Shuixia Guo

Depression uncouples brain hate circuit

It is increasingly recognized that we need a better understanding of how mental disorders such as depression alter the brains functional connections to improve both early diagnosis and therapy. A new holistic approach has been used to investigate functional connectivity changes in the brains of patients suffering from major depression using resting-state functional magnetic resonance imaging (fMRI) data. A canonical template of connectivity in 90 different brain regions was constructed from healthy control subjects and this identified a six-community structure with each network corresponding to a different functional system. This template was compared with functional networks derived from fMRI scans of both first-episode and longer-term, drug resistant, patients suffering from severe depression. The greatest change in both groups of depressed patients was uncoupling of the so-called ‘hate circuit’ involving the superior frontal gyrus, insula and putamen. Other major changes occurred in circuits related to risk and action responses, reward and emotion, attention and memory processing. A voxel-based morphometry analysis was also carried out but this revealed no evidence in the depressed patients for altered gray or white matter densities in the regions showing altered functional connectivity. This is the first evidence for the involvement of the ‘hate circuit’ in depression and suggests a potential reappraisal of the key neural circuitry involved. We have hypothesized that this may reflect reduced cognitive control over negative feelings toward both self and others.

Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.

Key functional circuitry altered in schizophrenia involves parietal regions associated with sense of self.

There is still no clear consensus as to which of the many functional and structural changes in the brain in schizophrenia are of most importance, although the main focus to date has been on those in the frontal and cingulate cortices. In the present study, we have used a novel holistic approach to identify brain‐wide functional connectivity changes in medicated schizophrenia patients, and functional connectivity changes were analyzed using resting‐state fMRI data from 69 medicated schizophrenia patients and 62 healthy controls. As far as we are aware, this is the largest population reported in the literature for a resting‐state study. Voxel‐based morphometry was also used to investigate gray and white matter volume changes. Changes were correlated with illness duration/symptom severity and a support vector machine analysis assessed predictive validity. A network involving the inferior parietal lobule, superior parietal gyrus, precuneus, superior marginal, and angular gyri was by far the most affected (68% predictive validity compared with 82% using all connections) and different components correlated with illness duration and positive and negative symptom severity. Smaller changes occurred in emotional memory and sensory and motor processing networks along with weakened interhemispheric connections. Our findings identify the key functional circuitry altered in schizophrenia involving the default network midline cortical system and the cortical mirror neuron system, both playing important roles in sensory and cognitive processing and particularly self‐processing, all of which are affected in this disorder. Interestingly, the functional connectivity changes with the strongest links to schizophrenia involved parietal rather than frontal regions. Hum Brain Mapp 35:123–139, 2014.

Beyond Element-Wise Interactions: Identifying Complex Interactions in Biological Processes

Background Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem.

Brain-wide functional inter-hemispheric disconnection is a potential biomarker for schizophrenia and distinguishes it from depression

Schizophrenia is associated with disconnectivity in the brain although it is still unclear whether changes within or between hemispheres are of greatest importance. In this paper, an analysis of 152 schizophrenia patients compared with 122 healthy controls was carried out. Comparisons were also made with 39 depression patients and 37 controls to examine whether brain-wide changes in inter- or intra-hemispheric functional connectivity are most associated with the disorder and can distinguish it from depression. The authors developed new techniques (first and second order symmetry) to investigate brain-wide changes in patients (45 regions per hemisphere) and their association with illness duration and symptom severity. Functional connectivity between the same regions in left- and right-hemispheres (first order symmetry) was significantly reduced as was that between the same pairs of regions in the left- and right-hemispheres (second order symmetry) or using all possible inter-hemispheric connections in schizophrenia patients. By contrast, no significant changes were found for brain-wide intra-hemispheric links. First order symmetry changes correlated significantly with positive and negative symptom severity for functional connections linked via the anterior commissure and negative symptoms for those linked via the corpus callosum. Support vector machine analysis revealed that inter-hemispheric symmetry changes had 73–81% accuracy in discriminating schizophrenia patients and either healthy controls or depressed patients. In conclusion, reduced brain-wide inter-hemispheric functional connectivity occurs in schizophrenia, is associated with symptom severity, and can discriminate schizophrenia patients from depressed ones or healthy controls. Brain-wide changes in inter-hemispheric connections may therefore provide a useful potential biomarker for schizophrenia.

Aberrant functional connectivity for diagnosis of major depressive disorder: A discriminant analysis

Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer‐assisted diagnosis.

The diminished interhemispheric connectivity correlates with negative symptoms and cognitive impairment in first-episode schizophrenia

BACKGROUND Previous studies imply that interhemispheric disconnectivity plays a more important role on information processing in schizophrenia. However, the role of the aberrant interhemispheric connection in the pathophysiology of this disorder remains unclear. Recently, resting-state functional Magnetic Resonance Imaging (fMRI) has reported to have potentials of mapping functional interactions between pairs of brain hemispheres. METHODS Resting-state whole-brain functional connectivity analyses were performed on 41 schizophrenia patients and 33 healthy controls. RESULTS The first-episode schizophrenia patients showed significant aberrant interhemispheric connection in the globus pallidus, medial frontal gyrus and inferior temporal gyrus. The correlation of Wechsler Adult Intelligence Scale scores with odds ratio of the aberrant interhemispheric connections revealed positive correlation in the pallidum (rho=0.335, p=.003) and medial frontal gyrus (rho=0.260, p=.025). The connection in the pallidum was also positively correlated with duration of illness (rho=-0.407, p=.009). Whereas, the aberrant interhemispheric connection in the inferior temporal gyrus was positively correlated with scores of Scale for the Assessment of Negative Symptoms (rho=0.393, p=.012). CONCLUSION The present study provides fMRI evidence for the aberrant interhemispheric resting-state functional connectivity within resting-state networks in first-episode schizophrenia patients. These aberrant interhemispheric connections, in particular the pallidum, due to its anatomical and functional connectivities, may be the primary disturbance for cognitive impairment, negative symptoms and chronicity of schizophrenia.

Altered functional connectivity links in neuroleptic-naïve and neuroleptic-treated patients with schizophrenia, and their relation to symptoms including volition

In order to analyze functional connectivity in untreated and treated patients with schizophrenia, resting-state fMRI data were obtained for whole-brain functional connectivity analysis from 22 first-episode neuroleptic-naïve schizophrenia (NNS), 61 first-episode neuroleptic-treated schizophrenia (NTS) patients, and 60 healthy controls (HC). Reductions were found in untreated and treated patients in the functional connectivity between the posterior cingulate gyrus and precuneus, and this was correlated with the reduction in volition from the Positive and Negative Symptoms Scale (PANSS), that is in the willful initiation, sustenance, and control of thoughts, behavior, movements, and speech, and with the general and negative symptoms. In addition in both patient groups interhemispheric functional connectivity was weaker between the orbitofrontal cortex, amygdala and temporal pole. These functional connectivity changes and the related symptoms were not treated by the neuroleptics. Differences between the patient groups were that there were more strong functional connectivity links in the NNS patients (including in hippocampal, frontal, and striatal circuits) than in the NTS patients. These findings with a whole brain analysis in untreated and treated patients with schizophrenia provide evidence on some of the brain regions implicated in the volitional, other general, and negative symptoms, of schizophrenia that are not treated by neuroleptics so have implications for the development of other treatments; and provide evidence on some brain systems in which neuroleptics do alter the functional connectivity.

Granger Causality: Theory and Applications

A question of great interest in systems biology is how to uncover complex network structures from experimental data[1, 3, 18, 38, 55]. With the rapid progress of experimental techniques, a crucial task is to develop methodologies that are both statistically sound and computationally feasible for analysing increasingly large datasets and reliably inferring biological interactions from them [16, 17, 22, 37, 40, 42]. The building block of such enterprise is to being able to detect relations (causal, statistical or functional) between nodes of the network. Over the past two decades, a number of approaches have been developed: information theory ([4]), control theory ([17]) or Bayesian statistics ([35]). Here we will be focusing on another successful alternative approach: Granger causality. In recent Cell papers [7, 12], the authors have come to the conclusion that the ordinary differential equation approach outperforms the other reverse engineering approaches (Bayesian network and information theory) in building causal networks. We have demonstrated that the Granger causality achieves better results than the ordinary differential approach [34]. The basic idea of Granger causality can be traced back to Wiener[47] who conceived the notion that, if the prediction of one time series is improved by incorporating the knowledge of a second time series, then the latter is said to have a causal influence on the first. Granger[23, 24] later formalized Wiener’s idea in the context of linear regression models. Specifically, two auto-regressive models are fitted to the first time series – with and without including the second time series – and the improvement of the prediction is measured by the ratio of the variance of the error terms. A ratio larger than one signifies an improvement, hence a causal connection. At worst, the ratio is 1 and signifies causal independence from the second time series to the first. Geweke’s decomposition of a vector autoregressive process ([20, 21]) led to a set of causality measures which have a spectral representation and make the interpretation more informative and useful by extending Granger causality to the frequency domain. In this chapter, we aim to present Granger causality and how its original formalism has been extended to address biological and computational issues, as summarized in Fig. 5.1.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

BackgroundReverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.ResultsHere we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.ConclusionsThe results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.