Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task

Neuroimaging features of cognitive impairments in schizophrenia and major depressive disorder

Cognitive dysfunctions are one of the key symptoms of schizophrenia (SZ) and major depressive disorder (MDD), which exist not only during the onset of diseases but also before the onset, even after the remission of psychiatric symptoms. With the development of neuroimaging techniques, these non-invasive approaches provide valuable insights into the underlying pathogenesis of psychiatric disorders and information of cognitive remediation interventions. This review synthesizes existing neuroimaging studies to examine domains of cognitive impairment, particularly processing speed, memory, attention, and executive function in SZ and MDD patients. First, white matter (WM) abnormalities are observed in processing speed deficits in both SZ and MDD, with distinct neuroimaging findings highlighting WM connectivity abnormalities in SZ and WM hyperintensity caused by small vessel disease in MDD. Additionally, the abnormal functions of prefrontal cortex and medial temporal lobe are found in both SZ and MDD patients during various memory tasks, while aberrant amygdala activity potentially contributes to a preference to negative memories in MDD. Furthermore, impaired large-scale networks including frontoparietal network, dorsal attention network, and ventral attention network are related to attention deficits, both in SZ and MDD patients. Finally, abnormal activity and volume of the dorsolateral prefrontal cortex (DLPFC) and abnormal functional connections between the DLPFC and the cerebellum are associated with executive dysfunction in both SZ and MDD. Despite these insights, longitudinal neuroimaging studies are lacking, impeding a comprehensive understanding of cognitive changes and the development of early intervention strategies for SZ and MDD. Addressing this gap is critical for advancing our knowledge and improving patient prognosis.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

The Brain Networks Basis for Deductive and Inductive Reasoning: A Functional Magnetic Resonance Imaging Study

Introduction

Frontoparietal (FPN) and cingulo-opercular network (CON) control cognitive functions needed in deductive and inductive reasoning via different functional frameworks. The FPN is a fast intuitive system while the CON is slow and analytical. The default-interventionist model presents a serial view of the interaction between intuitive and analytic cognitive systems. This study aims to examine the activity pattern of the FPN and CON from the perspective of the default-interventionist model via reasoning.

Methods

We employed functional magnetic resonance imaging (fMRI) to investigate cingulo-opercular and frontoparietal network activities in 24 healthy university students during Raven and Wason reasoning tasks. Due to the different operation times of the CON and FPN, the reaction time was assessed as a behavioral factor.

Results

During Raven's advanced progressive matrices (RAPM) test, both the CON and FPN were activated. Also, with the increase in the difficulty level of the Raven test, a linear increase in response time was observed. In contrast, during the Wason's selection task (WST) test, only the activity of FPN was observed.

Conclusion

The results of the study support the hypothesis that the default-interventionist model of dual-process theory provides an accurate explanation of the cognitive mechanisms involved in reasoning. Thus, the response method (intuitive/analytical) determines which cognitive skills and brain regions are involved in responding.

Highlights

The cingulo-opercular and fronto-parietal networks (FPNs) control cognitive functions and processes.The frontoparietal network is a fast intuitive system that utilizes short-time attention which is compatible with type 1 processing. In contrast, the cingulo-opercular network (CON) is an analytical time-consuming system that utilizes attention and working memory for a longer time, compatible with type 2 processing.The default-interventionist model of a dual-process theory states that our behaviors are controlled by type 1 processing unless we are confronted with novel and complex problems in which we have no prior experiences.

Plain Language Summary

The present study examined the activity of two task-based brain networks through performing diffrent type of reasoning tasks. Fronto-parietal and Cingulo-opercular are the two task-based brain networks that are responsible for cognitive control. These two brain networks direct the way to use cognitive skills and executive functions which are necessary to perform cognitive tasks especially higher-order ones as reasoning tasks. Since the two types of inductive and deductive reasoning tasks requier two different bottom-up and top-down cognitive control respectively, different cognitive skills would be needed which affect the activity of fronto-parietal and cingulo-opercular brain networks. Our results showed that through inductive reasoning task which examined by RAVEN, both of the fronto-parietal and cingulo-opercular brain networks were activated but deductive reasoning task which examined by Wason Selection Card test, just the fronto-parietal brain network was activated. It seems that in the case of deductive reasoninf task, there is a higher probability of errors which lead to giving less correct responses. Based on our results, subjects paid not enough attention to details, so had failure to update informations that leaded to responding with errors. Inactivity of cingulo-opercular network through dedeuctive reasoning task clearly showed that the bottom-up cognitive control did not happen successfully. As a result of that, information processing did not proceed properly.

Recurrence quantification analysis of periodic dynamics in the default mode network in first-episode drug-naïve schizophrenia

Abnormal functional connectivity (FC) within the default model network (DMN) in schizophrenia has been frequently reported in previous studies. However, traditional FC analysis was mostly linear correlations based, with the information on nonlinear or temporally lagged brain signals largely overlooked. Fifty-five first-episode drug-naïve schizophrenia (FES) patients and 53 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. The DMN was extracted using independent component analysis. Recurrence quantification analysis was used to measure the duration, predictability, and complexity of the periodic processes of the nonlinear DMN time series. The Mann‒Whitney U test was conducted to compare these features between FES patients and HCs. The support vector machine was applied to discriminate FES from HCs based on these features. Determinism, which means predictability of periodic process activity, between the ventromedial prefrontal cortex (vMPFC) and posterior cingulate and between the vMPFC and precuneus, was significantly decreased in FES compared with HCs. Determinism between the vMPFC and precuneus was positively correlated with category fluency scores in FES. The classifier achieved 77% accuracy. Our results suggest that synchronized periodicity among DMN brain regions is dysregulated in FES, and the periodicity in BOLD signals may be a promising indicator of brain functional connectivity.

Working memory processing deficit associated with a nonlinear response pattern of the anterior cingulate cortex in first-episode and drug-naïve schizophrenia

Impaired working memory (WM) is a core neuropsychological dysfunction of schizophrenia, however complex interactions among the information storage, information processing and attentional aspects of WM tasks make it difficult to uncover the psychophysiological mechanisms of this deficit. Thirty-six first-episode and drug-naïve schizophrenia and 29 healthy controls (HCs) were enrolled in this study. Here, we modified a WM task to isolate components of WM storage and WM processing, while also varying the difficulty level (load) of the task to study regional differences in load-specific activation using mixed effects models, and its relationship to distributed gene expression. Comparing patients with HCs, we found both attentional deficits and WM deficits, with WM processing being more impaired than WM storage in patients. In patients, but not controls, a linear modulation of brain activation was observed mainly in the frontoparietal and dorsal attention networks. In controls, an inverted U-shaped response pattern was identified in the left anterior cingulate cortex. The vertex of this inverted U-shape was lower in patients than controls, and a left-shifting axis of symmetry was associated with better WM performance in patients. Both the above linear and U-shaped modulation effects were associated with the expressions of the genes enriched in the dopamine neurotransmitter system across all cortical brain regions. These findings indicate that a WM processing deficit is evident in schizophrenia from an early stage before antipsychotic treatment, and associated with a dopamine pathway related aberration in nonlinear response pattern at the cingulate cortex when processing WM load.

Load-dependent inverted U-shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study

BACKGROUND

Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain.

METHODS

We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set.

RESULTS

Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the "2-back versus 0-back" contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%.

LIMITATIONS

We used a limited n-back paradigm that precluded the examination of higher working-memory loads.

CONCLUSION

Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia.

Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning

Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.

Altered resting-state functional connectivity in hiPSCs-derived neuronal networks from schizophrenia patients

Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connectivity. In the present work, we used Human Induced Pluripotent Stem Cells (hiPSCs)-derived neuronal cultures to study neural communicational dynamics during early development in SZ. We conducted gene and protein expression profiling, calcium imaging recordings, and applied a mathematical model to quantify the dynamism of functional connectivity (FC) in hiPSCs-derived neuronal networks. Along the neurodifferentiation process, SZ networks displayed altered gene expression of the glutamate receptor-related proteins HOMER1 and GRIN1 compared to healthy control (HC) networks, suggesting a possible tendency to develop hyperexcitability. Resting-state FC in neuronal networks derived from HC and SZ patients emerged as a dynamic phenomenon exhibiting connectivity configurations reoccurring in time (hub states). Compared to HC, SZ networks were less thorough in exploring different FC configurations, changed configurations less often, presented a reduced repertoire of hub states and spent longer uninterrupted time intervals in this less diverse universe of hubs. Our results suggest that alterations in the communicational dynamics of SZ emerging neuronal networks might contribute to the previously described brain FC anomalies in SZ patients, by compromising the ability of their neuronal networks for rapid and efficient reorganization through different activity patterns.

Adult outcome of preterm birth: Implications for neurodevelopmental theories of psychosis

Preterm birth is associated with an elevated risk of developmental and adult psychiatric disorders, including psychosis. In this review, we evaluate the implications of neurodevelopmental, cognitive, motor, and social sequelae of preterm birth for developing psychosis, with an emphasis on outcomes observed in adulthood. Abnormal brain development precipitated by early exposure to the extra-uterine environment, and exacerbated by neuroinflammation, neonatal brain injury, and genetic vulnerability, can result in alterations of brain structure and function persisting into adulthood. These alterations, including abnormal regional brain volumes and white matter macro- and micro-structure, can critically impair functional (e.g. frontoparietal and thalamocortical) network connectivity in a manner characteristic of psychotic illness. The resulting executive, social, and motor dysfunctions may constitute the basis for behavioural vulnerability ultimately giving rise to psychotic symptomatology. There are many pathways to psychosis, but elucidating more precisely the mechanisms whereby preterm birth increases risk may shed light on that route consequent upon early neurodevelopmental insult.

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

Background

Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.

Methods

Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.

Findings

GCN achieved an accuracy of 81·5% (95%CI: 80·5–82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.

Interpretation

These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.

Funding

This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

Surface-Based Spontaneous Oscillation in Schizophrenia: A Resting-State Functional Magnetic Resonance Imaging Study

Schizophrenia (SZ) is considered as a self-disorder with disordered local synchronous activation. Previous studies have reported widespread dyssynchrony of local activation in patients with SZ, which may be one of the crucial physiological mechanisms of SZ. To further verify this assumption, this work used a surface-based two-dimensional regional homogeneity (2dReHo) approach to compare the local neural synchronous spontaneous oscillation between patients with SZ and healthy controls (HC), instead of the volume-based regional homogeneity approach described in previous study. Ninety-seven SZ patients and 126 HC were recruited to this study, and we found the SZ showed abnormal 2dReHo across the cortical surface. Specifically, at the global level, the SZ patients showed significantly reduced global 2dReHo; at the vertex level, the foci with increased 2dReHo in SZ were located in the default mode network (DMN), frontoparietal network (FPN), and limbic network (LN); however, foci with decreased 2dReHo were located in the somatomotor network (SMN), auditory network (AN), and visual network (VN). Additionally, this work found positive correlations between the 2dReHo of bilateral rectus and illness duration, as well as a significant positive correlation between the 2dReHo of right orbital inferior frontal gyrus (OIFG) with the negative scores of the positive and negative syndrome scale in the SZ patients. Therefore, the 2dReHo could provide some effective features contributed to explore the pathophysiology mechanism of SZ.

Oxidative Stress and the Pathophysiology and Symptom Profile of Schizophrenia Spectrum Disorders

Schizophrenia is associated with increased levels of oxidative stress, as reflected by an increase in the concentrations of damaging reactive species and a reduction in anti-oxidant defences to combat them. Evidence has suggested that whilst not the likely primary cause of schizophrenia, increased oxidative stress may contribute to declining course and poor outcomes associated with schizophrenia. Here we discuss how oxidative stress may be implicated in the aetiology of schizophrenia and examine how current understanding relates associations with symptoms, potentially via lipid peroxidation induced neuronal damage. We argue that oxidative stress may be a good target for future pharmacotherapy in schizophrenia and suggest a multi-step model of illness progression with oxidative stress involved at each stage.

Functional near-infrared spectroscopy (fNIRS) as a tool to assist the diagnosis of major psychiatric disorders in a Chinese population

Advances in neuroimaging have promised the development of specific and objective biomarkers for the diagnosis and treatment of psychiatric disorders. Recently, functional near-infrared spectroscopy (fNIRS) has been used during cognitive tasks to measure cortical dysfunction associated with mental illnesses such as Schizophrenia (SCH), Major-Depressive disorder (MD) and Bipolar Disorder (BD). We investigated the ability of fNIRS as a clinically viable tool to successfully distinguish healthy individuals from those with major psychiatric disorders. 316 patients with major psychiatric disorders (198 SCH/54 MD/64 BP) and 101 healthy controls were included in this study. Changes in oxygenated-hemoglobin during a Chinese language verbal fluency test were measured using a 52-channel fNIRS machine over the bilateral temporal and frontal lobe areas. We evaluated the ability of two task-evoked features selected from prior studies the Integral and Centroid values, to identify individuals with major diagnoses. Both the integral value of frontal and centroid value of temporal showed sensitivity in classifying individuals with mental disorders from healthy controls. However, using a combined index featuring both the integral value and centroid value to differentiate psychiatric disorders from healthy controls with an AUC of 0.913, differentiate individuals with mood disorders from healthy controls showed an AUC of 0.899, while for schizophrenia the AUC was 0.737. Our data suggest that fNIRS can be used as a candidate biomarker during differential diagnosis individuals with mood or psychosis disorders and offer a step towards individualization of treatment.

Predicting individual variability in task-evoked brain activity in schizophrenia

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting‐state functional connectivity and brain activity during the well‐validated N‐back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine‐learning approach we were able to use resting‐state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task‐evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Cortical morphometric correlational networks associated with cognitive deficits in first episode schizophrenia

Schizophrenia (SCZ) is a chronic cognitive and behavioral disorder associated with abnormal cortical activity during information processing. Several brain structures associated with the seven performance domains evaluated using the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery (MCCB) have shown cortical volume loss in first episode schizophrenia (FES) patients. However, the relationship between morphological organization and MCCB performance remains unclear. Therefore, in the current observational study, high-resolution structural MRI scans were collected from 50 FES patients, and the morphometric correlation network (MCN) using cortical volume was established to characterize the cortical pattern associated with poorer MCCB performance. We also investigated topological properties, such as the modularity, the degree and the betweenness centrality. Our findings show structural volume was directly and strongly associated with the cognitive deficits of FES patients in the precuneus, anterior cingulate, and fusiform gyrus, as well as the prefrontal, parietal, and sensorimotor cortices. The medial orbitofrontal, fusiform, and superior frontal gyri were not only identified as the predominant nodes with high degree and betweenness centrality in the MCN, but they were also found to be critical in performance in several of the MCCB domains. Together, these results suggest a widespread cortical network is altered in FES patients and that performance on the MCCB domains is associated with the core pathophysiology of SCZ.

Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia

Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown. We investigated task-based connectivity of eight a priori network pairs in participants with schizophrenia (n = 29) and control participants (n = 31) during the performance of an RITL task. Multivariate pattern analysis was used to determine which network connectivity patterns predicted diagnostic group. Of all network pairs, only the connectivity between the cingulo-opercular network (CON) and salience network (SAN) during learning classified patients and control participants with significant accuracy (80%). CON-SAN connectivity during learning was significantly associated with task performance in participants with schizophrenia. These findings suggest that impaired interactions between identification of salient stimuli and maintenance of task goals contributes to RITL deficits in participants with schizophrenia.

Visual N80 latency as a marker of neuropsychological performance in schizophrenia: Evidence for bottom-up cognitive models

OBJECTIVE

Cognitive deficits and visual impairment in the magnocellular (M) pathway, have been independently reported in schizophrenia. The current study examined the association between neuropsychological (NPS) performance and visual evoked potentials (VEPs: N80/P1 to M- and P(parvocellular)-biased visual stimuli) in schizophrenia and healthy controls.

METHODS

NPS performance and VEPs were measured in n = 44 patients and n = 34 matched controls. Standardized NPS-scores were combined into Domains and a PCA (Principal Component Analysis) generated Composite. Group differences were assessed via (M)ANOVAs, association between NPS and VEP parameters via PCA, Pearson's coefficient and bootstrapping. Logistic regression was employed to assess classification power.

RESULTS

Patients showed general cognitive impairment, whereas group differences for VEP-parameters were non-significant. In patients, N80 latency across conditions loaded onto one factor with cognitive composite, showed significant negative correlations of medium effect sizes with NPS performance for M/P mixed stimuli and classified low and high performance with 70% accuracy.

CONCLUSION

The study provides no evidence for early visual pathway impairment but suggests a heightened association between early visual processing and cognitive performance in schizophrenia.

SIGNIFICANCE

Our results lend support to bottom-up models of cognitive function in schizophrenia and implicate visual N80 latency as a potential biomarker of cognitive deficits in schizophrenia.

Neurodevelopmental insights into circuit dysconnectivity in schizophrenia

Schizophrenia is increasingly being recognized as a disorder of brain circuits of developmental origin. Animal models, however, have been technically limited in exploring the effects of early developmental circuit abnormalities on the maturation of the brain and associated behavioural outputs. This review discusses evidence of the developmental emergence of circuit abnormalities in schizophrenia, followed by a critical assessment on how animal models need to be adapted through optimized tools in order to spatially and temporally manipulate early developmental events, thereby providing insight into the causal contribution of developmental perturbations to schizophrenia.

Decreased connection density and modularity of functional brain networks during n-back working memory paradigm

INTRODUCTION

Investigating how the brain adapts to increased mental workload through large-scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low-density setting.

METHODS

In this study, we utilized the n-back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n-times earlier. Electroencephalography recordings were performed under a control (0-back) and two task conditions of varying difficulty (2- and 3-back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures.

RESULTS

We found a substantial decrease in FC when transitioning from the 0- to the 2- or 3-back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands.

CONCLUSION

The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.

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

BACKGROUND

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Correlations between age, biomedical variables, and cognition in patients with schizophrenia

Objective

To illustrate the influence of clinical variables on cognition performance in patients with schizophrenia (SCZ).

Methods

Using the 66nao Brain Training device (a novel measurement tool), the cognitive performance of 99 patients with SCZ was evaluated. Patients were diagnosed by the ICD-10 diagnostic criteria for SCZ, and their age were 16–68 years old. Furthermore, we explored the relationship between age, biomedical variables and specific cognitive domains in patients with SCZ. Patients were divided into two groups: various of cognitive domains impairment group and non-impairment group according to the norm scores. All data were analyzed using RStudio Version 1.0.44 (RStudio, Inc.)

Results

Patients with SCZ had obvious cognitive impairment in total and five subdomains of cognitive function. We found that 1) SCZ patients with impaired cognitive total score experienced significant older age and longer illness duration compared with those with normal cognitive total score. 2) SCZ patients with impaired memory experienced significant older age compared with those with normal memory. 3) SCZ patients with impaired attention showed significant lower serum triglyceride (TG) level compared with those with normal attention. 4) SCZ patients with impaired flexibility performed significant longer illness duration compared with those with normal flexibility. 5) SCZ patients with impaired cognitive agility performed significant older age, longer duration, and higher systolic blood pressure (SBP) compared with those with normal cognitive agility. 6) The age, illness duration and SBP in patients with impaired time perception were marginally different from those of subjects with normal time perception.

Conclusion

There are five dimensions (memory, attention, flexibility, cognitive agility, and time perception) of cognitive dysfunction in SCZ patients. Age, illness duration, TG, and SBP might play vital roles in various subdomains of the cognitive deficits respectively in patients with SCZ.

Effective connectivity of the right anterior insula in schizophrenia: The salience network and task-negative to task-positive transition

Triple network dysfunction theory of schizophrenia postulates that the interaction between the default-mode and the fronto-parietal executive network is disrupted by aberrant salience signals from the right anterior insula (rAI). To date, it is not clear how the proposed resting-state disruption translates to task-processing inefficiency in subjects with schizophrenia. Using a contiguous resting and 2-back task performance fMRI paradigm, we quantified the change in effective connectivity that accompanies rest-to-task state transition in 29 clinically stable patients with schizophrenia and 31 matched healthy controls. We found an aberrant task-evoked increase in the influence of the rAI to both executive (Cohen's d = 1.35, p = 2.8 × 10-6) and default-mode (Cohen's d = 1.22, p = 1.5 × 10-5) network regions occur in patients when compared to controls. In addition, the effective connectivity from middle occipital gyrus (dorsal visual cortex) to insula is also increased in patients as compared with healthy controls. Aberrant insula to executive network influence is pronounced in patients with more severe negative symptom burden. These findings suggest that control signals from rAI are abnormally elevated and directed towards both task-positive and task-negative brain regions, when task-related demands arise in schizophrenia. This aberrant, undiscriminating surge in salience signalling may disrupt contextually appropriate allocation of resources in the neuronal workspace in patients with schizophrenia.

Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study

BACKGROUND

Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia.

METHODS

We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset.

RESULTS

Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy.

CONCLUSIONS

We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.

Functional network connectivity impairments and core cognitive deficits in schizophrenia

Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10^-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10^-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.

The Principles of Electroconvulsive Therapy Based on Correlations of Schizophrenia and Epilepsy: A View From Brain Networks

Electroconvulsive therapy (ECT) was established based on Meduna's hypothesis that there is an antagonism between schizophrenia and epilepsy, and that the induction of a seizure could alleviate the symptoms of schizophrenia. However, subsequent investigations of the mechanisms of ECT have largely ignored this originally established relationship between these two disorders. With the development of functional magnetic resonance imaging (fMRI), brain-network studies have demonstrated that schizophrenia and epilepsy share common dysfunctions in the default-mode network (DMN), saliency network (SN), dorsal-attention network (DAN), and central-executive network (CEN). Additionally, fMRI-defined brain networks have also been shown to be useful in the evaluation of the treatment efficacy of ECT. Here, we compared the ECT-induced changes in the pathological conditions between schizophrenia and epilepsy in order to offer further insight as to whether the mechanisms of ECT are truly based on antagonistic and/or affinitive relationships between these two disorders.

Activation-based association profiles differentiate network roles across cognitive loads

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Time Series Data-fMRI Study

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N − 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.