Hard to let it go: repetitive negative thinking and hoarding in later life in Japan.

Objectives: Hoarding in older adults can have a detrimental effect on daily life. Repetitive negative thinking (RNT) could result in a greater avoidance of discarding and increased saving behaviors; yet, the unique role of RNT on hoarding in older adults remains understudied. This study aimed to investigate whether the intensity of RNT contributes to hoarding in older adults. Methods: Two hundred and sixty-four older adults in Japan (ages 65-86 years, 132 males and 132 females) participated in an online survey. Hierarchical regression analyses were conducted to examine whether RNT could significantly explain the variance of hoarding after controlling for age, sex, years of education, self-reported cognitive impairment, and depression. Results: As we expected, RNT was significantly associated with greater hoarding behaviors, such as excessive acquisition (ß = .27, p = .005) and difficulty in discarding (ß = .27, p = .003). On the other hand, reflection, repetitive thinking without negative emotional valence, was significantly associated with higher scores on clutter (ß = .36 p < .001). Conclusion: Our findings highlight the importance of addressing RNT in the prevention and treatment of hoarding symptoms among older adults, potentially leading to more effective interventions and improved outcomes in managing hoarding behaviors in this population.

Intensity of repetitive negative thinking in depression is associated with greater functional connectivity between semantic processing and emotion regulation areas.

BACKGROUND: Repetitive negative thinking (RNT), a cognitive process that encompasses past (rumination) and future (worry) directed thoughts focusing on negative experiences and the self, is a transdiagnostic construct that is especially relevant for major depressive disorder (MDD). Severe RNT often occurs in individuals with severe levels of MDD, which makes it challenging to disambiguate the neural circuitry underlying RNT from depression severity. METHODS: We used a propensity score, i.e., a conditional probability of having high RNT given observed covariates to match high and low RNT individuals who are similar in the severity of depression, anxiety, and demographic characteristics. Of 148 MDD individuals, we matched high and low RNT groups (n = 50/group) and used a data-driven whole-brain voxel-to-voxel connectivity pattern analysis to investigate the resting-state functional connectivity differences between the groups. RESULTS: There was an association between RNT and connectivity in the bilateral superior temporal sulcus (STS), an important region for speech processing including inner speech. High relative to low RNT individuals showed greater connectivity between right STS and bilateral anterior insular cortex (AI), and between bilateral STS and left dorsolateral prefrontal cortex (DLPFC). Greater connectivity in those regions was specifically related to RNT but not to depression severity. CONCLUSIONS: RNT intensity is directly related to connectivity between STS and AI/DLPFC. This might be a mechanism underlying the role of RNT in perceptive, cognitive, speech, and emotional processing. Future investigations will need to determine whether modifying these connectivities could be a treatment target to reduce RNT.

Real-Time fMRI Functional Connectivity Neurofeedback Reducing Repetitive Negative Thinking in Depression: A Double-Blind, Randomized, Sham-Controlled Proof-of-Concept Trial.

INTRODUCTION: Repetitive negative thinking (RNT) is a cognitive process focusing on self-relevant and negative experiences, leading to a poor prognosis of major depressive disorder (MDD). We previously identified that connectivity between the precuneus/posterior cingulate cortex (PCC) and right temporoparietal junction (rTPJ) was positively correlated with levels of RNT. OBJECTIVE: In this double-blind, randomized, sham-controlled, proof-of-concept trial, we employed real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) to delineate the neural processes that may be causally linked to RNT and could potentially become treatment targets for MDD. METHODS: MDD-affected individuals were assigned to either active (n = 20) or sham feedback group (n = 19). RNT was measured by the Ruminative Response Scale-brooding subscale (RRS-B) before and 1 week after the intervention. RESULTS: Individuals in the active but not in the sham group showed a significant reduction in the RRS-B; however, a greater reduction in the PCC-rTPJ connectivity was unrelated to a greater reduction in the RRS-B. Exploratory analyses revealed that a greater reduction in the retrosplenial cortex (RSC)-rTPJ connectivity yielded a more pronounced reduction in the RRS-B in the active but not in the sham group. CONCLUSIONS: RtfMRI-nf was effective in reducing RNT. Considering the underlying mechanism of rtfMIR-nf, the RSC and rTPJ could be part of a network (i.e., default mode network) that might collectively affect the intensity of RNT. Understanding the relationship between the functional organization of targeted neural changes and clinical metrics, such as RNT, has the potential to guide the development of mechanism-based treatment of MDD.

Cognitive behavior therapy for autistic adolescents, awareness and care for my autistic traits program: a multicenter randomized controlled trial.

BACKGROUND: Autistic people demonstrate focused interests, sensitivity to sensory stimulation, and, compared with the general population, differences in social communication and interaction. We examined whether a combination of the Awareness and Care for My Autistic Traits (ACAT) program and treatment-as-usual is more effective than only treatment-as-usual in increasing the understanding of autistic attributes, reducing treatment stigma, and improving mental health and social adaptation among autistic adolescents and their parents/guardians. METHODS: Forty-nine adolescents and their parents/guardians were randomly assigned to either a combination of ACAT and treatment-as-usual or only treatment-as-usual. The combined group received six weekly 100-minute ACAT sessions, while the treatment-as-usual group received no additional intervention. The primary outcome was the change in understanding of autistic attributes (Autism Knowledge Quiz-Child), administered from pre- to post-intervention. The secondary outcomes included the change in Autism Knowledge Quiz-Parent, reduced treatment stigma, and improved mental health and social adaptation among autistic adolescents and their parents/guardians. A primary outcome measure scale was scored by assessors who were blind to the group assignment. RESULTS: The combined group (both autistic adolescents and their parents/guardians) showed an increase in Autism Knowledge Quiz scores compared to those in the treatment-as-usual group. Autistic adolescents in the combined group also demonstrated a decrease in treatment-related stigma and an improvement in general mental health compared to those in the treatment-as-usual group, while there were no group differences in the change in social adaptation. For parents/guardians, there were no group differences in the change in treatment-related stigma, general mental health, adaptive skills, or attitudes toward their children. CONCLUSIONS: The ACAT program could be an effective treatment modality to increase the understanding of autistic attributes among both autistic adolescents and their parents/guardians. The ACAT program positively affects self-understanding, reduces treatment stigma, and stabilizes behavioral issues for autistic adolescents as a part of mental health measures, but it does not effectively reduce treatment barriers or improve mental health for parents/guardians. Further research should consider whether additional support for parents/guardians could be beneficial. TRIAL REGISTRATION: The study was registered in UMIN (UMIN000029851, 06/01/2018).

Self-regulation of the posterior cingulate cortex with real-time fMRI neurofeedback augmented mindfulness training in healthy adolescents: A nonrandomized feasibility study.

Mindfulness training (MT) promotes the development of one's ability to observe and attend to internal and external experiences with objectivity and nonjudgment with evidence to improve psychological well-being. Real-time functional MRI neurofeedback (rtfMRI-nf) is a noninvasive method of modulating activity of a brain region or circuit. The posterior cingulate cortex (PCC) has been hypothesized to be an important hub instantiating a mindful state. This nonrandomized, single-arm study examined the feasibility and tolerability of training typically developing adolescents to self-regulate the posterior cingulate cortex (PCC) using rtfMRI-nf during MT. Thirty-four adolescents (mean age: 15 years; 14 females) completed the neurofeedback augmented mindfulness training task, including Focus-on-Breath (MT), Describe (self-referential thinking), and Rest conditions, across three neurofeedback and two non-neurofeedback runs (Observe, Transfer). Self-report assessments demonstrated the feasibility and tolerability of the task. Neurofeedback runs differed significantly from non-neurofeedback runs for the Focus-on-Breath versus Describe contrast, characterized by decreased activity in the PCC during the Focus-on-Breath condition (z = -2.38 to -6.27). MT neurofeedback neural representation further involved the medial prefrontal cortex, anterior cingulate cortex, dorsolateral prefrontal cortex, posterior insula, hippocampus, and amygdala. State awareness of physical sensations increased following rtfMRI-nf and was maintained at 1-week follow-up (Cohens' d = 0.69). Findings demonstrate feasibility and tolerability of rtfMRI-nf in healthy adolescents, replicates the role of PCC in MT, and demonstrate a potential neuromodulatory mechanism to leverage and streamline the learning of mindfulness practice. ( ClinicalTrials.gov identifier #NCT04053582; August 12, 2019).

Prevent breaking bad: A proof of concept study of rebalancing the brain's rumination circuit with real-time fMRI functional connectivity neurofeedback.

Rumination, repetitively thinking about the causes, consequences, and one's negative affect, has been considered as an important factor of depression. The intrusion of ruminative thoughts is not easily controlled, and it may be useful to visualize one's neural activity related to rumination and to use that information to facilitate one's self-control. Real-time fMRI neurofeedback (rtfMRI-nf) enables one to see and regulate the fMRI signal from their own brain. This proof-of concept study utilized connectivity-based rtfMRI-nf (cnf) to normalize brain functional connectivity (FC) associated with rumination. Healthy participants were instructed to brake or decrease FC between the precuneus and the right temporoparietal junction (rTPJ), associated with high levels of rumination, while engaging in a self-referential task. The cnf group (n = 14) showed a linear decrease in the precuneus-rTPJ FC across neurofeedback training (trend [112] = -0.180, 95% confidence interval [CI] -0.330 to -0.031, while the sham group (n = 14) showed a linear increase in the target FC (trend [112] = 0.151, 95% CI 0.017 to 0.299). Although the cnf group showed a greater reduction in state-rumination compared to the sham group after neurofeedback training (p < .05), decoupled precuneus-rTPJ FC did not predict attenuated state-rumination. We did not find any significant aversive effects of rtfMRI-nf in all study participants. These results suggest that cnf has the capacity to influence FC among precuneus and rTPJ of a ruminative brain circuit. This approach can be applied to mood and anxiety patients to determine the clinical benefits of reduction in maladaptive rumination.

Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network.

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.

Predictors of response to exposure and response prevention-based cognitive behavioral therapy for obsessive-compulsive disorder.

BACKGROUND: Cognitive behavioral therapy (CBT), which includes exposure and response prevention (ERP), is effective in improving symptoms of obsessive-compulsive disorder (OCD). However, whether poor cognitive functions and autism spectrum disorder (ASD) traits affect the therapeutic response of patients with OCD to ERP-based CBT remains unclear. This study aimed to identify factors predictive of the therapeutic response of Japanese patients with OCD to ERP-based CBT. METHODS: Forty-two Japanese outpatients with OCD were assessed using the Wechsler Adult Intelligence Scale-III (WAIS-III), Yale-Brown Obsessive-Compulsive Scale, Patient Health Questionnaire 9-item scale, and Autism Spectrum Quotient (AQ) at pre- and post-treatment. We used multiple regression analyses to estimate the effect on therapeutic response change. The treatment response change was set as a dependent variable in multiple regression analyses. RESULTS: Multiple regression analyses showed that among independent variables, communication as an AQ sub-scale and Letter Number Sequencing as a WAIS-III sub-test predict the therapeutic response to ERP-based CBT . CONCLUSIONS: Our results suggest that diminished working memory (Letter Number Sequencing), poor communication skill (AQ sub-scale) may undermine responsiveness to ERP-based CBT among patients with OCD. TRIAL REGISTRATION: UMIN, UMIN00024087 . Registered 20 September 2016 - Retrospectively registered (including retrospective data).

Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback.

Background: Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD. Methods: We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B). Results: Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037). Limitations: The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback. Conclusion: We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Clinical Response to Neurofeedback in Major Depression Relates to Subtypes of Whole-Brain Activation Patterns During Training.

Major Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. Significant symptom reduction was observed in the active group (t=-4.404, d=-0.704, p<0.001) but not in the control group (t=-1.609, d=-0.430, p=0.111). However, left amygdala activation did not account for the variability in clinical efficacy. To elucidate the brain training process underlying the clinical effect, we examined whole-brain activation patterns during two critical phases of the neurofeedback procedure: activation during the self-regulation period, and transient responses to feedback signal presentations. Using a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified two subtypes of regulation activation and three subtypes of brain responses to feedback signals. These subtypes were significantly associated with the clinical effect (regulation subtype: F=8.735, p=0.005; feedback response subtype: F=5.326, p=0.008; subtypes' interaction: F=3.471, p=0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity during training is more critical for clinical efficacy than the level of activation in the neurofeedback target region itself. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

Could neurofeedback improve therapist-patient communication? Considering the potential for neuroscience informed examinations of the psychotherapeutic relationship.

Empathic communication between a patient and therapist is an essential component of psychotherapy. However, finding objective neural markers of the quality of the psychotherapeutic relationship have been elusive. Here we conceptualize how a neuroscience-informed approach involving real-time neurofeedback, facilitated via existing functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) technologies, could provide objective information for facilitating therapeutic rapport. We propose several neurofeedback-assisted psychotherapy (NF-AP) approaches that could be studied as a way to optimize the experience of the individual patient and therapist across the spectrum of psychotherapeutic treatment. Finally, we consider how the possible strengths of these approaches are balanced by their current limitations and discuss the future prospects of NF-AP.

Sustained increase in depression and anxiety among psychiatrically healthy adolescents during late stage COVID-19 pandemic.

Background: Adolescents have experienced increases in anxiety, depression, and stress during the COVID-19 pandemic and may be at particular risk for suffering from long-term mental health consequences because of their unique developmental stage. This study aimed to determine if initial increases in depression and anxiety in a small sample of healthy adolescents after the onset of the COVID-19 pandemic were sustained at follow-up during a later stage of the pandemic. Methods: Fifteen healthy adolescents completed self-report measures at three timepoints (pre-pandemic [T1], early pandemic [T2], and later pandemic [T3]). The sustained effect of COVID-19 on depression and anxiety was examined using linear mixed-effect analyses. An exploratory analysis was conducted to investigate the relationship between difficulties in emotion regulation during COVID-19 at T2 and increases in depression and anxiety at T3. Results: The severity of depression and anxiety was significantly increased at T2 and sustained at T3 (depression: Hedges' g [T1 to T2] = 1.04, g [T1 to T3] = 0.95; anxiety: g [T1 to T2] = 0.79, g [T1 to T3] = 0.80). This was accompanied by sustained reductions in positive affect, peer trust, and peer communication. Greater levels of difficulties in emotion regulation at T2 were related to greater symptoms of depression and anxiety at T3 (rho = 0.71 to 0.80). Conclusion: Increased symptoms of depression and anxiety were sustained at the later stage of the pandemic in healthy adolescents. Replication of these findings with a larger sample size would be required to draw firm conclusions.

Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state.

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.

Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state.

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.

Thalamo-cortical circuits associated with trait- and state-repetitive negative thinking in major depressive disorder.

BACKGROUND: Repetitive negative thinking (RNT), often referred to as rumination in the mood disorders literature, is a symptom dimension associated with poor prognosis and suicide in major depressive disorder (MDD). Given the transdiagnostic nature of RNT, this study aimed to evaluate the hypothesis that neurobiological substrates of RNT in MDD may share the brain mechanisms underlying obsessions, particularly those involving cortico-striatal-thalamic-cortical (CSTC) circuits. METHODS: Thirty-nine individuals with MDD underwent RNT induction during fMRI. Trait-RNT was measured by the Ruminative Response Scale (RRS) and state-RNT was measured by a visual analogue scale. We employed a connectome-wide association analysis examining the association between RNT intensity with striatal and thalamic connectivity. RESULTS: A greater RRS score was associated with hyperconnectivity of the right mediodorsal thalamus with prefrontal cortex, including lateral orbitofrontal cortex, along with Wernicke's area and posterior default mode network nodes (t = 4.66-6.70). A greater state-RNT score was associated with hyperconnectivity of the right laterodorsal thalamus with bilateral primary sensory and motor cortices, supplementary motor area, and Broca's area (t = 4.51-6.57). Unexpectedly, there were no significant findings related to the striatum. CONCLUSIONS: The present results suggest RNT in MDD is subserved by abnormal connectivity between right thalamic nuclei and cortical regions involved in both visceral and higher order cognitive processing. Emerging deep-brain neuromodulation methods may be useful to establish causal relationships between dysfunction of right thalamic-cortical circuits and RNT in MDD.

Repetitive Negative Thinking-Specific and -Nonspecific White Matter Tracts Engaged by Historical Psychosurgical Targets for Depression.

BACKGROUND: Repetitive negative thinking (RNT) is a frequent symptom of major depressive disorder (MDD) that is associated with poor outcomes and treatment resistance. While most studies on RNT have focused on structural and functional characteristics of gray matter, this study aimed to examine the association between white matter (WM) tracts and interindividual variability in RNT. METHODS: A probabilistic tractography approach was used to characterize differences in the size and anatomical trajectory of WM fibers traversing psychosurgery targets historically useful in the treatment of MDD (anterior capsulotomy, anterior cingulotomy, and subcaudate tractotomy) in patients with MDD and low (n = 53) or high (n = 52) RNT, and healthy control subjects (n = 54). MDD samples were propensity matched on depression and anxiety severity and demographics. RESULTS: WM tracts traversing left hemisphere targets and reaching the ventral anterior body of the corpus callosum (thus extending to contralateral regions) were larger in the high-RNT MDD group compared with low-RNT (effect size D = 0.27, p = .042) and healthy control (D = 0.23, p = .02) groups. MDD was associated with greater size of tracts that converge onto the right medial orbitofrontal cortex regardless of RNT intensity. Other RNT-nonspecific findings in MDD involved tracts reaching the left primary motor and right primary somatosensory cortices. CONCLUSIONS: This study provides the first evidence to our knowledge that WM connectivity patterns, which could become targets of intervention, differ between high- and low-RNT participants with MDD. These WM differences extend to circuits that are not specific to RNT, possibly subserving reward mechanisms and psychomotor activity.

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments.

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets.

Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.

Attenuated interoceptive processing in individuals with major depressive disorder and high repetitive negative thinking.

Repetitive negative thinking (RNT) is a transdiagnostic symptom associated with poor outcomes in major depressive disorder (MDD). MDD is characterized by altered interoception, which has also been associated with poor outcomes. The present study investigated whether RNT is directly associated with altered interoceptive processing. Interoceptive awareness toward the heart and stomach was probed on the Visceral Interoceptive Attention (VIA) task with fMRI in MDD individuals who were propensity-matched on the severity of depression and anxiety symptoms and relevant demographics but different in RNT intensity (High RNT [H-RNT, n = 48] & Low RNT [L-RNT, n = 49]), and in matched healthy volunteers (HC, n = 27). Both H-RNT and L-RNT MDD individuals revealed reduced stomach interoceptive processing compared to HC in the left medial frontal region and insular cortex (H-RNT: ß = -1.04, L-RNT: ß = -0.97), perirhinal cortex (H-RNT: ß = -0.99, L-RNT: ß = -1.03), and caudate nucleus (H-RNT: ß = -1.06, L-RNT: ß = -0.89). However, H-RNT was associated with decreased right medial temporal lobe activity including the hippocampus and amygdala during stomach interoceptive trials (ß = -0.61) compared to L-RNT. Insular interoceptive processing was similar in H-RNT and L-RNT participants (ß = -0.07, p = 0.92). MDD individuals with high RNT exhibited altered gastric interoceptive responses in brain areas that are important for associating the information with specific contexts and emotions. Attenuated interoceptive processing may contribute to RNT generation, non-adaptive information processing, action selection, and thus poor treatment outcome.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal.

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.