Decreased Resting-State Interhemispheric Functional Connectivity Correlated with Neurocognitive Deficits in Drug-Naive First-Episode Adolescent-Onset Schizophrenia

A comparison of clinical characteristics and course predictors in early- and childhood-onset schizophrenia

AIM

The aim of this study was to compare the clinical characteristics of childhood-onset schizophrenia (COS) and early-onset schizophrenia (EOS) during the first- episode psychosis and the stable period, to examine psychopharmacological treatment approaches, and to investigate potential predictive factors for prognosis.

METHODS

Demographic, clinical, and psychopharmacological therapy data for 31 patients diagnosed with COS and 66 with EOS were retrieved from the file records in this multicenter study. Symptom distribution and disease severity and course were evaluated twice, in the acute psychotic stage and in the latest stable phase, during follow-up using the positive and negative syndrome scale (PANSS) and clinical global impression (CGI) scales.

RESULTS

A statistically significant difference was observed between the groups' CGI improvement rates and median last stable stage PANSS positive, negative, and general psychopathology symptom scores (p = .005, p = .031, p = .005, and p = .012, respectively). Premorbid neurodevelopmental disorder and obsessive-compulsive disorder and comorbidities were more common in the COS group (p = .025 and p = .030, respectively), and treatment required greater multiple antipsychotic use in that group (p = .013). When the independent variables affecting the difference between pre- and post-treatment PANSS scores were examined using linear regression analysis, the model established was found to be statistically significant (F = 5.393; p = .001), and the group variable (p = .024), initial disease severity (p = .001), and socioeconomic level (p = .022; p = .007) emerged as predictive factors for the disease course.

CONCLUSION

Although early diagnosis and treatment is an important factor in improving prognosis in schizophrenia, more specific predictors for schizophrenia need to be identified. Additionally, preventive programs and pharmacological methods need to be developed in children with neurodevelopmental problems, particularly those from low socioeconomic status families.

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis

BACKGROUND

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective.

METHODS

We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables.

RESULTS

A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance.

CONCLUSIONS

Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Influence of depression severity on interhemispheric functional integration: an analysis from the REST-meta-MDD database

Major depressive disorder (MDD) is a pervasive mental disorder that significantly impairs functional capabilities, underscoring the necessity for precise stratification of its severity to facilitate tailored treatment. This study investigated the utility of voxel-mirrored homotopic connectivity (VMHC) derived from resting-state functional magnetic resonance imaging (fMRI) data as a neuroimaging biomarker to differentiate varying severities of MDD in a sample drawn from the REST-meta-MDD project, which included 392 first-episode MDD patients and 440 healthy controls (HC) from 9 sites. Patients were classified into mild to moderate and severe depression groups according to the 17-item Hamilton Depression Scale (HAMD) scores. VMHC differences between these subgroups and their associations with HAMD scores were further examined. The results revealed significant reductions in VMHC within the fusiform gyrus for patients with mild to moderate depression compared to HCs, alongside more extensive reductions across the insula, postcentral gyrus, and angular gyrus in severe depression. Notably, increased VMHC in the middle cingulate cortex was identified in severe MDD patients relative to those with mild to moderate depression, with this increase showed a significant positive correlation with the HAMD scores. Additionally, receiver operating characteristic (ROC) curve analysis demonstrated that VMHC values in these regions effectively differentiate patients from HCs and across varying severities of MDD. These findings suggest that VMHC could serve as a valuable metric for clinical diagnosis and the stratification of depression severity, providing insights into the underlying neurobiological mechanisms associated with the disorder.

Schizophrenia and Neurodevelopment: Insights From Connectome Perspective

BACKGROUND

Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS).

STUDY DESIGN

We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field.

STUDY RESULTS

Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients.

CONCLUSIONS

Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.

A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data

Background

Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information.

Methods

We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals.

Results

Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features.

Conclusion

The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.

Association between homotopic connectivity and clinical symptoms in first-episode schizophrenia

Background

Abnormal functional connectivity (FC) in the brain has been observed in schizophrenia patients. However, studies on FC between homotopic brain regions are limited, and the results of these studies are inconsistent. The aim of this study was to compare homotopic connectivity between first-episode schizophrenia (FES) patients and healthy subjects and assess its correlation with clinical symptoms.

Methods

Thirty-one FES patients and thirty-three healthy controls (HC) were included in the study. The voxel-mirrored homotopic connectivity (VMHC) method of resting-state functional magnetic resonance imaging (rs-fMRI) was used to analyse the changes in homotopic connectivity between the two groups. The 5-factor PANSS model was used to quantitatively evaluate the severity of symptoms in FES patients. Partial correlation analysis was used to assess the correlation between homotopic connectivity changes and clinical symptoms.

Results

Compared to those in the HC group, VMHC values were decreased in the paracentral lobule (PL), thalamus, and superior temporal gyrus (STG) in the FES group (P < 0.05, FDR correction). No significant differences in white matter volume (WMV) within the subregion of the corpus callosum or in brain regions associated with reduced VMHC were observed between the two groups. Partial correlation analyses revealed that VMHC in the bilateral STG of FES patients was positively correlated with negative symptoms (rleft = 0.46, p < 0.05; rright = 0.47, p < 0.05), and VMHC in the right thalamus was negatively correlated with disorganized/concrete symptoms (rright = 0.45, p < 0.05).

Conclusion

Our study revealed that homotopic connectivity is altered in the resting-state brain of FES patients and correlates with the severity of negative symptoms; this change may be independent of structural changes in white matter. These findings may contribute to the development of the abnormal connectivity hypothesis in schizophrenia patients.

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia

BACKGROUND

Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.

METHODS

We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.

RESULTS

SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.

COMPARISON WITH EXISTING METHODS

Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.

CONCLUSIONS

This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.

A comparative study of interhemispheric functional connectivity in major depression and schizophrenia

BACKGROUND

Major depressive disorder (MDD) and schizophrenia (SZ) are serious psychiatric disorders that, despite exhibiting different diagnostic criteria, exhibit significant overlap regarding the biological and clinical features of affected patients. While prior evidence has shown that interhemispheric functional connectivity (FC) is abnormal in MDD and SZ, the particular similarities and differences that unify and characterize MDD and SZ regarding these interhemispheric FC patterns remain to be characterized. This study was thus designed to conduct an in-depth analysis of MDD- and SZ-related patterns of interhemispheric FC.

METHODS

This study enrolled MDD patients, SZ patients, and normal control (NC) individuals (n = 36 each). Resting-state functional MRI (rs-fMRI) studies of these patients were conducted, after which voxel-mirrored homotopic connectivity (VMHC) was used to analyze the preprocesses rs-fMRI data. The VMHC values in these different values were then compared through one-way ANOVAs and post hoc analyses.

RESULTS

Significant differences were observed in both the striatum and middle frontal gyrus (MFG) when comparing these three groups. Through pairwise comparisons, MDD patients but not SZ patients exhibited reduced MFG VMHC values relative to the NC individuals. Conversely, striatum VMHC values significantly increased in SZ patients relative to NC individuals and MDD patients.

CONCLUSION

These results support the interhemispheric functional disconnection hypothesis as a basis for the pathogenesis of MDD and SZ. The observed differences in interhemispheric FC in the MFG and striatum of MDD and SZ patients will offer a neuroimaging basis that can aid in the differential diagnosis of these debilitating conditions.

Abnormal interhemispheric functional cooperation in schizophrenia follows the neurotransmitter profiles

BACKGROUND

Interhemispheric cooperation is one of the most prominent functional architectures of the human brain. In patients with schizophrenia, interhemispheric cooperation deficits have been reported using increasingly powerful neurobehavioural and neuroimaging measures. However, these methods rely in part on the assumption of anatomic symmetry between hemispheres. In the present study, we explored interhemispheric cooperation deficits in schizophrenia using a newly developed index, connectivity between functionally homotopic voxels (CFH), which is unbiased by hemispheric asymmetry.

METHODS

Patients with schizophrenia and age- and sexmatched healthy controls underwent multimodal MRI, and whole-brain CFH maps were constructed for comparison between groups. We examined the correlations of differing CFH values between the schizophrenia and control groups using various neurotransmitter receptor and transporter densities.

RESULTS

We included 86 patients with schizophrenia and 86 matched controls in our analysis. Patients with schizophrenia showed significantly lower CFH values in the frontal lobes, left postcentral gyrus and right inferior temporal gyrus, and significantly greater CFH values in the right caudate nucleus than healthy controls. Moreover, the differing CFH values in patients with schizophrenia were significantly correlated with positive symptom score and illness duration. Functional connectivity within frontal lobes was significantly reduced at the voxel cluster level compared with healthy controls. Finally, the abnormal CFH map of patients with schizophrenia was spatially associated with the densities of the dopamine D1 and D2 receptors, fluorodopa, dopamine transporter, serotonin transporter and acetylcholine transporter.

CONCLUSION

Regional abnormalities in interhemispheric cooperation may contribute to the clinical symptoms of schizophrenia. These CFH abnormalities may be associated with dysfunction in neurotransmitter systems strongly implicated in schizophrenia.

Neuronal dysfunction in individuals at early stage of schizophrenia, A resting-state fMRI study

Schizophrenia has been associated with abnormal intrinsic brain activity, involving various cognitive impairments. Qualitatively similar abnormalities are seen in individuals at ultra-high risk (UHR) for psychosis. In this study, resting-state fMRI (rs-fMRI) data were collected from 44 drug-naïve first-episode schizophrenia (Dn-FES) patients, 48 UHR individuals, and 40 healthy controls (HCs). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC), were performed to evaluate resting brain function. A support vector machine (SVM) was applied for classification analysis. Compared to HCs, both clinical groups showed increased fALFF in the central executive network (CEN), decreased ReHo in the ventral visual pathway (VVP) and decreased FC in temporal-sensorimotor regions. Excellent performance was achieved by using fALFF value in distinguishing both FES (sensitivity=83.21%, specificity=80.58%, accuracy=81.37%, p=0.009) and UHR (sensitivity=75.88%, specificity=85.72%, accuracy=80.72%, p<0.001) from HC group. Moreover, the study highlighted the importance of frontal and temporal alteration in the pathogenesis of schizophrenia. However, no fMRI features were observed that could well distinguish Dn-FES from UHR group. To conclude, fALFF in the CEN may provide potential power for identifying individuals at the early stage of schizophrenia and the alteration in the frontal and temporal lobe may be important to these individuals.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

BACKGROUND

Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks.

METHODS

We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls).

RESULTS

We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05).

CONCLUSIONS

The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

The role of the insula in cognitive impairment of schizophrenia

Cognitive impairment is one of the core clinical symptom domains of schizophrenia. Research shows that cognitive deficits in this neuropsychiatric syndrome is associated with neurodevelopmental pathology affecting multiple brain regions such as the dorsolateral prefrontal cortex, the hippocampus and the parietal lobe. The insula is a relatively small structure that is highly connected with several brain regions as well as multiple brain networks. A large number of studies have reported the involvement of the insula in many of the psychotic and nonpsychotic manifestations of schizophrenia. Here we review the role of the insula as a hub across key neurocircuits which have been implicated in the various cognitive pathologies in schizophrenia. Structural and functional abnormalities in the right and left insulae may serve as a biomarker for susceptibility to schizophrenia.

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.

Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features

There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.

Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia

It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients' frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients' frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.

Identifying and revealing different brain neural activities of cognitive subtypes in early course schizophrenia

Background

Cognitive subtypes of schizophrenia may exhibit different neurobiological characteristics. This study aimed to reveal the underlying neurobiological features between cognitive subtypes in the early course of schizophrenia (ECS). According to prior studies, we hypothesized to identify 2–4 distinct cognitive subtypes. We further hypothesized that the subtype with relatively poorer cognitive function might have lower brain spontaneous neural activity than the subtype with relatively better cognitive function.

Method

Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Resting-state functional magnetic resonance imaging scanning was conducted for each individual. There were 155 ECS individuals and 97 healthy controls (HCs) included in the subsequent analysis. Latent profile analysis (LPA) was used to identify the cognitive subtypes in ECS individuals, and amplitude of low-frequency fluctuations (ALFFs) was used to measure brain spontaneous neural activity in ECS individuals and HCs.

Results

LPA identified two cognitive subtypes in ECS individuals, containing a severely impaired subtype (SI, n = 63) and a moderately impaired subtype (MI, n = 92). Compared to HCs, ECS individuals exhibited significantly increased ALFF in the left caudate and bilateral thalamus and decreased ALFF in the bilateral medial prefrontal cortex and bilateral posterior cingulate cortex/precuneus (PCC/PCu). In ECS cognitive subtypes, SI showed significantly higher ALFF in the left precentral gyrus (PreCG) and lower ALFF in the left PCC/PCu than MI. Furthermore, ALFFs of left PreCG were negatively correlated with several MCCB cognitive domains in ECS individuals, while ALFF of left PCC/PCu presented opposite correlations.

Conclusion

Our findings suggest that differences in the brain spontaneous neural activity of PreCG and PCC/PCu might be the potential neurobiological features of the cognitive subtypes in ECS, which may deepen our understanding of the role of PreCG and PCC/PCu in the pathogenesis of cognitive impairment in schizophrenia.

Increased resting-state interhemispheric functional connectivity of striatum in first-episode drug-naive adolescent-onset schizophrenia

BACKGROUND

Compared to adult-onset schizophrenia, relatively few neuroimaging studies have examined functional connectivity (FC) abnormalities in adolescent-onset schizophrenia (AOS). The present study was designed to investigate resting-state interhemispheric connectivity patterns among drug-naive first-episode AOS patients and potential changes following short-term antipsychotic drug treatment.

METHODS

This study included 107 drug-naïve, first-episode AOS patients (age: 15.33 ± 1.62, 45 males) and 67 matched healthy controls (age: 15.43 ± 1.86, 30 males). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans, and 34 AOS patients (age: 15.12 ± 1.68, 12 males) also underwent a follow-up scan after 8 weeks of antipsychotic drug treatment. Interhemispheric functional connectivity was measured by voxel-mirrored homotopic connectivity (VMHC).

RESULTS

Compared to healthy controls, AOS patients showed increased VMHC values in putamen and caudate. No significant differences were observed between the patients at baseline and after 8 weeks of treatment.

CONCLUSIONS

First-episode, drug-naive AOS patients demonstrate abnormalities in interhemispheric FC, and these are not mitigated by short-term antipsychotic treatment.

Resting-state electroencephalographic correlates of cognitive reserve: Moderating the age-related worsening in cognitive function

This exploratory study aimed to investigate the resting-state electroencephalographic (rsEEG) correlates of the cognitive reserve from a life span perspective. Current source density (CSD) and lagged-linear connectivity (LLC) measures were assessed to this aim. We firstly explored the relationship between rsEEG measures for the different frequency bands and a socio-behavioral proxy of cognitive reserve, the Cognitive Reserve Index (CRI). Secondly, we applied moderation analyses to assess whether any of the correlated rsEEG measures showed a moderating role in the relationship between age and cognitive function. Moderate negative correlations were found between the CRI and occipital CSD of delta and beta 2. Moreover, inter- and intrahemispheric LLC measures were correlated with the CRI, showing a negative association with delta and positive associations with alpha 1, beta 1, and beta 2. Among those correlated measures, just two rsEEG variables were significant moderators of the relationship between age and cognition: occipital delta CSD and right hemispheric beta 2 LLC between occipital and limbic regions. The effect of age on cognitive performance was stronger for higher values of both measures. Therefore, lower values of occipital delta CSD and lower beta 2 LLC between right occipital and limbic regions might protect or compensate for the effects of age on cognition. Results of this exploratory study might be helpful to allocate more preventive efforts to curb the progression of cognitive decline in adults with less CR, possibly characterized by these rsEEG parameters at a neural level. However, given the exploratory nature of this study, more conclusive work on these rsEEG measures is needed to firmly establish their role in the cognition-age relationship, for example, verifying if these measures moderate the relationship between brain structure and cognition.

Abnormal global-brain functional connectivity and its relationship with cognitive deficits in drug-naive first-episode adolescent-onset schizophrenia

Abnormal functional connectivity (FC) has been reported in drug-naive first-episode adolescent-onset schizophrenia (AOS) with inconsistent results due to differently selected regions of interest. The voxel-wise global-brain functional connectivity (GFC) analysis can help explore abnormal FC in an unbiased way in AOS. A total of 48 drug-naive first-episode AOS as well as 31 sex-, age- and education-matched healthy controls were collected. Data were subjected to GFC, correlation analysis and support vector machine analyses. Compared with healthy controls, the AOS group exhibited increased GFC in the right middle frontal gyrus (MFG), and decreased GFC in the right inferior temporal gyrus, left superior temporal gyrus (STG)/precentral gyrus/postcentral gyrus, right posterior cingulate cortex /precuneus and bilateral cuneus. After the Benjamini-Hochberg correction, significantly negative correlations between GFC in the bilateral cuneus and Trail-Making Test: Part A (TMT-A) scores (r=-0.285, p=0.049), between GFC in the left STG/precentral gyrus/postcentral gyrus and TMT-A scores (r=-0.384, p=0.007), and between GFC in the right MFG and the fluency scores (r=-0.335, p=0.020) in the patients. GFC in the left STG/precentral gyrus/postcentral gyrus has a satisfactory accuracy (up to 86.08%) in classifying patients from controls. AOS shows abnormal GFC in the brain areas of multiple networks, which bears cognitive significance. These findings suggest potential abnormalities in processing self-monitoring and sensory prediction, which further elucidate the pathophysiology of AOS.

Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia

Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.

Abnormal interhemispheric homotopic functional connectivity is correlated with gastrointestinal symptoms in patients with major depressive disorder

The severity of major depressive disorder (MDD) can be aggravated by gastrointestinal (GI) symptoms, but the neuroimaging mechanism underlying GI symptoms still remains unclear. In this study, we recruited 52 medication-free and first-episode MDD patients (35 with GI symptoms and 17 without GI symptoms) and 28 age-, sex-, and education-matched healthy controls to explore the inter-group differences in neuroimaging findings. All the participants underwent resting-state functional magnetic resonance imaging (fMRI) scan, and the functional connectivities that were reported to be abnormal in MDD were our focus of exploration. Voxel-mirrored homotopic connectivity (VMHC) method was used to explore the interhemispheric homotopic functional connectivity of all the subjects. Patients with MDD showed significantly different VMHC in brain regions in the default mode network (DMN), including the middle frontal gyrus, precuneus, inferior parietal lobule, and posterior cingulate cortex. Patients with GI symptoms exhibited significantly decreased interhemispheric homotopic functional connectivity in the middle frontal gyrus and superior frontal gyrus, compared with patients without GI symptoms. These results suggested that the DMN is involved in the neuropathology of MDD. Interhemispheric homotopic connectivity in specific regions could be applied as a biomarker to distinguish MDD patients with GI symptoms from those without GI symptoms.

Abnormal causal connectivity of left superior temporal gyrus in drug-naïve first- episode adolescent-onset schizophrenia: A resting-state fMRI study

This study aimed to investigate the alterations of causal connectivity between the brain regions in Adolescent-onset schizophrenia (AOS) patients. Thirty-two first-episode drug-naïve AOS patients and 27 healthy controls (HC) were recruited for resting-state functional MRI scanning. The brain region with the between-group difference in regional homogeneity (ReHo) values was chosen as a seed to perform the Granger causality analysis (GCA) and further detect the alterations of causal connectivity in AOS. AOS patients exhibited increased ReHo values in left superior temporal gyrus (STG) compared with HCs. Significantly decreased values of outgoing Granger causality from left STG to right superior frontal gyrus and right angular gyrus were observed in GC mapping for AOS. Significantly stronger causal outflow from left STG to right insula and stronger causal inflow from right middle occipital gyrus (MOG) to left STG were also observed in AOS patients. Based on assessments of the two strengthened causal connectivity of the left STG with insula and MOG, a discriminant model could identify all patients from controls with 94.9% accuracy. This study indicated that alterations of directional connections in left STG may play an important role in the pathogenesis of AOS and serve as potential biomarkers for the disease.

Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia

Background

Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients.

Methods

A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data.

Results

Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment.

Conclusions

This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.

Disrupted resting-state interhemispheric functional connectivity in systemic lupus erythematosus patients with and without neuropsychiatric lupus

PURPOSE

The aim of the study is to explore interhemispheric homotopic functional connectivity alterations in systemic lupus erythematosus (SLE) patients with and without neuropsychiatric lupus (NPSLE and non-NPSLE, respectively) and their potential correlations with clinical characteristics and neuropsychological performance.

METHODS

Based on resting-state functional MRI (rs-fMRI) data collected from SLE patients and matched healthy controls (HCs), the voxel-mirrored homotopic connectivity (VMHC) analysis was conducted to measure functional homotopy. Subsequently, correlations between altered functional homotopy and clinical/neuropsychological data were analyzed.

RESULTS

Compared with the HC group, both NPSLE and non-NPSLE groups showed attenuated homotopic connectivity in middle temporal gyrus (MTG), cuneus (CUN), middle occipital gyrus (MOG), angular gyrus (ANG), and postcentral gyrus (PoCG). NPSLE patients also exhibited decreased homotopic connectivity in inferior parietal gyrus (IPG) and middle frontal gyrus (MFG). Compared with non-NPSLE patients, NPSLE patients showed weaker interhemispheric homotopic functional connectivity in MOG. Decreased homotopic functional connectivity in PoCG, IPG, and MOG were associated with the anxiety state of SLE patients.

CONCLUSIONS

Our findings revealed attenuated functional homotopy in both NPSLE and non-NPSLE groups compared to the HC group, which appeared to be more severe in patients with comorbid neuropsychiatric lupus. Interhemispheric homotopy dysconnectivity may participate in the neuropathology of anxiety symptoms in SLE.

Contributions of neuroimmune and gut-brain signaling to vulnerability of developing substance use disorders

Epidemiology and clinical research indicate that only a subset of people who are exposed to drugs of abuse will go on to develop a substance use disorder. Numerous factors impact individual susceptibility to developing a substance use disorder, including intrinsic biological factors, environmental factors, and interpersonal/social factors. Given the extensive morbidity and mortality that is wrought as a consequence of substance use disorders, a substantial body of research has focused on understanding the risk factors that mediate the shift from initial drug use to pathological drug use. Understanding these risk factors provides a clear path for the development of risk mitigation strategies to help reduce the burden of substance use disorders in the population. Here we will review the rapidly growing body of literature that examines the importance of interactions between the peripheral immune system, the gut microbiome, and the central nervous system (CNS) in mediating the transition to pathological drug use. While these systems had long been viewed as distinct, there is growing evidence that there is bidirectional communication between both the immune system and the gut microbiome that drive changes in neural and behavioral plasticity relevant to substance use disorders. Further, both of these systems are highly sensitive to environmental perturbations and are implicated in numerous neuropsychiatric conditions. While the field of study examining these interactions in substance use disorders is in its relative infancy, clarifying the relationship between gut-immune-brain signaling and substance use disorders has potential to improve our understanding of individual propensity to developing addiction and yield important insight into potential treatment options.

Increased Resting-State Interhemispheric Functional Connectivity of Posterior Superior Temporal Gyrus and Posterior Cingulate Cortex in Congenital Amusia

Interhemispheric connectivity of the two cerebral hemispheres is crucial for a broad repertoire of cognitive functions including music and language. Congenital amusia has been reported as a neurodevelopment disorder characterized by impaired music perception and production. However, little is known about the characteristics of the interhemispheric functional connectivity (FC) in amusia. In the present study, we used a newly developed voxel-mirrored homotopic connectivity (VMHC) method to investigate the interhemispheric FC of the whole brain in amusia at resting-state. Thirty amusics and 29 matched participants underwent a resting-state functional magnetic resonance imaging (fMRI) scanning. An automated VMHC approach was used to analyze the fMRI data. Compared to the control group, amusics showed increased VMHC within the posterior part of the default mode network (DMN) mainly in the posterior superior temporal gyrus (pSTG) and posterior cingulate cortex (PCC). Correlation analyses revealed negative correlations between the VMHC value in pSTG/PCC and the music perception ability among amusics. Further ROC analyses showed that the VMHC value of pSTG/PCC showed a good sensibility/specificity to differentiate the amusics from the controls. These findings provide a new perspective for understanding the neural basis of congenital amusia and imply the immature state of DMN may be a credible neural marker of amusia.

Anti-TNF-α Compounds as a Treatment for Depression

Millions of people around the world suffer from psychiatric illnesses, causing unbearable burden and immense distress to patients and their families. Accumulating evidence suggests that inflammation may contribute to the pathophysiology of psychiatric disorders such as major depression and bipolar disorder. Copious studies have consistently shown that patients with mood disorders have increased levels of plasma tumor necrosis factor (TNF)-α. Given these findings, selective anti-TNF-α compounds were tested as a potential therapeutic strategy for mood disorders. This mini-review summarizes the results of studies that examined the mood-modulating effects of anti-TNF-α drugs.

Altered patterns of fractional amplitude of low-frequency fluctuation and regional homogeneity in abstinent methamphetamine-dependent users

Methamphetamine (MA) could induce functional and structural brain alterations in dependent subjects. However, few studies have investigated resting-state activity in methamphetamine-dependent subjects (MADs). We aimed to investigate alterations of brain activity during resting-state in MADs using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). We analyzed fALFF and ReHo between MADs (n = 70) and healthy controls (HCs) (n = 84) and performed regression analysis using MA use variables. Compared to HCs, abstinent MADs showed increased fALFF and ReHo values in the bilateral striatum, decreased fALFF in the left inferior frontal gyrus, and decreased ReHo in the bilateral anterior cingulate cortex, sensorimotor cortex, and left precuneus. We also observed the fALFF values of bilateral striatum were positively correlated with the age of first MA use, and negatively correlated with the duration of MA use. The fALFF value of right striatum was also positively correlated with the duration of abstinence. The alterations of spontaneous cerebral activity in abstinent MADs may help us probe into the neurological pathophysiology underlying MA-related dysfunction and recovery. Since MADs with higher fALFF in the right striatum had shorter MA use and longer abstinence, the increased fALFF in the right striatum might implicate early recovery during abstinence.

Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis

Characterizing neuroanatomical markers of different stages of schizophrenia (SZ) to assess pathophysiological models of how the disorder develops is an important target for the clinical practice. We performed a meta-analysis of voxel-based morphometry studies of genetic and clinical high-risk subjects (g-/c-HR), recently diagnosed (RDSZ) and chronic SZ patients (ChSZ). We quantified gray matter (GM) changes associated with these four conditions and compared them with contrast and conjunctional data. We performed the behavioral analysis and networks decomposition of alterations to obtain their functional characterization. Results reveal a cortical-subcortical, left-to-right homotopic progression of GM loss. The right anterior cingulate is the only altered region found altered among c-HR, RDSZ and ChSZ. Contrast analyses show left-lateralized insular, amygdalar and parahippocampal GM reduction in RDSZ, which appears bilateral in ChSZ. Functional decomposition shows involvement of the salience network, with an enlargement of the sensorimotor network in RDSZ and the thalamus-basal nuclei network in ChSZ. These findings support the current neuroprogressive models of SZ and integrate this deterioration with the clinical evolution of the disease.

Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression

BACKGROUND

Robust evidence shows that schizophrenia is associated with significant cognitive impairments, including deficits in visuospatial abilities. While other cognitive domains have sparked several functional neuroimaging studies in schizophrenia, only a few brain activation studies have examined the neural correlates of visuospatial abilities in schizophrenia.

PURPOSE

Here, we propose to perform a functional connectivity study on visuospatial processing in schizophrenia, and to determine the classification accuracy of the observed alterations.

METHODS

Thirty-nine schizophrenia patients and 42 healthy controls were scanned using functional magnetic resonance imaging while performing a mental rotation task. Task-based functional connectivity was examined using a region-of-interest (ROI) to ROI approach, as implemented in the CONN Toolbox. ROIs were selected from a previous meta-analysis on mental rotation. Logistic regression with Lasso regularization was performed, using train-test cross-validation.

RESULTS

Schizophrenia was associated with a complex pattern of dysconnectivity between the superior, middle and inferior frontal gyrus, the precentral gyrus, the superior parietal lobule (SPL) and the inferior lateral occipital cortex. The classification accuracy was 86.1%. Mental rotation performance was predicted by the dysconnectivity between the right and left superior frontal gyrus (SFG), as well as between the left SFG and left SPL.

CONCLUSION

The results of the current study highlight that visuospatial processing is useful for examining the widespread dysconnectivity between executive, motor and visual brain regions in schizophrenia. We also demonstrate that very good classification accuracy can be achieved using visuospatial-related functional connectivity data.

The role of gut-immune-brain signaling in substance use disorders

Substance use disorders (SUDs) are debilitating neuropsychiatric conditions that exact enormous costs in terms of loss of life and individual suffering. While much progress has been made defining the neurocircuitry and intracellular signaling cascades that contribute to SUDs, these studies have yielded limited effective treatment options. This has prompted greater exploration of non-traditional targets in addiction. Emerging data suggest inputs from peripheral systems, such as the immune system and the gut microbiome, impact multiple neuropsychiatric diseases, including SUDs. Until recently the gut microbiome, peripheral immune system, and the CNS have been studied independently; however, current work shows the gut microbiome and immune system critically interact to modulate brain function. Additionally, the gut microbiome and immune system intimately regulate one another via extensive bidirectional communication. Accumulating evidence suggests an important role for gut-immune-brain communication in the pathogenesis of substance use disorders. Thus, a better understanding of gut-immune-brain signaling could yield important insight to addiction pathology and potential treatment options.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Decreased interhemispheric connectivity and increased cortical excitability in unmedicated schizophrenia: A prefrontal interleaved TMS fMRI study

BACKGROUND

Prefrontal abnormalities in schizophrenia have consistently emerged from resting state and cognitive neuroimaging studies. However, these correlative findings require causal verification via combined imaging/stimulation approaches. To date, no interleaved transcranial magnetic stimulation and functional magnetic resonance imaging study (TMS fMRI) has probed putative prefrontal cortex abnormalities in schizophrenia.

OBJECTIVE

/Hypothesis: We hypothesized that subjects with schizophrenia would show significant hyperexcitability at the site of stimulation (BA9) and decreased interhemispheric functional connectivity.

METHODS

We enrolled 19 unmedicated subjects with schizophrenia and 22 controls. All subjects underwent brain imaging using a 3T MRI scanner with a SENSE coil. They also underwent a single TMS fMRI session involving motor threshold (rMT) determination, structural imaging, and a parametric TMS fMRI protocol with 10 Hz triplet pulses at 0, 80, 100 and 120% rMT. Scanning involved a surface MR coil optimized for bilateral prefrontal cortex image acquisition.

RESULTS

Of the original 41 enrolled subjects, 8 subjects with schizophrenia and 11 controls met full criteria for final data analyses. At equal TMS intensity, subjects with schizophrenia showed hyperexcitability in left BA9 (p = 0.0157; max z-score = 4.7) and neighboring BA46 (p = 0.019; max z-score = 4.47). Controls showed more contralateral functional connectivity between left BA9 and right BA9 through increased activation in right BA9 (p = 0.02; max z-score = 3.4). GM density in subjects with schizophrenia positively correlated with normalized prefrontal to motor cortex ratio of the corresponding distance from skull to cortex ratio (S-BA9/S-MC) (r = 0.83, p = 0.004).

CONCLUSIONS

Subjects with schizophrenia showed hyperexcitability in left BA9 and impaired interhemispheric functional connectivity compared to controls. Interleaved TMS fMRI is a promising tool to investigate prefrontal dysfunction in schizophrenia.

Advancing study of cognitive impairments for antipsychotic-naïve psychosis comparing high-income versus low- and middle-income countries with a focus on urban China: Systematic review of cognition and study methodology

BACKGROUND

Comparing the course of antipsychotic-naïve psychosis in low- and middle-income countries (LMIC) may help to illuminate core pathophysiologies associated with this condition. Previous reviews-primarily from high-income countries (HIC)-identified cognitive deficits in antipsychotic-naïve, first-episode psychosis, but did not examine whether individuals with psychosis with longer duration of untreated psychosis (DUP > 5 years) were included, nor whether LMIC were broadly represented.

METHOD

A comprehensive search of PUBMED from January 2002-August 2018 identified 36 studies that compared cognitive functioning in antipsychotic-naïve individuals with psychosis (IWP) and healthy controls, 20 from HIC and 16 from LMIC.

RESULTS

A key gap was identified in that LMIC study samples were primarily shorter DUP (<5 years) and were primarily conducted in urban China. Most studies matched cases and controls for age and gender but only 9 (24%) had sufficient statistical power for cognitive comparisons. Compared with healthy controls, performance of antipsychotic-naïve IWP was significantly worse in 81.3% (230/283) of different tests of cognitive domains assessed (90.1% in LMIC [118/131] and 73.7% [112/152] in HIC).

CONCLUSIONS

Most LMIC studies of cognition in antipsychotic-naïve IWP adopted standardized procedures and, like HIC studies, found broad-based impairments in cognitive functioning. However, these LMIC studies were often underpowered and primarily included samples typical of HIC: primarily male, young-adult, high-school educated IWP, in their first episode of illness with relatively short DUP (<5 years). To enhance understanding of the long-term natural course of cognitive impairments in untreated psychosis, future studies from LMIC should recruit community-dwelling IWP from rural areas where DUP may be longer.

Brain Functional Specialization Is Enhanced Among Tai Chi Chuan Practitioners

OBJECTIVE

To investigate the effect of long-term Tai Chi Chuan (TCC) practice on practitioners' brain functional specialization compare with the TCC novices.

DESIGN

A cross-sectional study.

SETTING

A psychology Institute.

PARTICIPANTS

TCC practitioners (N=22) (52.4±6.8y; 7 men; educated years: 12.18±3.03y) and 18 healthy adults (54.8±6.8y; 8 men; education years: 11.78±2.90y) matched by age, sex, and education were enrolled.

MAIN OUTCOME MEASURES

Participants underwent functional magnetic resonance imaging scanning and cognitive test to measure the differences in functional specialization and cognitive function. Functional specialization was evaluated by voxel-mirrored homotopic connectivity (VMHC) method.

RESULTS

Lower middle frontal gyrus VMHC in TCC practitioners compared to controls. For TCC practitioners, the longer they practice, the lower their VMHC in precentral and precuneus. TCC practitioners showed better cognition performance.

CONCLUSIONS

Changed VMHC indicated that TCC practice could enhance functional specialization in the middle frontal cortex of practitioners, which may be associated with higher-order cognitive ability.

Abnormal interhemispheric functional interactions in drug-naïve adult-onset first episode psychosis patients

Previous neuroimaging studies have shown that the cortical functional dysconnectivity contributed to the etiology of schizophrenia (SCZ). However, the interhemispheric functional interactions in SCZ remain not fully understood. The clinical heterogeneity of SCZ might lead to the variations in the findings and the effect of medication had a profound influence on the patient's functional features. In this study, we were aimed at investigating the interhemispheric functional interactions in drug-naïve adult-onset first episode psychosis (FEP) patients. Using voxel-mirrored homotopic connectivity (VMHC) analysis for functional magnetic resonance imaging (fMRI) data, we found decreased VMHC values in precuneus, posterior cingulate cortex (PCC), pallidum gyrus, inferior temporal gyrus and superior temporal gyrus in FEP patients. Additionally, the peak VMHC value of PCC was negatively correlated with the PANSS depression factor score. And the peak VMHC value of precuneus was positively correlated with the PANSS positive factor score. The results indicated that the disrupted interhemispheric functional connectivity of posterior default mode network (DMN) was related to emotion and consciousness dysregulation in FEP patients.

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.

Abnormal default-mode network homogeneity and its correlations with neurocognitive deficits in drug-naive first-episode adolescent-onset schizophrenia

The default mode network (DMN), is one of the most popularly employed resting-state networks applied in schizophrenia (SCZ) research. However, the homogeneity of this network in adolescent-onset SCZ (AOS) remains unknown. This study aims to use network homogeneity (NH) to explore the functional connectivity in the DMN of AOS patients. Resting-state functional magnetic resonance imaging scans were used to study 48 drug-naïve, first-episode AOS patients and 31 healthy age, gender, and education matched control. An automatic NH approach was employed to analyze the imaging dataset. Our results revealed that the patients had significantly higher NH values in the left medial prefrontal cortex (MPFC), and significantly lower values in the bilateral posterior cingulate cortex/precuneus (PCC/PCu) than those in healthy controls. We performed the receiver operating characteristic curve analysis to show that NH values of the left superior MPFC might be regarded as a potential marker in helping to identify patients. In addition, negative associations were found regarding abnormal values of NH in the left PCC/PCu as well as in the Maze and Stroop color-word tests in patients. The outcomes showed abnormal NH values in the DMN of drug-naïve, first-episode AOS suggesting specific functions of the DMN in the pathophysiology of SCZ.

Functional asymmetry of thalamocortical networks in subjects at ultra-high risk for psychosis and first-episode schizophrenia

Disrupted functional asymmetry has been implicated in schizophrenia. However, it remains unknown whether disrupted functional asymmetry originates from intra-hemispheric and/or inter-hemispheric functional connectivity (FC) in the patients, and whether it starts at very early stage of psychosis. Seventy-six patients with first-episode, drug-naive schizophrenia, 74 subjects at ultra-high risk for psychosis (UHR), and 71 healthy controls underwent resting-state functional magnetic resonance imaging. The 'Parameter of asymmetry' (PAS) metric was calculated and support vector machine (SVM) classification analysis was applied to analyze the data. Compared with healthy controls, patients exhibited decreased PAS in the left thalamus/pallidum, right hippocampus/parahippocampus, right inferior frontal gyrus/insula, right thalamus, and left inferior parietal lobule, and increased PAS in the left calcarine, right superior occipital gyrus/middle occipital gyrus, and right precentral gyrus/postcentral gyrus. By contrast, UHR subjects showed decreased PAS in the left thalamus relative to healthy controls. A negative correlation was observed between decreased PAS in the right hippocampus/parahippocampus and Brief Visuospatial Memory Test-Revised (BVMT-R) scores in the patients (r = -0.364, p = 0.002). Moreover, the PAS values in the left thalamus could discriminate the patients/UHR subjects from the controls with acceptable sensitivities (68.42%/81.08%). First-episode patients and UHR subjects shared decreased PAS in the left thalamus. This observed pattern of functional asymmetry highlights the involvement of the thalamus in the pathophysiology of psychosis and may also be applied as a very early marker for psychosis.

Interhemispheric connectivity and hemispheric specialization in schizophrenia patients and their unaffected siblings

Hemispheric integration and specialization are two prominent organizational principles for macroscopic brain function. Impairments of interhemispheric cooperation have been reported in schizophrenia patients, but whether such abnormalities should be attributed to effects of illness or familial risk remains inconclusive. Moreover, it is unclear how abnormalities in interhemispheric connectivity impact hemispheric specialization. To address these questions, we performed magnetic resonance imaging (MRI) in a large cohort of 253 participants, including 84 schizophrenia patients, 106 of their unaffected siblings and 63 healthy controls. Interhemispheric connectivity and hemispheric specialization were calculated from resting-state functional connectivity, and compared across groups. Results showed that schizophrenia patients exhibit lower interhemispheric connectivity as compared to controls and siblings. In addition, patients showed higher levels of hemispheric specialization as compared to siblings. Level of interhemispheric connectivity and hemispheric specialization correlated with duration of illness in patients. No significant alterations were identified in siblings relative to controls on both measurements. Furthermore, alterations in interhemispheric connectivity correlated with changes in hemispheric specialization in patients relative to controls and siblings. Taken together, these results suggest that lower interhemispheric connectivity and associated abnormalities in hemispheric specialization are features of established illness, rather than an expression of preexistent familial risk for schizophrenia.

Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

BACKGROUND

Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders.

OBJECTIVES

A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls.

METHODS

We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors.

RESULTS

Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network).

CONCLUSION

The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

Theoretical Modeling of Cognitive Dysfunction in Schizophrenia by Means of Errors and Corresponding Brain Networks

The current evidence of cognitive disturbances and brain alterations in schizophrenia does not provide the plausible explanation of the underlying mechanisms. Neuropsychological studies outlined the cognitive profile of patients with schizophrenia, that embodied the substantial disturbances in perceptual and motor processes, spatial functions, verbal and non-verbal memory, processing speed and executive functioning. Standardized scoring in the majority of the neurocognitive tests renders the index scores or the achievement indicating the severity of the cognitive impairment rather than the actual performance by means of errors. At the same time, the quantitative evaluation may lead to the situation when two patients with the same index score of the particular cognitive test, demonstrate qualitatively different performances. This may support the view why test paradigms that habitually incorporate different cognitive variables associate weakly, reflecting an ambiguity in the interpretation of noted cognitive constructs. With minor exceptions, cognitive functions are not attributed to the localized activity but eventuate from the coordinated activity in the generally dispersed brain networks. Functional neuroimaging has progressively explored the connectivity in the brain networks in the absence of the specific task and during the task processing. The spatio-temporal fluctuations of the activity of the brain areas detected in the resting state and being highly reproducible in numerous studies, resemble the activation and communication patterns during the task performance. Relatedly, the activation in the specific brain regions oftentimes is attributed to a number of cognitive processes. Given the complex organization of the cognitive functions, it becomes crucial to designate the roles of the brain networks in relation to the specific cognitive functions. One possible approach is to identify the commonalities of the deficits across the number of cognitive tests or, common errors in the various tests and identify their common "denominators" in the brain networks. The qualitative characterization of cognitive performance might be beneficial in addressing diffuse cognitive alterations presumably caused by the dysconnectivity of the distributed brain networks. Therefore, in the review, we use this approach in the description of standardized tests in the scope of potential errors in patients with schizophrenia with a subsequent reference to the brain networks.

Research Progress in Biological Studies of Schizophrenia in China in 2017

Schizophrenia is a severe mental disorder and its etiology and pathological mechanism are unknown. This article mainly introduces the progress of biological studies of schizophrenia in China in 2017, including neuroimaging, genetics, and immunology studies. It also introduces the research progress of high-risk psychotic syndrome and physiotherapy.