Targeting the superior temporal gyrus with real-time fMRI neurofeedback: A pilot study of the indirect effects on self-referential processes in schizophrenia.

BACKGROUND: Individuals with schizophrenia (SZ) and auditory hallucinations (AHs) display a distorted sense of self and self-other boundaries. Alterations of activity in midline cortical structures such as the prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) during self-reference as well as in the superior temporal gyrus (STG) have been proposed as neuromarkers of SZ and AHs. METHODS: In this randomized, participant-blinded, sham-controlled trial, 22 adults (18 males) with SZ spectrum disorders (SZ or schizoaffective disorder) and frequent medication-resistant AHs received one session of real-time fMRI neurofeedback (NFB) either from the STG (n = 11; experimental group) or motor cortex (n = 11; control group). During NFB, participants were instructed to upregulate their STG activity by attending to pre-recorded sentences spoken in their own voice and downregulate it by ignoring unfamiliar voices. Before and after NFB, participants completed a self-reference task where they evaluated if trait adjectives referred to themselves (self condition), Abraham Lincoln (other condition), or whether adjectives had a positive valence (semantic condition). FMRI activation analyses of self-reference task data tested between-group changes after NFB (self>semantic, post>pre-NFB, experimental>control). Analyses were pre-masked within a self-reference network. RESULTS: Activation analyses revealed significantly (p < 0.001) greater activation increase in the experimental, compared to the control group, after NFB within anterior regions of the self-reference network (mPFC, ACC, superior frontal cortex). CONCLUSIONS: STG-NFB was associated with activity increase in the mPFC, ACC, and superior frontal cortex during self-reference. Modulating the STG is associated with activation changes in other, not-directly targeted, regions subserving higher-level cognitive processes associated with self-referential processes and AHs psychopathology in SZ. CLINICALTRIALS: GOV: Rt-fMRI Neurofeedback and AH in Schizophrenia; https://clinicaltrials.gov/study/NCT03504579.

Mindfulness-based real-time fMRI neurofeedback: a randomized controlled trial to optimize dosing for depressed adolescents.

BACKGROUND: Adolescence is characterized by a heightened vulnerability for Major Depressive Disorder (MDD) onset, and currently, treatments are only effective for roughly half of adolescents with MDD. Accordingly, novel interventions are urgently needed. This study aims to establish mindfulness-based real-time fMRI neurofeedback (mbNF) as a non-invasive approach to downregulate the default mode network (DMN) in order to decrease ruminatory processes and depressive symptoms. METHODS: Adolescents (N = 90) with a current diagnosis of MDD ages 13-18-years-old will be randomized in a parallel group, two-arm, superiority trial to receive either 15 or 30 min of mbNF with a 1:1 allocation ratio. Real-time neurofeedback based on activation of the frontoparietal network (FPN) relative to the DMN will be displayed to participants via the movement of a ball on a computer screen while participants practice mindfulness in the scanner. We hypothesize that within-DMN (medial prefrontal cortex [mPFC] with posterior cingulate cortex [PCC]) functional connectivity will be reduced following mbNF (Aim 1: Target Engagement). Additionally, we hypothesize that participants in the 30-min mbNF condition will show greater reductions in within-DMN functional connectivity (Aim 2: Dosing Impact on Target Engagement). Aim 1 will analyze data from all participants as a single-group, and Aim 2 will leverage the randomized assignment to analyze data as a parallel-group trial. Secondary analyses will probe changes in depressive symptoms and rumination. DISCUSSION: Results of this study will determine whether mbNF reduces functional connectivity within the DMN among adolescents with MDD, and critically, will identify the optimal dosing with respect to DMN modulation as well as reduction in depressive symptoms and rumination. TRIAL REGISTRATION: This study has been registered with clinicaltrials.gov, most recently updated on July 6, 2023 (trial identifier: NCT05617495).

Functional connectivity MRI quality control procedures in CONN.

Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (N = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor - a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant's head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.

Reducing default mode network connectivity with mindfulness-based fMRI neurofeedback: a pilot study among adolescents with affective disorder history.

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed mindfulness-based fMRI neurofeedback (mbNF) for adolescents that aims to reduce default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and each participant's DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks associated with the emergence and persistence of depressive symptoms during adolescence.

Brain activation and connectivity in anorexia nervosa and body dysmorphic disorder when viewing bodies: relationships to clinical symptoms and perception of appearance.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are characterized by distorted perception of appearance, yet no studies have directly compared the neurobiology associated with body perception. We compared AN and BDD in brain activation and connectivity in relevant networks when viewing images of others' bodies and tested their relationships with clinical symptoms and subjective appearance evaluations. We acquired fMRI data from 64 unmedicated females (20 weight-restored AN, 23 BDD, 21 controls) during a matching task using unaltered or spatial-frequency filtered photos of others' bodies. Using general linear model and independent components analyses we compared brain activation and connectivity in visual, striatal, and parietal networks and performed univariate and partial least squares multivariate analyses to investigate relationships with clinical symptoms and appearance evaluations. AN and BDD showed partially overlapping patterns of hyperconnectivity in the dorsal visual network and hypoconnectivity in parietal network compared with controls. BDD, but not AN, demonstrated hypoactivity in dorsal visual and parietal networks compared to controls. Further, there were significant activity and connectivity differences between AN and BDD in both networks. In both groups, activity and/or connectivity were associated with symptom severity and appearance ratings of others' bodies. Thus, AN and BDD demonstrate both distinct and partially-overlapping aberrant neural phenotypes involved in body processing and visually encoding global features. Nevertheless, in each disorder, aberrant activity and connectivity show relationships to clinically relevant symptoms and subjective perception. These results have implications for understanding distinct and shared pathophysiology underlying perceptual distortions of appearance and may inform future novel treatment strategies.

Differentiating weight-restored anorexia nervosa and body dysmorphic disorder using neuroimaging and psychometric markers.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology-distorted perception of appearance, obsessions/compulsions, and limited insight-can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% ([Formula: see text]). In the multivariate model, reduced white matter global efficiency and better insight were associated more with AN than with BDD. These results improve our understanding of the relative contributions of the neurobiological characteristics and symptoms of these disorders. Moreover, this approach has the potential to aid clinicians in diagnosis, thereby leading to more tailored therapy.

Aberrant Dynamic Connectivity for Fear Processing in Anorexia Nervosa and Body Dysmorphic Disorder.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) share distorted perceptions of appearance with extreme negative emotion, yet the neural phenotypes of emotion processing remain underexplored in them, and they have never been directly compared. We sought to determine if shared and disorder-specific fronto-limbic connectivity patterns characterize these disorders. FMRI data was obtained from three unmedicated groups: BDD (n = 32), weight-restored AN (n = 25), and healthy controls (HC; n = 37), while they viewed fearful faces and rated their own degree of fearfulness in response. We performed dynamic effective connectivity modeling with medial prefrontal cortex (mPFC), rostral anterior cingulate cortex (rACC), and amygdala as regions-of-interest (ROI), and assessed associations between connectivity and clinical variables. HCs exhibited significant within-group bidirectional mPFC-amygdala connectivity, which increased across the blocks, whereas BDD participants exhibited only significant mPFC-to-amygdala connectivity (P < 0.05, family-wise error corrected). In contrast, participants with AN lacked significant prefrontal-amygdala connectivity in either direction. AN showed significantly weaker mPFC-to-amygdala connectivity compared to HCs (P = 0.0015) and BDD (P = 0.0050). The mPFC-to-amygdala connectivity was associated with greater subjective fear ratings (R2 = 0.11, P = 0.0016), eating disorder symptoms (R2 = 0.33, P = 0.0029), and anxiety (R2 = 0.29, P = 0.0055) intensity scores. Our findings, which suggest a complex nosological relationship, have implications for understanding emotion regulation circuitry in these related psychiatric disorders, and may have relevance for current and novel therapeutic approaches.

Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive-compulsive disorder.

Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive-compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual's potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.

Pregenual Anterior Cingulate Dysfunction Associated with Depression in OCD: An Integrated Multimodal fMRI/1H MRS Study.

Depression is a commonly occurring symptom in obsessive-compulsive disorder (OCD), and is associated with worse functional impairment, poorer quality of life, and poorer treatment response. Understanding the underlying neurochemical and connectivity-based brain mechanisms of this important symptom domain in OCD is necessary for development of novel, more globally effective treatments. To investigate biopsychological mechanisms of comorbid depression in OCD, we examined effective connectivity and neurochemical signatures in the pregenual anterior cingulate cortex (pACC), a structure known to be involved in both OCD and depression. Resting-state functional magnetic resonance imaging (fMRI) and 1H magnetic resonance spectroscopy (MRS) data were obtained from participants with OCD (n=49) and healthy individuals of equivalent age and sex (n=25). Granger causality-based effective (directed) connectivity was used to define causal networks involving the right and left pACC. The interplay between fMRI connectivity, 1H MRS and clinical data was explored by applying moderation and mediation analyses. We found that the causal influence of the right dorsal anterior midcingulate cortex (daMCC) on the right pACC was significantly lower in the OCD group and showed significant correlation with depressive symptom severity in the OCD group. Lower and moderate levels of glutamate (Glu) in the right pACC significantly moderated the interaction between right daMCC-pACC connectivity and depression severity. Our results suggest a biochemical-connectivity-psychological model of pACC dysfunction contributing to depression in OCD, particularly involving intracingulate connectivity and glutamate levels in the pACC. These findings have implications for potential molecular and network targets for treatment of this multi-faceted psychiatric condition.