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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.

BackgroundNeuroimage 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).MethodsSixty-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.ResultsASD-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-II, relative to TDC, 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. Non-verbal 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.LimitationsWe 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.ConclusionsASD-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 sub-populations on the spectrum in research.

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.

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.

Highlights • A longitudinal fixel-based analysis in young brain-injured patients. • Lower fiber density and fiber cross-section in young brain-injured patients. • Restoration of left-hemispheric sensorimotor tracts following balance training. • Balance training improved patients’ postural control., 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.

The double layer (DL) TFT consists of an IGZO channel layer with no oxygen flow (NOF) and an IGZO barrier layer with oxygen flow (OF). The DL-TFT demonstrates the field-effect mobility of 19 cm2/V-s, which is 1.6X of the NOF and the OF singe-layer TFTs (SL-TFTs) at the overdrive voltage of 18V and the drain voltage of 0.1V. The conduction band difference between NOF and OF IGZO is 0.28 eV, which was obtained by Tauc method, X-Ray photoelectron spectroscopy (XPS), and Kevin probe force microscopy (KPFM). The carriers in the DL-TFT are confined in the NOF layer by quantum confinement, where the OF layer serves as the barrier to reduce the Coulomb scattering between the channel electrons and oxide charge, and the surface roughness scattering from the IGZO/oxide interface. The results of positive bias temperature instability (PBTI) show that the threshold voltage shift of the DL-TFT is between the individual SL-TFT, and the DL-TFT is close to the lower one of the two SL-TFTs.

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.

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. ispartof: NEUROIMAGE vol:202 ispartof: location:United States status: published

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.Declarations of interestnone.

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.

In this study, we developed semitransparent perovskite (PVSK) photovoltaic (PV) cells with vivid colors, arising from optical interference in the microcavity systems, dependent on the thickness of their indium tin oxide (ITO) optical spacers. After embedding precisely controllable Ag/ITO/Ag microcavity structures, we obtained PVSK cells exhibiting strong coloration: reddish-orange, orange, yellow, yellow-green, yellowish-green, bluish-green, and blue. We obtained an optimized power conversion efficiency (PCE) of 7.2% for a yellow PVSK device under simulated AM 1.5G irradiation (100 mW cm–2). This device also exhibited high stability: After storage in the dark for 70 day in an Ar-filled glovebox, its PCE remained consistently high (up to 7.4%). This approach has potential for use in the preparation of highly efficient, inexpensive, and colorful building-integrated PVs.

The negative bias stress normally yields a negative threshold voltage shift of the thin film transistors due to the additional positive charges trapped in the gate dielectrics or at channel/gate insulator interface. However, a positive threshold voltage shift of the device with the post InGaZnO deposition annealing at 400oC is observed in our devices. The Na+ incorporation from Mo gate into the gate dielectric after 400oC annealing is responsible for this abnormal threshold voltage shift. The movement of Na+ ions toward the gate electrode by the negative gate bias decreases the distance between the gate electrode and the Na+ ions. Therefore, the voltage drop between the gate electrode and the Na+ ions reduces, and a corresponding positive threshold voltage shift is observed. Inserting a SiNx layer between the SiOx gate insulator and the Mo gate electrode can reduce the Na+ mobility, and thus a normal negative threshold voltage shift resumes. Experiment: A Mo layer with the thickness of 100 nm was sputtered on glass, and patterned as the gate electrode. Then 300 nm thick SiOx was deposited on the Mo gate electrode by plasma enhanced chemical vapor deposition (PECVD) at 350oC. A 50 nm a-IGZO active layer was sputtered on SiOx. PDA was performed with an O2/N2 gas with a mixing ratio of 1:4, and the annealing temperature is 350oC or 400oC for 1 hour. A 200 nm SiOx layer was deposited on the active layer by PECVD at 200oC as the etch-stop layer. After that, 100 nm Mo was sputtered as the source and drain electrodes. The channel width and length of all TFTs are 20μm and 10μm, respectively. Additional 100 nm thick SiOx was deposited by PECVD at 200oC as the passivation layer. Result and Discussion: The slightly negative VT shift of the device with 350oC PDA after negative bias stress (NBS) was mainly attributed to the hole-trapping in the gate insulator [1] or at the a-IGZO/gate insulator interface [2]-[3]. As the trapped holes increase, the VT shifts to the negative direction. However, the abnormally negative VT shift of the device with 400oC PDA after NBS was due to the motion of mobile Na+ in the gate insulator [4]. The Mo electrode has the Na+ concentration of ∼1×1015 cm−3, which was confirmed by second ion mass spectroscopy (SIMS). These results suggest that the origin of Na+ may be due to the contamination or the purity of Mo target. On the other hand, the peak concentration of Na+ in the device with 400oC PDA is approximately 5X of 350oC PDA. Fig. 1 shows the transfer characteristics of devices after NBS prebiased with a positive gate voltage (VGS=20V for 600 seconds). The larger positive VT shift of 400oC PDA TFT with prebias after NBS than the fresh devices is due to the large movement of Na+ toward gate electrode. Fig. 2(a) and (b) shows the band diagram to illustrate the Na+ effect. During the prebias period, Na+ near the SiOx/Mo gate electrode interface drifts toward the a-IGZO channel. When negative bias is applied at the Mo gate, Na+ moves toward the gate electrode. As the distance between Na+ and gate electrode decreases, the negative voltage drop at the gate electrode decreases, which “effectively” shifts the VT to the positive direction. In order to reduce the impact of Na+ movement, a 50nm SiNx layer was inserted between the SiOx insulator and the Mo gate electrode, and the VT shift after NBS with prebias recovered to negative direction. For SIMS measurement, the peak concentration of Na+ near SiOx and SiNx interface of the device with SiNx layer are smaller than near SiOx and Mo interface of the device without SiNx layer. On the other hand, the Na+ trapped in SiNx has low mobility and relatively low movement during NBS [5]. Therefore, the effectively positive voltage shift caused by Na+ movement is suppressed, and thus improves the reliability of 400oC PDA device. Acknowledgement: The support of INNOLUX and the Ministry of Science and Technology, Taiwan (MOST 108-2622-8-002-016, MOST 108-2218-E-002-027) is highly acknowledged. [1] Y. K. Moon et al., JJAP (2009) [2] E. N. Cho et al., Trans. Device Mater. Rel. (2011) [3] W. T. Chen et al., EDL (2011) [4] C. Lo et al., J-EDS (2016) [5] B.-D. Choi et al., JJAP (2005) Figure 1

Rare earth co-activated CeO2 phosphors are synthesized via hydrothermal process. Tb3+ and Yb3+ ions are deliberately incorporated in CeO2 host to convert high-energy photons into two near infrared photons with low energy for quantum cutting applications. The observed broad excitation ranging from 260 to 300 nm is associated with f-d transitions of Tb3+ ions. A strong green emission at 540 nm for Tb3+ ions along with typical Yb3+ emission band ranging from 950 to 1100 nm are observed under UV excitation. The PL intensity of Tb3+ ions starts to reduce with increase in Yb3+ emission as Yb3+ concentration increased, indicating an efficient energy transfer from Tb3+ to Yb3+. Photoluminescence decay times of CeO2:Tb3+/Yb3+ phosphors show reduction in lifetime from 2.32 to 0.83 ms with elevation of Yb3+ concentration from 0 to 12.5 mol%. Obtained results reveal the suitability of present phosphors in near-infrared quantum cutting applications.

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.

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.

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.