Microstructural white matter damage on MRI is associated with disease severity in Dutch-type cerebral amyloid angiopathy.

Peak width of skeletonized mean diffusivity (PSMD) is an emerging diffusion-MRI based marker to study subtle early alterations to white matter microstructure. We assessed PSMD over the clinical continuum in Dutch-type hereditary CAA (D-CAA) and its association with other CAA-related MRI-markers and cognitive symptoms. We included (pre)symptomatic D-CAA mutation-carriers and calculated PSMD from diffusion-MRI data. Associations between PSMD-levels, cognitive performance and CAA-related MRI-markers were assessed with linear regression models. We included 59 participants (25/34 presymptomatic/symptomatic; mean age 39/58 y). PSMD-levels increased with disease severity and were higher in symptomatic D-CAA mutation-carriers (median [range] 4.90 [2.77-9.50]mm2/s × 10-4) compared with presymptomatic mutation-carriers (2.62 [1.96-3.43]mm2/s × 10-4) p = <0.001. PSMD was positively correlated with age, CAA-SVD burden on MRI (adj.B [confidence interval] = 0.42 [0.16-0.67], p = 0.002), with number of cerebral microbleeds (adj.B = 0.30 [0.08-0.53], p = 0.009), and with both deep (adj.B = 0.46 [0.22-0.69], p = <0.001) and periventricular (adj.B = 0.38 [0.13-0.62], p = 0.004) white matter hyperintensities. Increasing PSMD was associated with decreasing Trail Making Test (TMT)-A performance (B = -0.42 [-0.69-0.14], p = 0.04. In D-CAA mutation-carriers microstructural white matter damage is associated with disease phase, CAA burden on MRI and cognitive impairment as reflected by a decrease in information processing speed. PSMD, as a global measure of alterations to the white matter microstructure, may be a useful tool to monitor disease progression in CAA.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Dynamic changes in perivascular space morphology predict signs of spaceflight-associated neuro-ocular syndrome in bed rest.

During long-duration spaceflight, astronauts experience headward fluid shifts and expansion of the cerebral perivascular spaces (PVS). A major limitation to our understanding of the changes in brain structure and physiology induced by spaceflight stems from the logistical difficulties of studying astronauts. The current study aimed to determine whether PVS changes also occur on Earth with the spaceflight analog head-down tilt bed rest (HDBR). We examined how the number and morphology of magnetic resonance imaging-visible PVS (MV-PVS) are affected by HDBR with and without elevated carbon dioxide (CO2). These environments mimic the headward fluid shifts, body unloading, and elevated CO2 observed aboard the International Space Station. Additionally, we sought to understand how changes in MV-PVS are associated with signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), ocular structural alterations that can occur with spaceflight. Participants were separated into two bed rest campaigns: HDBR (60 days) and HDBR + CO2 (30 days with elevated ambient CO2). Both groups completed multiple magnetic resonance image acquisitions before, during, and post-bed rest. We found that at the group level, neither spaceflight analog affected MV-PVS quantity or morphology. However, when taking into account SANS status, persons exhibiting signs of SANS showed little or no MV-PVS changes, whereas their No-SANS counterparts showed MV-PVS morphological changes during the HDBR + CO2 campaign. These findings highlight spaceflight analogs as models for inducing changes in MV-PVS and implicate MV-PVS dynamic compliance as a mechanism underlying SANS. These findings may lead to countermeasures to mitigate health risks associated with human spaceflight.

Unraveling the relations between post-traumatic stress symptoms, neurocognitive functioning, and limbic white matter in pediatric brain tumor patients.

Background: Pediatric brain tumor patients are at risk of developing neurocognitive impairments and associated white matter alterations. In other populations, post-traumatic stress symptoms (PTSS) impact cognition and white matter. This study aims to investigate the effect of PTSS on neurocognitive functioning and limbic white matter in pediatric brain tumor patients. Methods: Sixty-six patients (6-16 years) completed neuropsychological assessment and brain MRI (1-year post-diagnosis) and parents completed PTSS proxy questionnaires (CRIES-13; 1-3 months and 1-year post-diagnosis). Mean Z-scores and percentage impaired (>1SD) for attention, processing speed, executive functioning, and memory were compared to normscores (t-tests, chi-square tests). Multi-shell diffusion MRI data were analyzed for white matter tractography (fractional anisotropy/axial diffusivity). Effects of PTSS on neurocognition and white matter were explored with linear regression models (FDR correction for multiple testing), including age at diagnosis, treatment intensity, and tumor location as covariates. Neurocognition and limbic white matter associations were explored with correlations. Results: Attention (M = -0.49, 33% impaired; P < .05) and processing speed (M = -0.57, 34% impaired; P < .05) were significantly lower than healthy peers. PTSS was associated with poorer processing speed (ß = -0.64, P < .01). Treatment intensity, age at diagnosis, and tumor location, but not PTSS, were associated with limbic white matter metrics. Neurocognition and white matter metrics were not associated. Conclusions: Higher PTSS was associated with poorer processing speed, highlighting the need for monitoring, and timely referrals to optimize psychological well-being and neurocognitive functioning. Future research should focus on longitudinal follow-up and explore the impact of PTSS interventions on neurocognitive performance.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment.

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Improving advanced intraoperative MRI methods during pediatric neurosurgery.

Advanced intraoperative MR images (ioMRI) acquired during the resection of pediatric brain tumors could offer additional physiological information to preserve healthy tissue. With this work, we aimed to develop a protocol for ioMRI with increased sensitivity for arterial spin labeling (ASL) and diffusion MRI (dMRI), optimized for patient positioning regularly used in the pediatric neurosurgery setting. For ethical reasons, ASL images were acquired in healthy adult subjects that were imaged in the prone and supine position. After this, the ASL cerebral blood flow (CBF) was quantified and compared between both positions. To evaluate the impact of the RF coils setups on image quality, we compared different setups (two vs. four RF coils) by looking at T1-weighted (T1w) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), as well as undertaking a qualitative evaluation of T1w, T2w, ASL, and dMR images. Mean ASL CBF did not differ between the surgical prone and supine positions in any of the investigated regions of interest or the whole brain. T1w SNR (gray matter: p = 0.016, 34% increase; white matter: p = 0.016, 32% increase) and CNR were higher (p = 0.016) in the four versus two RF coils setups (18.0 ± 1.8 vs. 13.9 ± 1.8). Qualitative evaluation of T1w, T2w, ASL, and dMR images resulted in acceptable to good image quality and did not differ statistically significantly between setups. Only the nonweighted diffusion image maps and corticospinal tract reconstructions yielded higher image quality and reduced susceptibility artifacts with four RF coils. Advanced ioMRI metrics were more precise with four RF coils as the standard deviation decreased. Taken together, we have investigated the practical use of advanced ioMRI during pediatric neurosurgery. We conclude that ASL CBF quantification in the surgical prone position is valid and that ASL and dMRI acquisition with two RF coils can be performed adequately for clinical use. With four versus two RF coils, the SNR of the images increases, and the sensitivity to artifacts reduces.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images.

BACKGROUND: Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. NEW METHOD: We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy. RESULTS: PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels. COMPARISON WITH EXISTENT METHODS: Does not apply. CONCLUSIONS: The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Quantitative diffusion-weighted MRI response assessment in rhabdomyosarcoma: an international retrospective study on behalf of the European paediatric Soft tissue sarcoma Study Group Imaging Committee.

OBJECTIVE: To investigate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) as a predictive imaging marker after neoadjuvant chemotherapy in patients with rhabdomyosarcoma. MATERIAL AND METHODS: We performed a multicenter retrospective study including pediatric, adolescent and young adult patients with rhabdomyosarcoma, Intergroup Rhabdomyosarcoma Study group III/IV, treated according to the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 or MTS2008 studies. DW-MRI was performed according to institutional protocols. We performed two-dimensional single-slice tumor delineation. Areas of necrosis or hemorrhage were delineated to be excluded in the primary analysis. Mean, median and 5th and 95th apparent diffusion coefficient (ADC) were extracted. RESULTS: Of 134 included patients, 82 had measurable tumor at diagnosis and response and DW-MRI scans of adequate quality and were included in the analysis. Technical heterogeneity in scan acquisition protocols and scanners was observed. Mean ADC at diagnosis was 1.1 (95% confidence interval [CI]: 1.1-1.2) (all ADC expressed in * 10-3 mm2/s), versus 1.6 (1.5-1.6) at response assessment. The 5th percentile ADC was 0.8 (0.7-0.9) at diagnosis and 1.1 (1.0-1.2) at response. Absolute change in mean ADC after neoadjuvant chemotherapy was 0.4 (0.3-0.5). Exploratory analyses for association between ADC and clinical parameters showed a significant difference in mean ADC at diagnosis for alveolar versus embryonal histology. Landmark analysis at nine weeks after the date of diagnosis showed no significant association (hazard ratio 1.3 [0.6-3.2]) between the mean ADC change and event-free survival. CONCLUSION: A significant change in the 5th percentile and the mean ADC after chemotherapy was observed. Strong heterogeneity was identified in DW-MRI acquisition protocols between centers and in individual patients.

Advanced intraoperative MRI in pediatric brain tumor surgery.

Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides "real-time" imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging. Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care. Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent. Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information.

Neuroimaging standards for research into small vessel disease-advances since 2013.

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Corrigendum: Topology of diffusion changes in corpus callosum in Alzheimer's disease: An exploratory case-control study.

[This corrects the article DOI: 10.3389/fneur.2022.1005406.].

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging-What can we do better?

PURPOSE: Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma. MATERIALS AND METHODS: A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC. RESULTS: We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%. CONCLUSION: Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.

Neurological soft signs in adolescents are associated with brain structure.

Neurological soft signs (NSS) are minor deviations in motor performance. During childhood and adolescence, NSS are examined for functional motor phenotyping to describe development, to screen for comorbidities, and to identify developmental vulnerabilities. Here, we investigate underlying brain structure alterations in association with NSS in physically trained adolescents. Male adolescent athletes (n = 136, 13-16 years) underwent a standardized neurological examination including 28 tests grouped into 6 functional clusters. Non-optimal performance in at least 1 cluster was rated as NSS (NSS+ group). Participants underwent T1- and diffusion-weighted magnetic resonance imaging. Cortical volume, thickness, and local gyrification were calculated using Freesurfer. Measures of white matter microstructure (Free-water (FW), FW-corrected fractional anisotropy (FAt), axial and radial diffusivity (ADt, RDt)) were calculated using tract-based spatial statistics. General linear models with age and handedness as covariates were applied to assess differences between NSS+ and NSS- group. We found higher gyrification in a large cluster spanning the left superior frontal and parietal areas, and widespread lower FAt and higher RDt compared with the NSS- group. This study shows that NSS in adolescents are associated with brain structure alterations. Underlying mechanisms may include alterations in synaptic pruning and axon myelination, which are hallmark processes of brain maturation.

Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T.

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.

Anatomically informed multi-level fiber tractography for targeted virtual dissection.

OBJECTIVES: Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. METHODS: Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. RESULTS: The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: [Formula: see text] for the left and [Formula: see text] for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: [Formula: see text]. DISCUSSION: MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.

Topology of diffusion changes in corpus callosum in Alzheimer's disease: An exploratory case-control study.

Aim: This study aims to assess the integrity of white matter in various segments of the corpus callosum in Alzheimer's disease (AD) by using metrics derived from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and white matter tract integrity model (WMTI) and compare these findings to healthy controls (HC). Methods: The study was approved by the institutional ethics board. 12 AD patients and 12 HC formed the study population. All AD patients were recruited from a tertiary neurology memory clinic. A standardized battery of neuropsychological assessments was administered to the study participants by a trained rater. MRI scans were performed with a Philips Ingenia 3.0T scanner equipped with a 32-channel head coil. The protocol included a T1-weighted sequence, FLAIR and a dMRI acquisition. The dMRI scan included a total of 71 volumes, 8 at b = 0 s/mm2, 15 at b = 1,000 s/mm2 and 48 at b = 2,000 s/mm2. Diffusion data fit was performed using DKI REKINDLE and WMTI models. Results and discussion: We detected changes suggesting demyelination and axonal degeneration throughout the corpus callosum of patients with AD, most prominent in the mid-anterior and mid-posterior segments of CC. Axial kurtosis was the most significantly altered metric, being reduced in AD patients in almost all segments of corpus callosum. Reduced axial kurtosis in the CC segments correlated with poor cognition scores in AD patients in the visuospatial, language and attention domains.

Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization.

PURPOSE: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.

The transfer of global and local processing modes.

Förster and Dannenberg's (2010) GLOMOsys theory claims that people process perceived events and internal information in a more global or more local processing mode and that adopted modes should transfer to other, unrelated tasks. If so, global/local processing modes would qualify as metacontrol states that are assumed to regulate processing dilemmas, like persistence/flexibility, exploitation/exploration, or speed/accuracy (Goschke, 2000). Given increasing rates of nonreplications of previously demonstrated far transfer from prime tasks that are likely to induce a particular global or local processing bias to logically and temporally unrelated probe tasks, we tested whether near and far transfer can be demonstrated under conditions that should be optimal for such transfer. We reduced the temporal distance between prime and probe trials by integrating them into a dual-task paradigm and used probe tasks that were either almost identical to the prime task or at least shared the relevant modality and attentional demands. We obtained significant transfer effects between almost identical visual global/local tasks, irrespective of the degree of cognitive conflict that these tasks generated, but did not find any evidence for somewhat farer transfer to other visual tasks, like a flanker task and an attentional blink task. That is, any substantial change in the probe task's characteristics compared with the prime task eliminated almost any signs of transfer. Altogether, we conclude that either global/local processing modes as envisioned by GLOMOsys do not exist or they normally do not transfer from global/local tasks to other, unrelated tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury.

In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.