Removing the effects of the site in brain imaging machine-learning Measurement and extendable benchmark

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms

Characterising cognitive impairment in inflammatory demyelinating diseases, the impact of brain damage and compensatory mechanisms

[eng] INTRODUCTION: Cognitive impairment (CI) is one of the key symptoms in multiple sclerosis (MS), although it is highly variable in both severity and progression. In neuromyelitis optica spectrum disorders (NMOSD) cognitive dysfunction has probably been underestimated. Thus, the course of cognitive performance, the effect of cognitive reserve (CR) and brain damage on cognition, and the contribution of CI to the quality of life in these inflammatory demyelinating diseases remain poorly understood and unpredictable. HYPOTHESES: We believe that using mixed-effect models in a longitudinal study could help characterise the temporal dynamics of cognition in MS, and demographic, clinical and magnetic resonance imaging measures predict the development of CI. Cognition may be influenced by CR and its impact on brain connectivity, a fact that may differ between patients with or without CI. Microstructural integrity of certain brain areas may have predictive value on physical and cognitive disability in MS, and involve specific brain regions. Cognitive characteristics and how these affect quality of life in NMOSD patients, could be better understood through a multicentre collaborative study. OBJECTIVES: 1) Describe the temporal dynamics of cognition throughout the disease course and investigate CI predictors in MS patients. 2) Analyse the association between CR and brain structural connectivity integrity, and their impact on cognition in patients with and without CI. 3) Explore the value of the microstructural properties in predicting future disability, and identify areas related to motor and cognitive performance in MS patients. 4) Describe the cognitive profile of NMOSD patients, and assess the relationship between cognition and quality of life. METHODS: Conduct four observational studies, two of them longitudinal (first and third studies), and one multicentric (fourth study). RESULTS: Results of the first study showed that in MS patients cognition begins to deteriorate 5 years after disease onset, declining steadily over the next 25 years and more markedly affecting verbal memory. Predictors of future CI included education, disease severity, lesion burden, and volume of limbic structures. In the second study, we observed an impact of CR on brain structural connectivity in patients with CI. Furthermore, CR had a protective effect, and influenced cognitive performance in MS patients along with brain damage and ageing. Results from the third study showed that in MS patients, microstructural damage to white and grey matter was related to future physical and cognitive disability, respectively. Regional differences in areas associated with physical and cognitive performance were also found. The fourth study showed that CI was present in 34% of NMOSD patients and the most affected cognitive domain was visual memory. Cognitive performance was associated with quality of life. CONCLUSIONS: Taken together, these studies contribute to a deeper understanding of cognitive performance and the factors influencing the emergence of CI in patients with MS and NMOSD. They also consolidate a basis for developing more accurate predictive models that can be used to identify patients at risk of CI, and to develop a comprehensive approach focused on improving the patients’ quality of life.[spa] El deterioro cognitivo (DC) es uno de los síntomas clave en la esclerosis múltiple (EM) y en el trastorno del espectro de la neuromielitis óptica (NMOSD, por sus siglas en inglés). Hoy en día, la evolución del rendimiento cognitivo, el efecto de la reserva cognitiva (RC) y el daño cerebral en la cognición, así como la contribución del DC a la calidad de vida en estas enfermedades desmielinizantes siguen siendo poco conocidos, lo que dificulta su predicción en los pacientes. Creemos que el uso de modelos de efectos mixtos en un estudio longitudinal podría contribuir a caracterizar la dinámica temporal de la cognición en la EM, y las medidas demográficas, clínicas y de resonancia magnética podrían predecir el desarrollo del DC. La cognición podría estar influenciada por la RC y su impacto en la conectividad cerebral, un hecho que podría ser diferente en pacientes con o sin DC. La integridad microestructural de ciertas áreas cerebrales podría ayudar a predecir el estado físico y cognitivo en la EM, implicando diferentes regiones cerebrales. Las características cognitivas y su efecto en la calidad de vida de los pacientes con NMOSD, podrían entenderse mejor mediante un estudio colaborativo multicéntrico. Los principales objetivos son: 1) Describir la dinámica temporal de la cognición a lo largo del curso de la enfermedad, e investigar los predictores del DC en pacientes con EM. 2) Analizar la asociación entre la RC y la integridad de la conectividad estructural cerebral, así como su impacto en la cognición en pacientes con y sin DC. 3) Explorar el valor de las propiedades microestructurales para predecir la discapacidad futura, e identificar las áreas relacionadas con el deterioro físico y cognitivo en pacientes con EM. 4) Describir el perfil cognitivo en pacientes con NMOSD, y su relación con la calidad de vida. El primer estudio ha demostrado que en pacientes con EM la cognición se deteriora 5 años después del inicio de la enfermedad, disminuyendo de modo constante durante los siguientes 25 años y afectando de forma más marcada a la memoria verbal. Los predictores del futuro DC eran la educación, la gravedad de la enfermedad, la carga lesional y el volumen de las estructuras límbicas. En el segundo estudio, observamos un impacto de la RC en la conectividad estructural del cerebro en pacientes con DC. Además, la RC tuvo un efecto protector e influyó en el rendimiento cognitivo de los pacientes con EM, junto con el daño cerebral y el envejecimiento. En el tercer estudio en pacientes con EM, observamos que el daño microestructural en la materia blanca y gris se relacionaba con la futura discapacidad física y cognitiva, respectivamente. Además, se observaron diferencias regionales en áreas asociadas al rendimiento físico y cognitivo. El cuarto estudio mostró que el DC estaba presente en 34% de los pacientes con NMOSD y el dominio cognitivo más afectado era la memoria visual. El rendimiento cognitivo se asoció con la calidad de vida. En conjunto, estos estudios contribuyen a un conocimiento más profundo del rendimiento cognitivo y los factores que influyen en la aparición del DC en pacientes con EM y NMOSD. También consolidan las bases para el desarrollo de modelos predictivos más precisos para identificar a los pacientes en riesgo de DC, y para abordar un enfoque integral centrado en la mejora de la calidad de vida del paciente

Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value

Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p<0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8-77.2% to differentiate patients from HV, and 59.9-60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients

Cognitive Performance and Health-Related Quality of Life in Patients with Neuromyelitis Optica Spectrum Disorder

The frequency of cognitive impairment (CI) reported in neuromyelitis optica spectrum disorder (NMOSD) is highly variable, and its relationship with demographic and clinical characteristics is poorly understood. We aimed to describe the cognitive profile of NMOSD patients, and to analyse the cognitive differences according to their serostatus; furthermore, we aimed to assess the relationship between cognition, demographic and clinical characteristics, and other aspects linked to health-related quality of life (HRQoL).This cross-sectional study included 41 patients (median age, 44 years; 85% women) from 13 Spanish centres. Demographic and clinical characteristics were collected along with a cognitive z-score (Rao's Battery) and HRQoL patient-centred measures, and their relationship was explored using linear regression. We used the Akaike information criterion to model which characteristics were associated with cognition.Fourteen patients (34%) had CI, and the most affected cognitive domain was visual memory. Cognition was similar in AQP4-IgG-positive and -negative patients. Gender, mood, fatigue, satisfaction with life, and perception of stigma were associated with cognitive performance (adjusted R2 = 0.396, p < 0.001).The results highlight the presence of CI and its impact on HRQoL in NMOSD patients. Cognitive and psychological assessments may be crucial to achieve a holistic approach in patient care