Neuropsychiatric symptoms of cholinergic deficiency occur with degradation of the projections from the nucleus basalis of Meynert

This study aims to evaluate the relation between a cluster of neuropsychiatric symptoms related to cholinergic deficiency and degradation of the cortical cholinergic projections which project from the nucleus basalis of Meynert to the cerebral cortex. An atlas of the pathway from the nucleus basalis to the cortex (NbM cortical pathway) was constructed using diffusion tensor imaging and tractography in 87 memory clinic patients. Structural degradation was considered to be represented by lower fractional anisotropy (FA) and higher mean diffusivity (MD). Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory. A predefined cluster including agitation, anxiety, apathy, delusions, hallucinations, and irritability was labeled as the cholinergic deficiency syndrome (CDS). In regression analyses, lower FA and higher MD in the NbM cortical pathway were associated with CDS symptoms but not with other neuropsychiatric symptoms. These associations were independent of cerebral atrophy and overall FA or MD. There was no association between interruption of the NbM cortical pathway by white matter hyperintensities and CDS symptoms. Cox regression suggested a trend for higher mortality with lower FA in the NbM cortical pathway may exist. These findings provide anatomical support for the hypothesis that degradation of the cholinergic projections from the nucleus basalis of Meynert may lead to a distinct clinical syndrome. Future studies could use our results to test the utility of assessing NbM projection integrity to identify patients who may benefit from cholinergic treatment or with a worse prognosi

Value of infarct location in the prediction of functional outcome in patients with an anterior large vessel occlusion: results from the HERMES study

Purpose: Follow-up infarct volume (FIV) is moderately associated with functional outcome. We hypothesized that accounting for infarct location would strengthen the association of FIV with functional outcome. Methods: We included 252 patients from the HERMES collaboration with follow-up diffusion weighted imaging. Patients received endovascular treatment combined with best medical management (n = 52%) versus best medical management alone (n = 48%). FIV was quantified in low, moderate and high modified Rankin Scale (mRS)-relevant regions. We used binary logistic regression to study the relation between the total, high, moderate or low mRS-relevant FIVs and favorable outcome (mRS < 2) after 90 days. The strength of association was evaluated using the c-statistic. Results: Small lesions only occupied high mRS-relevant brain regions. Lesions additionally occupied lower mRS-relevant brain regions if FIV expanded. Higher FIV was associated with a higher risk of unfavorable outcome, as were volumes of tissue with low, moderate and high mRS relevance. In multivariable modeling, only the volume of high mRS-relevant infarct was significantly associated with favorable outcome. The c-statistic was highest (0.76) for the models that included high mRS-relevant FIV or the combination of high, moderate and low mRS-relevant FIV but was not significantly different from the model that included only total FIV (0.75). Conclusion: This study confirms the association of FIV and unfavorable functional outcome but showed no strengthened association if lesion location was taken into account

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

Rank-2 model-order selection in diffusion tensor MRI: Infromation complexity based on the total Kullback-Leibler divergence

Diffusion-weighted MRI (DW-MRI) can assess the integrity of white matter (WM) structures in the human brain. Multi-compartment analysis of DW-MRI requires an estimate of the number of compartments to permit unbiased estimation of the diffusion shape in a single fibers as well as crossing fascicles. We propose a new, rotation-invariant measure to assess the suitability of a model by a measure for information complexity (ICOMP) based on the total Kullback-Leibler divergence (TKLD). ICOMP-TKLD is evaluated on simulated data and on data from the Human Connectome Project. Compared to the state-of-the-art, ICOMP-TKLD is the only method that yields reliable model-order selection in both homogeneous and heterogeneous WM regions. Therefore, ICOM-TKLD may open the way for structure-adaptive estimation of diffusion properties of the entire brain

White matter by diffusion MRI following methylphenidate treatment: A randomized control trial in males with attention-deficit/hyperactivity disorder

Background: Methylphenidate (MPH) is highly effective in treating attention-deficit/hyperactivity disorder (ADHD). However, not much is known about its effect on the development of human brain white matter (WM). Purpose: To determine whether MPH modulates WM microstructure in an age-dependent fashion in a randomized double-blind placebo-controlled trial (Effects of Psychotropic Medication on Brain Development-Methylphenidate, or ePOD-MPH) among ADHD referral centers between October 13, 2011, and June 15, 2015, by using diffusion-tensor imaging (DTI). Materials and Methods: In this prospective study (NTR3103 and NL34509.000.10), 50 stimulant treatment-naive boys and 49 young adult men diagnosed with ADHD (all types) according to Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria were randomized to undergo treatment with MPH or placebo for 16 weeks. Before and 1 week after treatment cessation, study participants underwent MRI, including DTI. The outcome measure was change in fractional anisotropy (FA), which was assessed in three regions of interest (ROIs), as well as in a voxel-based analysis in brain WM. Data were analyzed by using intentionto- treat linear mixed models for ROI analysis and a permutation-based method for voxel-based analysis with family-wise error correction. Results: Fifty boys (n = 25 MPH group, n = 25 placebo group; age range, 10-12 years) and 48 men (n = 24 MPH group, n = 24 placebo group; age range, 23-40 years) were included. ROI analysis of FA yielded no main effect of time in any of the conditions. However, voxel-based analysis revealed significant (P , .05) time-by-medication-by-age interaction effects in several association tracts of the left hemisphere, as well as in the lateral aspect of the truncus of the corpus callosum, due to greater increase in FA (standardized effect size, 5.25) in MPH-treated boys. Similar changes were not present in boys receiving a placebo, nor in adult men. Conclusion: Four months of treatment with methylphenidate affects specific tracts in brain white matter in boys with attentiondeficit/ hyperactivity disorder. These effects seem to be age dependent, because they were not observed in adults treated with methylphenidate.Biomechatronics & Human-Machine ControlImPhys/Quantitative Imagin

Spatial versus angular resolution for tractography-assisted planning of deep brain stimulation

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel running white matter tracts that are targets for deep brain stimulation: the anterior thalamic radiation and the supero-lateral branch of the medial forebrain bundle. Both these tracts are situated in the ventral anterior limb of the internal capsule, and recent studies suggest that targeting a specific tract could improve treatment efficacy. Therefore, we scanned 19 healthy volunteers at 3T and 7T according to three diffusion MRI protocols with respectively standard clinical settings, increased spatial resolution of 1.4 mm, and increased angular resolution (64 additional gradient directions at b = 2200s/mm2). We performed probabilistic tractography for all protocols and quantified the separability of both tracts. The higher spatial resolution protocol improved separability by 41% with respect to the clinical standard, presumably due to decreased partial voluming. The higher angular resolution protocol resulted in increased apparent tract volumes and overlap, which is disadvantageous for application in precise treatment planning. We thus recommend to increase the spatial resolution for deep brain stimulation planning to 1.4 mm while maintaining angular resolution. This recommendation complements the general advice to aim for high angular resolution to resolve crossing fibers, confirming that the specific application and anatomical considerations are leading in clinical diffusion MRI protocol optimization.ImPhys/Quantitative Imagin

Recurrent inference machines as inverse problem solvers for MR relaxometry

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the Residual Neural Network (ResNet). The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods