Search

Autism Spectrum Disorder (ASD) is a phenotypically and etiologically heterogeneous developmental disorder typically diagnosed around 4 years of age. The development of biomarkers to help in earlier, presymptomatic diagnosis could facilitate earlier identification and therefore earlier intervention and may lead to better outcomes, as well as providing information to help better understand the underlying mechanisms of ASD. In this study, magnetic resonance imaging (MRI) scans of infants at high familial risk, from the Infant Brain Imaging Study (IBIS), at 6, 12 and 24 months of age were included in a morphological analysis, fitting a mixed-effects model to Tensor Based Morphometry (TBM) results to obtain voxel-wise growth trajectories. Subjects were grouped by familial risk and clinical diagnosis at 2 years of age. Several regions, including the posterior cingulate gyrus, the cingulum, the fusiform gyrus, and the precentral gyrus, showed a significant effect for the interaction of group and age associated with ASD, either as an increased or a decreased growth rate of the cerebrum. In general, our results showed increased growth rate within white matter with decreased growth rate found mostly in grey matter. Overall, the regions showing increased growth rate were larger and more numerous than those with decreased growth rate. These results detail, at the voxel level, differences in brain growth trajectories in ASD during the first years of life, previously reported in terms of overall brain volume and surface area., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Maternal psychological distress during pregnancy (PPD) 1 has been associated with changes in offspring amygdalar and hippocampal volumes. Studies on child amygdalae suggest that sex moderates the vulnerability of fetal brains to prenatal stress. However, this has not yet been observed in these structures in newborns. Newborn studies are crucial, as they minimize the confounding influence of postnatal life. We investigated the effects of maternal prenatal psychological symptoms on newborn amygdalar and hippocampal volumes and their interactions with newborn sex in 123 newborns aged 2-5 weeks (69 males, 54 females). Based on earlier studies, we anticipated small, but statistically significant effects of PPD on the volumes of these structures. Maternal psychological distress was measured at gestational weeks (GW) 2 14, 24 and 34 using Symptom Checklist-90 (SCL-90, anxiety scale) 3 and Edinburgh Postnatal Depression Scale (EPDS) 4 questionnaires. Newborn sex was found to moderate the relationship between maternal distress symptoms at GW 24 and the volumes of left and right amygdala. This relationship was negative and significant only in males. No significant main effect or sex-based moderation was found for hippocampal volumes. This newborn study provides evidence for a sex-dependent influence of maternal psychiatric symptoms on amygdalar structural development. This association may be relevant to later psychopathology., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Predicting cognitive decline and the eventual onset of dementia in patients with Mild Cognitive Impairment (MCI) is of high value for patient management and potential cohort enrichment in pharmaceutical trials. We used cognitive scores and MRI biomarkers from a single baseline visit to predict the onset of dementia due to AD in an amnestic MCI (aMCI) population over a nine-year follow-up period., Method: All aMCI subjects from ADNI1, ADNI2, and ADNI-GO with available baseline neurocognitive scores and T1w MRI were included in the study (n = 756). We built a Naïve Bayes classifier for every year over a 9-year follow-up period and tested each one with Leave one out cross validation., Results: We reached 87% prediction accuracy at five years follow-up with an AUC > 0.85 from two to seven years (peaking at 0.92 at five years). Both neurocognitive scores and MRI biomarkers were needed to make the prognostic models highly sensitive and specific, especially for longer follow-ups. MRI features are more sensitive, while cognitive features bring specificity to the prediction., Conclusion: Combining cognitive scores and MRI biomarkers yield accurate prediction years before onset of dementia. Such a tool may be helpful in selecting patients that would most benefit from lifestyle changes, and eventually early treatments that would slow cognitive decline and delay the onset of dementia., (Copyright © 2019. Published by Elsevier Inc.)

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 (R(2) = 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.

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.

Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive - pMCI vs. stable - sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.