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Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.

Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.

Neuroimaging studies have suggested the presence of abnormalities in the prefrontal–thalamic–cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal–thalamic–cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal–thalamic–cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term “dynamic rhythm information.” We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ($meanScZ:meanHC=0.715:0.777$). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ($meanScZ:meanHC=0.695:0.629$), and the degree of coupling was negatively correlated with the duration of disease ($r=-0.373$, $p=0.003$). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Early detection and intervention strategies for schizophrenia are receiving increasingly more attention. Dermatoglyphic patterns, such as the degree of asymmetry of the fingerprints, have been hypothesized to be indirect measures for early abnormal developmental processes that can lead to later psychiatric disorders such as schizophrenia. However, previous results have been inconsistent in trying to establish the association between dermatoglyphics and schizophrenia. The goal of this work is to try to resolve this problem by borrowing well-developed techniques from the field of fingerprint matching. Two dermatoglyphic asymmetry measures are proposed that draw on the orientation field of homologous fingers. To test the capability of these measures, fingerprint images were acquired digitally from 40 schizophrenic patients and 51 normal individuals. Based on these images, no statistically significant association between conventional dermatoglyphic asymmetry measures and schizophrenia was found. In contrast, the sample means of the proposed measures consistently identified the patient group as having a higher degree of asymmetry than the control group. These results suggest that the proposed measures are promising for detecting the dermatoglyphic patterns that can differentiate the patient and control groups.

The Lotka–Volterra–Haken equations have been frequently used in ecology and pattern formation. Recently, the equations have been proposed by several research groups as amplitude equations for task-related patterns of brain activity. In this theoretical study, the focus is on the circular causality aspect of pattern formation systems as formulated within the framework of synergetics. Accordingly, the stable modes of a pattern formation system inhibit the unstable modes, whereas the unstable modes excite the stable modes. Using this circular causality principle it is shown that under certain conditions the Lotka–Volterra–Haken amplitude equations can be derived from a general model of brain activity akin to the Wilson–Cowan model. The model captures the amplitude dynamics for brain activity patterns in experiments involving several consecutively performed multiple-choice tasks. This is explicitly demonstrated for two-choice tasks involving grasping and walking. A comment on the relevance of the theoretical framework for clinical psychology and schizophrenia is given as well.

To disclose the pathophysiological mechanism of schizophrenia from the general connectivity among distinct brain regions, the computational model proposed by Szalisznyó et al. is generalized by incorporating the effects of the hippocampus. Based on the center manifold theorem and normal form theory, the emergence of abnormal alpha oscillations, which are directly associated with symptoms of schizophrenia, is analyzed in this network with Hopf bifurcation as a precursor. It is found that the clinically significant hypoconnectivities between the anterior cingulate and the dorsolateral prefrontal cortex or from the dorsolateral prefrontal cortex to the hippocampus are helpful in inhibiting the abnormal alpha oscillations, while disconnection of the coupling from the anterior cingulate cortex to the striatum can induce schizophrenia. Moreover, polynomial chaos expansion and variance-based sensitivity analysis are applied to determine the contributions of these connection strengths to the variance of the neuronal population activity. Our model results are consistent with experimental observations and thus might be meaningful for clinical interference.

In pathological brain, a variety of morphological forms exist that reflect differences in functional requirements. To better understand microglia function in neurological disease, it is important to identify and quantify microglia morphology associated with specific neuropathologies. Traditional feature parameters such as area or cell diameter are not sufficient. In this study microglia were quantified by the box-counting fractal dimension (D_B). One hundred and four cells from post-mortem tissue were analyzed comprising cells of controls, Alzheimer's disease, schizophrenia and affective disorder. The D_B was significantly different from the control (1.36) compared to schizophrenia (1.41), Alzheimer's disease (1.41) and affective disorder (1.43) with p < 0.01. Thus fractal analysis provides a useful quantitative and objective measure of microglial form associated with normal function and diverse neuropathology. The distribution of fractal dimensions associated with microglia structure and activation with disease progression also differs, suggesting a different etiology for these diseases.

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一般來說，有精神病暴力紀錄的案主往往是令社會工作人員和專業人士最感頭痛的一群。工作人員往往覺得他們無法溝通、瞭解和建立關係，最後只能以控制、禁錮和強迫服藥作爲介入手法。其實在能耐取向的介入手法下，不少的研究都指出，精神分裂的症狀，如幻像、幻聽、妄想都是可以理解的，而暴力行爲也是可以預知的，並與妄想病人經驗支持系統有關。作者更以一實例顯示如何幫助案主建立互信和溝通讓他們發展所長、重返社會。

Clients with psychosis and record of violence has long been a challenge for social work interventions. Workers tend to insert social control by involuntary hospitalization and medical treatment. Based on a strengths perspective and by means of a case illustration, this paper shows that clients' delusion and hallucination are meaningful. Violent behaviours and outbreaks are predictable and related with stress in clients' psychosocial environments. Once their capabilities and strengths are properly developed, they can recover and enjoy a normal life as other persons in the society.

Analysis of the brain activity is the major research area in human neuroscience. Besides many works that have been conducted on analysis of brain activity in case of healthy subjects, investigation of brain activity in case of patients with different brain disorders also has aroused the attention of many researchers. An interesting category of works belong to the comparison of brain activity between healthy subjects and patients with brain disorders. In this research, for the first time, we compare the brain activity between adolescents with symptoms of schizophrenia and healthy subjects, by information-based analysis of their Electroencephalography (EEG) signals. For this purpose, we benefit from the Shannon entropy as the indicator of information content. Based on the results of analysis, EEG signal in case of healthy subjects contains more information than EEG signal in case of subjects with schizophrenia. The result of statistical analysis showed the significant variation in the Shannon entropy of EEG signal between healthy adolescents and adolescents with symptoms of schizophrenia in case of P3, O1 and O2 channels. The employed method of analysis in this research can be further extended in order to investigate the variations in the information content of EEG signal in case of subjects with other brain disorders versus healthy subjects.