Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

While emerging evidence suggests that neuroinflammation plays a crucial role in Alzheimer's disease, the impact of the microglia response in Alzheimer's disease remains a matter of debate. We aimed to study microglial activation in early Alzheimer's disease and its impact on clinical progression using a second-generation 18-kDa translocator protein positron emission tomography radiotracer together with amyloid imaging using Pittsburgh compound B positron emission tomography. We enrolled 96 subjects, 64 patients with Alzheimer's disease and 32 controls, from the IMABio3 study, who had both (11)C-Pittsburgh compound B and (18)F-DPA-714 positron emission tomography imaging. Patients with Alzheimer's disease were classified as prodromal Alzheimer's disease (n = 38) and Alzheimer's disease dementia (n = 26). Translocator protein-binding was measured using a simple ratio method with cerebellar grey matter as reference tissue, taking into account regional atrophy. Images were analysed at the regional (volume of interest) and at the voxel level. Translocator protein genotyping allowed the classification of all subjects in high, mixed and low affinity binders. Thirty high+mixed affinity binders patients with Alzheimer's disease were dichotomized into slow decliners (n = 10) or fast decliners (n = 20) after 2 years of follow-up. All patients with Alzheimer's disease had an amyloid positive Pittsburgh compound B positron emission tomography. Among controls, eight had positive amyloid scans (n = 6 high+mixed affinity binders), defined as amyloidosis controls, and were analysed separately. By both volumes of interest and voxel-wise comparison, 18-kDa translocator protein-binding was higher in high affinity binders, mixed affinity binders and high+mixed affinity binders Alzheimer's disease groups compared to controls, especially at the prodromal stage, involving the temporo-parietal cortex. Translocator protein-binding was positively correlated with Mini-Mental State Examination scores and grey matter volume, as well as with Pittsburgh compound B binding. Amyloidosis controls displayed higher translocator protein-binding than controls, especially in the frontal cortex. We found higher translocator protein-binding in slow decliners than fast decliners, with no difference in Pittsburgh compound B binding. Microglial activation appears at the prodromal and possibly at the preclinical stage of Alzheimer's disease, and seems to play a protective role in the clinical progression of the disease at these early stages. The extent of microglial activation appears to differ between patients, and could explain the overlap in translocator protein binding values between patients with Alzheimer's disease and amyloidosis controls.

The efficacy of convalescent plasma for coronavirus disease 2019 (COVID-19) is unclear. Although most randomized controlled trials have shown negative results, uncontrolled studies have suggested that the antibody content could influence patient outcomes. We conducted an open-label, randomized controlled trial of convalescent plasma for adults with COVID-19 receiving oxygen within 12 d of respiratory symptom onset (NCT04348656). Patients were allocated 2:1 to 500 ml of convalescent plasma or standard of care. The composite primary outcome was intubation or death by 30 d. Exploratory analyses of the effect of convalescent plasma antibodies on the primary outcome was assessed by logistic regression. The trial was terminated at 78% of planned enrollment after meeting stopping criteria for futility. In total, 940 patients were randomized, and 921 patients were included in the intention-to-treat analysis. Intubation or death occurred in 199/614 (32.4%) patients in the convalescent plasma arm and 86/307 (28.0%) patients in the standard of care arm—relative risk (RR) = 1.16 (95% confidence interval (CI) 0.94–1.43, P = 0.18). Patients in the convalescent plasma arm had more serious adverse events (33.4% versus 26.4%; RR = 1.27, 95% CI 1.02–1.57, P = 0.034). The antibody content significantly modulated the therapeutic effect of convalescent plasma. In multivariate analysis, each standardized log increase in neutralization or antibody-dependent cellular cytotoxicity independently reduced the potential harmful effect of plasma (odds ratio (OR) = 0.74, 95% CI 0.57–0.95 and OR = 0.66, 95% CI 0.50–0.87, respectively), whereas IgG against the full transmembrane spike protein increased it (OR = 1.53, 95% CI 1.14–2.05). Convalescent plasma did not reduce the risk of intubation or death at 30 d in hospitalized patients with COVID-19. Transfusion of convalescent plasma with unfavorable antibody profiles could be associated with worse clinical outcomes compared to standard care.

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SUMMARYObjective: To determine the main factors influencing metabolic changes in mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS). Methods: We prospectively studied 114 patients with MTLE (62 female; 60 left HS; 15-to 56-year-olds) with 18 F-fluorodeoxyglucose-positron emission tomography and correlated the results with the side of HS, structural atrophy, electroclinical features, gender, age at onset, epilepsy duration, and seizure frequency. Imaging processing was performed using statistical parametric mapping. Results: Ipsilateral hypometabolism involved temporal (mesial structures, pole, and lateral cortex) and extratemporal areas including the insula, frontal lobe, perisylvian regions, and thalamus, more extensively in right HS (RHS). A relative increase of metabolism (hypermetabolism) was found in the nonepileptic temporal lobe and in posterior areas bilaterally. Voxel-based morphometry detected unilateral hippocampus atrophy and gray matter concentration decrease in both frontal lobes, more extensively in left HS (LHS). Regardless of the structural alterations, the topography of hypometabolism correlated strongly with the extent of epileptic networks (mesial, anterior-mesiolateral, widespread mesiolateral, and bitemporal according to the ictal spread), which were larger in RHS. Notably, widespread perisylvian and bitemporal hypometabolism was found only in RHS. Mirror hypermetabolism was grossly proportional to the hypometabolic areas, coinciding partly with the default mode network. Gender-related effect was significant mainly in the contralateral frontal lobe, in which metabolism was higher in female patients. Epilepsy duration correlated with the contralateral temporal metabolism, positively in LHS and negatively in RHS. Opposite results were found with age at onset. High seizure frequency correlated negatively with the contralateral metabolism in LHS. Significance: Epileptic networks, as assessed by electroclinical correlations, appear to be the main determinant of hypometabolism in MTLE. Compensatory mechanisms reflected by a relative hypermetabolism in the nonepileptic temporal lobe and in extratemporal areas seem more efficient in LHS and in female patients, whereas long duration, late onset of epilepsy, and high seizure frequency may reduce these adaptive changes.