Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia.

Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

Mining Public Data to Investigate the Virome of Neglected Pollinators and Other Floral Visitors.

This study reports the virome investigation of pollinator species and other floral visitors associated with plants from the south of Bahia: Aphis aurantii, Atrichopogon sp., Dasyhelea sp., Forcipomyia taiwana, and Trigona ventralis hoozana. Studying viruses in insects associated with economically important crops is vital to understand transmission dynamics and manage viral diseases that pose as threats for global food security. Using literature mining and public RNA next-generation sequencing data deposited in the NCBI SRA database, we identified potential vectors associated with Malvaceae plant species and characterized the microbial communities resident in these insects. Bacteria and Eukarya dominated the metagenomic analyses of all taxon groups. We also found sequences showing similarity to elements from several viral families, including Bunyavirales, Chuviridae, Iflaviridae, Narnaviridae, Orthomyxoviridae, Rhabdoviridae, Totiviridae, and Xinmoviridae. Phylogenetic analyses indicated the existence of at least 16 new viruses distributed among A. aurantii (3), Atrichopogon sp. (4), Dasyhelea sp. (3), and F. taiwana (6). No novel viruses were found for T. ventralis hoozana. For F. taiwana, the available libraries also allowed us to suggest possible vertical transmission, while for A. aurantii we followed the infection profile along the insect development. Our results highlight the importance of studying the virome of insect species associated with crop pollination, as they may play a crucial role in the transmission of viruses to economically important plants, such as those of the genus Theobroma, or they will reduce the pollination process. This information may be valuable in developing strategies to mitigate the spread of viruses and protect the global industry.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.