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• Inflammation in schizophrenia may be related to heterogeneous mechanistic pathways. • IL-6 and IL-8 have the most widespread neuroanatomical impact. • IFN-γ plays a potentially protective role in schizophrenia. • Advanced semi-supervised machine-learning techniques can aid detection of inflammation subgroups based on peripheral, accessible blood-based biomarkers. Immune system dysfunction is hypothesised to contribute to structural brain changes through aberrant synaptic pruning in schizophrenia. However, evidence is mixed and there is a lack of evidence of inflammation and its effect on grey matter volume (GMV) in patients. We hypothesised that inflammatory subgroups can be identified and that the subgroups will show distinct neuroanatomical and neurocognitive profiles. The total sample consisted of 1067 participants (chronic patients with schizophrenia n = 467 and healthy controls (HCs) n = 600) from the Australia Schizophrenia Research Bank (ASRB) dataset, together with 218 recent-onset patients with schizophrenia from the external Benefit of Minocycline on Negative Symptoms of Psychosis: Extent and Mechanism (BeneMin) dataset. HYDRA (HeterogeneitY through DiscRiminant Analysis) was used to separate schizophrenia from HC and define disease-related subgroups based on inflammatory markers. Voxel-based morphometry and inferential statistics were used to explore GMV alterations and neurocognitive deficits in these subgroups. An optimal clustering solution revealed five main schizophrenia groups separable from HC: Low Inflammation, Elevated CRP, Elevated IL-6/IL-8, Elevated IFN-γ, and Elevated IL-10 with an adjusted Rand index of 0.573. When compared with the healthy controls, the IL-6/IL-8 cluster showed the most widespread, including the anterior cingulate, GMV reduction. The IFN-γ inflammation cluster showed the least GMV reduction and impairment of cognitive performance. The CRP and the Low Inflammation clusters dominated in the younger external dataset. Inflammation in schizophrenia may not be merely a case of low vs high, but rather there are pluripotent, heterogeneous mechanisms at play which could be reliably identified based on accessible, peripheral measures. This could inform the successful development of targeted interventions. [ABSTRACT FROM AUTHOR]

Many disease agents infect the central nervous system (CNS) of teleost fish causing severe losses for the fish farming sector. Yet, neurotropic fish pathogens remain poorly documented and immune responses in the teleost CNS essentially unknown. Previously thought to be devoid of an immune system, the mammalian CNS is now recognized to be protected from infection by diverse immune cells that mostly reside in the meningeal lymphatic system. Here we review the current body of work pertaining immune responses in the teleost CNS to infection. We identify important knowledge gaps with regards to CNS immunity in fish and make recommendations for rigorous experimentation and reporting in manuscripts so that fish immunologists can advance this burgeoning field. • We provide an overview of the teleost brain anatomy. • We summarize the literature pertaining immune responses in the teleost brain to infection. • We identify knowledge gaps in the field of brain immunity in teleosts. • We suggest experimental and reporting standards when investigating teleost brain immunity. [ABSTRACT FROM AUTHOR]

• Exposure to neglect makes the brain atypical at structural and functional levels. • Larger ACC was the one of structural atypicality. • Increased FC between the salience network and R.MFG was the functional atypicality. • Childhood neglect may have caused maladaptation of typical brain development. • This affects the network responsible for emotion regulation and social interaction. Child maltreatment can adversely affect brain development, leading to vulnerabilities in brain structure and function and various psychiatric disorders. Among the various types of child maltreatment, neglect has the highest incidence rate (76.0%); however, data on its sole adverse influence on the brain remain limited. This case-control brain magnetic resonance imaging (MRI) study identified the changes in gray matter structure and function that distinguish neglected children with no other type of maltreatment (Neglect group, n = 23) from typically developing children (TD group, n = 140), and investigated the association between these structural and functional differences and specific psychosocial phenotypes observed in neglected children. Our results showed that the Neglect group had a larger right and left anterior cingulate cortex (R/L.ACC) and smaller left angular gyrus (L.AG) gray matter volume. The larger R/L.ACC was associated with hyperactivity and inattention. Resting-state functional analysis showed increased functional connectivity (FC) between the left supramarginal gyrus (L.SMG) in the salience network (SN) and the right middle frontal gyrus (R.MFG) simultaneously with a decrease in FC with the L.ACC for the same seed. The increased FC for the R.MFG was associated with difficulty in peer problems and depressive symptoms; a mediating effect was evident for depressive symptoms. These results suggest that the structural atypicality of the R/L.ACC indirectly contributes to the disturbed FCs within the SN, thereby exacerbating depressive symptoms in neglected children. In conclusion, exposure to neglect in childhood may lead to maladaptive brain development, particularly neural changes associated with depressive symptoms. [ABSTRACT FROM AUTHOR]

• The proposed algorithm can help analyze the brain structural changes of hypertension patients. • Multiple types of features are extracted to train model. • LUPI improves the classification performance and optimizes the speed of diagnosis. • Using multi-kernel KRR can provide more information train a more effective model. • SPL optimizes the training process by gradually adopting samples classifier from easy to difficult. • A better control of blood pressure in the early stage can reduce the impact on brain injury. Hypertension can lead to changes in the brain structure and function, and different blood pressure levels (2017ACC/AHA) have different effects on brain structure. It is important to analyze these changes by machine learning methods, and various characteristics can provide rich information for the analysis of these changes. However, multiple feature extraction involves complex data processing. How to make a single feature achieve the same diagnosis effect as multiple features do is worth of study. Kernel ridge regression (KRR) is a kind of machine learning method, which shows faster learning speed and generalization ability in classification tasks. In order to knowledge transfer, we use privileged information (PI) to transfer information of multiple types of feature to single feature. This allows only one feature type to be used during the test stage. In the process of feature fusion, we need to consider all the samples' attribution making the classifier better. In this work, we propose a multi-kernel KRR+ framework based on self-paced learning to analyze the changes of the brain structure in patients with different blood pressure levels. Specifically, one kind of a feature is taken as main feature, and other features are input into the multi-kernel KRR as PI. These two inputs are fed into the final KRR classifier together. In addition, a self-paced learning method is introduced into sample selecting to avoid training the classifier using samples with a large loss value firstly, which improves the generalization performance of the classifier. Experimental results show that the proposed method can make full use of the information of various features and achieve better classification performance. This shows self-paced learning based KRR can help analyze brain structure of patients with different blood pressure levels. The discriminative features may help clinicians to make judgments of hypertension degrees on brain MRI images. [ABSTRACT FROM AUTHOR]

• For the first time, brain structural covariance in obesity was investigated in the present study. • Novel morphometric similarity network was used to construct individualized structural covariance network. • We observed opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity. • The key findings were validated in an independent sample. • The present study presents a novel mechanism that underlie the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches. Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches. [ABSTRACT FROM AUTHOR]

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions. [ABSTRACT FROM AUTHOR]

• This is the first work to construct the infant brain templates for Chinese babies using over one hundred infants. • The use of our constructed population-specific templates generates lower deformation energy together with smaller registration error when performing image registration, compared to the use of population-mismatched templates. • These templates can be used for investigating neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations. The infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requires a priori knowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 120 babies from 5 to 89 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain. [ABSTRACT FROM AUTHOR]

Sex differences exist in numerous parameters of the brain. Yet, sex-related factors are part of a large set of variables that interact to affect many aspects of brain structure and function. This raises questions regarding how to interpret findings of sex differences at the level of single brain measures and the brain as a whole. In the present study, we reanalyzed two datasets consisting of measures of oxytocin, vasopressin V1a, and mu opioid receptor binding densities in multiple brain regions in rats. At the level of single brain measures, we found that sex differences were rarely dimorphic and were largely persistent across estrous stage and parental status but not across age or context. At the level of aggregates of brain measures showing sex differences, we tested whether individual brains are 'mosaics' of female-typical and male-typical measures or are internally consistent, having either only female-typical or only male-typical measures. We found mosaicism for measures showing overlap between females and males. Mosaicism was higher a) with a larger number of measures, b) with smaller effect sizes of the sex difference in these measures, and c) in rats with more diverse life experiences. Together, these results highlight the limitations of the binary framework for interpreting sex effects on the brain and suggest two complementary pathways to studying the contribution of sex to brain function: (1) focusing on measures showing dimorphic and persistent sex differences and (2) exploring the relations between specific brain mosaics and specific endpoints. [Display omitted] • Dimorphic sex differences were rare for neuropeptide receptor measures in rat brains. • Sex differences were largely persistent across estrous cycle and parental status. • Few sex differences persisted across age and environment. • Mosaics were seen for measures showing overlap in scores between females and males. • Mosaicism was higher with more measures and with smaller effect sizes. [ABSTRACT FROM AUTHOR]

• Self-automatic processing is modulated by chronotype and time-of-day only in Evening-type participants. • There is a lack of modulation of others-controlled processing by chronotype and time-of-day. • Morning-types demonstrated greater flexibility and adaptability to non-optimal times-of-day. • Our results link the Self-Attention Network (SAN) more closely to the VMPFC than to the DLPFC. We assessed whether self-related automatic and others-related controlled processes are modulated by chronotype and time-of-day. Here, a shape-label matching task composed of three geometrical shapes arbitrarily associated with you , friend , and stranger was used. Twenty Morning-types, and twenty Evening-types performed the task at the optimal and non-optimal times of day (i.e., 8 AM, or 8:30 PM). Morning-types did not exhibit noticeable synchrony effects, thus proving the better adaptation of these participants to non-optimal moments of the day as compared to Evening-types. Contrary to our predictions regarding the absence of automatic-processing modulation and the presence of controlled-processing influences by time-of-day, we found an influence on self-related but not others-related processing only in Evening-type participants. Although brain structures are not directly tackled, we argue that such modulation may be due to the dependence of the activation of the ventromedial prefrontal cortex (VMPFC), an essential component of the self-attention network on circadian rhythms. [ABSTRACT FROM AUTHOR]

Localization within the nervous system provides context for neurological disease manifestations and treatment, with numerous disease mechanisms exhibiting predilect locations. In contrast, the molecular function of most disease-causing genes is generally considered dissociated from such brain regional correlations because most genes are expressed throughout the brain. We tested the factual basis for this dissociation by discerning between two distinct genetic disease mechanism possibilities: One, gene-specific, in which genetic disorders are poorly localizable because they are multiform at the molecular level, with each mutant gene acting more widely or complexly than via mere loss or gain of one function. The other, more general, where aspects shared by groups of genes such as membership in a gene set that sustains a concerted biological process accounts for a common or localizable phenotype. We analyzed mitochondrial substrate disorders as a paradigm of apparently heterogeneous diseases when considered from the point of view of their manifestations and individual function of their causal genes. We used publicly available transcriptomes, disease phenotypes published in peer-reviewed journals and Human Ontology classifications for 27 mitochondrial substrate metabolism diseases and analyzed if these disorders manifest common phenotypes and if this relates to common brain regions or cells as demarcated by their transcriptome. The most frequent phenotypic manifestations and brain structures involved were almost stereotypic regardless of the individual gene affected, correlating with the regional abundance of the transcriptome that served mitochondrial substrate metabolism. This also applied to the transcriptome of inhibitory neurons, which are dysfunctional in some mitochondrial diseases. This stands in contrast with resistance to dementia atrophy from other causes, which is known to also associate with greater expression of a similar fraction of the transcriptome. The results suggest that brain region or cell type dysfunction stemming from a broad process such as mitochondrial substrate metabolism is more relevant for disease manifestations than individual gene participation in specific molecular function. [ABSTRACT FROM AUTHOR]

• Depletion of gut microbiota disrupts the blood-brain barrier. • Depletion of gut microbiota induces brain infiltration of BM-derived cells. • Brain-infiltrating cells differentiate into microglia-like cells. • BM-derived monocytes are the major component of the brain-infiltrating cells. The crosstalk between intestinal bacteria and the central nervous system, so called "the gut-brain axis", is critically important for maintaining brain homeostasis and function. This study aimed to investigate the integrity of the blood-brain barrier (BBB) and migration of bone marrow (BM)-derived cells to the brain parenchyma after intestinal microbiota depletion in adult mice. Gut microbiota dysbiosis was induced with 5 non-absorbable antibiotics in drinking water in mice that had received bone marrow transplantation (BMT) from green fluorescent protein (GFP) transgenic mice. Antibiotic-induced microbiome depletion reduced expression of tight-junction proteins of the brain blood vessels and increased BBB permeability. Fecal microbiota transplantation of antibiotics treated mice with pathogen-free gut microbiota decreased BBB permeability and up-regulated the expression of tight junction proteins. The BM-derived GFP+ cells were observed to infiltrate specific brain regions, including the nucleus accumbens (NAc), the septal nucleus (SPT) and the hippocampus (CA3). The infiltrated cells acquired a ramified microglia-like morphology and Iba1, a microglia marker, was expressed in all GFP+ cells, whereas they were negative for the astrocyte marker GFAP. Furthermore, treatment with CCR2 antagonist (RS102895) suppressed the recruitment of BM-derived monocytes to the brain. We report for the first time the migration of BM-derived monocytes to the brain regions involved in regulating emotional behaviors after depletion of intestinal microbiota in BMT background mice. However, mechanisms responsible for the migration and functions of the microglia-like infiltrated cells in the brain need further investigation. These findings indicate that monocyte recruitment to the brain in response to gut microbiota dysbiosis may represent a novel cellular mechanism that contributes to the development of brain disorders. [ABSTRACT FROM AUTHOR]

• A two sample Mendelian randomization approach was used to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes. • Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites being replicated using independent exposure data. • Heterogeneity and pleiotropy analysis showed low to no deviation from null thus validating our associations as truly significant. • The study highlighted the role of proteolytic and anti-oxidative components in the development and functioning of the brain. • The results provide novel insight for understanding subcortical brain structure changes and could help in uncovering potential diagnostic markers and drug targets for the many disorders that are associated with changes in brain structures. Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes. [ABSTRACT FROM AUTHOR]

• Voxel-wise GLM analysis failed to capture fMRI signal variability. • ROI-based ICC analysis differentiated fMRI signal reliability between stimuli. • Stimuli with negative emotional valence are more reliable than positive valence ones. • Emotional valence more than stimulus habituation impacts fMRI signal reliability. The emotional domain is often impaired across many neurological diseases, for this reason it represents a relevant target of rehabilitation interventions. Functional changes in neural activity related to treatment can be assessed with functional MRI (fMRI) using emotion-generation tasks in longitudinal settings. Previous studies demonstrated that within-subject fMRI signal reliability can be affected by several factors such as repetition suppression, type of task and brain anatomy. However, the differential role of repetition suppression and emotional valence of the stimuli on the fMRI signal reliability and reproducibility during an emotion-generation task involving the vision of emotional pictures is yet to be determined. Sixty-two healthy subjects were enrolled and split into two groups: group A (21 subjects, test-retest reliability on same-day and with same-task-form), group B (30 subjects, test-retest reproducibility with 4-month-interval using two equivalent-parallel forms of the task). Test-retest reliability and reproducibility of fMRI responses and patterns were evaluated separately for positive and negative emotional valence conditions in both groups. The analyses were performed voxel-wise, using the general linear model (GLM), and via a region-of-interest (ROI)-based approach, by computing the intra-class correlation coefficient (ICC) on the obtained contrasts. The voxel-wise GLM test yielded no significant differences for both conditions in reliability and reproducibility analyses. As to the ROI-based approach, across all areas with significant main effects of the stimuli, the reliability, as measured with ICC, was poor (<0.4) for the positive condition and ranged from poor to excellent (0.4–0.75) for the negative condition. The ICC-based reproducibility analysis, related to the comparison of two different parallel forms, yielded similar results. The voxel-wise GLM analysis failed to capture the poor reliability of fMRI signal which was instead highlighted using the ROI-based ICC analysis. The latter showed higher signal reliability for negative valence stimuli with respect to positive ones. The implementation of two parallel forms allowed to exclude neural suppression as the predominant effect causing low signal reliability, which could be instead ascribed to the employment of different neural strategies to cope with emotional stimuli over time. This is an invaluable information for a better assessment of treatment and rehabilitation effects in longitudinal studies of emotional neural processing. [Display omitted] [ABSTRACT FROM AUTHOR]

• High quality 0.5 mm T1w and DTI templates of the older adult brain were constructed. • Data from a diverse, community cohort of 400 non-demented older adults were used. • The new templates are well-matched in space. • The new templates are most representative of the brain of non-demented older adults. • The new templates showed high performance when used as references for normalization. High quality, high resolution T 1 -weighted (T 1 w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T 1 w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T 1 w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T 1 w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T 1 w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults. [ABSTRACT FROM AUTHOR]

The participation of the hippocampal formation in consolidation and reconsolidation of contextual fear memories has been widely recognized and known to be dependent on the activation of the cAMP response element (CRE) binding protein (CREB) pathway. Recent findings have challenged the prevailing view that over time contextual fear memories migrate to neocortical circuits and no longer require the hippocampus for retrieval of remote fearful memories. It has also recently been found that this brain structure is important for the maintenance and recall of remote fear memories associated with aversive events, a common trait in stress-related disorders such as generalized anxiety disorder (GAD), major depression, and post-traumatic stress disorder. In view of these findings, here we examined the putative role of CREB in the hippocampus of an animal model of GAD during the retrieval of remote contextual fear memories. Specifically, we evaluated CREB phosphorylation in the hippocampus of male Carioca High- and Low-conditioned Freezing rats (CHF and CLF, respectively) upon re-exposure of animals to contextual cues associated to footshocks weeks after fear conditioning. Age-matched male rats from a randomized crossbreeding population served as controls (CTL). Adrenal catecholamine levels were also measured as a biological marker of stress response. Seven weeks after contextual fear conditioning, half of the sample of CHF (n = 9), CLF (n = 10) and CTL (n = 10) rats were randomly assigned to return to the same context chamber where footshocks were previously administrated (Context condition), while the remaining animals were individually placed in standard housing cages (Control condition). Western blot results indicated that pCREB levels were significantly increased in the hippocampus of CHF rats for both Context and Control conditions when compared to the other experimental groups. CHF rats in the Context condition also exhibited significant more freezing than that observed for both CLF and CTL rats. Lastly, CHF animals in the Context condition displayed significantly higher adrenal catecholamine levels than those in the Control condition, whereas no differences in catecholamine levels were observed between Context and Control conditions for CLF and CTL rats. These findings are discussed from a perspective in which the hippocampus plays a role in the maintenance and recall of remote contextual fear memories via the CREB pathway. [ABSTRACT FROM AUTHOR]

• IL-18 level significantly increased in mice with post-stroke depression. • IL-18 expression was mainly found in neurons at early phase and in microglia in late phase. • Exogenous IL-18 in to the amygdala caused severe depression-like behaviors. • The blockage of endogenous IL-18 by IL-18 binding protein rescued depressive phenotypes. • IL-18 KO mice exhibited the resistance to spatial restraint stress and cerebral ischemia injury. Post-stroke depression (PSD) is a common and serious complication that is affecting one thirds of stroke patients which leaves them with a poor quality of life, high mortality rate, high recurrent rate, and slow recovery. Recent studies showed that serum interleukin-18 (IL-18) level is a biomarker for patients with PSD. However, the role of IL-18 in the pathology of PSD is still unclear. In this study, we demonstrated that the IL-18 level in the ischemic brain significantly increased in mice with depression-like behaviors that were caused by the combined use of chronic spatial restraint stress and middle cerebral artery occlusion. Interestingly, IL-18 expression was mainly found in neurons at early phase and in microglia at a later phase. Injection of the exogenous IL-18 into the amygdala, but not the hippocampus or the striatum caused severe depression-like behaviors. On the contrary, the blockage of endogenous IL-18 by IL-18 binding protein, a specific antagonist of IL-18, repressed depressive phenotypes in SIR mice. IL-18 KO mice exhibited the resistance to spatial restraint stress and cerebral ischemia injury. Finally, we found that IL-18 mediated depressive behaviors by the interaction of IL-18 receptor and NKCC1, a sodium-potassium chloride co-transporter that is related to GABAergic inhibition. Administration of NKCC1 antagonist bumetanide exerted a therapeutic effect on the in IL-18-induced depressive mice. In conclusion, we demonstrated that increased IL-18 in the brain causes depression-like behaviors by promoting the IL-18 receptor/NKCC1 signaling pathway. Targeting IL-18 and its downstream pathway is a promising strategy for the prevention and treatment of PSD. [ABSTRACT FROM AUTHOR]

The infant brain undergoes drastic morphological and functional development during the first year of life. Three-dimensional T1-weighted Magnetic Resonance Imaging (3D T1w-MRI) is a major tool to characterize the brain anatomy, which however, manifests inherently low and rapidly changing contrast between white matter (WM) and gray matter (GM) in the infant brains (0-12 month-old). Despite the prior efforts made to maximize tissue contrast in the neonatal brains (≤1 months), optimization of imaging methods in the rest of the infancy (1–12 months) is not fully addressed, while brains in the latter period exhibit even more challenging contrast. Here, we performed a systematic investigation to improve the contrast between cortical GM and subcortical WM throughout the infancy. We first performed simultaneous T1 and proton density mapping in a normally developing infant cohort at 3T (n = 57). Based on the evolution of T1 relaxation times, we defined three age groups and simulated the relative tissue contrast between WM and GM in each group. Age-specific imaging strategies were proposed according to the Bloch simulation: inversion time (TI) around 800 ms for the 0-3 month-old group, dual TI at 500 ms and 700 ms for the 3-7 month-old group, and TI around 700 ms for 7-12 month-old group, using a centrically encoded 3D-MPRAGE sequence at 3T. Experimental results with varying TIs in each group confirmed improved contrast at the proposed optimal TIs, even in 3–7 month-old infants who had nearly isointense contrast. We further demonstrated the advantage of improved relative contrast in segmenting the neonatal brains using a multi-atlas segmentation method. The proposed age-specific optimization strategies can be easily adapted to routine clinical examinations, and the improved image contrast would facilitate quantitative analysis of the infant brain development. Image 1 • T1 and proton density maps were measured in 57 infant brains for Bloch simulation. • Age-specific T1 imaging strategies were proposed for 0–3, 3–7, and 7–12 monthold infants. • Relative contrast was improved at the proposed optimal inversion time in each age range. • The high tissue contrast improved segmentation accuracy in neonatal brains. • Contrast in the 3-7 month-old brains can be enhanced with dual TI acquisition. [ABSTRACT FROM AUTHOR]

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG 1 : 27 subjects; EEG 2 : 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity. • Canonical frequency bands disregard relevant subject-specific neural idiosyncrasies. • A novel frequency band segmentation approach is proposed, the CMB algorithm. • The CMB algorithm is based on the frequency-dependent topology of the functional connectivity. • The CMB algorithm provides an automatic user-specific band segmentation. • The CMB algorithm opens the way for novel, personalized, data-driven connectivity analyses. [ABSTRACT FROM AUTHOR]

• We studied changes in brain structure and function in children ages 9–13 years. • Cortical directed intracellular diffusion decreased, but subcortical increased. • Functional connectivity changes show network segregation, but limited integration. • Cortical microstructure varied most pre- or mid-puberty depending on the region. • Sparse sex differences show limited greater male variability in brain development. As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9–13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom. [ABSTRACT FROM AUTHOR]

The vacuolar protein sorting-associated protein 13B (VPS13B) is a large and highly conserved protein. Disruption of VPS13B causes the autosomal recessive Cohen syndrome, a rare disorder characterized by microcephaly and intellectual disability among other features, including developmental delay, hypotonia, and friendly-personality. However, the underlying mechanisms by which VPS13B disruption leads to brain dysfunction still remain unexplained. To gain insights into the neuropathogenesis of Cohen syndrome, we systematically characterized brain changes in Vps13b -mutant mice and compared murine findings to 235 previously published and 17 new patients diagnosed with VPS13B -related Cohen syndrome. We showed that Vps13b is differentially expressed across brain regions with the highest expression in the cerebellum, the hippocampus and the cortex with postnatal peak. Half of the Vps13b −/− mice die during the first week of life. The remaining mice have a normal lifespan and display the core phenotypes of the human disease, including microcephaly, growth delay, hypotonia, altered memory, and enhanced sociability. Systematic 2D and 3D brain histo-morphological analyses reveal specific structural changes in the brain starting after birth. The dentate gyrus is the brain region with the most prominent reduction in size, while the motor cortex is specifically thinner in layer VI. The fornix, the fasciculus retroflexus, and the cingulate cortex remain unaffected. Interestingly, these neuroanatomical changes implicate an increase of neuronal death during infantile stages with no progression in adulthood suggesting that VPS13B promotes neuronal survival early in life. Importantly, whilst both sexes were affected, some neuroanatomical and behavioral phenotypes were less pronounced or even absent in females. We evaluate sex differences in Cohen patients and conclude that females are less affected both in mice and patients. Our findings provide new insights about the neurobiology of VPS13B and highlight previously unreported brain phenotypes while defining Cohen syndrome as a likely new entity of non-progressive infantile neurodegeneration. • VPS13B has many roles in various developmental contexts • VPS13B controls brain anatomy and behavior • VPS13B promotes the survival of neurons after birth • VPS13B has a more important role in males than females [ABSTRACT FROM AUTHOR]

• We probed the effects of age on neuromagnetic signals during the resting-state in adults (18–88 years), interactions with sex, and correspondence with cortical thickness. • We found specific linear and non-linear associations of connectivity and power with age, indicating maturation and aging at various life stages. The findings varied regionally depending on the temporal properties of the signals and showed sex-specific effects in low-frequency bands. • Age trajectories of cortical thickness corresponded to aging patterns of delta and beta oscillations, and structure-function coupling was observed in the default mode, attention, and motor networks. Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18–88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity. [ABSTRACT FROM AUTHOR]

• A Kuramoto model with co-evolving structure and dynamics is introduced. • The model significantly improves prediction of empirical functional connectivity. • The novel, structural edge time series displays emergent network topology. • The model serves as a link between communication and dynamic systems models. Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function. [ABSTRACT FROM AUTHOR]

• We propose a new approach called principal parcellation analysis (PPA) for predicting human traits using the brain connectome. • PPA uses tractography-based brain connectome representation, clustering fiber endpoints to create a data-driven white matter parcellation. • With vector-valued representation, PPA facilitates easier statistical analysis, improving trait prediction power and model parsimony. • Applications to data from the human connectome project demonstrate the effectiveness of PPA. Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data. [ABSTRACT FROM AUTHOR]

• Extensive exploration of the relationship between intrinsic coupling modes and structural connectivity. • Phase and envelope coupling modes are specifically related to structural connectivity. • The observed relation between structural and functional coupling cannot be solely attributed to volume conduction. [Display omitted] Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex. [ABSTRACT FROM AUTHOR]

• Advances in neuroimaging have facilitated the study of EF development early in life. • Recent research continues to identify the pivotal role of the PFC in EF. • Important changes occur in the prefrontal cortex across development. • The importance of brain connectivity for EF is becoming increasingly clear. • Functional brain specialisation for EF continues to develop into adolescence. In the last decade, advances in neuroimaging technologies have given rise to a large number of research studies that investigate the neural underpinnings of executive function (EF). EF has long been associated with the prefrontal cortex (PFC) and involves both a unified, general element, as well as the distinct, separable elements of working memory, inhibitory control and set shifting. We will highlight the value of utilising advances in neuroimaging techniques to uncover answers to some of the most pressing questions in the field of early EF development. First, this review will explore the development and neural substrates of each element of EF. Second, the structural, anatomical and biochemical changes that occur in the PFC during infancy and throughout childhood will be examined, in order to address the importance of these changes for the development of EF. Third, the importance of connectivity between regions of the PFC and other brain areas in EF development is reviewed. Finally, throughout this review more recent developments in neuroimaging techniques will be addressed, alongside the implications for further elucidating the neural substrates of early EF development in the future. [ABSTRACT FROM AUTHOR]

Highlights • Own child activates the left hemisphere more than the right hemisphere in mothers. • Own child activates the left VLPFC, insula, amygdala, bilateral striatum and midbrain. • These brain regions underlie maternal motivation, salience and emotion processing. Abstract How special is her own child to a mother? Research that has focused on mothers' brain responses to their own child has revealed the involvement of multiple subcortical and cortical brain regions, but less is known about which brain regions are systematically activated across these studies. This meta-analysis aims to identify specific neural regions associated with "own child" compared to "other child". To ensure the consistency of the types of child stimuli across studies, the analysis focused on studies using neutral to positive visual stimuli of own and other children. Viewing their own child is associated with enhanced cerebral activation in cortical and subcortical regions including the midbrain, amygdala, striatum, ventrolateral prefrontal cortex, and insula. These regions are involved in reward and maternal motivation and promoting approach behavior as well as caregiving. Interestingly, own child faces activate regions in the left hemisphere more than in the right hemisphere in mothers. The current results may support the better understanding of deviation from expected maternal brain responses to own child, which could further inform neurological markers for innovative parental screening and intervention. [ABSTRACT FROM AUTHOR]

Abstract Stereotaxic systems and automatic tissue segmentation routines enable neuronavigation as well as reproducible processing of neuroimage datasets. Such systems have been developed for humans, non-human-primates, sheep, and rodents, but not for dogs. Although dogs share important neurofunctional and -anatomical features with humans, and in spite of their importance in translational neuroscience, little is known about the variability of the canine brain morphology and, possibly related, function. Moreover, we lack templates, tissue probability maps (TPM), and stereotaxic brain labels for implementation in standard software utilities such as Statistical Parametric Mapping (SPM). Hence, objective and reproducible, image-based investigations are currently impeded in dogs. We have created a detailed stereotaxic reference frame for dogs including TPM and tissue labels, enabling inter-individual and cross-study neuroimage analysis. T2w datasets were acquired from 16 neurologically inconspicuous dogs of different breeds by 3T MRI. The datasets were averaged after initial preprocessing using linear and nonlinear registration algorithms as implemented in SPM8. TPM for gray (GM) and white matter (WM) as well as cerebrospinal fluid (CSF) were created. Different cortical, subcortical, medullary, and CSF regions were manually labeled to create a spatial binary atlas being aligned with the template. A proof-of-concept for automatic determination of morphological and volumetrical characteristics was performed using additional canine datasets (n = 64) including a subgroup of laboratory beagles (n = 24). Overall, 21 brain regions were labeled using the segmented tissue classes of the brain template. The proof-of-concept trial revealed excellent suitability of the created tools for image processing and subsequent analysis. There was high intra-breed variability in frontal lobe and hippocampus volumes, and noticeable inter-breed corpus callosum volume variation. The T2w brain template provides important, breed-averaged canine brain anatomy features in a spatial standard coordinate system. TPM allows automatic tissue segmentation using SPM and enables unbiased automatic image processing or morphological characterization in different canine breeds. The reported volumetric and morphometric results may serve as a starting point for further research aimed at in vivo analysis of canine brain anatomy and function. Graphical abstract Image 1 Highlights • A breed-averaged MR template, probability map and 21 labels (n = 16) for dogs were established. • Post-processing were validated for various dog breeds including laboratory beagle. • Mean brain tissue and label volumes were given for n = 64 dogs. • Frontal lobe and hippocampus volumes are highly variable in laboratory beagle. • The inter-breed corpus callosum volume varys noticeable. [ABSTRACT FROM AUTHOR]

Abstract Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting-state functional magnetic resonance imaging (rsfMRI). The resting state patterns have been observed to be exhibiting complex spatio-temporal dynamics and substantial effort has been made to characterize the dynamic functional connectivity (dFC) configurations. However, the dynamics governing the state transitions that the brain undergoes and their relationship to stationary functional connectivity still remains an open problem. One class of approaches attempts to characterize the dynamics in terms of finite number of latent brain states, however, such attempts are yet to amalgamate the underlying anatomical structural connectivity (SC) with the dynamics. Another class of methods links individual dynamic FCs with the underlying SC but does not characterize the temporal evolution of FC. Further, the latent states discovered by previous approaches could not be directly linked to the SC, thereby motivating us to discover the underlying lower-dimensional manifold that represents the temporal structure. In the proposed approach, the discovered manifold is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to the latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) that inherently links dynamics to the structure and finally predicts the grand average FC of the test subjects by leveraging a state transition Markov model. The proposed solution does not make strong assumptions about the underlying data and is generally applicable to resting or task data for learning subject-specific state transitions and for successfully characterizing SC-dFC-FC relationship through a unifying framework. Training and testing were done using the rs-fMRI data of 46 healthy participants. tMKL model performs significantly better than the existing models for predicting resting state functional connectivity based on whole-brain dynamic mean-field model (DMF), single diffusion kernel (SDK) model and multiple kernel learning (MKL) model. Further, the learned model was tested on an independent cohort of 100 young, healthy participants from the Human Connectome Project (HCP) and the results establish the generalizability of the proposed solution. More importantly, the model retains sensitivity toward subject-specific anatomy, a unique contribution towards a holistic approach for SC-FC characterization. [ABSTRACT FROM AUTHOR]

Highlights • Medial prefrontal (mPFC) inactivation impairs both spatial and object memories. • Effects of dorsal hippocampal inactivation (DH) on memory depend on DREADD type. • Episodic memory consolidation requires concurrent activation of the DH and mPFC. Abstract The dorsal hippocampus (DH) and medial prefrontal cortex (mPFC) are brain regions essential for processing and storing episodic memory. In rodents, the DH has a well-established role in supporting the consolidation of episodic-like memory in tasks such as object recognition and object placement. However, the role of the mPFC in the consolidation of episodic-like memory tasks remains controversial. Therefore, the present study examined involvement of the DH and mPFC, alone and in combination, in object and spatial recognition memory consolidation in ovariectomized female mice. To this end, we utilized two types of inhibitory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) to inactivate the DH alone, the mPFC alone, or both brain regions concurrently immediately after object training to assess the role of each region in the consolidation of object recognition and spatial memories. Our results using single and multiplexed DREADDS suggest that excitatory activity in the DH and mPFC, alone or in combination, is required for the successful consolidation of object recognition and spatial memories. Together, these studies provide critical insight into how the DH and mPFC work in concert to facilitate memory consolidation in female mice. [ABSTRACT FROM AUTHOR]

Abstract Throughout infancy, childhood, and adolescence, our brains undergo remarkable changes. Processes including myelination and synaptogenesis occur rapidly across the first 2–3 years of life, and ongoing brain remodeling continues into young adulthood. Studies have sought to characterize the patterns of structural brain development, and early studies predominately relied upon gross anatomical measures of brain structure, morphology, and organization. MRI offers the ability to characterize and quantify a range of microstructural aspects of brain tissue that may be more closely related to fundamental neurodevelopmental processes. Techniques such as diffusion, magnetization transfer, relaxometry, and myelin water imaging provide insight into changing cyto- and myeloarchitecture, neuronal density, and structural connectivity. In this review, we focus on the growing body of literature exploiting these MRI techniques to better understand the microstructural changes that occur in brain white matter during maturation. Our review focuses on studies of normative brain development from birth to early adulthood (∼25 years), and places particular emphasis on longitudinal studies and newer techniques that are being used to study microstructural white matter development. All imaging methods demonstrate consistent, rapid microstructural white matter development over the first 3 years of life, suggesting increased myelination and axonal packing. Diffusion studies clearly demonstrate continued white matter maturation during later childhood and adolescence, though the lack of consistent findings in other modalities suggests changes may be mainly due to axonal packing. An emerging literature details differential microstructural development in boys and girls, and connects developmental trajectories to cognitive abilities, behaviour, and/or environmental factors, though the nature of these relationships remains unclear. Future research will need to focus on newer imaging techniques and longitudinal studies to provide more detailed information about microstructural white matter development, particularly in the childhood years. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Abstract Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods. Highlights • Validation methods should challenge the interpretation of dMRI. • dMRI contain information beyond the specificity of single validation techniques. • Sharing data, methods and software are key for dMRI development. [ABSTRACT FROM AUTHOR]

Abstract Diffusion microstructural imaging techniques have attracted great interest in the last decade due to their ability to quantify axon diameter and volume fraction in healthy and diseased human white matter. The estimates of compartment size and volume fraction continue to be debated, in part due to the lack of a gold standard for validation and quality control. In this work, we validate diffusion MRI estimates of compartment size and volume fraction using a novel textile axon ("taxon") phantom constructed from hollow polypropylene yarns with distinct intra- and extra-taxonal compartments to mimic white matter in the brain. We acquired a comprehensive set of diffusion MRI measurements in the phantom using multiple gradient directions, diffusion times and gradient strengths on a human MRI scanner equipped with maximum gradient strength (G max) of 300 mT/m. We obtained estimates of compartment size and restricted volume fraction through a straightforward extension of the AxCaliber/ActiveAx frameworks that enables estimation of mean compartment size in fiber bundles of arbitrary orientation. The voxel-wise taxon diameter estimates of 12.2 ± 0.9 μm were close to the manufactured inner diameter of 11.8 ± 1.2 μm with G max = 300 mT/m. The estimated restricted volume fraction demonstrated an expected decrease along the length of the fiber bundles in accordance with the known construction of the phantom. When G max was restricted to 80 mT/m, the taxon diameter was overestimated, and the estimates for taxon diameter and packing density showed greater uncertainty compared to data with G max = 300 mT/m. In conclusion, the compartment size and volume fraction estimates resulting from diffusion measurements on a human scanner were validated against ground truth in a phantom mimicking human white matter, providing confidence that this method can yield accurate estimates of parameters in simplified but realistic microstructural environments. Our work also demonstrates the importance of a biologically analogous phantom that can be applied to validate a variety of diffusion microstructural imaging methods in human scanners and be used for standardization of diffusion MRI protocols for neuroimaging research. Highlights • A method for estimating compartment size/density using diffusion MRI is proposed. • A novel hollow fiber phantom is used for validation of size/density estimates. • A comprehensive diffusion dataset was acquired on CONNECTOM scanner with the phantom. • The proposed method can resolve 12um compartment size with 300 mT/m gradient strength. [ABSTRACT FROM AUTHOR]

Abstract MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T 1 , T 2 , and T 2 * , and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists. Highlights • MRI relaxometry is sensitive to brain tissue microstructure. • Each of T 1 , T 2 , and T 2 ∗ relaxometry can be used. • Tissue models, acquisition methods, and analysis methods are important. [ABSTRACT FROM AUTHOR]

Abstract Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modeling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human research and clinical MRI. Highlights • Diffusion-weighted MRS (DW-MRS) allows to investigate brain metabolite diffusion. • Most brain metabolites are predominantly intracellular and cell specific. • Diffusion properties of brain metabolites provide exclusive cell-specific information. • The most recent methods for DW-MRS data acquisition and analysis are reviewed. • The potential relevance of DW-MRS for elucidating brain microstructure is discussed. [ABSTRACT FROM AUTHOR]

Abstract Recent breakthroughs in magnetic resonance imaging (MRI) enabled quantitative relaxometry and diffusion-weighted imaging with sub-millimeter resolution. Combined with biophysical models of MR contrast the emerging methods promise in vivo mapping of cyto- and myelo-architectonics, i.e., in vivo histology using MRI (hMRI) in humans. The hMRI methods require histological reference data for model building and validation. This is currently provided by MRI on post mortem human brain tissue in combination with classical histology on sections. However, this well established approach is limited to qualitative 2D information, while a systematic validation of hMRI requires quantitative 3D information on macroscopic voxels. We present a promising histological method based on optical 3D imaging combined with a tissue clearing method, Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY), adapted for hMRI validation. Adapting CLARITY to the needs of hMRI is challenging due to poor antibody penetration into large sample volumes and high opacity of aged post mortem human brain tissue. In a pilot experiment we achieved transparency of up to 8 mm-thick and immunohistochemical staining of up to 5 mm-thick post mortem brain tissue by a combination of active and passive clearing, prolonged clearing and staining times. We combined 3D optical imaging of the cleared samples with tailored image processing methods. We demonstrated the feasibility for quantification of neuron density, fiber orientation distribution and cell type classification within a volume with size similar to a typical MRI voxel. The presented combination of MRI, 3D optical microscopy and image processing is a promising tool for validation of MRI-based microstructure estimates. Highlights • CLARITY on post mortem human brain was adapted for validation of MRI-based histology. • Clearing of up to 8 mm, 3D imaging and staining of up to 5 mm thick samples was demonstrated. • Multiple stains were applied including markers for fibers, neurons and glia. • Characterization of cyto- and myelo-architectonics within an MRI-voxel was shown. [ABSTRACT FROM AUTHOR]

Abstract Magnetization transfer contrast has yielded insight into brain tissue microstructure changes across the lifespan and in a range of disorders. This progress has been aided by the development of quantitative magnetization transfer imaging techniques able to extract intrinsic properties of the tissue that are independent of the specifics of the data acquisition. While the tissue properties extracted by these techniques do not map directly onto specific cellular structures or pathological processes, a growing body of work from animal models and histopathological correlations aids the in vivo interpretation of magnetization transfer properties of tissue. This review examines the biophysical models that have been developed to describe magnetization transfer contrast in tissue as well as the experimental evidence for the biological interpretation of magnetization transfer data in health and disease. [ABSTRACT FROM AUTHOR]

Abstract Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R 1 , MT, R 2 *, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R 1 , MT: myelin, macromolecular content; R 2 *: myelin, paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18–39 years, N = 93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes – but increased tissue water content – when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure cross-sectionally, we found that parameters with sensitivity to tissue myelin (R 1 & MT) showed linear increases with age across frontal and parietal cortex (change 0.5–1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies. Highlights • We mapped multi-parameter maps (MPMs) across and within cortical regions. • We charted age effects (ages 18–39) on myelin and related processes. • MPMs sensitive to myelin (R 1 , MT) showed elevated values in primary areas over most cortical depths. • R 2 * map foci tended to overlap MPMs sensitive to myelin (R 1 , MT). • R 1 and MT increased with age (0.5–1.0% per year) at mid-depth in frontal and parietal cortex. [ABSTRACT FROM AUTHOR]

Abstract The MRI signal is dependent upon a number of sub-voxel properties of tissue, which makes it potentially able to detect changes occurring at a scale much smaller than the image resolution. This "microstructural imaging" has become one of the main branches of quantitative MRI. Despite the exciting promise of unique insight beyond the resolution of the acquired images, its widespread application is limited by the relatively modest ability of each microstructural imaging technique to distinguish between differing microscopic substrates. This is mainly due to the fact that MRI provides a very indirect measure of the tissue properties in which we are interested. A strategy to overcome this limitation lies in the combination of more than one technique, to exploit the relative contributions of differing physiological and pathological substrates to selected MRI contrasts. This forms the basis of multi-modal MRI, a broad concept that refers to many different ways of effectively combining information from more than one MRI contrast. This paper will review a range of methods that have been proposed to maximise the output of this combination, primarily falling into one of two approaches. The first one relies on data-driven methods, exploiting multivariate analysis tools able to capture overlapping and complementary information. The second approach, which we call "model-driven", aims at combining parameters extracted by existing biophysical or signal models to obtain new parameters, which are believed to be more accurate or more specific than the original ones. This paper will attempt to provide an overview of the advantages and limitations of these two philosophies. Graphical abstract Image 1 Highlights • Multiple MRI contrasts can be complementary. • Multi-modal MRI can provide information inaccessible through single modalities. • Multi-modal MRI can be achieved through model-driven or data-driven approaches. • The bias and limitations of these approaches need to be further explored. [ABSTRACT FROM AUTHOR]

Abstract This special issue summarizes an impressive body of work concerned with in vivo imaging of brain microstructure. Collectively, papers included here demonstrate the power of multi-modal magnetic resonance imaging (MRI) for mapping various structural properties of brain tissue. In this introduction, I provide a user's perspective vis-à-vis motivations for these efforts, review briefly the cellular composition of grey and white matter in the human brain, and provide a few examples of how we can bridge the gap between ex vivo and in vivo datasets to facilitate interpretation of studies measuring brain macro- and microstructure. [ABSTRACT FROM AUTHOR]

Abstract Microstructural imaging and connectomics are two research areas that hold great potential for investigating brain structure and function. Combining these two approaches can lead to a better and more complete characterization of the brain as a network. The aim of this work is characterizing the connectome from a novel perspective using the myelination measure given by the g-ratio. The g-ratio is the ratio of the inner to the outer diameters of a myelinated axon, whose aggregated value can now be estimated in vivo using MRI. In two different datasets of healthy subjects, we reconstructed the structural connectome and then used the g-ratio estimated from diffusion and magnetization transfer data to characterize the network structure. Significant characteristics of g-ratio weighted graphs emerged. First, the g-ratio distribution across the edges of the graph did not show the power-law distribution observed using the number of streamlines as a weight. Second, connections involving regions related to motor and sensory functions were the highest in myelin content. We also observed significant differences in terms of the hub structure and the rich-club organization suggesting that connections involving hub regions present higher myelination than peripheral connections. Taken together, these findings offer a characterization of g-ratio distribution across the connectome in healthy subjects and lay the foundations for further investigating plasticity and pathology using a similar approach. Highlights • We integrated myelination into the structural connectome using the g-ratio. • The average g-ratio and the number of streamlines follow different patterns. • A myelin blueprint can be observed looking at the hub structure. • The anatomical organization follows patterns consistent with existing literature. [ABSTRACT FROM AUTHOR]

Abstract Multivariate pattern analysis (MVPA) of fMRI data has allowed the investigation of neural representations of stimuli on the basis of distributed patterns of activity within a brain region, independently from overall brain activity. For instance, several studies on early visual cortex have reported reliable MVPA decoding of the identity of a stimulus representation that was kept in working memory or internally generated, despite the fact that the overall BOLD response was low or even at baseline levels. Here we ask how it is possible that reliable stimulus information can be decoded from early visual cortex even when the overall BOLD signal remains low. We reanalyzed a data set in which human participants (N = 24) imagined or kept in working memory an oriented visual grating. We divided voxels from V1, V2, and V3 into groups based on orientation preference, and compared the time course of mean BOLD responses to preferred and non-preferred orientations with the time course of the multivariate decoding performance. Decoding accuracy related to a numerically small, but reliable univariate difference in the mean BOLD response to preferred and non-preferred stimuli. The time course of the difference in BOLD responses to preferred and non-preferred orientations was highly similar to the time course of the multivariate pattern classification accuracy. The reliability of the classification strongly correlated with the magnitude of differences in BOLD signal between preferred and non-preferred stimuli. These activity differences were small compared to the large overall BOLD modulations. This suggests that a substantial part of the task-related BOLD response to visual stimulation might not be stimulus-specific. Rather, stimulus-evoked BOLD signals in early visual cortex during a task context may be an amalgam of small stimulus-specific responses and large task-related but non-stimulus-specific responses. The latter are not evident during the maintenance or internal generation of stimulus representations, but provide an explanation of how reliable stimulus information can be decoded from early visual cortex even though its overall BOLD signal remains low. Highlights • Orientation-selective V1 voxels are more active when maintaining preferred stimuli. • Orientation-selective activity differences in V1 correlate with decoding accuracy. • BOLD is an amalgam of stimulus-specific and non-stimulus-specific processes. [ABSTRACT FROM AUTHOR]

Abstract Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Highlights • Pitch processing is analyzed with a model-based fMRI encoding/decoding approach • The perceived pitch is modeled as a combination of height and salience • Lateral HG and STG responses reflect pitch height and salience • Pitch information improves the decoding of the fMRI responses to natural sounds [ABSTRACT FROM AUTHOR]

Purpose The high chemical shift separation at 9.4 T allows for selective saturation of proton pools in exchange with water protons. For the first time, highly selective and comprehensive chemical exchange saturation transfer (CEST) experiments were performed in the human brain at 9.4 T. This work provides insight into CEST signals in the human brain in comparison with existing animal studies, as well as with CEST effects in vivo at lower field strengths. Methods A novel snapshot-CEST method for human brain scans at 9.4 T was optimized and employed for highly-spectrally-resolved (95 offsets) CEST measurements in healthy subjects and one brain tumor patient. Reproducibility and stability between scans was verified in grey and white matter after B 0 , B 1 , and motion correction of the acquired 3D CEST volumes. Two-step Lorentzian fitting was used to further improve separation of spectrally discernible signals to create known and novel CEST contrast maps at 9.4 T. Results At a saturation power of B 1  = 0.5 μT most selective CEST effects could be obtained in the human brain with high inter-scan reproducibility. While contrast behavior of previously measured signals at lower field, namely amide-, guanidyl- and NOE-CEST effects, could be reproduced, novel signals at 2.7 ppm, and −1.6 ppm could be verified in healthy subjects and in a brain tumor patient for the first time. Conclusion High spectral resolution chemical exchange saturation transfer at 9.4 T allows deeper insights into the Z-spectrum structure of the human brain, and provides many different contrasts showing different correlations in healthy tissue and in tumor-affected areas of the brain, generating hypotheses for future investigations of in-vivo-CEST at UHF. [ABSTRACT FROM AUTHOR]

Highlights • Object representations are found within both the dorsal and ventral visual pathways. • Dorsal object areas represent 3-D shape, distance, and position information. • Dorsal cortex also integrates information about object motion over time and space. • Both 3-D form and motion are needed to guide object actions in dynamic environments. Abstract Although object-related areas were discovered in human parietal cortex a decade ago, surprisingly little is known about the nature and purpose of these representations, and how they differ from those in the ventral processing stream. In this article, we review evidence for the unique contribution of object areas of dorsal cortex to three-dimensional (3-D) shape representation, the localization of objects in space, and in guiding reaching and grasping actions. We also highlight the role of dorsal cortex in form-motion interaction and spatiotemporal integration, possible functional relationships between 3-D shape and motion processing, and how these processes operate together in the service of supporting goal-directed actions with objects. Fundamental differences between the nature of object representations in the dorsal versus ventral processing streams are considered, with an emphasis on how and why dorsal cortex supports veridical (rather than invariant) representations of objects to guide goal-directed hand actions in dynamic visual environments. [ABSTRACT FROM AUTHOR]

Highlights • The causal connectivity among evolutionary different regions of the cortex is reported. • Resting-state data show a strong rightward bias in the regions’ interactions. • There is also a top-down bias with the only exception of the right hippocampus. • The right ventrolateral cortex seems to be a prominent source of causal influences. • Several levels of brain cognitive-affective organization are implied by these results. Abstract By taking into account Bruce Bridgeman's interest in an evolutionary framing of human cognition, we examine effective (cause-and-effect) connectivity among cortical structures related to different parts of the triune phylogenetic stratification: archicortex, paleocortex and neocortex. Using resting-state functional magnetic resonance imaging data from 25 healthy subjects and spectral Dynamic Causal Modeling, we report interactions among 10 symmetrical left and right brain areas. Our results testify to general rightward and top-down biases in excitatory interactions of these structures during resting state, when self-related contemplation prevails over more objectified conceptual thinking. The right hippocampus is the only structure that shows bottom-up excitatory influences extending to the frontopolar cortex. The right ventrolateral cortex also plays a prominent role as it interacts with the majority of nodes within and between evolutionary distinct brain subdivisions. These results suggest the existence of several levels of cognitive-affective organization in the human brain and their profound lateralization. [ABSTRACT FROM AUTHOR]

There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly. [ABSTRACT FROM AUTHOR]

Oral mimicry is thought to represent an essential process for the neurodevelopment of spoken language systems in infants, the evolution of language in hominins, and a process that could possibly aid recovery in stroke patients. Using functional magnetic resonance imaging (fMRI), we previously reported a divergence of auditory cortical pathways mediating perception of specific categories of natural sounds. However, it remained unclear if or how this fundamental sensory organization by the brain might relate to motor output, such as sound mimicry. Here, using fMRI, we revealed a dissociation of activated brain regions preferential for hearing with the intent to imitate and the oral mimicry of animal action sounds versus animal vocalizations as distinct acoustic-semantic categories. This functional dissociation may reflect components of a rudimentary cortical architecture that links systems for processing acoustic-semantic universals of natural sound with motor-related systems mediating oral mimicry at a category level. The observation of different brain regions involved in different aspects of oral mimicry may inform targeted therapies for rehabilitation of functional abilities after stroke. [ABSTRACT FROM AUTHOR]

Fiber ball imaging (FBI) provides a means of calculating the fiber orientation density function (fODF) in white matter from diffusion MRI (dMRI) data obtained over a spherical shell with a b-value of about 4000 s/mm 2 or higher. By supplementing this FBI-derived fODF with dMRI data acquired for two lower b-value shells, it is shown that several microstructural parameters may be estimated, including the axonal water fraction (AWF) and the intrinsic intra-axonal diffusivity. This fiber ball white matter (FBWM) modeling method is demonstrated for dMRI data acquired from healthy volunteers, and the results are compared with those of the white matter tract integrity (WMTI) method. Both the AWF and the intra-axonal diffusivity obtained with FBWM are found to be significantly larger than for WMTI, with the FBWM values for the intra-axonal diffusivity being more consistent with recent results obtained using isotropic diffusion weighting. An important practical advantage of FBWM is that the only nonlinear fitting required is the minimization of a cost function with just a single free parameter, which facilitates the implementation of efficient and robust numerical routines. [ABSTRACT FROM AUTHOR]

RNA expression of lamprey gonadotropin-releasing hormone (lGnRH)-I, -II, and -III was demonstrated in the brains of larval, parasitic phase and adult sea lampreys, Petromyzon marinus , using a highly sensitive triple-label in situ hybridization technique. In female larval lampreys, lGnRH-I and-II were co-expressed in the same neurons throughout the olfactory bulbs, preoptic area (POA), and rhombencephalon (hindbrain); lGnRH-I, -II and -III were triple co-expressed in the hypothalamus and in the paranuclear region of neuronal somas in the rhombencephalon. In female parasitic phase lampreys, lGnRH-I and -II were co-expressed in the POA, thalamus, and preoptico-neurohypophyseal tract (PNT); lGnRH-III was minimally triple co-expressed with lGnRH-I and -II in the hypothalamus. In adult female lampreys, lGnRH-I and -III were co-expressed in the hypothalamus; lGnRH-I was also expressed in the neurohypophysis (NH). In adult male lampreys, lGnRH-I and-III were co-expressed in the primordial hippocampus, POA, thalamus, hypothalamus, NH, and PNT; lGnRH-I was also expressed in the epithalamus. In summary, we provide the first study using in situ hybridization of all three lGnRHs (lGnRH-I, -II, and -III) at three major life stages (larval, parasitic, and adult) of lampreys, which strongly supports previous immunohistological studies and suggests that lGnRH-I and -II are the predominant lGnRHs in larval and parasitic phase lampreys, and that lGnRH-I and -III are the predominant lGnRHs in adult female and male lampreys. Therefore, our results show that lGnRH-I, -II, and -III have different localization and co-expression in the development and sexual maturation of lampreys, which may suggest unique physiological roles at each life stage and sex in the developing and mature lamprey brain. [ABSTRACT FROM AUTHOR]

• Global network metrics are used to measure organizational properties of the brain. • Many metrics are highly correlated with each other and with topology-free metrics. • Network sparsity and variation in edge weights may yield greater independence between metrics. Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean | r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean | r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean | r| = 0.939) and with mean edge weight (mean | r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome. [ABSTRACT FROM AUTHOR]