Sensorimotor cortex beta oscillations reflect motor skill learning ability after stroke.

Recovery of skilled movement after stroke is assumed to depend on motor learning. However, the capacity for motor learning and factors that influence motor learning after stroke have received little attention. In this study, we first compared motor skill acquisition and retention between well-recovered stroke patients and age- and performance-matched healthy controls.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

Background: Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification.

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.

Objective: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD).

Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation.

Brain regions important for recovery after severe post-stroke upper limb paresis.

The ability to predict outcome after stroke is clinically important for planning treatment and for stratification in restorative clinical trials. In relation to the upper limbs, the main predictor of outcome is initial severity, with patients who present with mild to moderate impairment regaining about 70% of their initial impairment by 3 months post-stroke. However, in those with severe presentations, this proportional recovery applies in only about half, with the other half experiencing poor recovery.

Decoding post-stroke motor function from structural brain imaging.

Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question.

Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects.

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer's disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk.

Pattern changes of EEG oscillations and BOLD signals associated with temporal lobe epilepsy as revealed by a working memory task.

Background: It is known that the abnormal neural activity in epilepsy may be associated to the reorganization of neural circuits and brain plasticity in various ways. On that basis, we hypothesized that changes in neuronal circuitry due to epilepsy could lead to measurable variations in patterns of both EEG and BOLD signals in patients performing some cognitive task as compared to what would be obtained in normal condition. Thus, the aim of this study was to compare the cerebral areas involved in EEG oscillations versus fMRI signal patterns during a working memory (WM) task in normal controls and patients with refractory mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS).

SCoRS--A Method Based on Stability for Feature Selection and Mapping inNeuroimaging [corrected].

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features.

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines.

Pattern recognition methods have demonstrated to be suitable analyses tools to handle the high dimensionality of neuroimaging data. However, most studies combining neuroimaging with pattern recognition methods focus on two-class classification problems, usually aiming to discriminate patients under a specific condition (e.g.

Brain plasticity for verbal and visual memories in patients with mesial temporal lobe epilepsy and hippocampal sclerosis: an fMRI study.

We aimed to identify the brain areas involved in verbal and visual memory processing in normal controls and patients with unilateral mesial temporal lobe epilepsy (MTLE) associated with unilateral hippocampal sclerosis (HS) by means of functional magnetic resonance imaging (fMRI). The sample comprised nine normal controls, eight patients with right MTLE, and nine patients with left MTLE. All subjects underwent fMRI with verbal and visual memory paradigms, consisting of encoding and immediate recall of 17 abstract words and 17 abstract drawings.

Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI.

Background: Mesial temporal lobe epilepsy (MTLE), the most common type of focal epilepsy in adults, is often caused by hippocampal sclerosis (HS). Patients with HS usually present memory dysfunction, which is material-specific according to the hemisphere involved and has been correlated to the degree of HS as measured by postoperative histopathology as well as by the degree of hippocampal atrophy on magnetic resonance imaging (MRI). Verbal memory is mostly affected by left-sided HS, whereas visuo-spatial memory is more affected by right HS.

Electrical stimulation during gait promotes increase of muscle cross-sectional area in quadriplegics: a preliminary study.

Increases in muscular cross-sectional area (CSA) occur in quadriplegics after training, but the effects of neuromuscular electrical stimulation (NMES) along with training are unknown. Thus, we addressed two questions: (1) Does NMES during treadmill gait training increase the quadriceps CSA in complete quadriplegics?; and (2) Is treadmill gait training alone enough to observe an increase in CSA? Fifteen quadriplegics were divided into gait (n = 8) and control (n = 7) groups. The gait group performed training with NMES for 6 months twice a week for 20 minutes each time.

Muscle hypertrophy in quadriplegics with combined electrical stimulation and body weight support training.

We describe the analysis of muscle hypertrophy in complete quadriplegics after 6 months of treadmill gait training with neuromuscular electrical stimulation (NMES). We aim to evaluate the effect of treadmill gait training using NMES, with 30-50% body weight relief, on muscle mass. Fifteen quadriplegics were divided into gait (n=8) and control (n=7) groups.

Cerebral and corpus callosum atrophy in systemic lupus erythematosus.

Objective: To determine cerebral and corpus callosum volumes in patients with systemic lupus erythematosus (SLE), using semiautomatic magnetic resonance imaging (MRI) volumetric measurements, and to determine possible relationships between a reduction in cerebral volume and disease duration, total corticosteroid dose, neuropsychiatric manifestations, and the presence of antiphospholipid antibodies.

Methods: We studied 115 consecutive patients with SLE and 44 healthy volunteers. A complete clinical, laboratory, and neurologic evaluation was performed.