Privacy-preserving harmonization via distributed ComBat.

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

Characterization of the effects of outliers on ComBat harmonization for removing inter-site data heterogeneity in multisite neuroimaging studies.

Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are present in the data of one or more sites. It remains unclear how the presence of outliers could affect the effectiveness of data harmonization and consequently the results of analyses using harmonized data. To address this question, we generated a normal simulation dataset without outliers and a series of simulation datasets with outliers of varying properties (e.g., outlier location, outlier quantity, and outlier score) based on a real large-sample neuroimaging dataset. We first verified the effectiveness of the most commonly used ComBat harmonization method in the removal of inter-site heterogeneity using the normal simulation data, and then characterized the effects of outliers on the effectiveness of ComBat harmonization and on the results of association analyses between brain imaging-derived phenotypes and a simulated behavioral variable using the simulation datasets with outliers. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data and consequently improved the detection of the true brain-behavior relationships, the presence of outliers could damage severely the effectiveness of ComBat harmonization in the removal of data heterogeneity or even introduce extra heterogeneity in the data. Moreover, we found that the effects of outliers on the improvement of the detection of brain-behavior associations by ComBat harmonization were dependent on how such associations were assessed (i.e., by Pearson correlation or Spearman correlation), and on the outlier location, quantity, and outlier score. These findings help us better understand the influences of outliers on data harmonization and highlight the importance of detecting and removing outliers prior to data harmonization in multisite neuroimaging studies.

A source and ground motion study of earthquakes in and near Delhi (the National Capital Region), India.

We study source parameters of 10 local earthquakes (2.7 ≤  M w ≤ 4.5) that have occurred in the National Capital Region (NCR) since 2001 and the ground motions produced by these events. Moment rate spectra of the earthquakes retrieved from the recordings at hard sites after applying corrections for geometrical spreading (1/R, R ≤ 100 km), anelastic attenuation (Q = 253f 0.8) and cutoff frequency (f m = 35 Hz) are reasonably well fit by the Brune ω 2-source model with stress drop ranging between 0.9 and 13 MPa. Neglecting the outlier low-stress drop value, the average stress drop is 6 MPa. We apply a modified standard spectral ratio technique to estimate site effect at 38 soft sites in the NCR as well as the geometrical mean site effect with respect to a reference hard site. Application of the stochastic method, with source characterized by the Brune ω 2- model with stress drop of 6 MPa and the mean site effect for soft sites, yields peak horizontal ground acceleration and velocity curves that are in good agreement with the observed values. These results provide the parameters needed for the application of the stochastic method to predict ground motions at hard and soft sites in the NCR during postulated M w ≤ 5.5 earthquakes.

Simulating tree growth response to climate change in structurally diverse oak and beech forests.

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites. In our simulations under constant CO2 concentration ([CO2]cst) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO2 concentration ([CO2]var) mainly due to the CO2 fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO2]cst and [CO2]var, respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience.

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

Partial substitution of magnesium in lanthanum manganite perovskite for nitric oxide oxidation: The effect of substitution sites.

Lanthanum manganite (LaMnO3) with magnesium (Mg) substituted the A- and B-sites were reported for the first time in this work. The Mg substituted LaMnO3 catalysts were prepared by means of citric acid sol-gel method. The impact of partial substitution of Mg and its substitution site on the catalytic performance of LaMnO3 in nitric oxide (NO) oxidation was investigated. Through series of characterizations, it was found that Mg substitution at both A- and B-sites improves the specific surface area, Mn4+ content, reactivity of surface oxygen species, redox property and NO adsorption capacity of LaMnO3. However, Mg substitution at the A-site of LaMnO3 can cause the total charge imbalance of B-site, which significantly improves the amount and reactivity of surface active oxygen species. Therefore, the catalytic activity of the samples decreases in the order: La0.9Mg0.1MnO3 > LaMn0.9Mg0.1O3 > LaMnO3. This study will shed more light on the fundamental understanding and designing of perovskite catalyst.

Radon signals in geological (natural) geogas and in a simultaneous enhanced confined mode simulation experiment.

An enhanced confined mode (ECM) radon simulation experiment, tested in the laboratory in Jerusalem, was relocated to a subsurface geophysical observatory located 400 km apart, at a depth of 150 m and with a stable temperature. Five gamma sensors are placed around the ECM canister and lead shielding minimizes the influence of natural local gamma radiation. Simultaneous measurement of the geological radon and from radon in the ECM system indicates that the temporal variation of gamma radiation from radon in the ECM system contains annual, multi-day and daily signals, that correspond to signals in the local geological radon. This implies that a common external driver influences the radiation pattern of the geological radon and from radon inside the ECM canister. Once activated at BGO the typical variation pattern of the experimental system occurring in the laboratory changed to that occurring at the observatory. This is interpreted to indicate that the overall style of the temporal patterns of radiation from radon is site dependent. The outcome of this investigation conforms and further substantiates the recent suggestion that a component in solar radiation is driving the annual and daily periodic components in the variation of radon. New geophysical research potential is indicated.