The Network Structure of Cognitive Impairment: From Subjective Cognitive Decline to Alzheimer's Disease.

It was proposed that a reorganization of the relationships between cognitive functions occurs in dementia, a vision that surpasses the idea of a mere decline of specific domains. The complexity of cognitive structure, as assessed by neuropsychological tests, can be captured by exploratory graph analysis (EGA). EGA was applied to the neuropsychological assessment of people (humans) with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and Alzheimer's disease (AD; total N = 638). Both sexes were included. In AD, memory scores detach from the other cognitive functions, and memory subdomains reduce their reciprocal relation. SCD showed a pattern of segregated neuropsychological domains, and MCI showed a noisy and less stable pattern. Results suggest that AD drives a reorganization of cognitive functions toward a less-fractionated architecture compared with preclinical conditions. Cognitive functions show a reorganization that goes beyond the performance decline. Results also have clinical implications in test interpretations and usage.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.

BACKGROUND: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

Assessment of psychometric performance for the Chinese version of the Brief Inventory of Perceived Stress integrating exploratory graph analysis and confirmatory factor analysis.

OBJECTIVE: This study was to evaluate measurement properties of the Chinese version of the Brief Inventory of Perceived Stress (BIPS-C) and confirm possible solutions for measuring the constructs underlying perceived stress. METHODS: A total of 1356 community residents enrolled and were randomly split into two halves. The first half was used to explore the underlying constructs of the BIPS-C by exploratory graph analysis (EGA) and the second half was used to compare and confirm the constructs by confirmatory factor analysis (CFA). RESULTS: The EGA identified a one-factor model of the BIPS-C with an accuracy of 99.3%. One-factor, three-factor, second-order, and bifactor models were compared by CFAs. The bifactor model with one general and three specific factors was found to be the most adequate [comparative fit index (CFI) = 0.990; Tucker-Lewis index (TLI) = 0.979; root mean square error of approximation (RMSEA) = 0.058] and was superior to the other models. The related bifactor indices showed a stronger existence of the general factor. The bifactor model of the BIPS-C also showed adequate internal consistency with McDonald's omega and omega subscales ranging from moderate to strong (0.677-0.869). CONCLUSION: The BIPS-C demonstrates sufficient measurement properties for assessing general perceived stress.

A network perspective on real-life threat, anxiety, and avoidance.

BACKGROUND: Anxiety, approach, and avoidance motivation crucially influence mental and physical health, especially when environments are stressful. The interplay between anxiety and behavioral motivation is modulated by multiple individual factors. This proof-of-concept study applies graph-theoretical network analysis to explore complex associations between self-reported trait anxiety, approach and avoidance motivation, situational anxiety, stress symptoms, perceived threat, perceived positive consequences of approach, and self-reported avoidance behavior in real-life threat situations. METHODS: A total of 436 participants who were matched on age and gender (218 psychotherapy patients, 218 online-recruited nonpatients) completed an online survey assessing these factors in response to the COVID-19 pandemic. RESULTS AND DISCUSSION: The resulting cross-sectional psychological network revealed a complex pattern with multiple positive (e.g., between trait anxiety, avoidance motivation, and avoidance behavior) and negative associations (e.g., between approach and avoidance motivation). The patient and online subsample networks did not differ significantly, however, descriptive differences may inform future research.

A Network Analytic Approach to Dissociation: New Insights from Clinical Data.

The transdiagnostic construct of dissociation, characterized by a disintegration of specific psychological functions such as consciousness, memory, identity, perception, body representation, and behavior, remains elusive to a unified conceptualization. Specifically, its dimensionality is a matter of ongoing controversy. Empirical approaches applying factor analyses to the Dissociative Experiences Scale (DES) have yielded inconsistent findings. This study adopts a novel methodological approach, utilizing Exploratory Graph Analysis (EGA) to address this issue. In a sample of 668 day-hospital patients undergoing psychotherapy for a variety of mental disorders, a Gaussian graphical model was estimated for the 28 items of the DES. Additionally, the stability of the results was ensured by bootstrap procedures. While both the original EGA and the bootstrap EGA suggested four dimensions, the structural consistency of this solution was low due to an instability of 12 items. After excluding 10 of these unstable items, re-analyses again revealed a four-factor structure, but boot EGA indicated that one factor had unsatisfactory structural consistency due to the multidimensionality of its two items. Upon removing these, our final network consisted of 16 items mapping onto 3 dimensions. Our study, using data from a diagnostically heterogeneous sample, replicates and extends previous findings on the dimensionality of dissociation as captured by the DES. The three dimensions identified correspond to segregated processes, derealization/depersonalization, and absorption. This solution aligns with a bipartite model of dissociation with two broader categories referring to either altered states of consciousness (often named detachment) or to non-integrated mental modules (labeled as compartmentalization).

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Cognitive restraint, uncontrolled eating, and emotional eating. The Italian version of the Three Factor Eating Questionnaire-Revised 18 (TFEQ-R-18): a three-step validation study.

BACKGROUND: The Three Factor Eating Questionnaire-Revised 18 (TFEQ-R-18) is an extensively used questionnaire to measure three transdiagnostic features of eating behavior: cognitive restraint, uncontrolled eating, and emotional eating. OBJECTIVE: This research aims to investigate the psychometric properties of the Italian version of the TFEQ-R-18 in three large community samples. METHOD: Cross-sectional research designs were employed. In Study 1 (N = 537), an exploratory graph analysis (EGA) was used to examine item clustering within the TFEQ-R-18. In Study 2 (N = 645), a confirmatory factor analysis (CFA) was conducted to test its structural validity. In Study 3 (N = 346), a MANOVA was employed assessing mean differences across eating disorders (e.g., anorexia nervosa, bulimia nervosa, binge eating disorder). RESULTS: In Study 1, the EGA accurately identified the three original dimensions of the TFEQ-R-18. Study 2 showed that the Italian TFEQ-R-18 has good fit indexes (CFI = 0.989, RMSEA = 0.064; 90% CI [0.058, 0.070], SRMR = 0.062), and possesses robust psychometric properties. Study 3 reveals distinct, statistically significant differences among eating disorders. CONCLUSION: The TFEQ-R-18 proves to be a concise and precise tool for measuring transdiagnostic eating behaviors. Its applicability in the Italian context, supported by robust psychometric properties, suggests its utility for both research and clinical purposes. The findings affirm its potential to inform interventions aimed at enhancing psychological health. LEVEL OF EVIDENCE: Level V, descriptive study.

Exploring the factor structure of a Chinese version of the CORE-OM: insights from network approach and bifactor modeling.

OBJECTIVE: The Clinical Outcomes in Routine Evaluation (CORE-OM) is a measure of clinical outcomes that has been widely used in mental health research. Nevertheless, the exploration of the factor structure of the CORE-OM yields diverse results. This study aims to explore the factor structure with an innovative method known as exploratory graph analysis (EGA) and supplemented with bifactor modeling. METHOD: A Chinese version of the CORE-OM was administrated to a total of 1361 clinical college students. We first examined the factor structure of the CORE-OM using EGA, and then compared the model derived by EGA with other models using CFA to find the most reasonable model. RESULTS: The result of EGA indicated a four-factor model of CORE-OM. The CFA further suggested a bifactor model with a four-factor structure combined with a general factor. The bifactor modeling suggested a significant proportion of shared variance among the variables was attributed to the general factor. The four-factor bifactor model exhibited a satisfactory fit to the data. CONCLUSION: The results confirm the robustness and parsimonious nature of a four-factor bifactor model for the Chinese version of CORE-OM. It is suitable for measuring intrapersonal psychological distress, positive emotions, interpersonal problems, and risk-related issues among the Chinese population.

Subcortical influences on the topology of cortical networks align with functional processing hierarchies.

fMRI of the human brain reveals spatiotemporal patterns of functional connectivity (FC), forming distinct cortical networks. Lately, subcortical contributions to these configurations are receiving renewed interest, but investigations rarely focus explicitly on their effects on cortico-cortical FC. Here, we employ a straightforward multivariable approach and graph-theoretic tools to assess subcortical impact on topological features of cortical networks. Given recent evidence showing that structures like the thalamus and basal ganglia integrate input from multiple networks, we expect increased segregation between cortical networks after removal of subcortical effects on their FC patterns. We analyze resting state data of young and healthy participants (male and female; N = 100) from the human connectome project. We find that overall, the cortical network architecture becomes less segregated, and more integrated, when subcortical influences are accounted for. Underlying these global effects are the following trends: 'Transmodal' systems become more integrated with the rest of the network, while 'unimodal' networks show the opposite effect. For single nodes this hierarchical organization is reflected by a close correspondence with the spatial layout of the principal gradient of FC (Margulies et al., 2016). Lastly, we show that the limbic system is significantly less coherent with subcortical influences removed. The findings are validated in a (split-sample) replication dataset. Our results provide new insight regarding the interplay between subcortex and cortical networks, by putting the integrative impact of subcortex in the context of macroscale patterns of cortical organization.

Reduced resilience of brain state transitions in anti-N-methyl-D-aspartate receptor encephalitis.

Patients with anti-N-methyl-aspartate receptor (NMDA) receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. To this end, resting-state functional magnetic resonance imaging data were acquired in 73 patients with anti-NMDA receptor encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness and transition cost of these transition networks were compared between groups. Transition networks of patients showed significantly lower local efficiency (t = -2.41, pFDR  = .029), lower robustness (t = -2.01, pFDR  = .048) and higher leap size (t = 2.18, pFDR  = .037) compared with controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, alterations of brain state transitions correlated with disease severity. Together, these findings reveal systematic alterations of transition networks in patients, suggesting that anti-NMDA receptor encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

Comparison of latent variable and psychological network models in PROMIS data: output metrics and factor structure.

PURPOSE: Much research is still needed to compare traditional latent variable models such as confirmatory factor analysis (CFA) to emerging psychometric models such as the Gaussian graphical model (GGM). Previous comparisons of GGM centrality indices with factor loadings from CFA have discovered redundancies, and investigations into how well a GGM-based alternative to exploratory factor analysis (i.e., exploratory graph analysis, or EGA) is able to recover the hypothesized factor structure show mixed results. Importantly, such comparisons have not typically been examined in real mental and physical health symptom data, despite such data being an excellent candidate for the GGM. Our goal was to extend previous work by comparing the GGM and CFA using data from Wave 1 of the Patient Reported Outcomes Measurement Information System (PROMIS). METHODS: Models were fit to PROMIS data based on 16 test forms designed to measure 9 mental and physical health domains. Our analyses borrowed a two-stage approach for handling missing data from the structural equation modeling literature. RESULTS: We found weaker correspondence between centrality indices and factor loadings than found by previous research, but in a similar pattern of correspondence. EGA recommended a factor structure discrepant with PROMIS domains in most cases yet may be taken to provide substantive insight into the dimensionality of PROMIS domains. CONCLUSION: In real mental and physical health data, the GGM and EGA may provide complementary information to traditional CFA metrics.

Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation.

(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study.

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Network analysis of neuroimaging in mice.

Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.

Modulation of network centrality and gray matter microstructure using multi-session brain stimulation and memory training.

Neural mechanisms of behavioral improvement induced by repeated transcranial direct current stimulation (tDCS) combined with cognitive training are yet unclear. Previously, we reported behavioral effects of a 3-day visuospatial memory training with concurrent anodal tDCS over the right temporoparietal cortex in older adults. To investigate intervention-induced neural alterations we here used functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) datasets available from 35 participants of this previous study, acquired before and after the intervention. To delineate changes in whole-brain functional network architecture, we employed eigenvector centrality mapping. Gray matter alterations were analyzed using DTI-derived mean diffusivity (MD). Network centrality in the bilateral posterior temporooccipital cortex was reduced after anodal compared to sham stimulation. This focal effect is indicative of decreased functional connectivity of the brain region underneath the anodal electrode and its left-hemispheric homolog with other "relevant" (i.e., highly connected) brain regions, thereby providing evidence for reorganizational processes within the brain's network architecture. Examining local MD changes in these clusters, an interaction between stimulation condition and training success indicated a decrease of MD in the right (stimulated) temporooccipital cluster in individuals who showed superior behavioral training benefits. Using a data-driven whole-brain network approach, we provide evidence for targeted neuromodulatory effects of a combined tDCS-and-training intervention. We show for the first time that gray matter alterations of microstructure (assessed by DTI-derived MD) may be involved in tDCS-enhanced cognitive training. Increased knowledge on how combined interventions modulate neural networks in older adults, will help the development of specific therapeutic interventions against age-associated cognitive decline.

Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer's disease: a simultaneous PET/MR study.

PURPOSE: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). However, the effect of AD/MCI on the coupling between regional neural activity (measured by regional fluorodeoxyglucose imaging (rFDG)) and inter-regional FC (measured by resting-state functional magnetic resonance imaging (rs-fMRI)) is poorly understood. METHODS: We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/rs-fMRI and assessed rFDG and inter-regional FC metrics (i.e., clustering coefficient and degree centrality). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between rFDG and inter-regional FC metrics using hierarchical moderated multiple regression analysis. We also tested this effect by considering interaction between disease status and inter-regional FC metrics, as well as interaction between disease status and rFDG. RESULTS: Our findings revealed that both rFDG and inter-regional FC metrics were disrupted in MCI and AD. Moreover, AD altered the relationship between rFDG and inter-regional FC metrics. In particular, we found that AD moderated the effect of inter-regional FC metrics of the caudate, parahippocampal gyrus, angular gyrus, supramarginal gyrus, frontal pole, inferior temporal gyrus, middle frontal, lateral occipital, supramarginal gyrus, precuneus, and thalamus on predicting their rFDG. On the other hand, AD moderated the effect of rFDG of the parietal operculum on predicting its inter-regional FC metric. CONCLUSION: Our findings demonstrated that AD decoupled the link between regional neural activity and functional segregation and global connectivity across particular brain regions.

Conduct disorder symptomatology is associated with an altered functional connectome in a large national youth sample.

Conduct disorder (CD), characterized by youth antisocial behavior, is associated with a variety of neurocognitive impairments. However, questions remain regarding the neural underpinnings of these impairments. To investigate novel neural mechanisms that may support these neurocognitive abnormalities, the present study applied a graph analysis to resting-state functional magnetic resonance imaging (fMRI) data collected from a national sample of 4,781 youth, ages 9-10, who participated in the baseline session of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). Analyses were then conducted to examine the relationships among levels of CD symptomatology, metrics of global topology, node-level metrics for subcortical structures, and performance on neurocognitive assessments. Youth higher on CD displayed higher global clustering (ß = .039, 95% CIcorrected [.0027 .0771]), but lower Degreesubcortical (ß = -.052, 95% CIcorrected [-.0916 -.0152]). Youth higher on CD had worse performance on a general neurocognitive assessment (ß = -.104, 95% CI [-.1328 -.0763]) and an emotion recognition memory assessment (ß = -.061, 95% CI [-.0919 -.0290]). Finally, global clustering mediated the relationship between CD and general neurocognitive functioning (indirect ß = -.002, 95% CI [-.0044 -.0002]), and Degreesubcortical mediated the relationship between CD and emotion recognition memory performance (indirect ß = -.002, 95% CI [-.0046 -.0005]). CD appears associated with neuro-topological abnormalities and these abnormalities may represent neural mechanisms supporting CD-related neurocognitive disruptions.

TRIAC Treatment Improves Impaired Brain Network Function and White Matter Loss in Thyroid Hormone Transporter Mct8/Oatp1c1 Deficient Mice.

Dysfunctions of the thyroid hormone (TH) transporting monocarboxylate transporter MCT8 lead to a complex X-linked syndrome with abnormal serum TH concentrations and prominent neuropsychiatric symptoms (Allan-Herndon-Dudley syndrome, AHDS). The key features of AHDS are replicated in double knockout mice lacking MCT8 and organic anion transporting protein OATP1C1 (Mct8/Oatp1c1 DKO). In this study, we characterize impairments of brain structure and function in Mct8/Oatp1c1 DKO mice using multimodal magnetic resonance imaging (MRI) and assess the potential of the TH analogue 3,3',5-triiodothyroacetic acid (TRIAC) to rescue this phenotype. Structural and functional MRI were performed in 11-weeks-old male Mct8/Oatp1c1 DKO mice (N = 10), wild type controls (N = 7) and Mct8/Oatp1c1 DKO mice (N = 13) that were injected with TRIAC (400 ng/g bw s.c.) daily during the first three postnatal weeks. Grey and white matter volume were broadly reduced in Mct8/Oatp1c1 DKO mice. TRIAC treatment could significantly improve white matter thinning but did not affect grey matter loss. Network-based statistic showed a wide-spread increase of functional connectivity, while graph analysis revealed an impairment of small-worldness and whole-brain segregation in Mct8/Oatp1c1 DKO mice. Both functional deficits could be substantially ameliorated by TRIAC treatment. Our study demonstrates prominent structural and functional brain alterations in Mct8/Oatp1c1 DKO mice that may underlie the psychomotor deficiencies in AHDS. Additionally, we provide preclinical evidence that early-life TRIAC treatment improves white matter loss and brain network dysfunctions associated with TH transporter deficiency.

Exploring the structure of the GAD-7 scale in primary care patients with emotional disorders: A network analysis approach.

OBJECTIVE: Anxiety symptoms are one of the most frequent manifestations in people attending primary care, although how the symptoms are associated is unclear. This study aimed to establish the symptom structure of the Generalized Anxiety Disorder scale (GAD-7) using a novel network approach in combination with traditional analytical tools. METHODS: A sample of 1704 primary care patients with emotional disorders (i.e., anxiety, depression, and/or somatization) completed the GAD-7 to report their anxiety symptoms. We examined the GAD-7 structure using exploratory graph analysis (EGA) compared to exploratory factor analysis (EFA) and confirmatory factor analysis. RESULTS: The EFA results showed a one-factor solution, but EGA revealed a two-factor solution (cognitive-emotional and somatic). "Worrying too much" and "difficulty relaxing" were the most relevant symptoms. CONCLUSIONS: The results support the possible distinction between the somatic and cognitive-emotional components of the GAD-7, thus permitting more specific screening in primary care settings.

Centrality of drug targets in protein networks.

BACKGROUND: In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target's 'fitness' based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses. RESULTS: We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets' network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a 'good' target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein's 'fitness' as potential drug target. Correlations between protein nodes' network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions. CONCLUSIONS: Despite some entanglement with knowledge bias, like structure-oriented 'druggability' assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline.