Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function

Dynamic modular dysregulation in multilayer networks underlies cognitive and clinical deficits in first-episode schizophrenia

Schizophrenia has been identified to exhibit significant abnormalities in brain functional networks, which are likely to underpin the cognitive and functional impairments observed in patients. Graph theoretical analysis revealed the disrupted modularity in schizophrenia, however, the dynamic network abnormalities in schizophrenia remains unclear. We collected the resting-state functional magnetic resonance imaging data from 82 first-episode schizophrenia (FES) patients and 55 healthy control (HC) subjects. Dynamic functional connectivity matrices were constructed and a multilayer network model was employed to run the dynamic modularity analysis. We also performed correlation analyses to investigate the relationship between flexibility, cognitive function and clinical symptoms. Our findings indicate that FES patients exhibit higher multilayer modularity. The node flexibility of FES patients were found elevated in several brain regions, which were included in the default mode network, fronto-parietal network, salience network and visual network. The node flexibility metrics in aberrant brain regions were found to demonstrate significant correlations with cognitive function and negative symptoms in patients with FES. These findings suggest a pathological imbalance in brain network dynamics, where abnormal modular organization might contribute to the cognitive impairment and functional deficits in schizophrenia.

Macro- and Microstructural Alterations in the Midbrain in Early Psychosis Associates with Clinical Symptom Scores

Early psychosis (EP) is a critical period for psychotic disorders during which the brain undergoes rapid and significant functional and structural changes ( Shinn et al., 2017). The Human Connectome Project (HCP) is a global effort to map the human brain's connectivity in health and disease. Here we focus on HCP-EP subjects (i.e., those within 5 years of the initial psychotic episode) to determine macro- and microstructural alterations in EP (HCP-EP sample, n = 179: EP, n = 123, controls, n = 56) and their association with clinical outcomes (i.e., symptoms severity) in HCP-EP. We carried out analyses of deformation-based morphometry (DBM), scalar indices from the diffusion tensor imaging (DTI), and tract-based spatial statistics (TBSS). Lastly, we conducted correlation analyses focused on the midbrain (DBM and DTI) to examine associations between its structure and clinical symptoms. Our results show that the midbrain displays robust alteration in its structure (DBM and DTI) in the voxel-based analysis. Complimentary alterations were also observed for the hippocampus and putamen. A seed-based analysis centered around the midbrain confirms the voxel-based analysis of DBM and DTI. TBSS displays structural differences within the midbrain and complementary alterations in the corticospinal tract and cingulum. Correlations between the midbrain structures and behavior showed that the quantified features correlate with cognition and clinical scores. Our findings contribute to understanding the midbrain-focused circuitry involvement in EP and provide a path for future investigations to inform specific brain-based biomarkers of EP.

Reduced temporal variability of cortical excitation/inhibition ratio in schizophrenia

Altered neural excitation/inhibition (E/I) balance has long been suspected as a potential underlying cause for clinical symptoms in schizophrenia (SZ). Recent methodological advancements linking the spectral slope (β) of neurophysiological recordings - such as them electroencephalogram (EEG) - to E/I ratio provided much-needed tools to better understand this plausible relationship. Importantly, most approaches treat E/I ratio as a stationary feature in a single scaling range. On the other hand, previous research indicates that this property might change over time, as well as it can express different characteristics in low- and high-frequency regimes. In line, in this study we analyzed resting-state EEG recordings from 30 patients with SZ and 31 healthy controls (HC) and characterized E/I ratio via β separately for low- (1-4 Hz) and high- (20-45 Hz) frequency regimes in a time-resolved manner. Results from this analysis confirmed the bimodal nature of power spectra in both HC and SZ, with steeper spectral slopes in the high- compared to low-frequency ranges. We did not observe any between-group differences in stationary (i.e., time-averaged) neural signatures, however, the temporal variance of β in the 20-45 Hz regime was significantly reduced in SZ patients when compared to HC, predominantly over regions corresponding to the dorsal attention network. Furthermore, this alteration was found correlated to positive clinical symptom scores. Our study indicates that altered E/I ratio dynamics are a characteristic trait of SZ that reflect pathophysiological processes involving the parietal and occipital cortices, potentially responsible for some of the clinical features of the disorder.

On the varieties of conscious experiences: Altered Beliefs Under Psychedelics (ALBUS)

How is it that psychedelics so profoundly impact brain and mind? According to the model of "Relaxed Beliefs Under Psychedelics" (REBUS), 5-HT2a agonism is thought to help relax prior expectations, thus making room for new perspectives and patterns. Here, we introduce an alternative (but largely compatible) perspective, proposing that REBUS effects may primarily correspond to a particular (but potentially pivotal) regime of very high levels of 5-HT2a receptor agonism. Depending on both a variety of contextual factors and the specific neural systems being considered, we suggest opposite effects may also occur in which synchronous neural activity becomes more powerful, with accompanying "Strengthened Beliefs Under Psychedelics" (SEBUS) effects. Such SEBUS effects are consistent with the enhanced meaning-making observed in psychedelic therapy (e.g. psychological insight and the noetic quality of mystical experiences), with the imposition of prior expectations on perception (e.g. hallucinations and pareidolia), and with the delusional thinking that sometimes occurs during psychedelic experiences (e.g. apophenia, paranoia, engendering of inaccurate interpretations of events, and potentially false memories). With "Altered Beliefs Under Psychedelics" (ALBUS), we propose that the manifestation of SEBUS vs. REBUS effects may vary across the dose-response curve of 5-HT2a signaling. While we explore a diverse range of sometimes complex models, our basic idea is fundamentally simple: psychedelic experiences can be understood as kinds of waking dream states of varying degrees of lucidity, with similar underlying mechanisms. We further demonstrate the utility of ALBUS by providing neurophenomenological models of psychedelics focusing on mechanisms of conscious perceptual synthesis, dreaming, and episodic memory and mental simulation.

Aberrant Modular Dynamics of Functional Networks in Schizophrenia and Their Relationship with Neurotransmitter and Gene Expression Profiles

Introduction

Numerous studies have emphasized the time-varying modular architecture of functional brain networks and its relevance to cognitive functions in healthy participants. However, how brain modular dynamics change in schizophrenia and how these alterations relate to neurotransmitter and transcriptomic signatures have not been well elucidated.

Methods

We harmonized resting-state fMRI data from a multi-site sample including 223 patients and 279 healthy controls and applied the multilayer network method to estimate the regional module switching rate (flexibility) of functional brain connectomes. We examined aberrant flexibility in patients relative to controls and explored its relations to neurotransmitter systems and postmortem gene expression.

Results

Compared with controls, patients with schizophrenia had significantly higher flexibility in the somatomotor and right visual regions, and lower flexibility in the left parahippocampal gyrus, right supramarginal gyrus, right frontal-operculum-insula, bilateral precuneus posterior cingulate cortex, and bilateral inferior parietal gyrus. These alterations were associated with multiple neurotransmitter systems and weighted gene transcriptomic profiles. The most relevant genes were preferentially enriched for biological processes of transmembrane transport and brain development, specific cell types, and previously identified schizophrenia-related genes.

Conclusions

This study reveals aberrant modular dynamics in schizophrenia and its relations to neurotransmitter systems and schizophrenia-related transcriptomic profiles, providing insights into the understanding of the pathophysiology underlying schizophrenia.

Aberrant controllability of functional connectome during working memory tasks in patients with schizophrenia and unaffected siblings

Background

Working memory deficit, a key feature of schizophrenia, is a heritable trait shared with unaffected siblings. It can be attributed to dysregulation in transitions from one brain state to another.

Aims

Using network control theory, we evaluate if defective brain state transitions underlie working memory deficits in schizophrenia.

Method

We examined average and modal controllability of the brain's functional connectome in 161 patients with schizophrenia, 37 unaffected siblings and 96 healthy controls during a two-back task. We use one-way analysis of variance to detect the regions with group differences, and correlated aberrant controllability to task performance and clinical characteristics. Regions affected in both unaffected siblings and patients were selected for gene and functional annotation analysis.

Results

Both average and modal controllability during the two-back task are reduced in patients compared to healthy controls and siblings, indicating a disruption in both proximal and distal state transitions. Among patients, reduced average controllability was prominent in auditory, visual and sensorimotor networks. Reduced modal controllability was prominent in default mode, frontoparietal and salience networks. Lower modal controllability in the affected networks correlated with worse task performance and higher antipsychotic dose in schizophrenia (uncorrected). Both siblings and patients had reduced average controllability in the paracentral lobule and Rolandic operculum. Subsequent out-of-sample gene analysis revealed that these two regions had preferential expression of genes relevant to bioenergetic pathways (calmodulin binding and insulin secretion).

Conclusions

Aberrant control of brain state transitions during task execution marks working memory deficits in patients and their siblings.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

High modularity, more flexible of brain networks in patients with mild to moderate motor impairments after stroke

Stroke is recognized as a network communication disorder. Advances in neuroimaging technologies have enhanced our comprehension of dynamic cerebral alterations. However, different levels of motor function impairment after stroke may have different patterns of brain reorganization. Abnormal and adaptive patterns of brain activity in mild-to-moderate motor function impairments after stroke remain still underexplored. We aim to identify dynamic patterns of network remodeling in stroke patients with mild-to-moderate impairment of motor function. fMRI data were obtained from 30 stroke patients and 31 healthy controls to establish a spatiotemporal multilayer modularity model. Then, graph-theoretic measures, including modularity, flexibility, cohesion, and disjointedness, were calculated to quantify dynamic reconfiguration. Our findings reveal that the post-stroke brain exhibited higher modular organization, as well as heightened disjointedness, compared to HCs. Moreover, analyzing from the network level, we found increased disjointedness and flexibility in the Default mode network (DMN), indicating that brain regions tend to switch more frequently and independently between communities and the dynamic changes were mainly driven by DMN. Notably, modified functional dynamics positively correlated with motor performance in patients with mild-to-moderate motor impairment. Collectively, our research uncovered patterns of dynamic community reconstruction in multilayer networks following stroke. Our findings may offer new insights into the complex reorganization of neural function in post-stroke brain.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

Aberrant brain dynamics in major depressive disorder during working memory task

Working memory (WM) is a distributed and dynamic process, and WM deficits are recognized as one of the top-ranked endophenotype candidates for major depressive disorders (MDD). However, there is a lack of knowledge of brain temporal-spatial profile of WM deficits in MDD. We used the dynamical degree centrality (dDC) to investigate the whole-brain temporal-spatial profile in 40 MDD and 40 controls during an n-back task with 2 conditions (i.e., '0back' and '2back'). We explored the dDC temporal variability and clustered meta-stable states in 2 groups during different WM conditions. Pearson's correlation analysis was used to evaluate the relationship between the altered dynamics with clinical symptoms and WM performance. Compared with controls, under '2back vs. 0back' contrast, patients showed an elevated dDC variability in wide range of brain regions, including the middle frontal gyrus, orbital part of inferior frontal gyrus (IFGorb), hippocampus, and middle temporal gyrus. Furthermore, the increased dDC variability in the hippocampus and IFGorb correlated with worse WM performance. However, there were no significant group-related differences in the meta-stable states were observed. This study demonstrated the increased WM-related instability (i.e., the elevated dDC variability) was represented in MDD, and enhancing stability may help patients achieve better WM performance.

Increased whole-brain functional heterogeneity in psychosis during rest and task

Past work has shown that people with schizophrenia exhibit more cross-subject heterogeneity in their functional connectivity patterns. However, it remains unclear whether specific brain networks are implicated, whether common confounds could explain the results, or whether task activations might also be more heterogeneous. Unambiguously establishing the existence and extent of functional heterogeneity constitutes a first step toward understanding why it emerges and what it means clinically.

METHODS

We first leveraged data from the HCP Early Psychosis project. Functional connectivity (FC) was extracted from 718 parcels via principal components regression. Networks were defined via a brain network partition (Ji et al., 2019). We also examined an independent data set with controls, later-stage schizophrenia patients, and ADHD patients during rest and during a working memory task. We quantified heterogeneity by averaging the Pearson correlation distance of each subject's FC or task activity pattern to that of every other subject of the same cohort.

RESULTS

Affective and non-affective early psychosis patients exhibited more cross-subject whole-brain heterogeneity than healthy controls (ps < 0.001, Hedges' g > 0.74). Increased heterogeneity could be found in up to seven networks. In-scanner motion, medication, nicotine, and comorbidities could not explain the results. Later-stage schizophrenia patients exhibited heterogeneous connectivity patterns and task activations compared to ADHD and control subjects. Interestingly, individual connection weights, parcel-wise task activations, and network averages thereof were not more variable in patients, suggesting that heterogeneity becomes most obvious over large-scale patterns.

CONCLUSION

Whole-brain cross-subject functional heterogeneity characterizes psychosis during rest and task. Developmental and pathophysiological consequences are discussed.

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Macro- and Micro-Structural Alterations in the Midbrain in Early Psychosis

Introduction

Early psychosis (EP) is a critical period in the course of psychotic disorders during which the brain is thought to undergo rapid and significant functional and structural changes 1 . Growing evidence suggests that the advent of psychotic disorders is early alterations in the brain's functional connectivity and structure, leading to aberrant neural network organization. The Human Connectome Project (HCP) is a global effort to map the human brain's connectivity in healthy and disease populations; within HCP, there is a specific dataset that focuses on the EP subjects (i.e., those within five years of the initial psychotic episode) (HCP-EP), which is the focus of our study. Given the critically important role of the midbrain function and structure in psychotic disorders (cite), and EP in particular (cite), we specifically focused on the midbrain macro- and micro-structural alterations and their association with clinical outcomes in HCP-EP.

Methods

We examined macro- and micro-structural brain alterations in the HCP-EP sample (n=179: EP, n=123, Controls, n=56) as well as their associations with behavioral measures (i.e., symptoms severity) using a stepwise approach, incorporating a multimodal MRI analysis procedure. First, Deformation Based Morphometry (DBM) was carried out on the whole brain 3 Tesla T1w images to examine gross brain anatomy (i.e., seed-based and voxel-based volumes). Second, we extracted Fractional Anisotropy (FA), Axial Diffusivity (AD), and Mean Diffusivity (MD) indices from the Diffusion Tensor Imaging (DTI) data; a midbrain mask was created based on FreeSurfer v.6.0 atlas. Third, we employed Tract-Based Spatial Statistics (TBSS) to determine microstructural alterations in white matter tracts within the midbrain and broader regions. Finally, we conducted correlation analyses to examine associations between the DBM-, DTI- and TBSS-based outcomes and the Positive and Negative Syndrome Scale (PANSS) scores.

Results

DBM analysis showed alterations in the hippocampus, midbrain, and caudate/putamen. A DTI voxel-based analysis shows midbrain reductions in FA and AD and increases in MD; meanwhile, the hippocampus shows an increase in FA and a decrease in AD and MD. Several key brain regions also show alterations in DTI indices (e.g., insula, caudate, prefrontal cortex). A seed-based analysis centered around a midbrain region of interest obtained from freesurfer segmentation confirms the voxel-based analysis of DTI indices. TBSS successfully captured structural differences within the midbrain and complementary alterations in other main white matter tracts, such as the corticospinal tract and cingulum, suggesting early altered brain connectivity in EP. Correlations between these quantities in the EP group and behavioral scores (i.e., PANSS and CAINS tests) were explored. It was found that midbrain volume noticeably correlates with the Cognitive score of PA and all DTI metrics. FA correlates with the several dimensions of the PANSS, while AD and MD do not show many associations with PANSS or CAINS.

Conclusions

Our findings contribute to understanding the midbrain-focused circuitry involvement in EP and complimentary alteration in EP. Our work provides a path for future investigations to inform specific brain-based biomarkers of EP and their relationships to clinical manifestations of the psychosis course.

Modulatory effects of acupuncture on raphe nucleus-related brain circuits in patients with chronic neck pain: A randomized neuroimaging trial

OBJECTIVE

Acupuncture has shown promise in treating neck pain. Clinical trials have shown mixed results, possibly due to heterogeneous methodologies and the lack of knowledge regarding underlying brain circuit mechanism of action. In this study, we investigated the specific contribution of the serotonergic system in treating neck pain, and the specific brain circuits involved.

METHODS

A total of 99 patients with chronic neck pain (CNP) were randomized to receive true acupuncture (TA) or sham acupuncture (SA) 3 times weekly for 4 weeks. Patients with CNP in each group were assessed for primary outcomes by measuring the Visual Analog Scale (VAS) and the duration of each attack; secondary outcomes were measured using the Neck Disability Index (NDI), Northwick Park Neck Pain Questionnaire (NPQ), McGill Pain Questionnaire (MPQ), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS) and the 12-item Short Form Quality Life Scale (SF-12); levels of functional circuits connectivity were assessed using resting-state functional magnetic resonance imaging in the dorsal (DR) and median (MR) raphe nucleus, before and after undergoing acupuncture.

RESULTS

Patients receiving TA showed more extensive symptom improvement compared with SA. Regarding the primary outcomes, changes observed in the TA group were as follows: VAS = 16.9 mm (p < 0.001) and the duration of each attack = 4.30 h (p < 0.001); changes in the SA group: VAS = 5.41 mm (p = 0.138) and the duration of each attack = 2.06 h (p = 0.058). Regarding the secondary outcomes, changes in the TA group: NDI = 7.99 (p < 0.001), NPQ = 10.82 (p < 0.001), MPQ = 4.23 (p < 0.001), SAS = 5.82 (p < 0.001), SDS = 3.67 (p = 0.003), and SF-12 = 3.04 (p < 0.001); changes in the SA group: NDI = 2.97 (p = 0.138), NPQ = 5.24 (p = 0.035) and MPQ = 2.90 (p = 0.039), SAS = 1.48 (p = 0.433), SDS = 2.39 (p = 0.244), and SF-12 = 2.19 (p = 0.038). The modulatory effect of TA exhibited increased functional connectivity (FC) between the DR and thalamus, between the MR and parahippocampal gyrus, amygdala, and insula, with decreased FC between the DR and lingual gyrus and middle frontal gyrus, between the MR and middle frontal gyrus. Furthermore, changes in the DR-related circuit were specifically associated with the intensity and duration of pain, and the MR-related circuit was correlated with the quality of life with CNP.

CONCLUSION

These results demonstrated the effectiveness of TA in treating neck pain and suggested that it regulates CNP by reconfiguring the function of the raphe nucleus-related serotonergic system.

Aberrant Brain Dynamics in Schizophrenia During Working Memory Task: Evidence From a Replication Functional MRI Study

BACKGROUND AND HYPOTHESIS

The integration of information that typifies working memory (WM) operation requires a flexible, dynamic functional relationship among brain regions. In schizophrenia, though WM capacity is prominently impaired at higher loads, the mechanistic underpinnings are unclear. As a result, we lack convincing cognitive remediation of load-dependent deficits. We hypothesize that reduced WM capacity arises from a disruption in dynamic functional connectivity when patients face cognitive demands.

STUDY DESIGN

We calculate the dynamic voxel-wise degree centrality (dDC) across the functional connectome in 142 patients with schizophrenia and 88 healthy controls (HCs) facing different WM loads during an n-back task. We tested associations of the altered variability in dDC and clinical symptoms and identified intermediate connectivity configurations (clustered states) across time during WM operation. These analyses were repeated in another independent dataset of 169 subjects (102 with schizophrenia).

STUDY RESULTS

Compared with HCs, patients showed an increased dDC variability of supplementary motor area (SMA) for the "2back vs. 0back" contrast. This instability at the SMA seen in patients correlated with increased positive symptoms and followed a limited "U-shape" pattern at rest-condition and 2 loads. In the clustering analysis, patients showed reduced centrality in the SMA, superior temporal gyrus, and putamen. These results were replicated in a constrained search in the second independent dataset.

CONCLUSIONS

Schizophrenia is characterized by a load-dependent reduction of stable centrality in SMA; this relates to the severity of positive symptoms, especially disorganized behaviour. Restoring SMA stability in the presence of cognitive demands may have a therapeutic effect in schizophrenia.

Brain mechanisms underlying catatonia: A systematic review

BACKGROUND

Catatonia is a complex psychomotor disorder characterized by motor, affective, and behavioral symptoms. Despite being known for almost 150 years, its pathomechanisms are still largely unknown.

METHODS

A systematic research on PubMed, Web of Science, and Scopus was conducted to identify neuroimaging studies conducted on group or single individuals with catatonia. Overall, 33 studies employing structural magnetic resonance imaging (sMRI, n = 11), functional magnetic resonance imaging (fMRI, n = 10), sMRI and fMRI (n = 2), functional near-infrared spectroscopy (fNIRS, n = 1), single positron emission computer tomography (SPECT, n = 4), positron emission tomography (PET, n = 4), and magnetic resonance spectroscopy (MRS, n = 1), and 171 case reports were retrieved.

RESULTS

Observational sMRI studies showed numerous brain changes in catatonia, including diffuse atrophy and signal hyperintensities, while case-control studies reported alterations in fronto-parietal and limbic regions, the thalamus, and the striatum. Task-based and resting-state fMRI studies found abnormalities located primarily in the orbitofrontal, medial prefrontal, motor cortices, cerebellum, and brainstem. Lastly, metabolic and perfusion changes were observed in the basal ganglia, prefrontal, and motor areas. Most of the case-report studies described widespread white matter lesions and frontal, temporal, or basal ganglia hypoperfusion.

CONCLUSIONS

Catatonia is characterized by structural, functional, perfusion, and metabolic cortico-subcortical abnormalities. However, the majority of studies and case reports included in this systematic review are affected by considerable heterogeneity, both in terms of populations and neuroimaging techniques, which calls for a cautious interpretation. Further elucidation, through future neuroimaging research, could have great potential to improve the description of the neural motor and psychomotor mechanisms underlying catatonia.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features

Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.

Cross-module switching diversity of brain network nodes in resting and cognitive states

Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09894-z.

Examining the efficacy of a cardio-dance intervention on brain health and the moderating role of ABCA7 in older African Americans: a protocol for a randomized controlled trial

Introduction

African Americans are two to three times more likely to be diagnosed with Alzheimer's disease (AD) compared to White Americans. Exercise is a lifestyle behavior associated with neuroprotection and decreased AD risk, although most African Americans, especially older adults, perform less than the recommended 150 min/week of moderate-to-vigorous intensity exercise. This article describes the protocol for a Phase III randomized controlled trial that will examine the effects of cardio-dance aerobic exercise on novel AD cognitive and neural markers of hippocampal-dependent function (Aims #1 and #2) and whether exercise-induced neuroprotective benefits may be modulated by an AD genetic risk factor, ABCA7 rs3764650 (Aim #3). We will also explore the effects of exercise on blood-based biomarkers for AD.

Methods and analysis

This 6-month trial will include 280 African Americans (≥ 60 years), who will be randomly assigned to 3 days/week of either: (1) a moderate-to-vigorous cardio-dance fitness condition or (2) a low-intensity strength, flexibility, and balance condition for 60 min/session. Participants will complete health and behavioral surveys, neuropsychological testing, saliva and venipuncture, aerobic fitness, anthropometrics and resting-state structural and functional neuroimaging at study entry and 6 months.

Discussion

Results from this investigation will inform future exercise trials and the development of prescribed interventions that aim to reduce the risk of AD in African Americans.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

BACKGROUND

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

PURPOSE

To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.

STUDY TYPE

Prospective.

SUBJECTS

A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.

ASSESSMENT

Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.

STATISTICAL TESTS

The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).

DATA CONCLUSION

The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

Brain network flexibility as a predictor of skilled musical performance

Interactions between the body and the environment are dynamically modulated by upcoming sensory information and motor execution. To adapt to this behavioral state-shift, brain activity must also be flexible and possess a large repertoire of brain networks so as to switch them flexibly. Recently, flexible internal brain communications, i.e. brain network flexibility, have come to be recognized as playing a vital role in integrating various sensorimotor information. Therefore, brain network flexibility is one of the key factors that define sensorimotor skill. However, little is known about how flexible communications within the brain characterize the interindividual variation of sensorimotor skill and trial-by-trial variability within individuals. To address this, we recruited skilled musical performers and used a novel approach that combined multichannel-scalp electroencephalography, behavioral measurements of musical performance, and mathematical approaches to extract brain network flexibility. We found that brain network flexibility immediately before initiating the musical performance predicted interindividual differences in the precision of tone timbre when required for feedback control, but not for feedforward control. Furthermore, brain network flexibility in broad cortical regions predicted skilled musical performance. Our results provide novel evidence that brain network flexibility plays an important role in building skilled sensorimotor performance.

Developmental differences in functional organization of multispectral networks

Assessing brain connectivity during rest has become a widely used approach to identify changes in functional brain organization during development. Generally, previous works have demonstrated that brain activity shifts from more local to more distributed processing from childhood into adolescence. However, the majority of those works have been based on functional magnetic resonance imaging measures, whereas multispectral functional connectivity, as measured using magnetoencephalography (MEG), has been far less characterized. In our study, we examined spontaneous cortical activity during eyes-closed rest using MEG in 101 typically developing youth (9-15 years old; 51 females, 50 males). Multispectral MEG images were computed, and connectivity was estimated in the canonical delta, theta, alpha, beta, and gamma bands using the imaginary part of the phase coherence, which was computed between 200 brain regions defined by the Schaefer cortical atlas. Delta and alpha connectivity matrices formed more communities as a function of increasing age. Connectivity weights predominantly decreased with age in both frequency bands; delta-band differences largely implicated limbic cortical regions and alpha band differences in attention and cognitive networks. These results are consistent with previous work, indicating the functional organization of the brain becomes more segregated across development, and highlight spectral specificity across different canonical networks.

The acute effects of psychoactive drugs on emotional episodic memory encoding, consolidation, and retrieval: A comprehensive review

Psychoactive drugs modulate learning and emotional processes in ways that could impact their recreational and medical use. Recent work has revealed how drugs impact different stages of processing emotional episodic memories, specifically encoding (forming memories), consolidation (stabilizing memories), and retrieval (accessing memories). Drugs administered before encoding may preferentially impair (e.g., GABAA sedatives including alcohol and benzodiazepines, Δ9-tetrahydrocannabinol or THC, ketamine), enhance (e.g., dextroamphetamine and dextromethamphetamine), or both impair and enhance (i.e., ± 3,4-methylenedioxymethylamphetamine or MDMA) emotionally negative and positive compared to neutral memories. GABAA sedatives administered immediately post-encoding (during consolidation) can preferentially enhance emotional memories, though this selectivity may decline or even reverse (i.e., preferential enhancement of neutral memories) as the delay between encoding and retrieval increases. Finally, retrieving memories under the effects of THC, dextroamphetamine, MDMA, and perhaps GABAA sedatives distorts memory, with potentially greater selectively for emotional (especially positive) memories. We review these effects, propose neural mechanisms, discuss methodological considerations for future work, and speculate how drug effects on emotional episodic memory may contribute to drug use and abuse.

Discrimination of auditory verbal hallucination in schizophrenia based on EEG brain networks

Auditory verbal hallucinations (AVH) are a core positive symptom of schizophrenia and are regarded as a consequence of the functional breakdown in the related sensory process. Yet, the potential mechanism of AVH is still lacking. In the present study, we explored the difference between AVHs (n = 23) and non-AVHs (n = 19) in schizophrenia and healthy controls (n = 29) by using multidimensional electroencephalograms data during an auditory oddball task. Compared to healthy controls, both AVH and non-AVH groups showed reduced P300 amplitudes. Additionally, the results from brain networks analysis revealed that AVH patients showed reduced left frontal to posterior parietal/temporal connectivity compared to non-AVH patients. Moreover, using the fused network properties of both delta and theta bands as features for in-depth learning made it possible to identify the AVH from non-AVH patients at an accuracy of 80.95%. The left frontal-parietal/temporal networks seen in the auditory oddball paradigm might be underlying biomarkers of AVH in schizophrenia. This study demonstrated for the first time the functional breakdown of the auditory processing pathway in the AVH patients, leading to a better understanding of the atypical brain network of the AVH patients.

Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis.

Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network.

Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms.

Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network's quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network's temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.

Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia

Understanding how structural connectivity alterations affect aberrant dynamic function using network control theory will provide new mechanistic insights into the pathophysiology of schizophrenia. The study included 140 drug-naive schizophrenia patients and 119 healthy controls (HCs). The average controllability (AC) quantifying capacity of brain regions/networks to shift the system into easy-to-reach states was calculated based on white matter connectivity and was compared between patients and HCs as well as functional network topological and dynamic properties. The correlation analysis between AC and duration of untreated psychosis (DUP) were conducted to characterize the controllability progression pattern without treatment effects. Relative to HCs, patients exhibited reduced AC in multiple nodes, mainly distributed in default mode network (DMN), visual network (VN), and subcortical regions, and increased AC in somatomotor network. These networks also had impaired functional topology and increased temporal variability in dynamic functional connectivity analysis. Longer DUP was related to greater reductions of AC in VN and DMN. The current study highlighted potential structural substrates underlying altered functional dynamics in schizophrenia, providing a novel understanding of the relationship of anatomic and functional network alterations.

Robust dynamic brain coactivation states estimated in individuals

A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.

Increased flexibility of brain dynamics in patients with multiple sclerosis

Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, P_FDR = 0.024), promiscuity (t = 1.94, P_FDR = 0.038), entropy (t = 2.17, P_FDR = 0.027) and cohesion (t = 2.45, P_FDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.

Impaired dynamic functional brain properties and their relationship to symptoms in never treated first-episode patients with schizophrenia

Studies of dynamic functional connectivity (dFC) and topology can provide novel insights into the neurophysiology of brain dysfunction in schizophrenia and its relation to core symptoms of psychosis. Limited investigations of these disturbances have been conducted with never-treated first-episode patients to avoid the confounds of treatment or chronic illness. Therefore, we recruited 95 acutely ill, first-episode, never-treated patients with schizophrenia and examined brain dFC patterns relative to healthy controls using resting-state functional magnetic resonance imaging and a sliding-window approach. We compared the dynamic attributes at the group level and found patients spent more time in a hypoconnected state and correspondingly less time in a hyperconnected state. Patients demonstrated decreased dynamics of nodal efficiency and eigenvector centrality (EC) in the right medial prefrontal cortex, which was associated with psychosis severity reflected in Positive and Negative Syndrome Scale ratings. We also observed increased dynamics of EC in temporal and sensorimotor regions. These findings were supported by validation analysis. To supplement the group comparison analyses, a support vector classifier was used to identify the dynamic attributes that best distinguished patients from controls at the individual level. Selected features for case-control classification were highly coincident with the properties having significant between-group differences. Our findings provide novel neuroimaging evidence about dynamic characteristics of brain physiology in acute schizophrenia. The clinically relevant atypical pattern of dynamic shifting between brain states in schizophrenia may represent a critical aspect of illness pathophysiology underpinning its defining cognitive, behavioral, and affective features.

Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study

Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries-known as ego dissolution-surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A receptors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. SIGNIFICANCE STATEMENT: Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging findings by bridging the role between the 5-HT2A receptors and large-scale brain networks.

Functional brain reconfiguration during sustained pain

Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontoparietal regions, constituting an extended brain network of sustained pain. As pain decreased over time, the subcortical and frontoparietal regions were separated from this brain network and connected to multiple cerebellar regions. Machine-learning models based on these network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.

Skepticism about Recent Evidence That Psilocybin "Liberates" Depressed Minds

A recent paper in Nature Medicine found that psilocybin therapy in patients with depression decreased brain network modularity (measured with task-free functional magnetic resonance imaging), an effect supposedly not found with the selective serotonin reuptake inhibitor S-citalopram. This decrease in network modularity also correlated with depression. Here, we raise several issues with this paper, including inconsistencies in reports of the primary clinical outcome, statistical flaws including a one-tailed test, nonsignificant interaction, and regression to the mean, the ambiguity and overinterpretation of "resting state" data, and a missing reference for a conceptually similar study that exemplifies why a one-tailed test cannot be justified. Together, these issues make us question the uniqueness and impact of these findings, as well as the unwarranted media hype that they generated.

Visual system assessment for predicting a transition to psychosis

The field of psychiatry is far from perfect in predicting which individuals will transition to a psychotic disorder. Here, we argue that visual system assessment can help in this regard. Such assessments have generated medium-to-large group differences with individuals prior to or near the first psychotic episode or have shown little influence of illness duration in larger samples of more chronic patients. For example, self-reported visual perceptual distortions-so-called visual basic symptoms-occur in up to 2/3rds of those with non-affective psychosis and have already longitudinally predicted an impending onset of schizophrenia. Possibly predictive psychophysical markers include enhanced contrast sensitivity, prolonged backward masking, muted collinear facilitation, reduced stereoscopic depth perception, impaired contour and shape integration, and spatially restricted exploratory eye movements. Promising brain-based markers include visual thalamo-cortical hyperconnectivity, decreased occipital gamma band power during visual detection (MEG), and reduced visually evoked occipital P1 amplitudes (EEG). Potentially predictive retinal markers include diminished cone a- and b-wave amplitudes and an attenuated photopic flicker response during electroretinography. The foregoing assessments are often well-described mechanistically, implying that their findings could readily shed light on the underlying pathophysiological changes that precede or accompany a transition to psychosis. The retinal and psychophysical assessments in particular are inexpensive, well-tolerated, easy to administer, and brief, with few inclusion/exclusion criteria. Therefore, across all major levels of analysis-from phenomenology to behavior to brain and retinal functioning-visual system assessment could complement and improve upon existing methods for predicting which individuals go on to develop a psychotic disorder.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

Modulation effect of substantia nigra iron deposition and functional connectivity on putamen glucose metabolism in Parkinson's disease

Neurodegeneration of the substantia nigra affects putamen activity in Parkinson's disease (PD), yet in vivo evidence of how the substantia nigra modulates putamen glucose metabolism in humans is missing. We aimed to investigate how substantia nigra modulates the putamen glucose metabolism using a cross-sectional design. Resting-state fMRI, susceptibility-weighted imaging, and [18 F]-fluorodeoxyglucose-PET (FDG-PET) data were acquired. Forty-two PD patients and 25 healthy controls (HCs) were recruited for simultaneous PET/MRI scanning. The main measurements of the current study were R 2 * images representing iron deposition (28 PD and 25 HCs), standardized uptake value ratio (SUVr) images representing FDG-uptake (33 PD and 25 HCs), and resting state functional connectivity maps from resting state fMRI (34 PD and 25 HCs). An interaction term based on the general linear model was used to investigate the joint modulation effect of nigral iron deposition and nigral-putamen functional connectivity on putamen FDG-uptake. Compared with HCs, we found increased iron deposition in the substantia nigra (p = .007), increased FDG-uptake in the putamen (left: PFWE  < 0.001; right: PFWE  < 0.001), and decreased functional connectivity between the substantia nigra and the anterior putamen (left PFWE  < 0.001, right: PFWE  = 0.007). We then identified significant interaction effect of nigral iron deposition and nigral-putamen connectivity on FDG-uptake in the putamen (p = .004). The current study demonstrated joint modulation effect of the substantia nigra iron deposition and nigral-putamen functional connectivity on putamen glucose metabolic distribution, thereby revealing in vivo pathological mechanism of nigrostriatal neurodegeneration of PD.

Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

BACKGROUND

Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.

METHODS

Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.

RESULTS

We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, p_perm < .001) and increases in heart rate (R² = 0.075, p_perm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR² = 0.075, p_perm = .030) but not the presence or absence of a mood and anxiety disorder.

CONCLUSIONS

Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

Interictal dynamic network transitions in mesial temporal lobe epilepsy

OBJECTIVE

To reveal the possible routine of brain network dynamic alterations in patients with mesial temporal lobe epilepsy (mTLE) and to establish a predicted model of seizure recurrence during interictal periods.

METHODS

Seventy-nine unilateral mTLE patients with hippocampal sclerosis and 97 healthy controls from two centers were retrospectively enrolled. Dynamic brain configuration analyses were performed with resting-state functional magnetic resonance imaging (MRI) data to quantify the functional stability over time and the dynamic interactions between brain regions. Relationships between seizure frequency and ipsilateral hippocampal module allegiance were evaluated using a machine learning predictive model.

RESULTS

Compared to the healthy controls, patients with mTLE displayed an overall higher dynamic network, switching mainly in the epileptogenic regions (false discovery rate [FDR] corrected p-FDR < .05). Moreover, the dynamic network configuration in mTLE was characterized by decreased recruitment (intra-network communication), and increased integration (inter-network communication) among hippocampal systems and large-scale higher-order brain networks (p-FDR < .05). We further found that the dynamic interactions between the hippocampal system and the default-mode network (DMN) or control networks exhibited an opposite distribution pattern (p-FDR < .05). Strikingly, we showed that there was a robust association between predicted seizure frequency based on the ipsilateral hippocampal-DMN dynamics model and actual seizure frequency (p-perm < .001).

SIGNIFICANCE

These findings suggest that the interictal brain of mTLE is characterized by dynamical shifts toward unstable state. Our study provides novel insights into the brain dynamic network alterations and supports the potential use of DMN dynamic parameters as candidate neuroimaging markers in monitoring the seizure frequency clinically during interictal periods.

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01−0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01−0.25 Hz, 0.01−0.027 Hz, 0.027−0.073 Hz, 0.073−0.198 Hz, 0.198−0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Models of psychedelic drug action: modulation of cortical-subcortical circuits

Classic psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have recaptured the imagination of both science and popular culture, and may have efficacy in treating a wide range of psychiatric disorders. Human and animal studies of psychedelic drug action in the brain have demonstrated the involvement of the serotonin 2A (5-HT2A) receptor and the cerebral cortex in acute psychedelic drug action, but different models have evolved to try to explain the impact of 5-HT2A activation on neural systems. Two prominent models of psychedelic drug action (the cortico-striatal thalamo-cortical, or CSTC, model and relaxed beliefs under psychedelics, or REBUS, model) have emphasized the role of different subcortical structures as crucial in mediating psychedelic drug effects. We describe these models and discuss gaps in knowledge, inconsistencies in the literature and extensions of both models. We then introduce a third circuit-level model involving the claustrum, a thin strip of grey matter between the insula and the external capsule that densely expresses 5-HT2A receptors (the cortico-claustro-cortical, or CCC, model). In this model, we propose that the claustrum entrains canonical cortical network states, and that psychedelic drugs disrupt 5-HT2A-mediated network coupling between the claustrum and the cortex, leading to attenuation of canonical cortical networks during psychedelic drug effects. Together, these three models may explain many phenomena of the psychedelic experience, and using this framework, future research may help to delineate the functional specificity of each circuit to the action of both serotonergic and non-serotonergic hallucinogens.

Functional Integration and Segregation in a Multilayer Network Model of Patients with Schizophrenia

Research has shown that abnormal brain networks in patients with schizophrenia appear at different frequencies, but the relationship between these different frequencies is unclear. Therefore, it is necessary to use a multilayer network model to evaluate the integration of information from different frequency bands. To explore the mechanism of integration and separation in the multilayer network of schizophrenia, we constructed multilayer frequency brain network models in 50 patients with schizophrenia and 69 healthy subjects, and the entropy of the multiplex degree (EMD) and multilayer clustering coefficient (MCC) were calculated. The results showed that the ability to integrate and separate information in the multilayer network of patients was significantly higher than that of normal people. This difference was mainly reflected in the default mode network, sensorimotor network, subcortical network, and visual network. Among them, the subcortical network was different in both MCC and EMD outcomes. Furthermore, differences were found in the posterior cingulate gyrus, hippocampus, amygdala, putamen, pallidum, and thalamus. The thalamus and posterior cingulate gyrus were associated with the patient’s symptom scores. Our results showed that the cross-frequency interaction ability of patients with schizophrenia was significantly enhanced, among which the subcortical network was the most active. This interaction may serve as a compensation mechanism for intralayer dysfunction.

Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference

Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with the posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.

Dynamic changes in brain lateralization correlate with human cognitive performance

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders.

Hemispheric lateralization constitutes a core architectural principle of human brain organization, often argued to represent a stable, trait-like feature, but how does this fit with our increasing appreciation of the inherently dynamic nature of brain networks? This neuroimaging study reveals the dynamic nature of functional brain lateralization at resting-state and its relationship with language function and cognitive flexibility.

Temporal exponential random graph models of longitudinal brain networks after stroke

Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.

On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.

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

Background and Hypothesis

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

Study Design

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

Study Results

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

Conclusions

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