Effective connectivity during face processing in major depression - distinguishing markers of pathology, risk, and resilience.

BACKGROUND: Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression. METHODS: Thirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity. RESULTS: MDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls. CONCLUSIONS: Our results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.

Behavioural and functional evidence revealing the role of RBFOX1 variation in multiple psychiatric disorders and traits.

Common variation in the gene encoding the neuron-specific RNA splicing factor RNA Binding Fox-1 Homolog 1 (RBFOX1) has been identified as a risk factor for several psychiatric conditions, and rare genetic variants have been found causal for autism spectrum disorder (ASD). Here, we explored the genetic landscape of RBFOX1 more deeply, integrating evidence from existing and new human studies as well as studies in Rbfox1 knockout mice. Mining existing data from large-scale studies of human common genetic variants, we confirmed gene-based and genome-wide association of RBFOX1 with risk tolerance, major depressive disorder and schizophrenia. Data on six mental disorders revealed copy number losses and gains to be more frequent in ASD cases than in controls. Consistently, RBFOX1 expression appeared decreased in post-mortem frontal and temporal cortices of individuals with ASD and prefrontal cortex of individuals with schizophrenia. Brain-functional MRI studies demonstrated that carriers of a common RBFOX1 variant, rs6500744, displayed increased neural reactivity to emotional stimuli, reduced prefrontal processing during cognitive control, and enhanced fear expression after fear conditioning, going along with increased avoidance behaviour. Investigating Rbfox1 neuron-specific knockout mice allowed us to further specify the role of this gene in behaviour. The model was characterised by pronounced hyperactivity, stereotyped behaviour, impairments in fear acquisition and extinction, reduced social interest, and lack of aggression; it provides excellent construct and face validity as an animal model of ASD. In conclusion, convergent translational evidence shows that common variants in RBFOX1 are associated with a broad spectrum of psychiatric traits and disorders, while rare genetic variation seems to expose to early-onset neurodevelopmental psychiatric disorders with and without developmental delay like ASD, in particular. Studying the pleiotropic nature of RBFOX1 can profoundly enhance our understanding of mental disorder vulnerability.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Autobiographical Script-Driven Imagery Has No Detectable Effect on Emotion Regulation in Healthy Individuals.

INTRODUCTION: Emotion regulation (ER), the ability to actively modulate one's own emotion reactions, likely depends on the individual's current emotional state. Here, we investigated whether negative emotions induced by an interpersonal autobiographic script affect the neuronal processes underlying ER. METHODS: Twenty healthy participants were recruited and underwent functional magnetic resonance imaging (fMRI) during performance of distancing, a specific ER strategy, while viewing emotionally arousing pictures. Participants were instructed to either naturally experience ("permit" condition) or to actively downregulate ("regulate" condition) their emotional responses to the presented stimuli. Before each of the 4 runs in total, a neutral or negative autobiographical audio script was presented. The negative script comprised an emotionally negative event from childhood or adolescence that represented either emotional abuse or emotional neglect. The second event comprised an everyday neutral situation. We aimed at identifying the neural correlates of ER and their modulation by script-driven imagery. RESULTS: fMRI analyses testing for greater responses in the "regulate" than the "permit" condition replicated previously reported neural correlates of ER in the right dorsolateral prefrontal cortex and the right inferior parietal lobule. A significant ER effect was also observed in the left orbitofrontal cortex. In the amygdala, we found greater responses in the "permit" compared to the "regulate" condition. We did not observe a significant modulation of the ER effects in any of these regions by the negative emotional state induced by autobiographical scripts. Bayesian statistics confirmed the absence of such modulations by providing marginal evidence for null effects. DISCUSSION: While we replicated previously reported neural correlates of ER, we found no evidence for an effect of mood induction with individualized autobiographical scripts on the neural processes underlying ER in healthy participants.

Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2-94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0-21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

Amygdala functional connectivity in major depression - disentangling markers of pathology, risk and resilience.

BACKGROUND: Limbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience. METHODS: We analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA. RESULTS: Groups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching. CONCLUSION: Our results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

MAOA-VNTR genotype affects structural and functional connectivity in distributed brain networks.

Previous studies have linked the low expression variant of a variable number of tandem repeat polymorphism in the monoamine oxidase A gene (MAOA-L) to the risk for impulsivity and aggression, brain developmental abnormalities, altered cortico-limbic circuit function, and an exaggerated neural serotonergic tone. However, the neurobiological effects of this variant on human brain network architecture are incompletely understood. We studied healthy individuals and used multimodal neuroimaging (sample size range: 219-284 across modalities) and network-based statistics (NBS) to probe the specificity of MAOA-L-related connectomic alterations to cortical-limbic circuits and the emotion processing domain. We assessed the spatial distribution of affected links across several neuroimaging tasks and data modalities to identify potential alterations in network architecture. Our results revealed a distributed network of node links with a significantly increased connectivity in MAOA-L carriers compared to the carriers of the high expression (H) variant. The hyperconnectivity phenotype primarily consisted of between-lobe ("anisocoupled") network links and showed a pronounced involvement of frontal-temporal connections. Hyperconnectivity was observed across functional magnetic resonance imaging (fMRI) of implicit emotion processing (pFWE = .037), resting-state fMRI (pFWE = .022), and diffusion tensor imaging (pFWE = .044) data, while no effects were seen in fMRI data of another cognitive domain, that is, spatial working memory (pFWE = .540). These observations are in line with prior research on the MAOA-L variant and complement these existing data by novel insights into the specificity and spatial distribution of the neurogenetic effects. Our work highlights the value of multimodal network connectomic approaches for imaging genetics.

Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function.

Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

Dynamic reconfiguration of frontal brain networks during executive cognition in humans.

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of "dynamic network neuroscience" to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the "n-back" task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes "network flexibility," employs transient and heterogeneous connectivity between frontal systems, which we refer to as "integration." Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.

Sliding-window analysis tracks fluctuations in amygdala functional connectivity associated with physiological arousal and vigilance during fear conditioning.

We evaluated whether sliding-window analysis can reveal functionally relevant brain network dynamics during a well-established fear conditioning paradigm. To this end, we tested if fMRI fluctuations in amygdala functional connectivity (FC) can be related to task-induced changes in physiological arousal and vigilance, as reflected in the skin conductance level (SCL). Thirty-two healthy individuals participated in the study. For the sliding-window analysis we used windows that were shifted by one volume at a time. Amygdala FC was calculated for each of these windows. Simultaneously acquired SCL time series were averaged over time frames that corresponded to the sliding-window FC analysis, which were subsequently regressed against the whole-brain seed-based amygdala sliding-window FC using the GLM. Surrogate time series were generated to test whether connectivity dynamics could have occurred by chance. In addition, results were contrasted against static amygdala FC and sliding-window FC of the primary visual cortex, which was chosen as a control seed, while a physio-physiological interaction (PPI) was performed as cross-validation. During periods of increased SCL, the left amygdala became more strongly coupled with the bilateral insula and anterior cingulate cortex, core areas of the salience network. The sliding-window analysis yielded a connectivity pattern that was unlikely to have occurred by chance, was spatially distinct from static amygdala FC and from sliding-window FC of the primary visual cortex, but was highly comparable to that of the PPI analysis. We conclude that sliding-window analysis can reveal functionally relevant fluctuations in connectivity in the context of an externally cued task.

Evaluating the replicability, specificity, and generalizability of connectome fingerprints.

Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n = 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n = 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.

Identification of gene ontologies linked to prefrontal-hippocampal functional coupling in the human brain.

Functional interactions between the dorsolateral prefrontal cortex and hippocampus during working memory have been studied extensively as an intermediate phenotype for schizophrenia. Coupling abnormalities have been found in patients, their unaffected siblings, and carriers of common genetic variants associated with schizophrenia, but the global genetic architecture of this imaging phenotype is unclear. To achieve genome-wide hypothesis-free identification of genes and pathways associated with prefrontal-hippocampal interactions, we combined gene set enrichment analysis with whole-genome genotyping and functional magnetic resonance imaging data from 269 healthy German volunteers. We found significant enrichment of the synapse organization and biogenesis gene set. This gene set included known schizophrenia risk genes, such as neural cell adhesion molecule (NRCAM) and calcium channel, voltage-dependent, beta 2 subunit (CACNB2), as well as genes with well-defined roles in neurodevelopmental and plasticity processes that are dysfunctional in schizophrenia and have mechanistic links to prefrontal-hippocampal functional interactions. Our results demonstrate a readily generalizable approach that can be used to identify the neurogenetic basis of systems-level phenotypes. Moreover, our findings identify gene sets in which genetic variation may contribute to disease risk through altered prefrontal-hippocampal functional interactions and suggest a link to both ongoing and developmental synaptic plasticity.

Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

The application of global signal regression (GSR) to resting-state functional magnetic resonance imaging data and its usefulness is a widely discussed topic. In this article, we report an observation of segregated distribution of amygdala resting-state functional connectivity (rs-FC) within the fusiform gyrus (FFG) as an effect of GSR in a multi-center-sample of 276 healthy subjects. Specifically, we observed that amygdala rs-FC was distributed within the FFG as distinct anterior versus posterior clusters delineated by positive versus negative rs-FC polarity when GSR was performed. To characterize this effect in more detail, post hoc analyses revealed the following: first, direct overlays of task-functional magnetic resonance imaging derived face sensitive areas and clusters of positive versus negative amygdala rs-FC showed that the positive amygdala rs-FC cluster corresponded best with the fusiform face area, whereas the occipital face area corresponded to the negative amygdala rs-FC cluster. Second, as expected from a hierarchical face perception model, these amygdala rs-FC defined clusters showed differential rs-FC with other regions of the visual stream. Third, dynamic connectivity analyses revealed that these amygdala rs-FC defined clusters also differed in their rs-FC variance across time to the amygdala. Furthermore, subsample analyses of three independent research sites confirmed reliability of the effect of GSR, as revealed by similar patterns of distinct amygdala rs-FC polarity within the FFG. In this article, we discuss the potential of GSR to segregate face sensitive areas within the FFG and furthermore discuss how our results may relate to the functional organization of the face-perception circuit.

Alterations in neural Theory of Mind processing in euthymic patients with bipolar disorder and unaffected relatives.

OBJECTIVES: Behavioral deficits in the Theory of Mind (ToM) have been robustly demonstrated in bipolar disorder. These deficits may represent an intermediate phenotype of the disease. The aim of this study was: (i) to investigate alterations in neural ToM processing in euthymic patients with bipolar disorder, and (ii) to examine whether similar effects are present in unaffected relatives of patients with bipolar disorder suggesting that ToM functional activation may be, in part, due to genetic risk for the disease. METHODS: A total of 24 euthymic patients with bipolar disorder, 21 unaffected first-degree relatives, and 81 healthy controls completed a ToM task while undergoing functional magnetic resonance imaging. RESULTS: We observed reduced bilateral activation of the temporoparietal junction (TPJ) and diminished functional fronto-temporoparietal connectivity in patients compared to controls. Relatives tended towards intermediate temporoparietal activity and functional coupling with medial prefrontal areas. There was also evidence for a potentially compensatory enhanced recruitment of the right middle temporal gyrus and stronger connectivity between this region and the medial prefrontal cortex in relatives. CONCLUSIONS: These findings provide further evidence of altered neural ToM processing in euthymic patients with bipolar disorder. Further, our findings in relatives lend support to the idea that altered ToM processing may act as an intermediate phenotype of the disorder.

The temporal dynamics of resilience: Neural recovery as a biomarker.

Resilience can be defined as the capability of an individual to maintain health despite stress and adversity. Here we suggest to study the temporal dynamics of neural processes associated with affective perturbation and emotion regulation at different time scales to investigate the mechanisms of resilience. Parameters related to neural recovery might serve as a predictive biomarker for resilience.

Common and differential neural networks of emotion regulation by Detachment, Reinterpretation, Distraction, and Expressive Suppression: a comparative fMRI investigation.

Emotions are an indispensable part of our mental life. The term emotion regulation refers to those processes that influence the generation, the experience and the expression of emotions. There is a great variety of strategies to regulate emotions efficiently, which are used in daily life and that have been investigated by cognitive neuroscience. Distraction guides attention to a secondary task. Reinterpretation, a variant of cognitive reappraisal, works by changing the meaning of an emotional stimulus. Detachment, another reappraisal strategy, refers to distancing oneself from an emotional stimulus, thereby reducing its personal relevance. Expressive Suppression modifies the behavioral or physiological response to an emotional stimulus. These four strategies are not equally effective in terms of emotion regulation success and have been shown to partly rely on different neuronal systems. Here, we compare for the first time the neural mechanisms of these typical strategies directly in a common functional magnetic resonance imaging (fMRI) paradigm of downregulation of negative emotions. Our results indicate that three of those strategies (Detachment, Expressive Suppression and Distraction) conjointly increase brain activation in a right prefronto-parietal regulation network and significantly reduce activation of the left amygdala. Compared to the other regulation strategies, Reinterpretation specifically recruited a different control network comprising left ventrolateral prefrontal cortex and orbitofrontal gyrus and was not effective in downregulation of the amygdala. We conclude that Detachment, Distraction and Expressive Suppression recruit very similar emotion regulation networks, whereas Reinterpretation is associated with activation of a qualitatively different network, making this regulation strategy a special one. Notably, Reinterpretation also proved to be the least effective strategy in neural terms, as measured by downregulation of amygdala activation.

Replication of brain function effects of a genome-wide supported psychiatric risk variant in the CACNA1C gene and new multi-locus effects.

Variation in the CACNA1C gene has consistently been associated with psychosis in genome wide association studies. We have previously shown in a sample of n=110 healthy subjects that carriers of the CACNA1C rs1006737 risk variant exhibit hippocampal and perigenual anterior cingulate dysfunction (pgACC) during episodic memory recall. Here, we aimed to replicate our results, by testing for the effects of the rs1006737 risk variant in a new large cohort of healthy controls. We furthermore sought to refine these results by identifying the impact of a CACNA1C specific, gene-wide risk score in the absence of clinical pathology. An independent sample of 179 healthy subjects genotyped for rs1006737 underwent functional magnetic resonance imaging (fMRI) while performing an associative episodic memory task and underwent psychological testing similar to the discovery sample. The effect of gene-wide risk scores was analyzed in the combined sample of 289 subjects. We replicated our discovery findings of hippocampal and pgACC dysfunction in carriers of the rs1006737 risk variant. Additionally, we observed diminished activation of the dorsolateral prefrontal cortex, in the replication sample. Our replicated results as well as this new effect were also observable in the combined sample. Moreover, the same system-level phenotypes were significantly associated with the individual gene-based genetic risk score. Our findings suggest that altered hippocampal and frontolimbic function is associated with variants in the CACNA1C gene. Since CACNA1C variants have been associated repeatedly with psychosis at a genome-wide level, and preclinical data provide convergent evidence for the relevance of the CACNA1C gene for hippocampal and frontolimbic plasticity and adaptive regulation of stress, our data suggest a potential pathophysiological mechanism conferred by CACNA1C variants that may mediate risk for symptom dimensions shared among bipolar disorder, major depression, and schizophrenia.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.