Autism spectrum disorder is a multifactorial neurodevelopmental disorder with high genetic heterogeneity. Studies of brain networks in autism can provide new insights into the dynamics of information processing in individuals who suffer from such a condition. This paper proposes a method for automatic diagnosis of autism based on fMRI time series and machine learning algorithms. We verify that the left ventral posterior cingulate cortex region reduces the functional connectivity of the brain area in patients with autism spectrum disorder. Also, the brain networks of patients with autism spectrum disorder show more segregation, lower distribution of information, and less connectivity. Our methodology accurately differentiates control and autistic subjects providing an area under the curve close to higher than 95%.

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.

Schizophrenia is a severe mental disorder associated with persistent or recurrent psychosis, hallu- cinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization (WHO). Several studies encompass machine learning and deep learning algorithms to automate the diagnosis of this mental disorder. Others study schizophrenia brain networks to get new insights into the dynamics of information processing in patients suffering from the condition. In this paper, we offer a rigorous approach with machine learning 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 schizophrenia patients. For this purpose, we employed an fMRI and EEG dataset in a multimodal fashion. In addition, we combined EEG measures, i.e., Hjorth mobility and complexity, to complex network measurements to be analyzed in our model for the first time in the literature. When comparing the schizophrenia 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. In terms of 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 important measure in a multimodal fashion. Furthermore, the schizophrenia brain networks exhibit less segregation and lower distribution of information. As a final result, EEG measures outperformed complex networks in capturing the brain alterations associated with schizophrenia. As a result, our model achieved an AUC of 100%, an accuracy of 98% for the fMRI, an AUC of 95 %, and an accuracy of 95% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures for these results, which helped us better describe changes in the diseased brain.

The dynamical approach represents a new branch in the understanding of functional brain networks. Using simple indices to represent time connectivity and topological stability, we evaluated the hypothesis of increased brain stability during the meditative state in comparison to the relaxation state. We used a new way to consider the time evolution of synchronization patterns in electroencephalography (EEG) data. The time-varying graph approach and the motif synchronization method were combined to build a set of graphs representing time evolution for the synchronization of 29 EEG electrodes. We analysed these graphs during meditation and relaxation states in 17 experienced meditators. As result, we found significant increasing of time connectivity (t(15) $= -2.50$, p $= 0.023$) and topological stability (t(15) $= 1.23$, p $= 0.020$) in the meditation state when compared to the relaxation state. These findings suggest that dynamical properties of the synchronization network may revel aspects of brain activity in altered states of consciousness not possible to measure using a static approach. We concluded that the topological patterns evolution in the functional networks of experienced meditators are more stable in the meditative state than in the relaxation state.

Objective The aim of this study was to characterize the dynamic brain networks underlying the affective modulation of pleasant, unpleasant, and neutral image perception due to painful stimulations in healthy subjects. Methods Forty volunteers, 20 men and 20 women, participated in this study. Brain activity was recorded by 64-channel electroencephalography. After data cleaning, functional brain networks were built using the motif synchronization method. Results We found that increased cerebral connectivity in the left hemisphere under the pain condition broke the connection symmetry. Both women and men showed homophilic connections (intra-hemispheric), but women were more homophilic than men. The pain condition increased homophily in the left hemisphere and emotions could modulate pain. Frontal, central, and left temporal regions showed homophilic variation, depending on the emotional stimulus. Conclusions Pain and emotions altered brain activity. There was increased connectivity and homophily in the left brain hemisphere for the painful experience. The emotions modulated the brain activity in pain condition. Overall, the brain presented homophilic characteristics; homophily changed, depending on emotion or pain. The left brain hemisphere seems to be related to pain processing.