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Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression, while inconsistent findings have been reported. This study utilized connectome-based predictive modeling (CPM) to investigate whether RSFC and negative-thinking-state functional connectivity (NTFC) could predict RNT in individuals with Major Depressive Disorder (MDD). Although RSFC distinguished between healthy and depressed individuals, it did not predict trait RNT (as assessed by the Ruminative Responses Scale-Brooding subscale) in depressed individuals. Conversely, NTFC predicted trait RNT in depressed individuals with significant accuracy, but could not differentiate between healthy and depressed individuals. Connectome-wide analysis revealed that negative thinking in depression was associated with higher FC between default mode and executive control regions, which was not observed in RSFC. Our findings suggest that RNT in depression is associated with an active mental process involving multiple brain regions across functional networks that is not represented in the resting state.

Recent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrode’s montage. Here, we delineate how a closed-loop tES-fMRI study of frontoparietal network modulation can be designed and performed. We also discuss the challenges of running a concurrent tES-fMRI, describing how we can distinguish clinically meaningful physiological changes caused by tES from tES-related artifacts. There is a large methodological parameter space including electrode types, electrolytes, electrode montages, concurrent tES-fMRI hardware, online fMRI processing pipelines and closed-loop optimization algorithms that should be carefully selected for closed-loop tES-fMRI brain modulation. We also provide technical details on how safety and quality of tES-fMRI settings can be tested, and how these settings can be monitored during the study to ensure they do not exceed safety standards. The initial results of feasibility and applicability of closed-loop tES-fMRI are reported and potential hypotheses for the outcomes are discussed.Highlight pointsMethodological details of a closed-loop tES-fMRI study protocol are provided.The protocol is performed successfully on a frontoparietal network without side-effects.The temperature of electrodes in concurrent tES-fMRI remains in the safe range.Properly setup concurrent tES does not introduce MRI artifacts and noise.Simplex optimizer could be used to find an optimal tES stimulation parameter.

BackgroundLow intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation (tACS or tDCS), applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional magnetic resonance imaging (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies.ObjectiveTo develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency, and reproducibility (ContES Checklist).MethodsA two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists (EP) through the International Network of the tES-fMRI (INTF) Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC based on a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed using the checklist.ResultsExperts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (1) technological factors, (2) safety and noise tests, and (3) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article.ConclusionsUse of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies, and increase methodological transparency and reproducibility.

Electroencephalography microstates (EEG-ms) capture and reflect the spatio-temporal neural dynamics of the brain. A growing literature is employing EEG-ms-based analyses to study various mental illnesses and to evaluate brain mechanisms implicated in cognitive and emotional processing. The spatial and functional interpretation of the EEG-ms is still being investigated. Previous works studied the association of EEG-ms time courses with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal and suggested an association between EEG-ms and resting-state networks (RSNs). However, the distinctive association between EEG-ms temporal dynamics and brain neuronal activities is still not clear, despite the assumption that EEG-ms are an electrophysiological representation of RSNs activity. Recent works suggest a role for brain spontaneous EEG rhythms in contributing to and modulating canonical EEG-ms topographies and determining their classes (coined A through D) and metrics. This work simultaneously utilized EEG and fMRI to understand the EEG-ms and their properties further. We adopted the canonical EEG-ms analysis to extract three types of regressors for EEG-informed fMRI analyses: EEG-ms direct time courses, temporal activity per microstate, and pairwise temporal transitions among microstates (the latter two coined activity regressors). After convolving EEG-ms regressors with a hemodynamic response function, a generalized linear model whole-brain voxel-wise analysis was conducted to associate EEG-ms regressors with fMRI signals. The direct time course regressors replicated prior findings of the association between the fMRI signal and EEG-ms time courses but to a smaller extent. Notably, EEG-ms activity regressors were mostly anticorrelated with fMRI, including brain regions in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with no significant overlap for default mode, limbic or frontoparietal networks. A similar pattern emerged in using the transition regressors among microstates but not in self-transitions. The relatively short duration of each EEG-ms and the significant association of EEG-ms activity regressors with fMRI signals suggest that EEG-ms manifests successive transition from one brain functional state to another rather than being associated with specific brain functional state or RSN networks.

Highlights • Introduced connectome-wide approach of neurofeedback target identification. • Identified connectivity related to rumination severity with resting-state data. • Positively correlated precuneus-RTPJ connectivity with rumination severity. • Performed simulation analysis to design the optimal neurofeedback signal. • The two-point method was optimal online connectivity measure robust to motion., Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N = 223) and healthy controls (N = 45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, −54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, −49, 23 mm) was positively correlated with RRS scores (depressive, p