Dynamic Causal Modeling Study Research Articles

Direct Retrieval of Orthographic Representations in Chinese Handwritten Production: Evidence from a Dynamic Causal Modeling Study.

This present study identified an optimal model representing the relationship between orthography and phonology in Chinese handwritten production using dynamic causal modeling, and further explored how this model was modulated by word frequency and syllable frequency. Each model contained five volumes of interest in the left hemisphere (inferior frontal gyrus [IFG], middle frontal gyrus [MFG], angular gyrus [AG], supramarginal gyrus, and superior frontal gyrus), with the IFG as the driven input area. Results showed the superiority of a model in which both the MFG and the AG connected with the IFG, supporting the orthography autonomy hypothesis. Word frequency modulated the AG → superior frontal gyrus connection (information flow from the orthographic lexicon to the orthographic buffer), and syllable frequency affected the IFG → MFG connection (information transmission from the semantic system to the phonological lexicon). This study thus provides new insights into the connectivity architecture of neural substrates involved in writing.

Disrupted Effective Connectivity within the Fronto-Thalamic Circuit in Pontine Infarction: A Spectral Dynamic Causal Modeling Study.

This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Altered resting-state effective connectivity of trigeminal vascular system in migraine without aura: A spectral dynamic causal modeling study.

The trigeminal vascular system is an important part of the anatomical and physiological basis of migraine. The effective connectivity (EC) among the regions of interest (ROIs) in the trigeminal vascular system involved in migraine without aura (MWoA) remains unclear. In this cross-sectional study, 48 patients (mean [SD] age 38.06 [10.35] years; male, 14/48 [29%]) with MWoA during the interictal phase and 48 healthy controls of similar age and sex (mean [SD] age 38.96 [10.96] years; male, 14/48 [29%]) underwent resting-state functional magnetic resonance imaging (fMRI). Dynamic causal modeling analysis was conducted to investigate directional EC among ROIs in the trigeminal vascular system including the bilateral brainstem, the primary somatosensory cortex (S1), the thalamus, and the insula. Compared with the healthy control group, MWoA represented significantly reduced EC from the left brainstem (Brainstem.L) to the left insula (MWoA: mean [SD] -0.16 [0.36]; healthy controls: mean [SD] 0.11 [0.41]; Pcorrected = 0.021), reduced EC from the Brainstem.L to the right insula (MWoA: mean [SD] -0.15 [0.39]; healthy controls: mean [SD] 0.03 [0.35]; Pcorrected = 0.021), and decreased EC from the left thalamus (Thalamus.L) to the Brainstem.L (MWoA: mean [SD] -0.13 [0.56]; healthy controls: mean [SD] 0.10 [0.45]; Pcorrected = 0.021). Altered EC parameters were not significantly correlated with MWoA clinical data. These results further provide increasing evidence that disturbed homeostasis of the trigeminovascular nociceptive pathway is involved in the pathophysiological mechanisms of migraine. Patients with MWoA exhibited a regional interaction distinct from healthy controls in the neural pathway of the Bilateral Insula-Brainstem.L-Thalamus.L, which may shed light on the future understanding of brain mechanisms for MWoA. Future brain-based interventions are suggested to consider the dysregulation in the Bilateral Insula-Brainstem.L-Thalamus.L circuits.

Altered effective connectivity between reward and inhibitory control networks in people with binge eating episodes: A spectral dynamic causal modeling study

BackgroundConverging evidence points to the crucial role of brain connectivity involved in aberrant behavioral control and reward reactivity in the onset and maintenance of binge eating. However, the directional interaction pattern between brain's reward and inhibitory control systems in people with binge eating episodes is largely unknown. MethodsResting-state fMRI data were collected from 36 adults with binge eating episodes (age: 19.05 ± 0.90) and 36 well-matched controls (age: 18.88 ± 0.78). We applied spectral dynamic causal modeling approach to estimate effective connectivity of the executive control network (ECN) and reward network (RN) with 15 predefined regions of interest, and investigate the between-group differences in directional connectivity. ResultsCompared with controls, the positive connections within the ECN were significantly strengthened in individuals with binge eating episodes, while the negative connections from the ECN to RN and from the RN to ECN were significantly weakened. In adults with binge eating episodes, the RN→ECN connectivity was positively related to binge frequency even controlling for age, sex, and body mass index. ConclusionThis study represents an important first step in addressing the role of directional integration between reward and inhibitory control networks in binge eating, and provides novel evidence that the ability of people with binge eating episodes to maintain a balance between inhibitory control and reward reactivity is decreased, as reflected by diminished bidirectional negative effects of prefrontal-subcortical circuitry at rest.

Thalamo-cortical circuits during sensory attenuation in emerging psychosis: a combined magnetoencephalography and dynamic causal modelling study

Evidence suggests that schizophrenia (ScZ) involves impairments in sensory attenuation. It is currently unclear, however, whether such deficits are present during early-stage psychosis as well as the underlying network and the potential as a biomarker. To address these questions, Magnetoencephalography (MEG) was used in combination with computational modeling to examine M100 responses that involved a “passive” condition during which tones were binaurally presented, while in an “active” condition participants were asked to generate a tone via a button press. MEG data were obtained from 109 clinical high-risk for psychosis (CHR-P) participants, 23 people with a first-episode psychosis (FEP), and 48 healthy controls (HC). M100 responses at sensor and source level in the left and right thalamus (THA), Heschl’s gyrus (HES), superior temporal gyrus (STG) and right inferior parietal cortex (IPL) were examined and dynamic causal modeling (DCM) was performed. Furthermore, the relationship between sensory attenuation and persistence of attenuated psychotic symptoms (APS) and transition to psychosis was investigated in CHR-P participants. Sensory attenuation was impaired in left HES, left STG and left THA in FEP patients, while in the CHR-P group deficits were observed only in right HES. DCM results revealed that CHR-P participants showed reduced top-down modulation from the right IPL to the right HES. Importantly, deficits in sensory attenuation did not predict clinical outcomes in the CHR-P group. Our results show that early-stage psychosis involves impaired sensory attenuation in auditory and thalamic regions but may not predict clinical outcomes in CHR-P participants.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study.

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

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.

Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study.

Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression. We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores. The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.

Altered effective connectivity within the cingulo-frontal-parietal cognitive attention networks in chronic low back pain: a dynamic causal modeling study.

Dysfunction of the cingulo-frontal-parietal (CFP) cognitive attention network has been associated with the pathophysiology of chronic low back pain (cLBP). However, the direction of information processing within this network remains largely unknown. We aimed to study the effective connectivity among the CFP regions in 36 cLBP patients and 36 healthy controls by dynamic causal modeling (DCM). Both the resting-state and task-related (Multi-Source Interference Task, MSIT) functional magnetic resonance imaging (fMRI) data were collected and analyzed. The relationship between the effective connectivity of the CFP regions and clinical measures was also examined. Our results suggested that cLBP had significantly altered resting-state effective connectivity of the prefrontal cortex (PFC)-to-mid-cingulate cortex (MCC) (increased) and MCC-to-left superior parietal cortex (LPC) (decreased) pathways as compared with healthy controls. MSIT-related DCM suggested that the interference task could significantly increase the effective connectivity of the right superior parietal cortex (RPC)-to-PFC and RPC-to-MCC pathways in cLBP than that in healthy controls. The control task could significantly decrease the effective connectivity of the MCC-to-LPC and MCC-to-RPC pathways in cLBP than that in healthy controls. The endogenous connectivity of the PFC-to-RPC pathway in cLBP was significantly lower than that in healthy controls. No significant correlations were found between the effective connectivity within CFP networks and pain/depression scores in patients with cLBP. In summary, our findings suggested altered effective connectivity in multiple pathways within the CFP network in both resting-state and performing attention-demanding tasks in patients with cLBP, which extends our understanding of attention dysfunction in patients with cLBP.

Functional segregation within the dorsal frontoparietal network: a multimodal dynamic causal modeling study.

Discrimination and integration of motion direction requires the interplay of multiple brain areas. Theoretical accounts of perception suggest that stimulus-related (i.e., exogenous) and decision-related (i.e., endogenous) factors affect distributed neuronal processing at different levels of the visual hierarchy. To test these predictions, we measured brain activity of healthy participants during a motion discrimination task, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We independently modeled the impact of exogenous factors (task demand) and endogenous factors (perceptual decision-making) on the activity of the motion discrimination network and applied Dynamic Causal Modeling (DCM) to both modalities. DCM for event-related potentials (DCM-ERP) revealed that task demand impacted the reciprocal connections between the primary visual cortex (V1) and medial temporal areas (V5). With practice, higher visual areas were increasingly involved, as revealed by DCM-fMRI. Perceptual decision-making modulated higher levels (e.g., V5-to-Frontal Eye Fields, FEF), in a manner predictive of performance. Our data suggest that lower levels of the visual network support early, feature-based selection of responses, especially when learning strategies have not been implemented. In contrast, perceptual decision-making operates at higher levels of the visual hierarchy by integrating sensory information with the internal state of the subject.

Effective connectivity decreases in specific brain networks with postparalysis facial synkinesis: a dynamic causal modeling study.

Currently, the treatments for postparalysis facial synkinesis are still inadequate. However, neuroimaging mechanistic studies are very limited and blurred. Instead of mapping activation regions, we were devoted to characterizing the organizational features of brain regions to develop new targets for therapeutic intervention. Eighteen patients with unilateral facial synkinesis and 19 healthy controls were enrolled. They were instructed to perform task functional magnetic resonance imaging (eye blinking and lip pursing) examinations and resting-state scans. Then, we characterized group differences in task-state fMRI to identify three foci, including the contralateral precentral gyrus (PreCG), supramarginal gyrus (SMG), and superior parietal gyrus (SPG). Next, we employed a novel approach (using dynamic causal modeling) to identify directed connectivity differences between groups in different modes. Significant patterns in multiple regions in terms of regionally specific actions following synkinetic movements were demonstrated, although the resting state was not significant. The couplings from the SMG to the PreCG (p = 0.03) was significant in the task of left blinking, whereas the coupling from the SMG to the SPG (p = 0.04) was significant in the task of left smiling. We speculated that facial synkinesis affects disruption among the brain networks, and specific couplings that are modulated simultaneously can compensate for motor deficits. Therefore, behavioral or brain stimulation technique treatment could be applied to alter reorganization within specific couplings in the rehabilitation of facial function.

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

Enhanced Information Flow From Cerebellum to Secondary Visual Cortices Leads to Better Surgery Outcome in Degenerative Cervical Myelopathy Patients: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging.

Degenerative cervical myelopathy (DCM) damages the spinal cord, resulting in long-term neurological impairment including motor and visual deficits. Given that visual feedback is crucial in guiding movements, the visual disorder may be a cause of motor deficits in patients with DCM. It has been shown that increased functional connectivity between secondary visual cortices and cerebellum, which are functionally related to the visually guided movements, was correlated with motor function in patients with DCM. One possible explanation is that the information integration between these regions was increased to compensate for impaired visual acuity in patients with DCM and resulted in better visual feedback during motor function. However, direct evidence supporting this hypothesis is lacking. To test this hypothesis and explore in more detail the information flow within the “visual-cerebellum” system, we measured the effective connectivity (EC) among the “visual-cerebellum” system via dynamic causal modeling and then tested the relationship between the EC and visual ability in patients with DCM. Furthermore, the multivariate pattern analysis was performed to detect the relationship between the pattern of EC and motor function in patients with DCM. We found (1) significant increases of the bidirectional connections between bilateral secondary visual cortices and cerebellum were observed in patients with DCM; (2) the increased self-connection of the cerebellum was positively correlated with the impaired visual acuity in patients; (3) the amplitude of effectivity from the cerebellum to secondary visual cortices was positively correlated with better visual recovery following spinal cord decompression surgery; and (4) the pattern of EC among the visual-cerebellum system could be used to predict the pre-operative motor function. In conclusion, this study provided direct evidence that the increased information integration within the “visual-cerebellum” system compensated for visual impairments, which might have importance for sustaining better motor function in patients with DCM.

Hierarchical Learning of Statistical Regularities over Multiple Timescales of Sound Sequence Processing: A Dynamic Causal Modeling Study.

Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change over time. However, task-independent exposure to sound has revealed that strong first impressions exert a lasting impact on how the relevance of sound is contextualized. Dynamic causal modeling was applied to auditory ERPs collected during presentation of alternating pattern sequences. A local regularity (a rare p = .125 vs. common p = .875 sound) alternated to create a longer timescale regularity (sound probabilities alternated regularly creating a predictable block length), and the longer timescale regularity changed halfway through the sequence (the regular block length became shorter or longer). Predictions should be revised for local patterns when blocks alternated and for longer patterning when the block length changed. Dynamic causal modeling revealed an overall higher precision for the error signal to the rare sound in the first block type, consistent with the first impression. The connectivity changes in response to errors within the underlying neural network were also different for the two blocks with significantly more revision of predictions in the arrangement that violated the first impression. Furthermore, the effects of block length change suggested errors within the first block type exerted more influence on the updating of longer timescale predictions. These observations support the hypothesis that automatic sequential learning creates a high-precision context (first impression) that impacts learning rates and updates to those learning rates when predictions arising from that context are violated. The results further evidence automatic pattern learning over multiple timescales simultaneously, even during task-independent passive exposure to sound.

Counteracting Effect of Glutathione on the Glutamate-Driven Excitation/Inhibition Imbalance in First-Episode Schizophrenia: A 7T Mrs and Dynamic Causal Modeling Study

Oxidative stress plays a key role in the pathophysiology of schizophrenia. While free radicals produced by glutamatergic excess and oxidative metabolism have damaging effects on brain tissue, antioxidants such as glutathione (GSH) counteract these effects. The interaction between glutamate (GLU) and GSH is centered in NMDA receptors. GSH levels increase during glutamate-mediated excitatory neuronal activity, which serves as a checkpoint to protect neurons from oxidative damage and reduce excitatory overdrive.

Predictive Coding Over the Lifespan: Increased Reliance on Perceptual Priors in Older Adults-A Magnetoencephalography and Dynamic Causal Modeling Study.

Aging is accompanied by unisensory decline. To compensate for this, two complementary strategies are potentially relied upon increasingly: first, older adults integrate more information from different sensory organs. Second, according to the predictive coding (PC) model, we form “templates” (internal models or “priors”) of the environment through our experiences. It is through increased life experience that older adults may rely more on these templates compared to younger adults. Multisensory integration and predictive coding would be effective strategies for the perception of near-threshold stimuli, which may however come at the cost of integrating irrelevant information. Both strategies can be studied in multisensory illusions because these require the integration of different sensory information, as well as an internal model of the world that can take precedence over sensory input. Here, we elicited a classic multisensory illusion, the sound-induced flash illusion, in younger (mean: 27 years, N = 25) and older (mean: 67 years, N = 28) adult participants while recording the magnetoencephalogram. Older adults perceived more illusions than younger adults. Older adults had increased pre-stimulus beta-band activity compared to younger adults as predicted by microcircuit theories of predictive coding, which suggest priors and predictions are linked to beta-band activity. Transfer entropy analysis and dynamic causal modeling of pre-stimulus magnetoencephalography data revealed a stronger illusion-related modulation of cross-modal connectivity from auditory to visual cortices in older compared to younger adults. We interpret this as the neural correlate of increased reliance on a cross-modal predictive template in older adults leading to the illusory percept.

Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study.

Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation. Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Results indicated that trust and distrust can be two distinctive neural processes in human-automation interaction-distrust being a more complex network than trust, possibly due to the increased cognitive load. The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human-automation interface design but also in the proper use of automation in real-life situations.

Testing and tracking in the UK: A dynamic causal modelling study

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.

Counteracting Effects of Glutathione on the Glutamate-Driven Excitation/Inhibition Imbalance in First-Episode Schizophrenia: A 7T MRS and Dynamic Causal Modeling Study.

Oxidative stress plays a key role in the pathophysiology of schizophrenia. While free radicals produced by glutamatergic excess and oxidative metabolism have damaging effects on brain tissue, antioxidants such as glutathione (GSH) counteract these effects. The interaction between glutamate (GLU) and GSH is centered on N-Methyl-D-aspartate (NMDA) receptors. GSH levels increase during glutamate-mediated excitatory neuronal activity, which serves as a checkpoint to protect neurons from oxidative damage and reduce excitatory overdrive. We studied the possible influence of GSH on the glutamate-mediated dysconnectivity in 19 first-episode schizophrenia (FES) patients and 20 healthy control (HC) subjects. Using ultra-high field (7 Tesla) magnetic resonance spectroscopy (MRS) and resting state functional magnetic resonance imaging (fMRI), we measured GSH and GLU levels in the dorsal anterior cingulate cortex (dACC) and blood-oxygenation level-dependent activity in both the dACC and the anterior insula (AI). Using spectral dynamic causal modeling, we found that when compared to HCs, in FES patients inhibitory activity within the dACC decreased with GLU levels whereas inhibitory activity in both the dACC and AI increased with GSH levels. Our model explains how higher levels of GSH can reverse the downstream pathophysiological effects of a hyperglutamatergic state in FES. This provides an initial insight into the possible mechanistic effect of antioxidant system on the excitatory overdrive in the salience network (dACC-AI).