Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis

People with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are both at high risk for Alzheimer’s disease (AD). Behaviorally, both SCD and aMCI have subjective reports of cognitive decline, but the latter suffers a more severe objective cognitive impairment than the former. However, it remains unclear how the brain develops from SCD to aMCI. In the current study, we aimed to investigate the topological characteristics of the white matter (WM) network that can successfully identify individuals with SCD or aMCI from healthy control (HC) and to describe the relationship of pathological changes between these two stages. To this end, three groups were recruited, including 22 SCD, 22 aMCI, and 22 healthy control (HC) subjects. We constructed WM network for each subject and compared large-scale topological organization between groups at both network and nodal levels. At the network level, the combined network indexes had the best performance in discriminating aMCI from HC. However, no indexes at the network level can significantly identify SCD from HC. These results suggested that aMCI but not SCD was associated with anatomical impairments at the network level. At the nodal level, we found that the short-path length can best differentiate between aMCI and HC subjects, whereas the global efficiency has the best performance in differentiating between SCD and HC subjects, suggesting that both SCD and aMCI had significant functional integration alteration compared to HC subjects. These results converged on the idea that the neural degeneration from SCD to aMCI follows a gradual process, from abnormalities at the nodal level to those at both nodal and network levels.

Alteration of Cortical Functional Networks in Default-Mode Network-Related Regions in Mood Disorders with Resting-State Electroencephalography

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal, belonging to the default-mode network (DMN); however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes in DMN-related regions. Our findings might suggest more altered network in the DMN-related regions in patients with BD than in those with MDD.

FDG-PET/CT and Pathology in Newly Diagnosed Head and Neck Cancer: ACRIN 6685 Trial, FDG-PET/CT cN0

Objective FDG-PET/CT (fluorodeoxyglucose–positron emission tomography/computed tomography) is effective to assess for occult neck nodal disease. We report risks and patterns of nodal disease based on primary site and nodal level from data on the dissected cN0 per the results from ACRIN 6685. Study Design Prospective nonrandomized enrollment included participants with first-time head and neck squamous cell carcinoma and at least 1 cN0 neck side to be dissected. Setting Twenty-four ACRIN-certified centers internationally (American College of Radiology Imaging Network). Methods A total of 287 participants were enrolled. Preoperative FDG-PET/CT findings were centrally reviewed and compared with pathology. Incidence, relative risk, pattern of lymph node involvement, and impact upon neck dissection were reported. Results An overall 983 nodal levels were dissected (n = 261 necks, n = 203 participants). The highest percentages of ipsilateral positive nodes by primary location and nodal level were oral cavity (level I, 17/110, 15.5%), pharynx (level II, 6/30, 20.0%), and larynx (level VI, 1/3, 33.3%). Conclusion Levels at greatest risk for nodal disease in cN0 in terms of ipsilateral neck dissection are level I (oral cavity), II (pharynx), and VI (larynx). These data should be considered when treating patients presenting with cN0. This is the first study to comprehensively report the incidence, location, and risk of metastases in cN0 in the FDG-PET/CT era.

Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia

Background Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. Methods Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. Results At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. Conclusions These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder

Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder.

Nodal Level Adaptive Security for IOT-Enabled Sensors through Dynamic Reconfigurable Encryption

IOT-enabled sensors have been deployed in the wide area to perform various applications. Information security is an important aspect in wireless sensor networks. Since the attackers can be able to hack the information even at the node level, improved security mechanism have to be implemented. In this paper, nodal level security is done through dynamic encryption technique. The advantage of dynamic encryption is achieved by adaptive security. The proposed method involves a system-on-chip (SoC) design to provide a dynamically reconfigurable encryption methodology which leads to improved security level and also the energy efficiency. Dynamic encryption creates the confusion among the hackers about the tracking of security keys. The results shows that by dynamically selecting the encryption module through soft-core processor based on the available power budget, an energy efficient security solution is obtained for sensor nodes with reduced resources utilization.

Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.

Node-dependent kinematic elements for the dynamic analysis of beams with piezo-patches

This article extends the use of one-dimensional elements with node-dependent kinematics to the dynamic analysis of beam structures with piezo-patches. Node-dependent kinematics allows the kinematic assumptions to be defined individually on each finite element node, leading to finite element models with variable nodal kinematics. Derived from Carrera unified formulation, node-dependent kinematics facilitates the mathematical refinement to an arbitrary order at any desirable region on the nodal level while keeping the compactness of the formulation. As an ideal approach to simulate structures with special local features, node-dependent kinematics has been employed to model piezo-patches in static cases. In this work, the application of node-dependent beam elements in dynamic problems is demonstrated. Node-dependent kinematics is applied to increase the numerical accuracy in the areas where the piezo-patches lie in through sufficiently refined models, while lower order assumptions are used elsewhere. The dissimilar constitutive relations of neighboring components are appropriately considered with layer-wise models. Both open- and short-circuit conditions are considered. The results are compared against those from literature. The numerical study shows that the adoption of node-dependent kinematics allows accurate results to be obtained at reduced computational costs.