Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography

Structural Connectivity Differences Reflect Microstructural Heterogeneity of the Human Insular Cortex

The insular cortex is renowned for its multitude of functions, intricate structural connectivity patterns, and complex cytoarchitecture, yet a unified multimodal concept remains elusive. Microstructural parcellations provide a promising mediator to integrate connectome data into a combined structural-functional framework. While in the macaque insula, a clear relationship between anatomical connections and cytoarchitecture is well established, such correlation in the human insula remains unclear. By combining diffusion data from two large cohorts, including 914 and 204 subjects, respectively, as well as probabilistic tractography and the microstructural JulichBrain Atlas, we uncover how microstructural diversity reflects structural connectivity patterns in the human insula. Analyzing the connectivity of 16 cytoarchitectonic areas, we identified six clusters, two in the posterior and four in the anterior insula. Posterior clusters exhibited strong connectivity with temporal, occipital, and parietal areas encompassing auditory, visual, and somatosensory systems. Conversely, anterior clusters were specifically linked with (orbito)frontal areas, such as Broca's area or frontal opercular areas. Together, our data demonstrate that structural connectivity differences are reflected by fundamental principles of microstructural organization in the human insula. Additional whole-brain connectivity analyses reveal that two distinct areas within the anterior (Id6) and posterior (Id3) human insula may serve as integrative hubs, mediating between higher-order cognitive and limbic systems, as well as across sensory modalities. All clusters are openly available in MNI space to support future multimodal studies addressing the relations between cytoarchitecture, structure, functions, and pathologies in this complex region of the human neocortex.

A detailed spatiotemporal atlas of the white matter tracts for the fetal brain

This study presents the construction of a comprehensive spatiotemporal atlas of white matter tracts in the fetal brain for every gestational week between 23 and 36 wk using diffusion MRI (dMRI). Our research leverages data collected from fetal MRI scans, capturing the dynamic changes in the brain's architecture and microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), coinciding with the intensity of histogenic processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.

White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

Temporopolar blurring signifies abnormalities of white matter in mesial temporal lobe epilepsy

OBJECTIVE

The single-center retrospective cohort study investigated underlying pathogenic mechanisms and clinical significance of patients with temporal lobe epilepsy and hippocampal sclerosis (TLE-HS), in the presence/absence of gray-white matter abnormalities (usually called "blurring"; GMB) in ipsilateral temporopolar region (TPR) on MRI.

METHODS

The study involved 105 patients with unilateral TLE-HS (60 GMB+ and 45 GMB-) who underwent standard anterior temporal lobectomy, along with 61 healthy controls. Resected specimens were examined under light microscope. With combined T1-weighted and DTI data, we quantitatively compared large-scale morphometric features and exacted diffusion parameters of ipsilateral TPR-related superficial and deep white matter (WM) by atlas-based segmentation. Along-tract analysis was added to detect heterogeneous microstructural alterations at various points along deep WM tracts, which were categorized into inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and temporal cingulum.

RESULTS

Comparable seizure semiology and postoperative seizure outcome were found, while the GMB+ group had significantly higher rate of HS Type 1 and history of febrile seizures, contrasting with significantly lower proportion of interictal contralateral epileptiform discharges, HS Type 2, and increased wasteosomes in hippocampal specimens. Similar morphometric features but greater WM atrophy with more diffusion abnormalities of superficial WM was observed adjacent to ipsilateral TPR in the GMB+ group. Moreover, microstructural alterations resulting from temporopolar GMB were more localized in temporal cingulum while evenly and widely distributed along ILF and UF.

INTERPRETATION

Temporopolar GMB could signify more severe and widespread microstructural damage of white matter rather than a focal cortical lesion in TLE-HS, affecting selection of surgical procedures.

Comparative analysis of the chimpanzee and human brain superficial structural connectivities

Diffusion MRI tractography (dMRI) has fundamentally transformed our ability to investigate white matter pathways in the human brain. While long-range connections have extensively been studied, superficial white matter bundles (SWMBs) have remained a relatively underexplored aspect of brain connectivity. This study undertakes a comprehensive examination of SWMB connectivity in both the human and chimpanzee brains, employing a novel combination of empirical and geometric methodologies to classify SWMB morphology in an objective manner. Leveraging two anatomical atlases, the Ginkgo Chauvel chimpanzee atlas and the Ginkgo Chauvel human atlas, comprising respectively 844 and 1375 superficial bundles, this research focuses on sparse representations of the morphology of SWMBs to explore the little-understood superficial connectivity of the chimpanzee brain and facilitate a deeper understanding of the variability in shape of these bundles. While similar, already well-known in human U-shape fibers were observed in both species, other shapes with more complex geometry such as 6 and J shapes were encountered. The localisation of the different bundle morphologies, putatively reflecting the brain gyrification process, was different between humans and chimpanzees using an isomap-based shape analysis approach. Ultimately, the analysis aims to uncover both commonalities and disparities in SWMBs between chimpanzees and humans, shedding light on the evolution and organization of these crucial neural structures.

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter

In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

A, Hernandez Larzabal H, Román C, Poupon C, Mangin JF, Hernández C, Guevara P. Phybers: a package for brain tractography analysis

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

Direct Inside-Out Observation of Superficial White Matter Fasciculi in the Human Brain

Background: Recent methodological advances in the study of the cerebral white matter have left short association fibers relatively underexplored due to their compact and juxtacortical nature, which represent significant challenges for both post-mortem post-cortex removal dissection and magnetic resonance-based diffusion imaging. Objective: To introduce a novel inside-out post-mortem fiber dissection technique to assess short association fiber anatomy. Methods: Six cerebral specimens were obtained from a body donation program and underwent fixation in formalin. Following two freezing and thawing cycles, a standardized protocol involving peeling fibers from deep structures towards the cortex was developed. Results: The inside-out technique effectively exposed the superficial white matter. The procedure revealed distinguishable intergyral fibers, demonstrating their dissectability and enabling the identification of their orientation. The assessment of layer thickness was possible through direct observation and ex vivo morphological magnetic resonance imaging. Conclusion: The inside-out fiber technique effectively demonstrates intergyral association fibers in the post-mortem human brain. It adds to the neuroscience armamentarium, overcoming methodological obstacles and offering an anatomical substrate essential for neural circuit modeling and the evaluation of neuroimaging congruence. Impact statement The inside-out fiber dissection technique enables a totally new perception of cerebral connectivity as the observer navigates inside the parenchyma and looks toward the cerebral surface with the subcortical white matter and the cortical mantle in place. This approach has proven very effective for exposing intergyral association fibers, which have shown to be much more distinguishable from an inner perspective. It gave rise to unprecedented images of the human superficial white matter and allowed, for the first time, direct observation of this vast mantle of fascicles on entire cerebral hemisphere aspects.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Neurovascular coupling dysfunction of visual network organization in Parkinson's disease

Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.

Spatio-temporal evolution of human neural activity during visually cued hand movements

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.

Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.

Aberrant frontal lobe "U"-shaped association fibers in first-episode schizophrenia: A 7-Tesla Diffusion Imaging Study

Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been extensively studied (e.g. arcuate fasciculus), studies of short-ranged, "U"-shaped tracts have been limited in patients with schizophrenia, in part due to the sheer abundance of tracts present and due to the spatial variations across individuals that defy probabilistic characterization in the absence of reliable templates. In this study, we use diffusion magnetic resonance imaging (dMRI) to investigate frontal lobe superficial white matter that are present in the majority of study participants, comparing healthy controls and minimally treated patients with first-episode schizophrenia (<3 median days of lifetime treatment). Through group comparisons, 3 out of 63 frontal lobe "U"-shaped tracts were found to demonstrate localized aberrations affecting the microstructural tissue properties (via diffusion tensor metrics) in this early stage of disease. No associations were found in patients between aberrant segments of affected tracts and clinical or cognitive variables. Aberrations in the frontal lobe "U"-shaped tracts in early untreated stages of psychosis occur irrespective of symptom burden, and are distributed across critical functional networks associated with executive function and salience processing. While we limited the investigation to the frontal lobe, a framework has been developed to study such connections in other brain regions, enabling further extensive investigations jointly with the major deep white matter pathways.

Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions

It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.

Surface-based tracking for short association fibre tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.

Gyral and sulcal connectivity in the human cerebral cortex

The rapid evolution of image acquisition and data analytic methods has established in vivo whole-brain tractography as a routine technology over the last 20 years. Imaging-based methods provide an additional approach to classic neuroanatomical studies focusing on biomechanical principles of anatomical organization and can in turn overcome the complexity of inter-individual variability associated with histological and tractography studies. In this work we propose a novel, reliable framework for determining brain tracts resolving the anatomical variance of brain regions. We distinguished 4 region types based on anatomical considerations: (i) gyral regions at borders between cortical communities; (ii) gyral regions within communities; (iii) sulcal regions at invariant locations across subjects; and (iv) other sulcal regions. Region types showed strikingly different anatomical and connection properties. Results allowed complementing the current understanding of the brain’s communication structure with a model of its anatomical underpinnings.

Cytoarchitecture, intersubject variability, and 3D mapping of four new areas of the human anterior prefrontal cortex

The dorsolateral prefrontal cortex (DLPFC) plays a key role in cognitive control and executive functions, including working memory, attention, value encoding, decision making, monitoring, and controlling behavioral strategies. However, the relationships between this variety of functions and the underlying cortical areas, which specifically contribute to these functions, are not yet well-understood. Existing microstructural maps differ in the number, localization, and extent of areas of the DLPFC. Moreover, there is a considerable intersubject variability both in the sulcal pattern and in the microstructure of this region, which impedes comparison with functional neuroimaging studies. The aim of this study was to provide microstructural, cytoarchitectonic maps of the human anterior DLPFC in 3D space. Therefore, we analyzed 10 human post-mortem brains and mapped their borders using a well-established approach based on statistical image analysis. Four new areas (i.e., SFS1, SFS2, MFG1, and MFG2) were identified in serial, cell-body stained brain sections that occupy the anterior superior frontal sulcus and middle frontal gyrus, i.e., a region corresponding to parts of Brodmann areas 9 and 46. Differences between areas in cytoarchitecture were captured using gray level index profiles, reflecting changes in the volume fraction of cell bodies from the surface of the brain to the cortex-white matter border. A hierarchical cluster analysis of these profiles indicated that areas of the anterior DLPFC displayed higher cytoarchitectonic similarity between each other than to areas of the neighboring frontal pole (areas Fp1 and Fp2), Broca's region (areas 44 and 45) of the ventral prefrontal cortex, and posterior DLPFC areas (8d1, 8d2, 8v1, and 8v2). Area-specific, cytoarchitectonic differences were found between the brains of males and females. The individual areas were 3D-reconstructed, and probability maps were created in the MNI Colin27 and ICBM152casym reference spaces to take the variability of areas in stereotaxic space into account. The new maps contribute to Julich-Brain and are publicly available as a resource for studying neuroimaging data, helping to clarify the functional and organizational principles of the human prefrontal cortex.

Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.

Reduced asymmetry of the hand knob area and decreased sensorimotor u-fiber connectivity in middle-aged adults with autism

Individuals with autism spectrum disorder (ASD) frequently present with impairments in motor skills (e.g., limb coordination, handwriting and balance), which are observed across the lifespan but remain largely untreated. Many adults with ASD may thus experience adverse motor outcomes in aging, when physical decline naturally occurs. The 'hand knob' of the sensorimotor cortex is an area that is critical for motor control of the fingers and hands. However, this region has received little attention in ASD research, especially in adults after midlife. The hand knob area of the precentral (PrC_hand) and postcentral (PoC_hand) gyri was semi-manually delineated in 49 right-handed adults (25 ASD, 24 typical comparison [TC] participants, aged 41-70 years). Using multimodal (T1-weighted, diffusion-weighted, and resting-state functional) MRI, we examined the morphology, ipsilateral connectivity and laterality of these regions. We also explored correlations between hand knob measures with motor skills and autism symptoms, and between structural and functional connectivity measures. Bayesian analyses indicated moderate evidence of group effects with greater right PrC_hand volume and reduced leftward laterality of PrC_hand and PoC_hand volume in the ASD relative to TC group. Furthermore, the right PoC-PrC_hand u-fibers showed increased mean diffusivity in the ASD group. In the ASD group, right u-fiber volume positively correlated with corresponding functional connectivity but did not survive multiple comparisons correction. Correlations of hand knob measures and behavior were observed in the ASD group but did not survive multiple comparisons correction. Our findings suggest that morphological laterality and u-fiber connectivity of the sensorimotor network, putatively involved in hand motor/premotor function, may be diminished in middle-aged adults with ASD, perhaps rendering them more vulnerable to motor decline in old age. The altered morphology may relate to atypical functional motor asymmetries found in ASD earlier in life, possibly reflecting altered functional asymmetries over time.

Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach

BACKGROUND

Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD.

METHODS

We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas.

RESULTS

The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients.

CONCLUSIONS

We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.

Disentangling the variability of the superficial white matter organization using regional-tractogram-based population stratification

Each variation of the cortical folding pattern implies a particular rearrangement of the geometry of the fibers of the underlying white matter. While this rearrangement only impacts the ends of the long pathways, it may affect most of the trajectory of the short bundles. Therefore, mapping the short fibers of the human brain using diffusion-based tractography requires a dedicated strategy to overcome the variability of the folding patterns. In this paper, we propose a fiber-based stratification strategy splitting the population into homogeneous groups for disentangling the superficial white matter bundle organization. This strategy introduces a new refined fiber distance which includes angular considerations for inferring fine-grained atlases of the short bundles surrounding a specific sulcus and a subtractogram distance that quantifies the similitude between fiber sets of two different subjects. The stratification splits the population into groups with similar regional fiber organization using manifold learning. We first successfully test the hypothesis that the main source of variability of the regional fiber organization is the variability of the regional folding pattern. Then, in each group, we proceed with the automatic identification of the most stable bundles, at a higher granularity level than what can be achieved with the non-stratified whole population, enabling the disentanglement of the very variable configuration of the short fibers. Finally, the method searches for bundle correspondence across groups to build a population level atlas. As a proof of concept, the atlas refinement achieved by this strategy is illustrated for the fibers that surround the central sulcus and the superior temporal sulcus using the HCP dataset.

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

Linking Brain Structure, Activity, and Cognitive Function through Computation

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.

Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults

Tractography is a non-invasive technique to investigate the brain’s structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter.

Machine learning to investigate superficial white matter integrity in early multiple sclerosis

BACKGROUND AND PURPOSE

This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC).

METHODS

Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank-sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1-SWM, 2-DWM, 3-SWM and DWM, 4-cortical thickness, or 5-RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5-folds cross-validation. Area under the receiver operating characteristic curve (AUC_roc ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUC_roc values.

RESULTS

Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre- and post-central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUC_roc = 0.75). The SWM model provided higher AUC_roc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all-features (0.68) models (p < .001 for all).

CONCLUSION

Our results reveal a non-random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

Group-Wise Cortical Surface Parcellation Based on Inter-Subject Fiber Clustering. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:2655-2659. [PMID: 34891798 DOI: 10.1109/embc46164.2021.9631099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation was obtained using parcel probability maps to model and integrate the connectivity information of all subjects, and graphs to represent the overlap between parcels. Two inter-subject clustering schemes were tested, generating a total of 171 and 109 parcels, respectively. The resulting parcels were quantitatively compared with three state-of-the-art atlases. The best parcellation returned 69 parcels with a Dice similarity coefficient greater than 0.5. To the best of our knowledge, this is the first diffusion-based cortex parcellation method based on whole-brain inter-subject fiber clustering.

Stimulation of frontal pathways disrupts hand muscle control during object manipulation

The activity of frontal motor areas during hand-object interaction is coordinated by dense communication along specific white matter pathways. This architecture allows the continuous shaping of voluntary motor output and, despite extensively investigated in non-human primate studies, remains poorly understood in humans. Disclosure of this system is crucial for predicting and treatment of motor deficits after brain lesions. For this purpose, we investigated the effect of direct electrical stimulation on white matter pathways within the frontal lobe on hand-object manipulation. This was tested in thirty-four patients (15 left hemisphere, mean age 42 years, 17 male, 15 with tractography) undergoing awake neurosurgery for frontal lobe tumour removal with the aid of the brain mapping technique. The stimulation outcome was quantified based on hand-muscle activity required by task execution. The white matter pathways responsive to stimulation with an interference on muscles were identified by means of probabilistic density estimation of stimulated sites, tract-based lesion-symptom (disconnectome) analysis and diffusion tractography on the single patient level. Finally, we assessed the effect of permanent tracts disconnection on motor outcome in the immediate postoperative period using a multivariate lesion-symptom mapping approach. The analysis showed that stimulation disrupted hand-muscle activity during task execution in 66 sites within the white matter below dorsal and ventral premotor regions. Two different EMG interference patterns associated with different structural architectures emerged: 1) an arrest pattern, characterised by complete impairment of muscle activity associated with an abrupt task interruption, occurred when stimulating a white matter area below the dorsal premotor region. Local mid-U-shaped fibres, superior fronto-striatal, corticospinal and dorsal fronto-parietal fibres intersected with this region. 2) a clumsy pattern, characterised by partial disruption of muscle activity associated with movement slowdown and/or uncoordinated finger movements, occurred when stimulating a white matter area below the ventral premotor region. Ventral fronto-parietal and inferior fronto-striatal tracts intersected with this region. Finally, only resections partially including the dorsal white matter region surrounding the supplementary motor area were associated with transient upper-limb deficit (p = 0.05; 5000 permutations). Overall, the results identify two distinct frontal white matter regions possibly mediating different aspects of hand-object interaction via distinct sets of structural connectivity. We suggest the dorsal region, associated with arrest pattern and post-operative immediate motor deficits, to be functionally proximal to motor output implementation, while the ventral region may be involved in sensorimotor integration required for task execution.

Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers. Our approach implements all FFClust steps in parallel, improving execution times in all of them. In addition, our parallel approach includes a parallel Kmeans++ algorithm implementation and defines a new variant of Kmeans++ to reduce the impact of choosing outliers as initial centroids. The results show that our approach provides clustering quality results very similar to FFClust, and it requires an execution time of 3.5 s for processing about a million fibers, achieving a speedup of 11.5 times compared to FFClust.

Altered microstructural properties of superficial white matter in patients with Parkinson's disease

Parkinson's disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI) studies found abnormal DTI metrics in white matter bundles, such as the corpus callosum, cingulate, and frontal-parietal bundles, in PD patients. These studies mainly focused on alterations in microstructural features of long-range bundles within the deep white matter (DWM) that connects pairs of distant cortical regions. However, less is known about the DTI metrics of the superficial white matter (SWM) that connects local cortical regions in PD patients. To determine whether the DTI metrics of the SWM were different between the PD patients and the healthy controls, we recruited DTI data from 34 PD patients and 29 gender- and age-matched healthy controls. Using a probabilistic tractographic approach, we first defined a population-based SWM mask across all the subjects. Using a tract-based spatial statistical (TBSS) analytic approach, we then identified the SWM bundles showing abnormal DTI metrics in the PD patients. We found that the PD patients showed significantly lower DTI metrics in the SWM bundles connecting the sensorimotor cortex, cingulate cortex, posterior parietal cortex (PPC), and parieto-occipital cortex than the healthy controls. We also found that the clinical measures in the PD patients was significantly negatively correlated with the fractional anisotropy in the SWM (FA_SWM) that connects core regions in the default mode network (DMN). The FA_SWM in the bundles that connected the PPC was significantly positively correlated with cognitive performance in the PD patients. Our findings suggest that SWM may serve as the brain structural basis underlying the sensorimotor deficits and cognitive degeneration in PD patients.

A large-scale structural and functional connectome of social mentalizing

Humans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the brain-wide organization and mechanisms of mentalizing processing. Key connectomic features of the mentalizing network (MTN) have been delineated in exquisite detail. We found the structural architecture of MTN is organized by two parallel subsystems and constructed redundantly by local and long-range white matter fibers. We uncovered an intrinsic functional architecture that is synchronized according to the degree of mentalizing, and its hierarchy reflects the inherent information integration order. We also examined the correspondence between the structural and functional connectivity in the network and revealed their differences in network topology, individual variance, spatial specificity, and functional specificity. Finally, we scrutinized the connectome resemblance between the default mode network and MTN and elaborated their inherent differences in dynamic patterns, laterality, and homogeneity. Overall, our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other vicinity brain networks supporting autobiographical memory, semantic memory, self-referential, moral reasoning, and mental time travel.

ABrainVis: an android brain image visualization tool

Background

The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a continuous improvement in performance, different types of applications have been designed to help users in different work areas.

Results

We present, ABrainVis, an Android mobile tool that allows users to visualize different types of brain images, such as white matter diffusion tractographies, represented as fibers in 3D, segmented fiber bundles, MRI 3D images as rendered volumes and slices, and meshes. The tool enables users to choose and combine different types of brain imaging data to provide visual anatomical context for specific visualization needs. ABrainVis provides high performance over a wide range of Android devices, including tablets and cell phones using medium and large tractography datasets. Interesting visualizations including brain tumors and arteries, along with fiber, are given as examples of case studies using ABrainVis.

Conclusions

The functionality, flexibility and performance of ABrainVis tool introduce an improvement in user experience enabling neurophysicians and neuroscientists fast visualization of large tractography datasets, as well as the ability to incorporate other brain imaging data such as MRI volumes and meshes, adding anatomical contextual information.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00909-0.

Concurrent brain parcellation and connectivity estimation via co-clustering of resting state fMRI data: A novel approach

Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. We propose the co‐clustering‐based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co‐clustering‐based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs‐fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs‐fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.

The feasibility and value of extraoperative and adjuvant intraoperative stereoelectroencephalography in rolandic and perirolandic epilepsies

OBJECTIVE

The objective of this study was to illustrate the feasibility and value of extra- and intraoperative stereoelectroencephalography (SEEG) in patients who underwent resection in rolandic and perirolandic regions.

METHODS

The authors retrospectively reviewed all consecutive patients with at least 1 year of postoperative follow-up who underwent extra- and intraoperative SEEG monitoring between January 2015 and January 2017.

RESULTS

Four patients with pharmacoresistant rolandic and perirolandic focal epilepsy were identified, who underwent conventional extraoperative invasive SEEG evaluations followed by adjuvant intraoperative SEEG recordings. Conventional extraoperative SEEG evaluations demonstrated ictal and interictal epileptiform activities involving eloquent rolandic and perirolandic cortical areas in all patients. Following extraoperative monitoring, patients underwent preplanned staged resections guided by simultaneous and continuous adjuvant intraoperative SEEG monitoring. Resections, guided by electrode contacts of interest in 3D boundaries, were performed while continuous real-time electrographic data from SEEG recordings were obtained. Staged approaches of resections were performed until there was intraoperative resolution of synchronous rolandic/perirolandic cortex epileptic activities. All patients in the cohort achieved complete seizure freedom (Engel class IA) during the follow-up period ranging from 18 to 50 months. Resection resulted in minimal neurological deficit; 3 patients experienced transient, distal plantar flexion weakness (mild foot drop).

CONCLUSIONS

The seizure and functional outcome results of this highly preselected group of patients testifies to the feasibility and demonstrates the value of the combined benefits of both intra- and extraoperative SEEG recordings when resecting the rolandic and perirolandic areas. The novel hybrid method allows a more refined and precise identification of the epileptogenic zone. Consequently, tailored resections can be performed to minimize morbidity as well as to achieve adequate seizure control.

U-shape short-range extrinsic connectivity organisation around the human central sulcus

The central sulcus is probably one of the most studied folds in the human brain, owing to its clear relationship with primary sensory-motor functional areas. However, due to the difficulty of estimating the trajectories of the U-shape fibres from diffusion MRI, the short structural connectivity of this sulcus remains relatively unknown. In this context, we studied the spatial organization of these U-shape fibres along the central sulcus. Based on high quality diffusion MRI data of 100 right-handed subjects and state-of-the-art pre-processing pipeline, we first define a connectivity space that provides a comprehensive and continuous description of the short-range anatomical connectivity around the central sulcus at both the individual and group levels. We then infer the presence of five major U-shape fibre bundles at the group level in both hemispheres by applying unsupervised clustering in the connectivity space. We propose a quantitative investigation of their position and number of streamlines as a function of hemisphere, sex and functional scores such as handedness and manual dexterity. Main findings of this study are twofold: a description of U-shape short-range connectivity along the central sulcus at group level and the evidence of a significant relationship between the position of three hand related U-shape fibre bundles and the handedness score of subjects.

Disruption of Conscious Access in Psychosis Is Associated with Altered Structural Brain Connectivity

According to global neuronal workspace (GNW) theory, conscious access relies on long-distance cerebral connectivity to allow a global neuronal ignition coding for conscious content. In patients with schizophrenia and bipolar disorder, both alterations in cerebral connectivity and an increased threshold for conscious perception have been reported. The implications of abnormal structural connectivity for disrupted conscious access and the relationship between these two deficits and psychopathology remain unclear. The aim of this study was to determine the extent to which structural connectivity is correlated with consciousness threshold, particularly in psychosis. We used a visual masking paradigm to measure consciousness threshold, and diffusion MRI tractography to assess structural connectivity in 97 humans of either sex with varying degrees of psychosis: healthy control subjects (n = 46), schizophrenia patients (n = 25), and bipolar disorder patients with (n = 17) and without (n = 9) a history of psychosis. Patients with psychosis (schizophrenia and bipolar disorder with psychotic features) had an elevated masking threshold compared with control subjects and bipolar disorder patients without psychotic features. Masking threshold correlated negatively with the mean general fractional anisotropy of white matter tracts exclusively within the GNW network (inferior frontal-occipital fasciculus, cingulum, and corpus callosum). Mediation analysis demonstrated that alterations in long-distance connectivity were associated with an increased masking threshold, which in turn was linked to psychotic symptoms. Our findings support the hypothesis that long-distance structural connectivity within the GNW plays a crucial role in conscious access, and that conscious access may mediate the association between impaired structural connectivity and psychosis.

Reproducing macaque lateral grasping and oculomotor networks using resting state functional connectivity and diffusion tractography

Cortico-cortical networks involved in motor control have been well defined in the macaque using a range of invasive techniques. The advent of neuroimaging has enabled non-invasive study of these large-scale functionally specialized networks in the human brain; however, assessing its accuracy in reproducing genuine anatomy is more challenging. We set out to assess the similarities and differences between connections of macaque motor control networks defined using axonal tracing and those reproduced using structural and functional connectivity techniques. We processed a cohort of macaques scanned in vivo that were made available by the open access PRIME-DE resource, to evaluate connectivity using diffusion imaging tractography and resting state functional connectivity (rs-FC). Sectors of the lateral grasping and exploratory oculomotor networks were defined anatomically on structural images, and connections were reproduced using different structural and functional approaches (probabilistic and deterministic whole-brain and seed-based tractography; group template and native space functional connectivity analysis). The results showed that parieto-frontal connections were best reproduced using both structural and functional connectivity techniques. Tractography showed lower sensitivity but better specificity in reproducing connections identified by tracer data. Functional connectivity analysis performed in native space had higher sensitivity but lower specificity and was better at identifying connections between intrasulcal ROIs than group-level analysis. Connections of AIP were most consistently reproduced, although those connected with prefrontal sectors were not identified. We finally compared diffusion MR modelling with histology based on an injection in AIP and speculate on anatomical bases for the observed false negatives. Our results highlight the utility of precise ex vivo techniques to support the accuracy of neuroimaging in reproducing connections, which is relevant also for human studies.

Plis de passage in the superior temporal sulcus: Morphology and local connectivity

While there is a profusion of functional investigations involving the superior temporal sulcus (STS), our knowledge of the anatomy of this sulcus is still limited by a large individual variability. In particular, an accurate characterization of the "plis de passage" (PPs), annectant gyri inside the fold, is lacking to explain this variability. Performed on 90 subjects of the HCP database, our study revealed that PPs constitute landmarks that can be identified from the geometry of the STS walls. They were found associated with a specific U-shape white-matter connectivity between the two banks of the sulcus, the amount of connectivity being related to the depth of the PPs. These findings raise new hypotheses regarding the spatial organization of PPs, the relation between cortical anatomy and structural connectivity, as well as the possible role of PPs in the regional functional organization.

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

Motor Functional Reorganization Is Triggered by Tumor Infiltration Into the Primary Motor Area and Repeated Surgery

In patients with gliomas, motor deficits are not always observed, even though tumor cells infiltrate into the motor area. Currently, it is recognized that this phenomenon can occur through the neuroplasticity potential. The aim of this study is to investigate the characteristics of motor functional reorganization in gliomas. Out of 100 consecutive patients who underwent awake surgery, 29 patients were assessed as regards their motor function and were retrospectively explored to determine whether positive motor responses were elicited. A total of 73 positive mapping sites from 27 cases were identified, and their spatial anatomical locations and activated region by functional MRI were analyzed. Additionally, the factors promoting neuroplasticity were analyzed through multiple logistic regression analysis. As a result, a total of 60 points (21 cases) were found in place, while 13 points (17.8%) were found to be shifted from anatomical localization. Reorganizations were classified into three categories: Type 1 (move to ipsilateral different gyrus) was detected at nine points (four cases), and they moved into the postcentral gyrus. Type 2 (move within the ipsilateral precentral gyrus) was detected at four points (two cases). Unknown type (two cases) was categorized as those whose motor functional cortex was moved to other regions, although we could not find the compensated motor area. Two factors for the onset of reorganization were identified: tumor cells infiltrate into the primary motor area and repeated surgery (p < 0.0001 and p = 0.0070, respectively). Our study demonstrated that compensation can occur mainly in two ways, and it promoted repeated surgery and infiltration of tumor into the primary motor area.

Inter-Subject Clustering of Brain Fibers from Whole-Brain Tractography

This work presents an effective multiple subject clustering method using whole-brain tractography datasets. The method is able to obtain fiber clusters that are representative of the population. The proposed approach first applies a fast intra-subject clustering algorithm on each subject obtaining the cluster centroids for all subjects. Second, it compresses the collection of centroids to a latent space through the encoder of a trained autoencoder. Finally, it uses a modified HDBSCAN with adjusted parameters on the encoded centroids of all subjects to obtain the final inter-subject clusters. The results shows that the proposed method outperforms other clustering strategies, and it is able to retrieve known fascicles in a reasonable execution time, achieving a precision over 87% and F1 score above 86% on a collection of 20 simulated subjects.

FFClust: Fast fiber clustering for large tractography datasets for a detailed study of brain connectivity

Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases. The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies-Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8.6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.

Automatic group-wise whole-brain short association fiber bundle labeling based on clustering and cortical surface information

BACKGROUND

Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter.

METHODS

We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan-Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles.

RESULTS

Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h.

CONCLUSION

We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects.