Brain health in diverse settings: How age, demographics and cognition shape brain function.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis.

Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data. Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems.

Abnormal brain state distribution and network connectivity in a SYNGAP1 rat model.

Mutations in the SYNGAP1 gene are one of the common predictors of neurodevelopmental disorders, commonly resulting in individuals developing autism, intellectual disability, epilepsy, and sleep deficits. EEG recordings in neurodevelopmental disorders show potential to identify clinically translatable biomarkers to both diagnose and track the progress of novel therapeutic strategies, as well as providing insight into underlying pathological mechanisms. In a rat model of SYNGAP1 haploinsufficiency in which the exons encoding the calcium/lipid binding and GTPase-activating protein domains have been deleted (Syngap+/Δ-GAP ), we analysed the duration and occurrence of wake, non-rapid eye movement and rapid eye movement brain states during 6 h multi-electrode EEG recordings. We find that although Syngap+/Δ-GAP animals spend an equivalent percent time in wake and sleep states, they have an abnormal brain state distribution as the number of wake and non-rapid eye movement bouts are reduced and there is an increase in the average duration of both wake and non-rapid eye movement epochs. We perform connectivity analysis by calculating the average imaginary coherence between electrode pairs at varying distance thresholds during these states. In group averages from pairs of electrodes at short distances from each other, a clear reduction in connectivity during non-rapid eye movement is present between 11.5 Hz and 29.5 Hz, a frequency range that overlaps with sleep spindles, oscillatory phenomena thought to be important for normal brain function and memory consolidation. Sleep abnormalities were mostly uncorrelated to the electrophysiological signature of absence seizures, spike and wave discharges, as was the imaginary coherence deficit. Sleep spindles occurrence, amplitude, power and spread across multiple electrodes were not reduced in Syngap+/Δ-GAP rats, with only a small decrease in duration detected. Nonetheless, by analysing the dynamic imaginary coherence during sleep spindles, we found a reduction in high-connectivity instances between short-distance electrode pairs. Finally comparing the dynamic imaginary coherence during sleep spindles between individual electrode pairs, we identified a group of channels over the right somatosensory, association and visual cortices that have a significant reduction in connectivity during sleep spindles in mutant animals. This matched a significant reduction in connectivity during spindles when averaged regional comparisons were made. These data suggest that Syngap+/Δ-GAP rats have altered brain state dynamics and EEG connectivity, which may have clinical relevance for SYNGAP1 haploinsufficiency in humans.

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance- The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100.

Evaluation of EEG dynamic connectivity around seizure onset with principal component analysis.

Seizures represent a brain activity state charac-terised by extended synchronised firing in multiple regions that prevent normal brain functioning. It is important to develop methods to distinguish between normal and abnormal synchro-nisation in epilepsy, as well as to localise the networks involved in seizures. To this end, we perform a preliminary investigation in the use of principal components analysis (PCA) to assess the change in dynamic electroencephalogram (EEG) connectivity before and after seizure onset. Source estimation was performed for an openly available EEG dataset from 14 patients with epilepsy. By applying PCA onto the EEG data processed into dynamic connectivity (dFC) matrices, we identified a set of connectivity topologies (eigenconnectivities) that explain high levels of variance in the dynamic connectivity. We compare the dimensionality reduction results obtained on source-level vs. scalp-level connectivity. We identified eigenconnectivities with differences in preictal vs. ictal activity and the brain networks associated with these activations. The work illustrates a data-driven approach for identification of topologies of brain networks that change with seizure onset. Clinical relevance We identified networks that are signifi-cantly varying with preictal vs. ictal brain activity some of which verify preexistent epilepsy markers in a data-driven way.

Assessment of perivascular space filtering methods using a three-dimensional computational model.

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Multicriteria Decision Analysis for Updating of Quality Indicators for Inflammatory Bowel Disease Comprehensive Care Units in Spain.

BACKGROUND AND AIMS: Management of inflammatory bowel disease [IBD] is complex and IBD Comprehensive Care Units [ICCUs] facilitate the delivery of quality care to IBD patients. The objective of this study was to update the existing set of quality indicators [QIs] for ICCUs, based on a nationwide quality certification programme carried out in Spain, from a multi-stakeholder perspective and using multicriteria decision analysis [MCDA] methodology. METHODS: An MCDA comprising three different phases was conducted. In phase 1, a systematic literature review was performed, and after validation by a scientific committee comprising 11 experts, a preliminary set of QIs was developed. In phase 2, a larger group of 49 experts determined the relevance and relative importance of each QI by prioritising and weighing the preliminary set. Finally in phase 3, the scientific committee reviewed the results and made a final selection via a deliberative process. RESULTS: The final set comprised 67 QIs, classified as Structure [23 QIs], Process [35 QIs] and Outcome [9 QIs], which were ranked according to their relative importance. Multidisciplinary management was the most important requirement in ICCUs, followed by continuity of care, standardisation of clinical care and, especially, the incorporation of patients' reported outcomes. CONCLUSIONS: This updated set of QIs comprises a weighted and prioritised set of items that represent the essential minimum of criteria for ensuring appropriate quality of care in the management of IBD patients.

Antitumoral Effect of Plocabulin in High Grade Serous Ovarian Carcinoma Cell Line Models.

Ovarian cancer (OC) is a life-threatening tumor and the deadliest among gynecological cancers in developed countries. First line treatment with a carboplatin/paclitaxel regime is initially effective in the majority of patients, but most advanced OC will recur and develop drug resistance. Therefore, the identification of alternative therapies is needed. In this study, we employed a panel of high-grade serous ovarian cancer (HGSOC) cell lines, in monolayer and three-dimensional cell cultures. We evaluated the effects of a novel tubulin-binding agent, plocabulin, on proliferation, cell cycle, migration and invasion. We have also tested combinations of plocabulin with several drugs currently used in OC in clinical practice. Our results show a potent antitumor activity of plocabulin, inhibiting proliferation, disrupting microtubule network, and decreasing their migration and invasion capabilities. We did not observe any synergistic combination of plocabulin with cisplatin, doxorubicin, gemcitabine or trabectedin. In conclusion, plocabulin has a potent antitumoral effect in HGSOC cell lines that warrants further clinical investigation.

Prognosis Stratification Tools in Early-Stage Endometrial Cancer: Could We Improve Their Accuracy?

There are three prognostic stratification tools used for endometrial cancer: ESMO-ESGO-ESTRO 2016, ProMisE, and ESGO-ESTRO-ESP 2020. However, these methods are not sufficiently accurate to address prognosis. The aim of this study was to investigate whether the integration of molecular classification and other biomarkers could be used to improve the prognosis stratification in early-stage endometrial cancer. Relapse-free and overall survival of each classifier were analyzed, and the c-index was employed to assess accuracy. Other biomarkers were explored to improve the precision of risk classifiers. We analyzed 293 patients. A comparison between the three classifiers showed an improved accuracy in ESGO-ESTRO-ESP 2020 when RFS was evaluated (c-index = 0.78), although we did not find broad differences between intermediate prognostic groups. Prognosis of these patients was better stratified with the incorporation of CTNNB1 status to the 2020 classifier (c-index 0.81), with statistically significant and clinically relevant differences in 5-year RFS: 93.9% for low risk, 79.1% for intermediate merged group/CTNNB1 wild type, and 42.7% for high risk (including patients with CTNNB1 mutation). The incorporation of molecular classification in risk stratification resulted in better discriminatory capability, which could be improved even further with the addition of CTNNB1 mutational evaluation.

Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding.

Alzheimer's Disease (AD) shows both complex alterations of functional dependencies between brain regions and a decreased ability to perform Visual Short-Term Memory Binding (VSTMB) tasks. Recent advances in network neuroscience toward understanding the complexity of hierarchical brain function here enables us to establish a link between these two phenomena. Here, we study data on two types of dementia at Mild Cognitive Impairment (MCI) stage-familial AD patients (E280A mutation of the presenilin-1 gene) and elderly MCI patients at high risk of sporadic AD, both with age-matched controls. We analyzed Electroencephalogram (EEG) signals recorded during the performance of Visual Short-Term Memory (VSTM) tasks by these participants. Functional connectivity was computed using the phase-lag index in Alpha and Beta; and network analysis was employed using network indices of hierarchical spread (degree variance) and complexity. Hierarchical characteristics of EEG functional connectivity networks revealed abnormal patterns in familial MCI VSTMB function and sporadic MCI VSTMB function. The middle-aged familial MCI binding network displayed a larger degree variance in lower Beta compared to healthy controls (p = 0.0051, Cohen's d = 1.0124), while the elderly sporadic MCI binding network displayed greater hierarchical complexity in Alpha (p = 0.0140, Cohen's d = 1.1627). Characteristics in healthy aging were not shown to differ. These results indicate that activity in MCI exhibits cross-frequency network reorganization characterized by increased heterogeneity of node roles in the functional hierarchy. Aging itself is not found to cause VSTM functional hierarchy differences.

Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.

The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of subjects with sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes from EEG recordings is that noise and volume conduction effects are common and disruptive. It is known that the imaginary part of coherency (iCOH) can generate functional connectivity networks that mitigate against volume conduction, while also erasing true instantaneous activity (zero or π-phase). We aim to expose topological differences in these iCOH connectivity networks using a global network measure, eigenvector alignment (EA), shown to be robust to network alterations that emulate the erasure of connectivities by iCOH. Alignments assessed by EA capture the relationship between a pair of EEG channels from the similarity of their connectivity patterns. Significant alignments-from comparison with random null models-are seen to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks, where consistency of iCOH connectivities is also noted. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. Differences between the control and pre-dementia groupings are detected for significant correlation and iCOH connectivities, but only EA suggests a notable difference in network topology when comparing between subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD.

Prognostic implications of tumor-infiltrating T cells in early-stage endometrial cancer.

Patients with endometrial cancer differ in terms of the extent of T-cell infiltration; however, the association between T-cell subpopulations and patient outcomes remains unexplored. We characterized 285 early-stage endometrial carcinoma samples for T-cell infiltrates in a tissue microarray format using multiplex fluorescent immunohistochemistry. The proportion of T cells and their subpopulations were associated with clinicopathological features and relapse-free survival outcomes. CD3+ CD4+ infiltrates were more abundant in the patients with higher grade or non-endometrioid histology. Cytotoxic T cells (CD25+, PD-1+, and PD-L1+) were strongly associated with longer relapse-free survival. Moreover, CD3+ PD-1+ stromal cells were independent of other immune T-cell populations and clinicopathological factors in predicting relapses. Patients with high stromal T-cell fraction of CD3+ PD-1+ cells were associated with a 5-year relapse-free survival rate of 93.7% compared to 79.0% in patients with low CD3+ PD-1+ fraction. Moreover, in patients classically linked to a favorable outcome (such as endometrioid subtype and low-grade tumors), the stromal CD3+ PD-1+ T-cell fraction remained prognostically significant. This study supports that T-cell infiltrates play a significant prognostic role in early-stage endometrial carcinoma. Specifically, CD3+ PD-1+ stromal cells emerge as a promising novel prognostic biomarker.

Eribulin activity in soft tissue sarcoma monolayer and three-dimensional cell line models: could the combination with other drugs improve its antitumoral effect?

BACKGROUND: Eribulin has shown antitumour activity in some soft tissue sarcomas (STSs), but it has only been approved for advanced liposarcoma (LPS). METHODS: In this study, we evaluated the effect of eribulin on proliferation, migration and invasion capabilities in LPS, leiomyosarcoma (LMS) and fibrosarcoma (FS) models, using both monolayer (2D) and three-dimensional (3D) spheroid cell cultures. Additionally, we explored combinations of eribulin with other drugs commonly used in the treatment of STS with the aim of increasing its antitumour activity. RESULTS: Eribulin showed activity inhibiting proliferation, 2D and 3D migration and invasion in most of the cell line models. Furthermore, we provide data that suggest, for the first time, a synergistic effect with ifosfamide in all models, and with pazopanib in LMS as well as in myxoid and pleomorphic LPS. CONCLUSIONS: Our results support the effect of eribulin on LPS, LMS and FS cell line models. The combination of eribulin with ifosfamide or pazopanib has shown in vitro synergy, which warrants further clinical research.

Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks.

The visual design elements and principles (VDEPs) can trigger behavioural changes and emotions in the viewer, but their effects on brain activity are not clearly understood. In this paper, we explore the relationships between brain activity and colour (cold/warm), light (dark/bright), movement (fast/slow), and balance (symmetrical/asymmetrical) VDEPs. We used the public DEAP dataset with the electroencephalogram signals of 32 participants recorded while watching music videos. The characteristic VDEPs for each second of the videos were manually tagged for by a team of two visual communication experts. Results show that variations in the light/value, rhythm/movement, and balance in the music video sequences produce a statistically significant effect over the mean absolute power of the Delta, Theta, Alpha, Beta, and Gamma EEG bands (p < 0.05). Furthermore, we trained a Convolutional Neural Network that successfully predicts the VDEP of a video fragment solely by the EEG signal of the viewer with an accuracy ranging from 0.7447 for Colour VDEP to 0.9685 for Movement VDEP. Our work shows evidence that VDEPs affect brain activity in a variety of distinguishable ways and that a deep learning classifier can infer visual VDEP properties of the videos from EEG activity.

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

Objective.This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals.Approach.The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size.Main results.The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced.Significance.These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals.

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.

A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage.

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

Hierarchical Complexity of the Macro-Scale Neonatal Brain.

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.