Brain Magnetic Resonance Imaging Data Research Articles

White matter hyperintensities induce distal deficits in the connected fibers.

White matter hyperintensities (WMH) are common in elderly individuals and cause brain network deficits. However, it is still unclear how the global brain network is affected by the focal WMH. We aimed to investigate the diffusion of WMH‐related deficits along the connecting white matters (WM). Brain magnetic resonance imaging data and neuropsychological evaluations of 174 participants (aged 74 ± 5 years) were collected and analyzed. For each participant, WMH lesions were segmented using a deep learning method, and 18 major WM tracts were reconstructed using automated quantitative tractography. The diffusion characteristics of distal WM tracts (with the WMH penumbra excluded) were calculated. Multivariable linear regression analysis was performed. We found that a high burden of tract‐specific WMH was related to worse diffusion characteristics of distal WM tracts in a wide range of WM tracts, including the forceps major (FMA), forceps minor (FMI), anterior thalamic radiation (ATR), cingulum cingulate gyrus (CCG), corticospinal tract (CST), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus‐parietal (SLFP), superior longitudinal fasciculus‐temporal (SLFT), and uncinate fasciculus (UNC). Furthermore, a higher mean diffusivity (MD) of distal tracts was linked to worse attention and executive function in the FMI, right CCG, left ILF, SLFP, SLFT, and UNC. The effect of WMH on the microstructural integrity of WM tracts may propagate along tracts to distal regions beyond the penumbra and might eventually affect attention and executive function.

Reduced hippocampal volumes and memory deficits in adolescents with single ventricle heart disease.

IntroductionAdolescents with single ventricle congenital heart disease (SVHD) show functional deficits, particularly in memory and mood regulation. Hippocampi are key brain structures that regulate mood and memory; however, their tissue integrity in SVHD is unclear. Our study aim is to evaluate hippocampal volumes and their associations with memory, anxiety, and mood scores in adolescents with SVHD compared to healthy controls.MethodsWe collected brain magnetic resonance imaging data from 25 SVHD (age 15.9 ± 1.2 years; 15 male) and 38 controls (16.0 ± 1.1 years; 19 male) and assessed memory (Wide Range Assessment of Memory and Learning 2, WRAML2), anxiety (Beck Anxiety Inventory, BAI), and mood (Patient Health Questionnaire 9, PHQ‐9) functions. Both left and right hippocampi were outlined and global volumes, as well as three‐dimensional surfaces were compared between groups using ANCOVA and associations with cognitive and behavioral scores with partial correlations (covariates: age and total brain volume).ResultsThe SVHD group showed significantly higher BAI (p = .001) and PHQ‐9 (p < .001) scores, indicating anxiety and depression symptoms and significantly reduced WRAML2 scores (p < .001), suggesting memory deficits compared with controls. SVHD group had significantly reduced right global hippocampal volumes (p = .036) compared with controls, but not the left (p = .114). Right hippocampal volume reductions were localized in the CA1, CA4, subiculum, and dentate gyrus. Positive correlations emerged between WRAML2 scores and left (r = 0.32, p = .01) and right (r = 0.28, p = .03) hippocampal volumes, but BAI and PHQ‐9 did not show significant correlations.ConclusionAdolescents with SVHD show reduced hippocampal volumes, localized in several sites (CA1, CA4, subiculum, and dentate gyrus), which are associated with memory deficits. The findings indicate the need to explore ways to improve memory to optimize academic achievement and ability for self‐care in the condition.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Harmonization with Flow-Based Causal Inference.

Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well. This paper leverages a recently proposed normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM), in order to achieve harmonization of such data. A causal model is used to model observed effects (brain magnetic resonance imaging data) that result from known confounders (site, gender and age) and exogenous noise variables. Our formulation exploits the bijection induced by flow for the purpose of harmonization. We infer the posterior of exogenous variables, intervene on observations, and draw samples from the resultant SCM to obtain counterfactuals. This approach is evaluated extensively on multiple, large, real-world medical datasets and displayed better cross-domain generalization compared to state-of-the-art algorithms. Further experiments that evaluate the quality of confounder-independent data generated by our model using regression and classification tasks are provided.

Data Augmentation of 3D Brain Environment Using Deep Convolutional Refined Auto-Encoding Alpha GAN

Learning-based methods represent the state of the art in path planning problems. Their performance, however, depend on the number of medical images available for the training. Generative Adversarial Networks (GANs) are unsupervised neural networks that can be exploited to synthesize realistic images avoiding the dependency from the original data. In this article, we propose an innovative type of GAN, Deep Convolutional Refined Auto-Encoding Alpha GAN, able to successfully generate 3D brain Magnetic Resonance Imaging (MRI) data from random vectors by learning the data distribution. We combined a Variational Auto-Encoder GAN with a Code Discriminator to solve the common mode collapse problem and reduce the image blurriness. Finally, we inserted a Refiner in series with the Generator Network in order to smooth the shapes of the images and generate more realistic samples. A qualitative comparison between the generated images and the real ones has been used to test our model's quality. With the use of three indexes, namely the Multi-Scale Structural Similarity Metric, the Maximum Mean Discrepancy and the Intersection over Union, we also performed a quantitative analysis. The final results suggest that our model can be a suitable solution to overcome the shortage of medical images needed for learning-based methods.

Abnormal topological characteristics of brain white matter network relate to cognitive and emotional deficits of non-small cell lung cancer (NSCLC) patients prior to chemotherapy

Introduction Cancer and chemotherapy are correlated with brain functional and structural changes in cancer patients, which may lead to cognitive dysfunction. However, little is known about the structural abnormalities of brain in patients with non-small cell lung cancer (NSCLC). The aim of this study was to explore the topological properties within the brain white matter network of NSCLC patients prior to chemotherapy. Methods To explore the neurobiological biomarkers of NSCLC, brain magnetic resonance imaging (MRI) data were acquired in 24 non-nervous system metastatic NSCLC patients and 25 matched healthy controls. The topological properties of the brain structural networks of NSCLC were measured by the parameters of local and global efficiency. Results Treatment-naïve NSCLC patients showed cognitive and emotional deficits. In addition, NSCLC patients also exhibited decreased global efficacy in the left inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), right rolandic operculum, right gyrus rectus, right lenticular nucleus (putamen), left superior temporal gyrus and right inferior temporal gyrus. Decreased local efficacy were found in the left middle frontal gyrus (orbital part) and left superior temporal gyrus in NSCLC patients. Moreover, the aberrant brain regions were associated with the impaired cognitiion and emotion of NSCLC patients. Conclusion Overall our results suggested that altered local and global efficiency of brain white matter network were associated with cancer-induced cognitive and emotional deficits of NSCLC patients. These findings demonstrated that disrupted topological characteristics of the brain network might underlie the impaired cognition and emotion in NSCLC.

Automated detection system for texture feature based classification on different image datasets using S-transform

The objective of this study is to present a computer-aided diagnosis (CAD) system for automatic detection of brain tumors in brain magnetic resonance (MR) image data sets as we consider the brain image dataset from the different datasets. The proposed system initially pre-processes the input images using Fuzzy C-means (FCM) for image segmentation. Subsequently, it utilizes variant of S-transform namely discrete orthonormal S-transform (DOST) to extract the texture features and its dimensionality is reduced using Principal component analysis (PCA) and linear discriminant analysis (LDA). The reduced features are then supplied to the proposed Adaboost algorithm with Random Forest (ADBRF) classifier where the random forest is used as the base classifier for classifying the abnormal brain tumors in MRI image datasets. The simulation results based on the five runs of k-fold stratified cross-validation indicate that the proposed method yields superior accuracy (98.26%) as compared to existing schemes.

Association between pain sensitivity and gray matter properties in the sensorimotor network in women with irritable bowel syndrome.

BackgroundEnhanced perception of visceral stimuli is an important feature of Irritable Bowel Syndrome (IBS), but it is not known whether visceral sensitivity is associated with regional structural brain properties in IBS.MethodsStructural brain magnetic resonance imaging data from 216 women with IBS and 138 healthy women were parcellated with FreeSurfer to define regional gray matter morphometry (volume, cortical thickness, surface area and mean curvature) in the sensorimotor network. General linear models were used to detect group differences between IBS and health. In a second set of 48 female IBS patients, pain threshold, pain intensity ratings during rectal balloon distension, and reported levels of abdominal pain and bloating were correlated with brain regions that showed differences between IBS and health in the first data set.Key ResultsSeveral statistically significant differences between IBS patients and healthy controls were found, mainly higher gray matter volume and cortical thickness in primary somatosensory cortex, secondary somatosensory cortex, and subcortical regions, and lesser gray matter volume, surface area and cortical thickness in posterior insula and superior frontal gyrus. Pain intensity ratings during rectal distension were associated with left primary somatosensory cortical thickness, and pain threshold was associated with right nucleus accumbens volume.Conclusions and InferencesRegional gray matter differences in sensorimotor network are associated with visceral sensitivity and may represent neuroplastic changes in female IBS patients.

A Dynamic Particle Swarm Optimisation and Fuzzy Clustering Means Algorithm for Segmentation of Multimodal Brain Magnetic Resonance Image Data

Fuzzy Clustering Means (FCM) algorithm is a widely used clustering method in image segmentation, but it often falls into local minimum and is quite sensitive to initial values which are random in most cases. In this work, we consider the extension to FCM to multimodal data improved by a Dynamic Particle Swarm Optimization (DPSO) algorithm which by construction incorporates local and global optimization capabilities. Image segmentation of three-variate MRI brain data is achieved using FCM-3 and DPSOFCM-3 where the three modalities T1-weighted, T2-weighted and Proton Density (PD), are treated at once (the suffix -3 is added to distinguish our three-variate method from mono-variate methods usually using T1-weighted modality). FCM-3 and DPSOFCM-3 were evaluated on several Magnetic Resonance (MR) brain images corrupted by different levels of noise and intensity non-uniformity. By means of various performance criteria, our results show that the proposed method substantially improves segmentation results. For noisiest and most no-uniform images, the performance improved as much as 9% with respect to other methods

Modified Visual Magnetic Resonance Scale and Neuropsychometric Corelations in Alzheimer's disease

Amaç: Alzheimer hastalığı (AH) araştırmaları için daha ileri nörogörüntüleme teknikleri geliştirilmiş olsa da, yapısal manyetik rezonans görüntüleme (MRG) AH’nin klinik tanısında önemini korumaktadır. Birçok parametreyi değerlendiren kapsamlı görsel MRG derecelendirme ölçeklerinin, hastalığın kognitif ve davranışsal görünümleriyle ilişkisi yeterince araştırılmamıştır. Bu çalışmada, Modifiye Görsel Manyetik Rezonans Derecelendirme Skalası’nın (MGMRDS) 7 alt bölümünün, AH tanısı almış hastaların demografik, kognitif ve davranışsal verileri ile korelasyonunu değerlendirmek amaçlanmıştır. Gereç ve Yöntemler: Retrospektif çalışmamıza beyin MRG ve nöropsikometrik test (NPT) verileri olan, Ruhsal Bozuklukların Tanısal ve Sayımsal Elkitabı (DSM-IV-TR) ölçütleri ile Ulusal Nörolojik ve İletişimsel Bozukluklar ve İnme Enstitüsü &amp;amp; Alzheimer Hastalığı ve İlişkili Bozukluklar Derneği (NINCDS–ADRDA) kriterleri temelinde AH tanısı almış, 50 yaş ve üzeri toplam 42 hasta dahil edildi. Beyin MRG verileri, hasta yaş, cinsiyet ve tanı verilerine kör bir nöroradyolog tarafından MGMRDS kullanılarak değerlendirildi. MGMRDS verilerinin kognitif ve davranışsal test sonuçlarıyla korelasyonu incelendi. Bulgular: Hastaların %61,9’u kadın, yaş ortalaması 75,19±9,26 (53–92) yıl idi. Ortalama eğitim süresi 5,02±4,84 (0–15) yıl, ortalama hastalık süresi 4,52±2,94 yıldı. Ortalama Mini Mental Durum Testi skoru 18,51±5,43 (4–30) iken, ortalama Klinik Demans Derecelendirmesi (KDD) skoru 1,07±0,42 (0,5–2,0) idi. Sulkal atrofi puanları uzun süreli hatırlama ve yüz tanıma ile negatif korele idi; ventriküler atrofi skorları ise öğrenme puanları, meyve–insan ve yüz tanıma değişkenleri ile ters korelasyon gösterdi. Anlık hatırlama, öğrenme puanı, kendiliğinden hatırlama, meyve–insan ve KDD değişkenleri ile mediyal temporal atrofi değişkeni arasında anlamlı ilişki gözlendi. Tartışma ve Sonuç: Beyin MRG’ye dayalı görsel derecelendirme skalaları kullanmak, demans değerlendirmelerinde tanıyı doğrulayıcı, ucuz ve pratik bir yaklaşımdır. MGMRDS, kognitif (yürütücü işlevler, bellek, dikkat, dil) verilerle de anlamlı korelasyon göstermektedir. Bu ölçeği daha geniş hasta gruplarında değişik kognitif bozukluklarda değerlendirecek çalışmalar klinik açıdan faydalı olacaktır.

Association of Epigenetic Metrics of Biological Age With Cortical Thickness

Magnetic resonance imaging (MRI) studies of aging adults have shown substantial intersubject variability across various brain metrics, and some of this variability is likely attributable to chronological age being an imprecise measure of age-related change. Accurately quantifying one's biological age could allow better quantification of healthy and pathological changes in the aging brain. To investigate the association of DNA methylation (DNAm)-based biological age with cortical thickness and to assess whether biological age acceleration compared with chronological age captures unique variance in cortical thinning. This cross-sectional study used high-resolution structural brain MRI data collected from a sample of healthy aging adults who were participating in a larger ongoing neuroimaging study that began in May 2014. This population-based study accrued participants from the greater Omaha, Nebraska, metropolitan area. One hundred sixty healthy adults were contacted for the MRI component, 82 of whom participated in both DNAm and MRI study components. Data analysis was performed from March to June 2019. Vertexwise cortical thickness, DNAm-based biological age, and biological age acceleration compared with chronological age were measured. A pair of multivariable regression models were computed in which cortical thickness was regressed on DNAm-based biological age, controlling for sex in the first model and also controlling for chronological age in the second model. Seventy-nine adult participants (38 women; mean [SD] age, 43.82 [14.50] years; age range, 22-72 years) were included in all final analyses. Advancing biological age was correlated with cortical thinning across frontal, superior temporal, inferior parietal, and medial occipital regions. In addition, biological age acceleration relative to chronological age was associated with cortical thinning in orbitofrontal, superior and inferior temporal, somatosensory, parahippocampal, and fusiform regions. Specifically, for every 1 year of biological age acceleration, cortical thickness would be expected to decrease by 0.024 mm (95% CI, -0.04 to -0.01 mm) in the left orbitofrontal cortex (partial r, -0.34; P = .002), 0.014 mm (95% CI, -0.02 to -0.01 mm) in the left superior temporal gyrus (partial r, -0.36; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the left fusiform gyrus (partial r, -0.38; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the right fusiform gyrus (partial r, -0.43; P < .001), 0.019 mm (95% CI, -0.03 to -0.01 mm) in the right inferior temporal sulcus (partial r, -0.34; P = .002), and 0.011 mm (95% CI, -0.02 to -0.01 mm) in the right primary somatosensory cortex (partial r, -0.37; P = .001). To our knowledge, this is the first study to investigate vertexwise cortical thickness in relation to DNAm-based biological age, and the findings suggest that this metric of biological age may yield additional insight on healthy and pathological cortical aging compared with standard measures of chronological age alone.

Temporal lobe regions essential for preserved picture naming after left temporal epilepsy surgery.

To define left temporal lobe regions where surgical resection produces a persistent postoperative decline in naming visual objects. Pre- and postoperative brain magnetic resonance imaging data and picture naming (Boston Naming Test) scores were obtained prospectively from 59 people with drug-resistant left temporal lobe epilepsy. All patients had left hemisphere language dominance at baseline and underwent surgical resection or ablation in the left temporal lobe. Postoperative naming assessment occurred approximately 7months after surgery. Surgical lesions were mapped to a standard template, and the relationship between presence or absence of a lesion and the degree of naming decline was tested at each template voxel while controlling for effects of overall lesion size. Patients declined by an average of 15% in their naming score, with wide variation across individuals. Decline was significantly related to damage in a cluster of voxels in the ventral temporal lobe, located mainly in the fusiform gyrus approximately 4-6cm posterior to the temporal tip. Extent of damage to this region explained roughly 50% of the variance in outcome. Picture naming decline was not related to hippocampal or temporal pole damage. The results provide the first statistical map relating lesion location in left temporal lobe epilepsy surgery to picture naming decline, and they support previous observations of transient naming deficits from electrical stimulation in the basal temporal cortex. The critical lesion is relatively posterior and could be avoided in many patients undergoing left temporal lobe surgery for intractable epilepsy.

Articulation rehabilitation induces cortical plasticity in adults with non-syndromic cleft lip and palate.

In this study, we investigated brain morphological changes in adults with non-syndromic cleft lip and palate (NSCLP) after articulation rehabilitation (AR). High-resolution T1 weighted brain magnetic resonance imaging data were analyzed from 45 adults with NSCLP after palatoplasty: 24 subjects were assessed before AR (bNSCLP) and 21 subjects were assessed after AR (aNSCLP). In addition, there were 24 age and sex matched controls. Intergroup differences of grey matter volume were evaluated as a comprehensive measure of the cortex; cortical thickness and cortical complexity (gyrification and fractal dimensions) were also analyzed. As compared to controls, the bNSCLP subjects exhibited altered indexes in frontal, temporal, and parietal lobes; these morphological changes are characteristic for adults with NSCLP. Importantly, as compared to the bNSCLP and control subjects, the aNSCLP subjects exhibited cortical plasticity in the regions involved in language, auditory, pronunciation planning, and execution functions. The AR-mediated cortical plasticity in aNSCLP subjects may be caused by AR-induced cortical neurogenesis, which might reflect the underlying neural mechanism during AR.

Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

BackgroundAging-related cognitive decline is a primary risk factor for Alzheimer’s disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. MethodsUsing structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45–78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability. ResultsIn the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network’s small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. ConclusionsThese results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

Investigation of structure-function correlation among the young offspring of patients with bipolar disorder

Bipolar disorder (BD) has been associated with impaired executive functioning and integrity of fronto-limbic white matter tracts. The evaluation of these factors in young offspring of patients with BD (BDoff) as a high-risk group offers an opportunity to investigate factors that could predict vulnerability to the disorder. This study aims to examine the correlation between neurocognition and neuroimaging findings to evaluate the potential for these findings as biomarkers for the early recognition of BD. We enrolled BDoff (n = 16) who were aged between 12 and 18. Participants were assessed using clinical and neurocognitive tests. In addition, structural brain magnetic resonance and diffusion tensor imaging data were obtained. Mean fractional anisotropy (FA) and mean diffusivity (MD) values of the superior longitudinal fasciculus (SLF) and cingulum were extracted and correlations with neuropsychological data were analyzed. FA values in the SLF were negatively correlated with Stroop interference, the Wisconsin Card Sorting Test, and the Trail Making Test (B-A) scores. MD values in the cingulum were inversely correlated with the Child and Youth Resilience Measure and positively correlated with higher scores on the Barratt Impulsiveness Scale-Attentional. These findings provide a link between features of the brain and cognitive dysfunction in BDoff.

Anti-PDHA1 antibody is detected in a subset of patients with schizophrenia

Autoantibodies have been implicated in schizophrenia aetiology. Here, novel autoantibodies were isolated from patients with schizophrenia. Autoantibody candidates were searched using two-dimensional gel electrophoresis and western blotting with rat brain proteins as antigens and two sera pools (25 schizophrenia patients versus 25 controls) as antibodies. Immunoreactive antigens were identified by mass spectrometry. Antibody prevalence were evaluated by western blotting using human recombinant proteins. Furthermore, brain magnetic resonance imaging data (regional brain volumes and diffusion tensor imaging measures) were compared. Two proteins of the mitochondrial respiration pathway were identified as candidate antigens. Three patients with schizophrenia, but no controls, expressed antibodies targeting one of the candidate antigens, i.e., pyruvate dehydrogenase E1 component subunit alpha, somatic form, mitochondrial (PDHA1, EC 1.2.4.1), which is related to mitochondrial energy production. Anti-PDHA1 antibody-positive patients (n = 3) had increased volumes in the left occipital fusiform gyrus compared to both controls (n = 23, p = 0.017) and antibody-negative patients (n = 16, p = 0.009), as well as in the left cuneus compared to antibody-negative patients (n = 16, p = 0.018). This is the first report of an anti-PDHA1 antibody in patients with schizophrenia. Compatible with recent findings of mitochondrial dysfunction in schizophrenia, this antibody may be involved in the pathogenesis of a specific subgroup of schizophrenia.

Magnetic Resonance Imaging Findings and Their Association with Electroencephalogram Data in Children with Partial Epilepsy.

BackgroundIt is important to identify the neuroimaging features that are associated with partial epilepsy in children. Advances in technology have recently been made to localize focal epileptogenic lesions, especially high-resolution structural imaging with magnetic resonance imaging (MRI). The recommendation that electroencephalography (EEG) should be the gold standard and that MRI should be optional has been questioned. The present study aims to evaluate the efficacy of MRI in children with partial epilepsy and to compare the diagnostic yields of MRI and EEG data.MethodsThe present study was conducted among one hundred twelve 1- to 6-year-old children with partial epilepsy. All patients underwent EEG and brain MRI. The epileptogenic lesions were identified on the basis of the signal intensities and morphological abnormalities seen on MRI. The correlation between MRI and EEG abnormal findings was analyzed using a chi-square test.ResultsAbnormal MRI findings were present in 34.8% (n = 39) of the sample. The EEG and MRI data agreed with respect to classifications into abnormal or normal in 48.2% of the sample (n = 54). Of the 27 patients with normal EEG findings, six (22.2%) had abnormal MRI findings. Inter-rater agreement showed the compatibility between EEG and MRI not significant (weighted Kappa = 0.105).ConclusionA number of MRI abnormalities were found in our study of otherwise normal children, although the correlation between these results was not clear. The follow-up of these children will help us identify the important abnormalities. Despite the small sample size, our results showed that normal EEG findings do not predict normal brain MRI data in children with partial epilepsy.

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. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

The genetic architecture of the human cerebral cortex.

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Understanding the impact of bilateral brain injury in children with unilateral cerebral palsy

The presence of bilateral brain injury in patients with unilateral cerebral palsy (CP) may impact neuroplasticity in the ipsilateral hemisphere; however, this pattern of injury is typically under‐analyzed due to the lack of methods robust to severe injury. In this study, injury‐robust methods have been applied to structural brain magnetic resonance imaging (MRI) data of a cohort of 91 children with unilateral CP (37 with unilateral and 54 with bilateral brain injury, 4–17 years) and 44 typically developing controls (5–17 years), to determine how brain structure is associated with concurrent motor function, and if these associations differ between patients with unilateral or bilateral injury. Regression models were used to associate these measures with two clinical scores of hand function, with patient age, gender, brain injury laterality, and interaction effects included. Significant associations with brain structure and motor function were observed (Pearson's r = .494–.716), implicating several regions of the motor pathway, and demonstrating an accurate prediction of hand function from MRI, regardless of the extent of brain injury. Reduced brain volumes were observed in patients with bilateral injury, including volumes of the thalamus and corpus callosum splenium, compared to those with unilateral injury, and the healthy controls. Increases in cortical thickness in several cortical regions were observed in cohorts with unilateral and bilateral injury compared to controls, potentially suggesting neuroplasticity might be occurring in the inferior frontal gyrus and the precuneus. These findings identify prospective useful target regions for transcranial magnetic stimulation intervention.