Role of Hybrid Brain Imaging in Neuropsychiatric Disorders

Altered Language-Related Effective Connectivity in Patients with Benign Childhood Epilepsy with Centrotemporal Spikes

Benign childhood epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes and may be associated with language deficits. Resting-state functional magnetic resonance imaging (fMRI) data were collected from a total of 78 children: 52 patients with BECTS (28 drug-naïve and 24 medicated) and 26 healthy controls (HC). Granger causality analysis (GCA) was used to investigate alterations in effective connectivity (EC) between the language network core node (Broca's area) and the whole brain. EC from Broca's area to the left Heschl's gyrus (HG), right putamen, and anterior cingulate cortex (ACC) was significantly increased, while EC from the bilateral putamen and left ACC to Broca's area was significantly decreased in BECTS. Moreover, altered EC of Broca's area to the right putamen was significantly positively correlated with verbal IQ (VIQ), while altered EC of Broca's area to the ACC showed significantly negative correlations with the frequency of seizures. Altered EC from the left putamen to Broca's area was also significantly negatively correlated with performance IQ (PIQ) and full-scale IQ (FSIQ) in the drug-naïve group. In addition, there was a significant positive correlation between the EC of Broca's area to the left HG and the number of seizures, as well as between the EC of Broca's area to the right putamen and the age at onset in the medicated group. These findings suggest abnormal causal effects on the language network related to Broca's area in children with BECTS. Longitudinal investigation of language network development and further follow-up may be needed to illuminate the changes in organization and rebalancing over time.

Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review

The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.

Rationale, Relevance, and Limits of Stress-Induced Psychopathology in Rodents as Models for Psychiatry Research: An Introductory Overview

Emotional and cognitive information processing represent higher-order brain functions. They require coordinated interaction of specialized brain areas via a complex spatial and temporal equilibrium among neuronal cell-autonomous, circuitry, and network mechanisms. The delicate balance can be corrupted by stressful experiences, increasing the risk of developing psychopathologies in vulnerable individuals. Neuropsychiatric disorders affect twenty percent of the western world population, but therapies are still not effective for some patients. Elusive knowledge of molecular pathomechanisms and scarcity of objective biomarkers in humans present complex challenges, while the adoption of rodent models helps to improve our understanding of disease correlate and aids the search for novel pharmacological targets. Stress administration represents a strategy to induce, trace, and modify molecular and behavioral endophenotypes of mood disorders in animals. However, a mouse or rat model will only display one or a few endophenotypes of a specific human psychopathology, which cannot be in any case recapitulated as a whole. To override this issue, shared criteria have been adopted to deconstruct neuropsychiatric disorders, i.e., depression, into specific behavioral aspects, and inherent neurobiological substrates, also recognizable in lower mammals. In this work, we provide a rationale for rodent models of stress administration. In particular, comparing each rodent model with a real-life human traumatic experience, we intend to suggest an introductive guide to better comprehend and interpret these paradigms.

Neuroimaging correlates of neuropsychiatric symptoms in Alzheimer's disease: a review of 20 years of research

Assessing morphological, perfusion and metabolic brain changes preceding or associated with neuropsychiatric symptoms (NPSs) will help in the understanding of pathophysiological underlying processes in Alzheimer's disease (AD). This review aimed to highlight the main findings on significant associations between neuroimaging and NPSs, the pathophysiology to elucidate possible underlying mechanisms, and methodological issues to aid future research. Research papers published from January 1990 to October 2015 were identified in the databases PsycInfo, Embase, PubMed and Medline, using key words related to NPSs and imaging techniques. In addition to a semi-systematic search in the databases, we also performed hand searches based on reported citations identified to be of interest. Delusions, apathy and depression symptoms were particularly associated with brain changes in AD. The majority of studies disclosed an association between frontal lobe structural and/or metabolic changes and NPSs, implicating, interestingly, for all 12 NPSs studied, the anterior cingulate cortex although temporal, subcortical and parietal regions, and insula were also involved. Given the high degree of connectivity of these brain areas, frontal change correlates of NPSs may help in the understanding of neural network participation. This review also highlights crucial methodological issues that may reduce the heterogeneity of results to enable progress on the pathophysiological mechanisms and aid research on NPS treatments in AD. Based on a broad review of the current literature, a global brain pattern to support the huge heterogeneity of neuroimaging correlates of NPSs in AD and methodological strategies are suggested to help direct future research.