Adsorption of typical NDMA precursors by superfine powdered activated carbon: Critical role of particle size reduction.

Control of N-nitrosodimethylamine (NDMA) in drinking water could be achieved by removing its precursors as one practical way. Herein, superfine powdered activated carbons with a diameter of about 1 µm (SPACs) were successfully prepared by grinding powdered activated carbon (PAC, D50=24.3 µm) and applied to remove model NDMA precursors, i.e. ranitidine (RAN) and nizatidine (NIZ). Results from grain diameter experiments demonstrated that the absorption velocity increased dramatically with decreasing particle size, and the maximum increase in k2 was 26.8-folds for RAN and 33.4-folds for NIZ. Moreover, kinetic experiments explained that rapid absorption could be attributed to the acceleration of intraparticle diffusion due to the shortening of the diffusion path. Furthermore, performance comparison experiments suggested that the removal of RAN and NIZ (C0=0.5 mg/L) could reach 61.3% and 60%, respectively, within 5 min, when the dosage of SAPC-1.1 (D50=1.1 µm) was merely 5 mg/L, while PAC-24.3 could only eliminate 17.5% and 18.6%. The adsorption isotherm was well defined by Langmuir isotherm model, indicating that the adsorption of RAN/NIZ was a monolayer coverage process. The adsorption of RAN or NIZ by SAPC-1.1 and PAC-24.3 was strongly pH dependent, and high adsorption capacity could be observed under the condition of pH > pka+1. The coexistence of humic acid (HA) had no significant effect on the adsorption performance because RAN/NIZ may be coupled with HA and removed simultaneously. The coexistence of anions had little effect on the adsorption also. This study is expected to provide an alternative strategy for drinking water safety triggered by NDMA.

Evaluating the image quality and local tumor invasion of uterine cancer by MUSE DWI with RPG.

Diffusion-weighted imaging (DWI) is widely utilized for evaluating uterine diseases. However, the prevalent technique, single-shot echo planar imaging (ssEPI), is hindered by notable image distortion and low spatial resolution. Therefore, optimizing uterine DWI sequences is vital for improving image quality. To investigate the efficacy of multiplexed sensitivity encoding (MUSE) combined with reverse polarity gradient (RPG) in enhancing uterine DWI quality and assessing local invasion in endometrial and cervical cancer, we included 149 patients. Each patient underwent DWI of the uterus using ssEPI, MUSE, and RPG-MUSE techniques. We compared these three sequences regarding image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion rate (GDR), ADC values, accuracy in determining the extent of cancer invasion, and the Area Under the Curve (AUC) for identifying endometrial cancer and benign endometrial lesions using ADC values. The results indicated that RPG-MUSE DWI had less artifacts than MUSE and ssEPI (P < 0.05). Lesions were more apparent in MUSE and RPG-MUSE sequences compared to ssEPI (P < 0.05), with RPG-MUSE providing clearer lesion edges (P < 0.05). Additionally, RPG-MUSE DWI demonstrated higher SNR and CNR than ssEPI and MUSE (P < 0.05), along with a lower GDR (P < 0.05). The ADC values did not show significant differences among the three sequences (P > 0.05). Furthermore, the AUC of the ROC for detecting endometrial cancer and benign endometrial lesions using ADC values showed no significant differences across the sequences (P = 0.7609, 0.7186, and 0.8706, respectively). When combining each DWI sequence with T2WI for FIGO staging, RPG-MUSE and MUSE exhibited better alignment with pathology findings compared to ssEPI (P < 0.05). Overall, RPG-MUSE DWI showed fewer artifacts, higher SNR and CNR, reduced geometric distortion, and clearer lesion visualization compared to ssEPI and MUSE, leading to a more precise assessment of endometrial and cervical cancer invasion extent.

Comparative study of hybrid, tri-hybrid and tetra-hybrid nanoparticles in MHD unsteady flow with chemical reaction, activation energy, Soret-Dufour effect and sensitivity analysis over Non-Darcy porous stretching cylinder.

Present study investigates influence of Soret-Dufour effects on MHD unsteady flow of a tetra-hybrid nanofluid (Al2O3, Cu, SiO2 and TiO2 with base fluid water) within non-Darcy porous stretching cylinder. Additionally, chemical reaction, activation energy, and heat generation are considered. This research contributes to the understanding of how these nanofluids can optimize heat and mass transfer process in applications such as advanced cooling systems, solar collectors, biomedical devices, and chemical reactors. Tetra-hybrid nanofluids are selected as per novel aspects for their exceptional ability to adapt their properties for diverse applications, including advanced thermal management systems and scenarios requiring high thermal and electrical conductivity. The comparison between hybrid, tri-hybrid, and tetra-hybrid nanofluids serves to evaluate how increasing complexity and diversity in nanoparticle combinations impact thermal and flow characteristics. The prevailing PDE's undergo transformation into nonlinear ODE's through the utilization of similarity variables and numerically solved using fifth order Runge-Kutta Fehlberg method with shooting method. It is established that rising unsteady parameter values result in increasing velocity profile and rising shape factor parameter result in higher heat transfer. Specifically, the Nusselt number increases by 24 % in the tri-hybrid and 11 % in the tetra-hybrid with a higher Soret number, whereas the Sherwood number decreases by 38 % in the tri-hybrid and 26 % in the tetra-hybrid nanofluid. Employing sensitivity analysis, this study also aims to investigate impact of output responses such as local Nusselt number and local Sherwood number on input parameter Dufour number, Soret number and chemical reaction parameter for tri-hybrid and tetra-hybrid nanofluid. It is found out that Dufour number in tetra-hybrid nanofluid has the more significant impact on the Nusselt number, whereas the Soret number predominantly affects the Nusselt number in tri-hybrid nanofluid. These findings underscore the potential of tetra-hybrid nanofluid in enhancing the performance of various industrial and environmental processes.

Comparison of single-shot, FOCUS single-shot, MUSE, and FOCUS MUSE diffusion weighted imaging for pulmonary lesions: A pilot study.

Rationale and objectives: To compare the performance of SS, FOCUS SS, MUSE, and FOCUS MUSE DWI for pulmonary lesions to obtain a better technique for pulmonary DWI imaging. Materials and methods: 44 patients with pulmonary lesions were recruited to perform pulmonary DWI using SS, FOCUS SS, MUSE, and FOCUS MUSE sequences. Then, two radiologists with 12 and 10 years of chest MRI experiences assessed the overall image quality while another two radiologists both with 3 years of experiences evaluated the SNR, DR, and ADC of pulmonary lesions. Using interclass correlation coefficient (ICC) and kappa statistics to assess consistency of readers, Friedman test and Dunn-Bonferroni post hoc were used to calculate the difference between sequences. Mann-Whitney test and ROC curve were used to distinguish malignant from benign lesions. Results: All the assessed variables of the four sequences presented good to excellent intra-/inter-observer consistency. Compared with SS, FOCUS SS and MUSE, FOCUS MUSE demonstrated better image quality, including significantly higher 5-point Likert scale score (P < 0.001) and smaller DR (P < 0.001). SNR was comparable among SS, FOCUS SS, and FOCUS MUSE (P > 0.05) while MUSE presented with significantly higher SNR over them (P < 0.01). ADC of malignant was significantly smaller than that of benign for all the four sequences (P < 0.05). ROC analysis showed relatively better diagnostic performance of FOCUS MUSE (AUC = 0.820) over SS (AUC = 0.748), FOCUS SS (AUC = 0.778), and MUSE (AUC = 0.729) in distinguishing malignant from benign lesions. Conclusion: FOCUS MUSE possessed sufficient SNR and was better over SS, FOUCS SS, and MUSE for characterizing pulmonary lesions.

Role of Multiparametric MRI in the Preoperative Evaluation of Endometrial Carcinoma: A Cross-Sectional Study.

Background Endometrial carcinoma (EC) is a major global concern in females throughout the world with increasing incidence in India. Hence, early detection and prompt intervention will reduce morbidity and mortality associated with it. Multiple studies showed a promising role of multiparametric magnetic resonance imaging (mpMRI) in the evaluation and early detection of the disease. In view of the paucity of such studies in the Indian population, we assessed the role of mpMRI in the evaluation of EC by utilizing a 3T MR scanner. Objectives To assess the efficacy of mpMRI in detecting myometrial invasion and locoregional staging in suspected or diagnosed cases of EC. Materials and methods Nineteen cases of EC with mpMRI were included in the study, and 15 of these underwent surgicopathological staging. The preoperative staging was done using the International Federation of Gynecology and Obstetrics (FIGO) 2009 staging system based on mpMRI findings and compared with postoperative FIGO staging. All the data were compiled in a Microsoft Excel (Microsoft® Corp., Redmond, WA) file and analyzed in Statistical Product and Service Solutions (SPSS, version 21.0; IBM SPSS Statistics for Windows, Armonk, NY) using appropriate tools. Results In our study, EC was commonly seen in more than 50-year females with a predominant complaint being postmenopausal bleeding. EC most commonly appeared heterogeneously hyperintense on T2-weighted sequence (T2W) and areas of diffusion restriction on diffusion-weighted imaging (DWI) in all cases. Dynamic contrast-enhanced (DCE) MRI (DCE-MRI) showed mild heterogeneous enhancement in all phases with better delineation of adjacent myometrial infiltration in the equilibrium phase. Diffusion tensor imaging (DTI) parameters had significantly lower values in involved myometrium vis-a-vis uninvolved myometrium. A statistically significant correlation was seen between preoperative mpMRI FIGO staging utilizing T2W, DWI, DCE-MRI, and DTI with surgicopathological FIGO staging. Conclusion mpMRI, particularly T2W, DWI, DCE-MRI, and DTI, yields a significant correlation between MR imaging and histopathological findings in assessing myometrial infiltration and thereby could be helpful in preoperative staging and extent of lymph-nodal dissection.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning.

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Information Load from Neuromediator Diffusion to Extrasynaptic Space: The Interplay between the Injection Frequency and Clearance.

In our study, we simulate the release of glutamate, a neurotransmitter, from the presynaptic cell by modeling the diffusion of glutamate into both synaptic and extrasynaptic space around the synapse. We have also incorporated a new factor into our model: convection. This factor represents the process by which the body clears glutamate from the synapse. Due to this process, the physiological mechanisms that typically prevent glutamate from spreading beyond the synapse are altered. This results in a different distribution of glutamate concentrations, with higher levels outside the synapse than inside it. The variety of biological effects that occur in response to this extrasynaptic glutamate highlights the importance of preventing neurotransmitters from spreading beyond the synapse. We aim to explain the physical reasons behind these biological effects, which are observed as excitotoxicity. Our results show that preventing the spread of glutamate outside the synapse increases the amount of information exchanged within the synapse and its surroundings for frequencies of glutamate release up to 30-50 Hz, followed by a decrease. Additionally, we find that the rate at which glutamate is cleared from the synapse is effective at relatively low levels (≤0.5 nm/µs in our calculation grid) and remains constant at higher levels.

Analysis of Structural Changes of pH-Thermo-Responsive Nanoparticles in Polymeric Hydrogels.

The pH- and thermo-responsive behavior of polymeric hydrogels MC-co-MA have been studied in detail using dynamic light scattering DLS, scanning electron microscopy SEM, nuclear magnetic resonance (1H NMR) and rheology to evaluate the conformational changes, swelling-shrinkage, stability, the ability to flow and the diffusion process of nanoparticles at several temperatures. Furthermore, polymeric systems functionalized with acrylic acid MC and acrylamide MA were subjected to a titration process with a calcium chloride CaCl2 solution to analyze its effect on the average particle diameter Dz, polymer structure and the intra- and intermolecular interactions in order to provide a responsive polymer network that can be used as a possible nanocarrier for drug delivery with several benefits. The results confirmed that the structural changes in the sensitive hydrogels are highly dependent on the corresponding critical solution temperature CST of the carboxylic (-COOH) and amide (-CONH2) functional groups and the influence of calcium ions Ca2+ on the formation or breaking of hydrogen bonds, as well as the decrease in electrostatic repulsions generated between the polymer chains contributing to a particle agglomeration phenomenon. The temperature leads to a re-arrangement of the polymer chains, affecting the viscoelastic properties of the hydrogels. In addition, the diffusion coefficients D of nanoparticles were evaluated, showing a closeness among with the morphology, shape, size and temperature, resulting in slower diffusions for larger particles size and, conversely, the diffusion in the medium increasing as the polymer size is reduced. Therefore, the hydrogels exhibited a remarkable response to pH and temperature variations in the environment. During this research, the functionality and behavior of the polymeric nanoparticles were observed under different analysis conditions, which revealed notable structural changes and further demonstrated the nanoparticles promising high potential for drug delivery applications. Hence, these results have sparked significant interest in various scientific, industrial and technological fields.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

OBJECTIVE: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. METHODS: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). RESULTS: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. CONCLUSION: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. CRITICAL RELEVANCE STATEMENT: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. KEY POINTS: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Predictive markers for head and neck cancer treatment response: T1rho imaging in nasopharyngeal carcinoma.

OBJECTIVES: To investigate the potential of T1rho, a new quantitative imaging sequence for cancer, for pre and early intra-treatment prediction of treatment response in nasopharyngeal carcinoma (NPC) and compare the results with those of diffusion-weighted imaging (DWI). MATERIALS AND METHODS: T1rho and DWI imaging of primary NPCs were performed pre- and early intra-treatment in 41 prospectively recruited patients. The mean preT1rho, preADC, intraT1rho, intraADC, and % changes in T1rho (ΔT1rho%) and ADC (ΔADC%) were compared between residual and non-residual groups based on biopsy in all patients after chemoradiotherapy (CRT) with (n = 29) or without (n = 12) induction chemotherapy (IC), and between responders and non-responders to IC in the subgroup who received IC, using Mann-Whitney U-test. A p-value of < 0.05 indicated statistical significance. RESULTS: Significant early intra-treatment changes in mean T1rho (p = 0.049) and mean ADC (p < 0.01) were detected (using paired t-test), most showing a decrease in T1rho (63.4%) and an increase in ADC (95.1%). Responders to IC (n = 17), compared to non-responders (n = 12), showed higher preT1rho (64.0 ms vs 66.5 ms) and a greater decrease in ΔT1rho% (- 7.5% vs 1.3%) (p < 0.05). The non-residual group after CRT (n = 35), compared to the residual group (n = 6), showed higher intraADC (0.96 vs 1.09 × 10-3 mm2/s) and greater increase in ΔADC% (11.7% vs 27.0%) (p = 0.02). CONCLUSION: Early intra-treatment changes are detectable on T1rho and show potential to predict tumour shrinkage after IC. T1rho may be complementary to DWI, which, unlike T1rho, did not predict response to IC but did predict non-residual disease after CRT. CLINICAL RELEVANCE STATEMENT: T1rho has the potential to complement DWI in the prediction of treatment response. Unlike DWI, it predicted shrinkage of the primary NPC after IC but not residual disease after CRT. KEY POINTS: Changes in T1rho were detected early during cancer treatment for NPC. Pre-treatment and early intra-treatment change in T1rho predicted response to IC, but not residual disease after CRT. T1rho can be used to complement DWI with DWI predicting residual disease after CRT.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease.

OBJECTIVE: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI. METHODS: A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort. RESULTS: Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341). CONCLUSION: The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

Longitudinal assessment of glymphatic changes following mild traumatic brain injury: Insights from perivascular space burden and DTI-ALPS imaging.

Introduction: Traumatic brain injury (TBI) even in the mild form may result in long-lasting post-concussion symptoms. TBI is also a known risk to late-life neurodegeneration. Recent studies suggest that dysfunction in the glymphatic system, responsible for clearing protein waste from the brain, may play a pivotal role in the development of dementia following TBI. Given the diverse nature of TBI, longitudinal investigations are essential to comprehending the dynamic changes in the glymphatic system and its implications for recovery. Methods: In this prospective study, we evaluated two promising glymphatic imaging markers, namely the enlarged perivascular space (ePVS) burden and Diffusion Tensor Imaging-based ALPS index, in 44 patients with mTBI at two early post-injury time points: approximately 14 days (14Day) and 6-12 months (6-12Mon) post-injury, while also examining their associations with post-concussion symptoms. Additionally, 37 controls, comprising both orthopedic patients and healthy individuals, were included for comparative analysis. Results: Our key findings include: (1) White matter ePVS burden (WM-ePVS) and ALPS index exhibit significant correlations with age. (2) Elevated WM-ePVS burden in acute mTBI (14Day) is significantly linked to a higher number of post-concussion symptoms, particularly memory problems. (3) The increase in the ALPS index from acute (14Day) to the chronic (6-12Mon) phases in mTBI patients correlates with improvement in sleep measures. Furthermore, incorporating WM-ePVS burden and the ALPS index from acute phase enhances the prediction of chronic memory problems beyond socio-demographic and basic clinical information. Conclusion: ePVS burden and ALPS index offers distinct values in assessing glymphatic structure and activity. Early evaluation of glymphatic function could be crucial for understanding TBI recovery and developing targeted interventions to improve patient outcomes.

Exploring white matter microstructural alterations in mild cognitive impairment: a multimodal diffusion MRI investigation utilizing diffusion kurtosis and free-water imaging.

Background: Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear. Methods: This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI). Results: Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment. Conclusion: In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline.

Language and the Cerebellum: Structural Connectivity to the Eloquent Brain.

Neurobiological models of receptive language have focused on the left-hemisphere perisylvian cortex with the assumption that the cerebellum supports peri-linguistic cognitive processes such as verbal working memory. The goal of this study was to identify language-sensitive regions of the cerebellum then map the structural connectivity profile of these regions. Functional imaging data and diffusion-weighted imaging data from the Human Connectome Project (HCP) were analyzed. We found that (a) working memory, motor activity, and language comprehension activated partially overlapping but mostly unique subregions of the cerebellum; (b) the linguistic portion of the cerebello-thalamo-cortical circuit was more extensive than the linguistic portion of the cortico-ponto-cerebellar tract; (c) there was a frontal-lobe bias in the connectivity from the cerebellum to the cerebrum; (d) there was some degree of specificity; and (e) for some cerebellar tracts, individual differences in picture identification ability covaried with fractional anisotropy metrics. These findings yield insights into the structural connectivity of the cerebellum as relates to the uniquely human process of language comprehension.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Uncovering the hidden effects of repetitive subconcussive head impact exposure: A mega-analytic approach characterizing seasonal brain microstructural changes in contact and collision sports athletes.

Repetitive subconcussive head impacts (RSHI) are believed to induce sub-clinical brain injuries, potentially resulting in cumulative, long-term brain alterations. This study explores patterns of longitudinal brain white matter changes across sports with RSHI-exposure. A systematic literature search identified 22 datasets with longitudinal diffusion magnetic resonance imaging data. Four datasets were centrally pooled to perform uniform quality control and data preprocessing. A total of 131 non-concussed active athletes (American football, rugby, ice hockey; mean age: 20.06 ± 2.06 years) with baseline and post-season data were included. Nonparametric permutation inference (one-sample t tests, one-sided) was applied to analyze the difference maps of multiple diffusion parameters. The analyses revealed widespread lateralized patterns of sports-season-related increases and decreases in mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across spatially distinct white matter regions. Increases were shown across one MD-cluster (3195 voxels; mean change: 2.34%), one AD-cluster (5740 voxels; mean change: 1.75%), and three RD-clusters (817 total voxels; mean change: 3.11 to 4.70%). Decreases were shown across two MD-clusters (1637 total voxels; mean change: -1.43 to -1.48%), two RD-clusters (1240 total voxels; mean change: -1.92 to -1.93%), and one AD-cluster (724 voxels; mean change: -1.28%). The resulting pattern implies the presence of strain-induced injuries in central and brainstem regions, with comparatively milder physical exercise-induced effects across frontal and superior regions of the left hemisphere, which need further investigation. This article highlights key considerations that need to be addressed in future work to enhance our understanding of the nature of observed white matter changes, improve the comparability of findings across studies, and promote data pooling initiatives to allow more detailed investigations (e.g., exploring sex- and sport-specific effects).

Field-of-view extension for brain diffusion MRI via deep generative models.

Purpose: In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV. Results: For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved PSNR b 0 = 22.397 , SSIM b 0 = 0.905 , PSNR b 1300 = 22.479 , and SSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved PSNR b 0 = 21.304 , SSIM b 0 = 0.892 , PSNR b 1300 = 21.599 , and SSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank.

Background: During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods: We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results: Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions: We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.

In their study, Korbmacher et al. benchmark healthy aging processes in the brain's white matter. Findings of degrading white matter at higher ages were consistent with recent cross-sectional and longitudinal findings, particularly outlining changes in ventricle-near and cerebellar white matter. Degenerative processes were also found to accelerate at a higher age. Finally, the polygenic risk to develop psychiatric and neurodegenerative disorders was weakly associated with the white matter change in the otherwise healthily aging participants.

Automatic deep learning segmentation of the hippocampus on high-resolution diffusion magnetic resonance imaging and its application to the healthy lifespan.

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with limited brain coverage of 20 axial-oblique slices aligned along its long axis. However, manual segmentation is too laborious for large population studies, and it cannot be automatically segmented directly on the diffusion images using traditional T1 or T2 image-based methods because of the limited brain coverage and different contrast. An automatic method is proposed here that segments the hippocampus directly on high-resolution diffusion images based on an extension of well-known deep learning architectures like UNet and UNet++ by including additional dense residual connections. The method was trained on 100 healthy participants with previously performed manual segmentation on the 1-mm DTI, then evaluated on typical healthy participants (n = 53), yielding an excellent voxel overlap with a Dice score of ~ 0.90 with manual segmentation; notably, this was comparable with the inter-rater reliability of manually delineating the hippocampus on diffusion magnetic resonance imaging (MRI) (Dice score of 0.86). This method also generalized to a different DTI protocol with 36% fewer acquisitions. It was further validated by showing similar age trajectories of volumes, fractional anisotropy, and mean diffusivity from manual segmentations in one cohort (n = 153, age 5-74 years) with automatic segmentations from a second cohort without manual segmentations (n = 354, age 5-90 years). Automated high-resolution diffusion MRI segmentation of the hippocampus will facilitate large cohort analyses and, in future research, needs to be evaluated on patient groups.

Electrochemical Charge Transfer Kinetics of Ferrocene in the Light of Different Working Electrodes.

Ferrocene is an accidentally discovered organometallic compound that serves as a crucial redox probe in investigating electrochemical charge transfer dynamics. Besides solution phase studies, ferrocene derivatives are well-explored in molecular thin films, including self-assembled monolayers on various electrodes for understanding on-surface redox behavior, and in molecular electronics, and charge storage applications. Heterogeneous charge transfer is an imperative parameter for efficient charge transport in spin-dependent electrochemistry, photoelectrochemistry, and molecular electronic devices. In this work, we aim to study the electrochemical charge transfer of ferrocene on various electrodes such as commercially obtained glassy carbon, graphite rod, indium tin oxide (ITO), and as-prepared gold, and nickel to determine the impact of the nature of the working electrode on the electron transfer rate, diffusion coefficient, and reversibility of the redox process. Both the direct current and alternating current-based electrochemical experiments are performed, followed by digitization of the experimental results. The kinetics of electron transfer and electrochemical reversibility reveal a strong dependence on the nature of the working electrode, as the electrochemically driven oxidation and reduction of the material of interest are directly related to the Fermi energy and electronic structure of the working electrode.