Hongjun Peng

Increased suicide attempts in young depressed patients with abnormal temporal–parietal–limbic gray matter volume

BACKGROUND Suicide is a major cause of death throughout the world. Approximately 60% of all suicides have a history of depression. Previous studies of structural brain imaging have shown that suicide is often associated with abnormal fronto-limbic networks. However, the mechanism underlying suicide in depression remains poorly understood. METHOD Twenty sex- and age-matched suicidal unipolar patients were compared with 18 non-suicidal unipolar patients and 28 healthy controls. High-resolution T1-weighted 3T magnetic resonance imaging (MRI) scans were acquired. Hamilton Depressive Rating Scale (HAMD) and Self-Rating Depression scale (SDS) were evaluated. The criterion for suicidality was one or more documented lifetime suicide attempts. A whole-brain optimized voxel-based morphometry (VBM) approach was applied. The Dysfunctional Attitude Scale (DAS) was used to measure cognitive scheme in depressive patients. RESULTS Compared with controls, patients without suicide history showed significant decreased gray matter volume in the left insula lobe [-35 18 9], whereas patients with suicide history showed significantly decreased gray matter volume in the right middle temporal gyrus [60 -53 -8] and increased gray matter volume in the right parietal lobe [39 -39 60]. Compared with the non-suicidal depressed patient group, the suicidal group showed significant decreased gray matter volume in left limbic cingulated gyrus [-2 -21 28]. Moreover, the gray matter volume values in this significantly different brain region were negatively correlated with dysfunctional attitude scores in suicidal depressed patients. LIMITATIONS This study needs replication and further clarification in a larger patient population. CONCLUSIONS Suicide attempts in young depressed patients may be related to abnormal gray matter volumes in temporal-parietal-limbic networks. Specifically, small left limbic cingulate gyrus volumes may be a candidate for the prediction of suicide in young depressed patients.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

AbstractStructural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Changed Hub and Corresponding Functional Connectivity of Subgenual Anterior Cingulate Cortex in Major Depressive Disorder

Major depressive disorder (MDD) is one of the most prevalent mental disorders. In the brain, the hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of MDD remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in MDD. First, we constructed the whole-brain voxel-wise functional networks and calculated a functional connectivity strength (FCS) map in each subject in 34 MDD patients and 34 gender-, age- and education level-matched healthy controls (HCs). Next, the two-sample t-test was applied to compare the FCS maps between HC and MDD patients and identified significant decrease of FCS in subgenual anterior cingulate cortex (sgACC) in MDD patients. Subsequent functional connectivity analyses of sgACC showed disruptions in functional connectivity with posterior insula, middle and inferior temporal gyrus, lingual gyrus and cerebellum in MDD patients. Furthermore, the changed FCS of sgACC and functional connections to sgACC were significantly correlated with the Hamilton Depression Rating Scale (HDRS) scores in MDD patients. The results of the present study revealed the abnormal hub of sgACC and its corresponding disrupted frontal-limbic-visual cognitive-cerebellum functional networks in MDD. These findings may provide a new insight for the diagnosis and treatment of MDD.

Gray Matter Volume Abnormalities in Depressive Patients With and Without Anxiety Disorders

AbstractComorbidity with anxiety disorder is a relatively common occurrence in major depressive disorder. However, the unique and shared neuroanatomical characteristics of depression and anxiety disorders have not been fully identified. The aim of this study was to identify gray matter abnormalities and their clinical correlates in depressive patients with and without anxiety disorders.We applied voxel-based morphometry and region-of-interest analyses of gray matter volume (GMV) in normal controls (NC group, n = 28), depressive patients without anxiety disorder (DP group, n = 18), and depressive patients with anxiety disorder (DPA group, n = 20). The correlations between regional GMV and clinical data were analyzed.The DP group showed decreased GMV in the left insula (INS) and left triangular part of the inferior frontal gyrus when compared to the NC group. The DPA group showed greater GMV in the midbrain, medial prefrontal cortex, and primary motor/somatosensory cortex when compared to the NC group. Moreover, the DPA group showed greater GMV than the DP group in the frontal, INS, and temporal lobes. Most gray matter anomalies were significantly correlated with depression severity or anxiety symptoms. These correlations were categorized into 4 trend models, of which 3 trend models (ie, Models I, II, and IV) revealed the direction of the correlation between regional GMV and depression severity to be the opposite of that between regional GMV and anxiety symptoms. Importantly, the left INS showed a trend Model I, which might be critically important for distinguishing depressive patients with and without anxiety disorder.Our findings of gray matter abnormalities, their correlations with clinical data, and the trend models showing opposite direction may reflect disorder-specific symptom characteristics and help explain the neurobiological differences between depression and anxiety disorder.

Activation and Functional Connectivity of the Left Inferior Temporal Gyrus during Visual Speech Priming in Healthy Listeners and Listeners with Schizophrenia

Under a “cocktail-party” listening condition with multiple-people talking, compared to healthy people, people with schizophrenia benefit less from the use of visual-speech (lipreading) priming (VSP) cues to improve speech recognition. The neural mechanisms underlying the unmasking effect of VSP remain unknown. This study investigated the brain substrates underlying the unmasking effect of VSP in healthy listeners and the schizophrenia-induced changes in the brain substrates. Using functional magnetic resonance imaging, brain activation and functional connectivity for the contrasts of the VSP listening condition vs. the visual non-speech priming (VNSP) condition were examined in 16 healthy listeners (27.4 ± 8.6 years old, 9 females and 7 males) and 22 listeners with schizophrenia (29.0 ± 8.1 years old, 8 females and 14 males). The results showed that in healthy listeners, but not listeners with schizophrenia, the VSP-induced activation (against the VNSP condition) of the left posterior inferior temporal gyrus (pITG) was significantly correlated with the VSP-induced improvement in target-speech recognition against speech masking. Compared to healthy listeners, listeners with schizophrenia showed significantly lower VSP-induced activation of the left pITG and reduced functional connectivity of the left pITG with the bilateral Rolandic operculum, bilateral STG, and left insular. Thus, the left pITG and its functional connectivity may be the brain substrates related to the unmasking effect of VSP, assumedly through enhancing both the processing of target visual-speech signals and the inhibition of masking-speech signals. In people with schizophrenia, the reduced unmasking effect of VSP on speech recognition may be associated with a schizophrenia-related reduction of VSP-induced activation and functional connectivity of the left pITG.

Decreased functional connectivity and disrupted neural network in the prefrontal cortex of affective disorders: A resting-state fNIRS study

BACKGROUND Affective disorders (AD) have been conceptualized as neural network-level diseases. In this study, we utilized functional near infrared spectroscopy (fNIRS) to investigate the spontaneous hemodynamic activities in the prefrontal cortex (PFC) of the AD patients with or without medications. METHODS 42 optical channels were applied to cover the superior frontal gyrus (SFG), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG), which constitute one of the most important affective networks of the brain. We performed resting-state measurements on 28 patients who were diagnosed as having AD and 30 healthy controls (HC). Raw fNIRS data were preprocessed with independent component analysis (ICA) and a band-pass filter to remove artifacts and physiological noise. RESULTS By systematically analyzing the intra-regional, intrahemispheric, and interhemispheric connectivities based on the spontaneous oscillations of Δ[HbO], our results indicated that patients with AD exhibited significantly reduced intra-regional and symmetrically interhemispheric connectivities in the PFC when compared to HC. More specifically, relative to HC, AD patients showed significantly lower locally functional connectivity in the right IFG, and poor long-distance connectivity between bilateral IFG. In addition, AD patients without medication presented more disrupted cortical organizations in the PFC, and the severity of self-reported symptoms of depression was negatively correlated with the strength of intra-regional and symmetrically interhemispheric connectivity in the PFC. LIMITATIONS Regarding the measuring technique, fNIRS has restricted measurement depth and spatial resolution. During the study, the subgroups of AD, such as major depressive disorder, bipolar, comorbidity, or non-comorbidity, dosage of psychotropic drugs, as well as different types of pharmacological responses were not distinguished and systematically compared. Furthermore, due to the limitation of the research design, it was still not very clear how pharmacological treatment affected the resting state cortical organization of the prefrontal lobe, and the degree of the effect in patients with AD. CONCLUSION These results strongly supported that RSFC measured by fNIRS could be a useful and powerful way of delineating the neuropathology of AD.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

ABSTRACT This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study‐specific whole brain white matter parcellation using a well‐established data‐driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group‐wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between‐group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure. HIGHLIGHTSAn AAFC method to identify anatomical tracts from the whole brain tractography.Compute a tract anatomical label for each tract cluster in an automated fashion.Leverage group‐wise fiber geometry and cortical termination information.Application to study emotional processing and sensorimotor areas in MDD.

Prevalence of childhood trauma and correlations between childhood trauma, suicidal ideation, and social support in patients with depression, bipolar disorder, and schizophrenia in southern China

BACKGROUND Childhood trauma has long-term adverse effects on physical and psychological health. Previous studies demonstrated that suicide and mental disorders were related to childhood trauma. In China, there is insufficient research available on childhood trauma in patients with mental disorders. METHODS Outpatients were recruited from a psychiatric hospital in southern China, and controls were recruited from local communities. The demographic questionnaire, the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and the Social Support Rating Scale (SSRS) were completed by all participants, and the Self-rating Idea of Suicide Scale (SIOSS) were completed only by patients. Prevalence rates of childhood trauma were calculated. Kruskal-Wallis test and Dunnett test were used to compare CTQ-SF and SSRS scores between groups. Logistic regression was used to control demographic characteristics and examine relationships between diagnosis and CTQ-SF and SSRS scores. Spearmans rank correlation test was conducted to analyze relationships between suicidal ideation and childhood trauma and suicidal ideation and social support. RESULTS The final sample comprised 229 patients with depression, 102 patients with bipolar, 216 patient with schizophrenia, and 132 healthy controls. In our sample, 55.5% of the patients with depression, 61.8% of the patients with bipolar disorder, 47.2% of the patients with schizophrenia, and 20.5% of the healthy people reported at least one type of trauma. In patient groups, physical neglect (PN) and emotional neglect (EN) were most reported, and sexual abuse (SA) and physical abuse (PA) were least reported. CTQ-SF and SSRS total scores, and most of their subscale scores in patient groups were significantly different from the control group. After controlling demographic characteristics, mental disorders were associated with higher CTQ-SF scores and lower SSRS scores. CTQ-SF scores and number of trauma types were positively correlated with the SIOSS score. Negative correlations existed between SSRS scores and the SIOSS score. LIMITATIONS Our sample may not be sufficiently representative. Some results might have been interfered by demographic characteristics. The SIOSS was not completed by controls. Data from self-report scales were not sufficiently objective. CONCLUSIONS In southern China, childhood trauma is more severe and more prevalent in patients with mental disorders (depression, bipolar disorder and schizophrenia) than healthy people. Among patients with mental disorders in southern China, suicidal ideation is associated with childhood trauma and poor social support.

Schizophrenia affects speech-induced functional connectivity of the superior temporal gyrus under cocktail-party listening conditions

The superior temporal gyrus (STG) is involved in speech recognition against informational masking under cocktail-party-listening conditions. Compared to healthy listeners, people with schizophrenia perform worse in speech recognition under informational speech-on-speech masking conditions. It is not clear whether the schizophrenia-related vulnerability to informational masking is associated with certain changes in FC of the STG with some critical brain regions. Using sparse-sampling fMRI design, this study investigated the differences between people with schizophrenia and healthy controls in FC of the STG for target-speech listening against informational speech-on-speech masking, when a listening condition with either perceived spatial separation (PSS, with a spatial release of informational masking) or perceived spatial co-location (PSC, without the spatial release) between target speech and masking speech was introduced. The results showed that in healthy participants, but not participants with schizophrenia, the contrast of either the PSS or PSC condition against the masker-only condition induced an enhancement of functional connectivity (FC) of the STG with the left superior parietal lobule and the right precuneus. Compared to healthy participants, participants with schizophrenia showed declined FC of the STG with the bilateral precuneus, right SPL, and right supplementary motor area. Thus, FC of the STG with the parietal areas is normally involved in speech listening against informational masking under either the PSS or PSC conditions, and declined FC of the STG in people with schizophrenia with the parietal areas may be associated with the increased vulnerability to informational masking.

Using Support Vector Machine to Identify Imaging Biomarkers of Major Depressive Disorder and Anxious Depression

Comorbidity with anxiety disorders is a relatively common occurrence in major depressive disorder. However, there are no objective, neurological markers which can be used to identify depressive disorder with and without anxiety disorders. The aim of this study was to examine the diagnostic value of structural MRI to distinguish depressive patients with and without ss using support vector machine. In this paper, we applied voxel-based morphometry of gray matter volume (GMV), then choose discriminative features to classify different group using linear support vector machine (SVM) classifier. The experimental results showed that specific structural brain regions may be a potential biomarkers for disease diagnosis.