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• 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]

• 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]

• 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 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]

• 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]

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]

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]

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]

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]

• 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]

• Slow-wave origin and distribution are more posterior and asymmetric in children than in young adults. • Slow-wave amplitude is larger and globality is lower in children than in young adults. • Slow-waves with different synchronization efficiency undergo distinct maturational changes. • The observed changes may reflect the maturation of local and long-range connections. • Slow-wave-related indices may allow to track physiological and pathological development. Sleep slow wave activity, as measured using EEG delta power (<4 Hz), undergoes significant changes throughout development, mirroring changes in brain function and anatomy. Yet, age-dependent variations in the characteristics of individual slow waves have not been thoroughly investigated. Here we aimed at characterizing individual slow wave properties such as origin, synchronization, and cortical propagation at the transition between childhood and adulthood. We analyzed overnight high-density (256 electrodes) EEG recordings of healthy typically developing children (N = 21, 10.3 ± 1.5 years old) and young healthy adults (N = 18, 31.1 ± 4.4 years old). All recordings were preprocessed to reduce artifacts, and NREM slow waves were detected and characterized using validated algorithms. The threshold for statistical significance was set at p = 0.05. The slow waves of children were larger and steeper, but less widespread than those of adults. Moreover, they tended to mainly originate from and spread over more posterior brain areas. Relative to those of adults, the slow waves of children also displayed a tendency to more strongly involve and originate from the right than the left hemisphere. The separate analysis of slow waves characterized by high and low synchronization efficiency showed that these waves undergo partially distinct maturation patterns, consistent with their possible dependence on different generation and synchronization mechanisms. Changes in slow wave origin, synchronization, and propagation at the transition between childhood and adulthood are consistent with known modifications in cortico-cortical and subcortico-cortical brain connectivity. In this light, changes in slow-wave properties may provide a valuable yardstick to assess, track, and interpret physiological and pathological development. [ABSTRACT FROM AUTHOR]

• A method called APART-QSM is proposed to separate the opposing susceptibilities within a single voxel using a more comprehensive signal model. • APART-QSM is evaluated with phantom, ex-vivo and in-vivo experiments. • APART-QSM demonstrates an improved ability to accurately quantify iron and myelin, and also reveals fine brain subregion details. • APART-QSM can handle an arbitrary number of input GRE measurements. The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R 2 * with R 2 ′ in QSM. The magnitude decay kernel, describing the proportionality coefficient between R 2 ′ and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R 2 * relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R 2 ′ relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods. [ABSTRACT FROM AUTHOR]

• New methods to test statistical redundancy of network and gradient structure in fMRI data. • Unbiased and efficient generation of timeseries with network or gradient constraints. • Strong dependence between network and gradient structure in fMRI data. • Networks and gradients are considerably redundant representations of fMRI data. Many studies in human neuroscience seek to understand the structure of brain networks and gradients. Few studies, however, have tested the redundancy between these outwardly distinct features. Here, we developed methods to directly enable such tests. We built on insights from linear algebra to develop methods for unbiased and efficient sampling of timeseries with network or gradient constraints. We used these methods to show considerable redundancy between popular definitions of network and gradient structure in functional MRI data. On the one hand, we found that network constraints largely accounted for the structure of three major gradients. On the other hand, we found that gradient constraints largely accounted for the structure of seven major networks. Our results imply that some networks and gradients may denote discrete and continuous representations of the same aspects of functional MRI data. We suggest that integrated explanations can reduce redundancy by avoiding the attribution of independent existence or function to these features. [ABSTRACT FROM AUTHOR]

• We propose an extension of M-CCA for inter-individual alignment. • Method to transfer data from unseen individual into common M-CCA representation. • Show inter-individual transfer of pre-trained machine learning models in M-CCA space. • Demonstrate superiority of functional M-CCA alignment over geometric approaches. Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-subject alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-subject variability of sensor locations over the brain due to the use of a fixed helmet. Hence, an approach to combine MEG data over individual brains should relax the assumptions that a) brain anatomy and function are tightly linked and b) that the same sensors capture functionally comparable brain activation across individuals. Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activations recorded from 15 participants performing a grasping task. The M-CCA algorithm was applied to transform the data of a set of multiple participants into a common space with maximum correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this common representation. This makes it useful for applications that require transfer of models derived from a group of individuals to new individuals. We demonstrate the usefulness and superiority of the approach with respect to previously used approaches. Finally, we show that our approach requires only a small number of labeled data from the new participant. The proposed method demonstrates that functionally motivated common spaces have potential applications in reducing training time of online brain-computer interfaces, where models can be pre-trained on previous participants/sessions. Moreover, inter-subject alignment via M-CCA has the potential for combining data of different participants and could become helpful in future endeavors on large open datasets. [ABSTRACT FROM AUTHOR]

• A computational framework that jointly embeds both functional and structural connectomes. • A method to predict structural connectome from structural one. • Multimodal brain connectivity analysis and differences between diseased and healthy populations. Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. [ABSTRACT FROM AUTHOR]

• Lesion-deficit inference is so far dominated by frequentist statistical mapping. • We evaluated Bayesian lesion-deficit inference in silico and in real deficits. • Bayesian inference was statistically more liberal than frequentist mapping. • It transparently handles situations with low statistical power. • It complements the lesion mapping method portfolio with unique advantages. Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control , that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function –this has led to the concept of the ‘connectome’. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas’ functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness –the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions. [ABSTRACT FROM AUTHOR]

Biological sex influences brain anatomy across many species. Sex differences in brain anatomy have classically been attributed to differences in sex chromosome complement (XX versus XY) and/or in levels of gonadal sex steroids released from ovaries and testes. Using the four core genotype (4CG) mouse model in which gonadal sex and sex chromosome complement are decoupled, we previously found that sex hormones and chromosomes influence the volume of distinct brain regions. However, recent studies suggest there may be more complex interactions between hormones and chromosomes, and that circulating steroids can compensate for and/or mask underlying chromosomal effects. Moreover, the impact of pre vs post-pubertal sex hormone exposure on this sex hormone/sex chromosome interplay is not well understood. Thus, we used whole brain high-resolution ex-vivo MRI of intact and pre-pubertally gonadectomized 4CG mice to investigate two questions: 1) Do circulating steroids mask sex differences in brain anatomy driven by sex chromosome complement? And 2) What is the contribution of pre- versus post-pubertal hormones to sex-hormone-dependent differences in brain anatomy? We found evidence of both cooperative and compensatory interactions between sex chromosomes and sex hormones in several brain regions, but the interaction effects were of low magnitude. Additionally, most brain regions affected by sex hormones were sensitive to both pre- and post-pubertal hormones. This data provides further insight into the biological origins of sex differences in brain anatomy. [ABSTRACT FROM AUTHOR]

Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20 % improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise- and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g. , by identifying and rejecting the so-called ”bad” channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor- or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas. [ABSTRACT FROM AUTHOR]

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the organization of the brain functional network and found several consistent communities consisting of functionally connected but spatially separated brain regions across subjects. However, increasing evidence in many disciplines has shown that most realistic complex networks have overlapping community structure. Only recently has the overlapping community structure drawn increasing interest in the domain of brain network studies. Another issue is that the inter-subject variability is often not directly reflected in the process of community detection at the group level. In this paper, we propose a novel method called collective sparse symmetric non-negative matrix factorization (cssNMF) to address these issues. The cssNMF approach identifies the group-level overlapping communities across subjects and in the meantime preserves the information of individual variation in brain functional network organization. To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance comparison in the experiment. Experimental results show that the proposed cssNMF method accurately and stably identifies group-level overlapping communities across subjects as well as individual differences in network organization with neurophysiologically meaningful interpretations. This research extends our understanding of the common underlying community structures and individual differences in community strengths in brain functional network organization. [ABSTRACT FROM AUTHOR]

As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter. [ABSTRACT FROM AUTHOR]

Brain development involves spatiotemporally complex microstructural changes. A number of neuropsychiatric disorders are linked to the neural processes of development and aging. Thus, it is important to understanding the typical developmental patterns of various brain structures, which will help to define critical periods of vulnerability for neural maturation, as well as anatomical mechanisms of brain structure-related neuropathology. In this study, we used magnetic resonance imaging to assess development of the orbitofrontal cortex, cingulate cortex, amygdala, and hippocampus in a non-human primate species, the common marmoset ( Callithrix jacchus ). We collected a total of 114 T2-weighted and 91 diffusion-weighted scans from 23 animals from infancy to early adulthood. Quantitative and qualitative evaluation of age-related brain growth patterns showed non-linear structural developmental changes in all measured brain regions, consistent with reported human data. Overall, robust volumetric growth was observed from 1 to 3 months of age (from infancy to the early juvenile period). This rapid brain growth was associated with the largest decrease in mean, axial, and radial diffusivities of diffusion tensor imaging in all brain regions, suggesting an increase in the number and size of cells, dendrites, and spines during this period. After this developmental period, the volume of various brain regions steadily increased until adolescence (7–13 months of age, depending on the region). Further, structural connectivity derived from tractography data in various brain regions continuously changed from infancy to adolescence, suggesting that the increase in brain volume was related to continued axonal myelination during adolescence. Thereafter, the volume of the cortical regions decreased considerably, while there was no change in subcortical regions. Familial factors, rather than sex, contributed the development of the front-limbic brain regions. Overall, this study provides further data on the factors and timing important for normal brain development, and suggest that the common marmoset is a useful animal model for human neural development. [ABSTRACT FROM AUTHOR]

Primary somatosensory cortex (S1) processes somatosensory information and is composed of multiple subregions. In particular, tactile information from the skin is encoded in three subregions, namely Brodmann areas (BAs) 3b, 1 and 2, with each area representing a complete map of the contralateral body. Although, much is known about the somatotopic organization of the hand in human S1, less research has been carried out regarding the somatotopic maps of the foot and leg in S1. Moreover, a latero-medial S1 organization along the superior part of the postcentral gyrus has been reported when moving from hip to toes, yet to date there is no study investigating leg/foot maps within the different subregions of S1. Using ultra-high field MRI (7 T), we mapped six cortical representations of the lower limb (hip to toes) at the single subject level and performed this analysis separately for BAs 3b, 1 and 2. Analyzing the BOLD responses associated with tactile stimulations of the mapped foot and leg regions on each side, we quantified the extent and the strength of activation to determine somatotopic organization. In addition, we investigated whether each mapped representation also responded to the stimulation of other body parts (i.e. response selectivity) and conducted dissimilarity analysis relating these anatomical and functional properties of S1 to the physical structure of the lower limbs. Our data reveal somatotopy for the leg, but not for the foot in all investigated BAs, with large inter-subject variability. We found only minor differences between the properties of the three investigated BAs, suggesting that S1 maps for the lower limbs differ from those described for the hand. We also describe greater extent/strength of S1 activation for the big toe representation (compared to the other mapped representations) within all BAs, suggesting a possible homology between the first digit of upper and lower extremity in humans, and report different patterns of selectivity in the foot representations (i.e. lower selectivity) compared to the other leg representations (i.e. greater selectivity). These data provide a detailed description of human S1 subregions for the foot and leg, highlight the importance of high-resolution mapping studies and of single subject analysis, and indicate potential differences between the lower and the upper limb. [ABSTRACT FROM AUTHOR]

A defining feature of the basal ganglia is their anatomical organization into multiple cortico-striatal loops. A central tenet of this architecture is the idea that local striatal function is determined by its precise connectivity with cortex, creating a functional topography that is mirrored within cortex and striatum. Here we formally test this idea using both human anatomical and functional imaging, specifically asking whether within striatal subregions one can predict between-voxel differences in functional signals based on between-voxel differences in corticostriatal connectivity. We show that corticostriatal connectivity profiles predict local variation in reward signals in bilateral caudate nucleus and putamen, expected value signals in bilateral caudate nucleus, and response effector activity in bilateral putamen. These data reveal that, even within individual striatal regions, local variability in corticostriatal anatomical connectivity predicts functional differentiation. [ABSTRACT FROM AUTHOR]

• Starting in adolescence, a litter-effect is observed in mice brain and behaviors. • Variables patterns complexity explaining litter-effects increase within lifespan. • More litters of smaller size improve statistical power for litter modulated models. • The litter-effect and sex-effect are closely intertwined. • Litter modulated brain regions overlap with regions associated to social behaviors. Our current understanding of litter variability in neurodevelopmental studies using mice may limit translation of neuroscientific findings. Higher variance of measures across litters than within, often termed intra-litter likeness, may be attributable to both pre- and postnatal environment. This study aimed to assess the litter-effect within behavioral assessments (2 timepoints) and anatomy using T1-weighted magnetic resonance images across 72 brain region volumes (4 timepoints) (36 C57bl/6J inbred mice; 7 litters: 19F/17M). Between-litter comparisons of brain and behavioral measures and their associations were evaluated using univariate and multivariate techniques. A power analysis using simulation methods was then performed on modeled neurodevelopment and to evaluate trade-offs between number-of-litters, number-of-mice-per-litter, and sample size. Our results show litter-specific developmental effects, from the adolescent period to adulthood for brain structure volumes and behaviors, and for their associations in adulthood. Our power simulation analysis suggests increasing the number-of-litters in experimental designs to achieve the smallest total sample size necessary for detecting different rates of change in specific brain regions. Our results demonstrate how litter-specific effects may influence development and that increasing the litters to the total sample size ratio should be strongly considered when designing neurodevelopmental studies. [ABSTRACT FROM AUTHOR]

• Full FEM pipeline for electrophysiology (e-phys) forward modeling is proposed and integrated into brainstorm. • Realistic FEM mesh head models can be generated from the MR data (T1w /and T2w). • Realistic conductivity tensors can be generated from DWI and mapped to the FEM mesh. • E-phys FEM forward modeling can be performed either from an easy-to-use GUI or scripting using the DUNEuro solver. • Full anatomical data (MRI/DWI) and functional data (EEG/MEG) are distributed for easy analysis replication. Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community. [Display omitted] [ABSTRACT FROM AUTHOR]