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

• 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]

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]

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]

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]

• 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]

For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures. [ABSTRACT FROM AUTHOR]

Normative data for volumetric estimates of brain structures are necessary to adequately assess brain volume alterations in individuals with suspected neurological or psychiatric conditions. Although many studies have described age and sex effects in healthy individuals for brain morphometry assessed via magnetic resonance imaging, proper normative values allowing to quantify potential brain abnormalities are needed. We developed norms for volumetric estimates of subcortical brain regions based on cross-sectional magnetic resonance scans from 2790 healthy individuals aged 18 to 94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer , a widely used and freely available automated segmentation software. Models predicting subcortical regional volumes of each hemisphere were produced including age, sex, estimated total intracranial volume (eTIV), scanner manufacturer, magnetic field strength, and interactions as predictors. The mean explained variance by the models was 48%. For most regions, age, sex and eTIV predicted most of the explained variance while manufacturer, magnetic field strength and interactions predicted a limited amount. Estimates of the expected volumes of an individual based on its characteristics and the scanner characteristics can be obtained using derived formulas. For a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume can be obtained. Normative values were validated in independent samples of healthy adults and in adults with Alzheimer's disease and schizophrenia. [ABSTRACT FROM AUTHOR]

• A vast literature of subcortical studies is analysed from multiple axes: imaging protocols; regions of interest; methodology. • High-resolution atlases together with machine learning present a great opportunity for subcortical exploration with application to many clinical and research areas. • Detailed atlases target multiple typically ignored – small nuclei; machine learning is able to model the complex relationship between appearance and anatomy with fast inference. • Computational requirements, robustness and validation are associated challenges that we need to address for a widespread use of machine-learning in subcortical segmentation. This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning. [ABSTRACT FROM AUTHOR]

• We build a benchmark for the automatic tissue segmentation of distorted brains. • The dataset includes healthy subjects and patients with brain distortions. • We curated the annotation of the clinical dataset segmenting 6 brain tissues. • We computed the tissue segmentation using a 3D U-Net as ML baseline method. • We published the dataset and the code on the open science platform brainlife.io. T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis. [ABSTRACT FROM AUTHOR]

Reductions in gray matter volume of the medial prefrontal cortex (mPFC), especially the rostral and subgenual anterior cingulate cortex (rACC, sgACC) are a widely reported finding in major depressive disorder (MDD). Inflammatory mediators, which are elevated in a subgroup of patients with MDD, activate the kynurenine metabolic pathway and increase production of neuroactive metabolites such as kynurenic acid (KynA), 3-hydroxykynurenine (3HK) and quinolinic acid (QA) which influence neuroplasticity. It is not known whether the alterations in brain structure and function observed in major depressive disorders are due to the direct effect of inflammatory mediators or the effects of neurotoxic kynurenine metabolites. Here, using partial posterior predictive distribution mediation analysis, we tested whether the serum concentrations of kynurenine pathway metabolites mediated reductions in cortical thickness in mPFC regions in MDD. Further, we tested whether any association between C-reactive protein (CRP) and cortical thickness would be mediated by kynurenine pathway metabolites. Seventy-three unmedicated subjects who met DSM-IV-TR criteria for MDD and 91 healthy controls (HC) completed MRI scanning using a pulse sequence optimized for tissue contrast resolution. Automated cortical parcellation was performed using the PALS-B12 Brodmann area atlas as implemented in FreeSurfer in order to compare the cortical thickness and cortical area of six PFC regions: Brodmann areas (BA) 9, 10, 11, 24, 25, and 32. Serum concentrations of kynurenine pathway metabolites were determined by high performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS) detection, while high-sensitivity CRP concentration was measured immunoturbidimetrically. Compared with HCs, the MDD group showed a reduction in cortical thickness of the right BA24 ( p < 0.01) and BA32 ( p < 0.05) regions and MDD patients with a greater number of depressive episodes displayed thinner cortex in BA32 ( p < 0.05). Consistent with our previous findings in an overlapping sample, the KynA/3HK ratio and the log KynA/QA were reduced in the MDD group relative to the HC group ( p ’s < 0.05) and symptoms of anhedonia were negatively correlated with log KynA/QA in the MDD group ( p < 0.05). Both KynA/3HK and log KynA/QA at least partially mediated the relationship between diagnosis and cortical thickness of right BA32 ( p ’s < 0.05). CRP was inversely associated with BA32 thickness ( p < 0.01) and KynA/3HK partially mediated the relationship between CRP and the thickness of right BA32 ( p < 0.05). The results raise the possibility that the relative imbalance between KynA and neurotoxic kynurenine metabolites may partially explain the reductions in mPFC thickness observed in MDD, and further that these changes are more strongly linked to the putative effects of neuroactive kynurenine metabolites than those of inflammatory mediators. [ABSTRACT FROM AUTHOR]

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations. [ABSTRACT FROM AUTHOR]

Abnormalities in large-scale brain networks have been recently reported in dyslexia; however, it remains unclear whether these abnormalities are congenital (due to dyslexia per se) or arise later in development. Here, structural magnetic resonance imaging data of 17 Chinese reading disabled (RD) and 17 age-matched typically developing (TD) children were used to construct cortical thickness (sensitive to postnatal development) and surface area (sensitive to prenatal development) networks. In the thickness network, compared to TD, RD showed reduced nodal network properties (e.g., degree and betweenness) in the left hemisphere along with enhanced nodal properties mainly in the right hemisphere. As for the surface area network, compared to TD, RD demonstrated lower nodal properties in the posterior brain regions and higher nodal properties in the anterior brain regions. Furthermore, hubs in both the thickness and surface area networks in RD were more distributed in frontal areas and less distributed in parietal areas, whereas TD showed the opposite pattern. Altogether, these findings indicate that the aberrant structural connectivity in the dyslexic individuals was not only due to a late developmental effect reflected in the altered thickness network, but may also be a congenital effect during prenatal development, reflected in the altered surface network. [ABSTRACT FROM AUTHOR]

Previous studies distinguish between right hemisphere-dominant processing of prosodic/tonal information and left-hemispheric modulation of grammatical information as well as lexical tones. Swedish word accents offer a prime testing ground to better understand this division. Although similar to lexical tones, word accents are determined by words' morphosyntactic structure, which enables listeners to use the tone at the beginning of a word to predict its grammatical ending. We recorded electrophysiological and hemodynamic brain responses to words where stem tones matched or mismatched inflectional suffixes. Tones produced brain potential effects after 136 ms, correlating with subject variability in average BOLD in left primary auditory cortex, superior temporal gyrus, and inferior frontal gyrus. Invalidly cued suffixes activated the left inferior parietal lobe, arguably reflecting increased processing cost of their meaning. Thus, interaction of word accent tones with grammatical morphology yielded a rapid neural response correlating in subject variability with activations in predominantly left-hemispheric brain areas. [ABSTRACT FROM AUTHOR]

Brain structure can predict many aspects of human behavior, though the extent of this relationship in healthy adults, particularly for language-related skills, remains largely unknown. The objective of the present study was to explore this relation using magnetic resonance imaging (MRI) on a group of 21 healthy young adults who completed two language tasks: (1) semantic fluency and (2) sentence generation. For each region of interest, cortical thickness, surface area, and volume were calculated. The results show that verbal fluency scores correlated mainly with measures of brain morphology in the left inferior frontal cortex and bilateral insula. Sentence generation scores correlated with structure of the left inferior parietal and right inferior frontal regions. These results reveal that the anatomy of several structures in frontal and parietal lobes is associated with spoken language performance. The presence of both negative and positive correlations highlights the complex relation between brain and language. [ABSTRACT FROM AUTHOR]

Previous research has associated the inferior frontal and posterior temporal brain regions with a number of phonological processes. In order to identify how these specific brain regions contribute to phonological processing, we manipulated subsyllabic phonological complexity and stimulus modality during speech perception using fMRI. Subjects passively attended to visual or auditory pseudowords. Similar to previous studies, a bilateral network of cortical regions was recruited during the presentation of visual and auditory stimuli. Moreover, pseudowords recruited a similar network of regions as words and letters. Few regions in the whole-brain results revealed neural processing differences associated with phonological complexity independent of modality of presentation. In an ROI analysis, the only region sensitive to phonological complexity was the posterior part of the inferior frontal gyrus (IFGpo), with the complexity effect only present for print. In sum, the sensitivity of phonological brain areas depends on the modality of stimulus presentation and task demands. [ABSTRACT FROM AUTHOR]

• Developed a model-based reproducibility index for large-scale high-throughput MRI-based studies. • Provided an analytical tool to evaluate the sample size necessary for achieving a desirable model-based reproducibility. • Model-based reproducibility >0.99 was observed for a few large sample size analyses. • Both sample size and study-specific experimental factors play important roles in model-based reproducibility assessment. Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies. [ABSTRACT FROM AUTHOR]

While excessive noise exposure in childhood has been associated with reduced language ability, few studies have examined potential underlying neurobiological mechanisms that may account for noise-related differences in language skills. In this study, we tested the hypotheses that higher everyday noise exposure would be associated with 1) poorer language skills and 2) differences in language-related cortical structure. A socioeconomically diverse sample of children aged 5-9 (N = 94) completed standardized language assessments. High-resolution T1-weighted magnetic resonance imaging (MRI) scans were acquired, and surface area and cortical thickness of the left inferior frontal gyrus (IFG) and left superior temporal gyrus (STG) were extracted. Language Environmental Analysis (LENA) was used to measure levels of exposure to excessive environmental noise over the course of a typical day (n = 43 with complete LENA, MRI, and behavioral data). Results indicated that children exposed to excessive levels of noise exhibited reduced cortical thickness in the left IFG. These findings add to a growing literature that explores the extent to which home environmental factors, such as environmental noise, are associated with neurobiological development related to language development in children. [ABSTRACT FROM AUTHOR]

The three-dimensional dynamics and morphology of the human embryonic brain have not been previously analyzed using modern imaging techniques. The morphogenesis of the cerebral vesicles and ventricles was analyzed using images derived from human embryo specimens from the Kyoto Collection, which were acquired with a magnetic resonance microscope equipped with a 2.35-T superconducting magnet. A total of 101 embryos between Carnegie stages (CS) 13 and 23, without apparent morphological damage or torsion in the brain ventricles and axes, were studied. To estimate the uneven development of the cerebral vesicles, the volumes of the whole embryo and brain, prosencephalon, mesencephalon, and rhombencephalon with their respective ventricles were measured using image analyzing Amira™ software. The brain volume, excluding the ventricles (brain tissue), was 1.15 ± 0.43 mm 3 (mean ± SD) at CS13 and increased exponentially to 189.10 ± 36.91 mm 3 at CS23, a 164.4-fold increase, which is consistent with the observed morphological changes. The mean volume of the prosencephalon was 0.26 ± 0.15 mm 3 at CS13. The volume increased exponentially until CS23, when it reached 110.99 ± 27.58 mm 3 . The mean volumes of the mesencephalon and rhombencephalon were 0.20 ± 0.07 mm 3 and 0.69 ± 0.23 mm 3 at CS13, respectively; the volumes reached 21.86 ± 3.30 mm 3 and 56.45 ± 7.64 mm 3 at CS23, respectively. The ratio of the cerebellum to the rhombencephalon was approximately 7.2% at CS20, and increased to 12.8% at CS23. The ratio of the volume of the cerebral vesicles to that of the whole embryo remained nearly constant between CS15 and CS23 (11.6–15.5%). The non-uniform thickness of the brain tissue during development, which may indicate the differentiation of the brain, was visualized with surface color mapping by thickness. At CS23, the basal regions of the prosencephalon and rhombencephalon were thicker than the corresponding dorsal regions. The brain was further studied by the serial digital subtraction of layers of tissue from both the external and internal surfaces to visualize the core region (COR) of the thickening brain tissue. The COR, associated with the development of nuclei, became apparent after CS16; this was particularly visible in the prosencephalon. The anatomical positions of the COR were mostly consistent with the formation of the basal ganglia, thalamus, and pyramidal tract. This was confirmed through comparisons with serial histological sections of the human embryonic brain. The approach used in this study may be suitable as a convenient alternative method for estimating the development and differentiation of the neural ganglia and tracts. These findings contribute to a better understanding of brain and cerebral ventricle development. [ABSTRACT FROM AUTHOR]

We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test–retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr = 0.38, p -value < 10 − 6 ), precuneus (corr = − 0.44, p < 10 − 5 ), and combined default mode network regions (corr = − 0.45, p < 10 − 8 ) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development. [ABSTRACT FROM AUTHOR]

Among male patients affected by Kallmann syndrome, a genetically determined disease due to defective neural migration leading to hypogonadropic hypogonadism and hypo/anosmia, about 40% present the peculiar phenomenon of mirror movements, i.e. involuntary movements mirroring contralateral voluntary hand movements. Several pathogenic hypotheses have been proposed, but the ultimate neurological mechanisms are still elusive. The aim of the present study was to investigate brain anatomical substrates of mirror movements in Kallmann syndrome by means of a panel of quantitative MRI analyses. Forty-nine male Kallmann syndrome patients underwent brain MRI. The study protocol included 3D-T1-weighted gradient echo, fluid attenuated inversion recovery and diffusion tensor imaging. Voxel-based morphometry, sulcation, curvature and cortical thickness analyses and tract based spatial statistics were performed using SPM8, Freesurfer and FSL. All patients underwent a complete physical and neurological examination including the evaluation of mirror movements (according to the Woods and Teuber criteria). Kallmann syndrome patients presenting with mirror movements (16/49, 32%) displayed the following brain changes: 1) increased gray matter density in the depth of the left precentral sulcus behind the middle frontal gyrus; 2) decreased cortical thickness in the precentral gyrus bilaterally, in the depth of right precentral sulcus and in the posterior portion of the right superior frontal gyrus; and 3) decreased fractional anisotropy in the left hemisphere involving the temporal lobe and peritrigonal white matter. No differences were shown by cortical curvature and sulcation analyses. The composite array of brain changes observed in Kallmann syndrome patients with mirror movements likely represents the anatomical–structural underpinnings leading to the peculiar derangement of the complex circuitry committed to unilateral hand voluntary movements. [ABSTRACT FROM AUTHOR]

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. [ABSTRACT FROM AUTHOR]

• The relation of brain function with the underlying structural wiring is complex. • We propose new structure-informed graph signal processing (GSP) filtering of functional data. • GSP-derived features allow accurate task decoding and individual fingerprinting. • Functional connectivity from filtered data is more unique to subject and cognition. • The role of structurally aligned and liberal graph frequencies is elucidated. Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum. [ABSTRACT FROM AUTHOR]

• Ultra-fast acquisition for 3D multi-parametric quantitative MRI. • Simultaneous T 1 T 2 T 2 * PD and B 1 + mapping. • 3-minute scan at 1-mm isotropic resolution with whole-brain coverage. • Multi-parametric mapping at 700-µm isotropic resolution in 10 minutes. • Repeatable quantification and cortical-depth analysis. Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T 1 , T 2 , T 2 *, PD and B 1 + mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures. [ABSTRACT FROM AUTHOR]

Growing epidemiological evidence suggests that air pollution may increase the risk of cognitive decline and neurodegenerative disease. A hallmark of neurodegeneration and an important diagnostic biomarker is volume reduction of a key brain structure, the hippocampus. We aimed to investigate the possibility that outdoor air nitrogen dioxide (NO 2) and particulate matter with diameter ≤2.5 μm (PM 2.5) and ≤10 μm (PM 10) adversely affect hippocampal volume, through a meta-analysis. We considered studies that assessed the relation between outdoor air pollution and hippocampal volume by structural magnetic resonance imaging in adults and children, searching in Pubmed and Scopus databases from inception through July 13, 2021. For inclusion, studies had to report the correlation coefficient along with its standard error or 95% confidence interval (CI) between air pollutant exposure and hippocampal volume, to use standard space for neuroimages, and to consider at least age, sex and intracranial volume as covariates or effect modifiers. We meta-analyzed the data with a random-effects model, considering separately adult and child populations. We retrieved four eligible studies in adults and two in children. In adults, the pooled summary β regression coefficients of the association of PM 2.5 , PM 10 and NO 2 with hippocampal volume showed respectively a stronger association (summary β −7.59, 95% CI −14.08 to −1.11), a weaker association (summary β −2.02, 95% CI −4.50 to 0.47), and no association (summary β −0.44, 95% CI −1.27 to 0.40). The two studies available for children, both carried out in preadolescents, did not show an association between PM 2.5 and hippocampal volume. The inverse association between PM 2.5 and hippocampal volume in adults appeared to be stronger at higher mean PM 2.5 levels. Our results suggest that outdoor PM 2.5 and less strongly PM 10 could adversely affect hippocampal volume in adults, a phenomenon that may explain why air pollution has been related to memory loss, cognitive decline, and dementia. • Hippocampus is a key brain structure for cognitive functions. • Neurodegenerative diseases are characterized by hippocampal volume reduction. • Hippocampal volume from neuroimaging is an important diagnostic biomarker. • Particulate matter may adversely affect hippocampal volume. • The risk of neurodegenerative diseases may be increased by particulate matter. [ABSTRACT FROM AUTHOR]

In the present study, we describe a fingerprinting approach to analyze the time evolution of the MR signal and retrieve quantitative information about the microvascular network. We used a Gradient Echo Sampling of the Free Induction Decay and Spin Echo (GESFIDE) sequence and defined a fingerprint as the ratio of signals acquired pre- and post-injection of an iron-based contrast agent. We then simulated the same experiment with an advanced numerical tool that takes a virtual voxel containing blood vessels as input, then computes microscopic magnetic fields and water diffusion effects, and eventually derives the expected MR signal evolution. The parameter inputs of the simulations (cerebral blood volume [CBV], mean vessel radius [R], and blood oxygen saturation [SO2]) were varied to obtain a dictionary of all possible signal evolutions. The best fit between the observed fingerprint and the dictionary was then determined by using least square minimization. This approach was evaluated in 5 normal subjects and the results were compared to those obtained by using more conventional MR methods, steady-state contrast imaging for CBV and R and a global measure of oxygenation obtained from the superior sagittal sinus for SO2. The fingerprinting method enabled the creation of high-resolution parametric maps of the microvascular network showing expected contrast and fine details. Numerical values in gray matter (CBV=3.1±0.7%, R=12.6±2.4μm, SO2 =59.5±4.7%) are consistent with literature reports and correlated with conventional MR approaches. SO2 values in white matter (53.0±4.0%) were slightly lower than expected. Numerous improvements can easily be made and the method should be useful to study brain pathologies. [ABSTRACT FROM AUTHOR]

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. [ABSTRACT FROM AUTHOR]

Abstract: Fundamental to increasing our understanding of the role of white matter microstructure in normal/abnormal function in the living human is the development of MR-based metrics that provide increased specificity to distinct attributes of the white matter (e.g., local fibre architecture, axon morphology, and myelin content). In recent years, different approaches have been developed to enhance this specificity, and the Tractometry framework was introduced to combine the resulting multi-parametric data for a comprehensive assessment of white matter properties. The present work exploits that framework to characterise the statistical properties, specifically the variance and covariance, of these advanced microstructural indices across the major white matter pathways, with the aim of giving clear indications on the preferred metric(s) given the specific research question. A cohort of healthy subjects was scanned with a protocol that combined multi-component relaxometry with conventional and advanced diffusion MRI acquisitions to build the first comprehensive MRI atlas of white matter microstructure. The mean and standard deviation of the different metrics were analysed in order to understand how they vary across different brain regions/individuals and the correlation between them. Characterising the fibre architectural complexity (in terms of number of fibre populations in a voxel) provides clear insights into correlation/lack of correlation between the different metrics and explains why DT-MRI is a good model for white matter only some of the time. The study also identifies the metrics that account for the largest inter-subject variability and reports the minimal sample size required to detect differences in means, showing that, on the other hand, conventional DT-MRI indices might still be the safest choice in many contexts. [Copyright &y& Elsevier]

Abstract: State of the art magnetic resonance imaging (MRI) scanners are generally equipped with multi-element receive coils; 16 or 32 channel coils are common. Their development has been predominant for parallel imaging to enable faster scanning. Less consideration has been given to localized magnetic resonance spectroscopy (MRS). Multinuclear studies, for example 31P or 13C MRS, are often conducted with a single element coil located over the region of interest. 1H MRS studies have generally employed the same multi-element coils used for MRI, but little consideration has been given as to how the spectroscopic data from the different channels are combined. In many cases it is simply co-added with detrimental effect on the signal to noise ratio. In this study, we derive the optimum method for combining multi-coil data, namely weighting with the ratio of signal to the square of the noise. We show that provided that the noise is uncorrelated, this is the theoretical optimal combination. The method is demonstrated for in vivo proton MRS data acquired using a 32 channel receive coil at 7T in four different brain areas; left motor and right motor, occipital cortex and medial frontal cortex. [Copyright &y& Elsevier]

Abstract: Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally. To study the impact of these two steps, we apply the framework completely (“LL method”: Longitudinal registration and Longitudinal classification) and partially (“LC method”: Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework (“CC method”: Cross-sectional registration and Cross-sectional classification). The three methods are applied to (1) a scan–rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model. The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan–rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact. Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development. [Copyright &y& Elsevier]

Abstract: While significant differences in male and female brain structures have commonly been reported, only a few studies have focused on the sex differences in the way the cortex matures over time. Here, we investigated cortical thickness maturation between the age of 6 to 30years, using 209 longitudinally-acquired brain MRI scans. Significant sex differences in the trajectories of cortical thickness change with age were evidenced using non-linear mixed effects models. Similar statistical analyses were computed to quantify the differences between cortical gyrification changes with age in males and females. During adolescence, we observed a statistically significant higher rate of cortical thinning in females compared to males in the right temporal regions, the left temporoparietal junction and the left orbitofrontal cortex. This finding is interpreted as a faster maturation of the social brain areas in females. Concomitantly, statistically significant sex differences in cortical folding changes with age were observed only in one cluster of the right prefrontal regions, suggesting that the mechanisms underlying cortical thickness and gyrification changes with age are quite distinct. Sexual dimorphism in the developmental course of the cortical maturation may be associated with the different age of onset and clinical presentation of many psychiatric disorders between males and females. [Copyright &y& Elsevier]

Abstract: The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2mm isotropic resolution in 0.7s for fMRI, and 1.25mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. [Copyright &y& Elsevier]

Abstract: The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. [Copyright &y& Elsevier]

Abstract: Numerous studies have investigated inter-areal cortical networks using either diffusion MRI or axonal tract-tracing. While both techniques have been used in non-human primates only diffusion MRI can be used in human. The advantage of axonal tract-tracing is that unlike diffusion MRI it has a high single-cell resolution, and most importantly gives the laminar origins and terminations of inter-areal pathways. It, therefore, can be used to obtain the weighted and directed cortical graph. Axonal tract tracing has traditionally been collated from multiple experiments in order to determine the large-scale inter-areal network. Collated data of this kind present numerous problems due to lack of coherence across studies and incomplete exploitation. We have therefore developed a consistent data base which uses standardized experimental and parcellation procedures across brains. Here we review our recent publications analyzing the consistent database obtained from retrograde tracer injections in 29 cortical areas in a parcellation of 91 areas of the macaque cortex. Compared to collated data, our results show that the cortical graph is dense. Density is a graph theoretic measure, and refers to the number of observed connections in a square matrix expressed as a percentage of the possible connections. In our database 66% of the connections that can exist do exist which is considerably higher than the graph densities reported in studies using collated data (7–32%). The consistent data base reports 37% more pathways than previously reported, many of which are unidirectional. This latter and unexpected property has not been reported in earlier studies. Given the high density, the resulting cortical graph shows other unexpected properties. Firstly, the binary specificity is considerably higher than expected. As we show, this property is a consequence of the inter-areal connection probability declining with distance. Secondly, small groups of areas are found to receive high numbers of inputs. This is termed a high domination and is analyzed by a graph theoretic procedure known as a minimum dominating set analysis. We discuss these findings with respect to the long-distance connections, over half of which were previously not reported. These so called new found projections display high specificities and play an important integration role across large regions. It is to be expected that the future examination of the 62 remaining areas will disclose further levels of complexity and enable construction of a weighted directed graph revealing the hierarchical complexity of the cortex. [Copyright &y& Elsevier]

Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function. [ABSTRACT FROM AUTHOR]

Ketogenic diet (KD) is a high-fat and low-carbohydrate therapy for medically intractable epilepsy, and its applications in other neurological conditions, including those occurring in children, have been increasingly tested. However, how KD affects childhood neurodevelopment, a highly sensitive and plastic process, is not clear. In this study, we explored structural, metabolic, and functional consequences of a brief treatment of a strict KD (weight ratio of fat to carbohydrate plus protein is approximately 6.3:1) in naive juvenile mice of different inbred strains, using a multidisciplinary approach. Systemic measurements using magnetic resonance imaging revealed that unexpectedly, the volumes of most brain structures in KD-fed mice were about 90% of those in mice of the same strain but fed a standard diet. The reductions in volumes were nonselective, including different regions throughout the brain, the ventricles, and the white matter. The relative volumes of different brain structures were unaltered. Additionally, as KD is a metabolism-based treatment, we performed untargeted metabolomic profiling to explore potential means by which KD affected brain growth and to identify metabolic changes in the brain. We found that brain metabolomic profile was significantly impacted by KD, through both distinct and common pathways in different mouse strains. To explore whether the volumetric and metabolic changes induced by this KD treatment were associated with functional consequences, we recorded spontaneous EEG to measure brain network activity. Results demonstrated limited alterations in EEG patterns in KD-fed animals. In addition, we observed that cortical levels of brain-derived neurotrophic factor (BDNF), a critical molecule in neurodevelopment, did not change in KD-fed animals. Together, these findings indicate that a strict KD could affect volumetric development and metabolic profile of the brain in inbred juvenile mice, while global network activities and BDNF signaling in the brain were mostly preserved. Whether the volumetric and metabolic changes are related to any core functional consequences during neurodevelopment and whether they are also observed in humans need to be further investigated. In addition, our results indicate that certain outcomes of KD are specific to the individual mouse strains tested, suggesting that the physiological profiles of individuals may need to be examined to maximize the clinical benefit of KD. [ABSTRACT FROM AUTHOR]

• A method named DECOMPOSE-QSM is developed to decompose sub-voxel paramagnetic and diamagnetic susceptibilities only use multi-echo GRE data. • The method is validated with numerical simulations, phantom, ex vivo and in vivo experiments. • The resulting susceptibility composition maps reveal more detailed subregion structures than conventional QSM. • The proposed method may be applied to various susceptibility-based studies. A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model. Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model. Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM. [ABSTRACT FROM AUTHOR]

Abstract: Diffusion tensor imaging (DTI) provides information about the microstructure in the brain and spinal cord. While new neuroimaging techniques have significantly advanced the accuracy and sensitivity of DTI of the brain, the quality of spinal cord DTI data has improved less. This is in part due to the small size of the spinal cord (ca. 1cm diameter) and more severe instrumental (e.g. eddy current) and physiological (e.g. cardiac pulsation) artefacts present in spinal cord DTI. So far, the improvements in image quality and resolution have resulted from cardiac gating and new acquisition approaches (e.g. reduced field-of-view techniques). The use of retrospective correction methods is not well established for spinal cord DTI. The aim of this paper is to develop an improved post-processing pipeline tailored for DTI data of the spinal cord with increased quality. For this purpose, we compared two eddy current and motion correction approaches using three-dimensional affine (3D-affine) and slice-wise registrations. We also introduced a new robust-tensor-fitting method that controls for whole-volume outliers. Although in general 3D-affine registration improves data quality, occasionally it can lead to misregistrations and biassed tensor estimates. The proposed robust tensor fitting reduced misregistration-related bias and yielded more reliable tensor estimates. Overall, the combination of slice-wise motion correction, eddy current correction, and robust tensor fitting yielded the best results. It increased the contrast-to-noise ratio (CNR) in FA maps by about 30% and reduced intra-subject variation in fractional anisotropy (FA) maps by 18%. The higher quality of FA maps allows for a better distinction between grey and white matter without increasing scan time and is compatible with any multi-directional DTI acquisition scheme. [Copyright &y& Elsevier]

Abstract: Diffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction. [Copyright &y& Elsevier]

Abstract: This paper presents the growth pattern and sexual dimorphism of the thalamus and basal ganglia in a large-scale Asian neonatal cohort using both T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Our study observed a robust growth of the thalamus and basal ganglia (caudate, putamen, globus pallidus, and anterior limb of internal capsule) beyond the overall brain growth in the early postnatal period (36–43weeks of the gestational age). Additionally, the microstructure of the two structures was integrated as reflected by an increase in fractional anisotropy (FA) and a decrease in axial and radial water diffusivities in the first few weeks of life. Sexual dimorphism was only observed in the whole brain growth and the left thalamic volume but not in the other volumes or DTI measures of the basal ganglia and thalamus at birth. Even though the pattern of sexual dimorphism in the total brain volume is present at birth and persists throughout postnatal brain development, sexual dimorphisms of the basal ganglia and thalamus differ from those found in later stages of brain development, indicating that regionally distinct patterns of postnatal brain development between males and females arise after birth. [Copyright &y& Elsevier]

Abstract: Recently there has been renewed interest in the habenula; a pair of small, highly evolutionarily conserved epithalamic nuclei adjacent to the medial dorsal (MD) nucleus of the thalamus. The habenula has been implicated in a range of behaviours including sleep, stress and pain, and studies in non-human primates have suggested a potentially important role in reinforcement processing, putatively via its effects on monoaminergic neurotransmission. Over the last decade, an increasing number of neuroimaging studies have reported functional responses in the human habenula using functional magnetic resonance imaging (fMRI). However, standard fMRI analysis approaches face several challenges in isolating signal from this structure because of its relatively small size, around 30mm3 in volume. In this paper we offer a set of guidelines for locating and manually tracing the habenula in humans using high-resolution T1-weighted structural images. We also offer recommendations for appropriate pre-processing and analysis of high-resolution functional magnetic resonance imaging (fMRI) data such that signal from the habenula can be accurately resolved from that in surrounding structures. [Copyright &y& Elsevier]

Abstract: Previous anatomical volumetric studies have shown that healthy aging is associated with gray matter tissue loss in specific cerebral regions. However, these studies may have potentially missed critical elements of age-related brain changes, which largely exist within interrelationships among brain regions. This magnetic resonance imaging research aims to assess the effects of aging on the organization of gray matter structural covariance networks. Here, we used voxel-based morphometry on high-definition brain scans to compare the patterns of gray matter structural covariance networks that sustain different sensorimotor and high-order cognitive functions among young (n =88, mean age=23.5±3.1years, female/male=55/33) and older (n =88, mean age=67.3±5.9years, female/male=55/33) participants. This approach relies on the assumption that functionally correlated brain regions show correlations in gray matter volume as a result of mutually trophic influences or common experience-related plasticity. We found reduced structural association in older adults compared with younger adults, specifically in high-order cognitive networks. Major differences were observed in the structural covariance networks that subserve the following: a) the language-related semantic network, b) the executive control network, and c) the default-mode network. Moreover, these cognitive functions are typically altered in the older population. Our results indicate that healthy aging alters the structural organization of cognitive networks, shifting from a more distributed (in young adulthood) to a more localized topological organization in older individuals. [Copyright &y& Elsevier]

Abstract: Diffusion MRI streamlines tractography suffers from a number of inherent limitations, one of which is the accurate determination of when streamlines should be terminated. Use of an accurate streamlines propagation mask from segmentation of an anatomical image confines the streamlines to the volume of the brain white matter, but does not take full advantage of all of the information available from such an image. We present a modular addition to streamlines tractography, which makes more effective use of the information available from anatomical image segmentation, and the known properties of the neuronal axons being reconstructed, to apply biologically realistic priors to the streamlines generated; we refer to this as “Anatomically-Constrained Tractography”. Results indicate that some of the known false positives associated with tractography algorithms are prevented, such that the biological accuracy of the reconstructions should be improved, provided that state-of-the-art streamlines tractography methods are used. [Copyright &y& Elsevier]

Abstract: Women have consistently demonstrated better verbal memory on tests that evaluate immediate and delayed free recall. In patients with schizophrenia, these verbal memory processes are relatively more preserved in women than men. However an understanding of the brain anatomy of the female advantage for verbal memory is still unclear. 29 females and 59 males with schizophrenia made comparable to 21 female and 27 male healthy volunteers were scanned using structural magnetic resonance imaging (sMRI) in order to assess volumes of regions across the entire brain. Sex differences within and between groups in the covariance structure of memory circuitry regions were evaluated using a novel approach to covariance analysis (the Box M Test). Brain areas of interest included the prefrontal cortex (PFC), inferior parietal lobule (iPAR), anterior cingulate gyrus (ACG), parahippocampus, and hippocampus (HIPP). Results showed significant differences in the covariance matrices of females and males with schizophrenia compared with their healthy counterparts, in particular the relationships between iPAR–PFC, iPAR–ACG, and HIPP–PFC. Sex differences in the iPAR–PFC relationship were significantly associated with sex differences in verbal memory performance. In control women, but not in men ACG volume correlated strongly with memory performance. In schizophrenia, ACG volume was reduced in females, but not in men, relative to controls. Findings suggest that the relationship between iPAR and PFC is particularly important for understanding the relative preservation of verbal memory processing in females with schizophrenia and may compensate for ACG volume reductions. These results illustrate the utility of a unique covariance structure modeling approach that yields important new knowledge for understanding the nature of schizophrenia. [Copyright &y& Elsevier]

Abstract: We have developed a new method to provide a comprehensive quantitative analysis of brain anatomy in cerebral palsy patients, which makes use of two techniques: diffusion tensor imaging and automated 3D whole brain segmentation based on our brain atlas and a nonlinear normalization technique (large-deformation diffeomorphic metric mapping). This method was applied to 13 patients and normal controls. The reliability of the automated segmentation revealed close agreement with the manual segmentation. We illustrate some potential applications for individual characterization and group comparison. This technique also provides a framework for determining the impact of various neuroanatomic features on brain functions. [ABSTRACT FROM AUTHOR]

Abstract: Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1month, 1year, and 2years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network''s cost efficiency increases with the brain development to 1year and 2years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. [Copyright &y& Elsevier]

Abstract: In three neuroimaging experiments, face, novel object, and building stimuli were compared under conditions of restricted (aperture) viewing and normal (whole) viewing. Aperture viewing restricted the view to a single face/object feature at a time, with the subjects able to move the aperture continuously though time to reveal different features. An analysis of the proportion of time spent viewing different features showed stereotypical exploration patterns for face, object, and building stimuli, and suggested that subjects constrained their viewing to the features most relevant for recognition. Aperture viewing showed much longer response times than whole viewing, due to sequential exploration of the relevant isolated features. An analysis of BOLD activation revealed face-selective activation with both whole viewing and aperture viewing in the left and right fusiform face areas (FFA). Aperture viewing showed strong and sustained activation throughout exploration, suggesting that aperture viewing recruited similar processes as whole viewing, but for a longer time period. Face-selective recruitment of the FFA with aperture viewing suggests that the FFA is involved in the integration of isolated features for the purpose of recognition. [ABSTRACT FROM AUTHOR]

Abstract: Disruptions in the hippocampal-cortical functional connectivities have been implicated in schizophrenia but less is known about their anatomical disconnectivities and association with clinical symptoms. We assessed the anatomical relationships between hippocampal shape, cortical thickness, and integrity of white matter bundles interconnecting them in this study. A brain mapping technique, large deformation diffeomorphic metric mapping, was used to analyze structural magnetic resonance imaging and diffusion tensor imaging scans of 126 schizophrenia patients and 77 matched healthy controls. We found that schizophrenia patients had surface inward-deformation in bilateral anterior hippocampi and cortical thinning in the regions of bilateral prefrontal, temporal, and occipital cortices compared with healthy controls. Anterior hippocampal shape deformity was associated with cortical thinning in the brain regions involved in visuo-spatial and verbal memory pathways. Canonical analysis revealed that greater disruptions in the hippocampal-cortical connectivity were associated with more severe negative symptoms in schizophrenia. Furthermore, fractional anisotropy in the fornix and cingulum bundles were reduced indicating abnormal integration of white matter between hippocampus and cortex in schizophrenia. Our findings suggested that aberrant structural hippocampal-cortical connectivities may serve as a marker of the illness and provide further structural evidence to support the notion of schizophrenia as a disorder of brain connectivity. [ABSTRACT FROM AUTHOR]

Abstract: The encoding of the objects from the world around us is one of the major topics of cognitive psychology, yet the principles of object coding in the human brain remain unresolved. Beyond referring to the particular features commonly associated with objects, our ability to categorize and discuss objects in detailed linguistic propositions implies that we have access to generic concepts of each object category with well-specified boundaries between them. Consideration of the nature of generic object concepts reveals that they must have the structure of a probabilistic list array specifying the Bayesian prior on all possible features that the object can possess, together with mutual covariance matrices among the features. Generic object concepts must also be largely context independent for propositions to have communicable meaning. Although, there is good evidence for local feature processing in the occipital lobe and specific responses for a few basic object categories in the posterior temporal lobe, the encoding of the generic object concepts remains obscure. We analyze the conceptual underpinnings of the study of object encoding, draw some necessary clarifications in relation to its modality-specific and amodal aspects, and propose an analytic algebra with specific reference to functional Magnetic Resonance Imaging approaches to the issue of how generic (amodal) object concepts are encoded in the human brain. [Copyright &y& Elsevier]