Altered intrinsic and extrinsic connectivity in schizophrenia

Effective connectivity analysis of resting-state mentalizing brain networks in spinocerebellar ataxia type 2: A dynamic causal modeling study

Neuroimaging studies on healthy subjects described the causal effective connectivity of cerebellar-cerebral social mentalizing networks, revealing the presence of closed-loops. These studies estimated effective connectivity by applying Dynamic Causal Modeling on task-related fMRI data of healthy subjects performing mentalizing tasks. Thus far, few studies have applied Dynamic Causal Modeling to resting-state fMRI (rsfMRI) data to test the effective connectivity within the cerebellar-cerebral mentalizing network in the absence of experimental manipulations, and no study applied Dynamic Causal Modeling on fMRI data of patients with cerebellar disorders typically showing social cognition deficits. Thus, in this research we applied spectral Dynamic Causal Modeling, to rsfMRI data of 13 patients affected by spinocerebellar ataxia type 2 (SCA2) and of 23 matched healthy subjects. Specifically, effective connectivity was tested between acknowledged mentalizing regions of interest: bilateral cerebellar Crus II, dorsal and ventral medial prefrontal cortex, bilateral temporo-parietal junctions and precuneus. SCA2 and healthy subjects shared some similarities in cerebellar-cerebral mentalizing effective connectivity at rest, confirming the presence of closed-loops between cerebellar and cerebral mentalizing regions in both groups. However, relative to healthy subjects, SCA2 patients showed effective connectivity variations mostly in cerebellar-cerebral closed loops, namely weakened inhibitory connectivity from the cerebellum to the cerebral cortex, but stronger inhibitory connectivity from the cerebral cortex to the cerebellum. The present study demonstrated that effective connectivity changes affect a function-specific mentalizing network in SCA2 patients, allowing to deepen the direction and strength of the causal effective connectivity mechanisms driven by the cerebellar damage associated with SCA2.

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.

Altered effective connectivity among face-processing systems in major depressive disorder

BACKGROUND

Neuroimaging studies have revealed abnormal functional interaction during the processing of emotional faces in patients with major depressive disorder (MDD), thereby enhancing our comprehension of the pathophysiology of MDD. However, it is unclear whether there is abnormal directional interaction among face-processing systems in patients with MDD.

METHODS

A group of patients with MDD and a healthy control group underwent a face-matching task during functional magnetic resonance imaging. Dynamic causal modelling (DCM) analysis was used to investigate effective connectivity between 7 regions in the face-processing systems. We used a Parametric Empirical Bayes model to compare effective connectivity between patients with MDD and controls.

RESULTS

We included 48 patients and 44 healthy controls in our analyses. Both groups showed higher accuracy and faster reaction time in the shape-matching condition than in the face-matching condition. However, no significant behavioural or brain activation differences were found between the groups. Using DCM, we found that, compared with controls, patients with MDD showed decreased self-connection in the right dorsolateral prefrontal cortex (DLPFC), amygdala, and fusiform face area (FFA) across task conditions; increased intrinsic connectivity from the right amygdala to the bilateral DLPFC, right FFA, and left amygdala, suggesting an increased intrinsic connectivity centred in the amygdala in the right side of the face-processing systems; both increased and decreased positive intrinsic connectivity in the left side of the face-processing systems; and comparable task modulation effect on connectivity.

LIMITATIONS

Our study did not include longitudinal neuroimaging data, and there was limited region of interest selection in the DCM analysis.

CONCLUSION

Our findings provide evidence for a complex pattern of alterations in the face-processing systems in patients with MDD, potentially involving the right amygdala to a greater extent. The results confirm some previous findings and highlight the crucial role of the regions on both sides of face-processing systems in the pathophysiology of MDD.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Abnormal resting-state effective connectivity in large-scale networks among obsessive-compulsive disorder

BACKGROUND

Obsessive-compulsive disorder (OCD) is a chronic mental illness characterized by abnormal functional connectivity among distributed brain regions. Previous studies have primarily focused on undirected functional connectivity and rarely reported from network perspective.

METHODS

To better understand between or within-network connectivities of OCD, effective connectivity (EC) of a large-scale network is assessed by spectral dynamic causal modeling with eight key regions of interests from default mode (DMN), salience (SN), frontoparietal (FPN) and cerebellum networks, based on large sample size including 100 OCD patients and 120 healthy controls (HCs). Parametric empirical Bayes (PEB) framework was used to identify the difference between the two groups. We further analyzed the relationship between connections and Yale-Brown Obsessive Compulsive Scale (Y-BOCS).

RESULTS

OCD and HCs shared some similarities of inter- and intra-network patterns in the resting state. Relative to HCs, patients showed increased ECs from left anterior insula (LAI) to medial prefrontal cortex, right anterior insula (RAI) to left dorsolateral prefrontal cortex (L-DLPFC), right dorsolateral prefrontal cortex (R-DLPFC) to cerebellum anterior lobe (CA), CA to posterior cingulate cortex (PCC) and to anterior cingulate cortex (ACC). Moreover, weaker from LAI to L-DLPFC, RAI to ACC, and the self-connection of R-DLPFC. Connections from ACC to CA and from L-DLPFC to PCC were positively correlated with compulsion and obsession scores (r = 0.209, p = 0.037; r = 0.199, p = 0.047, uncorrected).

CONCLUSIONS

Our study revealed dysregulation among DMN, SN, FPN, and cerebellum in OCD, emphasizing the role of these four networks in achieving top-down control for goal-directed behavior. There existed a top-down disruption among these networks, constituting the pathophysiological and clinical basis.

Aberrant Effective Connectivity During Eye Gaze Processing Is Linked to Social Functioning and Symptoms in Schizophrenia

BACKGROUND

Patients with schizophrenia show abnormal gaze processing, which is associated with social dysfunction. These abnormalities are related to aberrant connectivity among brain regions that are associated with visual processing, social cognition, and cognitive control. In this study, we investigated 1) how effective connectivity during gaze processing is disrupted in schizophrenia and 2) how this may contribute to social dysfunction and clinical symptoms.

METHODS

Thirty-nine patients with schizophrenia/schizoaffective disorder (SZ) and 33 healthy control participants completed an eye gaze processing task during functional magnetic resonance imaging. Participants viewed faces with different gaze angles and performed explicit and implicit gaze processing. Four brain regions-the secondary visual cortex, posterior superior temporal sulcus, inferior parietal lobule, and posterior medial frontal cortex-were identified as nodes for dynamic causal modeling analysis.

RESULTS

Both the SZ and healthy control groups showed similar model structures for general gaze processing. Explicit gaze discrimination led to changes in effective connectivity, including stronger excitatory, bottom-up connections from the secondary visual cortex to the posterior superior temporal sulcus and inferior parietal lobule and inhibitory, top-down connections from the posterior medial frontal cortex to the secondary visual cortex. Group differences in top-down modulation from the posterior medial frontal cortex to the posterior superior temporal sulcus and inferior parietal lobule were noted, such that these inhibitory connections were attenuated in the healthy control group but further strengthened in the SZ group. Connectivity was associated with social dysfunction and symptom severity.

CONCLUSIONS

The SZ group showed notably stronger top-down inhibition during explicit gaze discrimination, which was associated with more social dysfunction but less severe symptoms among patients. These findings help pinpoint neural mechanisms of aberrant gaze processing and may serve as future targets for interventions that combine neuromodulation with social cognitive training.

Effective connectivity during face processing in major depression - distinguishing markers of pathology, risk, and resilience

BACKGROUND

Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression.

METHODS

Thirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity.

RESULTS

MDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls.

CONCLUSIONS

Our results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.

Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia

Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.

The maternal brain is more flexible and responsive at rest: effective connectivity of the parental caregiving network in postpartum mothers

The field of neuroscience has largely overlooked the impact of motherhood on brain function outside the context of responses to infant stimuli. Here, we apply spectral dynamic causal modelling (spDCM) to resting-state fMRI data to investigate differences in brain function between a group of 40 first-time mothers at 1-year postpartum and 39 age- and education-matched women who have never been pregnant. Using spDCM, we investigate the directionality (top-down vs. bottom-up) and valence (inhibition vs excitation) of functional connections between six key left hemisphere brain regions implicated in motherhood: the dorsomedial prefrontal cortex, ventromedial prefrontal cortex, posterior cingulate cortex, parahippocampal gyrus, amygdala, and nucleus accumbens. We show a selective modulation of inhibitory pathways related to differences between (1) mothers and non-mothers, (2) the interactions between group and cognitive performance and (3) group and social cognition, and (4) differences related to maternal caregiving behaviour. Across analyses, we show consistent disinhibition between cognitive and affective regions suggesting more efficient, flexible, and responsive behaviour, subserving cognitive performance, social cognition, and maternal caregiving. Together our results support the interpretation of these key regions as constituting a parental caregiving network. The nucleus accumbens and the parahippocampal gyrus emerging as 'hub' regions of this network, highlighting the global importance of the affective limbic network for maternal caregiving, social cognition, and cognitive performance in the postpartum period.

Effective cerebello-cerebral connectivity during implicit and explicit social belief sequence learning using dynamic causal modeling

To study social sequence learning, earlier functional magnetic resonance imaging (fMRI) studies investigated the neural correlates of a novel Belief Serial Reaction Time task in which participants learned sequences of beliefs held by protagonists. The results demonstrated the involvement of the mentalizing network in the posterior cerebellum and cerebral areas (e.g. temporoparietal junction, precuneus and temporal pole) during implicit and explicit social sequence learning. However, little is known about the neural functional interaction between these areas during this task. Dynamic causal modeling analyses for both implicit and explicit belief sequence learning revealed that the posterior cerebellar Crus I & II were effectively connected to cerebral mentalizing areas, especially the bilateral temporoparietal junction, via closed loops (i.e. bidirectional functional connections that initiate and terminate at the same cerebellar and cerebral areas). There were more closed loops during implicit than explicit learning, which may indicate that the posterior cerebellum may be more involved in implicitly learning sequential social information. Our analysis supports the general view that the posterior cerebellum receives incoming signals from critical mentalizing areas in the cerebrum to identify sequences of social actions and then sends signals back to the same cortical mentalizing areas to better prepare for others' social actions and one's responses to it.

Dynamic causal modeling of cerebello-cerebral connectivity when sequencing trait-implying actions

Prior studies suggest that the cerebellum contributes to the prediction of action sequences as well as the detection of social violations. In this dynamic causal modeling study, we explored the effective connectivity of the cerebellum with the cerebrum in processing social action sequences. A first model aimed to explore functional cerebello-cerebral connectivity when learning trait/stereotype-implying action sequences. We found many significant bidirectional connectivities between mentalizing areas of the cerebellum and the cerebrum including the temporo-parietal junction (TPJ) and medial prefrontal cortex (mPFC). Within the cerebrum, we found significant connectivity between the right TPJ and the mPFC, and between the TPJ bilaterally. A second model aimed to investigate cerebello-cerebral connectivity when conflicting information arises. We found many significant closed loops between the cerebellum and cerebral mentalizing (e.g. dorsal mPFC) and executive control areas (e.g. medial and lateral prefrontal cortices). Additional closed loops were found within the cerebral mentalizing and executive networks. The current results confirm prior research on effective connectivity linking the cerebellum with mentalizing areas in the cerebrum for predicting social sequences, and extend it to cerebral executive areas for social violations. Overall, this study emphasizes the critical role of cerebello-cerebral connectivity in understanding social sequences.

Abnormalities in visual cognition and associated impaired interactions between visual and attentional networks in schizophrenia and brief psychotic disorder

The extent and nature of cognitive impairment in brief psychotic disorder remains unclear, being rarely studied unlike schizophrenia. The present study hence sought to directly compare the visual cognitive dysfunction and its associated brain networks in brief psychotic disorder and schizophrenia. Data from picture completion (a complex visual task) and whole-brain functional connectome from resting-state fMRI were acquired from a sample of clinically stable patients with an established psychotic disorder (twenty with brief psychotic disorder, twenty with schizophrenia) and twenty-nine healthy controls. Group differences and the inter-relationships in task performances and brain networks were tested. Picture completion task deficits were found in brief psychotic disorder compared with healthy controls, though the deficits were less than schizophrenia. Task performance also correlated with severity of psychotic symptoms in patients. The task performance was inversely correlated with the functional connectivity between peripheral visual and attentional networks (dorsal attention and salience ventral attention), with increased functional connectivity in brief psychotic disorder compared with healthy controls and in schizophrenia compared with brief psychotic disorder. Present findings showed pronounced visual cognitive impairments in brief psychotic disorder that were worse in schizophrenia, underpinned by abnormal interactions between higher-order attentional and lower-order visual processing networks.

Prefrontal tDCS is unable to modulate mind wandering propensity or underlying functional or effective brain connectivity

There is conflicting evidence over the ability to modulate mind-wandering propensity with anodal transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (prefrontal tDCS). Here, 20 participants received 20-min of active and sham prefrontal tDCS while in the MRI scanner, in two separate sessions (counterbalanced). In each session, they completed two runs of a sustained attention to response task (before and during tDCS), which included probes recording subjective responses of mind-wandering. We assessed the effects of tDCS on behavioural responses as well as functional and effective dynamics, via dynamic functional network connectivity (dFNC) and dynamic causal modelling analyses over regions of the default mode, salience and executive control networks. Behavioural results provided substantial evidence in support of no effect of tDCS on task performance nor mind-wandering propensity. Similarly, we found no effect of tDCS on frequency (how often) or dwell time (time spent) of underlying brain states nor effective connectivity. Overall, our results suggest that prefrontal tDCS is unable to modulate mind-wandering propensity or influence underlying brain function. This expands previous behavioural replication failures in suggesting that prefrontal tDCS may not lead to even subtle (i.e., under a behavioural threshold) changes in brain activity during self-generated cognition.

Evidence for the contribution of HCN1 gene polymorphism (rs1501357) to working memory at both behavioral and neural levels in schizophrenia patients and healthy controls

Gene HCN1 polymorphism (rs1501357) has been proposed to be one of the candidate risk genes for schizophrenia in the second report of the Psychiatric Genomics Consortium–Schizophrenia Workgroup. Although animal studies repeatedly showed a role of this gene in working memory, its contribution to working memory in human samples, especially in schizophrenia patients, is still unknown. To explore the association between rs1501357 and working memory at both behavioral (Study 1) and neural (Study 2) levels, the current study involved two independent samples. Study 1 included 876 schizophrenia patients and 842 healthy controls, all of whom were assessed on a 2-back task, a dot pattern expectancy task (DPX), and a digit span task. Study 2 included 56 schizophrenia patients and 155 healthy controls, all of whom performed a 2-back task during functional magnetic resonance imaging (fMRI) scanning. In both studies, we consistently found significant genotype-by-diagnosis interaction effects. For Study 1, the interaction effects were significant for the three tasks. Patients carrying the risk allele performed worse than noncarriers, while healthy controls showed the opposite pattern. For Study 2, the interaction effects were observed at the parietal cortex and the medial frontal cortex. Patients carrying the risk allele showed increased activation at right parietal cortex and increased deactivation at the medial frontal cortex, while healthy controls showed the opposite pattern. These results suggest that the contributions of rs1501357 to working memory capability vary in different populations (i.e., schizophrenia patients vs. healthy controls), which expands our understanding of the functional impact of the HCN1 gene. Future studies should examine its associations with other cognitive functions.

Regional associations of white matter hyperintensities and early cortical amyloid pathology

White matter hyperintensities (WMHs) have a heterogeneous aetiology, associated with both vascular risk factors and amyloidosis due to Alzheimer’s disease. While spatial distribution of both amyloid and WM lesions carry important information for the underlying pathogenic mechanisms, the regional relationship between these two pathologies and their joint contribution to early cognitive deterioration remains largely unexplored.

We included 662 non-demented participants from three Amyloid Imaging to Prevent Alzheimer’s disease (AMYPAD)-affiliated cohorts: EPAD-LCS (N = 176), ALFA+ (N = 310), and EMIF-AD PreclinAD Twin60++ (N = 176). Using PET imaging, cortical amyloid burden was assessed regionally within early accumulating regions (medial orbitofrontal, precuneus, and cuneus) and globally, using the Centiloid method. Regional WMH volume was computed using Bayesian Model Selection. Global associations between WMH, amyloid, and cardiovascular risk scores (Framingham and CAIDE) were assessed using linear models. Partial least square (PLS) regression was used to identify regional associations. Models were adjusted for age, sex, and APOE-e4 status. Individual PLS scores were then related to cognitive performance in 4 domains (attention, memory, executive functioning, and language).

While no significant global association was found, the PLS model yielded two components of interest. In the first PLS component, a fronto-parietal WMH pattern was associated with medial orbitofrontal–precuneal amyloid, vascular risk, and age. Component 2 showed a posterior WMH pattern associated with precuneus-cuneus amyloid, less related to age or vascular risk. Component 1 was associated with lower performance in all cognitive domains, while component 2 only with worse memory.

In a large pre-dementia population, we observed two distinct patterns of regional associations between WMH and amyloid burden, and demonstrated their joint influence on cognitive processes. These two components could reflect the existence of vascular-dependent and -independent manifestations of WMH-amyloid regional association that might be related to distinct primary pathophysiology.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Atypically high influence of subcortical activity on primary sensory regions in autism

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The age-dependent decrease of subcortico-cortical connectivity is attenuated in ASD.

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Primary sensory regions remain less segregated from subcortical activity in ASD.

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This could underlie an excessive amount of sensory input relayed to the cortex.

Background

Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals.

Methods

To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups.

Results

We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD.

Conclusion

This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

Disparate insults relevant to schizophrenia converge on impaired spike synchrony and weaker synaptic interactions in prefrontal local circuits

Schizophrenia results from hundreds of known causes, including genetic, environmental, and developmental insults that cooperatively increase risk of developing the disease. In spite of the diversity of causal factors, schizophrenia presents with a core set of symptoms and brain abnormalities (both structural and functional) that particularly impact the prefrontal cortex. This suggests that many different causal factors leading to schizophrenia may cause prefrontal neurons and circuits to fail in fundamentally similar ways. The nature of convergent malfunctions in prefrontal circuits at the cell and synaptic levels leading to schizophrenia are not known. Here, we apply convergence-guided search to identify core pathological changes in the functional properties of prefrontal circuits that lie downstream of mechanistically distinct insults relevant to the disease. We compare the impacts of blocking NMDA receptors in monkeys and deleting a schizophrenia risk gene in mice on activity timing and effective communication in prefrontal local circuits. Although these manipulations operate through distinct molecular pathways and biological mechanisms, we found they produced convergent pathophysiological effects on prefrontal local circuits. Both manipulations reduced the frequency of synchronous (0-lag) spiking between prefrontal neurons and weakened functional interactions between prefrontal neurons at monosynaptic lags as measured by information transfer between the neurons. The two observations may be related, as reduction in synchronous spiking between prefrontal neurons would be expected to weaken synaptic connections between them via spike-timing-dependent synaptic plasticity. These data suggest that the link between spike timing and synaptic connectivity could comprise the functional vulnerability that multiple risk factors exploit to produce disease.

Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach

We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta-analysis was performed to determine common and disorder-specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty-four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta-analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal-temporal-cingulate-striatal-cerebellar regions. In addition, a disorder-specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging-based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.

Triple network hypothesis-related disrupted connections in schizophrenia: A spectral dynamic causal modeling analysis with functional magnetic resonance imaging

OBJECTIVE

The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients.

METHODS

We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS.

CONCLUSIONS

Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia.

Brain activity and connectivity differences in reward value discrimination during effort computation in schizophrenia

Negative symptoms in the motivational domain are strongly correlated with deficits in social and occupational functioning in schizophrenia. However, the neural substrates underlying these symptoms remain largely unknown. Twenty-eight adults with schizophrenia and twenty healthy volunteers underwent functional magnetic resonance while completing a lottery game designed to capture reward-related cognitive processes. Each trial demanded an initial investment of effort in form of key presses to increase the odds of winning. Brain activity in response to different reward cues (1 euro versus 1 cent) was compared between groups. Whereas controls invested more effort in improving their chances to win 1 euro compared to 1 cent in the lottery game, patients invested similarly high amounts of effort in both reward conditions. The neuroimaging analysis revealed lower neural activity in the bilateral caudate and cingulo-opercular circuits and decreased effective connectivity between reward-associated areas and neural nodes in the frontoparietal and salience network in response to high- versus low-reward conditions in schizophrenia patients compared to controls. Effective connectivity differences across conditions were associated with amotivation symptoms in patients. Overall, our data provide the evidence of alterations in neural activity in the caudate and cingulo-opercular "task maintenance" circuits and frontoparietal effective connectivity with reward-associated nodes as possible underlying mechanisms of reward value discrimination deficits affecting effort computation in schizophrenia.

Markov blankets in the brain

Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description.

Dynamic causal modeling of eye gaze processing in schizophrenia

BACKGROUND

Abnormal eye gaze perception is related to symptoms and social functioning in schizophrenia. However, little is known about the brain network mechanisms underlying these abnormalities. Here, we employed dynamic causal modeling (DCM) of fMRI data to discover aberrant effective connectivity within networks associated with eye gaze processing in schizophrenia.

METHODS

Twenty-seven patients (schizophrenia/schizoaffective disorder, SZ) and 22 healthy controls (HC) completed an eye gaze processing task during fMRI. Participants viewed faces with different gaze angles and performed explicit gaze discrimination (Gaze: "Looking at you?" yes/no) or implicit gaze processing (Gender: "male or female?"). Four brain regions, the secondary visual cortex (Vis), posterior superior temporal sulcus (pSTS), inferior parietal lobule (IPL), and posterior medial frontal cortex (pMFC) were identified as nodes for subsequent DCM analysis.

RESULTS

SZ and HC showed similar generative model structure, but SZ showed altered connectivity for specific self-connections, inter-regional connections during all gaze processing (reduced excitatory bottom-up and enhanced inhibitory top-down connections), and modulation by explicit gaze discrimination (increased frontal inhibition of visual cortex). Altered effective connectivity was significantly associated with poorer social cognition and functioning.

CONCLUSIONS

General gaze processing in SZ is associated with distributed cortical dysfunctions and bidirectional connectivity between regions, while explicit gaze discrimination involves predominantly top-down abnormalities in the visual system. These results suggest plausible neural mechanisms underpinning gaze processing deficits and may serve as bio-markers for intervention.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

Non-classical behavior of the default mode network regions during an information processing task

The default mode network (DMN) efficient deactivation and suppressed functional connectivity (FC) during goal-directed tasks, which require attentional resources, have been considered essential to healthy brain cognition. However, recent studies have shown that DMN regions do not always show the expected behavior. Then, we aimed to investigate the functional activation and connectivity of DMN nodes in young, healthy controls during a goal-directed task. We used an adaptation of the symbol digit modalities test (SDMT) to evaluate the information processing speed (IPS). Twenty-four subjects (10 women, age: 29 ± 7 years) underwent two functional Magnetic Resonance Imaging experiments: one during resting-state and one during a block-designed SDMT paradigm. We superimposed the templates of the DMN on the group activation map and observed the reorganization of the network. For the posterior cingulate cortex (PCC) node of the DMN, which is spatially extensive, comprising the precuneus (dorsal portion) and the posterior cingulate gyrus (PCG, ventral portion), the extent of each region was different between conditions, suggesting different functional roles for them. Therefore, for the functional connectivity (FC) analysis, we split the DMN-PCC region into two regions: left precuneus (BA 7) and PCG. The left precuneus (BA 7) was positively correlated with the left lingual gyrus (BA 17), a task-positive region, and negatively associated with the DMN nodes when comparing task performance with the resting-state condition. The other DMN regions presented the classical antagonistic role during the attentional task. In conclusion, we found that the activation and functional connectivity of the DMN is, in general, suppressed during the information processing. However, the left precuneus BA 7 presented a context-dependent modulatory behavior, working as a transient in-between hub connecting the DMN to task-positive areas. Such findings support studies that show increased activation and excitatory functional connectivity of DMN portions during goal-directed tasks. Moreover, our results may contribute to defining more precise functional correlates of IPS deficits in a wide range of clinical and neurological diseases.

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.

Obstetric Complications and Brain Imaging in Schizophrenia: A Systematic Review

Schizophrenia is a complex disorder in which clinical symptomatology typically reflects underlying brain abnormalities that coalign with multiple physical health comorbidities. The pathogenesis of schizophrenia involves the interplay between genetic and environmental factors, with obstetric complications widely described as key players in elevating the risk of psychosis. In this regard, understanding the anatomical and functional alterations associated with obstetric complications may help to elucidate potential mechanisms through which birth complications could contribute to schizophrenia pathogenesis. We conducted a systematic review of the extant literature describing brain abnormalities and obstetric complications in patients with schizophrenia and related disorders in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A total of 471 studies were retrieved and screened, and 33 studies met inclusion criteria for our review. Studies varied considerably in their methods, with 11 studies employing computed tomography, 1 using magnetic resonance spectroscopy, and 21 using magnetic resonance imaging. The scientific quality of the included studies was assessed and documented. Obstetric complications increase the risk of provoking brain abnormalities. These abnormalities range from decreased gray matter volume and abnormal brain-ventricle ratios to a reduction of volume in limbic regions-which relate to what is commonly observed in schizophrenia. However, current evidence from neuroimaging studies remains scant in relation to establishing obstetric complications as an independent risk factor for schizophrenia.

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

BACKGROUND

The ability to generate a precise internal model of statistical regularities is impaired in schizophrenia. Predictive coding accounts of schizophrenia suggest that psychotic symptoms may be explained by a failure to build precise beliefs or a model of the world. The precision of this model may vary with context. For example, in a noisy environment the model will be more imprecise compared to a model built in an environment with lower noise. However compelling, this idea has not yet been empirically studied in schizophrenia.

METHODS

In this study, 62 participants engaged in a stochastic mismatch negativity paradigm with high and low precision. We included inpatients with a schizophrenia spectrum disorder (N = 20), inpatients with a psychiatric disorder but without psychosis (N = 20), and healthy controls (N = 22), with comparable sex ratio and age distribution. Bayesian mapping and dynamic causal modelling were employed to investigate the underlying microcircuitry of precision encoding of auditory stimuli.

RESULTS

We found strong evidence (exceedance P > 0.99) for differences in the underlying connectivity associated with precision encoding between the three groups as well as on the continuum of psychotic-like experiences assessed across all participants. Critically, we show changes in interhemispheric connectivity between the two inpatient groups, with some connections further aligning on the continuum of psychotic-like experiences.

CONCLUSIONS

While our results suggest continuity in backward connectivity alterations with psychotic-like experiences regardless of diagnosis, they also point to specificity for the schizophrenia spectrum disorder group in interhemispheric connectivity alterations.

Glutamate and Dysconnection in the Salience Network: Neurochemical, Effective Connectivity, and Computational Evidence in Schizophrenia

BACKGROUND

Functional dysconnection in schizophrenia is underwritten by a pathophysiology of the glutamate neurotransmission that affects the excitation-inhibition balance in key nodes of the salience network. Physiologically, this manifests as aberrant effective connectivity in intrinsic connections involving inhibitory interneurons. In computational terms, this produces a pathology of evidence accumulation and ensuing inference in the brain. Finally, the pathophysiology and aberrant inference would partially account for the psychopathology of schizophrenia as measured in terms of symptoms and signs. We refer to this formulation as the 3-level hypothesis.

METHODS

We tested the hypothesis in core nodes of the salience network (the dorsal anterior cingulate cortex [dACC] and the anterior insula) of 20 patients with first-episode psychosis and 20 healthy control subjects. We established 3-way correlations between the magnetic resonance spectroscopy measures of glutamate, effective connectivity of resting-state functional magnetic resonance imaging, and correlations between measures of this connectivity and estimates of precision (inherent in evidence accumulation in the Stroop task) and psychopathology.

RESULTS

Glutamate concentration in the dACC was associated with higher and lower inhibitory connectivity in the dACC and in the anterior insula, respectively. Crucially, glutamate concentration correlated negatively with the inhibitory influence on the excitatory neuronal population in the dACC of subjects with first-episode psychosis. Furthermore, aberrant computational parameters of the Stroop task performance were associated with aberrant inhibitory connections. Finally, the strength of connections from the dACC to the anterior insula correlated negatively with severity of social withdrawal.

CONCLUSIONS

These findings support a link between glutamate-mediated cortical disinhibition, effective-connectivity deficits, and computational performance in psychosis.

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.

Heterogeneity of Outcomes and Network Connectivity in Early-Stage Psychosis: A Longitudinal Study

Imaging studies in psychotic disorders typically examine cross-sectional relationships between magnetic resonance imaging (MRI) signals and diagnosis or symptoms. We sought to examine changes in network connectivity identified using resting-state functional MRI (fMRI) corresponding to divergent functional recovery trajectories and relapse in early-stage psychosis (ESP). Prior studies have linked schizophrenia to hyperconnectivity in the default mode network (DMN). Given the correlations between the DMN and behavioral impairments in psychosis, we hypothesized that dynamic changes in DMN connectivity reflect the heterogeneity of outcomes in ESP. Longitudinal data were collected from 66 ESP patients and 20 healthy controls. Longitudinal cluster analysis identified subgroups of patients with similar trajectories in terms of symptom severity and functional outcomes. DMN connectivity was measured in a subset of patients (n = 36) longitudinally over 2 scans separated by a mean of 12 months. We then compared connectivity between patients and controls, and among the different outcome trajectory subgroups. Among ESP participants, 4 subgroups were empirically identified corresponding to: "Poor," "Middle," "Catch-up," and "Good" trajectory outcomes in the complete dataset (n = 36), and an independent replication (n = 30). DMN connectivity changes differed significantly between functional subgroups (F3,32 = 6.06, P-FDR corrected = .01); DMN connectivity increased over time in the "Poor" outcome cluster (β = +0.145) but decreased over time in the "Catch-up" cluster (β = -0.212). DMN connectivity is dynamic and correlates with a change in functional status over time in ESP. This approach identifies a brain-based marker that reflects important neurobiological processes required to sustain functional recovery.

Cognitive Control Errors in Nonhuman Primates Resembling Those in Schizophrenia Reflect Opposing Effects of NMDA Receptor Blockade on Causal Interactions Between Cells and Circuits in Prefrontal and Parietal Cortices

BACKGROUND

The causal biology underlying schizophrenia is not well understood, but it is likely to involve a malfunction in how neurons adjust synaptic connections in response to patterns of activity in networks. We examined statistical dependencies between neural signals at the cell, local circuit, and distributed network levels in prefrontal and parietal cortices of monkeys performing a variant of the AX continuous performance task paradigm. We then quantified changes in the pattern of neural interactions across levels of scale following NMDA receptor (NMDAR) blockade and related these changes to a pattern of cognitive control errors closely matching the performance of patients with schizophrenia.

METHODS

We recorded the spiking activity of 1762 neurons along with local field potentials at multiple electrode sites in prefrontal and parietal cortices concurrently, and we generated binary time series indicating the presence or absence of spikes in single neurons or local field potential power above or below a threshold. We then applied causal discovery analysis to the time series to detect statistical dependencies between the signals (causal interactions) and compared the pattern of these interactions before and after NMDAR blockade.

RESULTS

Global blockade of NMDAR produced distinctive and frequently opposite changes in neural interactions at the cell, local circuit, and network levels in prefrontal and parietal cortices. Cognitive control errors were associated with decreased interactions at the cell level and with opposite changes at the network level in prefrontal and parietal cortices.

CONCLUSIONS

NMDAR synaptic deficits change causal interactions between neural signals at different levels of scale that correlate with schizophrenia-like deficits in cognitive control.

Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.

Dynamic Causal Modelling suggests impaired effective connectivity in patients with schizophrenia spectrum disorders during gesture-speech integration

Integrating visual and auditory information during gesture-speech integration (GSI) is important for successful social communication, which is often impaired in schizophrenia. Several studies suggested the posterior superior temporal sulcus (pSTS) to be a relevant multisensory integration site. However, intact STS activation patterns were often reported in patients. Thus, here we used Dynamic Causal Modelling (DCM) to analyze whether information processing in schizophrenia spectrum disorders (SSD) is impaired during GSI on network level. We investigated GSI in three different samples. First, we replicated a recently published connectivity model for GSI in a healthy subject group (n = 19). Second, we investigated differences between patients with SSD and a matched healthy control group (n = 17 each). Participants were presented videos of an actor performing intrinsically meaningful gestures accompanied by spoken sentences in German or Russian, or just telling a German sentence without gestures. Across all groups, fMRI analyses revealed similar activation patterns, and DCM analyses resulted in the same winning model for GSI. This finding directly replicates previous results. However, patients revealed significantly reduced connectivity in the verbal pathway (from left middle temporal gyrus (MTG) to left STS). The clinical significance of this connection is supported by its correlations with the severity of concretism and a subscale of negative symptoms (SANS). Our model confirms the importance of the pSTS as integration site during audio-visual integration. Patients showed generally intact connectivity during GSI, but revealed impaired information transfer via the verbal pathway. This might be the basis of interpersonal communication problems in patients with SSD.

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

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

Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI

BACKGROUND

Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent.

METHODS

We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls.

RESULTS

A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula.

LIMITATIONS

The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results.

CONCLUSION

Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Structure learning in coupled dynamical systems and dynamic causal modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits

Individual differences in impulsivity and compulsivity is thought to underlie vulnerability to a broad range of disorders and are closely tied to cortical-striatal-thalamic-cortical function. However, whether impulsivity and compulsivity in clinical disorders is continuous with the healthy population and explains cortical-striatal-thalamic-cortical dysfunction across different disorders remains unclear. Here, we characterized the relationship between cortical-striatal-thalamic-cortical effective connectivity, estimated using dynamic causal modelling of resting-state functional magnetic resonance imaging data, and dimensional phenotypes of impulsivity and compulsivity in two symptomatically distinct but phenotypically related disorders, obsessive-compulsive disorder and gambling disorder. 487 online participants provided data for modelling of dimensional phenotypes. These data were combined with 34 obsessive-compulsive disorder patients, 22 gambling disorder patients, and 39 healthy controls, who underwent functional magnetic resonance imaging. Three core dimensions were identified: disinhibition, impulsivity, and compulsivity. Patients' scores on these dimensions were continuously distributed with the healthy participants, supporting a continuum model of psychopathology. Across all participants, higher disinhibition correlated with lower bottom-up connectivity in the dorsal circuit and greater bottom-up connectivity in the ventral circuit, and higher compulsivity correlated with lower bottom-up connectivity in the dorsal circuit. In patients, higher clinical severity was also linked to lower bottom-up connectivity in the dorsal circuit, but these findings were independent of phenotypic variation, demonstrating convergence towards behaviourally and clinically relevant changes in brain dynamics. Effective connectivity did not differ as a function of traditional diagnostic labels and only weak associations were observed for functional connectivity measures. Together, our results demonstrate that cortical-striatal-thalamic-cortical dysfunction across obsessive-compulsive disorder and gambling disorder may be better characterized by dimensional phenotypes than diagnostic comparisons, supporting investigation of quantitative liability phenotypes.

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.

A guide to group effective connectivity analysis, part 2: Second level analysis with PEB

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.

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This guide walks through a group effective connectivity study using DCM and PEB.

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It explains recently developed tools for hierarchical Bayesian modelling.

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The appendices clarify the technical detail of the PEB framework and its priors.

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An accompanying dataset is provided with step-by-step analysis instructions.

A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms

Ketamine is an uncompetitive N-methyl-d-aspartate (NMDA) glutamate receptor antagonist. It induces effects in healthy individuals that mimic symptoms associated with schizophrenia. We sought to root these experiences in altered brain function, specifically aberrant resting state functional connectivity (rsfMRI). In the present study, we acquired rsfMRI data under ketamine and placebo in a between-subjects design and analyzed seed-based measures of rsfMRI using large-scale networks, dorsolateral prefrontal cortex (DLPFC) and sub-nuclei of the thalamus. We found ketamine-induced alterations in rsfMRI connectivity similar to those seen in patients with schizophrenia, some changes that may be more comparable to early stages of schizophrenia, and other connectivity signatures seen in patients that ketamine did not recreate. We do not find any circuits from our regions of interest that correlates with positive symptoms of schizophrenia in our sample, although we find that DLPFC connectivity with ACC does correlate with a mood measure. These results provide support for ketamine's use as a model of certain biomarkers of schizophrenia, particularly for early or at-risk patients.

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Ketamine altered connectivity in default mode network, thalamus and DLPFC at rest.

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Similar patterns of altered connectivity are seen with ketamine as in psychotic patients.

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Mood symptoms correlated with DLPFC connectivity with ACC and frontal orbital cortex.

Self-other recognition impairments in individuals with schizophrenia: a new experimental paradigm using a double mirror

Clinical observations suggest early self-consciousness disturbances in schizophrenia. A double mirror combining the images of two individuals sitting on each side of the mirror was used to study self-other differentiation in 12 individuals with early onset schizophrenia (EOS) and 15 individuals with adult onset schizophrenia (AOS) compared to 27 typically developing controls (TDC) matched on age and sex. The effects of intermodal sensory perception (visual-tactile and visual-kinesthetic) on self-other recognition were also studied. The results showed that EOS and AOS individuals, independently of age and schizophrenia severity, were centered on their own image compared to TDC, with both significant earlier self-recognition and delayed other-recognition during the visual recognition task. In addition, there was no significant effect of intermodal sensory stimulation on self-other recognition in EOS and AOS patients, whereas self-centered functioning was significantly increased by visual-tactile stimulation and decreased by visual-kinesthetic stimulation in TDC. The findings suggest that self-other recognition impairments might be a possible endophenotypic trait of schizophrenia.

Dysregulation within the salience network and default mode network in hyperthyroid patients: a follow-up resting-state functional MRI study

This study investigated the aberrant connectivity of the salience network (SN) and default mode network (DMN) and the relevance between these abnormalities and symptom improvement in hyperthyroid patients using resting-state functional magnetic resonance imaging (rs-fMRI). Seed-based functional connectivity (FC) analyses were performed on state fMRI data to reveal possible differences in critical node connectivity in the SN and DMN between 41 new-onset, untreated hyperthyroid patients and 41 healthy controls. Subsequently, follow-up data were available for 25 patients treated with methimazole for one month. Compared with the healthy controls, the patients exhibited abnormal internetwork FC from the SN to the DMN and the executive control network (ECN) and decreased intra-network FC within the SN. Relative to the hyperthyroid state, the antithyroid therapy induced reversible connectivity of the left insula to the dorsal anterior cingulate cortex(dACC)and ECN, and persistently increased connectivity between the SN and DMN in patients with improved thyroid function. Finally, Pearson's correlation analyses were performed among the abnormal FC, neuropsychological assessment and serum free triiodothyronine(FT3)level data. The results indicated that aberrant intra- and internetwork FC in the SN and DMN might underlie the pathogenesis of hyperthyroidism, and antithyroid treatment could regulate the FC of certain key brain regions within the SN and DMN in hyperthyroid patients.