Vladimir Fonov

Early brain development in infants at high risk for autism spectrum disorder

Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6–12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

Reduced head and brain size for age and disproportionately smaller thalami in child-onset MS

Objective: Whole brain and regional volume measurement methods were used to quantify white matter, gray matter, and deep gray matter structure volumes in a population of patients with pediatric-onset multiple sclerosis (MS). Methods: Subjects included 38 patients (mean age 15.2 ± 2.4 years) and 33 age- and sex-matched healthy control (HC) participants. MRI measures included intracranial volume, normalized brain volume, normalized white and gray matter volume, and volumes of the thalamus, globus pallidus, putamen, and caudate. Because these volumes vary across age and sex in children, we normalized the volume measurements for MS and control groups by computing z scores using normative values obtained from healthy children enrolled in the MRI Study of Normal Brain Development. Results: The intracranial volume z score was significantly lower in the patients with MS (−0.45 ± 1.16; mean ± SD) compared with the HC participants (+0.25 ± 0.98; p = 0.01). Patients with MS also demonstrated significant decreases in normalized brain volume z scores (−1.09 ± 1.49 vs −0.05 ± 1.22; p = 0.002). After correction for global brain volume, thalamic volumes in the MS population remained lower than those of HCs (−0.68 ± 1.72 vs 0.15 ± 1.35; p = 0.02), indicating an even greater loss of thalamic tissue relative to more global brain measures. Moderate correlations were found between T2-weighted lesion load and normalized thalamic volumes (r = −0.44, p < 0.01) and normalized brain volume (r = −0.47, p < 0.01) and between disease duration and normalized thalamic volume (r = −0.58, p < 0.001) and normalized brain volume (r = −0.46, p < 0.01). Conclusions: When compared with age- and sex-matched control subjects, the onset of MS during childhood is associated with a smaller overall head size, brain volume, and an even smaller thalamic volume.

Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information

Mutual information (MI) quantifies the information that is shared between two random variables and has been widely used as a similarity metric for multi-modal and uni-modal image registration. A drawback of MI is that it only takes into account the intensity values of corresponding pixels and not of neighborhoods. Therefore, it treats images as “bag of words” and the contextual information is lost. In this work, we present Contextual Conditioned Mutual Information (CoCoMI), which conditions MI estimation on similar structures. Our rationale is that it is more likely for similar structures to undergo similar intensity transformations. The contextual analysis is performed on one of the images offline. Therefore, CoCoMI does not significantly change the registration time. We use CoCoMI as the similarity measure in a regularized cost function with a B-spline deformation field and efficiently optimize the cost function using a stochastic gradient descent method. We show that compared to the state of the art local MI based similarity metrics, CoCoMI does not distort images to enforce erroneous identical intensity transformations for different image structures. We further present the results on nonrigid registration of ultrasound (US) and magnetic resonance (MR) patient data from image-guided neurosurgery trials performed in our institute and publicly available in the BITE dataset. We show that CoCoMI performs significantly better than the state of the art similarity metrics in US to MR registration. It reduces the average mTRE over 13 patients from 4.12 mm to 2.35 mm, and the maximum mTRE from 9.38 mm to 3.22 mm.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Highlights • SVMs classified 6 versus 12 month-old infants above chance based on fcMRI data alone.• We carefully accounted for the effects of fcMRI motion artifact.• These results coincide with a period of dramatic change in infant development.• Two interpretations about connections supporting this age categorization are given.

The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder

BACKGROUND Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral features that emerge during the first years of life. Research indicates that abnormalities in brain connectivity are associated with these behavioral features. However, the inclusion of individuals past the age of onset of the defining behaviors complicates interpretation of the observed abnormalities: they may be cascade effects of earlier neuropathology and behavioral abnormalities. Our recent study of network efficiency in a cohort of 24-month-olds at high and low familial risk for ASD reduced this confound; we reported reduced network efficiencies in toddlers classified with ASD. The current study maps the emergence of these inefficiencies in the first year of life. METHODS This study uses data from 260 infants at 6 and 12 months of age, including 116 infants with longitudinal data. As in our earlier study, we use diffusion data to obtain measures of the length and strength of connections between brain regions to compute network efficiency. We assess group differences in efficiency within linear mixed-effects models determined by the Akaike information criterion. RESULTS Inefficiencies in high-risk infants later classified with ASD were detected from 6 months onward in regions involved in low-level sensory processing. In addition, within the high-risk infants, these inefficiencies predicted 24-month symptom severity. CONCLUSIONS These results suggest that infants with ASD, even before 6 months of age, have deficits in connectivity related to low-level processing, which contribute to a developmental cascade affecting brain organization and eventually higher-level cognitive processes and social behavior.

Rigid Registration of 3D Ultrasound and MRI: Comparing Two Approaches on Nine Tumor Cases

We present a new technique for registering ultrasound and magnetic resonance (MR) images in the context of neurosurgery. It involves generating a pseudo-ultrasound (pseudo-US) from a segmented MR image and uses cross-correlation as the cost function to register with ultrasound. The algorithm’s performance is compared to a state-of-the-art technique that uses a median filtered MR images to register with a Gaussian-blurred ultrasound using normalized mutual information (NMI). The two methods are tested on nine tumor cases, including both high- and low-grade gliomas. The pseudo-US method yielded significantly better alignment average than that obtained by NMI (p = 0.0009). If one case where NMI failed is excluded, the mean distance obtained by the pseudo-US approach (2.6 mm) is slightly lower than the one obtained by NMI (2.8mm), but not significantly so (p = 0.16). We conclude that the pseudo-US method is more robust for these cases.

Is It Possible to Differentiate the Impact of Pediatric Monophasic Demyelinating Disorders and Multiple Sclerosis After a First Episode of Demyelination

A first episode of acute demyelination of the central nervous system may be a monophasic transient illness or represent the first attack of multiple sclerosis (MS). This study investigates if it is possible to distinguish these two groups of patients retrospectively at the time of the first episode, in a pediatric population. For each patient, the method consists in fitting an individual brain growth curve using multiple follow-up time-points, and using this curve to predict 4 metrics at the first attack: brain volume, brain growth rate, thalamus volume normalized by the brain volume (called normalized thalamus) and normalized thalamus growth rate. These metrics were compared to age-and-sex matched healthy controls by computing z-scores.

A new template to study callosal growth shows specific growth in anterior and posterior regions of the corpus callosum in early childhood.

Most of the studies conducted on the development of the corpus callosum (CC) have been limited to a relatively simple assessment of callosal area, providing an estimation of the size of the CC in two dimensions rather than its actual measurement. The goal of this study was to revisit callosal development in childhood and adolescence by using a three‐dimensional (3D) magnetic resonance imaging template of the CC that considers the horizontal width of the CC and compares this with the two‐dimensional (2D) callosal area. We mapped callosal growth in a large sample of youths followed longitudinally (N = 370 at T1; N = 304 at T2; and N = 246 at T3). Both techniques were based on a five‐section subdivision of the CC. The results obtained with the 3D method revealed that the rate of CC growth over a 4‐year period in the rostrum, the genu, the anterior body and the splenium was significantly higher in the youngest age group (< 7 years) than in older groups, indicating an intense period of development in early childhood for the anterior and posterior parts of the CC. Similar results were obtained when 2D callosal area was used for the anterior and posterior parts of the CC. However, divergent results were found in the mid‐body and the caudal body of the CC. As shown by differences between 2D estimations and actual 3D measurements of callosal growth, our study highlights the importance of considering the horizontal width in measuring developmental changes in the CC.

Regional distribution of fibrillar amyloid deposition in the brain as a function of CSF beta-amyloid 1-42 and biomarkers of neurodegeneration

polygenetic profilewas predictive of age of AD diagnosis, AD vs control, and MCI conversion to AD over a three-year period. It was alsoweakly correlated to cognitive performance as measured by the modified ADAS-Cog and MMSE scores. As previously shown, imaging data is highly predictive of AD vs control and MCI conversion; we also show that it is correlated to age of AD diagnosis and cognitive performance. When imaging and genetic information is combined, all predictivemeasures improve (Tables 3& 4). Predictions using a polygenetic profile suggest that most of the MCI subjects are AD-like, whichmay indicate increased risk or environmental effects that have delayed the onset ofAD (Figure 1).Conclusions:Wedemonstrate that a polygenetic profile is predictive of AD, both in terms of AD vs control and in the approximate age of AD diagnosis. When combined with imaging data, the polygenetic profile improves prediction accuracy.

Differential impact of amyloidosis and tau pathology on brain metabolism

a small cluster in the frontal area. In contrast, CSF t-tau or p-tau showed correlation with [18F]florbetapir in frontal, temporal and parietal brain regions. No correlation was shown between global [18F]FDGSUVR and the binding of amyloid imaging agents (Figure1). Conclusions: The pattern of regional deposition of fibrillary amyloid in the brain is non-linearly associated with CSF Ab1-42 concentrations. However the link between p-tau, t-tau and fibrillary amyloid deposition seems to be dependent on the amyloid-imaging agent. While imaging and CSF measures of amyloid pathology are equivalent, [18F]FDG uptake seems to provide independent information from regional deposition of fibrillary amyloid.