ASL-MRICloud: An online tool for the processing of ASL MRI data.

Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud-based tool for ASL data processing, referred to as ASL-MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL-MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL-MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo-continuous ASL and pulsed ASL, and can process single-delay and multi-delay ASL data. The outputs of ASL-MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel-wise masks indicating regions with potential hyper-perfusion and hypo-perfusion, and an image quality index. The ASL tool is also integrated with a T1 -based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region-of-interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Neuroanatomical comparison of treatment-resistant and treatment-responsive schizophrenia patients using the cloud-based brain magnetic resonance image segmentation and parcellation system: An MRIcloud study.

Recent developments in neuroimaging have improved our understanding of the biological mechanisms underlying schizophrenia. However, neuroimaging findings in treatment-resistant schizophrenia (TRS) remain unclear. In the present study, we aimed to explore potential neuroanatomical regions that may be associated with treatment resistance in schizophrenia patients by comparing neuroanatomical regions of TRS and non-TRS patients using the MRICloud method. A total of 33 schizophrenia patients (meeting DSM 5 diagnostic criteria for schizophrenia) were included in the study. Patients were dichotomized into TRS (n = 18) and non-TRS (n = 15) groups, and all patients underwent MRI. Neuroanatomical regions of TRS and non-TRS patients were compared using the MRICloud method. Disease severity was measured using the Positive and Negative Syndrome Scale (PANSS). Interestingly, a statistically significant greater left Corpus Collosum (CC) thickness was found in TRS patients compared to non-TRS patients. It is clear that further studies comparing TRS patients with non-TRS patients are needed, and these studies should focus on the circuits in the corpus callosum that are thought to play a role in treatment resistance. Further longitudinal studies are also needed to complement the cross-sectional studies, using a multimodal imaging approach in the patients with clearly defined TRS criteria.

Subcortical Volume Changes in Schizophrenia and Bipolar Disorder: A Quantitative MRI Study

SUMMARY: Volume abnormalities in subcortical structures, including the hippocampus, amygdala, thalamus, caudate, putamen, and globus pallidus have been observed in schizophrenia (SZ) and bipolar disorder (BD), not all individuals with these disorders exhibit such changes. In addition, the specific patterns and severity of volume changes may vary between individuals and at different stages of the disease. The study aims to compare the volumes of these subcortical structures between healthy subjects and individuals diagnosed with SZ or BD. Volumetric measurements of lateral ventricle, globus palllidus, caudate, putamen, hippocampus, and amygdale were made by MRI in 52 healthy subjects (HS), 33 patients with SZ, and 46 patients with BD. Automatic segmentation methods were used to analyze the MR images with VolBrain and MRICloud. Hippocampus, amygdala and lateral ventricle increased in schizophrenia and bipolar disorder patients in comparison with control subjects using MRIcloud. Globus pallidus and caudate volume increased in patients with schizophrenia and bipolar disorder compared control subjects using Volbrain. We suggested that our results will contribute in schizophrenia and bipolar disorder patients that assessment of the sub-cortical progression, pathology, and anomalies of subcortical brain compositions. In patients with psychiatric disorders, VolBrain and MRICloud can detect subtle structural differences in the brain.

Se han observado anomalías de volumen en las estructuras subcorticales, incluidos el hipocampo, la amígdala, el tálamo, el núcleo caudado, el putamen y el globo pálido, en la esquizofrenia (SZ) y el trastorno bipolar (BD); no todos los individuos con estos trastornos presentan tales cambios. Además, los patrones específicos y la gravedad de los cambios de volumen pueden variar entre individuos y en diferentes etapas de la enfermedad. El estudio tuvo como objetivo comparar los volúmenes de estas estructuras subcorticales entre sujetos sanos e individuos diagnosticados con SZ o BD. Se realizaron mediciones volumétricas del ventrículo lateral, globo pálido, núcleo caudado, putamen, hipocampo y amígdala mediante resonancia magnética en 52 sujetos sanos (HS), 33 pacientes con SZ y 46 pacientes con BD. Se utilizaron métodos de segmentación automática para analizar las imágenes de resonancia magnética con VolBrain y MRICloud. El hipocampo, la amígdala y el ventrículo lateral aumentaron en pacientes con esquizofrenia y trastorno bipolar en comparación con sujetos de control que utilizaron MRIcloud. El globo pálido y el núcleo caudado aumentaron en pacientes con esquizofrenia y trastorno bipolar en comparación con los sujetos control que utilizaron Volbrain. Sugerimos que en pacientes con esquizofrenia y trastorno bipolar, nuestros resultados contribuirán a la evaluación de la progresión subcortical, la patología y las anomalías de las composiciones cerebrales subcorticales. En pacientes con trastornos psiquiátricos, VolBrain y MRICloud pueden detectar diferencias estructurales sutiles en el cerebro.