Cortical thickness of the inferior parietal lobule as a potential predictor of relapse in men with alcohol dependence.

Alcohol dependence is a disorder with a high recurrence rate that leads to a considerable public health burden. The risk of relapse appears to be related to a complex interplay of multiple factors. Herein, we aimed to explore the potential neural predictors of relapse in Chinese male patients with alcohol dependence. This study enrolled 58 male patients with alcohol dependence who had undergone acute detoxification. General demographic information and clinical features were collected. Magnetic resonance imaging data were used to measure cortical thickness across 34 regions of the brain. Patients were followed up at six months, and 51 patients completed the follow-up visit. These patients were divided into a relapser and an abstainer group. A binary logistic regression analysis was performed to investigate the potential risk factors of relapse. Compared to abstainers, relapsers showed higher inattention and non-planning impulsivity on the 11th version of the Barratt Impulsive Scale. The cortical thicknesses of the inferior-parietal lobules were significantly higher in abstainers compared with those in relapsers. Furthermore, binary logistic regression analysis showed that the thickness of the inferior parietal lobule predicted relapse, and lower non-planning impulse was a protective factor against relapse. Relapsers show poorer impulse control than abstainers, and structural magnetic resonance imaging revealed a decreased thickness of the inferior parietal lobule in relapsers. Our results indicate the thickness of the inferior parietal lobule as a potential relapse predictor in male patients with alcohol dependence.

Cortical thickness of the inferior parietal lobule as a potential predictor of relapse in men with alcohol dependence.

Background: Alcohol dependence (AD) is a disorder with a high recurrence rate that leads to a considerable public health burden. The risk of relapse appears to be related to a complex interplay of multiple factors. Herein, we aimed to explore the potential neural predictors of relapse in Chinese male patients with AD. Methods: This study enrolled 58 male patients with AD who had undergone acute detoxification. General demographic information and clinical features were collected. Magnetic resonance imaging (MRI) data were used to measure cortical thickness across 34 regions of the brain. Patients were followed up at 6 months, and 51 patients completed the follow-up visit. These patients were divided into a relapser and an abstainer group. A binary logistic regression analysis was performed to investigate the potential risk factors of relapse. Results: Compared to abstainers, relapsers showed higher inattention and non-planning impulsivity on the 11th version of the Barratt Impulsive Scale. The cortical thicknesses of the inferior-parietal lobule were significantly greater in abstainers compared with those in relapsers. Furthermore, binary logistic regression analysis showed that the thickness of the inferior parietal lobule predicted relapse. Conclusions: Relapsers show poorer impulse control than abstainers, and MRI imaging shows a decreased thickness of the inferior parietal lobule in relapsers. Our results indicate the thickness of the inferior parietal lobule as a potential relapse predictor in male patients with AD.

Association between functional and structural connectivity of the corticostriatal network in people with schizophrenia and unaffected first-degree relatives.

Background: Dysfunction of the corticostriatal network has been implicated in the pathophysiology of schizophrenia, but findings are inconsistent within and across imaging modalities. We used multimodal neuroimaging to analyze functional and structural connectivity in the corticostriatal network in people with schizophrenia and unaffected first-degree relatives. Methods: We collected resting-state functional magnetic resonance imaging and diffusion tensor imaging scans from people with schizophrenia (n = 47), relatives (n = 30) and controls (n = 49). We compared seed-based functional and structural connectivity across groups within striatal subdivisions defined a priori. Results: Compared with controls, people with schizophrenia had altered connectivity between the subdivisions and brain regions in the frontal and temporal cortices and thalamus; relatives showed different connectivity between the subdivisions and the right anterior cingulate cortex (ACC) and the left precuneus. Post-hoc t tests revealed that people with schizophrenia had decreased functional connectivity in the ventral loop (ventral striatum-right ACC) and dorsal loop (executive striatum-right ACC and sensorimotor striatum-right ACC), accompanied by decreased structural connectivity; relatives had reduced functional connectivity in the ventral loop and the dorsal loop (right executive striatum-right ACC) and no significant difference in structural connectivity compared with the other groups. Functional connectivity among people with schizophrenia in the bilateral ventral striatum-right ACC was correlated with positive symptom severity. Limitations: The number of relatives included was moderate. Striatal subdivisions were defined based on a relatively low threshold, and structural connectivity was measured based on fractional anisotropy alone. Conclusion: Our findings provide insight into the role of hypoconnectivity of the ventral corticostriatal system in people with schizophrenia.

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.

Erratum: Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

[This corrects the article DOI: 10.1038/s41537-017-0023-7.].

Altered Brain Network Connectivity as a Potential Endophenotype of Schizophrenia.

Abnormal functional brain connectivity could be considered an endophenotype of psychosis in schizophrenia. Identifying candidate endophenotypes may serve as a tool for elucidating its biological and neural mechanisms. The present study investigated the similarities and differences of features of brain network connectivity between patients and their first-degree relatives. Independent component analysis was conducted on imaging data collected from 34 healthy controls, 33 schizophrenia patients, and 30 unaffected first-degree relatives. The correlation between functional connectivity with neurocognitive performance and clinical symptoms were calculated. Abnormalities of between-network connectivity largely overlapped in patients and first-degree relatives, but the extent of such abnormalities was relatively minor in relatives. Negative connectivity between language networks and executive control networks was impaired in schizophrenia patients and their first-degree relatives, and this decreased connectivity was correlated with performance in language processing. Similar impairments were found in high-visual network and executive network coupling, and this decreased connection was correlated with the severity of positive symptoms in patients. The results indicated that abnormal functional connectivity within and between perceptual systems (i.e., high-visual and language) and executive control networks was related to the generic risk of schizophrenia, which makes it a potential endophenotype for schizophrenia.

Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome.

Psychopathology and personality traits associated with driving while intoxicated in Beijing, China: Implications for interventions.

BACKGROUND AND OBJECTIVES: Driving while intoxicated (DWI) represents an area of grave concern in China, yet little research has focused on it or on the personality traits and psychiatric disorders among these drivers. METHODS: We enrolled 325 of 382 residents charged with DWI in a compulsory detention center in Beijing, China. And 351 male drivers who had never had any alcohol arrests as control participants. All were screened for Axis I disorders using the Chinese version of the Structured Clinical Interview for the Diagnostic Statistical Manual of Mental Disorders IV-TR (SCID), the Minnesota Multiphasic Personality Inventory (MMPI-2), and the Sixteen Personality Factor Questionnaire (16PF). RESULTS: The subjects were all males with a mean (±SD) age of 34.41 ± 8.48 years, and almost 30% met DSM-IV criteria for alcohol abuse or dependence (n = 92). Compared to normal controls they showed greater Social Boldness, Abstractedness, Apprehension, Liveliness and Tension, and poorer Reasoning, Vigilance, Openness to Change, and Self-reliance. On the MMPI-2, DWI subjects showed greater Hypochondriasis, Psychopathic Deviate, Paranoia, Psychasthenia, Schizophrenia and Hypomania; and lower Social introversion. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Severe alcohol problems are more common in Beijing's drunk drivers than in the Chinese general population. These DWI drivers also have a broad variety of traits that increase their tendency to be venturesome and socially bold while enjoying excitement and risk-taking. (Am J Addict 2017;26:374-378).

Probing the Serotonin Transporter Availability Among Male Cigarette Smokers: A SPECT Study With [123I] ADAM.

OBJECTIVE: Genetic studies have suggested that the serotonin transporter (SERT) could be associated with cigarette smoking. However, evidence from neuroimaging is scarce. The aim of the present study was to examine the SERT availability among cigarette smokers by using single-photon emission computed tomography (SPECT). METHODS: Sixteen male smokers and 32 controls were enrolled. The SERT availability was measured by SPECT with a radiotracer, [I] ADAM, which is highly sensitive and specific to SERT. RESULTS: No significant difference in SERT availability was found between 2 groups in the midbrain (smokers: 2.12 ±â€Š0.70, nonsmokers: 2.13 ±â€Š0.63; P = 0.86), basal ganglia (smokers: 0.83 ±â€Š0.30, nonsmokers:0.90 ±â€Š0.39; P = 0.95), or thalamus (smokers: 1.14 ±â€Š0.41, nonsmokers: 1.20 ±â€Š0.38; P = 0.88). No significant association was found between the SERT availability, and either the breath carbon monoxide level or the score of the Fagerström Test for Nicotine Dependence. CONCLUSIONS: Whether the SERT availability in the brain is altered in smokers remains unclear.