A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called “Decentralized ComBat” which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets

A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called "Decentralized ComBat " which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets.</p

Modelos experimentales de anuros para estudiar los efectos de piretroides.

Los ecosistemas acuáticos están cada vez más expuestos a numerosos contaminantes ambientales, como los agroquímicos. En los últimos años se ha observado que los tests de toxicidad sólo evalúan los efectos a corto plazo (mortalidad) y no son suficientes para evaluar los riesgos de los ecosistemas. Por esta razón, son muy importantes las evaluaciones a largo plazo, ya que permiten estimar la incidencia de estos cambios sobre la biodiversidad y la salud humana. En el presente artículo evaluamos estadísticamente, bajo condiciones de laboratorio, el efecto agudo (mortalidad – supervivencia), las dosis subcrónicas (tasa de crecimiento y desarrollo) y las alteraciones a nivel subcelular e histológico, producidos por el pesticida cipermetrina. Los bioensayos de toxicidad fueron realizados con embriones y larvas de estadios críticos de dos especies regionales de anuros: Physalaemus biligonigerus y Bufo arenarum. Estas especies fueron elegidas debido a su sensibilidad a los biocidas y a su importancia ecológica. Adicionalmente, se realizó un análisis morfológico de los órganos target por microscopía óptica y electrónica, para evaluar el desarrollo de mecanismos adaptativos a las nuevas condiciones desfavorables. Se realizaron en forma complementaria estudios de in situ TUNEL y morfometría.Fil: Izaguirre, María Fernanda. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Biología. Laboratorio de Microscopía; ArgentinaFil: Marín, L.. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Biología. Laboratorio de Microscopía; ArgentinaFil: Vergara, M. N.;. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Biología. Laboratorio de Microscopía; ArgentinaFil: Lajmanovich, Rafael Carlos. Instituto Nacional de Limnología (inali-conicet),; ArgentinaFil: Peltzer, Paola. Instituto Nacional de Limnología (inali-conicet),; ArgentinaFil: Casco, Victor Hugo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Biología. Laboratorio de Microscopía; Argentin

The chronnectome as a model for Charcot's 'dynamic lesion' in functional movement disorders

This exploratory study set out to investigate dynamic functional connectivity (dFC) in patients with jerky and tremulous functional movement disorders (JT-FMD). The focus in this work is on dynamic brain states, which represent distinct dFC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 17 patients with JT-FMD and 17 healthy controls (HC). Symptom severity was measured using the Clinical Global Impression-Severity scale. Depression and anxiety were measured using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), respectively. Independent component analysis was used to extract functional brain components. After computing dFC, dynamic brain states were determined for every subject using k-means clustering. Compared to HC, patients with JT-FMD spent more time in a state that was characterized predominantly by increasing medial prefrontal, and decreasing posterior midline connectivity over time. They also tended to visit this state more frequently. In addition, patients with JT-FMD transitioned significantly more often between different states compared to HC, and incorporated a state with decreasing medial prefrontal, and increasing posterior midline connectivity in their attractor, i.e., the cyclic patterns of state transitions. Altogether, this is the first study that demonstrates altered functional brain network dynamics in JT-FMD that may support concepts of increased self-reflective processes and impaired sense of agency as driving factors in FMD

A resting-state fMRI pattern of spinocerebellar ataxia type 3 and comparison with F-18-FDG PET

Spinocerebellar ataxia type 3 (SCA3) is a rare genetic neurodegenerative disease. The neurobiological basis of SCA3 is still poorly understood, and up until now resting-state fMRI (rs-fMRI) has not been used to study this disease. In the current study we investigated (multi-echo) rs-fMRI data from patients with genetically confirmed SCA3 (n = 17) and matched healthy subjects (n = 16). Using independent component analysis (ICA) and subsequent regression with bootstrap resampling, we identified a pattern of differences between patients and healthy subjects, which we coined the fMRI SCA3 related pattern (fSCA3-RP) comprising cerebellum, anterior striatum and various cortical regions. Individual fSCA3-RP scores were highly correlated with a previously published F-18-FDG PET pattern found in the same sample (rho = 0.78, P = 0.0003). Also, a high correlation was found with the Scale for Assessment and Rating of Ataxia scores (r = 0.63, P = 0.007). No correlations were found with neuropsychological test scores, nor with levels of grey matter atrophy. Compared with the F-18-FDG PET pattern, the fSCA3-RP included a more extensive contribution of the mediofrontal cortex, putatively representing changes in default network activity. This rs-fMRI identification of additional regions is proposed to reflect a consequence of the nature of the BOLD technique, enabling measurement of dynamic network activity, compared to the more static F-18-FDG PET methodology. Altogether, our findings shed new light on the neural substrate of SCA3, and encourage further validation of the fSCA3-RP to assess its potential contribution as imaging biomarker for future research and clinical use

Microplastics and Their Effect in Horticultural Crops: Food Safety and Plant Stress

The presence of micro and nanoplastics in the food chain constitutes an emergent multifactorial food safety and physiological stress problem, which must be approached with a strategic perspective since it affects public health when consuming products that have this pollutant, such as fish and crustaceans, fruits, and vegetables. In this review, the authors present the results by scientists from different disciplines who are dedicated to discovering their chemical constitution and origin, the contents of these microparticles in edible plants, the contamination of water-irrigated soils, the mechanisms that concentrate microplastics in these soils, methods to determine them, contamination of freshwater sources of cities, and the negative effect of nano and microplastics on various food products and their detrimental impact on the environment. Recent findings of plant uptake mechanisms complement this, but more research is needed

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration