A modified methodology for extraction and quantification of microplastics in soil.

The ubiquitousness of microplastics (<5 mm) has become a pressing environmental concern globally due to the extensive use of plastics. Microplastics have been well-studied in aquatic environments but not well-characterized in soils. Present analytical processes to quantify microplastics accurately in soil samples are quite challenging and require improved and validated analytical steps to eliminate the obscurities and biases. We aimed to develop an effective method for the extraction and quantification of microplastics from soil samples. Different ratios of low-(NaCl) and high-density solutions (ZnCl2/ NaBr) were tested to determine the most efficient combination for density-dependent separation of microplastics from soil. The combination of low- (1:6) and high-density (1:3) solutions {as weight of soil(g)/volume of density solution(ml)} accounted for 95% recovery of the spiked microplastic particles from soil samples. Likewise, different soil-to-solution ratios of H2O2 were tested for the removal of soil organic matter with heating and non-heating steps. Prior removal of organic matter from soil samples achieved a clear supernatant that facilitated 99% recovery of microplastic particles. The validation of individually spiked microplastic particles of small (10-100 µm) and large scale (100-5000 µm) resulted in recovery ranging from 88 to 99%. A validated modified method with prior digestion followed by density-dependent separation was further tested using the field samples with microplastic contamination. The microplastics of different shapes, sizes, colours and polymeric compositions were reported efficiently and well characterized in the field-collected soil samples using this method.

Dual-task performance and brain morphologic characteristics in Parkinson's disease.

Introduction Parkinson's disease (PD) reduces an individual's capacity for automaticity which limits their ability to perform two tasks simultaneously, negatively impacting daily function. Understanding the neural correlates of dual-tasks (DT) may pave the way for targeted therapies. To better understand automaticity in PD, we aimed to explore whether individuals with differing DT performances possessed differences in brain morphologic characteristics. Methods Data were obtained from 34 individuals with PD and 47 healthy older adults including: 1) demographics (age, sex), 2) disease severity (Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn & Yahr, levodopa equivalent daily dose (LEDD)), 3) cognition (Montreal Cognitive Assessment), 4) Levodopa Equivalent Daily Dose, 5) single task- and DT- performance during a DT-Timed-Up-and-Go test utilizing a serial-subtraction task, and 6) Cortical thicknesses and subcortical volumes obtained from volumetric MRI. Participants were categorized as low or high DT performers if their combined DT-effect was greater than the previously determined mean value for healthy older adults (µ=74.2). Nonparametric testing using Quade's ANCOVA was conducted to compare cortical thicknesses and brain volumes between the highDT and lowDT groups while controlling for covariates: age, sex, MDS-UPDRS part III, LEDD, and intracranial volume. Secondarily, similar comparisons were made between the healthy older adult group the highDT and lowDT groups. Lastly, a hierarchical linear regression model was conducted regressing combined DT-effect on covariates (block one) and cortical thicknesses (block 2) in stepwise fashion. Results The highDT group had thicker cortices than the lowDT group in the right primary somatosensory (p=0.001), bilateral primary motor (p=<0.001, left; p=0.002, right), bilateral supplementary motor area (p=0.001, left; p<0.001, right), and mean of the bilateral hemispheres (p=0.001, left; p<0.001, right). Of note, left primary cortex thickness (p=0.002), left prefrontal cortex thickness (p<0.001), and right supplementary motor area thickness (p=0.003) differed when adding a healthy comparison group. Additionally, the regression analysis found that the left paracentral lobule thickness explained 20.8% of the variability in combined DT-effect (p=0.011) beyond the influence of covariates. Conclusions These results suggest regions underlying dual-task performance; specifically, a convergence of neural control relying sensorimotor integration, motor planning, and motor activation to achieve higher levels of DT performance for individuals with PD.

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.

Levodopa-Carbidopa Intestinal Gel may improve treatment-resistant freezing of gait in Parkinson's disease.

Introduction: Freezing of gait (FOG) is a highly disabling symptom in Parkinson's Disease (PD) with varying degree of benefits from oral dopaminergic medications and several subtypes that present with different medication states (e.g., off FOG, on FOG, pseudo-on FOG, supra-on FOG). Levodopa-Carbidopa Intestinal Gel (LCIG) greately reduces the variability of cerebral dopamine replacement inherent to oral therapies by continuous levodopa intestinal infusion. While LCIG may be superior to oral therapy in its ability to treat motor fluctuations and minimize off-time, there is no consensus regarding the overall effectiveness of LCIG specifically for the treatment of FOG in PD patients. Methods: A systematic literature review was conducted to understand the efficacy of LCIG to treat FOG in PD patients. A PubMed search was conducted using the search query "Intestinal AND (Levodopa OR L-dopa) AND Freezing of Gait AND Parkinson." Additional eligibility criteria included articles written in English and currently published journal articles. Articles were excluded if they did not have a clinical design or if they did not yield reportable data on FOG. Results: The literature search yielded 16 articles, of which 10 articles were included. Of the 10 studies included, there were 3 retrospective studies, 6 case reports or case series, and 1 open-label study. (n = 449 patients total and 318 FOG patients). Nine of the 10 studies concluded that LCIG has a favorable effect on FOG, though the metrics to evaluate benefits of LCIG on FOG varied among the articles. Conclusion: LCIG may be an effective treatment for PD patients suffering from FOG including those with poor response to oral medication, likely because of its ability to maintain steadier dopamine levels. Further research is necessary on LCIG as a therapy for refractory FOG, with particular attention to the different subtypes of FOG.

Corrigendum: Neural Correlates of Freezing of Gait in Parkinson's Disease: An Electrophysiology Mini-Review.

[This corrects the article DOI: 10.3389/fneur.2020.571086.].

Structural brain differences in recovering and weight-recovered adult outpatient women with anorexia nervosa.

BACKGROUND: Anorexia nervosa is a complex psychiatric illness that includes severe low body weight with cognitive distortions and altered eating behaviors. Brain structures, including cortical thicknesses in many regions, are reduced in underweight patients who are acutely ill with anorexia nervosa. However, few studies have examined adult outpatients in the process of recovering from anorexia nervosa. Evaluating neurobiological problems at different physiological stages of anorexia nervosa may facilitate our understanding of the recovery process. METHODS: Magnetic resonance imaging (MRI) images from 37 partially weight-restored women with anorexia nervosa (pwAN), 32 women with a history of anorexia nervosa maintaining weight restoration (wrAN), and 41 healthy control women were analyzed using FreeSurfer. Group differences in brain structure, including cortical thickness, areas, and volumes, were compared using a series of factorial f-tests, including age as a covariate, and correcting for multiple comparisons with the False Discovery Rate method. RESULTS: The pwAN and wrAN cohorts differed from each other in body mass index, eating disorder symptoms, and social problem solving orientations, but not depression or self-esteem. Relative to the HC cohort, eight cortical thicknesses were thinner for the pwAN cohort; these regions were predominately right-sided and in the cingulate and frontal lobe. One of these regions, the right pars orbitalis, was also thinner for the wrAN cohort. One region, the right parahippocampal gyrus, was thicker in the pwAN cohort. One volume, the right cerebellar white matter, was reduced in the pwAN cohort. There were no differences in global white matter, gray matter, or subcortical volumes across the cohorts. CONCLUSIONS: Many regional structural differences were observed in the pwAN cohort with minimal differences in the wrAN cohort. These data support a treatment focus on achieving and sustaining full weight restoration to mitigate possible neurobiological sequela of AN. In addition, the regions showing cortical thinning are similar to structural changes reported elsewhere for suicide attempts, anxiety disorders, and autistic spectrum disorder. Understanding how brain structure and function are related to clinical symptoms expressed during the course of recovering from AN is needed.

Anorexia nervosa is a life-threatening mental illness defined in part by an inability to maintain a body weight in the normal range. Malnutrition and low weight are factors typically present in the anorexia nervosa and can affect brain structure. We conducted a detailed analysis of brain structure using Freesurfer, focusing on regional cortical thicknesses, areas, and volumes, in adult outpatient women with anorexia nervosa. The study included both a partially weight-restored cohort with anorexia nervosa, a cohort sustaining a healthy body weight with history of anorexia nervosa, and a healthy comparison cohort. Reduced cortical thicknesses were observed in eight regions, primarily in the frontal lobe and cingulate for the cohort recently with anorexia nervosa but only one frontal region in the weight-maintained cohort. These data emphasize the importance of sustained weight-restoration for adult women with anorexia nervosa. Further, the impacted neural regions have been associated with impulsivity, attention, self-regulation, and social interactions in other clinical cohorts, suggesting that these neuropsychological processes may warrant study in patients recovering from anorexia nervosa. Future work should consider whether these factors have clinical relevance in the outpatient treatment of adults with anorexia nervosa.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments.

Previous neuroimaging studies have identified structural brain abnormalities in active professional fighters with repetitive head trauma and correlated these changes with fighters' neuropsychological impairments. However, functional brain changes in these fighters derived using neuroimaging techniques remain unclear. In this study, both static and dynamic functional connectivity alterations were investigated (1) between healthy normal control subjects (NC) and fighters and (2) between non-impaired and impaired fighters. Resting-state fMRI data were collected on 35 NC and 133 active professional fighters, including 68 impaired fighters and 65 non-impaired fighters, from the Professional Fighters Brain Health Study at our center. Impaired fighters performed worse on processing speed (PSS) tasks with visual-attention and working-memory demands. The static functional connectivity (sFC) matrix was estimated for every pair of regions of interest (ROI) using a subject-specific parcellation. The dynamic functional connectivity (dFC) was estimated using a sliding-window method, where the variability of each ROI pair across all windows represented the temporal dynamics. A linear regression model was fitted for all 168 subjects, and different t-contrast vectors were used for between-group comparisons. An association analysis was further conducted to evaluate FC changes associated with PSS task performances without creating artificial impairment group-divisions in fighters. Following corrections for multiple comparisons using network-based statistics, our study identified significantly reduced long-range frontal-temporal, frontal-occipital, temporal-occipital, and parietal-occipital sFC strengths in fighters than in NCs, corroborating with previously observed structural damages in corresponding white matter tracts in subjects experiencing repetitive head trauma. In impaired fighters, significantly decreased sFC strengths were found among key regions involved in visual-attention, executive and cognitive process, as compared to non-impaired fighters. Association analysis further reveals similar sFC deficits to worse PSS task performances in all 133 fighters. With our choice of dFC indices, we were not able to observe any significant dFC changes beyond a trend-level increased temporal variability among similar regions with weaker sFC strengths in impaired fighters. Collectively, our functional brain findings supplement previously reported structural brain abnormalities in fighters and are important to comprehensively understand brain changes in fighters with repetitive head trauma.

Neural Correlates of Freezing of Gait in Parkinson's Disease: An Electrophysiology Mini-Review.

Freezing of gait (FoG) is a disabling symptom characterized as a brief inability to step or by short steps, which occurs when initiating gait or while turning, affecting over half the population with advanced Parkinson's disease (PD). Several non-competing hypotheses have been proposed to explain the pathophysiology and mechanism behind FoG. Yet, due to the complexity of FoG and the lack of a complete understanding of its mechanism, no clear consensus has been reached on the best treatment options. Moreover, most studies that aim to explore neural biomarkers of FoG have been limited to semi-static or imagined paradigms. One of the biggest unmet needs in the field is the identification of reliable biomarkers that can be construed from real walking scenarios to guide better treatments and validate medical and therapeutic interventions. Advances in neural electrophysiology exploration, including EEG and DBS, will allow for pathophysiology research on more real-to-life scenarios for better FoG biomarker identification and validation. The major aim of this review is to highlight the most up-to-date studies that explain the mechanisms underlying FoG through electrophysiology explorations. The latest methodological approaches used in the neurophysiological study of FoG are summarized, and potential future research directions are discussed.

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.

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.

Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review.

Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.

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.

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.

Benzothiazole-pyridone and benzothiazole-pyrazole clubbed emissive azo dyes and dyeing application on polyester fabric: UPF, biological, photophysical and fastness properties with correlative computational assessments.

Positional isomers of benzothiazole-pyridone and benzothiazole-pyrazole containing disperse azo dyes are reported. These heterocyclic azo dyes are decorated with 'separate ESIPT core' and show emission in seven solvents of different polarity. After application on polyester fabric, "very good to excellent" light and washing fastness properties were observed. Thermal stability of 'dyed fabric' was analysed by sublimation fastness test- and found 'very good to excellent' ratings at 210 °C. Ultraviolet Protection Factor (UPF) analysis of four 'dyed fabric' indicates the blocking 96-97% of UV radiation. Dyes were found effective on gram positive and negative bacteria by agar diffusion method and all the 'dyed fabrics' also showed more than 92% or 94% reduction of S. aureus or K. pneumoniae respectively by 'AATCC 100' method. Structures of the dyes were optimized using Density Functional Theory (DFT) to deduce stable tautomeric form. Calculated HOMO-LUMO gap is then compared with antibacterial activities. Electrophilicity index and lightfastness property were also compared and found to have very good correlation.

Unique white matter structural connectivity in early-stage drug-naive Parkinson disease.

OBJECTIVE: To investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD). METHODS: We employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance. RESULTS: PD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network. CONCLUSION: By minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.

Longitudinal change in regional brain volumes with exposure to repetitive head impacts.

OBJECTIVE: This study tests the hypothesis that certain MRI-based regional brain volumes will show reductions over time in a cohort exposed to repetitive head impacts (RHI). METHODS: Participants were drawn from the Professional Fighters Brain Health Study, a longitudinal observational study of professional fighters and controls. Participants underwent annual 3T brain MRI, computerized cognitive testing, and blood sampling for determination of neurofilament light (NfL) and tau levels. Yearly change in regional brain volume was calculated for several predetermined cortical and subcortical brain volumes and the relationship with NfL and tau levels determined. RESULTS: A total of 204 participants who had at least 2 assessments were included in the analyses. Compared to controls, the active boxers had an average yearly rate of decline in volumes of the left thalamus (102.3 mm3/y [p = 0.0004], mid anterior corpus callosum (10.2 mm3/y [p = 0.018]), and central corpus callosum (16.5 mm3/y [p = <0.0001]). Retired boxers showed the most significant volumetric declines compared to controls in left (32.1 mm3/y [p = 0.002]) and right (30.6 mm3/y [p = 0.008]) amygdala and right hippocampus (33.5 mm3/y [p = 0.01]). Higher baseline NfL levels were associated with greater volumetric decline in left hippocampus and mid anterior corpus callosum. CONCLUSION: Volumetric loss in different brain regions may reflect different pathologic processes at different times among individuals exposed to RHI.

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.