A traditional and widely used approach for linking neurological symptoms to specific brain regions involves identifying overlap in lesion location across patients with similar symptoms, termed lesion mapping. This approach is powerful and broadly applicable, but has limitations when symptoms do not localize to a single region or stem from dysfunction in regions connected to the lesion site rather than the site itself. A newer approach sensitive to such network effects involves functional neuroimaging of patients, but this requires specialized brain scans beyond routine clinical data, making it less versatile and difficult to apply when symptoms are rare or transient. In this article we show that the traditional approach to lesion mapping can be expanded to incorporate network effects into symptom localization without the need for specialized neuroimaging of patients. Our approach involves three steps: (i) transferring the three-dimensional volume of a brain lesion onto a reference brain; (ii) assessing the intrinsic functional connectivity of the lesion volume with the rest of the brain using normative connectome data; and (iii) overlapping lesion-associated networks to identify regions common to a clinical syndrome. We first tested our approach in peduncular hallucinosis, a syndrome of visual hallucinations following subcortical lesions long hypothesized to be due to network effects on extrastriate visual cortex. While the lesions themselves were heterogeneously distributed with little overlap in lesion location, 22 of 23 lesions were negatively correlated with extrastriate visual cortex. This network overlap was specific compared to other subcortical lesions (P < 10(-5)) and relative to other cortical regions (P < 0.01). Next, we tested for generalizability of our technique by applying it to three additional lesion syndromes: central post-stroke pain, auditory hallucinosis, and subcortical aphasia. In each syndrome, heterogeneous lesions that themselves had little overlap showed significant network overlap in cortical areas previously implicated in symptom expression (P < 10(-4)). These results suggest that (i) heterogeneous lesions producing similar symptoms share functional connectivity to specific brain regions involved in symptom expression; and (ii) publically available human connectome data can be used to incorporate these network effects into traditional lesion mapping approaches. Because the current technique requires no specialized imaging of patients it may prove a versatile and broadly applicable approach for localizing neurological symptoms in the setting of brain lesions.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individualspecific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects.More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 minutes) showed comparable generalizability as parcellations estimated by two state-of-theart methods using five sessions (50 minutes). We also showed that behavioral phenotypes across cognition, personality and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individualspecific network topography might also serve as a fingerprint of human behavior. substantially across participants (Harrison et al., 2015;Laumann et al., 2015;Wang et al., 2015;Glasser et al., 2016;Gordon et al., 2017aGordon et al., , 2017bGordon et al., , 2017cBraga and Buckner, 2017).Yet, the possible behavioral relevance of individual differences in network size and network topography (location and spatial arrangement) remains unknown.We proposed a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific network parcellations of the cerebral cortex and investigated whether individual-specific network topography and size are associated with human behavior. The multiple layers of the MS-HBM allowed explicit separation of inter-subject (betweensubject) and intra-subject (within-session) functional connectivity variability. Previous individual-specific network mappings only accounted for inter-subject variability, but not intra-subject variability. However, inter-subject and intra-subject RSFC variability can be markedly different across regions (Mueller et al., 2013;Chen et al., 2015;Laumann et al., 2015). For example, the motor cortex exhibits high intra-subject functional connectivity variability, but low inter-subject functional connectivity variability .Therefore, observed RSFC variability in the motor cortex might be incorrectly attributed to inter-subject spatial variability of brain networks, rather than just intra-...

No abstract

No abstract

Background Systemic infiltration of the brain by tumor cells is a hallmark of glioma pathogenesis which may cause disturbances in functional connectivity. We hypothesized that aggressive high-grade tumors cause more damage to functional connectivity than low-grade tumors. Methods We designed an imaging tool based on resting-state functional (f)MRI to individually quantify abnormality of functional connectivity and tested it in a prospective cohort of patients with newly diagnosed glioma. Results Thirty-four patients were analyzed (World Health Organization [WHO] grade II, n = 13; grade III, n = 6; grade IV, n = 15; mean age, 48.7 y). Connectivity abnormality could be observed not only in the lesioned brain area but also in the contralateral hemisphere with a close correlation between connectivity abnormality and aggressiveness of the tumor as indicated by WHO grade. Isocitrate dehydrogenase 1 (IDH1) mutation status was also associated with abnormal connectivity, with more alterations in IDH1 wildtype tumors independent of tumor size. Finally, deficits in neuropsychological performance were correlated with connectivity abnormality. Conclusion Here, we suggested an individually applicable resting-state fMRI marker in glioma patients. Analysis of the functional connectome using this marker revealed that abnormalities of functional connectivity could be detected not only adjacent to the visible lesion but also in distant brain tissue, even in the contralesional hemisphere. These changes were associated with tumor biology and cognitive function. The ability of our novel method to capture tumor effects in nonlesional brain suggests a potential clinical value for both individualizing and monitoring glioma therapy.

Solid-state flexible aqueous Zn-ion battery was fabricated with nanostructured polyaniline–cellulose papers as the cathode and Zn-grown graphite papers as the anode. The separator was a flexible gel electrolyte with high ionic conductivity, based on cellulose nanofibers. The Zn-ion battery exhibited energy density of 117.5 and 67.8 mW·h/g at power density of 0.16 and 3.34 W/g, respectively (estimated from total active mass of both cathode and anode). The energy density of the Zn-ion battery was much higher than that of asymmetric supercapacitors with aqueous electrolytes, while maintaining a comparable power density. Meanwhile, good cyclic stability was achieved with a high capacity retention of 84.7% after 1000 charge/discharge cycles at a current density of 4 A/g. More importantly, specific capacity changed little under mechanical bending, and there was only 9% loss after 1000 bending cycles. The solid-state flexible Zn-ion battery has great potential as an energy-storage device for flexible displays and wearable electronics.

Active contour model (ACM) has been a successful method for image segmentation. The existing ACMs poorly segment the images with intensity inhomogeneity or non-homogeneity, and the results highly depend on the initial position of the contour. To overcome these disadvantages, we proposed a fuzzy region-based active contour driven by weighting global and local fitting energy, wherein we propose a fuzzy region energy with local spatial image information, which has been proved convex and ensures the segmentation results independent of initialization, to motivate an initial evolving curve of pseudo level set function (LSF), followed by the pseudo LSF and further smoothed by an edge energy to accurately extract the object boundaries and maintain its distance feature. In addition, in the fuzzy region energy, instead of using the Euler-Lagrange equation to minimize the energy functional, we develop a more direct method to calculate the change of the fuzzy region energy. The experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models, and takes less running time. The code is available at: https://github.com/fangchj2002/FRAGL.

Objective: Current understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease. Methods: Fourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired. Results: Two neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia. Interpretation: Concurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS.