Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study

Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder

OBJECTIVE

Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms.

METHODS

Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups.

RESULTS

Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively.

CONCLUSION

This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.

A systematic review of structural and functional magnetic resonance imaging studies on the neurobiology of depressive symptoms in schizophrenia spectrum disorders

Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies' findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond "total score" depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.

Functional brain networks in Developmental Topographical Disorientation

Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.

Abnormal prefrontal functional network in adult obstructive sleep apnea: A resting-state fNIRS study

To assess prefrontal brain network abnormality in adults with obstructive sleep apnea (OSA), resting-state functional near infrared spectroscopy (rs-fNIRS) was used to evaluate 52 subjects, including 27 with OSA and 25 healthy controls (HC). The study found that patients with OSA had a decreased connection edge number, particularly in the connection between the right medial frontal cortex (MFG-R) and other right-hemisphere regions. Graph-based analysis also revealed that patients with OSA had a lower global efficiency, local efficiency, and clustering coefficient than the HC group. Additionally, the study found a significant positive correlation between the Montreal Cognitive Assessment (MoCA) score and both the connection edge number and the graph-based indicators in patients with OSA. These preliminary results suggest that prefrontal rs-fNIRS could be a useful tool for objectively and quantitatively assessing cognitive function impairment in patients with OSA.

Discriminative analysis of schizophrenia patients using an integrated model combining 3D CNN with 2D CNN: A multimodal MR image and connectomics analysis

OBJECTIVE

Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients.

METHOD

Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier.

RESULTS

The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively.

CONCLUSIONS

Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.

Structural brain networks in schizophrenia based on nonnegative matrix factorization

Schizophrenia is a severe mental disease with significant morphometric reductions in gray matter volume and cortical thickness in a variety of brain regions. However, most studies only focused on the voxel level alterations in specific cerebral regions and ignored the spatial relationship between voxels. In the present study, we used a novel, data-driven technique-nonnegative matrix factorization (NMF) to group voxels with similar information into a network, and studied the structural covariance at the network level in schizophrenia. Our sample included 36 patients with schizophrenia and 21 healthy controls. Compared with healthy controls, patients with schizophrenia showed significant gray matter volume reductions in six structural covariance networks (dorsal striatum, thalamus, hippocampus-parahippocampus, supplementary motor area-fusiform, middle/inferior temporal network, frontal-parietal-occipital network). Our findings confirmed the assumption of a disturbance in the cortical-subcortical circuit in schizophrenia and suggested that NMF is a useful multivariate method to identify brain networks, which provides a new perspective to study the neural mechanism in schizophrenia.

The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics

The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.

Brain structural network to investigate the mechanism of cognitive impairment in patients with acoustic neuroma

Objective

Acoustic neuroma (AN) is a common benign tumor. Little is known of neuropsychological studies in patients with acoustic neuroma, especially cognitive neuropsychology, and the neuropsychological abnormalities of patients affect their life quality. The purpose of this study was to explore the changes in the cognitive function of patients with acoustic neuroma, and the possible mechanism of these changes by structural magnetic resonance imaging.

Materials and methods

We used a neuropsychological assessment battery to assess cognitive function in 69 patients with acoustic neuroma and 70 healthy controls. Then, we used diffusion tensor imaging data to construct the structural brain network and calculate topological properties based on graph theory, and we studied the relation between the structural brain network and cognitive function. Moreover, three different subnetworks (short-range subnetwork, middle-range subnetwork, and long-range subnetwork) were constructed by the length of nerve fibers obtained from deterministic tracking. We studied the global and local efficiency of various subnetworks and analyzed the correlation between network metrics and cognitive function. Furthermore, connectome edge analysis directly assessed whether there were differences in the number of fibers in the different brain regions. We analyzed the relation between the differences and cognitive function.

Results

Compared with the healthy controls, the general cognitive function, memory, executive function, attention, visual space executive ability, visual perception ability, movement speed, and information processing speed decreased significantly in patients with acoustic neuroma. A unilateral hearing loss due to a left acoustic neuroma had a greater impact on cognitive function. The results showed that changes in the global and local metrics, the efficiency of subnetworks, and cognitively-related fiber connections were associated with cognitive impairments in patients with acoustic neuroma.

Conclusion

Patients exhibit cognitive impairments caused by the decline of the structure and function in some brain regions, and they also develop partial compensation after cognitive decline. Cognitive problems are frequent in patients with acoustic neuroma. Including neuropsychological aspects in the routine clinical evaluation and appropriate treatments may enhance the clinical management and improve their life quality.

Low-frequency rTMS treatment alters the topographical organization of functional brain networks in schizophrenia patients with auditory verbal hallucination

Auditory verbal hallucinations (AVH) are an important characteristic of schizophrenia. Repeated transcranial magnetic stimulation (rTMS) has been evidence to be effective in treating AVH. We evaluated the topological properties of resting-state functional brain networks in schizophrenia patients with AVH (n = 32) who received 1-Hz rTMS treatment and matched healthy controls (n = 33). The results showed that the psychotic symptoms and certain neurocognitive performances in patients were improved by rTMS treatment. Furthermore, the pretreatment patients showed abnormal global topological metrics compared with the controls, including lower global efficiency (E_glob, represents the relative quality of information transmission between all nodes in the network) and higher characteristic path length (L_p, characterizes the mean shortest distance between any two nodes in the network). The pretreament patients also showed decreased local topological metrics relative to the controls, including the nodal shortest path (NL_p, quantifies the mean distance between the given node and the other nodes in the network) and nodal efficiency (N_e, measures the information interchange among the neighbor nodes when one node is removed), mainly located in the prefrontal cortex, occipital cortex, and subcortical regions. While the abnormal global and local topological patterns were normalized in patients after rTMS treatment. The multiple linear regression analysis indicated that the baseline topological metrics could be associated with the clinical responses after treatment in the patient group. The results suggested that the topological organization of the functional brain network was globally and regionally altered in schizophrenia patients with AVH after rTMS treatment and may be a potential therapeutic effect for AVH in schizophrenia.

Functional reconfiguration of cerebellum-cerebral neural loop in schizophrenia following electroconvulsive therapy

Recent evidence highlights the role of the cerebellum-cerebral loop in the pathophysiology of schizophrenia (SZ). Electroconvulsive therapy (ECT) is clinically applied to augment the effect of antipsychotic drugs. The study aims to address whether the cerebellum-cerebral loop is involved in the mechanisms of ECT's augmentation effect. Forty-two SZ patients and 23 healthy controls (HC) were recruited and scanned using resting-state functional MRI (rs-fMRI). Twenty-one patients received modified ECT plus antipsychotics (MSZ group), and 21 patients took antipsychotics only (DSZ group). All patients were re-scanned four weeks later. Brain functional network was constructed according to the graph theory. The sub-network exhibited longitudinal changes after ECT or medications were constructed. For the MSZ group, a sub-network involving default-mode network and cerebellum showed significant longitudinal changes. For the DSZ group, a different sub-network involving the thalamus, frontal and occipital cortex was found to be altered in the follow-up scan. In addition, the changing FC of the left cerebellar crus2 region was correlated with the changing scores of the psychotic symptoms only in the MSZ group but not in the DSZ group. In conclusion, the cerebral-cerebellum loop is possibly involved in the antipsychotic mechanisms of ECT for schizophrenia.

Functional network connectivity and topology during naturalistic stimulus is altered in first-episode psychosis

BACKGROUND

Psychotic disorders have been suggested to derive from dysfunctional integration of signaling between brain regions. Earlier studies have found several changes in functional network synchronization as well as altered network topology in patients with psychotic disorders. However, studies have used mainly resting-state that makes it more difficult to link functional alterations to any specific stimulus or experience. We set out to examine functional connectivity as well as graph (topological) measures and their association to symptoms in first-episode psychosis patients during movie viewing. Our goal was to understand whole-brain functional dynamics of complex naturalistic information processing in psychosis and changes in brain functional organization related to symptoms.

METHODS

71 first-episode psychosis patients and 57 control subjects watched scenes from the movie Alice in Wonderland during 3 T fMRI. We compared functional connectivity and graph measures indicating integration, segregation and centrality between groups, and examined the association between topology and symptom scores in the patient group.

RESULTS

We identified a subnetwork with predominantly decreased links of functional connectivity in first-episode psychosis patients. The subnetwork was mainly comprised of nodes of and links between the cingulo-opercular, sensorimotor and default-mode networks. In topological measures, we observed between-group differences in properties of centrality.

CONCLUSIONS

Functional brain networks are affected during naturalistic information processing already in the early stages of psychosis, concentrated in salience- and cognitive control-related hubs and subnetworks. Understanding these aberrant dynamics could add to better targeted cognitive and behavioral interventions in the early stages of psychotic disorders.

Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity

BACKGROUND

Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear.

METHOD

We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls.

RESULTS

The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing.

CONCLUSION

These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.

The relation of integrated psychological therapy to resting state functional brain connectivity networks in patients with schizophrenia

Functional brain dysconnectivity measured with resting state functional magnetic resonance imaging (rsfMRI) has been linked to cognitive impairment in schizophrenia. This study investigated the effects on functional brain connectivity of Integrated Psychological Therapy (IPT), a cognitive behavioral oriented group intervention program, in 31 patients with schizophrenia. Patients received IPT or an equal intensity non-specific psychological treatment in a non-randomized design. Evidence of improvement in executive and social functions, psychopathology and overall level of functioning was observed after treatment completion at six months only in the IPT treatment group and was partially sustained at one-year follow up. Independent Component Analysis and Isometric Mapping (ISOMAP), a non-linear manifold learning algorithm, were used to construct functional connectivity networks from the rsfMRI data. Functional brain dysconnectivity was observed in patients compared to a group of 17 healthy controls, both globally and specifically including the default mode (DMN) and frontoparietal network (FPN). DMN and FPN connectivity were reversed towards healthy control patterns only in the IPT treatment group and these effects were sustained at follow up for DMN but not FPN. These data suggest the use of rsfMRI as a biomarker for accessing and monitoring the therapeutic effects of cognitive remediation therapy in schizophrenia.

Investigating the diagnostic utility of speech patterns in schizophrenia and their symptom associations

BACKGROUND

Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms.

METHODS

Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered.

RESULTS

Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms.

CONCLUSIONS

The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.

Interindividual variability of functional connectome in schizophrenia

Schizophrenia is a complex psychiatric disorder that displays an outstanding interindividual variability in clinical manifestation and neurobiological substrates. A better characterization and quantification of this heterogeneity could guide the search for both common abnormalities (linked to lower intersubject variability) and the presence of biological subtypes (leading to a greater heterogeneity across subjects). In the current study, we address interindividual variability in functional connectome by means of resting-state fMRI in a large sample of patients with schizophrenia and healthy controls. Among the different metrics of distance/dissimilarity used to assess variability, geodesic distance showed robust results to head motion. The main findings of the current study point to (i) a higher between subject heterogeneity in the functional connectome of patients, (ii) variable levels of heterogeneity throughout the cortex, with greater variability in frontoparietal and default mode networks, and lower variability in the salience network, and (iii) an association of whole-brain variability with levels of clinical symptom severity and with topological properties of brain networks, suggesting that the average functional connectome overrepresents those patients with lower functional integration and with more severe clinical symptoms. Moreover, after performing a graph theoretical analysis of brain networks, we found that patients with more severe clinical symptoms had decreased connectivity at both whole-brain level and within the salience network, and that patients with higher negative symptoms had large-scale functional integration deficits.

Decline of anterior cingulate functional network efficiency in first-episode, medication-naïve somatic symptom disorder and its relationship with catastrophizing

The high prevalence of somatic symptom disorder (SSD) led to cumulative burdens to the medical system. However, the pathogenesis of this disease still remains unclear. Graph theoretical analysis discovered altered network topology across various psychiatric disorders, yet alteration in the topological structure of brain functional network in SSD patients is still unexplored. Catastrophizing is a common cognitive distortion in SSD. We hypothesize that the network topological metrics of SSD should be altered, and should correlate with catastrophizing scales. 32 medication-naïve, first-episode SSD patients and 30 age, gender matched HCs were recruited. The 17-item Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA) and Cognitive Emotion Regulation Questionnaire (CERQ) were accessed. Functional MRI were scanned and brain functional networks were constructed based on 166 anatomically cerebrum regions from the automated anatomical labeling 3 (AAL3) template. Network topological metrics were calculated and compared between the two groups. Correlation between these metrics and clinical scales were also calculated. Network global efficiency of SSD was significantly lower than that of HC. Nodal global efficiency of the left subgenual anterior cingulate cortex (sgACC) of SSD was significantly lower than that of HC. FCs between the left sgACC and other 21 seed nodes were significantly declined in SSD in comparison with HC. In SSD group, HAMD total score was significantly negatively correlated with the connection between the left medial superior frontal gyrus and the left sgACC. CERQ catastrophizing score was significantly negatively correlated with nodal global efficiency of left sgACC and with the FCs between the left sgACC and other 13 seed nodes. Catastrophizing could reflect the specific sgACC-centered dysfunction of brain network global efficiency of SSD. The left sgACC may be a future treatment target of dealing with catastrophizing, which is a core cognitive distortion of SSD.

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia

Social behaviour requires the brain to efficiently integrate multiple social processes, but it is not clear what neural substrates underlie general social behaviour. While psychosis patients and individuals with subclinical symptoms are characterized by social dysfunction, the neural mechanisms underlying social dysfunctions in schizophrenia spectrum disorders remains unclear. We first constructed a general social brain network (SBN) using resting-state functional connectivity (FC) with regions of interest based on the automatic meta-analysis results from NeuroSynth. We then examined the general SBN and its relationship with social network (SN) characteristics in 30 individuals with schizophrenia (SCZ) and 33 individuals with social anhedonia (SA). We found that patients with SCZ exhibited deficits in their SN, while SA individuals did not. SCZ patients showed decreased segregation and functional connectivity in their SBN, while SA individuals showed a reversed pattern with increased segregation and functional connectivity of their SBN. Sparse canonical correlation analysis showed that both SCZ patients and SA individuals exhibited reduced correlation between SBN and SN characteristics compared with their corresponding healthy control groups. These preliminary findings suggest that both SCZ and SA participants exhibit abnormality in segregation and functional connectivity within the general SBN and reduced correlation with SN characteristics. These findings could guide the development of non-pharmacological interventions for social dysfunction in SCZ spectrum disorders.

Anhedonia Relates to the Altered Global and Local Grey Matter Network Properties in Schizophrenia

Anhedonia is one of the major negative symptoms in schizophrenia and defined as the loss of hedonic experience to various stimuli in real life. Although structural magnetic resonance imaging has provided a deeper understanding of anhedonia-related abnormalities in schizophrenia, network analysis of the grey matter focusing on this symptom is lacking. In this study, single-subject grey matter networks were constructed in 123 patients with schizophrenia and 160 healthy controls. The small-world property of the grey matter network and its correlations with the level of physical and social anhedonia were evaluated using graph theory analysis. In the global scale whole-brain analysis, the patients showed reduced small-world property of the grey matter network. The local-scale analysis further revealed reduced small-world property in the default mode network, salience/ventral attention network, and visual network. The regional-level analysis showed an altered relationship between the small-world properties and the social anhedonia scale scores in the cerebellar lobule in patients with schizophrenia. These results indicate that anhedonia in schizophrenia may be related to abnormalities in the grey matter network at both the global whole-brain scale and local-regional scale.

Divergent Alterations of Structural-Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients

Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural-functional connectivity couplings (SC-FC couplings) have been previously reported in schizophrenia patients, the alterations in SC-FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC-FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC-FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC-FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.

Attenuated Resting-State Functional Anticorrelation between Attention and Executive Control Networks in Schizotypal Personality Disorder

Exploring the disruptions to intrinsic resting-state networks (RSNs) in schizophrenia-spectrum disorders yields a better understanding of the disease-specific pathophysiology. However, our knowledge of the neurobiological underpinnings of schizotypal personality disorders mostly relies on research on schizotypy or schizophrenia. This study aimed to investigate the RSN abnormalities of schizotypal personality disorder (SPD) and their clinical implications. Using resting-state data, the intra- and inter-network of the higher-order functional networks (default mode network, DMN; frontoparietal network, FPN; dorsal attention network, DAN; salience network, SN) were explored in 22 medication-free, community-dwelling, non-help seeking individuals diagnosed with SPD and 30 control individuals. Consequently, while there were no group differences in intra-network functional connectivity across DMN, FPN, DAN, and SN, the SPD participants exhibited attenuated anticorrelation between the right frontal eye field region of the DAN and the right posterior parietal cortex region of the FPN. The decreases in anticorrelation were correlated with increased cognitive–perceptual deficits and disorganization factors of the schizotypal personality questionnaire, as well as reduced independence–performance of the social functioning scale for all participants together. This study, which links SPD pathology and social functioning deficits, is the first evidence of impaired large-scale intrinsic brain networks in SPD.

Functional network connectivity in early-stage schizophrenia

Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed.

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

BACKGROUND

Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.

METHODS

Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.

RESULTS

A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.

CONCLUSIONS

Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

The negative correlation between energy consumption and communication efficiency in motor network

OBJECTIVE

Motor network plays an important role in people's daily lives. However, until now, the energy consumption mechanism of motor network remains unclear. In this study, we aimed to investigate the energy consumption of motor network.

MATERIALS AND METHODS

Fluorine-18-fluorodeoxyglucose PET ([F]FDG PET) data of 81 healthy male Sprague-Dawley rats were included in this study. Metabolic motor network was constructed on the basis of group independent component analysis. Properties of motor network such as degree and nodal efficiency were investigated using graph theory-based analysis. Furthermore, the relationships between [F]FDG standardized uptake value ratio and these properties of each node were investigated.

RESULTS

A motor network comprising of the following 11 regions were found: left primary motor cortex, right primary motor cortex, left secondary motor cortex, right secondary motor cortex, left primary somatosensory cortex, right primary somatosensory cortex, left secondary somatosensory cortex, right secondary somatosensory cortex, left insular cortex, right insular cortex, and left orbital cortex. Graph theory-based analysis indicated that right primary somatosensory cortex and left secondary somatosensory cortex were the hubs of motor network, and the nodal efficiency and nodal degree share the same order. Further investigation found a significantly negative correlation between nodal efficiency and [F]FDG standardized uptake value ratios.

CONCLUSION

This study investigated the energy consumption of motor network and found a relationship between energy consumption and communication efficiency. These results may provide insights into the understanding of energy consumption mechanism underlying motor network.Video abstract: http://links.lww.com/NMC/A142.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Disrupted modular organization of primary sensory brain areas in schizophrenia

Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel resolution limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures

Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia.

Short-range connections in the developmental connectome during typical and atypical brain maturation

The human brain is remarkably complex with connectivity constituting its basic organizing principle. Although long-range connectivity has been focused on in most research, short-range connectivity is characterized by unique and spatiotemporally heterogeneous dynamics from infancy to adulthood. Alterations in the maturational dynamics of short-range connectivity has been associated with neuropsychiatric disorders, such as autism and schizophrenia. Recent advances in neuroimaging techniques, especially diffusion magnetic resonance imaging (dMRI), resting-state functional MRI (rs-fMRI), electroencephalography (EEG) and magnetoencephalography (MEG), have made quantification of short-range connectivity possible in pediatric populations. This review summarizes findings on the development of short-range functional and structural connections at the macroscale. These findings suggest an inverted U-shaped pattern of maturation from primary to higher-order brain regions, and possible "hyper-" and "hypo-" short-range connections in autism and schizophrenia, respectively. The precisely balanced short- and long-range connections contribute to the integration and segregation of the connectome during development. The mechanistic relationship among short-range connectivity maturation, the developmental connectome and emerging brain functions needs further investigation, including the refinement of methodological approaches.

Current and potential pharmacological and psychosocial interventions for anxiety symptoms and disorders in patients with schizophrenia: structured review

OBJECTIVE

Between 30% and 62% of patients with schizophrenia present with co-morbid anxiety disorders that are associated with increased overall burden. Our aim was to summarize current and potential interventions for anxiety in schizophrenia.

DESIGN

Structured review, summarizing pharmacological and psychosocial interventions used to reduce anxiety in schizophrenia and psychosis.

RESULTS

Antipsychotics have been shown to reduce anxiety, increase anxiety, or have no effect. These may be augmented with another antipsychotic, anxiolytic, or antidepressant. Novel agents, such as L-theanine, pregabalin, and cycloserine, show promise in attenuating anxiety in schizophrenia. Psychosocial therapies have been developed to reduce the distress of schizophrenia. Cognitive behavioural therapy (CBT) has shown that benefit and refinements in the therapy have been successful, for example, for managing worry in schizophrenia. CBT usually involves more than 16 sessions, as short courses of CBT do not attenuate the presentation of anxiety in schizophrenia. To address time and cost, the development of manualized CBT to address anxiety in schizophrenia is being developed.

CONCLUSIONS

The presence of coexisting anxiety symptoms and co-morbid anxiety disorders should be ascertained when assessing patients with schizophrenia or other psychoses as a range of pharmacological and psychosocial treatments are available.

Multivariate brain network graph identification in functional MRI

Motivated by recent interest in identification of functional brain networks, we develop a new multivariate approach for functional brain network identification and name it as Multivariate Vector Regression-based Connectivity (MVRC). The proposed MVRC method regresses time series of all regions to those of other regions simultaneously and estimates pairwise association between two regions with consideration of influence of other regions and builds the adjacency matrix. Next, modularity method is applied on the adjacency matrix to detect communities or functional brain networks. We compare the proposed MVRC method with existing methods ranging from simple Pearson correlation to advanced Multivariate Adaptive Sparse Representation (ASR) methods. Experimental results on simulated and real fMRI dataset demonstrate that MVRC is able to extract functional brain networks that are consistent with the literature. Also, the proposed MVRC method is 650-750 times faster compared to the existing ASR method on 90 node network.

Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Functional brain networks in treatment-resistant schizophrenia

INTRODUCTION

Up to 20% of individuals with schizophrenia show minimal or no response to medication and are considered to have 'treatment-resistant' schizophrenia (TRS). Unlike early and established schizophrenia, few studies have investigated resting-state functional connectivity (rs-FC) in TRS. Here, we test for disruptions in FC and altered efficiency of functional brain networks in a well-characterized cohort of TRS patients.

METHODS

Resting-state functional magnetic resonance imaging was used to investigate functional brain networks in 42 TRS participants prescribed clozapine (30 males, mean age=41.3(10)) and 42 healthy controls (24 males, mean age=38.4(10)). Graph analysis was used to characterize between-group differences in local and global efficiency of functional brain network organization as well as the strength of FC.

RESULTS

Global brain FC was reduced in TRS patients (p=0.0001). Relative to controls, 3.4% of all functional connections showed reduced strength in TRS (p<0.001), predominantly involving fronto-temporal, fronto-occipital and temporo-occipital connections. Global efficiency was reduced in TRS (p=0.0015), whereas local efficiency was increased (p=0.0042).

CONCLUSIONS

TRS is associated with widespread reductions in rs-FC and altered network topology. Increased local functional network efficiency coupled with decreased global efficiency suggests that hub-to-hub connections are preferentially affected in TRS. These findings further our understanding of the neurobiological impairments in TRS.

Structural brain network analysis in schizophrenia using minimum spanning tree

Schizophrenia is a mental disorder in which functional and structural brain networks are disrupted. Classical network analysis has been used by many researchers to quantify brain networks and to study the network changes in schizophrenia, but unfortunately metrics used in this classical method highly depend on the networks' density and weight; the comparisons made by this method are biased. The minimum spanning tree (MST) is an alternative method to solve this problem, but its usefulness in studying the schizophrenic brain network has not been examined yet. In the present study, we quantified structural brain networks using MST metrics to conduct group analysis between age and sex matched schizophrenic patients and healthy controls. Many MST metrics including Kappa, gamma, max, Betweenness centrality (BC), leaf number, and diameter were found to have significantly changed between two groups that implied a disruption in the whole brain integrity. This was unlike the brain segregation, which was not altered in the schizophrenia group. These results have consistency with Classical network analysis works and demonstrate the MST potential as a powerful method to be used in researches, studying schizophrenic brain connectome.

Cingulo-opercular network efficiency mediates the association between psychotic-like experiences and cognitive ability in the general population

BACKGROUND

Psychosis is hypothesized to occur on a spectrum between psychotic disorders and healthy individuals. In the middle of the spectrum are individuals who endorse psychotic-like experiences (PLEs) that may not impact daily functioning or cause distress. Individuals with PLEs show alterations in both cognitive ability and functional connectivity of several brain networks, but the relationship between PLEs, cognition, and functional networks remains poorly understood.

METHODS

We analyzed resting-state fMRI data, a range of neuropsychological tasks, and questions from the Achenbach Adult Self Report (ASR) in 468 individuals from the Human Connectome Project. We aimed to determine whether global efficiency of specific functional brain networks supporting higher-order cognition (the fronto-parietal network (FPN), cingulo-opercular network (CON), and default mode network (DMN)) was associated with PLEs and cognitive ability in a non-psychiatric sample.

RESULTS

21.6% of individuals in our sample endorsed at least one PLE. PLEs were significantly negatively associated with higher-order cognitive ability, CON global efficiency, and DMN global efficiency, but not crystallized knowledge. Higher-order cognition was significantly positively associated with CON and DMN global efficiency. Interestingly, the association between PLEs and cognitive ability was partially mediated by CON global efficiency and, in a subset of individuals who tested negative for drugs (N=405), the participation coefficient of the right anterior insula (a hub within the CON).

CONCLUSIONS

These findings suggest that CON integrity may represent a shared mechanism that confers risk for psychotic experiences and the cognitive deficits observed across the psychosis spectrum.

How do abnormalities in the brain's spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology

Schizophrenia is a complex neuropsychiatric disorder with a variety of symptoms that include sensorimotor, affective, cognitive, and social changes. The exact neuronal mechanisms underlying these symptoms remain unclear though. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's spontaneous activity, otherwise known as its resting state activity. While various spatial and temporal abnormalities have been observed in spontaneous activity in schizophrenia, their meaning and significance for the different psychopathological symptoms in schizophrenia, are yet to be defined. The first aim in this paper is to provide an overview of recent findings concerning changes in the spatial (e.g., functional connectivity) and temporal (e.g., couplings between different frequency fluctuations) properties of spontaneous activity in schizophrenia. The second aim is to link these spatiotemporal changes to the various psychopathological symptoms of schizophrenia, with a specific focus on basic symptoms, formal thought disorder, and ego-disturbances. Based on the various findings described, we postulate that the spatiotemporal changes on the neuronal level of the brain's spontaneous activity transform into corresponding spatiotemporal changes on the psychological level which, in turn, leads to the different kinds of psychopathological symptoms. We consequently suggest a spatiotemporal rather than cognitive or sensory approach to the condition, amounting to what we describe as "Spatiotemporal Psychopathology".