Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT. METHODS: We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR). RESULTS: In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given. CONCLUSION: DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed. CLINICAL RELEVANCE STATEMENT: White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied. KEY POINTS: White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.

Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed. MATERIALS AND METHODS: Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3 T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in terms of self-referenced quality metrics, image resolution, patient groups and reconstruction time. In ten scans, the contrast ratio (CR) was determined between lesions and white matter. The effect of acceleration factor was assessed in a publicly available fully sampled dataset, since ground truth data are not available in prospectively accelerated clinical scans. Specifically, 451 FLAIR scans, including scans with white matter lesions, were adopted from the FastMRI database to evaluate structural similarity (SSIM) and the CR of lesions and white matter on ranging acceleration factors from four-fold up to 12-fold. RESULTS: Interventional neuroradiologists significantly preferred the CIRIM for imaging artifacts, anatomic conspicuity, and contrast. One rater significantly preferred the CIRIM in terms of sharpness and diagnostic confidence. The pediatric neuroradiologist preferred CS for imaging artifacts and sharpness. Compared to CS, the CIRIM reconstructions significantly improved in terms of imaging artifacts and anatomic conspicuity (p < 0.01) for higher resolution scans while yielding a 28% higher SNR (p = 0.001) and a 5.8% lower CR (p = 0.04). There were no differences between patient groups. Additionally, CIRIM was five times faster than CS was. An increasing acceleration factor did not lead to changes in CR (p = 0.92), but led to lower SSIM (p = 0.002). DISCUSSION: Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times.

Prognostic Value of Thrombus Volume and Interaction With First-Line Endovascular Treatment Device Choice.

BACKGROUND: A larger thrombus in patients with acute ischemic stroke might result in more complex endovascular treatment procedures, resulting in poorer patient outcomes. Current evidence on thrombus volume and length related to procedural and functional outcomes remains contradicting. This study aimed to assess the prognostic value of thrombus volume and thrombus length and whether this relationship differs between first-line stent retrievers and aspiration devices for endovascular treatment. METHODS: In this multicenter retrospective cohort study, 670 of 3279 patients from the MR CLEAN Registry (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) for endovascularly treated large vessel occlusions were included. Thrombus volume (0.1 mL) and length (0.1 mm) based on manual segmentations and measurements were related to reperfusion grade (expanded Treatment in Cerebral Infarction score) after endovascular treatment, the number of retrieval attempts, symptomatic intracranial hemorrhage, and a shift for functional outcome at 90 days measured with the reverted ordinal modified Rankin Scale (odds ratio >1 implies a favorable outcome). Univariable and multivariable linear and logistic regression were used to report common odds ratios (cORs)/adjusted cOR and regression coefficients (B/aB) with 95% CIs. Furthermore, a multiplicative interaction term was used to analyze the relationship between first-line device choice, stent retrievers versus aspiration device, thrombus volume, and outcomes. RESULTS: Thrombus volume was associated with functional outcome (adjusted cOR, 0.83 [95% CI, 0.71-0.97]) and number of retrieval attempts (aB, 0.16 [95% CI, 0.16-0.28]) but not with the other outcome measures. Thrombus length was only associated with functional independence (adjusted cOR, 0.45 [95% CI, 0.24-0.85]). Patients with more voluminous thrombi had worse functional outcomes if endovascular treatment was based on first-line stent retrievers (interaction cOR, 0.67 [95% CI, 0.50-0.89]; P=0.005; adjusted cOR, 0.74 [95% CI, 0.55-1.0]; P=0.04). CONCLUSIONS: In this study, patients with a more voluminous thrombus required more endovascular thrombus retrieval attempts and had a worse functional outcome. Patients with a lengthier thrombus were less likely to achieve functional independence at 90 days. For more voluminous thrombi, first-line stent retrieval compared with first-line aspiration might be associated with worse functional outcome.

A densely interconnected network for deep learning accelerated MRI.

OBJECTIVE: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. MATERIALS AND METHODS: A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). RESULTS: The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2-4%, 4-9%, and 0.5-1%, respectively. CONCLUSION: The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.

Utilizing 7-Tesla Subthalamic Nucleus Connectivity in Deep Brain Stimulation for Parkinson Disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a highly effective surgical treatment for patients with advanced Parkinson disease (PD). Combining 7.0-Tesla (7T) T2- and diffusion-weighted imaging (DWI) sequences allows for selective segmenting of the motor part of the STN and, thus, for possible optimization of DBS. MATERIALS AND METHODS: 7T T2 and DWI sequences were obtained, and probabilistic segmentation of motor, associative, and limbic STN segments was performed. Left- and right-sided motor outcome (Movement Disorders Society Unified Parkinson's Disease Rating Scale) scores were used for evaluating the correspondence between the active electrode contacts in selectively segmented STN and the clinical DBS effect. The Bejjani line was reviewed for crossing of segments. RESULTS: A total of 50 STNs were segmented in 25 patients and proved highly feasible. Although the highest density of motor connections was situated in the dorsolateral STN for all patients, the exact partitioning of segments differed considerably. For all the active electrode contacts situated within the predominantly motor-connected segment of the STN, the average hemi-body Unified Parkinson's Disease Rating Scale motor improvement was 80%; outside this segment, it was 52% (p < 0.01). The Bejjani line was situated in the motor segment for 32 STNs. CONCLUSION: The implementation of 7T T2 and DWI segmentation of the STN in DBS for PD is feasible and offers insight into the location of the motor segment. Segmentation-guided electrode placement is likely to further improve motor response in DBS for PD. However, commercially available DBS software for postprocessing imaging would greatly facilitate widespread implementation.

A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine.

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

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.

Sharpness in motion corrected quantitative imaging at 7T.

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R1, R2* and Quantitative Susceptibility Maps and T1-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

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.

Gray and white matter are both affected in classical galactosemia: An explorative study on the association between neuroimaging and clinical outcome.

BACKGROUND: Classical Galactosemia (CG) is an inherited disorder of galactose metabolism caused by a deficiency of the galactose-1-phosphate uridylyltransferase (GALT) enzyme resulting in neurocognitive complications. As in many Inborn Errors of Metabolism, the metabolic pathway of CG is well-defined, but the pathophysiology and high variability in clinical outcome are poorly understood. The aim of this study was to investigate structural changes of the brain of CG patients on MRI and their association with clinical outcome. METHODS: In this prospective cohort study an MRI protocol was developed to evaluate gray matter (GM) and white matter (WM) volume of the cerebrum and cerebellum, WM hyperintensity volume, WM microstructure and myelin content with the use of conventional MRI techniques, diffusion tensor imaging (DTI) and quantitative T1 mapping. The association between several neuroimaging parameters and both neurological and intellectual outcome was investigated. RESULTS: Twenty-one patients with CG (median age 22 years, range 8-47) and 24 controls (median age 30, range 16-52) were included. Compared to controls, the WM of CG patients was lower in volume and the microstructure of WM was impaired both in the whole brain and corticospinal tract (CST) and the lower R1 values of WM, GM and the CST were indicative of less myelin. The volume of WM lesions were comparable between patients and controls. The 9/16 patients with a poor neurological outcome (defined as the presence of a tremor and/or dystonia), demonstrated a lower WM volume, an impaired WM microstructure and lower R1 values of the WM indicative of less myelin content compared to 7/16 patients without movement disorders. In 15/21 patients with a poor intellectual outcome (defined as an IQ < 85) both GM and WM were affected with a lower cerebral and cerebellar WM and GM volume compared to 6/21 patients with an IQ ≥ 85. Both the severity of the tremor (as indicated by the Tremor Rating Scale) and IQ (as continuous measure) were associated with several neuroimaging parameters such as GM volume, WM volume, CSF volume, WM microstructure parameters and R1 values of GM and WM. CONCLUSION: In this explorative study performed in patients with Classical Galactosemia, not only WM but also GM pathology was found, with more severe brain abnormalities on MRI in patients with a poor neurological and intellectual outcome. The finding that structural changes of the brain were associated with the severity of long-term complications indicates that quantitative MRI techniques could be of use to explain neurological and cognitive dysfunction as part of the disease spectrum. Based on the clinical outcome of patients, the absence of widespread WM lesions and the finding that both GM and WM are affected, CG could be primarily a GM disease with secondary damage to the WM as a result of neuronal degeneration. To investigate this further the course of GM and WM should be evaluated in longitudinal research, which could also clarify if CG is a neurodegenerative disease.

MP2RAGEME: T1 , T2* , and QSM mapping in one sequence at 7 tesla.

Quantitative magnetic resonance imaging generates images of meaningful physical or chemical variables measured in physical units that allow quantitative comparisons between tissue regions and among subjects scanned at the same or different sites. Here, we show that we can acquire quantitative T1 , T2* , and quantitative susceptibility mapping (QSM) information in a single acquisition, using a multi-echo (ME) extension of the second gradient-echo image of the MP2RAGE sequence. This combination is called MP2RAGE ME, or MP2RAGEME. The simultaneous acquisition results in large time savings, perfectly coregistered data, and minimal image quality differences compared to separately acquired data. Following a correction for residual transmit B1+ -sensitivity, quantitative T1 , T2* , and QSM values were in excellent agreement with those obtained from separately acquired, also B1+ -corrected, MP2RAGE data and ME gradient echo data. The quantitative values from reference regions of interests were also in very good correspondence with literature values. From the MP2RAGEME data, we further derived a multiparametric cortical parcellation, as well as a combined arterial and venous map. In sum, our MP2RAGEME sequence has the benefit in large time savings, perfectly coregistered data and minor image quality differences.

White Matter by Diffusion MRI Following Methylphenidate Treatment: A Randomized Control Trial in Males with Attention-Deficit/Hyperactivity Disorder.

BackgroundMethylphenidate (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).PurposeTo 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 MethodsIn 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 intention-to-treat linear mixed models for ROI analysis and a permutation-based method for voxel-based analysis with family-wise error correction.ResultsFifty 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.ConclusionFour months of treatment with methylphenidate affects specific tracts in brain white matter in boys with attention-deficit/hyperactivity disorder. These effects seem to be age dependent, because they were not observed in adults treated with methylphenidate.© RSNA, 2019Online supplemental material is available for this article.

No Evidence for Accelerated Aging-Related Brain Pathology in Treated Human Immunodeficiency Virus: Longitudinal Neuroimaging Results From the Comorbidity in Relation to AIDS (COBRA) Project.

Background: Despite successful antiretroviral therapy, people living with human immunodeficiency virus (PLWH) experience higher rates of age-related morbidity, including abnormal brain structure, brain function, and cognitive impairment. This has raised concerns that PLWH may experience accelerated aging-related brain pathology. Methods: We performed a multicenter longitudinal study of 134 virologically suppressed PLWH (median age, 56.0 years) and 79 demographically similar human immunodeficiency virus (HIV)-negative controls (median age, 57.2 years). To measure cognitive performance and brain pathology, we conducted detailed neuropsychological assessments and multimodality neuroimaging (T1-weighted, T2-weighted, diffusion magnetic resonance imaging [MRI], resting-state functional MRI, spectroscopy, arterial spin labeling) at baseline and at 2 years. Group differences in rates of change were assessed using linear mixed effects models. Results: One hundred twenty-three PLWH and 78 HIV-negative controls completed longitudinal assessments (median interval, 1.97 years). There were no differences between PLWH and HIV-negative controls in age, sex, years of education, smoking or alcohol use. At baseline, PLWH had poorer global cognitive performance (P < .01), lower gray matter volume (P = .04), higher white matter hyperintensity load (P = .02), abnormal white matter microstructure (P < .005), and greater brain-predicted age difference (P = .01). Longitudinally, there were no significant differences in rates of change in any neuroimaging measure between PLWH and HIV-negative controls (P > .1). Cognitive performance was longitudinally stable in both groups. Conclusions: We found no evidence that middle-aged PLWH, when receiving successful treatment, are at increased risk of accelerated aging-related brain changes or cognitive decline over 2 years.

Structural Brain Abnormalities in Successfully Treated HIV Infection: Associations With Disease and Cerebrospinal Fluid Biomarkers.

Background: Brain structural abnormalities have been reported in persons living with human immunodeficiency virus (HIV; PLWH) who are receiving suppressive combination antiretroviral therapy (cART), but their pathophysiology remains unclear. Methods: We investigated factors associated with brain tissue volumes and white matter microstructure (fractional anisotropy) in 134 PLWH receiving suppressive cART and 79 comparable HIV-negative controls, aged ≥45 years, from the Comorbidity in Relation to AIDS cohort, using multimodal neuroimaging and cerebrospinal fluid biomarkers. Results: Compared with controls, PLWH had lower gray matter volumes (-13.7 mL; 95% confidence interval, -25.1 to -2.2) and fractional anisotropy (-0.0073; 95% confidence interval, -.012 to -.0024), with the largest differences observed in those with prior clinical AIDS. Hypertension and the soluble CD14 concentration in cerebrospinal fluid were associated with lower fractional anisotropy. These associations were independent of HIV serostatus (Pinteraction = .32 and Pinteraction = .59, respectively) and did not explain the greater abnormalities in brain structure in relation to HIV infection. Conclusions: The presence of lower gray matter volumes and more white matter microstructural abnormalities in well-treated PLWH partly reflect a combination of historical effects of AIDS, as well as the more general influence of systemic factors, such as hypertension and ongoing neuroinflammation. Additional mechanisms explaining the accentuation of brain structure abnormalities in treated HIV infection remain to be identified.

Effect of Long-Term Vascular Care on Progression of Cerebrovascular Lesions: Magnetic Resonance Imaging Substudy of the PreDIVA Trial (Prevention of Dementia by Intensive Vascular Care).

BACKGROUND AND PURPOSE: This study aimed to evaluate the effect of a nurse-led multidomain cardiovascular intervention on white matter hyperintensity (WMH) progression and incident lacunar infarcts in community-dwelling elderly with hypertension. METHODS: The preDIVA trial (Prevention of Dementia by Intensive Vascular Care) was an open-label, cluster-randomized controlled trial in community-dwelling individuals aged 70 to 78 years. General practices were assigned by computer-generated randomization to 6-year nurse-led, multidomain intensive vascular care or standard care. Of 3526 preDIVA participants, 195 nondemented participants with a systolic blood pressure ≥140 mm Hg were consecutively recruited to undergo magnetic resonance imaging at 2 to 3 and 5 to 6 years after baseline. WMH volumes were measured automatically, lacunar infarcts assessed visually, blinded to treatment allocation. RESULTS: One hundred and twenty-six participants were available for longitudinal analysis (64 intervention and 62 control). Annual WMH volume increase in milliliter was similar for intervention (mean=0.73, SD=0.84) and control (mean=0.70, SD=0.59) participants (adjusted mean difference, -0.08 mL; 95% confidence interval, -0.30 to 0.15; P=0.50). Analyses suggested greater intervention effects with increasing baseline WMH volumes (P for interaction=0.03). New lacunar infarcts developed in 6 (9%) intervention and 2 (3%) control participants (odds ratio, 2.2; 95% confidence interval, 0.4-12.1; P=0.36). CONCLUSIONS: Nurse-led vascular care in hypertensive community-dwelling older persons did not diminish WMH accumulation over 3 years. However, our results do suggest this type of intervention could be effective in persons with high WMH volumes. There was no effect on lacunar infarcts incidence but numbers were low. CLINICAL TRIAL INFORMATION: URL: http://www.isrctn.com/ISRCTN29711771. Unique identifier: ISRCTN29711771.

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

Age-related differences in autism: The case of white matter microstructure.

Autism spectrum disorder (ASD) is typified as a brain connectivity disorder in which white matter abnormalities are already present early on in life. However, it is unknown if and to which extent these abnormalities are hard-wired in (older) adults with ASD and how this interacts with age-related white matter changes as observed in typical aging. The aim of this first cross-sectional study in mid- and late-aged adults with ASD was to characterize white matter microstructure and its relationship with age. We utilized diffusion tensor imaging with head motion control in 48 adults with ASD and 48 age-matched controls (30-74 years), who also completed a Flanker task. Intra-individual variability of reaction times (IIVRT) measures based on performance on the Flanker interference task were used to assess IIVRT-white matter microstructure associations. We observed primarily higher mean and radial diffusivity in white matter microstructure in ASD, particularly in long-range fibers, which persisted after taking head motion into account. Importantly, group-by-age interactions revealed higher age-related mean and radial diffusivity in ASD, in projection and association fiber tracts. Subtle dissociations were observed in IIVRT-white matter microstructure relations between groups, with the IIVRT-white matter association pattern in ASD resembling observations in cognitive aging. The observed white matter microstructure differences are lending support to the structural underconnectivity hypothesis in ASD. These reductions seem to have behavioral percussions given the atypical relationship with IIVRT. Taken together, the current results may indicate different age-related patterns of white matter microstructure in adults with ASD. Hum Brain Mapp 38:82-96, 2017. © 2016 Wiley Periodicals, Inc.

Prenatal famine exposure has sex-specific effects on brain size.

Early nutritional deprivation might cause irreversible damage to the brain. Prenatal exposure to undernutrition has been shown to be associated with increased central nervous system anomalies at birth and decreased cognitive function in adulthood. Little is known about the potential effect on the brain in older age. We investigated brain size and structure at age 68 years after prenatal famine exposure. T1-weighted structural magnetic resonance images of the brain were made in 118 Dutch famine birth cohort members. Of these 118 (44% male, age range 65-69 years), 41 had been exposed to famine in early gestation and 77 had been prenatally unexposed. Structural volumes were automatically assessed using FreeSurfer. Diffusion tensor imaging was performed and anisotropy and diffusivity were computed. Fluid attenuated inversion recovery was performed to assess white matter hyperintensities. Exposure to famine in early gestation was associated with smaller intracranial volume in males, but not females. Volumes of total brain, grey and white matter were also smaller in early exposed males, but these differences disappeared after adjusting for intracranial volume. Prenatally exposed males but not females, had a smaller intracranial and total brain volume compared to unexposed subjects. Our findings show that prenatal undernutrition permanently affected brain size.media-1vid110.1093/brain/aww132_video_abstractaww132_video_abstract.

White matter integrity between left basal ganglia and left prefrontal cortex is compromised in gambling disorder.

Pathological gambling (PG) is a behavioral addiction characterized by an inability to stop gambling despite the negative consequences, which may be mediated by cognitive flexibility deficits. Indeed, impaired cognitive flexibility has previously been linked to PG and also to reduced integrity of white matter connections between the basal ganglia and the prefrontal cortex. It remains unclear, however, how white matter integrity problems relate to cognitive inflexibility seen in PG. We used a cognitive switch paradigm during functional magnetic resonance imaging in pathological gamblers (PGs; n = 26) and healthy controls (HCs; n = 26). Cognitive flexibility performance was measured behaviorally by accuracy and reaction time on the switch task, while brain activity was measured in terms of blood oxygen level-dependent responses. We also used diffusion tensor imaging on a subset of data (PGs = 21; HCs = 21) in combination with tract-based spatial statistics and probabilistic fiber tracking to assess white matter integrity between the basal ganglia and the dorsolateral prefrontal cortex. Although there were no significant group differences in either task performance, related neural activity or tract-based spatial statistics, PGs did show decreased white matter integrity between the left basal ganglia and prefrontal cortex. Our results complement and expand similar findings from a previous study in alcohol-dependent patients. Although we found no association between white matter integrity and task performance here, decreased white matter connections may contribute to a diminished ability to recruit prefrontal networks needed for regulating behavior in PG. Hence, our findings could resonate an underlying risk factor for PG, and we speculate that these findings may extend to addiction in general.

White matter alterations in cocaine users are negatively related to the number of additionally (ab)used substances.

Diffusion tensor imaging studies have provided evidence for white matter (WM) alterations in cocaine users. While polysubstance use is a widespread phenomenon among cocaine users, its role in WM alterations in cocaine users is currently unknown. This study examined the relation between the number of substances that are used(cocaine, alcohol and marijuana) and WM alterations in 67 male non-drug users and 67 male regular cocaine users, who were classified into five groups based on the number of used substances. Diffusion-weighted images were acquired on a 3.0 T magnetic resonance imaging scanner. Using tract-based spatial statistics we demonstrated that there was a negative relation between the number of used substances and fractional anisotropy, a global measure of WM integrity. Also, we demonstrated a positive relation between the number of used substance and radial diffusivity within the prefrontal lobe, suggesting an increase in demyelination with the number of used substances. We did not find a dose-effect between the level of substance use and WM alterations. The results of the current study may reflect the presence of a pre-existing vulnerability to polysubstance use resulting from prefrontal WM abnormalities and related impaired cognitive control although WM alterations because of polysubstance use cannot be fully excluded. This study is an important first step in understanding the problems related to polysubstance use among cocaine users.