Large-scale network functional interactions during distraction and reappraisal in remitted bipolar and unipolar patients.

OBJECTIVES: The human brain is organized into large-scale networks that dynamically interact with each other. Extensive evidence has shown characteristic changes in certain large-scale networks during transitions from internally directed to externally directed attention. The aim of the present study was to compare these context-dependent network interactions during emotion regulation and to examine potential alterations in remitted unipolar and bipolar disorder patients. METHODS: We employed a multi-region generalized psychophysiological interactions analysis to quantify connectivity changes during distraction vs reappraisal pair-wise across 90 regions placed throughout the four networks of interest (default-mode, frontoparietal, salience, and dorsal attention networks). Using network contingency analysis and permutation testing, we estimated the likelihood that the number of significant condition-dependent connectivity changes in every pair of networks exceeds the number expected by chance. We first examined the pattern of functional connectivity in 42 healthy subjects (sample I) and then compared these connectivity patterns across healthy individuals (n=23) and remitted bipolar (n=21) and unipolar disorder patients (n=21) in an independent sample II. RESULTS: In sample I, distraction compared to reappraisal was characterized by reduced connectivity within the default-mode network and between the default-mode and two cognitive control networks and increased connectivity among the cognitive control networks. In sample II, both patient groups exhibited abnormally increased default-mode intra- and inter-network connectivity during distraction compared to reappraisal. CONCLUSIONS: The present study highlights the role of large-scale network interactions in emotion regulation and provides preliminary evidence of default-mode inter- and intra-network connectivity impairments in remitted bipolar and unipolar patients during emotion regulation.

Simultaneous EEG and fMRI reveals a causally connected subcortical-cortical network during reward anticipation.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to study the neural correlates of reward anticipation, but the interrelation of EEG and fMRI measures remains unknown. The goal of the present study was to investigate this relationship in response to a well established reward anticipation paradigm using simultaneous EEG-fMRI recording in healthy human subjects. Analysis of causal interactions between the thalamus (THAL), ventral-striatum (VS), and supplementary motor area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blood oxygenation level-dependent (BOLD) activity is mediating intermodal correlations between the EEG contingent negative variation (CNV) signal and the fMRI BOLD signal in SMA and VS, (2) the underlying causal connectivity network consists of top-down regulation from SMA to VS and SMA to THAL along with an excitatory information flow through a THAL→VS→SMA route during reward anticipation, and (3) the EEG CNV signal is best predicted by a combination of THAL fMRI BOLD response and strength of top-down regulation from SMA to VS and SMA to THAL. Collectively, these findings represent a likely neurobiological mechanism mapping a primarily subcortical process, i.e., reward anticipation, onto a cortical signature.

Reactivation strength during cued recall is modulated by graph distance within cognitive maps.

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

The cross-sectional GRAS sample: a comprehensive phenotypical data collection of schizophrenic patients.

BACKGROUND: Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia. METHODS: For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected. RESULTS: The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail. CONCLUSIONS: The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study.

The functional connectome is organized into several separable intrinsic connectivity networks (ICNs) that are thought to be the building blocks of the mind. However, it is currently not well understood how these networks are engaged by emotionally salient information, and how such engagement fits into emotion theories. The current study assessed how ICNs respond during the processing of angry and fearful faces in a large sample (N = 843) and examined how connectivity changes relate to the ICNs. All ICNs were modulated by emotional faces and showed functional interactions, a finding which is in line with the "theory of constructed emotions" that assumes that basic emotion do not arise from separable ICNs but from their interplay. We further identified a set of brain regions whose connectivity changes during the tasks suggest a special role as "affective hubs" in the brain. While hubs were located in all ICNs, we observed high selectivity for the amygdala within the subcortical network, a finding which also fits into "primary emotion" theory. The topology of hubs corresponded closely to a set of brain regions that has been implicated in anxiety disorders, pointing towards a clinical relevance of the present findings. The present data are the most comprehensive mapping of connectome-wide changes in functionally connectivity evoked by an affective processing task thus far and support two competing views on how emotions are represented in the brain, suggesting that the connectome paradigm might help with unifying the two ideas.

Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations.

Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.

Hippocampal-dorsolateral prefrontal coupling as a species-conserved cognitive mechanism: a human translational imaging study.

Hippocampal-prefrontal cortex (HC-PFC) interactions are implicated in working memory (WM) and altered in psychiatric conditions with cognitive impairment such as schizophrenia. While coupling between both structures is crucial for WM performance in rodents, evidence from human studies is conflicting and translation of findings is complicated by the use of differing paradigms across species. We therefore used functional magnetic resonance imaging together with a spatial WM paradigm adapted from rodent research to examine HC-PFC coupling in humans. A PFC-parietal network was functionally connected to hippocampus (HC) during task stages requiring high levels of executive control but not during a matched control condition. The magnitude of coupling in a network comprising HC, bilateral dorsolateral PFC (DLPFC), and right supramarginal gyrus explained one-fourth of the variability in an independent spatial WM task but was unrelated to visual WM performance. HC-DLPFC coupling may thus represent a systems-level mechanism specific to spatial WM that is conserved across species, suggesting its utility for modeling cognitive dysfunction in translational neuroscience.

A CAG repeat polymorphism of KCNN3 predicts SK3 channel function and cognitive performance in schizophrenia.

KCNN3, encoding the small conductance calcium-activated potassium channel SK3, harbours a polymorphic CAG repeat in the amino-terminal coding region with yet unproven function. Hypothesizing that KCNN3 genotypes do not influence susceptibility to schizophrenia but modify its phenotype, we explored their contribution to specific schizophrenic symptoms. Using the Göttingen Research Association for Schizophrenia (GRAS) data collection of schizophrenic patients (n = 1074), we performed a phenotype-based genetic association study (PGAS) of KCNN3. We show that long CAG repeats in the schizophrenic sample are specifically associated with better performance in higher cognitive tasks, comprising the capacity to discriminate, select and execute (p < 0.0001). Long repeats reduce SK3 channel function, as we demonstrate by patch-clamping of transfected HEK293 cells. In contrast, modelling the opposite in mice, i.e. KCNN3 overexpression/channel hyperfunction, leads to selective deficits in higher brain functions comparable to those influenced by SK3 conductance in humans. To conclude, KCNN3 genotypes modify cognitive performance, shown here in a large sample of schizophrenic patients. Reduction of SK3 function may constitute a pharmacological target to improve cognition in schizophrenia and other conditions with cognitive impairment.

Modification of cognitive performance in schizophrenia by complexin 2 gene polymorphisms.

CONTEXT: Schizophrenia is the collective term for a heterogeneous group of mental disorders with a still obscure biological basis. In particular, the specific contribution of risk or candidate gene variants to the complex schizophrenic phenotype is largely unknown. OBJECTIVE: To prepare the ground for a novel "phenomics" approach, a unique schizophrenia patient database was established by GRAS (Göttingen Research Association for Schizophrenia), designed to allow association of genetic information with quantifiable phenotypes.Because synaptic dysfunction plays a key role in schizophrenia, the complexin 2 gene (CPLX2) was examined in the first phenotype-based genetic association study (PGAS) of GRAS [corrected] DESIGN: Subsequent to a classic case-control approach, we analyzed the contribution of CPLX2 polymorphisms to discrete cognitive domains within the schizophrenic population. To gain mechanistic insight into how certain CPLX2 variants influence gene expression and function, peripheral blood mononuclear cells of patients, Cplx -null mutant mice, and transfected cells were investigated. SETTING: Coordinating research center (Max Planck Institute of Experimental Medicine) and 23 collaborating psychiatric centers all over Germany. PARTICIPANTS: One thousand seventy-one patients with schizophrenia (DSM-IV) examined by an invariant investigator team, resulting in the GRAS database with more than 3000 phenotypic data points per patient, and 1079 healthy control subjects of comparable ethnicity. Main Outcome Measure Cognitive performance including executive functioning, reasoning, and verbal learning/memory. RESULTS: Six single-nucleotide polymorphisms, distributed over the whole CPLX2 gene, were found to be highly associated with current cognition of schizophrenic subjects but only marginally with premorbid intelligence. Correspondingly, in Cplx2 -null mutant mice, prominent cognitive loss of function was obtained only in combination with a minor brain lesion applied during puberty, modeling a clinically relevant environmental risk ("second hit") for schizophrenia. In the human CPLX2 gene, 1 of the identified 6 cognition-relevant single-nucleotide polymorphisms, rs3822674 in the 3' untranslated region, was detected to influence microRNA-498 binding and gene expression. The same marker was associated with differential expression of CPLX2 in peripheral blood mononuclear cells. CONCLUSIONS: The PGAS allows identification of marker-associated clinical/biological traits. Current cognitive performance in schizophrenic patients is modified by CPLX2 variants modulating posttranscriptional gene expression.