Molecular age prediction using skull bone samples from individuals with and without signs of decomposition: a multivariate approach combining analysis of posttranslational protein modifications and DNA methylation.

The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4-11.8 years, RMSE: 10.4-15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6-12 years, RMSE: 8.1-15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases.

Evidence for differences in DNA methylation between Germans and Japanese.

As a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included "robust CpGs" prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for "robust" CpG markers-which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman's rank correlation. Differences in DNAm between the groups "healthy children" and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

Age estimation based on different molecular clocks in several tissues and a multivariate approach: an explorative study.

Several molecular modifications accumulate in the human organism with increasing age. Some of these "molecular clocks" in DNA and in proteins open up promising approaches for the development of methods for forensic age estimation. A natural limitation of these methods arises from the fact that the chronological age is determined only indirectly by analyzing defined molecular changes that occur during aging. These changes are not linked exclusively to the expired life span but may be influenced significantly by intrinsic and extrinsic factors in the complex process of individual aging. We tested the hypothesis that a combined use of different molecular clocks in different tissues results in more precise age estimates because this approach addresses the complex aging processes in a more comprehensive way. Two molecular clocks (accumulation of D-aspartic acid (D-Asp), accumulation of pentosidine (PEN)) in two different tissues (annulus fibrosus of intervertebral discs and elastic cartilage of the epiglottis) were analyzed in 95 cases, and uni- and multivariate models for age estimation were generated. The more parameters were included in the models for age estimation, the smaller the mean absolute errors (MAE) became. While the MAEs were 7.5-11.0 years in univariate models, a multivariate model based on the two protein clocks in the two tissues resulted in a MAE of 4.0 years. These results support our hypothesis. The tested approach of a combined analysis of different molecular clocks analyzed in different tissues opens up new possibilities in postmortem age estimation. In a next step, we will add the epigenetic clock (DNA methylation) to our protein clocks (PEN, D-Asp) and expand our set of tissues.

Urethral length and bladder neck behavior: can dynamic magnetic resonance imaging give the same results as introital ultrasound?

PURPOSE: To compare dynamic magnetic resonance imaging (dMRI) and introital ultrasound results with regard to urethral length measurements and the evaluation of bladder neck changes. METHODS: Retrospective analyses of urethral length measurements and detection of bladder neck changes (rotated/vertical bladder neck descent, urethral funneling) were conducted in women-scheduled for surgical treatment with alloplastic material-who had undergone introital ultrasound and dMRI presurgery and 3 months postsurgery. Measurement differences between both imaging modalities were evaluated by assessing the confidence interval for the difference in means between the datasets using bootstrap analysis. RESULTS: Based on data from 40 patients (320 image series), the urethra could be clearly measured on every pre- and postsurgical dMRI dataset but not on preoperative ultrasound images in nine women during Valsalva maneuver due to a large cystocele. The estimation of the mean difference distribution based on 500,000 bootstrap resamples indicated that the urethral length was measured shorter by dMRI pre- and postsurgery at rest and postsurgery during Valsalva maneuver (median 1.6-3.1 mm) but longer by dMRI (median 0.2 mm) during Valsalva maneuver presurgery. Rotated/vertical bladder neck descent and urethral funneling diagnoses showed concordance of 67-74% in the direct comparison of patients; the estimation of the concordance indicated poorer outcomes with 50-72%. CONCLUSIONS: Metric information on urethral length from dMRI is comparable to that from introital ultrasound. dMRI is more advantageous in cases with an extended organ prolapse. At present, dMRI does not give the same diagnosis on bladder neck changes as introital ultrasound does.

Definition and characterization of an extended multiple-demand network.

Neuroimaging evidence suggests that executive functions (EF) depend on brain regions that are not closely tied to specific cognitive demands but rather to a wide range of behaviors. A multiple-demand (MD) system has been proposed, consisting of regions showing conjoint activation across multiple demands. Additionally, a number of studies defining networks specific to certain cognitive tasks suggest that the MD system may be composed of a number of sub-networks each subserving specific roles within the system. We here provide a robust definition of an extended MDN (eMDN) based on task-dependent and task-independent functional connectivity analyses seeded from regions previously shown to be convergently recruited across neuroimaging studies probing working memory, attention and inhibition, i.e., the proposed key components of EF. Additionally, we investigated potential sub-networks within the eMDN based on their connectional and functional similarities. We propose an eMDN network consisting of a core whose integrity should be crucial to performance of most operations that are considered higher cognitive or EF. This then recruits additional areas depending on specific demands.

Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke.

Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1-16 days) with hand motor deficits were enrolled in a sham-controlled design and pseudo-randomized into 2 groups. iTBS was administered prior to physiotherapy on 5 consecutive days either over ipsilesional primary motor cortex (M1-stimulation group) or parieto-occipital vertex (control-stimulation group). Hand motor function, cortical excitability, and resting-state fMRI were assessed 1 day prior to the first stimulation and 1 day after the last stimulation. Recovery of grip strength was significantly stronger in the M1-stimulation compared to the control-stimulation group. Higher levels of motor network connectivity were associated with better motor outcome. Consistently, control-stimulated patients featured a decrease in intra- and interhemispheric connectivity of the motor network, which was absent in the M1-stimulation group. Hence, adding iTBS to prime physiotherapy in recovering stroke patients seems to interfere with motor network degradation, possibly reflecting alleviation of post-stroke diaschisis.

Neural correlates of reactive aggression in children with attention-deficit/hyperactivity disorder and comorbid disruptive behaviour disorders.

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) is often linked with impulsive and aggressive behaviour, indexed by high comorbidity rates between ADHD and disruptive behaviour disorders (DBD). The present study aimed to investigate underlying neural activity of reactive aggression in children with ADHD and comorbid DBD using functional neuroimaging techniques (fMRI). METHOD: Eighteen boys with ADHD (age 9-14 years, 10 subjects with comorbid DBD) and 18 healthy controls were administered a modified fMRI-based version of the 'Point Subtraction Aggression Game' to elicit reactive aggressive behaviour. Trials consisted of an 'aggression phase' (punishment for a fictitious opponent) and an 'outcome phase' (presentation of the trial outcome). RESULTS: During the aggression phase, higher aggressive responses of control children were accompanied by higher activation of the ventral anterior cingulate cortex and the temporoparietal junction. Patients displayed inverted results. During the outcome phase, comparison between groups and conditions showed differential activation in the dorsal striatum and bilateral insular when subjects gained points. Losing points was accompanied by differential activation of regions belonging to the insula and the middle temporal sulcus. CONCLUSION: Data support the hypothesis that deficient inhibitory control mechanisms are related to increased impulsive aggressive behaviour in young people with ADHD and comorbid DBD.

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in "individual" patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6-87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment.

Co-activation based parcellation of the human frontal pole.

Historically, the human frontal pole (FP) has been considered as a single architectonic area. Brodmann's area 10 is located in the frontal lobe with known contributions in the execution of various higher order cognitive processes. However, recent cytoarchitectural studies of the FP in humans have shown that this portion of cortex contains two distinct cytoarchitectonic regions. Since architectonic differences are accompanied by differential connectivity and functions, the frontal pole qualifies as a candidate region for exploratory parcellation into functionally discrete sub-regions. We investigated whether this functional heterogeneity is reflected in distinct segregations within cytoarchitectonically defined FP-areas using meta-analytic co-activation based parcellation (CBP). The CBP method examined the co-activation patterns of all voxels within the FP as reported in functional neuroimaging studies archived in the BrainMap database. Voxels within the FP were subsequently clustered into sub-regions based on the similarity of their respective meta-analytically derived co-activation maps. Performing this CBP analysis on the FP via k-means clustering produced a distinct 3-cluster parcellation for each hemisphere corresponding to previously identified cytoarchitectural differences. Post-hoc functional characterization of clusters via BrainMap metadata revealed that lateral regions of the FP mapped to memory and emotion domains, while the dorso- and ventromedial clusters were associated broadly with emotion and social cognition processes. Furthermore, the dorsomedial regions contain an emphasis on theory of mind and affective related paradigms whereas ventromedial regions couple with reward tasks. Results from this study support previous segregations of the FP and provide meta-analytic contributions to the ongoing discussion of elucidating functional architecture within human FP.

Neural network of cognitive emotion regulation--an ALE meta-analysis and MACM analysis.

Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders.

Cytoarchitecture, probability maps and functions of the human frontal pole.

The frontal pole has more expanded than any other part in the human brain as compared to our ancestors. It plays an important role for specifically human behavior and cognitive abilities, e.g. action selection (Kovach et al., 2012). Evidence about divergent functions of its medial and lateral part has been provided, both in the healthy brain and in psychiatric disorders. The anatomical correlates of such functional segregation, however, are still unknown due to a lack of stereotaxic, microstructural maps obtained in a representative sample of brains. Here we show that the human frontopolar cortex consists of two cytoarchitectonically and functionally distinct areas: lateral frontopolar area 1 (Fp1) and medial frontopolar area 2 (Fp2). Based on observer-independent mapping in serial, cell-body stained sections of 10 brains, three-dimensional, probabilistic maps of areas Fp1 and Fp2 were created. They show, for each position of the reference space, the probability with which each area was found in a particular voxel. Applying these maps as seed regions for a meta-analysis revealed that Fp1 and Fp2 differentially contribute to functional networks: Fp1 was involved in cognition, working memory and perception, whereas Fp2 was part of brain networks underlying affective processing and social cognition. The present study thus disclosed cortical correlates of a functional segregation of the human frontopolar cortex. The probabilistic maps provide a sound anatomical basis for interpreting neuroimaging data in the living human brain, and open new perspectives for analyzing structure-function relationships in the prefrontal cortex. The new data will also serve as a starting point for further comparative studies between human and non-human primate brains. This allows finding similarities and differences in the organizational principles of the frontal lobe during evolution as neurobiological basis for our behavior and cognitive abilities.

Interoperable atlases of the human brain.

The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro- and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.

Aberrant resting-state connectivity in non-psychotic individuals with auditory hallucinations.

BACKGROUND: Although auditory verbal hallucinations (AVH) are a core symptom of schizophrenia, they also occur in non-psychotic individuals, in the absence of other psychotic, affective, cognitive and negative symptoms. AVH have been hypothesized to result from deviant integration of inferior frontal, parahippocampal and superior temporal brain areas. However, a direct link between dysfunctional connectivity and AVH has not yet been established. To determine whether hallucinations are indeed related to aberrant connectivity, AVH should be studied in isolation, for example in non-psychotic individuals with AVH. METHOD: Resting-state connectivity was investigated in 25 non-psychotic subjects with AVH and 25 matched control subjects using seed regression analysis with the (1) left and (2) right inferior frontal, (3) left and (4) right superior temporal and (5) left parahippocampal areas as the seed regions. To correct for cardiorespiratory (CR) pulsatility rhythms in the functional magnetic resonance imaging (fMRI) data, heartbeat and respiration were monitored during scanning and the fMRI data were corrected for these rhythms using the image-based method for retrospective correction of physiological motion effects RETROICOR. RESULTS: In comparison with the control group, non-psychotic individuals with AVH showed increased connectivity between the left and the right superior temporal regions and also between the left parahippocampal region and the left inferior frontal gyrus. Moreover, this group did not show a negative correlation between the left superior temporal region and the right inferior frontal region, as was observed in the healthy control group. CONCLUSIONS: Aberrant connectivity of frontal, parahippocampal and superior temporal brain areas can be specifically related to the predisposition to hallucinate in the auditory domain.

Imitation and observational learning of hand actions: prefrontal involvement and connectivity.

The first aim of this event-related fMRI study was to identify the neural circuits involved in imitation learning. We used a rapid imitation task where participants directly imitated pictures of guitar chords. The results provide clear evidence for the involvement of dorsolateral prefrontal cortex, as well as the fronto-parietal mirror circuit (FPMC) during action imitation when the requirements for working memory are low. Connectivity analyses further indicated a robust connectivity between left prefrontal cortex and the components of the FPMC bilaterally. We conclude that a mechanism of automatic perception-action matching alone is insufficient to account for imitation learning. Rather, the motor representation of an observed, complex action, as provided by the FPMC, only serves as the 'raw material' for higher-order supervisory and monitoring operations associated with the prefrontal cortex. The second aim of this study was to assess whether these neural circuits are also recruited during observational practice (OP, without motor execution), or only during physical practice (PP). Whereas prefrontal cortex was not consistently activated in action observation across all participants, prefrontal activation intensities did predict the behavioural practice effects, thus indicating a crucial role of prefrontal cortex also in OP. In addition, whilst OP and PP produced similar activation intensities in the FPMC when assessed during action observation, during imitative execution, the practice-related activation decreases were significantly more pronounced for PP than for OP. This dissociation indicates a lack of execution-related resources in observationally practised actions. More specifically, we found neural efficiency effects in the right motor cingulate-basal ganglia circuit and the FPMC that were only observed after PP but not after OP. Finally, we confirmed that practice generally induced activation decreases in the FPMC during both action observation and imitation sessions and outline a framework explaining the discrepant findings in the literature.

Meta-analytical definition and functional connectivity of the human vestibular cortex.

Contrary to most other sensory systems, no consensus has been reached within the scientific community about the exact locations and functions of human cortical areas processing vestibular information. Metaanalytical modelling using activation likelihood estimation (ALE) for the integration of neuroimaging results has already been successfully applied to several distinct tasks, thereby revealing the cortical localization of cognitive functions. We used the same algorithm and technique with all available and suitable PET and fMRI studies employing a vestibular stimulus. Most consistently across 28 experiments vestibular stimuli evoked activity in the right hemispheric parietal opercular area OP 2 implicating it as the core region for vestibular processing. Furthermore, we took our primary results as a seeding point and fed them into a functional connectivity analysis based on resting-state oscillations in 100 healthy subjects. This subsequent calculation confirmed direct connections of the area OP 2 with every other region found in the meta-analysis, in particular temporo-parietal regions, premotor cortex, and the midcingulate gyrus. Thus revealing a joint vestibular network in accordance with a concept from animal literature termed the inner vestibular circle. Moreover, there was also a significant vestibular connectivity overlap with frontal but not parietal cortical centres responsible for the generation of saccadic eye movements, likely to be involved in nystagmus fast phase generation. This was shown in an additional ocular motor meta-analysis. We conclude that the cytoarchitectonic area OP 2 in the parietal operculum, embedded in a joint vestibular network, should be the primary candidate for the human vestibular cortex. This area may represent the human homologue to the vestibular area PIVC as proposed by Guldin and Grüsser in non-human primates.

Modelling neural correlates of working memory: a coordinate-based meta-analysis.

Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca's region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task set and load effects. Nevertheless, a consistent but more restricted "core" network emerged from conjunctions across analyses of specific task designs and contrasts. This "core" network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focussing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders.

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Endocrine stress response in pregnancy and 12 weeks postpartum - Exploring risk factors for postpartum depression.

Pregnancy and the postpartum period are characterized by physiological alterations in cortisol and cortisone levels. In the present study, we sought to explore the risk factors for postpartum depression (PPD) and self-remitting postpartum adjustment disorder (AD) and whether cortisol/cortisone metabolism might have any bearing on them. Hair samples from 196 participants (mean age = 31.44, SD = 4.71) were collected at two time points (1-6 days after childbirth and 12 weeks postpartum) to determine the cumulative hair cortisol (HCC) and hair cortisone (HCNC) exposure in the third trimester and during the 12 weeks postpartum. Compared to the non-depressed group (ND, n = 141), more women in the AD (n = 28) and PPD (n = 27) groups had a personal or family history of depression and more stressful life events. Compared to ND and PPD, more women in the AD group had birth-related complications with their children being more often transferred to a pediatric ward. The factors associated with PPD were found to include being unmarried and having a lower household income, less support at home, more subjectively perceived stress after childbirth and lower maternal sensitivity. The natural decrease in HCC concentration from the third trimester to 12 weeks postpartum was significant only in the ND and AD groups, but not in PPD. In summary, prolonged subjectively perceived postpartum stress associated with living situations may contribute to the development of PPD while birth- and child-related complications are likely to trigger brief episodes of AD. Only in ND and AD, the pregnancy-related physiological changes in glucocorticoid levels return to the pre-pregnancy baseline after 12 weeks. Our observations point to the difference between the ND and PPD groups in glucocorticoid metabolism-related postpartum adjustment, which may be a factor in the development of PPD.

A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System.

While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.