Tractome: a visual data mining tool for brain connectivity analysis

A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study

The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.

Connectivity by the Frontal Aslant Tract (FAT) Explains Local Functional Specialization of the Superior and Inferior Frontal Gyri in Humans When Choosing Predictive over Reactive Strategies: A Tractography-Guided TMS Study

Predictive and reactive behaviors represent two mutually exclusive strategies in a sensorimotor task. Predictive behavior consists in internally estimating timing and features of a target stimulus and relies on a cortical medial frontal system [superior frontal gyrus (SFG)]. Reactive behavior consists in waiting for actual perception of the target stimulus and relies on the lateral frontal cortex [inferior frontal gyrus (IFG)]. We investigated whether SFG-IFG connections by the frontal aslant tract (FAT) can mediate predictive/reactive interactions. In 19 healthy human volunteers, we applied online transcranial magnetic stimulation (TMS) to six spots along the medial and lateral terminations of the FAT, during the set period of a delayed reaction task. Such scenario can be solved using either predictive or reactive strategies. TMS increased the propensity toward reactive behavior if applied to a specific portion of the IFG and increased predictive behavior when applied to a specific SFG spot. The two active spots in the SFG and IFG were directly connected by a sub-bundle of FAT fibers as indicated by diffusion-weighted imaging (DWI) tractography. Since FAT connectivity identifies two distant cortical nodes with opposite functions, we propose that the FAT mediates mutually inhibitory interactions between SFG and IFG to implement a "winner takes all" decisional process. We hypothesize such role of the FAT to be domain-general, whenever competition occurs between internal predictive and external reactive behaviors. Finally, we also show that anatomic connectivity is a powerful factor to explain and predict the spatial distribution of brain stimulation effects.SIGNIFICANCE STATEMENT We interact with sensory cues adopting two main mutually-exclusive strategies: (1) trying to anticipate the occurrence of the cue or (2) waiting for the GO-signal to be manifest and react to it. Here, we showed, by using noninvasive brain stimulation [transcranial magnetic stimulation (TMS)], that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which mediates the direct competition and exclusiveness between predictive and reactive strategies. More generally, implementing anatomic connectivity in TMS studies strongly reduces spatial noise.

Segregated circuits for phonemic and semantic fluency: A novel patient-tailored disconnection study

Phonemic and semantic fluency are neuropsychological tests widely used to assess patients' language and executive abilities and are highly sensitive tests in detecting language deficits in glioma patients. However, the networks that are involved in these tasks could be distinct and suggesting either a frontal (phonemic) or temporal (semantic) involvement. 42 right-handed patients (26 male, mean age = 52.5 years, SD=±13.3) were included in this retrospective study. Patients underwent awake (54.8%) or asleep (45.2%) surgery for low-grade (16.7%) or high-grade-glioma (83.3%) in the frontal (64.3%) or temporal lobe (35.7%) of the left (50%) or right (50%) hemisphere. Pre-operative tractography was reconstructed for each patient, with segmentation of the inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), third branch of the superior longitudinal fasciculus (SLF-III), frontal aslant tract (FAT), and cortico-spinal tract (CST). Post-operative percentage of damage and disconnection of each tract, based on the patients' surgical cavities, were correlated with verbal fluencies scores at one week and one month after surgery. Analyses of differences between fluency scores at these timepoints (before surgery, one week and one month after surgery) were performed; lesion-symptom mapping was used to identify the correlation between cortical areas and post-operative scores. Immediately after surgery, a transient impairment of verbal fluency was observed, that improved within a month. Left hemisphere lesions were related to a worse verbal fluency performance, being a damage to the left superior frontal or temporal gyri associated with phonemic or semantic fluency deficit, respectively. At a subcortical level, disconnection analyses revealed that fluency scores were associated to the involvement of the left FAT and the left frontal part of the IFOF for phonemic fluency, and the association was still present one month after surgery. For semantic fluency, the correlation between post-surgery performance emerged for the left AF, UF, ILF and the temporal part of the IFOF, but disappeared at the follow-up. This approach based on the patients' pre-operative tractography, allowed to trace for the first time a dissociation between white matter pathways integrity and verbal fluency after surgery for glioma resection. Our results confirm the involvement of a frontal anterior pathway for phonemic fluency and a ventral temporal pathway for semantic fluency. Finally, our longitudinal results suggest that the frontal executive pathway requires a longer interval to recover compared to the semantic one.

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.

Prediction of postoperative deficits using an improved diffusion-weighted imaging maximum a posteriori probability analysis in pediatric epilepsy surgery

OBJECTIVE

This study is aimed at improving the clinical utility of diffusion-weighted imaging maximum a posteriori probability (DWI-MAP) analysis, which has been reported to be useful for predicting postoperative motor, language, and visual field deficits in pediatric epilepsy surgery. The authors determined the additive value of a new clustering mapping method in which average direct-flip distance (ADFD) reclassifies the outliers of original DWI-MAP streamlines by referring to their minimum distances to the exemplar streamlines (i.e., medoids).

METHODS

The authors studied 40 children with drug-resistant focal epilepsy (mean age 8.7 ± 4.8 years) who had undergone resection of the presumed epileptogenic zone and had five categories of postoperative deficits (i.e., hemiparesis involving the face, hand, and/or leg; dysphasia requiring speech therapy; and/or visual field cut). In pre- and postoperative images of the resected hemisphere, DWI-MAP identified a total of nine streamline pathways: C1 = face motor area, C2 = hand motor area, C3 = leg motor area, C4 = Broca's area-Wernicke's area, C5 = premotor area-Broca's area, C6 = premotor area-Wernicke's area, C7 = parietal area-Wernicke's area, C8 = premotor area-parietal area, and C9 = occipital lobe-lateral geniculate nucleus. For each streamline of the identified pathway, the minimal ADFD to the nine exemplars corrected the pathway membership. Binary logistic regression analysis was employed to determine how accurately two fractional predictors, Δ1-9 (postoperative volume change of C1-9) and γ1-9 (preoperatively planned volume of C1-9 resected), predicted postoperative motor, language, and visual deficits.

RESULTS

The addition of ADFD to DWI-MAP analysis improved the sensitivity and specificity of regression models for predicting postoperative motor, language, and visual deficits by 28% for Δ1-3 (from 0.62 to 0.79), 13% for Δ4-8 (from 0.69 to 0.78), 13% for Δ9 (from 0.77 to 0.87), 7% for γ1-3 (from 0.81 to 0.87), 1% for γ4-8 (from 0.86 to 0.87), and 24% for γ9 (from 0.75 to 0.93). Preservation of the eloquent pathways defined by preoperative DWI-MAP analysis with ADFD (up to 97% of C1-4,9) prevented postoperative motor, language, and visual deficits with sensitivity and specificity ranging from 88% to 100%.

CONCLUSIONS

The present study suggests that postoperative functional outcome substantially differs according to the extent of resected white matter encompassing eloquent cortex as determined by preoperative DWI-MAP analysis. The preservation of preoperative DWI-MAP-defined pathways may be crucial to prevent postoperative deficits. The improved DWI-MAP analysis may provide a complementary noninvasive tool capable of guiding the surgical margin to minimize the risk of postoperative deficits for children.

Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism, and compared our models with current state-ofthe-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73 -100%) for the objective detection of functionally-important white matter pathways including ESM determined eloquent areas such as primary motor, aphasia, speech arrest, auditory, and visual functions. The ?ndings of this study encourage further investigations to determine if DWICNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level, but also the underlying ?bers connecting these cortical areas, to minimize or predict postsurgical functional de?cits. This study translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM, fMRI) are suboptimal. The implementation will be released at https://github. com/HaotianMXu/Brain-?ber-classi?cation-using-CNNs.

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

White Matter Tract Segmentation as Multiple Linear Assignment Problems

Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.

Visualization, Interaction and Tractometry: Dealing with Millions of Streamlines from Diffusion MRI Tractography

Recently proposed tractography and connectomics approaches often require a very large number of streamlines, in the order of millions. Generating, storing and interacting with these datasets is currently quite difficult, since they require a lot of space in memory and processing time. Compression is a common approach to reduce data size. Recently such an approach has been proposed consisting in removing collinear points in the streamlines. Removing points from streamlines results in files that cannot be robustly post-processed and interacted with existing tools, which are for the most part point-based. The aim of this work is to improve visualization, interaction and tractometry algorithms to robustly handle compressed tractography datasets. Our proposed improvements are threefold: (i) An efficient loading procedure to improve visualization (reduce memory usage up to 95% for a 0.2 mm step size); (ii) interaction techniques robust to compressed tractograms; (iii) tractometry techniques robust to compressed tractograms to eliminate biased in tract-based statistics. The present work demonstrates the need of correctly handling compressed streamlines to avoid biases in future tractometry and connectomics studies.

Alignment of Tractograms As Graph Matching

The white matter pathways of the brain can be reconstructed as 3D polylines, called streamlines, through the analysis of diffusion magnetic resonance imaging (dMRI) data. The whole set of streamlines is called tractogram and represents the structural connectome of the brain. In multiple applications, like group-analysis, segmentation, or atlasing, tractograms of different subjects need to be aligned. Typically, this is done with registration methods, that transform the tractograms in order to increase their similarity. In contrast with transformation-based registration methods, in this work we propose the concept of tractogram correspondence, whose aim is to find which streamline of one tractogram corresponds to which streamline in another tractogram, i.e., a map from one tractogram to another. As a further contribution, we propose to use the relational information of each streamline, i.e., its distances from the other streamlines in its own tractogram, as the building block to define the optimal correspondence. We provide an operational procedure to find the optimal correspondence through a combinatorial optimization problem and we discuss its similarity to the graph matching problem. In this work, we propose to represent tractograms as graphs and we adopt a recent inexact sub-graph matching algorithm to approximate the solution of the tractogram correspondence problem. On tractograms generated from the Human Connectome Project dataset, we report experimental evidence that tractogram correspondence, implemented as graph matching, provides much better alignment than affine registration and comparable if not better results than non-linear registration of volumes.