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The perivascular space (PVS) is integral to glymphatic function, facilitating fluid exchange and waste clearance in the brain. Diffusion tensor imaging along the perivascular space (DTI‐ALPS) has been proposed as a noninvasive marker of perivascular diffusion, yet its specificity remains unclear. ALPS measures assume radial symmetry in white matter (characterized by equal transverse diffusion eigenvalues, λ2 = λ3) and interpret deviations (i.e., radial asymmetry, where λ2 > λ3) as reflecting PVS contributions. However, anatomical and microstructural confounds may influence these metrics. We systematically evaluated potential biases in ALPS‐derived measures using high‐resolution, multishell diffusion MRI from the Human Connectome Project (HCP) and high‐field imaging. Specifically, we examined (1) the prevalence of radial asymmetry across white matter, (2) the influence of crossing fibers on ALPS indices, (3) the impact of axonal undulations and dispersion, and (4) the spatial alignment of vasculature with white matter in ALPS‐associated regions. Radial asymmetry is widespread across white matter and persists even at high b‐values, suggesting a dominant contribution from axonal geometry rather than faster PVS‐specific diffusion. Crossing fibers significantly inflate ALPS indices, with greater radial asymmetry observed in regions with a greater prevalence of crossing fibers. Furthermore, anisotropic axonal dispersion and undulations introduce systematic asymmetry independent of perivascular diffusion. Finally, high‐resolution vascular imaging reveals substantial heterogeneity in medullary vein orientation, challenging the assumption that PVS consistently aligns with the left–right axis in ALPS regions. ALPS indices are significantly influenced by white matter microstructure, including fiber crossings, undulations, and dispersion. These findings suggest that ALPS‐derived metrics may not provide a direct measure of glymphatic function but rather reflect underlying axonal geometry. Interpretations of ALPS‐derived metrics as biomarkers of glymphatic function must consider these anatomical complexities, and future studies should integrate advanced modeling approaches to disentangle perivascular contributions from white matter structure. Our findings reveal that the DTI‐ALPS index, a proposed marker of glymphatic function, is strongly influenced by white matter geometry—including fiber crossings, dispersion, and undulations. These findings challenge the specificity of ALPS and highlight the need for careful interpretation in studies linking ALPS metrics to glymphatic function.

Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.

Postoperative aphasia (POA) is a common complication in patients undergoing surgery for language‐eloquent lesions. This study aimed to enhance the prediction of POA by leveraging preoperative navigated transcranial magnetic stimulation (nTMS) language mapping and diffusion tensor imaging (DTI)‐based tractography, incorporating deep learning (DL) algorithms. One hundred patients with left‐hemispheric lesions were retrospectively enrolled (43 developed postoperative aphasia, as the POA group; 57 did not, as the non‐aphasia (NA) group). Fiber tracking was performed at fractional anisotropy threshold (FAthres) of 0.10 and 0.15, analyzing the total fiber volume (Vfibertotal), average fractional anisotropy (FAwhole), and number of visualized tracts. Additionally, the visualization ratio (VR) and FA‐sensitive visualization were assessed for individual tractography. The NA group demonstrated significantly higher Vfibertotal (FAthres = 0.10: 61.1 vs. 51.7 cm3, p = 0.029; FAthres = 0.15: 36.9 vs. 29.6 cm3, p = 0.008), higher FAwhole (FAthres = 0.10: 0.38 vs. 0.35, p = 0.006; FAthres = 0.15: 0.42 vs. 0.39, p = 0.006), and greater tract numbers (FAthres = 0.10: 6.1 vs. 5.7, p = 0.111; FAthres = 0.15: 5.6 vs. 4.8, p = 0.004). Among individual fiber tracts, the corticothalamic fibers (CtF) showed significantly higher VR in the NA group (86.0% vs. 58.1%, p = 0.003), whereas FA‐sensitive visualization of CtF was higher in the POA group (11.6% vs. 0.0%, p = 0.013). A binary DL model developed to predict POA achieved a sensitivity of 72.3% and specificity of 85.3%, with an area underthecurve (AUC) of 0.82. Our findings demonstrate the potential of nTMS‐based tractography to predict POA by integrating DL. The CtF showed the most significant potential in predicting aphasia risk and understanding the complexity of the language network, whereas their individual predictive contribution within the model remained limited. This study integrates navigated transcranial magnetic stimulation (nTMS) language mapping and diffusion tensor imaging (DTI)‐based individual tractography with deep learning model to evaluate postoperative aphasia (POA) risk in patients with language‐eloquent lesions. General properties of language network and specific features of individual language‐related tracts jointly inform the classifier and contribute to feature prioritization, enabling preoperative risk stratification and highlighting the deep cerebral fiber tract with the strongest predictive potential.

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies. •Significant site and scanner effects exist in DTI scalar maps.•Several multi-site harmonization methods are proposed.•ComBat performs the best at removing site effects in FA and MD.•Voxels associated with age in FA and MD are more replicable after ComBat.•ComBat is generalizable to other imaging modalities.

INTRODUCTION Diffusion tensor image analysis along the perivascular space (DTI‐ALPS) index was proposed for assessing glymphatic clearance function. This study evaluated DTI‐ALPS as a biomarker for cerebral small vessel disease (cSVD) related vascular cognitive impairment and dementia (VCID). METHODS Four independent cohorts were examined. A composite score of executive function (UDS3‐EF) was used to evaluate EF status. The association between the ALPS index and UDS3‐EF scores and the mediator effect of free water in white matter (WM‐FW) on such association was analyzed. RESULTS The ALPS index was significantly associated with UDS3‐EF scores in all cohorts. Additionally, WM‐FW mediates the relationship between the ALPS index and UDS3‐EF scores. DISCUSSION Lower ALPS index may be a surrogate marker of glymphatic dysfunction, which is associated with impaired EF, and this association is mediated by the interstitial fluid (ISF) drainage ISF in WM, providing a clinical rationale for using ALPS index as a biomarker for cSVD‐related VCID. Highlights This is the first study to investigate the mediation role of interstitial FW fraction (WM‐FW) on the relationship between glymphatic clearance (ALPS index) and EF (UDS3‐EF scores) in four independent middle to aged cohorts at risk for cSVD. This study identified that ALPS index was independently associated with UDS3‐EF scores after adjusting for demographics, VRFs, and WM hyperintensity burden and that WM‐FW mediated this association in all middle to aged cohorts. Our findings suggest that in middle to aged individuals, glymphatic dysfunction (reflected by ALPS index) is strongly associated with EF and that this association is mediated by the ISF drainage in WM. This study provides a strong clinical rationale for the use of the ALPS index as a marker of cognitive function in multi‐site observational studies and clinical trials to monitor and prevent VCID.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 8%-12% of children worldwide. Throughout an individual's lifetime, ADHD can significantly increase risk for other psychiatric disorders, educational and occupational failure, accidents, criminality, social disability and addictions. No single risk factor is necessary or sufficient to cause ADHD. The multifactorial causation of ADHD is reflected in the heterogeneity of this disorder, as indicated by its diversity of psychiatric comorbidities, varied clinical profiles, patterns of neurocognitive impairment and developmental trajectories, and the wide range of structural and functional brain anomalies. Although evidence-based treatments can reduce ADHD symptoms in a substantial portion of affected individuals, there is yet no curative treatment for ADHD. A number of theoretical models of the emergence and developmental trajectories of ADHD have been proposed, aimed at providing systematic guides for clinical research and practice. We conducted a comprehensive review of the current status of research in understanding the heterogeneity of ADHD in terms of etiology, clinical profiles and trajectories, and neurobiological mechanisms. We suggest that further research focus on investigating the impact of the etiological risk factors and their interactions with developmental neural mechanisms and clinical profiles in ADHD. Such research would have heuristic value for identifying biologically homogeneous subgroups and could facilitate the development of novel and more tailored interventions that target underlying neural anomalies characteristic of more homogeneous subgroups.

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0–5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community. •Complete description of the UNC/UMN Baby Connectome Project (BCP) protocol.•The importanc'e of dense longitudinal sampling.•Protocol optimization and preliminary results of optimized imaging protocol.•BCP study data as a unique resource for the scientific community.

PurposeThe diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain’s glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation.Materials and methodsSeven healthy volunteers were enrolled in this study. Image acquisition was performed for this test–retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above.ResultsThis test–retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index.ConclusionALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically. •We investigate tract-based spatial statistics (TBSS) considering potential pitfalls.•TBSS is not tract-specific and we show how this may falsify results.•User defined parameters strongly influence the final TBSS-derived results.•We provide suggestions that improve the validity and increase the impact of TBSS.

The neural mechanisms induced by cerebral small vessel disease (CSVD) in the vascular dementia (VaD) is extremely complex. Recent studies have identified altered lymphatic function as a key factor contributing to the development of cognitive deficits, whether they linked to iron deposition and reduced cerebral blood flow remains unclear. The study involved 59 participants, comprising 30 healthy controls and 29 patients with VaD. Each participant underwent QSM, ASL, and DTI imaging scans and clinical measurements. The ALPS index based on DTI, QSM, and ASL for the left and right hemispheres, as well as for the whole brain were calculated and compared between groups. Regional mean susceptibility and mean CBF based on AAL template were calculated and compared between groups. Correlation analyses were performed between altered indexes and clinical measurements. Finally, mediation analyses were conducted to determine whether the mean susceptibility rate and mean CBF were involved in the modulation process of the ALPS. DTI-ALPS based analysis demonstrated a significantly lower ALPS index for the left, right, and whole brain in the VaD as compared to healthy controls. Mean susceptibility in the bilateral superior occipital gyrus and bilateral middle occipital gyrus was significantly higher, whereas mean CBF in the eft inferior frontal gyrus, opercular part, right rolandic operculum, right insula, and right heschl gyrus was significantly lower in the VaD. Further correlation analyses revealed associations between elevated mean susceptibility, reduced ALPS index, and cognitive impairments. Additionally, the results of correlation and mediation analyses suggested that CBF may be involved in the regulatory process of DTI-ALPS. Our results demonstrated that altered lymphatic function is a key pathological factor in VaD, and reduced cerebral blood flow may play a role in this process. These findings provide a theoretical basis for identifying new targets in the treatment of VaD.