Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach

Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks' organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors.

Mapping alterations in the local synchrony of the cerebral cortex in schizophrenia

BACKGROUND

Observations from different fields of research coincide in indicating that a defective gamma-aminobutyric acid (GABA) interneuron system may be among the primary factors accounting for the varied clinical expression of schizophrenia. GABA interneuron deficiency is locally expressed in the form of neural activity desynchronization. We mapped the functional anatomy of local synchrony in the cerebral cortex in schizophrenia using functional connectivity MRI.

METHODS

Data from 86 patients with schizophrenia and 137 control subjects were obtained from publicly available repositories. Resting-state functional connectivity maps based on Iso-Distant Average Correlation measures across three distances were estimated detailing the local functional structure of the cerebral cortex.

RESULTS

Patients with schizophrenia showed weaker local functional connectivity (i.e., lower MRI signal synchrony) in (i) prefrontal lobe areas, (ii) somatosensory, auditory, visual, and motor cortices, (iii) paralimbic system at the anterior insula and anterior cingulate cortex, and (iv) hippocampus. The distribution of the defect in cortical area synchrony largely coincided with the synchronization effect of the GABA agonist alprazolam previously observed using identical functional connectivity measures. There was also a notable resemblance between the anatomy of our findings and cortical areas showing higher density of parvalbumin (prefrontal lobe and sensory cortices) and somatostatin (anterior insula and anterior cingulate cortex) GABA interneurons in humans.

CONCLUSIONS

Our results thus provide detail of the functional anatomy of synchrony changes in the cerebral cortex in schizophrenia and suggest which elements of the interneuron system are affected. Such information could ultimately be relevant in the search for specific treatments.

[Changes of brain network in patients with insomnia following radiotherapy for cervical cancer and their correlation with IT-TMS treatment efficacy: a graph-theory analysis]

OBJECTIVE

To analyze the changes of brain small-world and node function network properties in patients with insomnia following radiotherapy for cervical cancer based on graph theory analysis and explore the correlation between functional networks and the clinical efficacy of individual-target transcranial magnetic stimulation (IT-TMS) for treatment of insomnia.

METHODS

The resting state functional magnetic resonance imaging (rs-fMRI) data were collected from 30 patients with insomnia following radiotherapy for cervical cancer and 30 matched healthy individuals. All the patients received accelerated intelligent neuromodulation TMS therapy. Using graph theory analysis and GRETNA software, the functional connectivity matrices were constructed and the attribute features were extracted. The scores on the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) of the participants were collected before and after IT-TMS, and the correlation between improvement in insomnia and the functional network was investigated.

RESULTS

The two groups matched for age, gender, and education level (P>0.05) showed significant differences in PSQI, ISI, SAS and SDS scores (P<0.05). Both groups showed attributes of the small-world network. Compared with the healthy individuals, the patients showed significantly decreased σ, EI, Cp and Lp (P<0.05) and increased Eg (P<0.05) at baseline, which, along with insomnia symptoms, were all improved after IT-TMS treatment. The patients showed reduced functional connections of the node network at follow-up compared with the baseline, and the low functional connectivity between the right insula and left superior frontal gyrus was correlated with the improvement of ISI scores.

CONCLUSION

The patients with insomnia following radiotherapy for cervical cancer have impaired information integration ability of the brain network, IT-TMS can significantly improve insomnia symptoms by reducing the hyperconnectivity between the default mode network and the salience network.

Hierarchical functional system development supports executive function

In this perspective, we describe how developmental improvements in youth executive function (EF) are supported by hierarchically organized maturational changes in functional brain systems. We first highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis of cortical organization. We then review data showing that functional system developmental profiles vary along this axis: systems near the associative end become more functionally segregated, while those in the middle become more integrative. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication. We propose a central role for attention and frontoparietal control systems in the maturation of healthy EF and suggest that reduced functional system differentiation across the sensorimotor-association axis contributes to transdiagnostic EF deficits.

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.

Tracking functional network connectivity dynamics in the elderly

Introduction

Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages.

Method

This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination.

Results

The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree.

Discussion

The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.

Decreased integration of default-mode network during a working memory task in schizophrenia with severe attention deficits

Background

Working memory (WM) and attention deficits are both important features of schizophrenia. WM is closely related to attention, for it acted as an important characteristic in activating and manipulating WM. However, the knowledge of neural mechanisms underlying the relationship between WM and attention deficits in schizophrenia is poorly investigated.

Methods

Graph theory was used to examine the network topology at the whole-brain and large-scale network levels among 125 schizophrenia patients with different severity of attention deficits (65 mild attention deficits; 46 moderate attention deficits; and 14 severe attention deficits) and 53 healthy controls (HCs) during an N-back WM task. These analyses were repeated in the same participants during the resting state.

Results

In the WM task, there were omnibus differences in small-worldness and normalized clustering coefficient at a whole-brain level and normalized characterized path length of the default-mode network (DMN) among all groups. Post hoc analysis further indicated that all patient groups showed increased small-worldness and normalized clustering coefficient of the whole brain compared with HCs, and schizophrenia with severe attention deficits showed increased normalized characterized path length of the DMN compared with schizophrenia with mild attention deficits and HCs. However, these observations were not persisted under the resting state. Further correlation analyses indicated that the increased normalized characterized path length of the DMN was correlated with more severe attentional deficits and poorer accuracy of the WM task.

Conclusion

Our research demonstrated that, compared with the schizophrenia patients with less attention deficits, disrupted integration of the DMN may more particularly underlie the WM deficits in schizophrenia patients with severe attention deficits.

Improved Functional Organization in Patients With Primary Insomnia After Individually-Targeted Transcranial Magnetic Stimulation

Primary insomnia (PI) is among the most prevalent sleep-related disorders and has a far-reaching impact on daytime functioning. Repetitive transcranial magnetic stimulation (rTMS) has drawn attention because of its effectiveness and safety. The purpose of the current study was to detect changes in the topological organization of whole-brain functional networks and to determine their associations with the clinical treatment effects of rTMS. Resting-state functional magnetic resonance imaging (rsfMRI) data from 32 patients with PI were collected and compared with findings from 32 age- and gender-matched healthy controls (HCs). The patients were treated with Stanford accelerated intelligent neuromodulation therapy, which is a recently validated neuroscience-informed accelerated intermittent theta-burst stimulation protocol. Graph theoretical analysis was used to construct functional connectivity matrices and to extract the attribute features of small-world networks in insomnia. Scores on the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and the associations between these clinical characteristics and functional metrics, were the primary outcomes. At baseline, the patients with PI showed inefficient small-world property and aberrant functional segregation and functional integration compared with the HCs. These properties showed renormalization after individualized rTMS treatment. Furthermore, low functional connectivity between the right insula and left medial frontal gyrus correlated with improvement in ISI scores. We highlight functional network dysfunctions in PI patients and provide evidence into the pathophysiological mechanisms involved and the possible mode of action of rTMS.

A Possible White Matter Compensating Mechanism in the Brain of Relatives of People Affected by Psychosis Inferred from Repeated Long-Term DTI Scans

Background and Hypothesis

An existing model suggests that some brain features of relatives of people affected by psychosis can be distinguished from both the probands and a control group.  Such findings can be interpreted as representing a compensating mechanism.

Study Design

We studied white matter features using diffusion tensor imaging in a cohort of 82 people affected by psychosis, 122 of their first-degree relatives, and 89 control subjects that were scanned between two to three times with an interval of approximately 3 years between consecutive scans. We measured both fractional anisotropy and other standard diffusivity measures such as axial diffusivity. Additionally, we calculated standard connectivity measures such as path length based on probabilistic or deterministic tractography. Finally, by averaging the values of the different measures over the two or three consecutive scans, we studied epoch-averagely the difference between these three groups.

Study Results

For several tracts and several connectivity measures, the relatives showed distinct features from both the probands and the control groups. In those cases, the relatives did not necessarily score between the probands and the control group. An aggregate analysis in the form of a group-dependent score for the different modes of the analysis (e.g., for fractional anisotropy) supported this observation.

Conclusions

We interpret these results as evidence supporting a compensation mechanism in the brain of relatives that may be related to resilience that some of them exhibit in the face of the genetic risk they have for being affected by psychosis.

Frontal-posterior functional imbalance and aberrant function developmental patterns in schizophrenia

Schizophrenia (SZ) is a neurodevelopmental disorder. There remain significant gaps in understanding the neural trajectory across development in SZ. A major research focus is to clarify the developmental functional changes of SZ and to identify the specific timing, the specific brain regions, and the underlying mechanisms of brain alterations during SZ development. Regional homogeneity (ReHo) characterizing brain function was collected and analyzed on humans with SZ (hSZ) and healthy controls (HC) cross-sectionally, and methylazoxymethanol acetate (MAM) rats, a neurodevelopmental model of SZ, and vehicle rats longitudinally from adolescence to adulthood. Metabolomic and proteomic profiling in adult MAM rats and vehicle rats was examined and bioanalyzed. Compared to HC or adult vehicle rats, similar ReHo alterations were observed in hSZ and adult MAM rats, characterized by increased frontal (medial prefrontal and orbitofrontal cortices) and decreased posterior (visual and associated cortices) ReHo. Longitudinal analysis of MAM rats showed aberrant ReHo patterns as decreased posterior ReHo in adolescence and increased frontal and decreased posterior ReHo in adulthood. Accordingly, it was suggested that the visual cortex was a critical locus and adolescence was a sensitive window in SZ development. In addition, metabolic and proteomic alterations in adult MAM rats suggested that central carbon metabolism disturbance and mitochondrial dysfunction were the potential mechanisms underlying the ReHo alterations. This study proposed frontal-posterior functional imbalance and aberrant function developmental patterns in SZ, suggesting that the adolescent visual cortex was a critical locus and a sensitive window in SZ development. These findings from linking data between hSZ and MAM rats may have a significant translational contribution to the development of effective therapies in SZ.

Topological Organization Alterations of Whole-Brain Functional Networks in Patients with Childhood Absence Epilepsy: Associations with Treatment Effects

PURPOSE

The purpose of the current study is to detect changes of topological organization of whole-brain functional networks and their relationship with the clinical treatment effects of antiepileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). Patients and Methods. RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 age- and gender-matched healthy controls (HCs). The patients were treated with first-line AEDs for 46.03 months before undergoing a second RS-fMRI scan.

RESULTS

CAE children at baseline showed a reduced clustering coefficient (Cp) and local efficiency (El) than the HC group, implying the reduction of functional segregation. CAE children at baseline also showed smaller characteristic path length (Lp) and higher global efficiency (Eg) compared with the HC group, implying the impairment of functional segregation. However, those metrics showed no significant differences between CAE children at follow-up and the HC group which indicated a clear renormalization of topological organization after AED treatments. CAE at follow-up also showed significantly decreased connectivity between several network regions, with which the thalamus is mainly involved. Furthermore, the reduced connectivity change between the left superior parietal gyrus and the left thalamus is positively correlated with the symptom improvements after AED treatment.

CONCLUSION

We highlighted the convergence and divergence of brain functional network dysfunctions in CAE patients and provided crucial insights into pathophysiological mechanisms and the AED effects.

Common neurobiological correlates of resilience and personality traits within the triple resting-state brain networks assessed by 7-Tesla ultra-high field MRI

Despite numerous studies investigating resilience and personality trials, a paucity of information regarding their neurobiological commonalities at the level of the large resting-state networks (rsNWs) remains. Here we address this topic using the advantages of ultra-high-field (UHF) 7T-MRI, characterized by higher signal-to-noise ratio and increased sensitivity. The association between resilience, personality traits and three fMRI measures (fractional amplitude of low-frequency fluctuations (fALFF), degree centrality (DC) and regional homogeneity (ReHo)) determined for three core rsNWs (default mode (DMN), salience (SN), and central executive network (CEN)) were examined in 32 healthy volunteers. The investigation revealed a significant role of SN in both resilience and personality traits and a tight association of the DMN with resilience. DC in CEN emerged as a significant moderator for the correlations of resilience with the personality traits of neuroticism and extraversion. Our results indicate that the common neurobiological basis of resilience and the Big Five personality traits may be reflected at the level of the core rsNWs.

The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SD_BOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSD_BOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSD_BOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSD_BOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSD_BOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSD_BOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.

Abnormal Function in Dentate Nuclei Precedes the Onset of Psychosis: A Resting-State fMRI Study in High-Risk Individuals

OBJECTIVE

The cerebellum serves a wide range of functions and is suggested to be composed of discrete regions dedicated to unique functions. We recently developed a new parcellation of the dentate nuclei (DN), the major output nuclei of the cerebellum, which optimally divides the structure into 3 functional territories that contribute uniquely to default-mode, motor-salience, and visual processing networks as indexed by resting-state functional connectivity (RsFc). Here we test for the first time whether RsFc differences in the DN, precede the onset of psychosis in individuals at risk of developing schizophrenia.

METHODS

We used the magnetic resonance imaging (MRI) dataset from the Shanghai At Risk for Psychosis study that included subjects at high risk to develop schizophrenia (N = 144), with longitudinal follow-up to determine which subjects developed a psychotic episode within 1 year of their functional magnetic resonance imaging (fMRI) scan (converters N = 23). Analysis used the 3 functional parcels (default-mode, salience-motor, and visual territory) from the DN as seed regions of interest for whole-brain RsFc analysis.

RESULTS

RsFc analysis revealed abnormalities at baseline in high-risk individuals who developed psychosis, compared to high-risk individuals who did not develop psychosis. The nature of the observed abnormalities was found to be anatomically specific such that abnormal RsFc was localized predominantly in cerebral cortical networks that matched the 3 functional territories of the DN that were evaluated.

CONCLUSIONS

We show for the first time that abnormal RsFc of the DN may precede the onset of psychosis. This new evidence highlights the role of the cerebellum as a potential target for psychosis prediction and prevention.

Shared and specific dynamics of brain segregation and integration in bipolar disorder and major depressive disorder: A resting-state functional magnetic resonance imaging study

BACKGROUND

When bipolar disorder (BD) presents as the depressive state, it is often misdiagnosed as major depressive disorder (MDD). However, few studies have focused on dynamic differences in local brain activity and connectivity between BD and MDD. Therefore, the present study explored shared and specific patterns of abnormal dynamic brain segregation and integration in BD and MDD patients.

METHODS

BD Patients (n = 106), MDD patients (n = 114), and 130 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (fMRI). We first used a sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF) and, based on the altered dALFF, further analyzed the dynamic functional connectivity (dFC) using a seed-based approach.

RESULTS

Both the BD and MDD groups showed decreased temporal variability of the dALFF (less dynamic segregation) in the bilateral posterior cingulate cortex (PCC)/precuneus compared with the HCs. The MDD group showed increased temporal variability of the dALFF (more dynamic segregation) in the left putamen compared with the controls, but there was no significant difference between the BD and HCs. The dFC analysis also showed that both the BD and MDD groups had reduced dFC (less dynamic integration) between the bilateral PCC/ precuneus and the left inferior parietal lobule compared with the HCs.

LIMITATIONS

This study was cross-sectional and did not examine data from remitted BD and MDD patients.

CONCLUSION

Our findings indicated disrupted dynamic balance between segregation and integration within the default mode network in both BD and MDD. Moreover, we found MDD-specific abnormal brain dynamics in the putamen.

Classification of Schizophrenia by Seed-based Functional Connectivity using Prefronto-Temporal Functional Near Infrared Spectroscopy

BACKGROUND

Schizophrenia is one of the most serious mental disorders. Currently, the diagnosis of schizophrenia mainly relies on scales and doctors' experience. Recently, functional near infrared spectroscopy (fNIRS) has been used to distinguish schizophrenia from other mental disorders. The conventional classification methods utilized time-course features from single or multiple fNIRS channels.

NEW METHOD

The fNIRS data were obtained from 52 channels covering the frontotemporal cortices in 200 patients with schizophrenia and 100 healthy subjects during a Chinese verbal fluency task. The channels with significant between-group differences were selected as the seeds. Functional connectivity (FC) was calculated for each seed, and FCs with significant between-group differences were selected as the features for classification.

RESULTS

The proposed method reduced the number of channels to 26 while achieving overall classification accuracy, sensitivity and specificity values as high as 89.67%, 93.00% and 86.00%, respectively, outperforming most of the reported results. The superior performance was attributed to the cross-scale neurological changes related to schizophrenia, which were employed by the classification method. In addition, the method provided multiple classification criteria with similar accuracy, consequently increasing the flexibility and reliability of the results.

COMPARISON WITH EXISTING METHODS

This is the first fNIRS study to classify schizophrenia based on FCs. This method integrated information from regional modulation, segregation and integration. The classification performance outperformed most of the classification methods described in previous studies.

CONCLUSIONS

Our findings suggest a reliable method with a high level of accuracy and a low level of instrumental complexity to identify patients with schizophrenia.

The role of altered brain structural connectivity in resilience, vulnerability, and disease expression to schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a highly heritable disorder associated with brain connectivity changes. Although the mechanism of disease expression and vulnerability of SCZ have been reported by previous studies, the mechanism of resilience to SCZ based on the brain structural connectivity is poorly understood. The goal of the present study was to identify the structural brain connectivity related with the resilience to SCZ, which is defined here as the capacity to avoid or delay the onset of SCZ in unaffected siblings of SCZ probands.

METHOD

We collected diffusion tensor imaging (DTI) data of 49 medication-naive, first-episode SCZ (FE-SCZ) patients, 56 unaffected siblings of SCZ probands (SIB-SCZ), and 90 healthy controls. Then we used graph theoretical approach to calculate the topological properties of the brain structural network, including global, subnetwork, and regional parameters. Finally, we compared the parameters between the three groups, and identified the brain structural network related to the resilience, vulnerability and disease expression to SCZ.

RESULTS

With respect to resilience, only the SIB-SCZ showed significantly increased connectivity in the subnetworks of the left cuneus-precuneus and left posterior cingulate gyrus-precuneus, and in brain areas of right supramarginal gyrus and right inferior temporal gyrus. With respect to vulnerability, both the FE-SCZ and SIB-SCZ had decreased cluster coefficients and local efficiency, and decreased nodal efficiency in the right medial superior frontal gyrus and right medial orbital superior frontal gyrus compared with the healthy controls. With respect to disease expression, only the FE-SCZ group showed decreased or increased global, subnetwork, and nodal connectivity in broader brain regions compared with the healthy controls.

CONCLUSION

Difference in the topological properties of brain structural connectivity not only reflect the underlying mechanism of vulnerability but also that of resilience to schizophrenia. Alteration in the brain structural connectivity associating with resilience and disease expression may contribute to the onset of SCZ.

Does an Over-Connected Visual Cortex Undermine Efforts to Stay Sober After Treatment for Alcohol Use Disorder?

A fine-tuned interplay of highly synchronized activity within and between the brain's communities is a crucial feature of the brain's functional organization. We wanted to investigate in individuals with alcohol use disorder (AUD) the degree to which the interplay of the brain's community-architecture and the extended brain reward system (eBRS) is affected by drinking status (relapse or abstinence). We used Graph Theory Analysis of resting-state fMRI data from treatment seekers at 1 month of abstinence to model the brain's intrinsic community configuration and their follow-up data as abstainers or relapsers 3 months later to quantify the degree of global across-community interaction between the eBRS and the intrinsic communities at both timepoints. After 1 month of abstinence, the ventromedial PFC in particular showed a significantly higher global across-community interaction in the 22 future relapsers when compared to 30 light/non-drinking controls. These differences were no longer present 3 months later when the relapsers had resumed drinking. We found no significant differences between abstainers and controls at either timepoint. Post hoc tests revealed that one eBRS region, the ventromedial PFC, showed a significant global across-community interaction with a community comprising the visual cortex in relapsers at baseline. In contrast, abstainers showed a significant negative association of the ventromedial PFC with the visual cortex. The increased across-community interaction of the ventromedial PFC and the visual cortex in relapsers at timepoint 1 may be an early indicator for treatment failure in a subgroup of AUD patients.

Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia

Schizophrenia (SZ) is a highly heritable disease with neurodevelopmental origins and significant functional brain network dysfunction. Functional network is heavily influenced by neurodevelopment processes and can be characterized by the degree of segregation and integration. This study examines functional segregation and integration in SZ and their first-degree relatives (high risk [HR]) to better understand the dynamic changes in vulnerability and resiliency, and disease markers. Resting-state functional magnetic resonance imaging data acquired from 137 SZ, 89 HR, and 210 healthy controls (HCs). Small-worldness σ was computed at voxel level to quantify balance between segregation and integration. Interregional functional associations were examined based on Euclidean distance between regions and reflect degree of segregation and integration. Distance strength maps were used to localize regions of altered distance-based functional connectivity. σ was significantly decreased in SZ compared to HC, with no differences in high risk (HR). In three-group comparison, significant differences were noted in short-range connectivity (primarily in the primary sensory, motor and their association cortices, and the thalamus) and medium/long-range connectivity (in the prefrontal cortices [PFCs]). Decreased short- and increased medium/long-range connectivity was found in SZ. Decreased short-range connectivity was seen in SZ and HR, while HR had decreased medium/long-range connectivity. We observed disrupted balance between segregation and integration in SZ, whereas relatively preserved in HR. Similarities and differences between SZ and HR, specific changes of SZ were found. These might reflect dynamic changes of segregation in primary cortices and integration in PFCs in vulnerability and resilience, and disease markers in SZ.