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Schizophrenia is characterized by hypoconnectivity or decreased intrinsic functional connectivity (iFC) between prefrontal-limbic cortices and thalamic nuclei, as well as hyperconnectivity or increased iFC between primary-sensorimotor cortices and thalamic nuclei. However, cortico-thalamic iFC overlaps with larger, structurally defined cortico-striato-pallido-thalamo-cortical (CSPTC) circuits. If such an overlap is relevant for intrinsic hypo-/hyperconnectivity, it suggests (i) that patterns of cortico-subcortical hypo-/hyperconnectivity extend consistently from thalamus to basal ganglia nuclei; and (ii) such consistent hypo-/hyperconnectivity might link distinctively but consonant with different symptom dimensions, namely cognitive and psychotic impairments. To test this hypothesis, 57 patients with schizophrenia and 61 healthy controls were assessed by resting-state functional magnetic resonance imaging (fMRI) and clinical–behavioral testing. IFC from intrinsic cortical networks into thalamus, striatum, and pallidum was estimated by partial correlations between fMRI time courses. In patients, the salience network covering prefrontal-limbic cortices was hypoconnected with the mediodorsal thalamus and ventral parts of striatum and pallidum; these iFC-hypoconnectivity patterns were correlated both among each other and specifically with patients’ impaired cognition. In contrast, the auditory-sensorimotor network covering primary-sensorimotor cortices was hyperconnected with the anterior ventral nucleus of the thalamus and dorsal parts of striatum and pallidum; these iFC-hyperconnectivity patterns were likewise correlated among each other and specifically with patients’ psychotic symptoms. The results demonstrate that prefrontal-limbic hypoconnectivity and primary-sensorimotor hyperconnectivity extend consistently across subcortical nuclei and specifically across distinct symptom dimensions. Data support the model of consistent cortico-subcortical hypo-/hyperconnectivity within CSPTC circuits in schizophrenia.

Major depressive disorder (MDD), anxiety disorders (ANX), and chronic pain (CP) are closely-related disorders with both high degrees of comorbidity among them and shared risk factors. Considering this multi-level overlap, but also the distinct phenotypes of the disorders, we hypothesized both common and disorder-specific changes of large-scale brain systems, which mediate neural mechanisms and impaired behavioral traits, in MDD, ANX, and CP. To identify such common and disorder-specific brain changes, we conducted a transdiagnostic, multimodal meta-analysis of structural and functional MRI-studies investigating changes of gray matter volume (GMV) and intrinsic functional connectivity (iFC) of large-scale intrinsic brain networks across MDD, ANX, and CP. The study was preregistered at PROSPERO (CRD42019119709). 320 studies comprising 10,931 patients and 11,135 healthy controls were included. Across disorders, common changes focused on GMV-decrease in insular and medial-prefrontal cortices, located mainly within the so-called default-mode and salience networks. Disorder-specific changes comprised hyperconnectivity between default-mode and frontoparietal networks and hypoconnectivity between limbic and salience networks in MDD; limbic network hyperconnectivity and GMV-decrease in insular and medial-temporal cortices in ANX; and hypoconnectivity between salience and default-mode networks and GMV-increase in medial temporal lobes in CP. Common changes suggested a neural correlate for comorbidity and possibly shared neuro-behavioral chronification mechanisms. Disorder-specific changes might underlie distinct phenotypes and possibly additional disorder-specific mechanisms.

Cognitive reward control (CRC) refers to the cognitive control of one’s craving for hedonic stimuli, like food, sex, or drugs. Numerous functional magnetic resonance imaging (fMRI) studies have investigated neural sources of CRC. However, a consistent pattern of brain activation across stimulus types has not been identified so far. We addressed this question using coordinate-based meta-analysis of task-fMRI studies during CRC. To further characterize such a potential common CRC activation pattern, we extended our approach to three additional questions: (i) Do CRC meta-analytic results overlap with those during the control of emotional states, such as in cognitive regulation of aversive emotions (cognitive emotion regulation, CER)? (ii) How does the control of motivational/emotional states link to the control of action states with less motivational/emotional valence such as in response inhibition paradigms, i.e., do meta-anyltic result maps overlap? (iii) Does the control of motivational/emotional states constitute a consistent pattern of organized (i.e., coherent) ongoing or intrinsic brain activity? This question was tested by a seed-based intrinsic functional connectivity (iFC) analysis in an independent data set of resting-state fMRI. We found consistent CRC activation mainly in supplementary motor, dorsolateral prefrontal, and ventrolateral prefrontal cortices across studies. This activation pattern overlapped largely with CER-related activation, except for left-sided lateral temporal and parietal cortex activation, which was more pronounced during CER. It overlapped partly with activation during response inhibition in (pre-)supplementary motor, insular, and parietal cortices, but differed from it in dorsolateral and ventrolateral prefrontal cortices. Furthermore, it remarkably defined an iFC network covering activation patterns of both CRC and CER. Results demonstrate a consistent activation pattern of CRC across stimulus types, which overlaps largely with those of CER but only partly with those of response inhibition and constitutes an intrinsic co-activity network. These data suggest a common mechanism for the cognitive control of both motivational and emotional stimuli. •Cognitive reward control activates lateral prefrontal and supplementary motor cortices.•This activation pattern overlaps largely with that of cognitive emotion regulation.•Furthermore, it defines an intrinsic co-activity network of control.•Data suggest a common mechanism of motivational/emotional control.

Brain imaging reveals schizophrenia as a disorder of macroscopic brain networks. In particular, default mode and salience network (DMN, SN) show highly consistent alterations in both interacting brain activity and underlying brain structure. However, the same networks are also altered in major depression. This overlap in network alterations induces the question whether DMN and SN changes are different across both disorders, potentially indicating distinct underlying pathophysiological mechanisms. To address this question, we acquired T1-weighted, diffusion-weighted, and resting-state functional MRI in patients with schizophrenia, patients with major depression, and healthy controls. We measured regional gray matter volume, inter-regional structural and intrinsic functional connectivity of DMN and SN, and compared these measures across groups by generalized Wilcoxon rank tests, while controlling for symptoms and medication. When comparing patients with controls, we found in each patient group SN volume loss, impaired DMN structural connectivity, and aberrant DMN and SN functional connectivity. When comparing patient groups, SN gray matter volume loss and DMN structural connectivity reduction did not differ between groups, but in schizophrenic patients, functional hyperconnectivity between DMN and SN was less in comparison to depressed patients. Results provide evidence for distinct functional hyperconnectivity between DMN and SN in schizophrenia and major depression, while structural changes in DMN and SN were similar. Distinct hyperconnectivity suggests different pathophysiological mechanism underlying aberrant DMN-SN interactions in schizophrenia and depression.

Decision-making alterations are present in psychiatric illnesses like major depressive disorder (MDD), obsessive–compulsive disorder (OCD), and schizophrenia, linked to symptoms of the respective disorders. We sought to analyze unique and shared decision-making alterations in these disorders, which is crucial for early diagnosis and treatment, especially given potential comorbidities. Using 2 computational modelling approaches — logistic regression and hierarchical Bayesian modelling — we analyzed alterations in model-based and model-free decision-making in a transdiagnostic cohort of patients with MDD, OCD, or schizophrenia. Our aim was to identify disorder-specific and shared alterations and their associations with symptoms. We included 23 patients with MDD, 25 patients with OCD, 27 patients with schizophrenia, and 25 controls. Overall, participants of all groups relied on model-free decision-making. Patients with schizophrenia had the lowest learning rate and highest switching rate, indicating low perseverance. Furthermore, patients with OCD were more random in both task stages than controls and patients with MDD. All patient groups exhibited more randomness in responses than controls, with the schizophrenia group showing the highest levels. Increased model-free behaviour correlated with elevated depressive symptoms, and more model-based decision-making was linked to lower anhedonia levels across all patient groups. The sample size in each group was small. This study highlights disorder-specific and shared decision-making alterations among people with MDD, OCD, or schizophrenia. Our findings suggest that anhedonia and depressive symptoms, which are present in all 3 disorders, share underlying behavioural mechanisms. Improving model-based behaviour may be a target for intervention and treatment. Furthermore, completely random behaviour in the 2-step task appears to distinctly differentiate patients with schizophrenia in remission.

In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.

BackgroundPET (positron emission tomography) biokinetic modelling relies on accurate quantitative data. One of the main corrections required in PET imaging to obtain high quantitative accuracy is tissue attenuation correction (AC). Incorrect non-uniform PET-AC may result in local bias in the emission images, and thus in relative activity distributions and time activity curves for different regions. MRI (magnetic resonance imaging)-based AC is an active area of research in PET/MRI neuroimaging, where several groups developed in the last few years different methods to calculate accurate attenuation (μ-)maps. Some AC methods have been evaluated for different PET radioisotopes and pathologies. However, AC in PET/MRI has scantly been investigated in dynamic PET studies where the aim is to get quantitative kinetic parameters, rather than semi-quantitative parameters from static PET studies. In this work, we investigated the impact of AC accuracy in PET image absolute quantification and, more importantly, in the slope of the Patlak analysis based on the simplified reference tissue model, from a dynamic [18F]-fluorodopa (FDOPA) PET/MRI study. In the study, we considered the two AC methods provided by the vendor and an in-house AC method based on the dual ultrashort time echo MRI sequence, using as reference a multi-atlas-based AC method based on a T1-weighted MRI sequence.ResultsNon-uniform bias in absolute PET quantification across the brain, from − 20% near the skull to − 10% in the central region, was observed using the two vendor’s μ-maps. The AC method developed in-house showed a − 5% and 1% bias, respectively. Our study resulted in a 5–9% overestimation of the PET kinetic parameters with the vendor-provided μ-maps, while our in-house-developed AC method showed < 2% overestimation compared to the atlas-based AC method, using the cerebellar cortex as reference region. The overestimation obtained using the occipital pole as reference region resulted in a 7–10% with the vendor-provided μ-maps, while our in-house-developed AC method showed < 6% overestimation.ConclusionsPET kinetic analyses based on a reference region are especially sensitive to the non-uniform bias in PET quantification from AC inaccuracies in brain PET/MRI. Depending on the position of the reference region and the bias with respect to the analysed region, kinetic analyses suffer different levels of bias. Considering bone in the μ-map can potentially result in larger errors, compared to the absence of bone, when non-uniformities in PET quantification are introduced.

The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders. The resting‐state CAP states have gradually varying spatial and temporal patterns across frequency sub‐bands in the healthy brain. Particularly, CAP states in slow‐4 exhibited stronger coactivation in several subcortical regions than slow‐5. As slow‐5 showed larger inter‐subject differences, slow‐4 achieved a better schizophrenia prediction accuracy.

A potential pathophysiological mechanism of cognitive difficulties in schizophrenia is a dysregulated cholinergic system. Particularly, the cholinergic basal forebrain nuclei (BFCN), the source of cortical cholinergic innervation, support multiple cognitive functions, ranging from attention to decision-making. We hypothesized that BFCN structural integrity is altered in schizophrenia and associated with patients’ attentional deficits. We assessed gray matter (GM) integrity of cytoarchitectonically defined BFCN region-of-interest in 72 patients with schizophrenia and 73 healthy controls, matched for age and gender, from the COBRE open-source database, via structural magnetic resonance imaging (MRI)–based volumetry. MRI-derived measures of GM integrity (i.e., volumes) were linked with performance on a symbol coding task (SCT), a paper-pencil-based metric that assesses attention, by correlation and mediation analysis. To assess the replicability of findings, we repeated the analyses in an independent dataset comprising 26 patients with schizophrenia and 24 matched healthy controls. BFCN volumes were lower in patients (t(139)=2.51, p = 0.01) and significantly associated with impaired SCT performance (r = 0.31, p = 0.01). Furthermore, lower BFCN volumes mediated the group difference in SCT performance. When including global GM volumes, which were lower in patients, as covariates-of-no-interest, these findings disappeared, indicating that schizophrenia did not have a specific effect on BFCN relative to other regional volume changes. We replicated these findings in the independent cohort, e.g., BFCN volumes were lower in patients and mediated patients’ impaired SCT performance. Results demonstrate lower BFCN volumes in schizophrenia, which link with patients’ attentional deficits. Data suggest that a dysregulated cholinergic system might contribute to cognitive difficulties in schizophrenia via impaired BFCN.