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Supplementary Material 1

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero., Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitted

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.

In recent years, several automatic methods have been proposed to differentiate between brain tumor recurrence and treatment-related changes following radiotherapy, based on conventional and advanced MRI methods. Vascular parameters extracted from dynamic contrast-enhanced (DCE) were suggested as potential markers for disease progression. Yet, most studies that proposed machine-learning methods or relayed on threshold values of DCE for classification, did not analyze separately Glioblastoma (GBM) and brain metastasis, and often offer the same method/threshold values, neglecting the different vascularity of these two tumor types. Understanding and quantifying the differences between tumors type can improve early diagnosis of recurrence and may improve the reliability of identifying treatment-related changes. The aim of this study was to quantify vascular parameters of tumor recurrence and to assess differences between GBM and brain metastasis, specifically metastasis of breast cancer. METHOD: 41 MRI scans were included: 24 from patients with GBM: 20 with recurrence and 4 with treatment-related changes, and 17 scans from patients with brain metastasis of breast cancer: 10 with recurrence and 7 with treatment-related changes (diagnosis were based on radiological follow-up/histopathology, when available). MRIs were performed on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Prisma) and included T1, T1+contrast, and DCE images. DCE analysis was performed using DUSTER and Ktrans, Vp and Kep pharmacokinetic parameters were compared between groups. RESULTS: Patients with GBM often demonstrated both tumor recurrence and areas with treatment-related changes. Significantly differences were detected between tumor recurrence and treatment-related changes, and higher Vp and Ktrans were detected in recurrent GBM as compared to recurrent breast cancer metastasis. Cutoff for differentiation between tumor recurrence and treatment-related changes will be presented specifically for each tumor type. To conclude, when defining quantitative threshold values for clinical decision-making, the inherent differences between primary and metastatic brain tumors should be taken into account.

Background: Current evidence suggest that 25%–33% of stroke-survivors develop post-stroke cognitive impairment (PSCI). The licensed drug Maraviroc, a CCR5-antagonist, is postulated to act via a neuroprotective mechanism that may offer the potential of preventing progression to vascular dementia. Our hypothesis: Maraviroc may have the potential to augment learning skills and cognitive performance by affecting synaptic plasticity, along with neuro-inflammatory modulation in patients with cerebral small vessel disease (SVD) and PSCI. Design: MARCH is a multi-center, double-blind randomized-control Phase-II trial of Maraviroc 150 or 600 mg/day versus placebo for 12-months in five stroke centers in Israel. Included are patients diagnosed with recent (1–24 months) subcortical stroke who experience mild PSCI and have evidence of white matter lesions and SVD on neuroimaging. Outcomes: Primary outcomes: 1. Change in cognitive scores. 2. Drug related adverse events. Secondary outcomes: change in functional and affective scores, MRI-derived measures, inflammatory markers, carotid atherosclerosis, cerebrospinal-fluid biomarkers in a sub-study. A sample size of 60 in each treatment group and 30 in the placebo group (total – 150 participants) provides 80% power between the treatment and the placebo groups. Conclusions: The results of this work could lead to a novel, readily available, therapeutic avenue to reduce PSCI, and possibly other pathologies. This study will test safety and effectiveness of Maraviroc in limiting cognitive deterioration and/or post stroke cognitive impairment in patients with cerebral small vessel disease. Schedule: First-patient first-visit was May 2021. Recruitment to complete in 2023, follow-up to complete in 2024.

Non-manifesting carriers (NMCs) of Parkinson’s disease (PD)-related mutations such as LRRK2 and GBA are at an increased risk for developing PD. Dopamine transporter (DaT)-spectral positron emission computed tomography is widely used for capturing functional nigrostriatal dopaminergic activity. However, it does not reflect other ongoing neuronal processes; especially in the prodromal stages of the disease. Resting-state fMRI (rs-fMRI) has been proposed as a mode for assessing functional alterations associated with PD, but its relation to dopaminergic deficiency remains unclear. We aimed to study the association between presynaptic striatal dopamine uptake and functional connectivity (FC) patterns among healthy first-degree relatives of PD patients with mutations in LRRK2 and GBA genes. N = 85 healthy first-degree subjects were enrolled and genotyped. All participants underwent DaT and rs-fMRI scans, as well as a comprehensive clinical assessment battery. Between-group differences in FC within striatal regions were investigated and compared with striatal binding ratios (SBR). N = 26 GBA-NMCs, N = 25 LRRK2-NMCs, and N = 34 age-matched nonmanifesting noncarriers (NM-NCs) were included in each study group based on genetic status. While genetically-defined groups were similar across clinical measures, LRRK2-NMCs demonstrated lower SBR in the right putamen compared with NM-NCs, and higher right putamen FC compared to GBA-NMCs. In this group, higher striatal FC was associated with increased risk for PD. The observed differential SBR and FC patterns among LRRK2-NMCs and GBA-NMCs indicate that DaTscan and FC assessments might offer a more sensitive prediction of the risk for PD in the pre-clinical stages of the disease.

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice., 15 pages, 8 figures, presented in CARS 2020, submitted to IJCARS

A novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus causing coronavirus disease 2019 (COVID-19) disease, which emerged as a global pandemic. Data regarding the implications of COVID-19 disease at early gestation on fetal and obstetric outcomes is scarce. Thus, our aim was to investigate the effect of first and second trimester maternal COVID-19 disease on fetal and perinatal outcomes. This was a prospective cohort study of pregnant women with a laboratory-proven SARS-COV-2 infection contracted prior to 26 weeks gestation. Women were followed at a single tertiary medical center by serial sonographic examinations every 4–6 weeks to assess fetal well-being, growth, placental function, anatomic evaluation and signs of fetal infection. Amniocentesis was offered to assess amniotic fluid SARS-COV-2-PCR (polymerase chain reaction) and fetal brain magnetic resonance imaging (MRI) was offered at 30–32 weeks gestation. Demographic, obstetric and neonatal data were collected from history intake, medical charts or by telephone survey. Perinatal outcomes were compared between women infected at first vs. second trimester. 55 women with documented COVID-19 disease at early gestation were included and followed at our center. The mean maternal age was 29.6 ± 6.2 years and the mean gestational age at viral infection was 14.2 ± 6.7 weeks with 28 (51%) women infected at the first trimester and 27 (49%) at the second trimester. All patients but one experienced asymptomatic to mild symptoms. Of 22 patients who underwent amniocentesis, none had evidence of vertical transmission. None of the fetuses exhibited signs of central nervous system (CNS) disease, growth restriction and placental dysfunction on serial ultrasound examinations and fetal MRI. Pregnancies resulted in perinatal survival of 100% to date with mean gestational age at delivery of 38.6 ± 3.0 weeks and preterm birth &lt, 37 weeks rate of 3.4%. The mean birthweight was 3260 ± 411 g with no cases of small for gestational age infants. The obstetric and neonatal outcomes were similar among first vs. second trimester infection groups. We conclude SARS-CoV-2 infection at early gestation was not associated with vertical transmission and resulted in favorable obstetric and neonatal outcomes.

Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; pp Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

Contains fulltext : 181510.pdf (Publisher’s version ) (Open Access) Atypical brain development and early brain injury have profound and long lasting impact on the development, skill acquisition, and subsequent independence of a child. Heterogeneity is present at the brain level and at the motor level; particularly with respect to phenomena of bilateral activation and mirrored movements (MMs). In this multiple case study we consider the feasibility of using several modalities to explore the relationship between brain structure and/or activity and hand function: Electroencephalography (EEG), both structural and functional Magnetic Resonance Imaging (sMRI, fMRI), diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), Electromyography (EMG) and hand function assessments. Methods : 15 children with unilateral CP (ages: 9.4±2.5 years) undertook hand function assessments and at least two additional neuroimaging and/or neurophysiological procedures: MRI/DTI/fMRI (n=13), TMS (n=11), and/or EEG/EMG (n=8). During the fMRI scans and EEG measurements, a motor task was performed to study cortical motor control activity during simple hand movements. DTI tractography analysis was used to study the corpus-callosum (CC) and cortico-spinal tracts (CST). TMS was used to study cortico-spinal connectivity pattern. Results: Type and range of severity of brain injury was evident across all levels of manual ability with the highest radiological scores corresponded to children poorer manual ability. Evidence of MMs was found in 7 children, mostly detected when moving the affected hand, and not necessarily corresponding to bilateral brain activation. When moving the affected hand, bilateral brain activation was seen in 6/11 children while 3/11 demonstrated unilateral activation in the contralateral hemisphere, and one child demonstrated motor activation predominantly in the supplementary motor area (SMA). TMS revealed three types of connectivity patterns from the cortex to the affected hand: a contralateral (n=3), an ipsilateral (n=4) and a mixed (n=1) connectivity pattern; again without clear association with MMs. No differences were found between children with and without MMs in lesion scores, motor fMRI laterality indices, CST diffusivity values, and upper limb function. In the genu, midbody, and splenium of the CC, higher fractional anisotropy values were found in children with MMs compared to children without MMs. The EEG data indicated a stronger mu-restoration above the contralateral hemisphere in 6/8 children and above the ipsilateral hemisphere in 2/8 children. Conclusion: The current results demonstrate benefits from the use of different modalities when studying upper-limb function in children with CP; not least to accommodate to the variations in tolerance and feasibility of implementation of the differing methods. These exposed multiple individual brain-reorganization patterns corresponding to different functional motor abilities. Additional research is warranted to understand the transactional influences of early brain injury, neuroplasticity and developmental and environmental factors on hand function in order to develop targeted interventions. 12 p.

This study proposes a method for territorial segmentation and volumetric flow rate (VFR) distribution measurement of cerebral territories based on time-resolved contrast enhanced magnetic-resonance-angiography (MRA). The method uses an iterative region-growing algorithm based on bolus-arrival-time with increased temporal resolution. Eight territories were segmented: (1) right and (2) left internal carotid arteries, including the middle cerebral artery (ICA+MCA), excluding the anterior cerebral arteries (ACA); (3) right and left ACA (R+L-ACA); (4) right and (5) left external carotid arteries (ECA); (6) right and (7) left posterior cerebral arteries (PCA); and (8) vertebrobasilar territory. VFR percentage, relative to the entire brain (rVFR), was measured based on territorial volume as a function of time. Mean rVFR values of fifteen healthy subjects were: ICA+MCA = 23 ± 2%, R + L-ACA = 17 ± 3%, ECA = 4 ± 2%, PCA = 12 ± 2%, and vertebrobasilar territory = 31 ± 4%. Excluding the ECA-rVFR, which is underestimated, these values are comparable to previously reported values. Six subjects were scanned twice, demonstrating comparable and even higher reproducibility than previously reported using phase-contrast, yet with faster scan time (∼1 min). This method was implemented in one patient with MCA occlusion and one with Moyamoya syndrome scanned before and after bypass surgery, demonstrating its clinical potential for quantitative assessment of the degree of occlusion and the effect of surgery.

Purpose In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or to improve Signal to Noise Ratio (SNR). In this paper the authors present a framework, referred to as FASTMER, for fast MRI by exploiting a reference image. Methods The proposed approach utilizes the possible similarity of the reference image to the acquired image, which exists in many clinical MRI imaging scenarios. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scan and similarity between different scans of the same patient. To account for the fact that the reference image may exhibit low similarity with the acquired image the authors develop an iterative weighted reconstruction approach, which tunes the weights according to the degree of similarity. Results Experimental results demonstrate the performance of the method in three different clinical MRI scenarios: The first example demonstrates SNR improvement in high resolution brain MRI, the second scenario exploits similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and the last application utilizes similarity between baseline and follow-up scans for fast follow-up acquisition. The results show that FASTMER outperforms image reconstruction of existing state-of-the-art methods. Conclusions The authors present a framework for fast MRI by exploiting a reference image. Recovery is based on an iterative algorithm that supports cases in which similarity to the reference scan is not guaranteed. This extends the applicability of the FASTMER to different MRI scanning scenarios. Thanks to the existence of reference images in various clinical imaging tasks, the proposed framework can play a major role in improving reconstruction in many MR applications.

INTRODUCTION: Peripheral neurofibromas (NF) represent one of the most common clinical presentations of the NF1. About 10% of patients with NF1 develop malignant peripheral nerve-sheath tumours (MPNST), which is a major cause of morbidity in NF1 patients. Improved prognosis can be achieved if MPNST are diagnosed at an early stage permitting ablative surgery with wide resection margins. Better understanding regarding the natural history and biology of MPNST has been obtained in the last decade, however existing imaging modalities are still imperfect for diagnosis of MPNST. The aim of this study was to use a machine learning method in order to differentiate between benign plexiform neurofibromas from MPNST in patients with NF1 based on conventional MRI methods. METHODS: Among 23 NF1 patients with pathologically-defined MPNST treated in the last decade in TASMC, seven patients with available preoperative MRI studies were included in the study. For comparison, 7 patients with benign plexiform neurofibromas were chosen from the database of the Gilbert Israeli Neurofibromatosis Center. Regions of interest at tumor areas were extracted from the post contrast 2D T(1)-weighted spin-echo images acquired on 1.5/3T MRI systems. For each patient, speeded-up-robust (SURF) features were extracted from lesion areas using Matlab bagOfFeatures algorithm. The algorithm was trained with number of visual words =500, grid point selection method, strongest features=0.8 and grid step size (in pixels)=[5 5]. The algorithm was tested with 100 iterations, with the data being randomly split into training (80%) and testing (20%) datasets. RESULTS: A code-book of lesion representations was generated for each group and was given as input to the support-vector-machine (SVM) classifier. Classification results achieved mean accuracy=80%, mean sensitivity=72% and mean specificity=87%. CONCLUSION: Our preliminary results suggest that a quantitative image representation method based on Bag-of-Features may assist in the classification between benign plexiform neurofibromas and MPNST in NF1.

INTRODUCTION: High grade brain tumors and brain metastasis are the two most common malignant brain tumors in adults. There is high clinical importance for differentiating between the two, as well as for sub-classification of metastases based on their origin, since medical staging, surgical planning, and therapeutic decisions are vastly different for each tumor type. MRI is the modality of choice for the assessment of patients with brain tumors. However, in many cases, differentiation between the tumor types may be challenging due to their similar appearance on MRI. The aim of this study was to develop an automatic method for segmentation and classification of brain tumors based on conventional MRI. METHODS: A retrospective analysis of 439 MRI data sets: 212 from patients with high grade glioma and 227 from patients with brain metastasis of breast, lung and other origins, was performed. Data include post contrast 3D T1 weighted images acquired at 1.5/3.0 Tesla MRI and clinical information including age, weight and gender. Following preprocessing and automatic segmentation of the enhancing tumor, several features were extracted: bag of features, first and second order statistical, morphological and wavelet features. The classifiers were trained in a 5-fold manner on 80% of the dataset and tested on the remaining 20% of the dataset. Sensitivity, specificity and accuracy were calculated. RESULTS: Best classification results obtained when classifying between high grade brain tumors and brain metastasis, using quadratic SVM classifier with mean accuracy=0.85, sensitivity=0.82 and specificity=0.87. Classifying brain metastases by their origin achieved best results with cosin KNN classifier with mean specificity=0.85 but low sensitivity=0.46. CONCLUSION: The proposed study demonstrates the use of machine learning methods for automatic segmentation and classification of brain tumors based on conventional MRI. Such methods may have great clinical importance in assisting in the diagnosis of new patients.

We report on a series of nine patients with high grade glioma (HGG) who developed punctate contrast-enhancing foci on MRI, several years after tumor treatment. The lesions were considered suspicious for tumor progression at their onset, but were proven over time to be benign in nature. Five of the patients were treated for glioblastoma; 3 for anaplastic astrocytoma, and one for anaplastic oligodendroglioma. Median age of the patients at diagnosis was 48 years (31–62 years). The mean time of onset of “suspicious spots” following treatment was 51 months after diagnosis (6–170 months). Of note, median overall survival of the group is 96 months; with none of the nine patients deceased, and only 2 patients known to have progression. The remainder of the 7 patients are free of active tumor and being followed off treatment. No apparent clinical manifestations of the lesions have been observed. The majority of the punctate lesions were of a dynamic nature; they appeared and spontaneously disappeared over time, without intervention. All lesions developed within the field of radiation treatment; some were contralateral to the tumor. Radiological characteristics of the lesions including analysis of SWI, DWI, and DCE magnetic resonance imaging sequences will be reported. Punctate enhancing foci in the region of radiation treatment are phenomena which may be manifestation of delayed treatment sequelae in HGG. Patients with these lesions are on average of younger age and have a particularly long survival. These lesions appeared several years following oncological treatment. The foci should be initially followed more closely until shown to be of a non-progressive nature; they should not be presumed to be indicative of tumor recurrence. Possible etiology of the lesions may be related to vascular changes induced by radiation.

We present a novel method for fiber-based comparison of diffusion tensor imaging (DTI) scans of groups of subjects. The method entails initial preprocessing and fiber reconstruction by tractography of each brain in its native coordinate system. Several diffusion parameters are sampled along each fiber and used in subsequent comparisons. A spatial correspondence between subjects is established based on geometric similarity between fibers in a template set (several choices for template are explored), and fibers in all other subjects. Diffusion parameters between groups are compared statistically for each template fiber. Results are presented at single fiber resolution. As an initial exploratory step in neurological population studies this method points to the locations affected by the pathology of interest, without requiring a hypothesis. It does not make any grouping assumptions on the fibers and no manual intervention is needed. The framework was applied here to 18 healthy subjects and 23 amyotrophic lateral sclerosis (ALS) patients. The results are compatible with previous findings and with the tract based spatial statistics (TBSS) method. Hum Brain Mapp 37:477-490, 2016. © 2015 Wiley Periodicals, Inc.

Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. Results: The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. Conclusions: The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Introduction: Post-stroke patients are at high risk of developing cognitive decline. Previous studies have examined only limited magnetic resonance imaging (MRI) parameters for prediction of cognitive status following stroke Aim: To determine radiological markers associated with different cognitive domains in post-stroke patients. Materials and Methods: Patients from the TABASCO (Tel-Aviv Brain Acute Stroke Cohort), a prospective cohort of first-ever mild-moderate ischemic stroke patients, were Included (N=141). All patients underwent a 3T MRI and were cognitively assessed at admission and 2 later. Multiple regression models were used to assess which of the imaging measures best associated with cognitive scores 2 years after the index event. Results: After controlling for both age and education, the comparisons between cognitively intact and impaired patients revealed significant differences between the two groups in relative CSF volume, white matter lesion (WML) load and white matter microstructural integrity, reflected as mean diffusivity (MD), radial diffusivity (Dr) and axial diffusivity (Da) values ( p’s Discussion: Our results highlight the role of hippocampal MD and WML load as markers for cognitive performances. We suggest that these imaging markers best demonstrated "brain intergrity"

INTRODUCTION: Pituitary adenomas are the third most common primary brain tumor in adults, with an incidence of 73–94/100000 cases. Most adenomas can be effectively resected using endoscopic endonasal approach, yet in about 10% of cases the tumor may be fibrous requiring a more complex procedure. Preoperative assessment of pituitary adenoma consistency and vascularity is thus important for surgical planning and risk assessment. The aim of this study was to assess pituitary macroadenoma consistency and vascularity using texture analysis of conventional MRI. METHODS: Retrospective analysis of pre-operative MRI data of 109 patients with pituitary adenoma was performed. Tumor consistency (hard vs soft) and vascularity (vascular vs avascular) were determined intraoperatively by the surgeon. Radiomics analysis was performed on T(2)-weighted (T(2)W) and post contrast T(1)-weighted (T(1)W+C) images, and apparent diffusion coefficient (ADC) maps (available in 57 patients). Fifteen features were extracted from the lesion area for each modality, including: mean, minimum, maximum, median, percentile 10,25,75,90, standard deviation, kurtosis, skewness, entropy, root mean square (RMS), and energy. Between group differences were tested using independent samples T-Test. RESULTS: Consistency: Significant group differences (p

The goal of this work was to study white matter maturation in young children with autism following previous reports of increased cerebral volume during early development, as well as arguments for abnormal neural growth patterns and regulation at this critical developmental period. We applied diffusion tensor imaging (DTI) and high b value diffusion-weighted imaging (DWI) to young children diagnosed with autism and to a typically developing (TD) control group. Fractional anisotropy (FA), probability and displacement were measured in overall analysis as well as in regions of interest (ROI). Individual data points of children with autism were compared to the developmental curves obtained from typically developing children. Increased restriction, reflected in significantly increased FA and probability along with reduced displacement values, was detected in overall analysis as well as in several brain regions. Increased restriction, suggesting an early and accelerated abnormal maturation of white matter, was more dominant in the left hemisphere and was mainly detected in the frontal lobe. No changes were detected in the occipital lobes. These results support previous claims of abnormal brain overgrowth in young children with autism and are in contrast to the decreased restricted diffusion reported in previous studies in adolescent with autism.

Background. Motor-learning interventions may improve hand function in children with unilateral cerebral palsy (UCP) but with inconsistent outcomes across participants. Objective. To examine if pre-intervention brain imaging predicts benefit from bimanual intervention. Method. Twenty children with UCP with Manual Ability Classification System levels I to III, aged 7-16 years, participated in an intensive bimanual intervention. Assessments included the Assisting Hand Assessment (AHA), Jebsen Taylor Test of Hand Function (JTTHF) and Children’s Hand Experience Questionnaire (CHEQ) at baseline (T1), completion (T2) and 8-10 weeks post-intervention (T3). Imaging at baseline included conventional structural (radiological score), functional (fMRI) and diffusion tensor imaging (DTI). Results. Improvements were seen across assessments; AHA ( P = 0.04), JTTHF ( P < .001) and CHEQ ( P < 0.001). Radiological score significantly correlated with improvement at T2; AHA ( r = .475) and CHEQ ( r = .632), but negatively with improvement on unimanual measures at T3 (JTTFH r = −.514). fMRI showed negative correlations between contralesional brain activation when moving the affected hand and AHA improvements (T2: r = −.562, T3: r = −0.479). Fractional Anisotropy in the affected posterior limb of the internal capsule correlated negatively with increased bimanual use on CHEQ at T2 ( r = −547) and AHA at T3 ( r = −.656). Conclusions. Children with greater structural, functional and connective brain damage showed enhanced responses to bimanual intervention. Baseline imaging may identify parameters predicting response to intervention in children with UCP.

Neuroplasticity studies examining children with hemiparesis (CH) have focused predominantly on unilateral interventions. CH also have bimanual coordination impairments with bimanual interventions showing benefits. We explored neuroplasticity following hand-arm bimanual intensive therapy (HABIT) of 60 hours in twelve CH (6 females, mean age 11 ± 3.6 y). Serial behavioral evaluations and MR imaging including diffusion tensor (DTI) and functional (fMRI) imaging were performed before, immediately after, and at 6-week follow-up. Manual skills were assessed repeatedly with the Assisting Hand Assessment, Children’s Hand Experience Questionnaire, and Jebsen-Taylor Test of Hand Function. Beta values, indicating the level of activation, and lateralization index (LI), indicating the pattern of brain activation, were computed from fMRI. White matter integrity of major fibers was assessed using DTI. 11/12 children showed improvement after intervention in at least one measure, with 8/12 improving on two or more tests. Changes were retained in 6/8 children at follow-up. Beta activation in the affected hemisphere increased at follow-up, and LI increased both after intervention and at follow-up. Correlations between LI and motor function emerged after intervention. Increased white matter integrity was detected in the corpus callosum and corticospinal tract after intervention in about half of the participants. Results provide first evidence for neuroplasticity changes following bimanual intervention in CH. This project was funded by grants from Guy’s and St Thomas’ Charity, Marnie Kimelman Trust and ILAN, the Israeli Association for Disabled children. Beit Issie Shapiro funded and provided the camp venue. D. Green was supported by a grant from the Department of Immigration and Absorption during 2010-2011.

Purpose: To evaluate the sensitivity of high b value diffusion weight magnetic resonance imaging (DWI) in detecting normal white matter maturation, compare it to conventional diffusion tensor imaging (DTI), and to obtain normative quantitative data using this method. Materials and Methods: High b value DWI (bmax 6000 sec/mm 2 ) using q-space analysis and conventional DTI (b 1000 sec/mm 2 ) were performed on 36 healthy subjects aged 4 months to 23 years. Fractional-anisotropy (FA), apparent-displacement, and apparent-probability values were measured in all slices and in six regions of interest (ROIs) of large fiber tracks. Values were correlated with each other and with age using regression analysis. Results: FA, displacement, and probability indices from all slices were highly correlated with each other (r 0.87, P 0.0001) and with age (r 0.82, P 0.0001). All age-related changes in the six pre-determined ROIs were best fitted by mono-exponential functions. Changes in the splenium extended to a later age when compared with the genu of the corpus-callosum, while the centrum semi-ovale demonstrated the latest changes with age. Conclusions: High b-value DWI and DTI showed changes in white matter from infancy through adulthood. However, high b-value detects a signal that is likely to originate mainly from the intra-axonal water population, and thus may represent different aspects of development and different sensitivity to pathology.

The functional anatomy of syntactic transformations, a major computational operation invoked in sentence processing, was identified through a functional magnetic resonance imaging investigation. A grammaticality judgment task was used, presented through a novel hidden-blocks design. Subjects listened to transformational and non-transformational sentences in which a host of other complexity generators (number of words, prepositions, embeddings, etc.) were kept constant. A series of analyses revealed that the neural processing of transformations is localizable, evoking a highly lateralized and localized activation in the left inferior frontal gyrus (Broca's region) and bilateral activation in the posterior superior temporal sulcus. The pattern of activation associated with transformational analysis was distinct from the one observed in neighboring regions, and anatomically separable from the effects of verb complexity, which yielded significant activation in the left posterior superior temporal sulcus. Taken together with neuropsychological evidence, these results uncover the neural reality of syntactic transformations.

To what extent does emotional traumatic context affect sensory processing in the brain? A striking example of emotional impact on sensation is manifested in posttraumatic stress disorder (PTSD), in which a severe emotional trauma produces recurrent and vivid unpleasant sensory recollections. Here we report on an fMRI study exploring the sensory processing of trauma-related pictures in the visual cortex and amygdala in respect to PTSD. The impact of traumatic experience on brain responses was tested in relation to stimuli content and its level of recognition in a parametric factorial design. Twenty combat veterans, 10 with and 10 without PTSD, viewed backward-masked images of combat and noncombat content, presented at below, near, and above recognition thresholds. The response to combat content evoked more activation in the visual cortex in PTSD subjects than in non-PTSD subjects, only when images were presented at below recognition threshold. By contrast, the amygdala demonstrated increased activation in PTSD subjects irrespective of content and recognition threshold of the images. These intriguing findings are compatible with the notion that in PTSD, emotional traumatic experience could modify visual processing already at the preattentive level. On the other hand, lack of content specificity in the amygdala point to a possible predisposed mechanism for pathological processing of traumatic experience. The differential sensitivity of the amygdala and visual cortex to traumatic context implies distinct roles of limbic and sensory regions in the registration and recollection of emotional experience in the brain.

An important characteristic of visual perception is the fact that object recognition is largely immune to changes in viewing conditions. This invariance is obtained within a sequence of ventral stream visual areas beginning in area V1 and ending in high order occipito-temporal object areas (the lateral occipital complex, LOC). Here we studied whether this transformation could be observed in the contrast response of these areas. Subjects were presented with line drawings of common objects and faces in five different contrast levels (0, 4, 6, 10, and 100%). Our results show that indeed there was a gradual trend of increasing contrast invariance moving from area V1, which manifested high sensitivity to contrast changes, to the LOC, which showed a significantly higher degree of invariance at suprathreshold contrasts (from 10 to 100%). The trend toward increased invariance could be observed for both face and object images; however, it was more complete for the face images, while object images still manifested substantial sensitivity to contrast changes. Control experiments ruled out the involvement of attention effects or hemodynamic “ceiling” in producing the contrast invariance. The transition from V1 to LOC was gradual with areas along the ventral stream becoming increasingly contrast-invariant. These results further stress the hierarchical and gradual nature of the transition from early retinotopic areas to high order ones, in the build-up of abstract object representations.

Identification of early changes in Dopamine-Transporter (DaT) SPECT imaging expected in the prodromal phase of Parkinson’s disease (PD), are usually overlooked. Carriers of the G2019S LRRK2 mutation are known to be at high risk for developing PD, compared to non-carriers. In this work we aimed to study early changes in Dopamine uptake in non-manifesting PD carriers (NMC) of the G2019S LRRK2 mutation using quantitative DaT-SPECT analysis and to examine the potential for early prediction of PD. Eighty Ashkenazi-Jewish subjects were included in this study: eighteen patients with PD; thirty-one NMC and thirty-one non-manifesting non-carriers (NMNC). All subjects underwent a through clinical assessment including evaluation of motor, olfactory, affective and non-motor symptoms and DaT-SPECT imaging. A population based DaT-SPECT template was created based on the NMNC cohort, and data driven volumes-of-interest (VOIs) were defined. Comparisons between groups were performed based on VOIs and voxel-wise analysis. The striatum area of all three cohorts was segmented into four VOIs, corresponding to the right/left dorsal and ventral striatum. Significant differences in clinical measures were found between patients with PD and non-manifesting subjects with no differences between NMC and NMNC. Significantly lower uptake (p

Gamma knife radiosurgery (GK-RS) is a technique applied in selected cases of mesial-temporal epilepsy, although still limited to centres with adequate instrumentation and expertise. Here, we report a case of radio surgery targeted with the aid of electrical source imaging that localizes the cortical area generating the scalp epileptic discharges. The patient, a 39-year-old male, presented with a right mesio-temporal lesion; electrical source imaging localization partially overlapped with the lesional area but showed an important activation of the omolateral frontal area, concordant with the epileptic network. The patient underwent GK-RS, with good neurosurgical and clinical results. A radiosurgical ellipsoidal treatment volume area of 2 × 2 × 2 cm³, located over the right temporo-mesial region within a centre showing abnormal signal intensity, was considered. Seven months after treatment, the patient developed brain oedema that gradually resolved after one year. After three years of follow-up, the patient was seizure-free (Engel class I). Our very preliminary experience suggests that electrical source imaging appears to be a useful supporting tool for the definition of the radiosurgical treatment volume in selected patients with temporo-mesial lesional epilepsy.

How are objects represented in the human visual cortex? Two conflicting theories suggest either a holistic representation, in which objects are represented by a collection of object templates, or a part-based representation, in which objects are represented as collections of features or object parts. We studied this question using a gradual object-scrambling paradigm in which pictures of objects (faces and cars) were broken in a stepwise manner into an increasing number of blocks. Our results reveal a hierarchical axis oriented anterior--posteriorly in the organization of ventral object-areas. Along this axis, representations are arranged in bands of increasing sensitivity to image scrambling. The axis starts in early visual areas through retinotopic areas V4/V8 and continues into the lateral-occipital sulcus dorsally and the posterior fusiform girus ventrally, corresponding together to the previously described object-related lateral occipital complex (LOC). Regions showing the highest sensitivity to scrambling tended to be located at the most anterior-lateral regions of the complex. In these more anterior regions, breaking the images into 16 parts produced a significant reduction in activation. Interestingly, activation was not affected when images were cut in two halves, either horizontally or vertically. Car images generally produced a weaker activation compared to faces in the lateral occipital complex but showed the same tendency of increased scrambling sensitivity along the anterior--posterior axis. These results suggest the existence of a hierarchical axis along ventral occipito-temporal object-areas, in which the neuronal properties shift from sensitivity to local object features to a more global and holistic representation.

Background and Purpose— Inflammation may contribute to cognitive impairment after stroke. Inflammatory markers are associated with hippocampal atrophy. We tested whether markers of inflammation, erythrocyte sedimentation rate (ESR), and serum levels of C-reactive protein are associated with reduced hippocampal volume and poor cognitive performance among stroke survivors. Methods— We analyzed 368 consecutive cases from our prospective study of first-ever mild–moderate stroke patients. MRI, cognitive tests, and inflammatory markers were determined. Patients were reevaluated 6 and 12 months after the event. Results— ESR remained unchanged in follow-up examinations, suggesting a chronic inflammation background in some patients. Higher levels of C-reactive protein and ESR were associated with worse performance in cognitive tests, particularly memory scores. This association was maintained for ESR (but not C-reactive protein) after adjustment for confounders ( P =0.002). Patients with smaller hippocampi had inferior cognitive results. Moreover, in a multivariate regression model, higher ESR values (but not C-reactive protein) were related to reduced hippocampal volume ( P =0.049). Conclusions— This report shows a strong relationship between ESR and hippocampal volume, as well as with cognitive performance among poststroke patients. This could plausibly relate to incipient cognitive decline via hippocampal pathways.

Aim This study investigated the effects of a theme-based (‘magic’) variation of the hand–arm bimanual intensive therapy programme, in two different countries, in improving activity performance for children with hemiplegia, including those with severe movement restrictions. Method Twenty-three children with spastic hemiplegia (13 males, 10 females; mean age 10y 7mo, range 7–15y; Manual Ability Classification System level I, two; level II, 13; level III, eight), participated in one of three, 2-week, summer camps. A within-participant experimental design was used with the Assisting Hand Assessment and Children's Hand Experience Questionnaire as primary outcome measures. Evaluations occurred immediately before the first day, on the last day, and 3 months after intervention. Two groups underwent additional assessments 2 weeks before the camp. Results Significant intervention effects were seen on the Assisting Hand Assessment (p=0.002) and on the Children's Hand Experience Questionnaire (p

We present a new method for the estimation of the next brain MR scan in a longitudinal tumor follow-up study. Our method effectively incorporates information of the past scans in the time series to predict the future scan of the patient. Its advantages are that it requires no user intervention and does not assume any particular tumor growth model. Instead, the patient-specific tumor growth parameters are estimated individually from the past patient scans. To validate our method, we conducted an experimental study on four patients with Optic Path Gliomas (OPGs) and four patients with glioblastomas multiforma (GBM), each scanned at five time points. The tumor volumes in the predicted and actual future scans, both segmented by an expert radiologist, yield a mean volume overlap difference of 13.65% for the OPG patients, and 34.23% for the GBM patients.

Blood oxygenation level dependence (BOLD) imaging under either hypercapnia or hyperoxia has been used to study neuronal activation and for assessment of various brain pathologies. We evaluated the benefit of a combined protocol of BOLD imaging during both hyperoxic and hypercapnic challenges (termed hemodynamic response imaging (HRI)). Nineteen healthy controls and seven patients with primary brain tumors were included: six with glioblastoma (two newly diagnosed and four with recurrent tumors) and one with atypical-meningioma. Maps of percent signal intensity changes (ΔS) during hyperoxia (carbogen; 95%O2+5%CO2) and hypercapnia (95%air+5%CO2) challenges and vascular reactivity mismatch maps (VRM; voxels that responded to carbogen with reduced/absent response to CO2) were calculated. VRM values were measured in white matter (WM) and gray matter (GM) areas of healthy subjects and used as threshold values in patients. Significantly higher response to carbogen was detected in healthy subjects, compared to hypercapnia, with a GM/WM ratio of 3.8 during both challenges. In patients with newly diagnosed/treatment-naive tumors (n = 3), increased response to carbogen was detected with substantially increased VRM response (compared to threshold values) within and around the tumors. In patients with recurrent tumors, reduced/absent response during both challenges was demonstrated. An additional finding in 2 of 4 patients with recurrent glioblastoma was a negative response during carbogen, distant from tumor location, which may indicate steal effect. In conclusion, the HRI method enables the assessment of blood vessel functionality and reactivity. Reference values from healthy subjects are presented and preliminary results demonstrate the potential of this method to complement perfusion imaging for the detection and follow up of angiogenesis in patients with brain tumors.

Autism spectrum disorder (ASD) is a disorder of neuronal connectivity. It has been suggested that disturbed, abnormal and disorganized interand intra–cortical connections are one of the core issues in autism1, resulting in poorly synchronized and weakly responsive networks, which in turn lead to abnormal cognitive and neurological functioning. Evidence accumulated in recent years has led to a shift in the conceptualization of autism, from a localized neurological abnormality to a disorder of distributed networks throughout the brain. What is connectivity? Two fundamental principles of brain organization have been proposed: functional specialization and functional integration (Friston, 1994, 2002), with the understanding that these two principles are complementary. Functional specialization is usually inferred by the presence of activation foci while functional integration is regarded as a process mediated by connectivity, which reflects the patterns of interaction between neuronal populations, either during the performance of specific tasks or during resting state (Friston, 1994, 2002, 2009a, 2009b; Honey et al., 2009). Functional integration relies on functional and structural connectivity, while taking into account that these two are not necessarily co-referential (Honey et al., 2009), and that the functional-structural relationship is not straightforward (Damoiseaux & Greicius, 2009). Functional connectivity, as studied by functional magnetic resonance imaging (fMRI), refers to the temporal synchronization of the blood oxygenation level dependent (BOLD) signal of two or more brain areas. Structural connectivity on the other hand, as measured by diffusion tensor imaging (DTI), refers to the physical properties of structural connections the way in which different brain regions are connected, at the macro level (bundles of axonal tracts) (Mori & van Zijl, 2002).