Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of the Dynamic Brain Functional Networks.

BACKGROUND: Alzheimer's Disease (AD), identified as the most common type of dementia, presents considerable heterogeneity in clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS: We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 AD patients, 441 MCI patients, and 1160 normal controls (NC). Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method and comprehensively assessed their similarity and differences. RESULTS: Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in both their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as well as those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and these MCI have a more severe cognitive decline rate. CONCLUSIONS: This study quantifies neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identifies disease subtypes in AD and MCI using a comprehensive resting-state fMRI multicenter dataset. It provides new insights for developing early prevention and personalized treatment strategies for AD.

Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia.

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.

Reversibility of Impaired Large-Scale Functional Brain Networks in Cushing's Disease after Surgery Treatment: A Longitudinal Study.

INTRODUCTION: Chronic exposure to excessive endogenous cortisol leads to brain changes in Cushing's disease (CD). However, it remains unclear how CD affects large-scale functional networks (FNs) and whether these effects are reversible after treatment. This study aimed to investigate functional network changes of CD patients and their reversibility in a longitudinal cohort. METHODS: Active CD patients (N = 37) were treated by transsphenoidal pituitary surgery and reexamined 3 months later. FNs were computed from resting-state fMRI data of the CD patients and matched normal controls (NCs, N = 37). A pattern classifier was built on the FNs to distinguish active CD patients from controls and applied to FNs of the CD patients at the 3-month follow-up. Two subgroups of endocrine-remitted CD patients were identified according to their classification scores, referred to as image-based phenotypically (IBP) recovered and unrecovered CD patients, respectively. The informative FNs identified by the classification model were compared between NCs, active CD patients, and endocrine-remitted patients as well as between IBP recovered and unrecovered CD patients to explore their functional network reversibility. RESULTS: All 37 CD patients reached endocrine remission after treatment. The classification model identified three informative FNs, including cerebellar network (CerebN), fronto-parietal network (FPN), and default mode network. Among them, CerebN and FPN partially recovered toward normal at 3 months after treatment. Moreover, the informative FNs were correlated with 24-h urinary-free cortisol and emotion scales in CD patients. CONCLUSION: These findings suggest that CD patients have aberrant FNs that are partially reversible toward normal after treatment.

Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy.

Significance: Accurate evaluation of consciousness in patients with prolonged disorders of consciousness (DOC) is critical for designing therapeutic plans, determining rehabilitative services, and predicting prognosis. Effective ways for detecting consciousness in patients with DOC are still needed. Aim: Evaluation of the residual awareness in patients with DOC and investigation of the spatiotemporal differences in the hemodynamic responses between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) groups using active command-driven motor imagery (MI) tasks. Approach: In this study, functional near-infrared spectroscopy (fNIRS) was used to measure the changes of hemodynamic responses in 19 patients with DOC (9 MCS and 10 UWS) using active command-driven MI tasks. The characteristics of the hemodynamic responses were extracted to compare the differences between the MCS and UWS groups. Moreover, the correlations between the hemodynamic responses and the clinical behavioral evaluations were also studied. Results: The results showed significant differences in the spatiotemporal distribution of the hemodynamic responses between the MCS and UWS groups. For the patients with MCS, significant increases in task-evoked hemodynamic responses occurred during the "YES" questions of the command-driven MI tasks. Importantly, these changes were significantly correlated with their coma-recovery scale-revised (CRS-R) scores. However, for the patients with UWS, no significant changes of the hemodynamic responses were found. Additionally, the results did not show any statistical correlation between the hemodynamic responses and their CRS-R scores. Conclusions: The fNIRS-based command-driven MI tasks can be used as a promising tool for detecting residual awareness in patients with DOC. We hope that the findings and the active paradigm used in this study will provide useful insights into the diagnosis, therapy, and prognosis of this challenging patient population.

Non-invasive detection of the content of white blood cells in the blood of humans based on dynamic spectrum.

Objective. Changes in white blood cell content have been shown to be useful in determining whether the body is in a healthy state. We propose an improved data processing and modeling approach, which helps to accommodate blood component content detection and improve prediction accuracy.Approach. In this experiment, the finger-end transmission method was used for spectral measurement, and we collected a total of 440 sample data. In this paper, we first use the method of CEEMDAN combined with wavelet threshold to denoise the PPG signal, and then use the integral method to extract the spectral features, which makes up for the defects of the single-edge method using incomplete data and the deviation of the slope of the rising segment from the actual signal. We further improve the screening of samples and wavelengths, and used PLS regression modeling combine the double nonlinear correction method to build the most stable and universal model.Main results. The model has been applied to 332 subjects' finger transmission spectral data to predict the concentration of leukocytes. The correlation coefficient of the final training set result was 0.927, and the root mean square error (RMSE) is 0.569×109l-1, the correlation coefficient of the prediction set result is 0.817, and the RMSE is 0.826×109l-1, which proves the practicability of the proposed method.Significance. We propose a non-invasive method for detecting leukocyte concentration in blood that can also be generalized to detect other blood components.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Understanding the heterogeneity of dynamic functional connectivity patterns in first-episode drug naïve depression using normative models.

BACKGROUND: The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD. METHOD: Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling. RESULTS: The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities. CONCLUSIONS: This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study.

Recent advances in neuroimaging technologies have provided insights into detecting residual consciousness and assessing cognitive abilities in patients with disorders of consciousness (DOC). Functional near-infrared spectroscopy (fNIRS) is non-invasive and portable and can be used for longitudinal bedside monitoring, making it uniquely suited for evaluating brain function in patients with DOC at appropriate spatiotemporal resolutions. In this pilot study, an active command-driven motor imagery (MI) paradigm based on fNIRS was used to detect residual consciousness in patients with prolonged DOC. A support vector machine (SVM) classifier was used to classify yes-or-no responses. The results showed that relatively reliable responses were detected from three out of five patients in a minimally consciousness state (MCS). One of the patients answered all the questions accurately when assessed according to this method. This study confirmed the feasibility of using portable fNIRS technology to detect residual cognitive ability in patients with prolonged DOC by active command-driven motor imagery. We hope to detect the exact level of consciousness in DOC patients who may have a higher level of consciousness.

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.

COMT-Val158Met polymorphism modulates antipsychotic effects on auditory verbal hallucinations and temporal lobe gray matter volumes in healthy individuals-symptom relief accompanied by worrisome volume reductions.

Investigation of auditory verbal hallucinations (AVHs) in schizophrenics is complicated by psychiatric symptoms. Investigating healthy individuals with AVHs (H-AVHs) can obviate such confounding factors. The objective of this study was to explore the effects of antipsychotic treatment on AVHs and gray matter volumes (GMVs) in H-AVH subjects and whether such are effects are influenced by COMT-Val158Met genotype. Magnetic resonance imaging (MRI) and genotyping studies were completed for 42 H-AVH subjects and 42 well-matched healthy controls (HCs). COMT-Met/Met homozygotes (158th codon) were identified as COMT-Met genotype; COMT-Met/Val heterozygotes and COMT-Val/Val homozygotes were identified as COMT-Val genotype. Data were compared across groups (H-AVH vs. HC, and between genotypes) with two-sample t-tests. The H-AVH COMT-Met group showed a stronger response to antipsychotic treatment than the H-AVH COMT-Val group (p < 0.001). Both H-AVH genotype groups exhibited temporal lobe GMV reductions after treatment, and relative to their respective genotype-matched HC groups. Antipsychotic treatment effects in H-AVH subjects were influenced by COMT-Val158Met genotype and associated with widespread GMV reductions. These findings provide clues for further exploration of treatment targets for AVHs. Treatment associated GMV reductions, however, raise concerns about use of antipsychotics in H-AVH subjects.

Altered large-scale functional brain networks in neurological Wilson's disease.

Wilson's disease patients with neurological symptoms have motor symptoms and cognitive deficits, including frontal executive, visuospatial processing, and memory impairments. Although the brain structural abnormalities associated with Wilson's disease have been documented, it remains largely unknown how Wilson's disease affects large-scale functional brain networks. In this study, we investigated functional brain networks in Wilson's disease. Particularly, we analyzed resting state functional magnetic resonance images of 30 Wilson's disease patients and 26 healthy controls. First, functional brain networks for each participant were extracted using an independent component analysis method. Then, a computationally efficient pattern classification method was developed to identify discriminative brain functional networks associated with Wilson's disease. Experimental results indicated that Wilson's disease patients, compared with healthy controls, had altered large-scale functional brain networks, including the dorsal anterior cingulate cortex and basal ganglia network, the middle frontal gyrus, the dorsal striatum, the inferior parietal lobule, the precuneus, the temporal pole, and the posterior lobe of cerebellum. Classification models built upon these networks distinguished between neurological WD patients and HCs with accuracy up to 86.9% (specificity: 86.7%, sensitivity: 89.7%). The classification scores were correlated with the United Wilson's Disease Rating Scale measures and durations of disease of the patients. These results suggest that Wilson's disease patients have multiple aberrant brain functional networks, and classification scores derived from these networks are associated with severity of clinical symptoms.

Does Depressive-Type Schizophrenia Exist? How Do We Prove It?: An Updated Review and Overview.

Depressive symptoms can occur at any point in the duration of schizophrenia. However, we are unable to predict if or when depression will occur in schizophrenic patients. Simultaneously, the standard treatment of depression in schizophrenic patients is the combination of antidepressants and antipsychotics, which has been minimally effective for most patients. Based on several studies, we hypothesized the existence of depressive-type schizophrenia and reviewed the substantial evidence supporting the hypothesis of depressive-type schizophrenia. Simultaneously, we propose technical methods to explore the neuropathology of depressive-type schizophrenia in order to identify the disease during its early stages and to predict how patients will respond to the standard treatment strategies. We believe that the new classification of depressive-type schizophrenia will differentiate it from other forms of depression. In return, this will aid in the discovery of new therapeutic strategies for combatting this disease.

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.

The Effect of Dopamine Antagonist Treatment on Auditory Verbal Hallucinations in Healthy Individuals Is Clearly Influenced by COMT Genotype and Accompanied by Corresponding Brain Structural and Functional Alterations: An Artificially Controlled Pilot Study.

Few studies have been conducted to explore the influence of the catechol-o-methyltransferase (COMT) genotype on the severity of and treatment efficacy on auditory verbal hallucination (AVH) symptoms in healthy individuals with AVHs (Hi-AVHs). We hypothesized that the efficacy of dopamine antagonist treatment on AVHs in Hi-AVHs may be influenced by their COMT genotype and may be accompanied by corresponding brain alterations. To preliminarily investigate and test our hypothesis in an artificially controlled pilot study, we enrolled 42 Hi-AVHs as subjects and used magnetic resonance imaging and genetic methods to explore the basis brain features to investigate whether the efficacy of dopamine antagonist treatment on AVHs in Hi-AVH subjects was influenced by their COMT genotype or not. We found that COMT-met genotype subjects' treatment response was better than that of COMT-val subjects. Although COMT-met genotype subjects demonstrated an increase in global functional connectivity density (gFCD) but no difference on gray matter volume (GMV) compared to COMT-val genotype subjects at baseline, notably, we found that both groups demonstrated gFCD and GMV reduction after treatment, but the reduction was more widespread in COMT-met genotype subjects than in COMT-val genotype subjects. This is the first study to report that Hi-AVH subjects' baseline brain functional features are influenced by their COMT genotypes and that the COMT-met genotype subjects exhibit better responses to dopamine antagonists but have more widespread GMV and gFCD reduction than subjects with the COMT-val genotype. Despite several limitations, these findings may provide auxiliary information to further explain the mechanisms of AVHs and provide a clue for scholars to further explore specific treatment targets for AVHs in Hi-AVH subjects or in schizophrenia patients.

Topiramate and Metformin Are Effective Add-On Treatments in Controlling Antipsychotic-Induced Weight Gain: A Systematic Review and Network Meta-Analysis.

Background: Antipsychotic drugs may lead to side effects such as obesity, diabetes, dyslipidemia, and cardiovascular disease. The current systematic review and network meta-analysis analyzes and provides an update on the clinical performance of these add-ons in comparison to placebo on body weight and body mass index (BMI) reductions. Methods: A comprehensive literature search was performed on electronic databases: PubMed (1946-), Embase (1974-), Cochrane library (1992-), and OpenGrey (2000-) until 31 July 2018. Network meta-analyses, comparing the body weight change, BMI change and withdrawn due to adverse events of different pharmacological add-ons, was performed using a multivariate meta-regression model with random-effects, adopting a frequentist approach. To rank the prognosis for all add-ons, we used surface under the cumulative ranking (SUCRA) values. Outcomes: From 614 potential studies identified, 27 eligible studies (n = 1,349 subjects) were included. All the studies demonstrated low to moderate risk of bias. For the analysis of body weight change, all add-ons except Ranitidine showed significant weight reductions comparing to placebo. The effectiveness rank based on SUCRA results from highest to lowest was Sibutramine, Topiramate, Metformin, Reboxetine, Ranitidine, and placebo. A similar pattern was seen for BMI change. The analysis of safety outcome did not detect significantly increased withdrawn number from the add-ons. Current evidence showed relatively good tolerance and safety of using the pharmacological add-ons. Interpretation: Topiramate and Metformin are effective add-on treatments in controlling antipsychotic-induced weight gain, comparing to placebo. They are well tolerated in short-term period. Although Sibutramine has the highest rank of the effectiveness, its license has been withdrawn in many countries due to its adverse effects. Hence, Sibutramine should not be adopted to treat antipsychotic-induced weight gain.

GABRA2 rs279858-linked variants are associated with disrupted structural connectome of reward circuits in heroin abusers.

The reward system plays a vital role in drug addiction. The purpose of this study is to investigate the structural connectivity characteristics and driving-control subnetwork patterns of reward circuits in heroin abusers and assess the genetic modulation on the reward network. We first defined the reward network based on systematic literature review, and built the reward network based on diffusion tensor imaging data of 78 heroin abusers (HAs) and 79 healthy controls (HCs) using structural connectomics. Then we assessed genetic factors that might modulate changes in the reward network by performing imaging-genetic screening for 22 addiction-related polymorphisms. The genetic association was validated by performing genetic associations (1032 HAs and 2863 HCs) and expanded-variant analysis. Finally, we estimated the association between these genetic variations, reward network, and clinical performance. We found that HAs had widespread deficiencies in the structural connectivity of the reward circuit (center in VTA-linked connections), which correlated with cognition deficiency. The disruptions synchronously were shown on the reward driving system and reward control system. GABRA2 rs279858-linked variants might be a key genetic modulator for heroin vulnerability by affecting the connections of reward network and cognition. The role of the reward network connections that mediates the effects of rs279858 on cognition would be disrupted by heroin addiction. These findings provide new insights into the neurocircuitry and genetic mechanisms of addiction.

Distinct pattern of cerebral blood flow alterations specific to schizophrenics experiencing auditory verbal hallucinations with and without insight: a pilot study.

Schizophrenia is associated with widespread and complex cerebral blood flow (CBF) disturbance. Auditory verbal hallucinations (AVH) and insight are the core symptoms of schizophrenia. However, to the best of our knowledge, very few studies have assessed the CBF characteristics of the AVH suffered by schizophrenic patients with and without insight. Based on our previous findings, Using a 3D pseudo-continuous ASL (pcASL) technique, we investigated the differences in AVH-related CBF alterations in schizophrenia patients with and without insight. We used statistical parametric mapping (SPM8) and statistical non-parametric mapping (SnPM13) to perform the fMRI analysis. We found that AVH-schizophrenia patients without insight showed an increased CBF in the left temporal pole and a decreased CBF in the right middle frontal gyrus when compared to AVH-schizophrenia patients with insight. Our novel findings suggest that AVH-schizophrenia patients without insight possess a more complex CBF disturbance. Simultaneously, our findings also incline to support the idea that the CBF aberrant in some specific brain regions may be the common neural basis of insight and AVH. Our findings support the mostly current hypotheses regarding AVH to some extent. Although our findings come from a small sample, it provide the evidence that indicate us to conduct a larger study to thoroughly explore the mechanisms of schizophrenia, especially the core symptoms of AVHs and insight.