Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging.

During the third trimester, the human brain undergoes rapid cellular and molecular processes that reshape the structural architecture of the cerebral cortex. Knowledge of cortical differentiation obtained predominantly from histological studies is limited in localized and small cortical regions. How cortical microstructure is differentiated across cortical regions in this critical period is unknown. In this study, the cortical microstructural architecture across the entire cortex was delineated with non-Gaussian diffusion kurtosis imaging as well as conventional diffusion tensor imaging of 89 preterm neonates aged 31-42 postmenstrual weeks. The temporal changes of cortical mean kurtosis (MK) or fractional anisotropy (FA) were heterogeneous across the cortical regions. Cortical MK decreases were observed throughout the studied age period, while cortical FA decrease reached its plateau around 37 weeks. More rapid decreases in MK were found in the primary visual region, while faster FA declines were observed in the prefrontal cortex. We found that distinctive cortical microstructural changes were coupled with microstructural maturation of associated white matter tracts. Both cortical MK and FA measurements predicted the postmenstrual age of preterm infants accurately. This study revealed a differential 4D spatiotemporal cytoarchitectural signature inferred by non-Gaussian diffusion barriers inside the cortical plate during the third trimester. The cytoarchitectural processes, including dendritic arborization and neuronal density decreases, were inferred by regional cortical FA and MK measurements. The presented findings suggest that cortical MK and FA measurements could be used as effective imaging markers for cortical microstructural changes in typical and potentially atypical brain development.

Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications. Extensive effort has been devoted to improving fMRI data quality, but in the last two decades, there is no consensus reached which technique is more effective. In this study, we developed a novel deep neural network for denoising fMRI data, named denoising neural network (DeNN). This deep neural network is 1) applicable without requiring externally recorded data to model noise; 2) spatially and temporally adaptive to the variability of noise in different brain regions at different time points; 3) automated to output denoised data without manual interference; 4) trained and applied on each subject separately and 5) insensitive to the repetition time (TR) of fMRI data. When we compared DeNN with a number of nuisance regression methods for denoising fMRI data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, only DeNN had connectivity for functionally uncorrelated regions close to zero and successfully identified unbiased correlations between the posterior cingulate cortex seed and multiple brain regions within the default mode network or task positive network. The whole brain functional connectivity maps computed with DeNN-denoised data are approximately three times as homogeneous as the functional connectivity maps computed with raw data. Furthermore, the improved homogeneity strengthens rather than weakens the statistical power of fMRI in detecting intrinsic functional differences between cognitively normal subjects and subjects with Alzheimer's disease.

CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.

In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a single workstation with a high-quality GPU card, CAST can segment a new subject in less than 1 â€‹min even with GPU acceleration disabled, thus this method is more time efficient than current automated methods and manual segmentation. This toolbox is highly flexible with either a single modality or multiple modalities and can be easily set up to be trained with a researcher's unique data. A 3D multi-scale deep convolutional neural network is the key algorithm used in the toolbox. The main merit of multi-scale images is the capability to capture more global structural information from down-sampled images without dramatically increasing memory and computational burden. The original images capture more local information to refine the boundary between subfields. Residual learning is applied to alleviate the vanishing gradient problem and improve the performance with a deeper network. We applied CAST with the same settings on two datasets, one 7T dataset (the UMC dataset) with only the T2 image and one 3T dataset (the MNI dataset) with both T1 and T2 images available. The segmentation accuracy of both CAST and the state-of-the-art automated method ASHS, in terms of the dice similarity coefficient (DSC), were comparable. CAST significantly improved the reliability of segmenting small subfields, such as CA2, CA3, and the entorhinal cortex (ERC), in terms of the intraclass correlation coefficient (ICC). Both ASHS and manual segmentation process some subfields (e.g. CA2 and ERC) with high DSC values but low ICC values, consequently increasing the difficulty of judging segmentation quality. CAST produces very consistent DSC and ICC values, with a maximal discrepancy of 0.01 (DSC-ICC) across all subfields. The pre-trained model, source code, and settings for the CAST toolbox are publicly available.

Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: A validation study.

During the past ten years, dynamic functional connectivity (FC) has been extensively studied using the sliding-window method. A fixed window-size is usually selected heuristically, since no consensus exists yet on choice of the optimal window-size. Furthermore, without a known ground-truth, the validity of the computed dynamic FC remains unclear and questionable. In this study, we computed single-scale time-dependent (SSTD) window-sizes for the sliding-window method. SSTD window-sizes were based on the frequency content at every time point of a time series and were computed without any prior information. Therefore, they were time-dependent and data-driven. Using simulated sinusoidal time series with frequency shifts, we demonstrated that SSTD window-sizes captured the time-dependent period (inverse of frequency) information at every time point. We further validated the dynamic FC values computed with SSTD window-sizes with both a classification analysis using fMRI data with a low sampling rate and a regression analysis using fMRI data with a high sampling rate. Specifically, we achieved both a higher classification accuracy in predicting cognitive impairment status in fighters and a larger explained behavioral variance in healthy young adults when using dynamic FC matrices computed with SSTD window-sizes as features, as compared to using dynamic FC matrices computed with the conventional fixed window-sizes. Overall, our study computed and validated SSTD window-sizes in the sliding-window method for dynamic FC analysis. Our results demonstrate that dynamic FC matrices computed with SSTD window-sizes can capture more temporal dynamic information related to behavior and cognitive function.

Red Emitting Hydroxybenzazole (HBX) Based Azo Dyes: Linear and Non Linear Optical Properties, Optical Limiting, Z Scan Analysis with DFT Assessments.

Herein, we report the hydroxybenzazole (HBX) containing azo dyes for "linear and non-linear optical" (NLO) applications. These bi-heterocyclic dyes have HBX scaffold (decorated with ESIPT core) and connected to another thiazole moietiy through azo bond. In DMF and DMSO, dyes are "emissive in yellow-red region" and "large Stokes shift" in the range of 62-121 nm were observed. "Nonlinear absorptive coefficient" (ß), "nonlinear refractive index" (ƞ2), "third order non-linear optical susceptibility" (χ3) in DMSO, ethanol and methanol were calculated using simple and effective "Z-scan technique" having "Nd: YAG laser" at 532 nm wavelength. 4.46 × 10-13 (e.s.u.) was the highest (χ3) was observed in DMSO among all the dyes. Optical Limiting (OL) values are in the range of 7.61-19.06 J cm-2 in solvents. Thermo Gravimetric Analysis (TGA) supports that, these compounds are useful for numerous high-temperature practices in the construction of electronic as well as optical devices. Band gap was calculated by CV as well as by DFT in acetonitrile. The same trend was observed when these HOMO-LUMO gaps were correlated in between CV and DFT. To gain more insights into structural parameters, molecular geometries were optimized at "B3LYP-6-311 + G (d,p)" level of theory. Further, "Molecular Electrostatic Potential" (MEP), "Frontier Molecular Orbitals" (FMO) were presented using "Density Functional Theory (DFT)". Global hybrid functional (B3LYP, BHandHLYP) and range separated hybrid functionals (RSH) i.e. CAM-B3LYP, ωB97, ωB97X, and ωB97XD were used to calculate linear and NLO properties. Graphical Abstract.

Multivariate group-level analysis for task fMRI data with canonical correlation analysis.

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Structural network maturation of the preterm human brain.

During the 3rd trimester, large-scale neural circuits are formed in the human brain, resulting in a highly efficient and segregated connectome at birth. Despite recent findings identifying important preterm human brain network properties such as rich-club organization, how the structural network develops differentially across brain regions and among different types of connections in this period is not yet known. Here, using high resolution diffusion MRI of 77 preterm-born and full-term neonates scanned at 31.9-41.7 postmenstrual weeks (PMW), we constructed structural connectivity matrices and performed graph-theory-based analyses. Faster increases of nodal efficiency were mainly located at the brain hubs distributed in primary sensorimotor regions, superior-middle frontal, and precuneus regions during 31.9-41.7PMW. Higher rates of edge strength increases were found in the rich-club and within-module connections, compared to other connections. The edge strength of short-range connections increased faster than that of long-range connections. Nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed more rapid efficiency increases of the hub and rich-club connections as well as higher developmental rates of edge strength in short-range and within-module connections. These jointly underlie network segregation and differentiated emergence of brain functions.

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks.

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters.

Long-term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph-theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph-theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD.

3D spatially-adaptive canonical correlation analysis: Local and global methods.

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.

Multimodal MR Imaging Signatures of Cognitive Impairment in Active Professional Fighters.

Purpose To investigate whether combining multiple magnetic resonance (MR) imaging modalities such as T1-weighted and diffusion-weighted MR imaging could reveal imaging biomarkers associated with cognition in active professional fighters. Materials and Methods Active professional fighters (n = 297; 24 women and 273 men) were recruited at one center. Sixty-two fighters (six women and 56 men) returned for a follow-up examination. Only men were included in the main analysis of the study. On the basis of computerized testing, fighters were separated into the cognitively impaired and nonimpaired groups on the basis of computerized testing. T1-weighted and diffusion-weighted imaging were performed, and volume and cortical thickness, along with diffusion-derived metrics of 20 major white matter tracts were extracted for every subject. A classifier was designed to identify imaging biomarkers related to cognitive impairment and was tested in the follow-up dataset. Results The classifier allowed identification of seven imaging biomarkers related to cognitive impairment in the cohort of active professional fighters. Areas under the curve of 0.76 and 0.69 were obtained at baseline and at follow-up, respectively, with the optimized classifier. The number of years of fighting had a significant (P = 8.8 × 10-7) negative association with fractional anisotropy of the forceps major (effect size [d] = 0.34) and the inferior longitudinal fasciculus (P = .03; d = 0.17). A significant difference was observed between the impaired and nonimpaired groups in the association of fractional anisotropy in the forceps major with number of fights (P = .03, d = 0.38) and years of fighting (P = 6 × 10-8, d = 0.63). Fractional anisotropy of the inferior longitudinal fasciculus was positively associated with psychomotor speed (P = .04, d = 0.16) in nonimpaired fighters but no association was observed in impaired fighters. Conclusion Without enforcement of any a priori assumptions on the MR imaging-derived measurements and with a multivariate approach, the study revealed a set of seven imaging biomarkers that were associated with cognition in active male professional fighters. © RSNA, 2017 Online supplemental material is available for this article.

Photostability of Coumarin Laser Dyes - a Mechanistic Study Using Global and Local Reactivity Descriptors.

Using density functional theory approach, the trend in photostability of fluorescent coumarin laser dyes were investigated with the help global reactivity descriptors. The effect of different basis sets on global reactivity descriptors i.e. electrophilicity index (ω) is studied. The local reactivity descriptors such as Fukui function, local softness and local electrophilicity index have been used to understand the reactive site in the molecule. Photodegradation mechanism of fluorescent coumarin laser dyes in presence of singlet oxygen is explained using density functional theory.

Structural Development of Human Fetal and Preterm Brain Cortical Plate Based on Population-Averaged Templates.

We hypothesized that the distinct maturational processes take place across different cortical areas from middle fetal stage to normal time of birth and these maturational processes are altered in late third trimester. Fractional anisotropies (FA) from diffusion tensor imaging (DTI) infer the microstructures of the early developing cortical plate. High-resolution DTI of 11 fetal brain specimens at postmenstrual age of 20 weeks (or simplified as 20 weeks), 19 in vivo brains at 35 weeks, and 17 in vivo brains at normal time of birth at term (40 weeks) were acquired. Population-averaged age-specific DTI templates were established with large deformation diffeomorphic metric mapping for subject groups at 20, 35, and 40 weeks. To alleviate partial volume effects, skeletonized FA values were used for mapping averaged cortical FA to the cortical surface and measuring FA at 12 functionally distinctive cortical regions. Significant and heterogeneous FA decreases take place in distinct cortical areas from 20 to 35 weeks and from 35 to 40 weeks, suggesting differentiated cortical development patterns. Temporally nonuniform FA decrease patterns during 35-40 weeks compared with those during 20-35 weeks were observed in higher-order association cortex. Measured skeletonized FA suggested dissociated changes between cerebral cortex and white matter during 35-40 weeks.

Atypical age-dependent effects of autism on white matter microstructure in children of 2-7 years.

Atypical age-dependent changes of white matter (WM) microstructure play a central role in abnormal brain maturation of the children with autism spectrum disorder (ASD), but their early manifestations have not been systematically characterized. The entire brain core WM voxels were surveyed to detect differences in WM microstructural development between 31 children with ASD of 2-7 years and 19 age-matched children with typical development (TD), using measurements of fractional anisotropy (FA) and radial diffusivity (RD) from diffusion tensor imaging (DTI). The anatomical locations, distribution, and extent of the core WM voxels with atypical age-dependent changes in a specific tract or tract group were delineated and evaluated by integrating the skeletonized WM with a digital atlas. Exclusively, unidirectional FA increases and RD decreases in widespread WM tracts were revealed in children with ASD before 4 years, with bi-directional changes found for children with ASD of 2-7 years. Compared to progressive development that raised FA and lowered RD during 2-7 years in the TD group, flattened curves of WM maturation were found in multiple major WM tracts of all five tract groups, particularly associational and limbic tracts, in the ASD group with trend lines of ASD and TD crossed around 4 years. We found atypical age-dependent changes of FA and RD widely and heterogeneously distributed in WM tracts of children with ASD. The early higher WM microstructural integrity before 4 years reflects abnormal neural patterning, connectivity, and pruning that may contribute to aberrant behavioral and cognitive development in ASD. Hum Brain Mapp 37:819-832, 2016. © 2015 Wiley Periodicals, Inc.

Development of human brain structural networks through infancy and childhood.

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers.

Toward tract-specific fractional anisotropy (TSFA) at crossing-fiber regions with clinical diffusion MRI.

PURPOSE: White matter fractional anisotropy (FA), a measure suggesting microstructure, is significantly underestimated with single diffusion tensor model at crossing-fiber regions (CFR). We propose a tract-specific FA (TSFA), corrected for the effects of crossing-fiber geometry and free water at CFR, and adapted for tract analysis with diffusion MRI (dMRI) in clinical research. METHODS: At CFR voxels, the proposed technique estimates free water fraction (fiso ) as a linear function of mean apparent diffusion coefficient (mADC), fits the dual tensors and estimates TSFA. Digital phantoms were designed for testing the accuracy of fiso and fitted dual-anisotropies at CFR. The technique was applied to clinical dMRI of normal subjects and hereditary spastic paraplegia (HSP) patients to test the effectiveness of TSFA. RESULTS: Phantom simulation showed unbiased estimates of dual-tensor anisotropies at CFR and high accuracy of fiso as a linear function of mADC. TSFA at CFR was highly consistent to the single tensor FA at non-CFR within the same tract with normal human dMRI. Additional HSP imaging biomarkers with significant correlation to clinical motor function scores could be identified with TSFA. CONCLUSION: Results suggest the potential of the proposed technique in estimating unbiased TSFA at CFR and conducting tract analysis in clinical research.

Special Issue "Microbial Endophytes: Functional Biology and Applications": Editorial.

Plants harbour various microbial communities, including bacteria, fungi, actinomycetes, and nematodes, inside or outside their tissues [...].

Topological reorganization of functional hubs in patients with Parkinson's disease with freezing of gait.

BACKGROUND AND PURPOSE: Resting-state functional MRI (rs-fMRI) studies in Parkinson's disease (PD) patients with freezing of gait (FOG) have implicated dysfunctional connectivity over multiple resting-state networks (RSNs). While these findings provided network-specific insights and information related to the aberrant or altered regional functional connectivity (FC), whether these alterations have any effect on topological reorganization in PD-FOG patients is incompletely understood. Understanding the higher order functional organization, which could be derived from the "hub" and the "rich-club" organization of the functional networks, could be crucial to identifying the distinct and unique pattern of the network connectivity associated with PD-FOG. METHODS: In this study, we use rs-fMRI data and graph theoretical approaches to explore the reorganization of RSN topology in PD-FOG when compared to those without FOG. We also compared the higher order functional organization derived using the hub and rich-club measures in the FC networks of these PD-FOG patients to understand whether there is a topological reorganization of these hubs in PD-FOG. RESULTS: We found that the PD-FOG patients showed a noticeable reorganization of hub regions. Regions that are part of the prefrontal cortex, primary somatosensory, motor, and visuomotor coordination areas were some of the regions exhibiting altered hub measures in PD-FOG patients. We also found a significantly altered feeder and local connectivity in PD-FOG. CONCLUSIONS: Overall, our findings demonstrate a widespread topological reorganization and disrupted higher order functional network topology in PD-FOG that may further assist in improving our understanding of functional network disturbances associated with PD-FOG.

Regional changes of cortical mean diffusivities with aging after correction of partial volume effects.

Accurately measuring the cortical mean diffusivity (MD) derived from diffusion tensor imaging (DTI) at the comprehensive lobe, gyral and voxel level of young, elderly healthy brains and those with Alzheimer's disease (AD) may provide insights on heterogeneous cortical microstructural changes caused by aging and AD. Due to partial volume effects (PVE), the measurement of cortical MD is overestimated with contamination of cerebrospinal fluid (CSF). The bias is especially severe for aging and AD brains because of significant cortical thinning of these brains. In this study, we aimed to quantitatively characterize the unbiased regional cortical MD changes due to aging and AD and delineate the effects of cortical thinning of elderly healthy and AD groups on MD measurements. DTI and T1-weighted images of 14 young, 15 elderly healthy subjects and 17 AD patients were acquired. With the parcellated cortical gyri and lobes from T1 weighted image transformed to DTI, regional cortical MD of all subjects before and after PVE correction were measured. CSF contamination model was used to correct bias of MD caused by PVE. Compared to cortical MD of young group, significant increases of corrected MD for elderly healthy and AD groups were found only in frontal and limbic regions, respectively, while there were significant increases of uncorrected MD all over the cortex. Uncorrected MD are significantly higher in limbic and temporal gyri in AD group, compared to those in elderly healthy group but higher MD only remained in limbic gyri after PVE correction. Cortical thickness was also measured for all groups. The correlation slopes between cortical MD and thickness for elderly healthy and AD groups were significantly decreased after PVE correction compared to before correction while no significant change of correlation slope was detected for young group. It suggests that the cortical thinning in elderly healthy and AD groups is a significant contributor to the bias of uncorrected cortical MD measurement. The established comprehensive unbiased cortical MD profiles of young, elderly healthy subjects and AD patients at the lobe, gyral and voxel level may serve as clinical references for cortical microstructure.