Diffusion MRI Tractography Research Articles

Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation.

White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.

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.

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

Diffusion MRI tractography processing pipeline requires a large number of steps (typically 20+ steps). If parameters of these steps, number of threads, and random seed generators are not carefully controlled, the resulting tractography can easily be non-reproducible and non-replicable, even in test-test experiments. To handle these issues, we developed TractoFlow. TractoFlow is fully automatic from raw diffusion weighted images to tractography. The pipeline also outputs classical diffusion tensor imaging measures and several fiber orientation distribution function measures. TractoFlow supports the recent Brain Imaging Data Structure (BIDS) format as input and is based on two engines: Nextflow and Singularity. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100%, depending on parameter choices. Moreover, it is easy to use for non-technical users, with little to no installation requirements. TractoFlow is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

Frontostriatal Brain Wiring Organization in Male and Female Healthy Subjects Using a Novel Diffusion Imaging Fiber Cluster Analysis

We assessed frontostriatal brain wiring organization using diffusion MRI tractography in 100 healthy subjects (HSs) from the Human Connectome Project; age: 22 to 35; sex: 46 females and 54 males.

Principles of temporal association cortex organisation as revealed by connectivity gradients

To establish the link between structure and function of any large area of the neocortex, it is helpful to identify its principles of organisation. One way to establish such principles is to investigate how differences in whole-brain connectivity are structured across the area. Here, we use Laplacian eigenmaps on diffusion MRI tractography data to investigate the organisational principles of the human temporal association cortex. We identify three overlapping gradients of connectivity that are, for the most part, consistent across hemispheres. The first gradient reveals an inferior–superior organisation of predominantly longitudinal tracts and separates visual and auditory unimodal and multimodal cortices. The second gradient radiates outward from the posterior middle temporal cortex with the arcuate fascicle as a distinguishing feature; the third gradient is concentrated in the anterior temporal lobe and emanates towards its posterior end. We describe the functional relevance of each of these gradients through the meta-analysis of data from the neuroimaging literature. Together, these results unravel the overlapping dimensions of structural organization of the human temporal cortex and provide a framework underlying its functional multiplicity.

Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan

Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8–90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE). A DCNN tract classification deeply learned spatial trajectories of DWI white matter pathways linking electrical stimulation mapping (ESM) findings from 89 children with FE, and then automatically identified white matter pathways associated with eloquent functions (i.e., primary motor, language, and vision). Clinical utility was examined by 1) measuring the nearest distance between DCNN-determined pathways and ESM, 2) evaluating the effectiveness of DCNN-determined pathways to optimize surgical margins via Kalman filter analysis, and 3) evaluating how accurately changes in DCNN-determined language pathway volume can predict changes in language ability via canonical correlation analysis. DCNN tract classification outperformed other existing methods, achieving an excellent accuracy of 98 % while non-invasively detecting eloquent areas within the spatial resolution of ESM (i.e., 1cm). The Kalman filter analysis found that the preservation of brain areas within a surgical margin determined by DCNN tract classification predicted lack of postoperative deficit with a high accuracy of 92 %. Postoperative change of DCNN-determined language pathway volume showed a significant correlation with postoperative changes in language ability (R = 0.7, p 0.001). Our findings demonstrate that postoperative functional deficits substantially differ according to the extent of resected white matter, and that DCNN tract classification may offer key translational information by identifying these pathways in pediatric epilepsy surgery. DCNN tract classification may be an effective tool to improve surgical outcome of children with FE.

How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning

Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes “sufficient” is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.

Tractostorm: The what, why, and how of tractography dissection reproducibility.

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44–77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ​≤ ​|β| ​≤ ​0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ​≤ ​|β| ​≤ ​0.406) than the consistency-based approach which retained only 30% of connections (0.140 ​≤ ​|β| ​≤ ​0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC ​= ​0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ​≤ ​|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ​≤ ​|0.219|, p ​< ​0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

Individualized Cortical Parcellation Based on Diffusion MRI Tractography.

The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure-function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure-function relationships in the human brain.

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis

Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the conventional measures of connectivity. We mapped g-ratio-based connectomes in 14 patients with MS and compared them with those of 14 age- and sex-matched healthy controls. For comparison, probabilistic tractography was also used to map connectomes based on the number of streamlines (NOS). We found that g-ratio- and NOS-based connectomes comprised significant connectivity reductions in patients with MS, predominantly in the motor, somatosensory, visual, and limbic regions. However, only the g-ratio-based connectome enabled detection of significant increases in nodal strength in patients with MS. Finally, we found that the g-ratio-weighted nodal strength in motor, visual, and limbic regions significantly correlated with inter-individual variation in measures of disease severity. The g-ratio-based connectome can serve as a sensitive biomarker for diagnosing and monitoring disease progression.

Differential tractography as a track-based biomarker for neuronal injury

Diffusion MRI tractography has been used to map the axonal structure of the human brain, but its ability to detect neuronal injury is yet to be explored. Here we report differential tractography, a new type of tractography that utilizes repeat MRI scans and a novel tracking strategy to map the exact segment of fiber pathways with a neuronal injury. We examined differential tractography on multiple sclerosis, Huntington’s disease, amyotrophic lateral sclerosis, and epileptic patients. The results showed that the affected pathways shown by differential tractography matched well with the unique clinical symptoms of the patients, and the false discovery rate of the findings could be estimated using a sham setting to provide a reliability measurement. This novel approach enables a quantitative and objective method to monitor neuronal injury in individuals, allowing for diagnostic and prognostic evaluation of brain diseases.

Tractography delineation of the vertical occipital fasciculus using quantitative T1 mapping

The vertical occipital fasciculus (VOF) is a white-matter tract that connects the ventral and dorsal visual streams. The precise borders of the VOF have been a matter of dispute since its discovery in the 19th century. The presence of an adjacent vertical pathway, the posterior arcuate fasciculus, makes it especially hard to determine the anterior extent of the VOF. By integrating diffusion MRI tractography with quantitative T1 mapping we found that the vertical streamlines originating in the ventral occipito-temporal cortex show a pattern of lower T1 in more posterior streamlines. We used this pattern to develop an automatic procedure for VOF identification based on a sharp increase in the streamline T1 signature along the posterior-anterior axis. We studied the cortical endpoints of the VOF and their relation to known cytoarchitectonic and functional divisions of the cortex. These results show that multi-modal MRI information, which characterizes local tissue microstructure such as myelination, can be used to delineate white-matter tracts in vivo.

White matter information flow mapping from diffusion MRI and EEG

The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.

Structural connectivity-based topography of the human globus pallidus: Implications for therapeutic targeting in movement disorders.

Understanding the topographical organization of the cortico-basal ganglia circuitry is of pivotal importance because of the spreading of techniques such as DBS and, more recently, MR-guided focused ultrasound for the treatment of movement disorders. A growing body of evidence has described both direct cortico- and dento-pallidal connections, although the topographical organization in vivo of these pathways in the human brain has never been reported. To investigate the topographical organization of cortico- and dento-pallidal pathways by means of diffusion MRI tractography and connectivity based parcellation. High-quality data from 100 healthy subjects from the Human Connectome Project repository were utilized. Constrained spherical deconvolution-based tractography was used to reconstruct structural cortico- and dento-pallidal connectivity. Connectivity-based parcellation was performed with a hypothesis-driven approach at three different levels: functional regions (limbic, associative, sensorimotor, and other), lobes, and gyral subareas. External globus pallidus segregated into a ventral associative cluster, a dorsal sensorimotor cluster, and a caudal "other" cluster on the base of its cortical connectivity. Dento-pallidal connections clustered only in the internal globus pallidus, where also associative and sensorimotor clusters were identified. Lobar parcellation revealed the presence in the external globus pallidus of dissociable clusters for each cortical lobe (frontal, parietal, temporal, and occipital), whereas in internal globus pallidus only frontal and parietal clusters were found out. We mapped the topographical organization of both internal and external globus pallidus according to cortical and cerebellar connections. These anatomical data could be useful in DBS, radiosurgery and MR-guided focused ultrasound targeting for treating motor and nonmotor symptoms in movement disorders. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering.

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.

Diffusion-based tractography atlas of the human acoustic radiation

Diffusion MRI tractography allows in-vivo characterization of white matter architecture, including the localization and description of brain fibre bundles. However, some primary bundles are still only partially reconstructed, or not reconstructed at all. The acoustic radiation (AR) represents a primary sensory pathway that has been largely omitted in many tractography studies because its location and anatomical features make it challenging to reconstruct. In this study, we investigated the effects of acquisition and tractography parameters on the AR reconstruction using publicly available Human Connectome Project data. The aims of this study are: (i) using a subgroup of subjects and a reference AR for each subject, define an optimum set of parameters for AR reconstruction, and (ii) use the optimum parameters set on the full group to build a tractography-based atlas of the AR. Starting from the same data, the use of different acquisition and tractography parameters lead to very different AR reconstructions. Optimal results in terms of topographical accuracy and correspondence to the reference were obtained for probabilistic tractography, high b-values and default tractography parameters: these parameters were used to build an AR probabilistic tractography atlas. A significant left-hemispheric lateralization was found in the AR reconstruction of the 34 subjects.

Uncovering the inferior fronto-occipital fascicle and its topological organization in non-human primates: the missing connection for language evolution.

Whether brain networks underlying the multimodal processing of language in humans are present in non-human primates is an unresolved question in primate evolution. Conceptual awareness in humans, which is the backbone of verbal and non-verbal semantic elaboration, involves intracerebral connectivity via the inferior fronto-occipital fascicle (IFOF). While non-human primates can communicate through visual information channels, there has been no formal demonstration that they possess a functional homologue of the human IFOF. Therefore, we undertook a post-mortem diffusion MRI tractography study in conjunction with Klingler micro-dissection to search for IFOF fiber tracts in brain of Old-World (vervet) monkeys. We found clear and concordant evidence from both techniques for the existence of bilateral fiber tracts connecting the frontal and occipital lobes. These tracts closely resembled the human IFOF with respect to trajectory, topological organization, and cortical terminal fields. Moreover, these fibers are clearly distinct from other bundles previously described in this region of monkey brain, i.e., the inferior longitudinal and uncinate fascicles, and the external and extreme capsules. This demonstration of an IFOF in brain of a species that diverged from the human lineage some 22millions years ago enhances our comprehension about the evolution of language and social behavior.

Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI?

Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs’ weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ∼70–90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies.