Technological Advances in Stroke Rehabilitation: Robotics and Virtual Reality.

Robotic technology and virtual reality (VR) have been widely studied technologies in stroke rehabilitation over the last few decades. Both technologies have typically been considered as ways to enhance recovery through promoting intensive, repetitive, and engaging therapies. In this review, we present the current evidence from interventional clinical trials that employ either robotics, VR, or a combination of both modalities to facilitate post-stroke recovery. Broadly speaking, both technologies have demonstrated some success in improving post-stroke outcomes and complementing conventional therapy. However, more high-quality, randomized, multicenter trials are required to confirm our current understanding of their role in precision stroke recovery.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Resting state functional connectivity associated with impaired proprioception post-stroke.

Deficits in proprioception, the knowledge of limb position and movement in the absence of vision, occur in ~50% of all strokes; however, our lack of knowledge of the neurological mechanisms of these deficits diminishes the effectiveness of rehabilitation and prolongs recovery. We performed resting-state functional magnetic resonance imaging (fMRI) on stroke patients to determine functional brain networks that exhibited changes in connectivity in association with proprioception deficits determined by a Kinarm robotic exoskeleton assessment. Thirty stroke participants were assessed for proprioceptive impairments using a Kinarm robot and underwent resting-state fMRI at 1 month post-stroke. Age-matched healthy control (n = 30) fMRI data were also examined and compared to stroke data in terms of the functional connectivity of brain regions associated with proprioception. Stroke patients exhibited reduced connectivity of the supplementary motor area and the supramarginal gyrus, relative to controls. Functional connectivity of these regions plus primary somatosensory cortex and parietal opercular area was significantly associated with proprioceptive function. The parietal lobe of the lesioned hemisphere is a significant node for proprioception after stroke. Assessment of functional connectivity of this region after stroke may assist with prognostication of recovery. This study also provides potential targets for therapeutic neurostimulation to aid in stroke recovery.

Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke.

Proprioceptive impairments occur in ~50% of stroke survivors, with 20-40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke.

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.

PURPOSE: Multiple medical imaging modalities are used for clinical follow-up ischemic stroke analysis. Mixed-modality datasets are challenging, both for clinical rating purposes and for training machine learning models. While image-to-image translation methods have been applied to harmonize stroke patient images to a single modality, they have only been used for paired data so far. In the more common unpaired scenario, the standard cycle-consistent generative adversarial network (CycleGAN) method is not able to translate the stroke lesions properly. Thus, the aim of this work was to develop and evaluate a novel image-to-image translation regularization approach for unpaired 3D follow-up stroke patient datasets. METHODS: A modified CycleGAN was used to translate images between 238 non-contrast computed tomography (NCCT) and 244 fluid-attenuated inversion recovery (FLAIR) MRI datasets, two of the most relevant follow-up modalities in clinical practice. We introduced an additional attention-guided mechanism to encourage an improved translation of the lesion and a gradient-consistency loss to preserve structural brain morphology. RESULTS: The proposed modifications were able to preserve the overall quality provided by the CycleGAN translation. This was confirmed by the FID score and gradient correlation results. Furthermore, the lesion preservation was significantly improved compared to a standard CycleGAN. This was evaluated for location and volume with segmentation models, which were trained on real datasets and applied to the translated test images. Here, the Dice score coefficient resulted in 0.81 and 0.62 for datasets translated to FLAIR and NCCT, respectively, compared to 0.57 and 0.50 for the corresponding datasets translated using a standard CycleGAN. Finally, an analysis of the distribution of mean lesion intensities showed substantial improvements. CONCLUSION: The results of this work show that the proposed image-to-image translation method is effective at preserving stroke lesions in unpaired modality translation, supporting its potential as a tool for stroke image analysis in real-life scenarios.

Association of stroke lesion shape with newly detected atrial fibrillation - Results from the MonDAFIS study.

Paroxysmal Atrial fibrillation (AF) is often clinically silent and may be missed by the usual diagnostic workup after ischemic stroke. We aimed to determine whether shape characteristics of ischemic stroke lesions can be used to predict AF in stroke patients without known AF at baseline. Lesion shape quantification on brain MRI was performed in selected patients from the intervention arm of the Impact of standardized MONitoring for Detection of Atrial Fibrillation in Ischemic Stroke (MonDAFIS) study, which included patients with ischemic stroke or TIA without prior AF. Multiple morphologic parameters were calculated based on lesion segmentation in acute brain MRI data. Multivariate logistic models were used to test the association of lesion morphology, clinical parameters, and AF. A stepwise elimination regression was conducted to identify the most important variables. A total of 755 patients were included. Patients with AF detected within 2 years after stroke (n = 86) had a larger overall oriented bounding box (OBB) volume (p = 0.003) and a higher number of brain lesion components (p = 0.008) than patients without AF. In the multivariate model, OBB volume (OR 1.72, 95%CI 1.29-2.35, p < 0.001), age (OR 2.13, 95%CI 1.52-3.06, p < 0.001), and female sex (OR 2.45, 95%CI 1.41-4.31, p = 0.002) were independently associated with detected AF. Ischemic lesions in patients with detected AF after stroke presented with a more dispersed infarct pattern and a higher number of lesion components. Together with clinical characteristics, these lesion shape characteristics may help in guiding prolonged cardiac monitoring after stroke.

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging.

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.

Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.

Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.

Lesion-symptom mapping with NIHSS sub-scores in ischemic stroke patients.

BACKGROUND: Lesion-symptom mapping (LSM) is a statistical technique to investigate the population-specific relationship between structural integrity and post-stroke clinical outcome. In clinical practice, patients are commonly evaluated using the National Institutes of Health Stroke Scale (NIHSS), an 11-domain clinical score to quantitate neurological deficits due to stroke. So far, LSM studies have mostly used the total NIHSS score for analysis, which might not uncover subtle structure-function relationships associated with the specific sub-domains of the NIHSS evaluation. Thus, the aim of this work was to investigate the feasibility to perform LSM analyses with sub-score information to reveal category-specific structure-function relationships that a total score may not reveal. METHODS: Employing a multivariate technique, LSM analyses were conducted using a sample of 180 patients with NIHSS assessment at 48-hour post-stroke from the ESCAPE trial. The NIHSS domains were grouped into six categories using two schemes. LSM was conducted for each category of the two groupings and the total NIHSS score. RESULTS: Sub-score LSMs not only identify most of the brain regions that are identified as critical by the total NIHSS score but also reveal additional brain regions critical to each function category of the NIHSS assessment without requiring extensive, specialised assessments. CONCLUSION: These findings show that widely available sub-scores of clinical outcome assessments can be used to investigate more specific structure-function relationships, which may improve predictive modelling of stroke outcomes in the context of modern clinical stroke assessments and neuroimaging. TRIAL REGISTRATION NUMBER: NCT01778335.

Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study.

Background: Clinical stroke rehabilitation decision making relies on multi-modal data, including imaging and other clinical assessments. However, most previously described methods for predicting long-term stroke outcomes do not make use of the full multi-modal data available. The aim of this work was to develop and evaluate the benefit of nested regression models that utilise clinical assessments as well as image-based biomarkers to model 30-day NIHSS. Method: 221 subjects were pooled from two prospective trials with follow-up MRI or CT scans, and NIHSS assessed at baseline, as well as 48-hours and 30 days after symptom onset. Three prediction models for 30-day NIHSS were developed using a support vector regression model: one clinical model based on modifiable and non-modifiable risk factors (MCLINICAL) and two nested regression models that aggregate clinical and image-based features that differed with respect to the method used for selection of important brain regions for the modelling task. The first model used the widely accepted RreliefF (MRELIEF) machine learning method for this purpose, while the second model employed a lesion-symptom mapping technique (MLSM) often used in neuroscience to investigate structure-function relationships and identify eloquent regions in the brain. Results: The two nested models achieved a similar performance while considerably outperforming the clinical model. However, MRELIEF required fewer brain regions and achieved a lower mean absolute error than MLSM while being less computationally expensive. Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.

Neural correlates of the impulse dyscontrol domain of mild behavioral impairment.

OBJECTIVES: Agitation and aggression are common in dementia and pre-dementia. The dementia risk syndrome mild behavioral impairment (MBI) includes these symptoms in the impulse dyscontrol domain. However, the neural circuitry associated with impulse dyscontrol in neurodegenerative disease is not well understood. The objective of this work was to investigate if regional micro- and macro-structural brain properties were associated with impulse dyscontrol symptoms in older adults with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD). METHODS: Clinical, neuropsychiatric, and T1-weighted and diffusion-tensor magnetic resonance imaging (DTI) data from 80 individuals with and 123 individuals without impulse dyscontrol were obtained from the AD Neuroimaging Initiative. Linear mixed effect models were used to assess if impulse dyscontrol was related to regional DTI and volumetric parameters. RESULTS: Impulse dyscontrol was present in 17% of participants with NC, 43% with MCI, and 66% with AD. Impulse dyscontrol was associated with: (1) lower fractional anisotropy (FA), and greater mean, axial, and radial diffusivity in the fornix; (2) lesser FA and greater radial diffusivity in the superior fronto-occipital fasciculus; (3) greater axial diffusivity in the cingulum; (4) greater axial and radial diffusivity in the uncinate fasciculus; (5) gray matter atrophy, specifically, lower cortical thickness in the parahippocampal gyrus. CONCLUSION: Our findings provide evidence that well-established atrophy patterns of AD are prominent in the presence of impulse dyscontrol, even when disease status is controlled for, and possibly in advance of dementia. Our findings support the growing evidence for impulse dyscontrol symptoms as an early manifestation of AD.

Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning.

Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, genetics and clinical and demographic characteristics. As a post-hoc analysis, we aimed to explore the connection between novel selected features and GC more precisely and to investigate whether this relationship is specific to GC or is driven by specific cognitive domains. 101 idiopathic PD patients had a cognitive assessment, structural MRI and blood draw. ML was performed on 102 input features including demographics, cortical thickness and subcortical measures, and several genetic variants (APOE, MAPT, SNCA, etc.). Using the combination of RRELIEFF and Support Vector Regression, 11 features were found to be predictive of GC including sex, rs894280, Edinburgh Handedness Inventory, UPDRS-III, education, five cortical thickness measures (R-parahippocampal, L-entorhinal, R-rostral anterior cingulate, L-middle temporal, and R-transverse temporal), and R-caudate volume. The rs894280 of SNCA gene was selected as the most novel finding of ML. Post-hoc analysis revealed a robust association between rs894280 and GC, attention, and visuospatial abilities. This variant indicates a potential role for the SNCA gene in cognitive impairments of idiopathic PD.

Supervised machine learning tools: a tutorial for clinicians.

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.

The Association of Saliva Cytokines and Pediatric Sports-Related Concussion Outcomes.

OBJECTIVES: This study aimed to explore cytokine alterations following pediatric sports-related concussion (SRC) and whether a specific cytokine profile could predict symptom burden and time to return to sports (RTS). SETTING: Sports Medicine Clinic. PARTICIPANTS: Youth ice hockey participants (aged 12-17 years) were recruited prior to the 2013-2016 hockey season. DESIGN: Prospective exploratory cohort study. MAIN MEASURE: Following SRC, saliva samples were collected and a Sport Concussion Assessment Tool version 3 (SCAT3) was administered within 72 hours of injury and analyzed for cytokines. Additive regression of decision stumps was used to model symptom burden and length to RTS based on cytokine and clinical features. RRelieFF feature selection was used to determine the predictive value of each cytokine and clinical feature, as well as to identify the optimal cytokine profile for the symptom burden and RTS. RESULTS: Thirty-six participants provided samples post-SRC (81% male; age 14.4 ± 1.3 years). Of these, 10 features, sex, number of previous concussions, and 8 cytokines, were identified to lead to the best prediction of symptom severity (r = 0.505, P = .002), while 12 cytokines, age, and history of previous concussions predicted the number of symptoms best (r = 0.637, P < .001). The prediction of RTS led to the worst results, requiring 21 cytokines, age, sex, and number of previous concussions as features (r = -0.320, P = .076). CONCLUSIONS: In pediatric ice hockey participants following SRC, there is evidence of saliva cytokine profiles that are associated with increased symptom burden. However, further studies are needed.

The Impact of Covariates in Voxel-Wise Lesion-Symptom Mapping.

Background: Voxel-wise lesion-symptom mapping (VLSM) is a statistical technique to infer the structure-function relationship in patients with cerebral strokes. Previous VLSM research suggests that it is important to adjust for various confounders such as lesion size to minimize the inflation of true effects. The aim of this work is to investigate the regional impact of covariates on true effects in VLSM. Methods: A total of 222 follow-up datasets of acute ischemic stroke patients with known NIH Stroke Scale (NIHSS) score at 48-h post-stroke were available for this study. Patient age, lesion volume, and follow-up imaging time were tested for multicollinearity using variance inflation factor analysis and used as covariates in VLSM analyses. Covariate importance maps were computed from the VLSM results by standardizing the beta coefficients of general linear models. Results: Covariates were found to have distinct regional importance with respect to lesion eloquence in the brain. Age has a relatively higher importance in the superior temporal gyrus, inferior parietal lobule, and in the pre- and post-central gyri. Volume explains more variability in the opercular area of the insula, inferior frontal gyrus, and caudate. Follow-up imaging time accounts for most of the variance in the globus pallidus, ventromedial- and dorsolateral putamen, dorsal caudate, pre-motor thalamus, and the dorsal insula. Conclusions: This is the first study investigating and revealing distinctive regional patterns of importance for covariates typically used in VLSM. These covariate importance maps can improve our understanding of the lesion-deficit relationships in patients and could prove valuable for patient-specific treatment and rehabilitation planning.

Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction.

Cerebral cortex thinning and cerebral blood flow (CBF) reduction are typically observed during normal healthy aging. However, imaging-based age prediction models have primarily used morphological features of the brain. Complementary physiological CBF information might result in an improvement in age estimation. In this study, T1-weighted structural magnetic resonance imaging and arterial spin labeling CBF images were acquired in 146 healthy participants across the adult life span. Sixty-eight cerebral cortex regions were segmented, and the cortical thickness and mean CBF were computed for each region. Linear regression with age was computed for each region and data type, and laterality and correlation matrices were computed. Sixteen predictive models were trained with the cortical thickness and CBF data alone as well as a combination of both data types. The age explained more variance in the cortical thickness data (average R2 of 0.21) than in the CBF data (average R2 of 0.09). All 16 models performed significantly better when combining both measurement types and using feature selection, and thus, we conclude that the inclusion of CBF data marginally improves age estimation.

Structural integrity of white matter tracts as a predictor of acute ischemic stroke outcome.

BACKGROUND AND PURPOSE: Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS). MATERIAL AND METHODS: A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2-9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation. RESULTS: The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only. CONCLUSION: White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data.

BACKGROUND: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. OBJECTIVES: To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. METHOD: Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. RESULTS: In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73). CONCLUSION: Baseline NPS, categorized for MBI domain and duration, have prognostic utility in addition to brain morphology measures for predicting diagnosis change using ML. MBI total score, followed by impulse dyscontrol and affective dysregulation were most predictive of future diagnosis.

Recanalization following Endovascular treatment and imaging of PErfusion, Regional inFarction and atrophy to Understand Stroke Evolution-NA1 (REPERFUSE-NA1).

RATIONALE: Following endovascular treatment, poor clinical outcomes are more frequent if the initial infarct core or volume of irreversible brain damage is large. Clinical outcomes may be improved using neuroprotective agents that reduce stroke volume and improve recovery. AIM: The aim of the REPERFUSE NA1 was to replicate the preclinical neuroprotection study that significantly reduced infarct volume in a primate model of ischemia reperfusion. Specifically, REPERFUSE NA1 will determine if administration of the neuroprotectant NA1 prior to endovascular therapy can significantly reduce early (Day 2 subtract Day 1 diffusion-weighted imaging volume) and delayed secondary infarct (90-day whole brain atrophy plus FLAIR volume-Day 1 diffusion-weighted imaging volume) growth, as measured by magnetic resonance imaging. METHODS AND DESIGN: REPERFUSE-NA1 is a magnetic resonance imaging observational substudy of ESCAPE-NA1 (ClinicalTrialGov NCT02930018). A total of 150 acute stroke patients will be recruited (including 20% attrition) that have been randomized to either NA1 or placebo in the ESCAPE-NA1 trial. STUDY OUTCOMES: Primary-Early infarct growth measured using diffusion-weighted imaging will be at least 30% smaller in patients receiving NA1 compared to placebo. Secondary-Delayed secondary stroke injury at 90 days will be significantly reduced in patients receiving NA1 compared to placebo, as well as delayed secondary growth at 90 days. CONCLUSION: REPERFUSE-NA1 will demonstrate the effect of NA1 neuroprotection on reducing the early and delayed stroke injury after reperfusion treatment.

High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly.

Normative brain atlases are a standard tool for neuroscience research and are, for example, used for spatial normalization of image datasets prior to voxel-based analyses of brain morphology and function. Although many different atlases are publicly available, they are usually biased with respect to an imaging modality and the age distribution. Both effects are well known to negatively impact the accuracy and reliability of the spatial normalization process using non-linear image registration methods. An important and very active neuroscience area that lacks appropriate atlases is lesion-related research in elderly populations (e.g. stroke, multiple sclerosis) for which FLAIR MRI and non-contrast CT are often the clinical imaging modalities of choice. To overcome the lack of atlases for these tasks and modalities, this paper presents high-resolution, age-specific FLAIR and non-contrast CT atlases of the elderly generated using clinical images.