Nicolas Guizard

Brain Volume and Metabolism in Fetuses With Congenital Heart Disease Evaluation With Quantitative Magnetic Resonance Imaging and Spectroscopy

Background— Adverse neurodevelopmental outcome is an important source of morbidity in children with congenital heart disease (CHD). A significant proportion of newborns with complex CHD have abnormalities of brain size, structure, or function, which suggests that antenatal factors may contribute to childhood neurodevelopmental morbidity. Methods and Results— Brain volume and metabolism were compared prospectively between 55 fetuses with CHD and 50 normal fetuses with the use of 3-dimensinal volumetric magnetic resonance imaging and proton magnetic resonance spectroscopy. Fetal intracranial cavity volume, cerebrospinal fluid volume, and total brain volume were measured by manual segmentation. Proton magnetic resonance spectroscopy was used to measure the cerebral N-acetyl aspartate: choline ratio (NAA:choline) and identify cerebral lactate. Complete fetal echocardiograms were performed. Gestational age at magnetic resonance imaging ranged from 25 1/7 to 37 1/7 weeks (median, 30 weeks). During the third trimester, there were progressive and significant declines in gestational age–adjusted total brain volume and intracranial cavity volume in CHD fetuses relative to controls. NAA:choline increased progressively over the third trimester in normal fetuses, but the rate of rise was significantly slower (P<0.001) in CHD fetuses. On multivariable analysis adjusted for gestational age and weight percentile, cardiac diagnosis and percentage of combined ventricular output through the aortic valve were independently associated with total brain volume. Independent predictors of lower NAA:choline included diagnosis, absence of antegrade aortic arch flow, and evidence of cerebral lactate (P<0.001). Conclusions— Third-trimester fetuses with some forms of CHD have smaller gestational age– and weight-adjusted total brain volumes than normal fetuses and evidence of impaired neuroaxonal development and metabolism. Hemodynamic factors may play an important role in this abnormal development.

BEaST: brain extraction based on nonlocal segmentation technique.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimers Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

Injury to the Premature Cerebellum: Outcome is Related to Remote Cortical Development

Cerebellar injury is an important complication of preterm birth with far-reaching neuropsychiatric sequelae. We have previously shown a significant association between isolated injury to the premature cerebellum and subsequent impairment of regional volumetric growth in the contralateral cerebrum. In the current study, we examine the relationship between these remote regional impairments of cerebral volumetric growth and domain-specific functional deficits in these children. In 40 ex-preterm infants with isolated cerebellar injury, we performed neurodevelopmental evaluations and quantitative magnetic resonance imaging (MRI) studies at a mean age of 34 months. We measured cortical gray matter volumes in 8 parcellated regions of each cerebral hemisphere, as well as right and left cerebellar volumes. We show highly significant associations between early signs of autism and dorsolateral prefrontal cortex volume (P < 0.001); gross motor scores and sensorimotor cortical volumes (P < 0.001); and cognitive and expressive language scores and premotor and mid-temporal cortical volumes (P < 0.001). By multivariate analyses, each unit increase in the corresponding regional cerebral volume was associated with lower odds of abnormal outcome score, adjusted for age at MRI and contralateral cerebellar volume. This is the first report linking secondary impairment of remote cerebral cortical growth and functional disabilities in survivors of prematurity-related cerebellar brain injury.

Cerebellar Injury in the Premature Infant Is Associated With Impaired Growth of Specific Cerebral Regions

We have shown that cerebellar injury in the premature infant is followed by significant growth impairment of the contralateral cerebral hemisphere evident as early as term adjusted age. In this study, we hypothesize that this remote growth restriction is region specific in the cerebrum. In a prospectively enrolled cohort of 38 expreterm infants with isolated cerebellar injury by neonatal MRI, we performed follow-up volumetric MRI studies at a mean postnatal age of 35.5 ± 13.8 mo. We measured volumes of cortical and subcortical gray matter, and cerebral white matter within eight parcellated regions for each cerebral hemisphere. Unilateral cerebellar injury (n = 24) was associated with significantly smaller volumes of cortical gray and cerebral white matter in the following regions of the contralateral (versus ipsilateral) cerebral hemisphere: dorsolateral prefrontal, premotor (PM), sensorimotor, and midtemporal regions (p < 0.001 for all except midtemporal cortical gray, p = 0.01), as well as subcortical gray matter in the PM region (p < 0.001). Conversely, in cases of bilateral cerebellar injury (n = 14), there was no significant interhemispheric difference in tissue volumes for any of the cerebral regions studied. These findings suggest that regional cerebral growth impairment results from interruption of cerebellocerebral connectivity and loss of neuronal activation critical for development.

Jacobian integration method increases the statistical power to measure gray matter atrophy in multiple sclerosis

Gray matter atrophy provides important insights into neurodegeneration in multiple sclerosis (MS) and can be used as a marker of neuroprotection in clinical trials. Jacobian integration is a method for measuring volume change that uses integration of the local Jacobian determinants of the nonlinear deformation field registering two images, and is a promising tool for measuring gray matter atrophy. Our main objective was to compare the statistical power of the Jacobian integration method to commonly used methods in terms of the sample size required to detect a treatment effect on gray matter atrophy. We used multi-center longitudinal data from relapsing–remitting MS patients and evaluated combinations of cross-sectional and longitudinal pre-processing with SIENAX/FSL, SPM, and FreeSurfer, as well as the Jacobian integration method. The Jacobian integration method outperformed these other commonly used methods, reducing the required sample size by a factor of 4–5. The results demonstrate the advantage of using the Jacobian integration method to assess neuroprotection in MS clinical trials.

Rotation-invariant multi-contrast non-local means for MS lesion segmentation

Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden, determining disease progression and measuring the impact of new clinical treatments. MS lesions can vary in size, location and intensity, making automatic segmentation challenging. In this paper, we propose a new supervised method to segment MS lesions from 3D magnetic resonance (MR) images using non-local means (NLM). The method uses a multi-channel and rotation-invariant distance measure to account for the diversity of MS lesions. The proposed segmentation method, rotation-invariant multi-contrast non-local means segmentation (RMNMS), captures the MS lesion spatial distribution and can accurately and robustly identify lesions regardless of their orientation, shape or size. An internal validation on a large clinical magnetic resonance imaging (MRI) dataset of MS patients demonstrated a good similarity measure result (Dice similarity = 60.1% and sensitivity = 75.4%), a strong correlation between expert and automatic lesion load volumes (R2 = 0.91), and a strong ability to detect lesions of different sizes and in varying spatial locations (lesion detection rate = 79.8%). On the independent MS Grand Challenge (MSGC) dataset validation, our method provided competitive results with state-of-the-art supervised and unsupervised methods. Qualitative visual and quantitative voxel- and lesion-wise evaluations demonstrated the accuracy of RMNMS method.

Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training

Training to understand the feelings and thoughts of others induces structural changes in two divergent social brain networks. Although neuroscientific research has revealed experience-dependent brain changes across the life span in sensory, motor, and cognitive domains, plasticity relating to social capacities remains largely unknown. To investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology, we collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age. By means of various daily mental exercises and weekly instructed group sessions, training protocols specifically addressed three functional domains: (i) mindfulness-based attention and interoception, (ii) socio-affective skills (compassion, dealing with difficult emotions, and prosocial motivation), and (iii) socio-cognitive skills (cognitive perspective-taking on self and others and metacognition). MRI-based cortical thickness analyses, contrasting the different training modules against each other, indicated spatially diverging changes in cortical morphology. Training of present-moment focused attention mostly led to increases in cortical thickness in prefrontal regions, socio-affective training induced plasticity in frontoinsular regions, and socio-cognitive training included change in inferior frontal and lateral temporal cortices. Module-specific structural brain changes correlated with training-induced behavioral improvements in the same individuals in domain-specific measures of attention, compassion, and cognitive perspective-taking, respectively, and overlapped with task-relevant functional networks. Our longitudinal findings indicate structural plasticity in well-known socio-affective and socio-cognitive brain networks in healthy adults based on targeted short daily mental practices. These findings could promote the development of evidence-based mental training interventions in clinical, educational, and corporate settings aimed at cultivating social intelligence, prosocial motivation, and cooperation.

Cerebellar malformations alter regional cerebral development

Aim  The aim of this study was to compare total and regional cerebral volumes in children with isolated cerebellar malformations (CBMs) with those in typically developing children, and to examine the extent to which cerebellar volumetric reductions are associated with total and regional cerebral volumes.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimers disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimers disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24 months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: − 1.4% to − 2.2% (AD) and − 0.35% to − 0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: − 1.5% to − 7.0% (AD) and − 0.4% to − 1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12 month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinsons disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre‐surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label‐fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored. Hum Brain Mapp 35:4330–4344, 2014.