Standard Space Research Articles

Radiofrequency thalamotomy for tremor produces focused and predictable lesions shown on magnetic resonance images.

Radiofrequency thalamotomy is a neurosurgical management option for medically-refractory tremor. In this observational study, we evaluate the MRI features of the resultant lesion, their temporal dynamics, and how they vary depending on surgical factors. We report on lesion characteristics including size and location, as well as how these vary over time and across different MRI sequences. Data from 12 patients (2 essential tremor, 10 Parkinson's disease) who underwent unilateral radiofrequency thalamotomy for tremor were analysed. Lesion characteristics were compared across five structural sequences. Volumetric analysis of lesion features was performed at early (<5 weeks) and late (>5 months) timepoints by manual segmentation. Lesion location was determined after registration of lesions to standard space. All patients showed tremor improvement (clinical global impressions scale) postoperatively. Chronic side-effects included balance disturbances (n = 4) and worsening mobility due to parkinsonism progression (n = 1). Early lesion features including a necrotic core, cytotoxic oedema and perilesional oedema were best demarcated on T2-weighted sequences. Multiple lesions were associated with greater cytotoxic oedema compared with single lesions (T2-weighted mean volume: 537 ± 112 mm³ versus 302 ± 146 mm³, P = 0.028). Total lesion volume reduced on average by 90% between the early and late scans (T2-weighted mean volume: 918 ± 517 versus 75 ± 50 mm³, t = 3.592, P = 0.023, n = 5), with comparable volumes demonstrated at ∼6 months after surgery. Lesion volumes on susceptibility-weighted images were larger than those of T2-weighted images at later timepoints. Radiofrequency thalamotomy produces focused and predictable lesion imaging characteristics over time. T2-weighted scans distinguish between the early lesion core and oedema characteristics, while lesions may remain more visible on susceptibility-weighted images in the months following surgery. Scanning patients in the immediate postoperative period and then at 6 months is clinically meaningful for understanding the anatomical basis of the transient and permanent effects of thalamotomy.

Display of battery items in web and mail surveys: Grids versus item-by-item and radio versus wide buttons

How to display questions that are part of a battery in self-administered surveys is an important decision. Battery items may be displayed in a grid in a mail survey or computer web survey, but are often displayed as individual items on mobile devices. Although past research has compared grids to item-by-item displays in computer and mobile web surveys, almost no work has compared these displays in mail surveys. Additionally, many web survey templates use wide rectangular buttons to select response options in individual items using a mobile-optimized design, different from the standard round answer space format typically used in mail surveys. In this study, we experimentally test grid versus item-by-item displays and round radio buttons versus wide rectangular buttons for battery items in a probability-based general population mixed-mode mail + web survey of adults in Nebraska. Consistent with past research, we find that item-by-item displays reduce straightlining rates compared to grid designs. We also find that respondents are less likely to select the last two response categories in the item-by-item displays than in the grid displays. Smartphone and computer web respondents have higher item nonresponse rates than mail respondents, and web respondents have lower straightlining rates than mail respondents, accounting for respondent characteristics. Reassuringly, there is no difference in data quality outcomes across radio button versus wide button formats. These findings replicate past research that item-by-item displays reduce straightlining but may shift answer categories. These findings suggest that questionnaire designers can combine round radio button answer spaces on mail surveys with wide buttons on web surveys on battery items with little difference in data quality.

The Dimensionality of Genetic Information

This paper investigates the dimensionality of genetic information from the perspective of optimal representation. Recently it has been shown that optimal coding of information is in terms of the noninteger dimension of e, which is accompanied by the property of scale invariance. Since Nature is optimal, we should see this dimension reflected in the organization of the genetic code. With this as background, this paper investigates the problem of the logic behind the nature of the assignment of codons to amino acids, for they take different values that range from 1 to 6. It is shown that the non-uniformity of this assignment, which goes against mathematical coding theory that demands a near uniform assignment, is consistent with noninteger dimensions. The reason why the codon assignment for different amino acids varies is because uniformity is a requirement for optimality only in a standard vector space, and is not so in the noninteger dimensional space. It is noteworthy that there are 20 different covering regions in an e-dimensional information space, which is equal to the number of amino acids. The problem of the visualization of data that originates in an e-dimensional space but examined in a 3-dimensional vector space is also discussed. It is shown that the assignment of the codons to the amino acids is fractal-like that is well modeled by the Zipf distribution which is a power law. It is remarkable that the Zipf distribution that holds for the letter frequencies of words in a natural language also applies to the rank order of triplets in the code for amino acids.

Performance Comparison of Classical Methods and Neural Networks for Colour Correction.

Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods. In the first part of this paper, a leading neural network approach is compared and contrasted with regression methods. We find that, although the neural network model supports improved colour correction compared with simple least-squares regression, it performs less well than the more advanced root-polynomial regression. Moreover, the relative improvement afforded by NNs, compared to linear least-squares, is diminished when the regression methods are adapted to minimise a perceptual colour error. Problematically, unlike linear and root-polynomial regressions, the NN approach is tied to a fixed exposure (and when exposure changes, the afforded colour correction can be quite poor). We explore two solutions that make NNs more exposure-invariant. First, we use data augmentation to train the NN for a range of typical exposures and second, we propose a new NN architecture which, by construction, is exposure-invariant. Finally, we look into how the performance of these algorithms is influenced when models are trained and tested on different datasets. As expected, the performance of all methods drops when tested with completely different datasets. However, we noticed that the regression methods still outperform the NNs in terms of colour correction, even though the relative performance of the regression methods does change based on the train and test datasets.

An artificial intelligence approach for segmenting and classifying brain lesions caused by stroke

ABSTRACT Brain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes’ territory localisation. All models’ architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications.

Action Mapping: A Reinforcement Learning Method for Constrained-Input Systems.

Existing approaches to constrained-input optimal control problems mainly focus on systems with input saturation, whereas other constraints, such as combined inequality constraints and state-dependent constraints, are seldom discussed. In this article, a reinforcement learning (RL)-based algorithm is developed for constrained-input optimal control of discrete-time (DT) systems. The deterministic policy gradient (DPG) is introduced to iteratively search the optimal solution to the Hamilton-Jacobi-Bellman (HJB) equation. To deal with input constraints, an action mapping (AM) mechanism is proposed. The objective of this mechanism is to transform the exploration space from the subspace generated by the given inequality constraints to the standard Cartesian product space, which can be searched effectively by existing algorithms. By using the proposed architecture, the learned policy can output control signals satisfying the given constraints, and the original reward function can be kept unchanged. In our study, the convergence analysis is given. It is shown that the iterative algorithm is convergent to the optimal solution of the HJB equation. In addition, the continuity of the iterative estimated Q-function is investigated. Two numerical examples are provided to demonstrate the effectiveness of our approach.

Investigation on the thermal properties of carbon fibres with silica-based aerogel composites

In this research, thermal protection systems of space hardware have prompted the development and study of silica aerogel composites bonded with a variety of Carbon fibres. Carbon fibres were woven into a matrix of tetra ethoxy silane (Ts) and vinyl tri-methoxy silane (Vs) to create these composites. Many different composites were developed, each with its own set of characteristics and capabilities because to the wide range of fibres used. The measured bulk density values were often relatively low, with the lowest recorded value being only 180 kg m−3. The thermal conductivity was reduced to below 30 mW m−1 K−1 and the stability was maintained up to 600°C. Nanocomposites consisting of longer fibres containing meta-Carbons are superior insulators, while short-fiber composites are stiffer and have lower thermal conductivities. The aerogel composites compliance with Space conditions was evaluated using standard Space materials qualification procedures like thermal cycling and outgassing, proving their fitness for use in this context.

A Study on General State Model of Differential Drive Wheeled Mobile Robots

This study introduces a novel approach by representing a multi-input-multi-output (MIMO) differential drive wheel mobile robot (DDWMR) using the standard state space representation for the first time. This representation facilitates the application of analysis and control system design techniques to MIMO systems. Specifically, the investigation delves into stability, controllability, observability, input-output interaction, and the relative gain array of the DDWMR model. To demonstrate the concept, the established methodology employs the conventional pole placement controller design technique to formulate a state feedback control law for trajectory tracking in the DDWMR system, utilizing both a nominal and a generalized model. The generalized model incorporates distinct parameters for the left and right motor-wheel systems, unlike the nominal model where they are assumed to be identical. Simulation results highlight that accounting for the asymmetric characteristics through the controller derived from the generalized model yields superior performance compared to the nominal model-based controller. Furthermore, the proposed model can be served as an illustrative platform for evaluating innovative MIMO control methodologies in prospective studies.

Progressive Changes in Cerebral Apparent Diffusion Values in Fabry Disease: A 5-Year Follow-up MRI Study.

White matter lesions are commonly found in patients with Fabry disease. Existing studies have shown elevated diffusivity in healthy-appearing brain regions that are commonly associated with white matter lesions, suggesting that DWI could help detect white matter lesions at an earlier stage This study explores whether diffusivity changes precede white matter lesion formation in a cohort of patients with Fabry disease undergoing yearly MR imaging examinations during a 5-year period. T1-weighted anatomic, FLAIR, and DWI scans of 48 patients with Fabry disease (23 women; median age, 44 years; range, 15-69 years) were retrospectively included. White matter lesions and tissue probability maps were segmented and, together with ADC maps, were transformed into standard space. ADC values were determined within lesions before and after detection on FLAIR images and compared with normal-appearing white matter ADC. By means of linear mixed-effects modeling, changes in ADC and ΔADC (relative to normal-appearing white matter) across time were investigated. ADC was significantly higher within white matter lesions compared with normal-appearing white matter (P < .01), even before detection on FLAIR images. ADC and ΔADC were significantly affected by sex, showing higher values in men (60.1 [95% CI, 23.8-96.3] ×10-6mm2/s and 35.1 [95% CI, 6.0-64.2] ×10-6mm2/s), respectively. ΔADC increased faster in men compared with women (0.99 [95% CI, 0.27-1.71] ×10-6mm2/s/month). ΔADC increased with time even when only considering data from before detection (0.57 [95% CI, 0.01-1.14] ×10-6mm2/s/month). Our results indicate that in Fabry disease, changes in diffusion precede the formation of white matter lesions and that microstructural changes progress faster in men compared with women. These findings suggest that DWI may be of predictive value for white matter lesion formation in Fabry disease.

Local neuroanatomical and tract-based proxies of optimal subcallosal cingulate deep brain stimulation

BackgroundDeep brain stimulation of the subcallosal cingulate area (SCC-DBS) is a promising neuromodulatory therapy for treatment-resistant depression (TRD). Biomarkers of optimal target engagement are needed to guide surgical targeting and stimulation parameter selection and to reduce variance in clinical outcome. Objective/HypothesisWe aimed to characterize the relationship between stimulation location, white matter tract engagement, and clinical outcome in a large (n = 60) TRD cohort treated with SCC-DBS. A smaller cohort (n = 22) of SCC-DBS patients with differing primary indications (bipolar disorder/anorexia nervosa) was utilized as an out-of-sample validation cohort. MethodsVolumes of tissue activated (VTAs) were constructed in standard space using high-resolution structural MRI and individual stimulation parameters. VTA-based probabilistic stimulation maps (PSMs) were generated to elucidate voxelwise spatial patterns of efficacious stimulation. A whole-brain tractogram derived from Human Connectome Project diffusion-weighted MRI data was seeded with VTA pairs, and white matter streamlines whose overlap with VTAs related to outcome (‘discriminative’ streamlines; Puncorrected < 0.05) were identified using t-tests. Linear modelling was used to interrogate the potential clinical relevance of VTA overlap with specific structures. ResultsPSMs varied by hemisphere: high-value left-sided voxels were located more anterosuperiorly and squarely in the lateral white matter, while the equivalent right-sided voxels fell more posteroinferiorly and involved a greater proportion of grey matter. Positive discriminative streamlines localized to the bilateral (but primarily left) cingulum bundle, forceps minor/rostrum of corpus callosum, and bilateral uncinate fasciculus. Conversely, negative discriminative streamlines mostly belonged to the right cingulum bundle and bilateral uncinate fasciculus. The best performing linear model, which utilized information about VTA volume overlap with each of the positive discriminative streamline bundles as well as the negative discriminative elements of the right cingulum bundle, explained significant variance in clinical improvement in the primary TRD cohort (R = 0.46, P < 0.001) and survived repeated 10-fold cross-validation (R = 0.50, P = 0.040). This model was also able to predict outcome in the out-of-sample validation cohort (R = 0.43, P = 0.047). Conclusion(s)These findings reinforce prior indications of the importance of white matter engagement to SCC-DBS treatment success while providing new insights that could inform surgical targeting and stimulation parameter selection decisions.

A survey on compositional algorithms for verification and synthesis in supervisory control

AbstractThis survey gives an overview of the current research on compositional algorithms for verification and synthesis of modular systems modelled as interacting finite-state machines. Compositional algorithms operate by repeatedly simplifying individual components of a large system, replacing them by smaller so-called abstractions, while preserving critical properties. In this way, the exponential growth of the state space can be limited, making it possible to analyse much bigger state spaces than possible by standard state space exploration. This paper gives an introduction to the principles underlying compositional methods, followed by a survey of algorithmic solutions from the recent literature that use compositional methods to analyse systems automatically. The focus is on applications in supervisory control of discrete event systems, particularly on methods that verify critical properties or synthesise controllable and nonblocking supervisors.

Intersections of Poisson [formula omitted]-flats in constant curvature spaces

Poisson processes in the space of k-dimensional totally geodesic subspaces (k-flats) in a d-dimensional standard space of constant curvature κ∈{−1,0,1} are studied, whose distributions are invariant under the isometries of the space. We consider the intersection processes of order m together with their (d−m(d−k))-dimensional Hausdorff measure within a geodesic ball of radius r. Asymptotic normality for fixed r is shown as the intensity of the underlying Poisson process tends to infinity for all m satisfying d−m(d−k)≥0. For κ∈{−1,0} the problem is also approached in the set-up where the intensity is fixed and r tends to infinity. Again, if 2k≤d+1 a central limit theorem is shown for all possible values of m. However, while for κ=0 asymptotic normality still holds if 2k>d+1, we prove for κ=−1 convergence to a non-Gaussian infinitely divisible limit distribution in the special case m=1. The proof of asymptotic normality is based on the analysis of variances and general bounds available from the Malliavin–Stein method. We also show for general κ∈{−1,0,1} that, roughly speaking, the variances within a general observation window W are maximal if and only if W is a geodesic ball having the same volume as W. Along the way we derive a new integral-geometric formula of Blaschke–Petkantschin type in a standard space of constant curvature.

CLRM-11 ALK TYROSINE KINASE INHIBITOR THERAPY AND BRAIN METASTASES IN NON-SMALL CELL LUNG CANCER: IMPACT ON TUMOR SIZE AND DISTRIBUTION

Abstract Non-small cell lung cancer (NSCLC) with anaplastic lymphoma kinase rearrangement (ALK+) is known to have a high propensity to form brain metastases (BrM), with over 50% of ALK+ lung cancer patients developing BrM despite effective ALK tyrosine kinase inhibitor (TKI) therapy with central nervous system (CNS) activity. Pharmacokinetic (PK) data from other CNS-active lung cancer TKIs have suggested that there may be differences in drug concentration between white and gray matter in the brain, which may play a role in BrM formation patterns and response. In this study, we aimed to compare the size and distribution of ALK+ NSCLC BrMs at diagnosis in a TKI-naïve and TKI-exposed cohort. Brain MRI data from patients with ALK+ NSCLC were retrospectively reviewed, and each tumor was marked in a standard space brain model using 3D Slicer-4.11. FreeSurfer white-gray matter atlases were used to assess BrM distribution. We found that TKI-exposed patients had significantly smaller BrM diameters than TKI-naïve patients (6.1±3.8 vs. 10.2±5.5mm, p=0.02) and were more likely to have white matter-exclusive (3.5±4.4 vs. 1.4±2.0, p=0.05) and deep white matter metastases (3.2±4.3 vs. 1.3±2.0, p=0.06). The metastatic burden was similar between the groups, while the mean number of BrM per patient was numerically higher in the TKI-exposed group (10.6±11.9 vs. 6.2±9.5; p=0.22). These findings suggest that TKI therapy may result in smaller individual lesions that are more likely to be exclusive to the white matter, where drug concentrations may be significantly lower. This suboptimal CNS distribution of TKIs in the white matter may contribute to the progression of brain metastases in ALK+ patients despite TKI therapy. Further analyses are ongoing to evaluate ALK TKIs of varying CNS penetrance and later disease time points in more granular anatomic regions.

A Study on the Spatial Pattern of Traditional Villages from the Perspective of Courtyard House Distribution

As clan and blood relations are the essential core of rural social attributes, the rise and fall of the main family names in a village not only affects the spatial arrangement of their internal courtyard houses but also influences the overall spatial pattern of the village. Taking Pei Cheng Village as an example, in this study, quantitative analyses, such as those entailing standard deviation ellipse, kernel density, and space syntax, are incorporated to investigate the changes in rural family names’ courtyard houses and the historical context for these changes to reveal the influence of the evolution of courtyard house distribution under a given social structure on the overall spatial structure of the countryside at the micro- and meso-scales. The results in this study show that there is strong aggregation and bonding associated with the main family surname in a village and that such surnames have an important correlation with the village centre and spatial structure formation. Notably, the distribution of the surname-bearing clan courtyard houses has a certain degree of consistency with the spatial pattern of the village, with the main space corresponding to the main family surname, the secondary space corresponding to the secondary family, and the underdeveloped space corresponding to mixed family surnames. On this basis, the spatial system grading of Pei Cheng Village is summarized, providing a fundamental basis for future planning of Pei Cheng Village facilities and other aspects of planning, such as the selection of locations.

FIESTA: Autoencoders for accurate fiber segmentation in tractography

White matter bundle segmentation is a cornerstone of modern tractography to study the brain’s structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework

Analysis of separation properties of attractors of the product of fuzzy iterated function systems

The theory of fractals mainly deals with analysis of highly irregular shapes having infinitely complex patterns in all scales. Mathematically, fractal geometry uses iteration of different functions defined on the same space to generate different irregular shapes. Fuzzy fractal is indeed an extensive mathematical combination. The separation properties of finite product of fractals formed by repeated iterations of systems consisting of fuzzy contractions in the standard fuzzy metric space with regard to the standard Hausdorff fuzzy metric is the primary topic of this work. Characterizations for the product of fuzzy iterated function systems to become fuzzy disconnected and fuzzy overlapping are obtained. Some mandatory requirements for the product of fuzzy iterated function systems to be fuzzy incident are also given. Along with that, the separation properties of finite product of fuzzy iterated function systems with condensation are attempted.

Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout.Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors.We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

Exploration of the effect of blood lipids on the lesion distribution pattern in acute ischemic stroke based on MRI study with population standard spatial analysis

Objective: To explore the effect of blood lipids on the lesion distribution pattern in patients with acute ischemic stroke by using MRI technology based on population standard spatial analysis. Methods: The MRI data of 1 202 patients with acute ischemic stroke in General Hospital of Eastern Theater Command from January 2015 to December 2020 and Nanjing First Hospital from January 2013 to December 2021 were retrospectively collected, including 871 males and 331 females, aged 26 to 94 (64±11) years. According to the condition of blood lipids, they were divided into the dyslipidemia group (n=683) and the normal blood lipids group (n=519). After the automatic segmentation of diffusion-weighted imaging (DWI) images by artificial intelligence, the infarct sites were registered to the standard space which was used to draw the frequency heat map. The chi-square test was used to compare the difference in lesion location between the two groups. Generalized linear model regression analysis was used to observe the correlation between each blood lipid index and lesion site, and inter-group comparison and correlation analysis were used to observe the relationship between each blood lipid index and lesion volume. Results: Compared with the normal blood lipid group, the lesions in the dyslipidemia group were more extensive, mostly distributed in the occipital temporal region of the right posterior cerebral artery and the frontal region of the left middle cerebral artery. The brain regions of higher triglyceride(TG) and higher low-density lipoprotein cholesterol(LDL-C) groups were concentrated in the posterior circulation. The brain regions in the higher total cholesterol(TC) and lower high-density lipoprotein cholesterol(HDL-C) groups were concentrated in the anterior circulation(all P<0.05). In the anterior circulation infarct volume, the higher TC group was significantly higher than the normal TC group[(27.58±5.34) vs (17.73±1.18)ml, P=0.029]. In the posterior circulation infarct volume, the higher LDL-C group and the TG group were significantly higher than the normal LDL-C and TG groups[(7.55±2.51) vs (3.55±0.31) ml; (5.76±1.19) vs (3.36±0.30) ml](both P<0.05). Correlation analysis showed that TC and LDL-C were non-linearly (U-shaped) correlated with anterior circulation infarct volume (both P<0.05). Conclusions: Different blood lipids have effects on the distribution pattern and volume of ischemic stroke infarcts. Different hyperlipidemia is related to the specific distribution site and the larger extent of infarction.

Patterns of gray and white matter functional networks involvement in glioblastoma patients: indirect mapping from clinical MRI scans.

Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS). We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS. 99 patients were included (70 males, mean age 62 years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema (p < 0.001). 5 main patterns of GBM core distribution across functional networks were found, while edema localization was less classifiable. ANOVA showed significant differences between mean overlap percentages, separately for GMNs and WMNs (p-values<0.0001). Core-N12 overlap predicts higher OS, although its inclusion does not increase the explained OS variance. Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival.

Severe cholinergic terminal loss in newly diagnosed dementia with Lewy bodies.

Cholinergic changes play a fundamental role in the natural history of dementia with Lewy bodies and Lewy body disease in general. Despite important achievements in the field of cholinergic research, significant challenges remain. We conducted a study with four main objectives: (i) to examine the integrity of cholinergic terminals in newly diagnosed dementia with Lewy bodies; (ii) to disentangle the cholinergic contribution to dementia by comparing cholinergic changes in Lewy body patients with and without dementia; (iii) to investigate the in vivo relationship between cholinergic terminal loss and atrophy of cholinergic cell clusters in the basal forebrain at different stages of Lewy body disease; and (iv) to test whether any asymmetrical degeneration in cholinergic terminals would correlate with motor dysfunction and hypometabolism. To achieve these objectives, we conducted a comparative cross-sectional study of 25 newly diagnosed dementia with Lewy bodies patients (age 74 ± 5 years, 84% male), 15 healthy control subjects (age 75 ± 6 years, 67% male) and 15 Parkinson's disease patients without dementia (age 70 ± 7 years, 60% male). All participants underwent 18F-fluoroetoxybenzovesamicol PET and high-resolution structural MRI. In addition, we collected clinical 18F-fluorodeoxyglucose PET images. Brain images were normalized to standard space and regional tracer uptake and volumetric indices of basal forebrain degeneration were extracted. Patients with dementia showed spatially distinct reductions in cholinergic terminals across the cerebral cortex, limbic system, thalamus and brainstem. Also, cholinergic terminal binding in cortical and limbic regions correlated quantitatively and spatially with atrophy of the basal forebrain. In contrast, patients without dementia showed decreased cholinergic terminal binding in the cerebral cortex despite preserved basal forebrain volumes. In patients with dementia, cholinergic terminal reductions were most severe in limbic regions and least severe in occipital regions compared to those without dementia. Interhemispheric asymmetry of cholinergic terminals correlated with asymmetry of brain metabolism and lateralized motor function. In conclusion, this study provides robust evidence for severe cholinergic terminal loss in newly diagnosed dementia with Lewy bodies, which correlates with structural imaging measures of cholinergic basal forebrain degeneration. In patients without dementia, our findings suggest that loss of cholinergic terminal function occurs 'before' neuronal cell degeneration. Moreover, the study supports that degeneration of the cholinergic system is important for brain metabolism and may be linked with degeneration in other transmitter systems. Our findings have implications for understanding how cholinergic system pathology contributes to the clinical features of Lewy body disease, changes in brain metabolism and disease progression patterns.