White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis.

BACKGROUND: Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. METHODS: Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. RESULTS: The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aß42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). CONCLUSION: Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aß42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism.

Whether brain stimulation could modulate brain structure in autism remains unknown. This study explored the impact of continuous theta burst stimulation (cTBS) over the left dorsolateral prefrontal cortex (DLPFC) on white matter macro/microstructure in intellectually able children and emerging adults with autism. Sixty autistic participants were randomized (30 active) and received active or sham cTBS for eight weeks twice per week, 16 total sessions using a double-blind (participant-, rater-, analyst-blinded) design. All participants received high-angular resolution diffusion MR imaging at baseline and week 8. Twenty-eight participants in the active group and twenty-seven in the sham group with good imaging quality entered the final analysis. With longitudinal fixel-based analysis and network-based statistics, we found no significant difference between the active and sham groups in changes of white matter macro/microstructure and connections following cTBS. In addition, we found no association between baseline white matter macro/microstructure and autistic symptom changes from baseline to week 8 in the active group. In conclusion, we did not find a significant impact of left DLPFC cTBS on white matter macro/microstructure and connections in children and emerging adults with autism. These findings need to be interpreted in the context that the current intellectually able cohort in a single university hospital site limits the generalizability. Future studies are required to investigate if higher stimulation intensities and/or doses, other personal factors, or rTMS parameters might confer significant brain structural changes visible on MRI in ASD.

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo.

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism.

Following the published behavioral and cognitive results of this single-blind parallel sham-controlled randomized clinical trial, the current study aimed to explore the impact of intermittent theta burst stimulation (iTBS), a variant of excitatory transcranial magnetic stimulation, over the bilateral posterior superior temporal sulci (pSTS) on white matter macro/microstructure in intellectually able children and adolescents with autism. Participants were randomized and blindly received active or sham iTBS for 4 weeks (the single-blind sham-controlled phase). Then, all participants continued to receive active iTBS for another 4 weeks (the open-label phase). The clinical results were published elsewhere. Here, we present diffusion magnetic resonance imaging data on potential changes in white matter measures after iTBS. Twenty-two participants in Active-Active group and 27 participants in Sham-Active group underwent multi-shell high angular resolution diffusion imaging (64-direction for b = 2000 & 1000 s/mm2, respectively) at baseline, week 4, and week 8. With longitudinal fixel-based analysis, we found no white matter changes following iTBS from baseline to week 4 (a null treatment by time interaction and a null within-group paired comparison in the Active-Active group), nor from baseline to week 8 (null within-group paired comparisons in both Active-Active and Sham-Active groups). As for the brain-symptoms relationship, we did not find baseline white matter metrics associated with symptom changes at week 4 in either group. Our results raise the question of what the minimal cumulative stimulation dose required to induce the white matter plasticity is.

A diffusion MRI study of brain white matter microstructure in adolescents and adults with a Fontan circulation: Investigating associations with resting and peak exercise oxygen saturations and cognition.

INTRODUCTION: Adolescents and adults with a Fontan circulation are at risk of cognitive dysfunction; Attention and processing speed are notable areas of concern. Underlying mechanisms and brain alterations associated with worse long-term cognitive outcomes are not well determined. This study investigated brain white matter microstructure in adolescents and adults with a Fontan circulation and associations with resting and peak exercise oxygen saturations (SaO2), predicted maximal oxygen uptake during exercise (% pred VO2), and attention and processing speed. METHODS: Ninety-two participants with a Fontan circulation (aged 13-49 years, ≥5 years post-Fontan completion) had diffusion MRI. Averaged tract-wise diffusion tensor imaging (DTI) metrics were generated for 34 white matter tracts of interest. Resting and peak exercise SaO2 and % pred VO2 were measured during cardiopulmonary exercise testing (CPET; N = 81). Attention and processing speed were assessed using Cogstate (N = 67 and 70, respectively). Linear regression analyses adjusted for age, sex, and intracranial volume were performed to investigate associations between i) tract-specific DTI metrics and CPET variables, and ii) tract-specific DTI metrics and attention and processing speed z-scores. RESULTS: Forty-nine participants were male (53%), mean age was 23.1 years (standard deviation (SD) = 7.8 years). Mean resting and peak exercise SaO2 were 93.1% (SD = 3.6) and 90.1% (SD = 4.7), respectively. Mean attention and processing speed z-scores were -0.63 (SD = 1.07) and -0.72 (SD = 1.44), respectively. Resting SaO2 were positively associated with mean fractional anisotropy (FA) of the left corticospinal tract (CST) and right superior longitudinal fasciculus I (SLF-I) and negatively associated with mean diffusivity (MD) and radial diffusivity (RD) of the right SLF-I (p ≤ 0.01). Peak exercise SaO2 were positively associated with mean FA of the left CST and were negatively associated with mean RD of the left CST, MD of the left frontopontine tract, MD, RD and axial diffusivity (AD) of the right SLF-I, RD of the left SLF-II, MD, RD and AD of the right SLF-II, and MD and RD of the right SLF-III (p ≤ 0.01). Percent predicted VO2 was positively associated with FA of the left uncinate fasciculus (p < 0.01). Negative associations were identified between mean FA of the right arcuate fasciculus, right SLF-II and right SLF-III and processing speed (p ≤ 0.01). No significant associations were identified between DTI-based metrics and attention. CONCLUSION: Chronic hypoxemia may have long-term detrimental impact on white matter microstructure in people living with a Fontan circulation. Paradoxical associations between processing speed and tract-specific DTI metrics could be suggestive of compensatory white matter remodeling. Longitudinal investigations focused on the mechanisms and trajectory of altered white matter microstructure and associated cognitive dysfunction in people with a Fontan circulation are required to better understand causal associations.

White matter microstructural and morphometric alterations in autism: implications for intellectual capabilities.

BACKGROUND: Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). METHODS: Sixty-five participants with ASD (ASD-Whole; 16.6 ± 5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3 ± 5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross section (FC), and a combined index (FDC), and brain symptom/cognition associations. RESULTS: ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-IA, relative to TDC, had no significant discrepancies, while ASD-II showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Nonverbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. LIMITATIONS: We could not preclude the potential effects of age and sex from the ASD cohort, although statistical tests suggested that these factors were not influential. Our results could be confounded by variable psychiatric comorbidities and psychotropic medication uses in our ASD participants recruited from outpatient clinics, which is nevertheless closer to a real-world presentation of ASD. The outcomes related to ASD-MV were considered preliminaries due to the small sample size within this subgroup. Finally, our study design did not include intellectual impairment-only participants without ASD to disentangle the mixture of autistic and intellectual symptoms. CONCLUSIONS: ASD-associated white matter alterations appear driven by individuals with II and potentially further by MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied subpopulations on the spectrum in research.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study.

Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.

Structural Connectivity Remote From Lesions Correlates With Somatosensory Outcome Poststroke.

Background and Purpose: Changes in connectivity of white matter fibers remote to a stroke lesion, suggestive of structural connectional diaschisis, may impact on clinical impairment and recovery after stroke. However, until recently, we have not had tract-specific techniques to map changes in white matter tracts in vivo in humans to enable investigation of potential mechanisms and clinical impact of such remote changes. Our aim was to identify and quantify white matter tracts that are affected remote from a stroke lesion and to investigate the associations between reductions in tract-specific connectivity and impaired touch discrimination function after stroke. Methods: We applied fixel-based analysis to diffusion magnetic resonance imaging data from 37 patients with stroke (right lesion =16; left lesion =21) and 26 age-matched healthy adults. Three quantitative metrics were compared between groups: fiber density; fiber-bundle cross-section; and a combined measure of both (fiber-bundle cross-section) that reflects axonal structural connectivity. Results: Compared with healthy adults, patients with stroke showed significant common fiber-bundle cross-section and fiber density reductions in 4 regions remote from focal lesions that play roles in somatosensory and spatial information processing. Structural connectivity along the somatosensory fibers of the lesioned hemisphere was correlated with contralesional hand touch function. Touch function of the ipsilesional hand was associated with connectivity of the superior longitudinal fasciculus, and, for the right-lesion group, the corpus callosum. Conclusions: Remote tract-specific reductions in axonal connectivity indicated by diffusion imaging measures are observed in the somatosensory network after stroke. These remote white matter connectivity reductions, indicative of structural connectional diaschisis, are associated with touch impairment in patients with stroke.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges.

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Longitudinal fixel-based analysis reveals restoration of white matter alterations following balance training in young brain-injured patients.

BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is one of the leading causes of death and disability in children and adolescents. Young TBI patients suffer from gross motor deficits, such as postural control deficits, which can severely compromise their daily life activities. However, little attention has been devoted to uncovering the underlying white matter changes in response to training in TBI. In this study, we used longitudinal fixel-based analysis (FBA), an advanced diffusion imaging analysis technique, to investigate the effect of a balance training program on white matter fibre density and morphology in a group of young TBI patients. METHODS: Young patients with moderate-to-severe TBI (N = 17, 10 females, mean age = 13 ± 3 years) and age-matched controls (N = 17) underwent a home-based balance training program. Diffusion MRI scans together with gross motor assessments, including the gross motor items of the Bruininks-Oseretsky Test of Motor Proficiency, the Activities-Specific Balance Confidence (ABC) Scale, and the Sensory Organization Test (SOT) were administered before and at completion of 8-weeks of training. We used FBA to compare microstructural differences in fibre density (FD), macrostructural (morphological) changes in fibre cross-section (FC), and the combined FD and FC (FDC) metric across the whole brain. We then performed a longitudinal analysis to test whether training restores the white matter in the regions found to be damaged before treatment. RESULTS: Whole-brain fixel-based analysis revealed lower FD and FC in TBI patients compared to the control group across several commissural tracts, association fibres and projection fibres, with FD reductions of up to 50%. Following training, TBI patients showed a significant interaction effect between Group and Time for the SOT test, as well as significant increases in macrostructural white matter (i.e., FC & FDC) in left sensorimotor tracts. The amount of change in FC and FDC over time was, however, not associated with behavioural changes. DISCUSSION: Our fixel-based findings identified both microstructural and macrostructural abnormalities in young TBI patients. The longitudinal results provide a deeper understanding of the neurobiological mechanisms underlying balance training, which will allow clinicians to make more effective treatment decisions in everyday clinical practice with brain-injured patients.

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.

Optic Radiation Tractography in Pediatric Brain Surgery Applications: A Reliability and Agreement Assessment of the Tractography Method.

BACKGROUND: Optic radiation (OR) tractography may help predict and reduce post-neurosurgical visual field deficits. OR tractography methods currently lack pediatric and surgical focus. PURPOSE: We propose a clinically feasible OR tractography strategy in a pediatric neurosurgery setting and examine its intra-rater and inter-rater reliability/agreements. METHODS: Preoperative and intraoperative MRI data were obtained from six epilepsy and two brain tumor patients on 3 Tesla MRI scanners. Four raters with different clinical experience followed the proposed strategy to perform probabilistic OR tractography with manually drawing anatomical landmarks to reconstruct the OR pathway, based on fiber orientation distributions estimated from high angular resolution diffusion imaging data. Intra- and inter-rater reliabilities/agreements of tractography results were assessed using intraclass correlation coefficient (ICC) and dice similarity coefficient (DSC) across various tractography and OR morphological metrics, including the lateral geniculate body positions, tract volumes, and Meyer's loop position from temporal anatomical landmarks. RESULTS: Good to excellent intra- and inter-rater reproducibility was demonstrated for the majority of OR reconstructions (ICC = 0.70-0.99; DSC = 0.84-0.89). ICC was higher for non-lesional (0.82-0.99) than lesional OR (0.70-0.99). The non-lesional OR's mean volume was 22.66 cm3; the mean Meyer's loop position was 29.4 mm from the temporal pole, 5.89 mm behind of and 10.26 mm in front of the temporal ventricular horn. The greatest variations (± 1.00-3.00 mm) were observed near pathology, at the tract edges or at cortical endpoints. The OR tractography were used to assist surgical planning and guide lesion resection in all cases, no patient had new visual field deficits postoperatively. CONCLUSION: The proposed tractography strategy generates reliable and reproducible OR tractography images that can be reliably implemented in the routine, non-emergency pediatric neurosurgical setting.

MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.

MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.

Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential.

When using diffusion MRI streamlines tractograms to construct structural connectomes, ideally, each streamline should connect exactly 2 regions-of-interest (i.e. network nodes) as defined by a given brain parcellation scheme. However, the ill-posed nature of termination criteria in many tractography algorithms can cause streamlines apparently being associated with zero, one, or more than two grey matter (GM) nodes; streamlines that terminate in white matter or cerebrospinal fluid may even end up being assigned to nodes if the definitions of these nodes are not strictly constrained to genuine GM areas, resulting in a misleading connectome in non-trivial ways. Based on both in-house MRI data and state-of-the-art data provided by the Human Connectome Project, this study investigates the actual influence of streamline-to-node assignment methods, and their interactions with fibre-tracking terminations and brain parcellations, on the construction of pairwise regional connectivity and subsequent connectomic measures. Our results show that the frequency of generating successful pairwise connectivity is heavily affected by the convoluted interactions between the applied strategies for connectome construction, and that minor changes in the mechanism can cause significant variations in the within- and between-module connectivity strengths as well as in the commonly-used graph theory metrics. Our data suggest that these fundamental processes should not be overlooked in structural connectomics research, and that improved data quality is not in itself sufficient to solve the underlying problems associated with assigning streamlines to brain nodes. We demonstrate that the application of advanced fibre-tracking techniques that are designed to correct for inaccuracies of track terminations with respect to anatomical information at the fibre-tracking stage is advantageous to the subsequent connectome construction process, in which pairs of parcellation nodes can be more robustly identified from streamline terminations via a suitable assignment mechanism.

A Novel Method for Extracting Hierarchical Functional Subnetworks Based on a Multisubject Spectral Clustering Approach.

Brain network modularity analysis has attracted increasing interest due to its capability in measuring the level of integration and segregation across subnetworks. Most studies have focused on extracting modules at a single level, although brain network modules are known to be organized in a hierarchical manner. A few techniques have been developed to extract hierarchical modularity in human functional brain networks using resting-state functional magnetic resonance imaging (fMRI) data; however, the focus of those methods is binary networks produced by applying arbitrary thresholds of correlation coefficients to the connectivity matrices. In this study, we propose a new multisubject spectral clustering technique, called group-level network hierarchical clustering (GNetHiClus), to extract the hierarchical structure of the functional network based on full weighted connectivity information. The most reliable results of hierarchical clustering are then estimated using a bootstrap aggregation algorithm. Specifically, we employ a voting-based ensemble method, that is, majority voting; random subsamples with replacement are created for clustering brain regions, which are further aggregated to select the most reliable clustering results. The proposed method is evaluated over a range of group sample sizes, based on resting-state fMRI data from the Human Connectome Project. Our results show that GNetHiClus can extract relatively consistent hierarchical network structures across a range of sample sizes investigated. In addition, the results demonstrate that GNetHiClus can hierarchically cluster brain functional networks into specialized subnetworks from upper-to-lower level, including the high-level cognitive and the low-level perceptual networks. Conversely, from lower-to-upper level, information processed by specialized lower level subnetworks is integrated into upper level for achieving optimal efficiency for brain functional communications. Importantly, these findings are consistent with the concept of network segregation and integration, suggesting that the proposed technique can be helpful to promote the understanding of brain network from a hierarchical point of view.

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.

Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes.

The human brain is a complex network, in which some brain regions, denoted as 'hub' regions, play critically important roles. Some of these hubs are highly interconnected forming a rich-club organization, which has been identified based on the degree metric from structural connectomes constructed using diffusion tensor imaging (DTI)-based fiber tractography. However, given the limitations of DTI, the yielded structural connectomes are largely compromised, possibly affecting the characterization of rich-club organizations. Recent progress in diffusion MRI and fiber tractography now enable more reliable but also very dense structural connectomes to be achieved. However, while the existing rich-club analysis method is based on weighted networks, it is essentially built upon degree metric and, therefore, not suitable for identifying rich-club organizations from such dense networks, as it yields nodes with indistinguishably high degrees. Therefore, we propose a novel method, i.e. Rich-club organization Identification using Combined H-degree and Effective strength to h-degree Ratio (RICHER), to identify rich-club organizations from dense weighted networks. Overall, it is shown that more robust rich-club organizations can be achieved using our proposed framework (i.e., state-of-the-art fiber tractography approaches and our proposed RICHER method) in comparison to the previous method focusing on weighted networks based on degree, i.e., RC-degree. Furthermore, by simulating network attacks in 3 ways, i.e., attack to non-rich-club/non-rich-club edges (NRC2NRC), rich-club/non-rich-club edges (RC2NRC), and rich-club/rich-club edges (RC2RC), brain network damage consequences have been evaluated in terms of global efficiency (GE) reductions. As expected, significant GE reductions have been detected using our proposed framework among conditions, i.e., NRC2NRC < RC2NRC, NRC2NRC < RC2RC and RC2NRC < RC2RC, which however have not been detected otherwise.

Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics.

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.