Neurophenomenological Investigation of Mindfulness Meditation "Cessation" Experiences Using EEG Network Analysis in an Intensively Sampled Adept Meditator.

Mindfulness meditation is a contemplative practice that is informed by Buddhism. It has been proven effective for improving mental and physical health in clinical and non-clinical contexts. To date, mainstream dialogue and scientific research on mindfulness has focused primarily on short-term mindfulness training and applications of mindfulness for reducing stress. Understanding advanced mindfulness practice has important implications for mental health and general wellbeing. According to Theravada Buddhist meditation, a "cessation" event is a dramatic experience of profound clarity and equanimity that involves a complete discontinuation in experience, and is evidence of mastery of mindfulness meditation. Thirty-seven cessation events were captured in a single intensively sampled advanced meditator (over 6,000 h of retreat mindfulness meditation training) while recording electroencephalography (EEG) in 29 sessions between November 12, 2019 and March 11, 2020. Functional connectivity and network integration were assessed from 40 s prior to cessations to 40 s after cessations. From 21 s prior to cessations there was a linear decrease in large-scale functional interactions at the whole-brain level in the alpha band. In the 40 s following cessations these interactions linearly returned to prior levels. No modulation of network integration was observed. The decrease in whole-brain functional connectivity was underlain by frontal to left temporal and to more posterior decreases in connectivity, while the increase was underlain by wide-spread increases in connectivity. These results provide neuroscientific evidence of large-scale modulation of brain activity related to cessation events that provides a foundation for future studies of advanced meditation.

Long-term trajectories of depressive symptoms in deployed military personnel: A 10-year prospective study.

BACKGROUND: Military missions, especially those involving combat exposure, are associated with an increased risk of depression. Understanding the long-term course of depressive symptoms post-deployment is important to improve decision-making regarding deployment and mental health policies in the military. This study investigates trajectories of depressive symptoms in the Dutch army, exploring the influence of factors such as demographics, early-life trauma, posttraumatic stress disorder (PTSD) symptoms, and deployment stressors. METHODS: A cohort of 1032 military men and women deployed to Afghanistan (2005-2008) was studied from pre- to 10 years post-deployment. Depressive and PTSD symptoms were assessed using the Symptom CheckList-90 and the Self-Rating Inventory for PTSD. Demographics, early trauma, and deployment experiences were collected at baseline and after deployment, respectively. Latent Class Growth Analysis was used to explore heterogeneity in trajectories of depressive symptoms over time. RESULTS: Four trajectories were found: resilient (65%), intermediate-stable (20%), symptomatic-chronic (9%), and late-onset-increasing (6%). The resilient group experienced fewer deployment stressors, while the symptomatic-chronic group reported more early life traumas. Trajectories with elevated depressive symptoms consistently demonstrated higher PTSD symptoms. LIMITATIONS: Potential nonresponse bias and missing information due to the longitudinal design and extensive follow-up times. CONCLUSIONS: This study identified multiple trajectories of depressive symptoms in military personnel up to 10 years post-deployment, associated with early trauma, deployment stressors, adverse life events and PTSD symptoms. The prevalence of the resilient trajectory suggests a substantial level of resilience among deployed military personnel. These findings provide valuable insights and a foundation for further research.

Intensive whole-brain 7T MRI case study of volitional control of brain activity in deep absorptive meditation states.

Jhanas are profound states of mind achieved through advanced meditation, offering valuable insights into the nature of consciousness and tools to enhance well-being. Yet, its neurophenomenology remains limited due to methodological difficulties and the rarity of advanced meditation practitioners. We conducted a highly exploratory study to investigate the neurophenomenology of jhanas in an intensively sampled adept meditator case study (4 hr 7T fMRI collected in 27 sessions) who performed jhana meditation and rated specific aspects of experience immediately thereafter. Linear mixed models and correlations were used to examine relations among brain activity and jhana phenomenology. We identified distinctive patterns of brain activity in specific cortical, subcortical, brainstem, and cerebellar regions associated with jhana. Furthermore, we observed correlations between brain activity and phenomenological qualities of attention, jhanic qualities, and narrative processing, highlighting the distinct nature of jhanas compared to non-meditative states. Our study presents the most rigorous evidence yet that jhana practice deconstructs consciousness, offering unique insights into consciousness and significant implications for mental health and well-being.

Investigation of advanced mindfulness meditation "cessation" experiences using EEG spectral analysis in an intensively sampled case study.

Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of "cessation" events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.

Pattern classification based on the amygdala does not predict an individual's response to emotional stimuli.

Functional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi-voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region-of-interest analysis) or the whole-brain (secondary exploratory analysis). The accuracy score of the amygdala-based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28-58%; chance-level: 33%). In contrast, the accuracy score of the whole-brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across-subjects mean and standard deviation of 49% ± 6% (range: 33-62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real-time fMRI neurofeedback training.

Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response.

Introduction: Trauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success. Methods: Functional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed. Results: No differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion. Discussion: These results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.

The effect on relapse rate and psychiatric symptomatology: Switching a combination of first- and second-generation antipsychotic polypharmacy to antipsychotic monotherapy in long-term inpatients with schizophrenia and related disorders. A pragmatic randomized open-label trial (SwAP trial).

BACKGROUND: There is little evidence to support the use of antipsychotic polypharmacy, and there are concerns about safety and side effects. Nonetheless, it is commonly used in the treatment of long-term inpatients with schizophrenia. This study investigated the effects of switching from a combination of first- and second-generation antipsychotics (FGA and SGA) to monotherapy (FGA or SGA) on relapse rates and psychiatric symptomatology. METHODS: Institutionalized patients with chronic psychotic disorders using a combination of SGA and FGA (n = 136) participated in a randomized open-label trial. The SWITCH group discontinued either FGA or SGA, the STAY group continued combination treatment. Relapse and psychotic symptoms were measured at baseline and during follow-up at 3, 6, and 9 months. Psychiatric symptomatology was measured using the Brief Psychiatric Rating Scale (BPRS). Relapse was defined as (i) an increase in BPRS score of at least 2 points on any item, or (ii) an increase of at least 4 points in total BPRS score and an adjustment of antipsychotics. RESULTS: A logistic regression model, corrected for sex, showed that the probability of relapse was significantly lower in the SWITCH group: 0.29 (95% CI 0.13-0.62). The protective effect of switching to monotherapy was attributable to patients continuing clozapine as monotherapy. For patients who did not experience a relapse nor dropped out, BPRS total scores decreased significantly more in the SWITCH group (p = 0.0001). CONCLUSION: Switching from a combination of FGA and SGA to monotherapy in long-term inpatients does not increase the relapse rate and may even reduce it.

Voxel-based morphometry and cortical thickness in combat veterans suffering from impulsive aggression.

BACKGROUND: Problems with impulsive aggression occur in many forms of psychiatric dysfunction, and are a common complaint among combat veterans. The present study sought to examine the neuroanatomical correlates of combat-related impulsive aggression. METHODS: T1-weighted magnetic resonance images were acquired from 29 male veterans with impulsive aggression and 30 non-aggressive combat controls. Subcortical volumetry was conducted with the amygdala and hippocampus and their main constituent subdivisions as regions-of-interest (ROIs) (basolateral, centromedial amygdala; head, body, tail of hippocampus). Cortical thickness measurements were extracted for the dorsolateral prefrontal cortex, orbitofrontal cortex, and anterior cingulate cortex. Within-group correlations with psychometric measures were also explored. RESULTS: No significant group differences in cortical thickness or subcortical grey matter volumes were observed for any of the ROIs. Also, no significant correlations with any of the psychometric measures were recorded. Exploratory whole-brain analysis of cortical thickness revealed a significant group × anxiety interaction effect in a cluster located in the left lingual gyrus. CONCLUSIONS: The current findings indicate that problems with impulsive aggression may not be directly associated with alterations in cortical thickness or amygdalar/hippocampal (sub)volumes. The observed interplay between impulsive aggression problems and anxiety-related symptoms is consistent with prior work showing the two phenomena may share the same underlying (neural) mechanisms.

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking.

Current treatments for smoking yield suboptimal outcomes, partly because of an inability to reduce cue-induced smoking. Mindfulness training (MT) has shown preliminary efficacy for smoking cessation, yet its neurobiological target remains unknown. Our prior work with nonsmokers indicates that MT reduces posterior cingulate cortex (PCC) activity. In individuals who smoke, the PCC, consistently a main hub of the "default mode network," activates in response to smoking cues. In this randomized controlled trial, we tested the effects of app-delivered MT on PCC reactivity to smoking cues and whether individual differences in MT-mediated PCC changes predicted smoking outcomes. Smoking cue-induced PCC reactivity was measured using functional magnetic resonance imaging at baseline and 1 month after receiving smartphone app-based MT (n = 33) vs. an active control (National Cancer Institute's QuitGuide, n = 34). Whether individual differences in treatment-related changes in PCC activity predicted smoking behavior was assessed. The MT group demonstrated a significant correlation between a reduction in PCC reactivity to smoking cues and a decline in cigarette consumption (r = 0.39, p = 0.02). No association was found in the control group (r = 0.08, p = 0.65). No effects of group alone were found in PCC or cigarette reduction. Post hoc analysis revealed this association is sex specific (women, r = 0.49, p = 0.03; men: r = -0.08, p = 0.79). This initial report indicates that MT specifically reduces smoking cue-induced PCC activity in a subject-specific manner, and the reduction in PCC activity predicts a concurrent decline in smoking. These findings link the hypothesized behavioral effects of MT for smoking to neural mechanisms particularly in women. This lays the groundwork for identifying individuals who may benefit from targeted digital therapeutic treatments such as smartphone-based MT, yielding improved clinical outcomes.

From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.

OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system. METHODS: A linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage. RESULTS: Nearly 4500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population. CONCLUSION: Our Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings. SIGNIFICANCE: A translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.

Improving efficiency in neuroimaging research through application of Lean principles.

INTRODUCTION: "Lean" is a set of management principles which focus on increasing value and efficiency by reducing or avoiding waste (e.g., overproduction, defects, inventory, transportation, waiting, motion, over processing). It has been applied to manufacturing, education, and health care, leading to optimized process flow, increased efficiency and increased team empowerment. However, to date, it has not been applied to neuroimaging research. METHODS: Lean principles, such as Value stream mapping (e.g. a tool with which steps in the workflow can be identified on which to focus improvement efforts), 5S (e.g. an organizational method to boost workplace efficiency and efficacy) and Root-cause analysis (e.g. a problem-solving approach to identify key points of failure in a system) were applied to an ongoing, large neuroimaging study that included seven research visits per participant. All team members participated in a half-day exercise in which the entire project flow was visualized and areas of inefficiency were identified. Changes focused on removing obstacles, standardization, optimal arrangement of equipment and root-cause-analysis. A process for continuous improvement was also implemented. Total time of an experiment was recorded before implementation of Lean for two participants and after implementation of Lean for two participants. Satisfaction of team members was assessed anonymously on a 5-item Likert scale, ranging from much worsened to much improved. RESULTS: All team members (N = 6) considered the overall experience of conducting an experiment much improved after implementation of Lean. Five out of six team members indicated a much-improved reduction in time, with the final team member considering this somewhat improved. Average experiment time was reduced by 13% after implementation of Lean (from 142 and 147 minutes to 124 and 128 minutes). DISCUSSION: Lean principles can be successfully applied to neuroimaging research. Training in Lean principles for junior research scientists is recommended.

Can Mindfulness Address Maladaptive Eating Behaviors? Why Traditional Diet Plans Fail and How New Mechanistic Insights May Lead to Novel Interventions.

Emotional and other maladaptive eating behaviors develop in response to a diversity of triggers, from psychological stress to the endless external cues in our modern food environment. While the standard approach to food- and weight-related concerns has been weight-loss through dietary restriction, these interventions have produced little long-term benefit, and may be counterproductive. A growing understanding of the behavioral and neurobiological mechanisms that underpin habit formation may explain why this approach has largely failed, and pave the way for a new generation of non-pharmacologic interventions. Here, we first review how modern food environments interact with human biology to promote reward-related eating through associative learning, i.e., operant conditioning. We also review how operant conditioning (positive and negative reinforcement) cultivates habit-based reward-related eating, and how current diet paradigms may not directly target such eating. Further, we describe how mindfulness training that targets reward-based learning may constitute an appropriate intervention to rewire the learning process around eating. We conclude with examples that illustrate how teaching patients to tap into and act on intrinsic (e.g., enjoying healthy eating, not overeating, and self-compassion) rather than extrinsic reward mechanisms (e.g., weighing oneself), is a promising new direction in improving individuals' relationship with food.

Meditation is associated with increased brain network integration.

INTRODUCTION: This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. METHODS: Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands. RESULTS: Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. CONCLUSION: These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

Source-space EEG neurofeedback links subjective experience with brain activity during effortless awareness meditation.

BACKGROUND: Meditation is increasingly showing beneficial effects for psychiatric disorders. However, learning to meditate is not straightforward as there are no easily discernible outward signs of performance and thus no direct feedback is possible. As meditation has been found to correlate with posterior cingulate cortex (PCC) activity, we tested whether source-space EEG neurofeedback from the PCC followed the subjective experience of effortless awareness (a major component of meditation), and whether participants could volitionally control the signal. METHODS: Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators were briefly trained to perform a basic meditation practice to induce the subjective experience of effortless awareness in a progressively more challenging neurofeedback test-battery. Experienced meditators performed a self-selected meditation practice to induce this state in the same test-battery. Neurofeedback was provided based on gamma-band (40-57Hz) PCC activity extracted using a beamformer algorithm. Associations between PCC activity and the subjective experience of effortless awareness were assessed by verbal probes. RESULTS: Both groups reported that decreased PCC activity corresponded with effortless awareness (P<0.0025 for each group), with high median confidence ratings (novices: 8 on a 0-10 Likert scale; experienced: 9). Both groups showed high moment-to-moment median correspondence ratings between PCC activity and subjective experience of effortless awareness (novices: 8, experienced: 9). Both groups were able to volitionally control the PCC signal in the direction associated with effortless awareness by practicing effortless awareness meditation (novices: median % of time=77.97, P=0.001; experienced: 89.83, P<0.0005). CONCLUSIONS: These findings support the feasibility of using EEG neurofeedback to link an objective measure of brain activity with the subjective experience of effortless awareness, and suggest potential utility of this paradigm as a tool for meditation training.

Auditory Hallucinations and the Brain's Resting-State Networks: Findings and Methodological Observations.

In recent years, there has been increasing interest in the potential for alterations to the brain's resting-state networks (RSNs) to explain various kinds of psychopathology. RSNs provide an intriguing new explanatory framework for hallucinations, which can occur in different modalities and population groups, but which remain poorly understood. This collaboration from the International Consortium on Hallucination Research (ICHR) reports on the evidence linking resting-state alterations to auditory hallucinations (AH) and provides a critical appraisal of the methodological approaches used in this area. In the report, we describe findings from resting connectivity fMRI in AH (in schizophrenia and nonclinical individuals) and compare them with findings from neurophysiological research, structural MRI, and research on visual hallucinations (VH). In AH, various studies show resting connectivity differences in left-hemisphere auditory and language regions, as well as atypical interaction of the default mode network and RSNs linked to cognitive control and salience. As the latter are also evident in studies of VH, this points to a domain-general mechanism for hallucinations alongside modality-specific changes to RSNs in different sensory regions. However, we also observed high methodological heterogeneity in the current literature, affecting the ability to make clear comparisons between studies. To address this, we provide some methodological recommendations and options for future research on the resting state and hallucinations.

Structural Brain Network Disturbances in the Psychosis Spectrum.

BACKGROUND: Individuals with subclinical psychotic symptoms provide a unique window on the pathophysiology of psychotic experiences as these individuals are free of confounders such as hospitalization, negative and cognitive symptoms and medication use. Brain network disturbances of white matter connections are thought to play a central role in the pathophysiology of psychosis. Based on the structural network disconnection hypothesis in schizophrenia, we expect less and weaker connections, and altered brain network organization in individuals with clinical and those with subclinical psychotic symptoms. METHODS: We used diffusion tensor imaging to study 35 patients with a psychotic disorder, 35 subjects with subclinical psychotic symptoms, and 36 healthy controls. The structural brain network was analyzed on 3 levels: connection density, white matter microstructure (fractional anisotropy, mean diffusivity, and magnetic transfer ratio), and network organization. Network organization was studied with minimum spanning tree analysis, a method to reconstruct a backbone of structural highways in the brain. RESULTS: Decreased fractional anisotropy and increased mean diffusivity was found in both groups with psychotic symptoms, while their network topology showed decreased overlap with a healthy reference network. Decreased centrality was found in several brain regions, including parietal hubs and language areas, in both groups with psychotic symptoms. Deviation of network characteristics was more apparent in clinical subjects than in subclinical subjects. DISCUSSION: Weaker connections and decreased centrality of parietal hubs characterize the structural brain network in subjects with psychotic symptoms. These differences are more notable in clinical than in subclinical subjects with psychotic experiences.

Cortical thickness in individuals with non-clinical and clinical psychotic symptoms.

Symptoms that are linked to psychosis are also experienced by individuals who are not in need of care. In the present study, cortical thickness was investigated in these individuals. Fifty individuals with non-clinical auditory verbal hallucinations (most of them also experienced other non-clinical psychotic symptoms), 50 patients with a psychotic disorder and auditory verbal hallucinations, and 50 healthy control subjects underwent structural magnetic resonance imaging. Data were analysed using FreeSurfer. Cortical thickness in the pars orbitalis, paracentral lobule, fusiform gyrus and inferior temporal gyrus was lowest in patients, intermediate in the non-clinical hallucinating group, and highest in control subjects. The patients also showed thinning in widespread additional areas compared to the two other groups, whereas both hallucinating groups showed similar levels of thinning in the insula. Ranking the levels of cortical thickness per brain region across groups revealed that for 88% of brain regions, cortical thickness was lowest in patients, intermediate in the non-clinical hallucinating group, and highest in controls. These findings show that individuals with non-clinical psychotic symptoms show a similar but less pronounced pattern of cortical thinning as patients with a psychotic disorder, which is suggestive of a similar, but milder underlying pathophysiology in this group compared to the psychosis group.

Symptom dimensions of the psychotic symptom rating scales in psychosis: a multisite study.

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

Review of the efficacy of transcranial magnetic stimulation for auditory verbal hallucinations.

With an increase of the number of studies exploring repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations (AVH), an update is provided on the efficacy of different paradigms. A literature search was performed from 1966 through April 2013. Twenty-five randomized controlled trials using the severity of AVH or psychosis as outcome measures were included. Standardized mean weighted effect sizes were computed; a qualitative review of the literature was performed to assess the effects of various rTMS paradigms. rTMS versus sham treatment for AVH yielded a mean weighted effect size of .44. No significant mean weighted effect size was found for the severity of psychosis (i.e., .21). For patients with medication-resistant AVH, the mean weighted effect size was .45. rTMS applied at the left temporoparietal area with a frequency of 1 Hz yielded a moderate mean weighted effect size of .63, indicating superiority of this paradigm. Various other paradigms failed to show superior effects. rTMS applied at the right temporoparietal area was not superior to sham treatment. rTMS, especially when applied at the left temporoparietal area with a frequency of 1 Hz, is effective for the treatment of AVH, including in patients with medication-resistant AVH. The results for other rTMS paradigms are disappointing thus far. A next step should be to explore the effects of rTMS in medication-free individuals, for example, during the initial phases of psychosis, and in patients with diagnoses other than schizophrenia who do not have comorbid psychotic symptoms.

Network analysis of auditory hallucinations in nonpsychotic individuals.

BACKGROUND: Auditory verbal hallucinations (AVH) are a cardinal feature of schizophrenia and can severely disrupt behavior and decrease quality of life. Identification of areas with high functional connectivity (so-called hub regions) that are associated with the predisposition to hallucinate may provide potential targets for neuromodulation in the treatment of AVH. METHODS: Resting-state fMRI scans during which no hallucinations had occurred were acquired from 29 nonpsychotic individuals with AVH and 29 matched controls. These nonpsychotic individuals with AVH provide the opportunity to study AVH without several confounds associated with schizophrenia, such as antipsychotic medication use and other symptoms related to the illness. Hub regions were identified by assessing weighted connectivity strength and betweenness centrality across groups using a permutation analysis. RESULTS: Nonpsychotic individuals with AVH exhibited increased functioning as hub regions in the temporal cortices and the posterior cingulate/precuneus, which is an important area in the default mode network (DMN), compared to the nonhallucinating controls. In addition, the right inferior temporal gyrus, left paracentral lobule and right amygdala were less important as a hub region in the AVH group. CONCLUSIONS: These results suggest that the predisposition to hallucinate may be related to aberrant functioning of the DMN and the auditory cortices.