Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment. METHODS: Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4-6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern. RESULTS: We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction).Limitations: The linear model was constructed separately for each region of interest. CONCLUSION: This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets.

INTRODUCTION: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. METHODS: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. RESULTS: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52-0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. CONCLUSION: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography.

BACKGROUND: Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. METHODS: We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). RESULTS: The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). CONCLUSION: Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Symptom dimensions of major depression in a large community-based cohort.

BACKGROUND: Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community. METHODS: This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or 'symptom dimensions' via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records. RESULTS: Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations. CONCLUSIONS: An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

Relationship of neurite architecture to brain activity during task-based fMRI.

Functional MRI (fMRI) has been widely used to examine changes in neuronal activity during cognitive tasks. Commonly used measures of gray matter macrostructure (e.g., cortical thickness, surface area, volume) do not consistently appear to serve as structural correlates of brain function. In contrast, gray matter microstructure, measured using neurite orientation dispersion and density imaging (NODDI), enables the estimation of indices of neurite density (neurite density index; NDI) and organization (orientation dispersion index; ODI) in gray matter. Our study explored the relationship among neurite architecture, BOLD (blood-oxygen-level-dependent) fMRI, and cognition, using a large sample (n = 750) of young adults of the human connectome project (HCP) and two tasks that index more cortical (working memory) and more subcortical (emotion processing) targeting of brain functions. Using NODDI, fMRI, structural MRI and task performance data, hierarchical regression analyses revealed that higher working memory- and emotion processing-evoked BOLD activity was related to lower ODI in the right DLPFC, and lower ODI and NDI values in the right and left amygdala, respectively. Common measures of brain macrostructure (i.e., DLPFC thickness/surface area and amygdala volume) did not explain any additional variance (beyond neurite architecture) in BOLD activity. A moderating effect of neurite architecture on the relationship between emotion processing task-evoked BOLD response and performance was observed. Our findings provide evidence that neuro-/social-affective cognition-related BOLD activity is partially driven by the local neurite organization and density with direct impact on emotion processing. In vivo gray matter microstructure represents a new target of investigation providing strong potential for clinical translation.

Moving beyond the mean: Subgroups and dimensions of brain activity and cognitive performance across domains.

Human neuroimaging during cognitive tasks has provided unique and important insights into the neurobiology of cognition. However, the vast majority of research relies on group aggregate or average statistical maps of activity, which do not fully capture the rich intersubject variability in brain function. In order to fully understand the neurobiology of cognitive processes, it is necessary to explore the range of variability in activation patterns across individuals. To better characterize individual variability, hierarchical clustering was performed separately on six fMRI tasks in 822 participants from the Human Connectome Project. Across all tasks, clusters ranged from a predominantly 'deactivating' pattern towards a more 'activating' pattern of brain activity, with significant differences in out-of-scanner cognitive test scores between clusters. Cluster stability was assessed via a resampling approach; a cluster probability matrix was generated, as the probability of any pair of participants clustering together when both were present in a random subsample. Rather than forming distinct clusters, participants fell along a spectrum or into pseudo-clusters without clear boundaries. A principal components analysis of the cluster probability matrix revealed three components explaining over 90% of the variance in clustering. Plotting participants in this lower-dimensional 'similarity space' revealed manifolds of variations along an S 'snake' shaped spectrum or a folded circle or 'tortilla' shape. The 'snake' shape was present in tasks where individual variability related to activity along covarying networks, while the 'tortilla' shape represented multiple networks which varied independently.

Functional magnetic resonance imaging study of external source memory and its relation to cognitive insight in non-clinical subjects.

AIM: Previous research has linked cognitive insight (a measure of self-reflectiveness and self-certainty) in psychosis with neurocognitive and neuroanatomical disturbances in the fronto-hippocampal neural network. The authors' goal was to use functional magnetic resonance imaging (fMRI) to investigate the neural correlates of cognitive insight during an external source memory paradigm in non-clinical subjects. METHODS: At encoding, 24 non-clinical subjects travelled through a virtual city where they came across 20 separate people, each paired with a unique object in a distinct location. fMRI data were then acquired while participants viewed images of the city, and completed source recognition memory judgments of where and with whom objects were seen, which is known to involve prefrontal cortex. Cognitive insight was assessed with the Beck Cognitive Insight Scale. RESULTS: External source memory was associated with neural activity in a widespread network consisting of frontal cortex, including ventrolateral prefrontal cortex (VLPFC), temporal and occipital cortices. Activation in VLPFC correlated with higher self-reflectiveness and activation in midbrain correlated with lower self-certainty during source memory attributions. Neither self-reflectiveness nor self-certainty significantly correlated with source memory accuracy. CONCLUSION: By means of virtual reality and in the context of an external source memory paradigm, the study identified a preliminary functional neural basis for cognitive insight in the VLPFC in healthy people that accords with our fronto-hippocampal theoretical model as well as recent neuroimaging data in people with psychosis. The results may facilitate the understanding of the role of neural mechanisms in psychotic disorders associated with cognitive insight distortions.

Genetic architecture of the structural connectome.

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Clinical neuroscience research is going through a translational crisis largely due to the challenges of producing meaningful and generalizable results. Two critical limitations within clinical neuroscience research are the use of univariate statistics and between-study methodological variation. Univariate statistics may not be sensitive enough to detect complex relationships between several variables, and methodological variation poses challenges to the generalizability of the results. We compared two widely used multivariate statistical approaches, canonical correlations analysis (CCA) and partial least squares correlation (PLS), to determine the generalizability and stability of their solutions. We show that the properties of the measures inputted into the analysis likely play a more substantial role in the generalizability and stability of results compared to the specific approach applied (i.e., CCA or PLS).

A systematic review of structural and functional magnetic resonance imaging studies on the neurobiology of depressive symptoms in schizophrenia spectrum disorders.

Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies' findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond "total score" depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.

Functional phenotypes in schizophrenia spectrum disorders: defining the constructs and identifying biopsychosocial correlates using data-driven methods.

Functional impairments contribute to poor quality of life in schizophrenia spectrum disorders (SSD). We sought to (Objective I) define the main functional phenotypes in SSD, then (Objective II) identify key biopsychosocial correlates, emphasizing interpretable data-driven methods. Objective I was tested on independent samples: Dataset I (N = 282) and Dataset II (N = 317), with SSD participants who underwent assessment of multiple functioning areas. Participants were clustered based on functioning. Objective II was evaluated in Dataset I by identifying key features for classifying functional phenotype clusters from among 65 sociodemographic, psychological, clinical, cognitive, and brain volume measures. Findings were replicated across latent discriminant analyses (LDA) and one-vs.-rest binomial regularized regressions to identify key predictors. We identified three clusters of participants in each dataset, demonstrating replicable functional phenotypes: Cluster 1-poor functioning across domains; Cluster 2-impaired Role Functioning, but partially preserved Independent and Social Functioning; Cluster 3-good functioning across domains. Key correlates were Avolition, anhedonia, left hippocampal volume, and measures of emotional intelligence and subjective social experience. Avolition appeared more closely tied to role functioning, and anhedonia to independent and social functioning. Thus, we found three replicable functional phenotypes with evidence that recovery may not be uniform across domains. Avolition and anhedonia were both critical but played different roles for different functional domains. It may be important to identify critical functional areas for individual patients and target interventions accordingly.

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis.

BACKGROUND: Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD. METHODS: Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability. RESULTS: Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01). CONCLUSIONS: Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Bayesian Optimization of Neurostimulation (BOONStim).

BACKGROUND: Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed. OBJECTIVE: The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline. METHODS: BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization. RESULTS: Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time. CONCLUSIONS: BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Neural activity related to self-initiating elaborative semantic encoding in associative memory.

During episodic memory encoding, elaborative encoding strategies have been related to greater performance on later memory tests. However, many clinical populations display a deficit in self-initiating encoding strategies. We designed an fMRI study to examine the neural correlates of self-initiating elaborative encoding. Twenty-three healthy participants were presented triads of objects in which either neither, one or both objects in the bottom of the triad were related to the top object, and given two encoding instructions that required them to indicate the number of semantic ("related?") or physical ("smaller?") relationships in the triad. Reaction time decreased with more semantic relationships for both encoding instructions, indicating that semantic analysis was performed during the non-semantic encoding task. Recognition memory was better for the semantic encoding condition ("related?"), but there was no modulation of the number of semantic links on memory performance for either encoding condition. We performed a conjunction analysis on the fMRI data to find areas with greater activity for the non-semantic>semantic encoding tasks that were modulated by increasing semantic relationships during non-semantic encoding. Activity was found in the left dorsolateral prefrontal cortex (DLPFC) and bilaterally in the supramarginal gyrus. We suggest that the DLPFC is the most likely candidate region for the self-initiation of elaborative encoding while the supramarginal activity is likely related to attentional effects.

Auditory-motor adaptation to frequency-altered auditory feedback occurs when participants ignore feedback.

BACKGROUND: Auditory feedback is important for accurate control of voice fundamental frequency (F(0)). The purpose of this study was to address whether task instructions could influence the compensatory responding and sensorimotor adaptation that has been previously found when participants are presented with a series of frequency-altered feedback (FAF) trials. Trained singers and musically untrained participants (nonsingers) were informed that their auditory feedback would be manipulated in pitch while they sang the target vowel [/α /]. Participants were instructed to either 'compensate' for, or 'ignore' the changes in auditory feedback. Whole utterance auditory feedback manipulations were either gradually presented ('ramp') in -2 cent increments down to -100 cents (1 semitone) or were suddenly ('constant') shifted down by 1 semitone. RESULTS: Results indicated that singers and nonsingers could not suppress their compensatory responses to FAF, nor could they reduce the sensorimotor adaptation observed during both the ramp and constant FAF trials. CONCLUSIONS: Compared to previous research, these data suggest that musical training is effective in suppressing compensatory responses only when FAF occurs after vocal onset (500-2500 ms). Moreover, our data suggest that compensation and adaptation are automatic and are influenced little by conscious control.

Within-subject reliability of concurrent TMS-fMRI during a single session.

Concurrent transcranial magnetic stimulation with functional MRI (concurrent TMS-fMRI) allows real-time causative probing of brain connectivity. However, technical challenges, safety, and tolerability may limit the number of trials employed during a concurrent TMS-fMRI experiment. We leveraged an existing data set with 100 trials of active TMS compared to a sub-threshold control condition to assess the reliability of the evoked BOLD response during concurrent TMS-fMRI. This data will permit an analysis of the minimum number of trials that should be employed in a concurrent TMS-fMRI protocol in order to achieve reliable spatial changes in activity. Single-subject maps of brain activity were created by splitting the trials within the same experimental session into groups of 50, 40, 30, 25, 20, 15, or 10 trials, correlations (R) between t-maps derived from paired subsets of trials within the same individual were calculated as reliability. R was moderate-high for 50 trials (mean R = .695) and decreased as the number of trials decreased. Consistent with previous findings of high individual variability in the spatial patterns of evoked neuronal changes following a TMS pulse, the spatial pattern of Rs differed across participants, but regional R was correlated with the magnitude of TMS-evoked activity. These results demonstrate concurrent TMS-fMRI produces a reliable pattern of activity at the individual level at higher trial numbers, particularly within localized regions. The spatial pattern of reliability is individually idiosyncratic and related to the individual pattern of evoked changes.

Spatial-extent inference for testing variance components in reliability and heritability studies.

Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.

Subgenual cingulate connectivity as a treatment predictor during low-frequency right dorsolateral prefrontal rTMS: A concurrent TMS-fMRI study.

INTRODUCTION: Repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) is effective in alleviating treatment-resistant depression (TRD). It has been proposed that regions within the left DLPFC that are anti-correlated with the right subgenual anterior cingulate cortex (sgACC) may represent optimal individualized target sites for high-frequency left rTMS (HFL). OBJECTIVE/HYPOTHESIS: This study aimed to explore the effects of low-frequency right rTMS (LFR) on left sgACC connectivity during concurrent TMS-fMRI. METHODS: 34 TRD patients underwent an imaging session that included both a resting-state fMRI run (rs-fMRI0) and a run during which LFR was applied to the right DLPFC (TMS-fMRI). Participants subsequently completed four weeks of LFR treatment. The left sgACC functional connectivity was compared between the rs-fMRI0 run and TMS-fMRI run. Personalized e-fields and a region-of-interest approach were used to calculate overlap of left sgACC functional connectivity at the TMS target and to assess for a relationship with treatment effects. RESULTS: TMS-fMRI increased left sgACC functional connectivity to parietal regions within the ventral attention network; differences were not significantly associated with clinical improvements. Personalized e-fields were not significant in predicting treatment outcomes (p = 0.18). CONCLUSION: This was the first study to examine left sgACC anti-correlation with the right DLPFC during an LFR rTMS protocol. In contrast to studies that targeted the left DLPFC, we did not find that higher anti-correlation was associated with clinical outcomes. Our results suggest that the antidepressant mechanism of action of LFR to the right DLPFC may be different than for HFL.

Protocol for a systematic review and meta-analysis of coordinate-based network mapping of brain structure in bipolar disorder across the lifespan.

BACKGROUND: Studies about brain structure in bipolar disorder have reported conflicting findings. These findings may be explained by the high degree of heterogeneity within bipolar disorder, especially if structural differences are mapped to single brain regions rather than networks. AIMS: We aim to complete a systematic review and meta-analysis to identify brain networks underlying structural abnormalities observed on T1-weighted magnetic resonance imaging scans in bipolar disorder across the lifespan. We also aim to explore how these brain networks are affected by sociodemographic and clinical heterogeneity in bipolar disorder. METHOD: We will include case-control studies that focus on whole-brain analyses of structural differences between participants of any age with a standardised diagnosis of bipolar disorder and controls. The electronic databases Medline, PsycINFO and Web of Science will be searched. We will complete an activation likelihood estimation analysis and a novel coordinate-based network mapping approach to identify specific brain regions and brain circuits affected in bipolar disorder or relevant subgroups. Meta-regressions will examine the effect of sociodemographic and clinical variables on identified brain circuits. CONCLUSIONS: Findings from this systematic review and meta-analysis will enhance understanding of the pathophysiology of bipolar disorder. The results will identify brain circuitry implicated in bipolar disorder, and how they may relate to relevant sociodemographic and clinical variables across the lifespan.