compared with the known applied volume change using ICC and mean absolute difference. As assessed by the ICC, the agreement between our developed software and the applied volume change for different structures ranged from 0.999–1 for hemisphere, 0.976–0.998 for putamen, and 0.985–0.999 for caudate nucleus. The mean absolute differences between measured and applied volume change were 109–332 μL for hemisphere, 2.9–11.9 μL for putamen, and 2.2–10.1 μL for caudate nucleus. This method offers a reliable and robust measurement of volume change using MR images and could potentially be used to standardize clinical measurement of atrophy rates. Keywords: Brain atrophy, MRI, Standardization, Phantom, Segmentatio