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Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Intelligible communication with others as well as covert conscious thought requires us to combine a representation of the external world with inner abstract concepts. Interaction with the external world through sensory perception and motor execution is arranged as sequences in time and space, whereas abstract thought and invariant categories are independent of the moment. Using advanced MRI-based fibre tracking on high resolution data from 183 participants in the Human Connectome Project, we identified two large supramodal systems comprising specific cortical regions and their connecting fibre tracts; a dorsal one for processing of sequences in time and space, and a ventral one for concepts and categories. We found that two hub regions exist in the executive front and the perceptive back of the brain where these two cognitive processes converge, constituting a dual-loop model. The hubs are located in the onto- and phylogenetically youngest regions of the cortex. We propose that this hub feature serves as the neural substrate for the more abstract sense of syntax in humans, i.e. for the system populating sequences with content in all cognitive domains. The hubs bring together two separate systems (dorsal and ventral) at the front and the back of the brain and create a closed-loop. The closed-loop facilitates recursivity and forethought, which we use twice; namely, for communication with others about things that are not there and for covert thought., Competing Interests: Declaration of Competing Interest Authors declare that they have no competing interests., (Copyright © 2022. Published by Elsevier Inc.)

The brain hemispheres can be divided into an upper dorsal and a lower ventral system. Each system consists of distinct cortical regions connected via long association tracts. The tracts cross the central sulcus or the limen insulae to connect the frontal lobe with the posterior brain. The dorsal stream is associated with sensorimotor mapping. The ventral stream serves structural analysis and semantics in different domains, as visual, acoustic or space processing. How does the prefrontal cortex, regarded as the platform for the highest level of integration, incorporate information from these different domains? In the current view, the ventral pathway consists of several separate tracts, related to different modalities. Originally the assumption was that the ventral path is a continuum, covering all modalities. The latter would imply a very different anatomical basis for cognitive and clinical models of processing. To further define the ventral connections, we used cutting-edge in vivo global tractography on high-resolution diffusion tensor imaging (DTI) data from 100 normal subjects from the human connectome project and ex vivo preparation of fiber bundles in the extreme capsule of 8 humans using the Klingler technique. Our data showed that ventral stream tracts, traversing through the extreme capsule, form a continuous band of fibers that fan out anteriorly to the prefrontal cortex, and posteriorly to temporal, occipital and parietal cortical regions. Introduction of additional volumes of interest in temporal and occipital lobes differentiated between the inferior fronto-occipital fascicle (IFOF) and uncinate fascicle (UF). Unequivocally, in both experiments, in all subjects a connection between the inferior frontal and middle-to-posterior temporal cortical region, otherwise known as the temporo-frontal extreme capsule fascicle (ECF) from nonhuman primate brain-tracing experiments was identified. In the human brain, this tract connects the language domains of "Broca's area" and "Wernicke's area". The differentiation in the three tracts, IFOF, UF and ECF seems arbitrary, all three pass through the extreme capsule. Our data show that the ventral pathway represents a continuum. The three tracts merge seamlessly and streamlines showed considerable overlap in their anterior and posterior course. Terminal maps identified prefrontal cortex in the frontal lobe and association cortex in temporal, occipital and parietal lobes as streamline endings. This anatomical substrate potentially facilitates the prefrontal cortex to integrate information across different domains and modalities., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Copyright © 2021. Published by Elsevier Inc.)

Regulation of the internal homeostasis is modulated by the central autonomic system. So far, the view of this system is determined by animal and human research focusing on cortical and subcortical grey substance regions. To provide an overview based on white matter architecture, we used a global tractography approach to reconstruct a network of tracts interconnecting brain regions that are known to be involved in autonomic processing. Diffusion weighted imaging data were obtained from subjects of the human connectome project (HCP) database. Resulting tracts are in good agreement with previous studies assuming a division of the central autonomic system into a cortical (CAN) and a subcortical network (SAN): the CAN consist of three subsystems that encompass all cerebral lobes and overlap within the insular cortex: a parieto-anterior-temporal pathway (PATP), an occipito-posterior-temporo-frontal pathway (OPTFP) and a limbic pathway. The SAN on the other hand connects the hypothalamus to the periaqueductal grey and locus coeruleus, before it branches into a dorsal and a lateral part that target autonomic nuclei in the rostral medulla oblongata. Our approach furthermore reveals how the CAN and SAN are interconnected: the hypothalamus can be considered as the interface-structure of the SAN, whereas the insula is the central hub of the CAN. The hypothalamus receives input from prefrontal cortical fields but is also connected to the ventral apex of the insular cortex. Thus, a holistic view of the central autonomic system could be created that may promote the understanding of autonomic signaling under physiological and pathophysiological conditions., Competing Interests: Declaration of Competing Interest There are no conflicts of interest., (Copyright © 2021. Published by Elsevier Inc.)

Fiber tractography based on diffusion-weighted MRI provides a non-invasive characterization of the structural connectivity of the human brain at the macroscopic level. Quantification of structural connectivity strength is challenging and mainly reduced to "streamline counting" methods. These are however highly dependent on the topology of the connectome and the particular specifications for seeding and filtering, which limits their intra-subject reproducibility across repeated measurements and, in consequence, also confines their validity. Here we propose a novel method for increasing the intra-subject reproducibility of quantitative estimates of structural connectivity strength. To this end, the connectome is described by a large matrix in positional-orientational space and reduced by Principal Component Analysis to obtain the main connectivity "modes". It was found that the proposed method is quite robust to structural variability of the data., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Fiber tractography assisted Deep Brain Stimulation (DBS) has been performed by different groups for more than 10 years to now. Groups around the world have adapted initial approaches to currently embrace the fiber tractography technology mainly for treating tremor (DBS and lesions), psychiatric indications (OCD and major depression) and pain (DBS). Despite the advantages of directly visualizing the target structure, the technology is demanding and is vulnerable to inaccuracies especially since it is performed on individual level. In this contribution, we will focus on tremor and psychiatric indications, and will show future applications of sophisticated tractography applications for subthalamic nucleus (STN) DBS surgery and stimulation steering as an example., Competing Interests: Conflict of interest statement Dr. Coenen has received grants for clinical trials (IITs) from Boston Scientific, USA and Medtronic, USA. He receives an ongoing collaborative grant from BrainLab (Munich, Germany). Dr. Coenen is scientific advisor for CereGate (Hamburg, Germany), INBRAIN (Barcelona, Spain) and Cortec (Freiburg). Dr. Reisert has nothing to declare. No specific external funding was received for this work which is based on departmental funds only., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Biophysical modeling lies at the core of evaluating tissue cellular structure using diffusion-weighted MRI, albeit with shortcomings. The challenges lie not only in the complexity of the diffusion phenomenon, but also in the need to know the diffusion-specific properties of diverse cellular compartments in vivo. The likelihood function obtained from the commonly acquired Stejskal-Tanner diffusion-weighted MRI data is degenerate with different parameter constellations explaining the signal equally well, thereby hindering an unambiguous parameter estimation. The aim of this study is to measure the intra-axonal water diffusivity which is one of the central parameters of white matter models. Estimating intra-axonal diffusivity is complicated by (i) the presence of other compartments, and (ii) the orientation dispersion of axons. Our measurement involves an efficient signal suppression of water in extra-axonal space and all cellular processes oriented outside a narrow cone around the principal fiber direction. This is achieved using a planar water mobility filter that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. After the planar filter, the diffusivity of the remaining intra-axonal signal is measured using linear and spherical diffusion encoding. We find the average intra-axonal diffusivity D 0 =2.25±0.03μm 2 /ms for the timing of the applied gradients, which gives D 0 (∞) ≈2.0μm 2 /ms when extrapolated to infinite diffusion time. The result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Understanding diffusion-weighted MR signal in brain white matter (WM) has been a long-sought-after goal. Modern research pursues this goal by focusing on the biological compartments that contributes essentially to the signal. In this study, we experimentally address the apparent presence of a compartment in which water motion is restricted in all spatial directions. Using isotropic diffusion encoding, we establish an upper bound on the fraction of such a compartment, which is shown to be about 2% of the unweighted signal for moderate diffusion times. This helps to eliminate such a compartment that have been assumed in literature on biophysical modeling. We also used the diffusion decay curve obtained from the isotropic encoding to establish a lower limit on the mean diffusivities of either of intra- or extra-axonal compartment as a function of their relative water fraction., (Copyright © 2017 Elsevier Inc. All rights reserved.)

As quantitative measures derived from fiber tractography are increasingly being used to characterize the structural connectivity of the brain, it is important to establish their reproducibility. However, no such information is as yet available for global tractography. Here we provide the first comprehensive analysis of the reproducibility of streamline counts derived from global tractography as quantitative estimates of structural connectivity. In a sample of healthy young adults scanned twice within one week, within-session and between-session test-retest reproducibility was estimated for streamline counts of connections based on regions of the AAL atlas using the intraclass correlation coefficient (ICC) for absolute agreement. We further evaluated the influence of the type of head-coil (12 versus 32 channels) and the number of reconstruction repetitions (reconstructing streamlines once or aggregated over ten repetitions). Factorial analyses demonstrated that reproducibility was significantly greater for within- than between-session reproducibility and significantly increased by aggregating streamline counts over ten reconstruction repetitions. Using a high-resolution head-coil incurred only small beneficial effects. Overall, ICC values were positively correlated with the streamline count of a connection. Additional analyses assessed the influence of different selection variants (defining fuzzy versus no fuzzy borders of the seed mask; selecting streamlines that end in versus pass through a seed) showing that an endpoint-based variant using fuzzy selection provides the best compromise between reproducibility and anatomical specificity. In sum, aggregating quantitative indices over repeated estimations and higher numbers of streamlines are important determinants of test-retest reproducibility. If these factors are taken into account, streamline counts derived from global tractography provide an adequately reproducible quantitative measure that can be used to gauge the structural connectivity of the brain in health and disease., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Tractography based on diffusion-weighted MRI investigates the large scale arrangement of the neurite fibers in brain white matter. It is usually assumed that the signal is a convolution of a fiber specific response function (FRF) with a fiber orientation distribution (FOD). The FOD is the focus of tractography. While in the past the FRF was estimated beforehand and was usually assumed to be fix, more recent approaches estimate the response function during tractography. This work proposes a novel objective function independent of the FRF, just aiming for FOD reconstruction. The objective is integrated into global tractography showing promising results., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Oxygen metabolism is altered in brain tumor regions and is quantified by the cerebral metabolic rate of oxygen consumption (CMRO 2 ). Direct dynamic 17 O MRI with inhalation of isotopically enriched 17 O 2 gas can be used to quantify CMRO 2 ; however, pixel-wise CMRO 2 quantification in human brain is challenging due to low natural abundance of 17 O isotope and, thus, the low signal-to-noise ratio (SNR) of 17 O MR images. To test the feasibility CMRO 2 mapping at a clinical 3 T MRI system, a new iterative reconstruction was proposed, which uses the edge information contained in a co-registered 1 H gradient image to construct a non-homogeneous anisotropic diffusion (AD) filter. AD-constrained reconstruction of 17 O MR images was compared to conventional Kaiser-Bessel gridding without and with Hanning filtering, and to iterative reconstruction with a total variation (TV) constraint. For numerical brain phantom and in two in vivo data sets of one healthy volunteer, AD-constrained reconstruction provided 17 O images with improved resolution of fine brain structures and resulted in higher SNR. CMRO 2 values of 0.78 - 1.55µmol/g tissue /min (white brain matter) and 1.03 - 2.01µmol/g tissue /min (gray brain matter) as well as the CMRO 2 maps are in a good agreement with the results of 15 O-PET and 17 O MRI at 7 T and at 9.4 T. In conclusion, the proposed AD-constrained reconstruction enabled calculation of 3D CMRO 2 maps at 3 T MRI system, which is an essential step towards clinical translation of 17 O MRI for non-invasive CMRO 2 quantification in tumor patients., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Connectomics of brain disorders seeks to reveal how altered brain function emerges from the architecture of cerebral networks; however the causal impact of targeted cellular damage on the whole brain functional and structural connectivity remains unknown. In the central nervous system, demyelination is typically the consequence of an insult targeted at the oligodendrocytes, the cells forming and maintaining the myelin. This triggered perturbation generates cascades of pathological events that most likely alter the brain connectome. Here we induced oligodendrocyte death and subsequent demyelinating pathology via cuprizone treatment in mice and combining mouse brain resting state functional Magnetic Resonance Imaging and diffusion tractography we established functional and structural pathology-to-network signatures. We demonstrated that demyelinated brain fundamentally reorganizes its intrinsic functional connectivity paralleled by widespread damage of the structural scaffolding. We evidenced default mode-like network as core target of demyelination-induced connectivity modulations and hippocampus as the area with strongest connectional perturbations., (Copyright © 2017 Elsevier Inc. All rights reserved.)

In a longitudinal mouse study we evaluated whether diffusion tensor imaging (DTI) can monitor microstructural changes after administration of the neuromodulating drug EPO and whether erythropoietin (EPO) has an effect on cognitive performance. Twelve mice (2 groups with 6 mice each) were scanned in a 7T Bruker Biospin animal scanner with a highly resolved DTI sequence before and 16 days after intraperitoneal injections of EPO or saline. All mice underwent behavioral testing (Morris water maze) and histologic evaluation of hippocampal and corpus callosum cell proliferation and oligodendrogenesis. Whole brain DTI analysis showed significant Trace, RD and AD decrease within the dentate gyrus, subiculum, primary motor, somatosensory, and supplementary somatosensory areas and FA increase in the hippocampus, corpus callosum, and fimbria fornix in EPO treated mice only. ROI-based DTI analysis showed significant Trace and RD decrease and FA increase only in the corpus callosum of EPO treated mice, whereas in the dentate gyrus significant Trace, RD, and AD decrease occurred in both, EPO- and control-group. Behavioral tests showed that EPO treated mice performed better and learned faster than controls. Histologically, the number of BrdU-positive nuclei and optical density of DCX-labeled juvenile neurons significantly increased within the dentate gyrus, corpus callosum and fimbria fornix and the number of NG2-positive oligodendrocyte progenitors in corpus callosum and fimbria fornix, respectively. In conclusion we were able to monitor microstructural changes with DTI and showed EPO treatment-related alterations correlating with enhanced dentate gyrus and corpus callosum cell proliferation and better learning capabilities., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients., (Copyright © 2015 Elsevier Inc. All rights reserved.)

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional-structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the 'default mode network' (DMN) showed the highest agreement of structure-function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional-structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations., (© 2013 Elsevier Inc. All rights reserved.)

Diffusion-weighted magnetic resonance imaging provides information about the nerve fiber bundle geometry of the human brain. While the inference of the underlying fiber bundle orientation only requires single q-shell measurements, the absolute determination of their volume fractions is much more challenging with respect to measurement techniques and analysis. Unfortunately, the usually employed multi-compartment models cannot be applied to single q-shell measurements, because the compartment's diffusivities cannot be resolved. This work proposes an equation for fiber orientation densities that can infer the absolute fraction up to a global factor. This equation, which is inspired by the classical mass preservation law in fluid dynamics, expresses the fiber conservation associated with the assumption that fibers do not terminate in white matter. Simulations on synthetic phantoms show that the approach is able to derive the densities correctly for various configurations. Experiments with a pseudo ground truth phantom show that even for complex, brain-like geometries the method is able to infer the densities correctly. In-vivo results with 81 healthy volunteers are plausible and consistent. A group analysis with respect to age and gender show significant differences, such that the proposed maps can be used as a quantitative measure for group and longitudinal analysis., (Copyright © 2013 Elsevier Inc. All rights reserved.)

MR-encephalography allows the observation of functional signal in the brain at a frequency of 10 Hz, permitting filtering of physiological "noise" and the detection of single event activations. High temporal resolution is achieved by the use of undersampled non-Cartesian trajectories, parallel imaging and regularized image reconstruction. MR-encephalography is based on 3D-encoding, allowing undersampling in two dimensions and providing advantages in terms of signal to noise ratio. Long readout times, which are necessary for single shot whole brain imaging (up to 75 ms), cause off-resonance artifacts. To meet this issue, a spherical stack of spirals trajectory is proposed in this work. By examining the trajectories in local k-space, it is shown that in areas of strong susceptibility gradients spatial information is fundamentally lost, making a meaningful image reconstruction impossible in the affected areas. It is shown that the loss of spatial information is reduced when using a stack of spirals trajectory compared to concentric shells., (Copyright © 2013 Elsevier Inc. All rights reserved.)

As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to fibercup09@gmail.com and updates will be published on the webpage., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Global fiber reconstruction aims at providing a consistent view of the fiber architecture in the whole volume of cerebral white matter on the basis of diffusion-sensitized magnetic resonance imaging. A new realization of this principle is presented. The method utilizes data acquired with high angular resolution diffusion imaging (HARDI), a measurement method that fulfills clinical requirements. For the first time among global reconstruction methods, the computation time is acceptable for a broad class of practical applications. The method does not involve any boundary conditions that prescribe the location of the ends of reconstructed fibers. This helps to minimize necessary user interaction and operator dependence. Results obtained in a physical phantom demonstrate a high reconstruction quality. In vivo results have been obtained in several volunteers. The algorithm found a number of prominent fascicles including those in the limbic system, which had been problematic for a previously published version of global tracking., (Copyright © 2010 Elsevier Inc. All rights reserved.)