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.

A technical review of canonical correlation analysis for neuroscience applications.

Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.

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.

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.

A family of locally constrained CCA models for detecting activation patterns in fMRI.

Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

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.

Cardiac Chemical Exchange Saturation Transfer MR Imaging Tracking of Cell Survival or Rejection in Mouse Models of Cell Therapy.

Purpose To examine whether cardiac chemical exchange saturation transfer (CEST) imaging can be serially and noninvasively used to probe cell survival or rejection after intramyocardial implantation in mice. Materials and Methods Experiments were compliant with the National Institutes of Health Guidelines on the Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee. One million C2C12 cells labeled with either europium (Eu) 10-(2-hydroxypropyl)-1,4,7-tetraazacyclododecane-1,4,7-triacetic acid (HP-DO3A) or saline via the hypotonic swelling technique were implanted into the anterior-lateral left ventricular wall in C57BL/6J (allogeneic model, n = 17) and C3H (syngeneic model, n = 13) mice. Imaging (frequency offsets of ±15 parts per million) was performed 1, 10, and 20 days after implantation, with the asymmetrical magnetization transfer ratio (MTRasym) calculated from image pairs. Histologic examination was performed at the conclusion of imaging. Changes in MTRasym over time and between mice were assessed by using two-way repeated-measures analysis of variance. Results MTRasym was significantly higher in C3H and C57BL/6J mice in grafts of Eu-HP-DO3A-labeled cells (40.2% ± 5.0 vs 37.8% ± 7.0, respectively) compared with surrounding tissue (-0.67% ± 1.7 vs -1.8% ± 5.3, respectively; P < .001) and saline-labeled grafts (-0.4% ± 6.0 vs -1.2% ± 3.6, respectively; P < .001) at day 1. In C3H mice, MTRasym remained increased (31.3% ± 9.2 on day 10, 28.7% ± 5.2 on day 20; P < .001 vs septum) in areas of in Eu-HP-DO3A-labeled cell grafts. In C57BL/6J mice, corresponding MTRasym values (11.3% ± 8.1 on day 10, 5.1% ± 9.4 on day 20; P < .001 vs day 1) were similar to surrounding myocardium by day 20 (P = .409). Histologic findings confirmed cell rejection in C57BL/6J mice. Estimation of graft area was similar with cardiac CEST imaging and histologic examination (R2 = 0.89). Conclusion Cardiac CEST imaging can be used to image cell survival and rejection in preclinical models of cell therapy. © RSNA, 2016 Online supplemental material is available for this article.

Advanced cardiac chemical exchange saturation transfer (cardioCEST) MRI for in vivo cell tracking and metabolic imaging.

An improved pre-clinical cardiac chemical exchange saturation transfer (CEST) pulse sequence (cardioCEST) was used to selectively visualize paramagnetic CEST (paraCEST)-labeled cells following intramyocardial implantation. In addition, cardioCEST was used to examine the effect of diet-induced obesity upon myocardial creatine CEST contrast. CEST pulse sequences were designed from standard turbo-spin-echo and gradient-echo sequences, and a cardiorespiratory-gated steady-state cine gradient-echo sequence. In vitro validation studies performed in phantoms composed of 20 mM Eu-HPDO3A, 20 mM Yb-HPDO3A, or saline demonstrated similar CEST contrast by spin-echo and gradient-echo pulse sequences. Skeletal myoblast cells (C2C12) were labeled with either Eu-HPDO3A or saline using a hypotonic swelling procedure and implanted into the myocardium of C57B6/J mice. Inductively coupled plasma mass spectrometry confirmed cellular levels of Eu of 2.1 × 10(-3) ng/cell in Eu-HPDO3A-labeled cells and 2.3 × 10(-5) ng/cell in saline-labeled cells. In vivo cardioCEST imaging of labeled cells at ±15 ppm was performed 24 h after implantation and revealed significantly elevated asymmetric magnetization transfer ratio values in regions of Eu-HPDO3A-labeled cells when compared with surrounding myocardium or saline-labeled cells. We further utilized the cardioCEST pulse sequence to examine changes in myocardial creatine in response to diet-induced obesity by acquiring pairs of cardioCEST images at ±1.8 ppm. While ventricular geometry and function were unchanged between mice fed either a high-fat diet or a corresponding control low-fat diet for 14 weeks, myocardial creatine CEST contrast was significantly reduced in mice fed the high-fat diet. The selective visualization of paraCEST-labeled cells using cardioCEST imaging can enable investigation of cell fate processes in cardioregenerative medicine, or multiplex imaging of cell survival with imaging of cardiac structure and function and additional imaging of myocardial creatine.

Uptake of 18F-AV45 in the Putamen Provides Additional Insights into Alzheimer's Disease beyond the Cortex.

Cortical uptake in brain amyloid positron emission tomography (PET) is increasingly used for the biological diagnosis of Alzheimer's disease (AD); however, the clinical and biological relevance of the striatum beyond the cortex in amyloid PET scans remains unclear. A total of 513 amyloid-positive participants having 18F-AV45 amyloid PET scans available were included in the analysis. The associations between cognitive scores and striatal uptake were analyzed. The participants were categorized into three groups based on the residual from the linear fitting between 18F-AV45 uptake in the putamen and the cortex in the order of HighP > MidP > LowP group. We then examined the differences between these three groups in terms of clinical diagnosis, APOE genotype, CSF phosphorylated tau (ptau) concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate to evaluate the additional insights provided by the putamen beyond the cortex. The 18F-AV45 uptake in the putamen was more strongly associated with ADAS-cog13 and MoCA scores (p < 0.001) compared to the uptake in the caudate nucleus. Despite comparable cortical uptakes, the HighP group had a two-fold higher risk of being ε4-homozygous or diagnosed with AD dementia compared to the LowP group. These three groups had significantly different CSF ptau concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate. These findings suggest that the assessment of 18F-AV45 uptake in the putamen is of unique value for evaluating disease severity and predicting disease progression.

Accelerated hypometabolism with disease progression associated with faster cognitive decline among amyloid positive patients.

Objective: To evaluate the progression of brain glucose metabolism among participants with biological signature of Alzheimer's disease (AD) and its relevance to cognitive decline. Method: We studied 602 amyloid positive individuals who underwent 18F-fluorodeoxyglucose PET (FDG-PET) scan, 18F-AV-45 amyloid PET (AV45-PET) scan, structural MRI scan and neuropsychological examination, including 116 cognitively normal (CN) participants, 314 participants diagnosed as mild cognitive impairment (MCI), and 172 participants diagnosed as AD dementia. The first FDG-PET scan satisfying the inclusion criteria was considered as the baseline scan. Cross-sectional analysis were conducted with the baseline FDG-PET data to compare the regional differences between diagnostic groups after adjusting confounding factors. Among these participants, 229 participants (55 CN, 139 MCI, and 35 AD dementia) had two-year follow-up FDG-PET data available. Regional glucose metabolism was computed and the progression rates of regional glucose metabolism were derived from longitudinal FDG-PET scans. Then the group differences of regional progression rates were examined to assess whether glucose metabolism deficit accelerates or becomes stable with disease progression. The association of cognitive decline rate with baseline regional glucose metabolism, and progression rate in longitudinal data, were evaluated. Results: Participants with AD dementia showed substantial glucose metabolism deficit than CN and MCI at left hippocampus, in addition to the traditionally reported frontal and parietal-temporal lobe. More substantial metabolic change was observed with the contrast AD - MCI than the contrast MCI - CN, even after adjusting time duration since cognitive symptom onset. With the longitudinal data, glucose metabolism was observed to decline the most rapidly in the AD dementia group and at a slower rate in MCI. Lower regional glucose metabolism was correlated to faster cognitive decline rate with mild-moderate correlations, and the progression rate was correlated to cognitive decline rate with moderate-large correlations. Discussion and conclusion: Hippocampus was identified to experience hypometabolism in AD pathology. Hypometabolism accelerates with disease progression toward AD dementia. FDG-PET, particularly longitudinal scans, could potentially help predict how fast cognition declines and assess the impact of treatment in interventional trials.

Microglial Imaging in Alzheimer's Disease and Its Relationship to Brain Amyloid: A Human 18F-GE180 PET Study.

BACKGROUND: Emerging evidence suggests a potential causal role of neuroinflammation in Alzheimer's disease (AD). Using positron emission tomography (PET) to image overexpressed 18 kDA translocator protein (TSPO) by activated microglia has gained increasing interest. The uptake of 18F-GE180 TSPO PET was observed to co-localize with inflammatory markers and have a two-stage association with amyloid PET in mice. Very few studies evaluated the diagnostic power of 18F-GE180 PET in AD population and its interpretation in human remains controversial about whether it is a marker of microglial activation or merely reflects disrupted blood-brain barrier integrity in humans. OBJECTIVE: The goal of this study was to study human GE180 from the perspective of the previous animal observations. METHODS: With data from twenty-four participants having 18F-GE180 and 18F-AV45 PET scans, we evaluated the group differences of 18F-GE180 uptake between participants with and without cognitive impairment. An association analysis of 18F-GE180 and 18F-AV45 was then conducted to test if the relationship in humans is consistent with the two-stage association in AD mouse model. RESULTS: Elevated 18F-GE180 was observed in participants with cognitive impairment compared to those with normal cognition. No regions showed reduced 18F-GE180 uptake. Consistent with mouse model, a two-stage association between 18F-GE180 and 18F-AV45 was observed. CONCLUSIONS: 18F-GE180 PET imaging showed promising utility in detecting pathological alterations in a symptomatic AD population. Consistent two-stage association between 18F-GE180 and amyloid PET in human and mouse suggested that 18F-GE180 uptake in human might be considerably influenced by microglial activation.

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.

Clinical and biological relevance of glial fibrillary acidic protein in Alzheimer's disease.

INTRODUCTION: There is a tremendous need for identifying reliable blood-based biomarkers for Alzheimer's disease (AD) that are tied to the biological ATN (amyloid, tau and neurodegeneration) framework as well as clinical assessment and progression. METHODS: One hundred forty-four elderly participants underwent 18F-AV45 positron emission tomography (PET) scan, structural magnetic resonance imaging (MRI) scan, and blood sample collection. The composite standardized uptake value ratio (SUVR) was derived from 18F-AV45 PET to assess brain amyloid burden, and the hippocampal volume was determined from structural MRI scans. Plasma glial fibrillary acidic protein (GFAP), phosphorylated tau-181 (ptau-181), and neurofilament light (NfL) measured by single molecular array (SIMOA) technology were assessed with respect to ATN framework, genetic risk factor, age, clinical assessment, and future functional decline among the participants. RESULTS: Among the three plasma markers, GFAP best discriminated participants stratified by clinical diagnosis and brain amyloid status. Age was strongly associated with NfL, followed by GFAP and ptau-181 at much weaker extent. Brain amyloid was strongly associated with plasma GFAP and ptau-181 and to a lesser extent with plasma NfL. Moderate association was observed between plasma markers. Hippocampal volume was weakly associated with all three markers. Elevated GFAP and ptau-181 were associated with worse cognition, and plasma GFAP was the most predictive of future functional decline. Combining GFAP and ptau-181 together was the best model to predict brain amyloid status across all participants (AUC = 0.86) or within cognitively impaired participants (AUC = 0.93); adding NfL as an additional predictor only had a marginal improvement. CONCLUSION: Our findings indicate that GFAP is of potential clinical utility in screening amyloid pathology and predicting future cognitive decline. GFAP, NfL, and ptau-181 were moderately associated with each other, with discrepant relevance to age, sex, and AD genetic risk, suggesting their relevant but differential roles for AD assessment. The combination of GFAP with ptau-181 provides an accurate model to predict brain amyloid status, with the superior performance of GFAP over ptau-181 when the prediction is limited to cognitively impaired participants.

Sex Modulates the Pathological Aging Effect on Caudate Functional Connectivity in Mild Cognitive Impairment.

Purpose: To assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process. Materials and Methods: Two hundred and seventy-seven functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson's correlation was used to characterize the functional connectivity (FC) between caudate nuclei and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4, and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status, and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region's connectivity with caudate nuclei. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI. Results: The MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P = 6.23 × 10-7, right: P = 3.37 × 10-8), but not for men (P > 0.3 for bilateral caudate nuclei). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI. Conclusion: Sex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.

Brain functional topology differs by sex in cognitively normal older adults.

Introduction: Late onset Alzheimer's disease (AD) is the most common form of dementia, in which almost 70% of patients are women. Hypothesis: We hypothesized that women show worse global FC metrics compared to men, and further hypothesized a sex-specific positive correlation between FC metrics and cognitive scores in women. Methods: We studied cognitively healthy individuals from the Alzheimer's Disease Neuroimaging Initiative cohort, with resting-state functional Magnetic Resonance Imaging. Metrics derived from graph theoretical analysis and functional connectomics were used to assess the global/regional sex differences in terms of functional integration and segregation, considering the amyloid status and the contributions of APOE E4. Linear mixed effect models with covariates (education, handedness, presence of apolipoprotein [APOE] E4 and intra-subject effect) were utilized to evaluate sex differences. The associations of verbal learning and memory abilities with topological network properties were assessed. Result: Women had a significantly lower magnitude of the global and regional functional network metrics compared to men. Exploratory association analysis showed that higher global clustering coefficient was associated with lower percent forgetting in women and worse cognitive scores in men. Conclusion: Women overall show lower magnitude on measures of resting state functional network topology and connectivity. This factor can play a role in their different vulnerability to AD. Significance statement: Two thirds of AD patients are women but the reasons for these sex difference are not well understood. When this late onset form dementia arises is too late to understand the potential causes of this sex disparities. Studies on cognitively healthy elderly population are a fundamental approach to explore in depth this different vulnerability to the most common form of dementia, currently affecting 6.2 million Americans aged 65 and older are, which means that >1 in 9 people (11.3%) 65 and older are affected by AD. Approaches such as resting-state functional network topology and connectivity may play a key role in understanding and elucidate sex-dependent differences relevant to late-onset dementia syndromes.

Sex Differences of Brain Functional Topography Revealed in Normal Aging and Alzheimer's Disease Cohort.

We applied graph theory analysis on resting-state functional magnetic resonance imaging data to evaluate sex differences of brain functional topography in normal controls (NCs), early mild cognitive impairment (eMCI), and AD patients. These metrics were correlated with RAVLT verbal learning and memory scores. The results show NCs have better functional connectivity (FC) metrics than eMCI and AD, and NC women show worse FC metrics compared to men, despite performing better on the RAVLT. FC differences between men and women diminished in eMCI and disappeared in AD. Within women, better FC metrics relate to better RAVLT learning in NCs and eMCI groups.