Resting-state fMRI in patients with refractory epilepsy with and without drop attacks: exploring the connectivity of sensorimotor cortex.

OBJECTIVE: Patients with multifocal or generalized epilepsies manifesting with drop attacks have severe refractory seizures and significant cognitive and behavioural abnormalities. It is unclear to what extent these features relate to network abnormalities and how networks in sensorimotor cortex differ from those in patients with refractory focal epilepsies. Thus, in this study we sought to provide preliminary data on connectivity of sensorimotor cortex in patients with epileptic drop attacks, in comparison to patients with focal refractory epilepsies. METHODS: Resting-state fMRI (rs-fMRI) data was available for 5 patients with epileptic drop attacks and 15 with refractory focal epilepsies undergoing presurgical evaluation. Functional connectivity was analyzed with a seed-based protocol, with primary seeds placed at the precentral gyrus, the postcentral gyrus and the premotor cortex. For each seed, the subjects' timeseries were extracted and transformed to Z scores. Between-group analysis was then performed using the 3dttest+ + AFNI program. RESULTS: Two clusters of reduced connectivity in the group with drop attacks (DA group) in relation to those with focal epilepsies were found in the between-group analysis: the precentral seed showed reduced connectivity in the surrounding motor area, and the postcentral seed, reduced connectivity with the ipsilateral posterior cingulate gyrus. In the intra-group analyses, sensorimotor and premotor networks were abnormal in the DA group, whereas patients with focal epilepsies had the usual connectivity maps with each seed. CONCLUSION: This pilot study shows differences in the cerebral connectivity in the sensorimotor cortex of patients with generalized epilepsies and drop attacks which should be further explored to better understand the biological bases of the seizure generation and cognitive changes in these people.

Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample.

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory.

Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.

Greater Empathic Abilities and Resting State Brain Connectivity Differences in Psychotherapists Compared to Non-psychotherapists.

In a therapeutic environment a proper regulation of the empathic response strengthens the patient-therapist relationship. Thus, it is important that psychotherapists constantly regulate their own perspective and emotions to better understand the other's affective state. We compared the empathic abilities of a group of 52 psychotherapists with a group of 92 non-psychotherapists and found psychometric differences. Psychotherapists showed greater scores in Fantasy and Perspective Taking, both cognitive empathy constructs, and lower scores in the use of expressive suppression, an emotional regulation strategy that hampers the empathic response, suggesting that psychotherapists exert top-down processes that influence their empathic response. In addition, the expected sex differences in empathic concern and expressive suppression were only present in the non-psychotherapist group. To see if such psychometric differences were related to a distinctive functional organization of brain networks, we contrasted the resting state functional connectivity of empathy-related brain regions between a group of 18 experienced psychotherapists and a group of 18 non-psychotherapists. Psychotherapists showed greater functional connectivity between the left anterior insula and the dorsomedial prefrontal cortex, and less connectivity between rostral anterior cingulate cortex and the orbito prefrontal cortex. Both associations correlated with Perspective Taking scores. Considering that the psychometric differences between groups were in the cognitive domain and that the functional connectivity associations involve areas related to cognitive regulation processes, these results suggest a relationship between the functional brain organization of psychotherapists and the cognitive regulation of their empathic response.

Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks.

In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To improve the technique, the elimination of artifacts through Independent Components Analysis (ICA) has been proposed, as this can separate neural signal and noise, opening possibilities for automatic classification. The main classification techniques have focused on processes based on typical machine learning. However, there are currently more robust approaches such as convolutional neural networks, which can deal with complex problems directly from the data without feature selection and even with data that does not have a simple interpretation, being limited by the amount of data necessary for training and its high computational cost. This research focused on studying four methods of volume reduction mitigating the computational cost for the training of 3 models based on convolutional neural networks. One of the reduction techniques is a novel approach that we call Reduction by Consecutive Binary Patterns (RCBP), which was shown to preserve the spatial features of the independent components. In addition, the RCBP showed networks in components associated with neuronal activity more clearly. The networks achieved accuracy above 98 % in classification, and one network was even found to be over 99 % accurate, outperforming most machine learning-based classification algorithms.

Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed.

Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.