A voxel-wise assessment of growth differences in infants developing autism spectrum disorder

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Pediatric neuroimaging study of Autism Spectrum Disorder.

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Longitudinal Tensor Based Morphometry of the presymptomatic period of ASD.

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Differences in voxelwise growth trajectories of children with ASD.

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Regions with differences have been implicated in the core symptoms of ASD.

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.

The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT

Even though age-related white matter hyperintensities (WMH) begin to emerge in middle age, their effect on brain micro- and macrostructure in this age group is not fully elucidated. We have examined how presence of WMH and load of WMH affect regional brain volumes and microstructure in a validated, representative general population sample of 873 individuals between 50 and 66 years. Presence of WMH was determined as Fazakas grade ≥1. WMH load was WMH volume from manual tracing of WMHs divided on intracranial volume. The impact of age appropriate WMH (Fazakas grade 1) on the brain was also investigated. Major novel findings were that even the age appropriate WMH group had widespread macro- and microstructural changes in gray and white matter, showing that the mere presence of WMH, not just WMH load is an important clinical indicator of brain health. With increasing WMH load, structural changes spread centrifugally. Further, we found three major patterns of FA and MD changes related to increasing WMH load, demonstrating a heterogeneous effect on white matter microstructure, where distinct patterns were found in the proximity of the lesions, in deep white matter and in white matter near the cortex. This study also raises several questions about the onset of WMH related pathology, in particular, whether some of the aberrant brain structural and microstructural findings are present before the emergence of WMH. We also found, similar to other studies, that WMH risk factors had low explanatory power for WMH, making it unclear which factors lead to WMH.

Regional gray matter atrophy in relapsing remitting multiple sclerosis: baseline analysis of multi-center data

Regional gray matter (GM) atrophy in multiple sclerosis (MS) at disease onset and its temporal variation can provide objective information regarding disease evolution. An automated pipeline for estimating atrophy of various GM structures was developed using tensor based morphometry (TBM) and implemented on a multi-center sub-cohort of 1008 relapsing remitting MS (RRMS) patients enrolled in a Phase 3 clinical trial. Four hundred age and gender matched healthy controls were used for comparison. Using the analysis of covariance, atrophy differences between MS patients and healthy controls were assessed on a voxel-by-voxel analysis. Regional GM atrophy was observed in a number of deep GM structures that included thalamus, caudate nucleus, putamen, and cortical GM regions. General linear regression analysis was performed to analyze the effects of age, gender, and scanner field strength, and imaging sequence on the regional atrophy. Correlations between regional GM volumes and expanded disability status scale (EDSS) scores, disease duration (DD), T2 lesion load (T2 LL), T1 lesion load (T1 LL), and normalized cerebrospinal fluid (nCSF) were analyzed using Pearson׳s correlation coefficient. Thalamic atrophy observed in MS patients compared to healthy controls remained consistent within subgroups based on gender and scanner field strength. Weak correlations between thalamic volume and EDSS (r=-0.133; p<0.001) and DD (r=-0.098; p=0.003) were observed. Of all the structures, thalamic volume moderately correlated with T2 LL (r=-0.492; P-value<0.001), T1 LL (r=-0.473; P-value<0.001) and nCSF (r=-0.367; P-value<0.001).

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

Multi-modal MRI of mild traumatic brain injury

Multi-modal magnetic resonance imaging (MRI) that included high resolution structural imaging, diffusion tensor imaging (DTI), magnetization transfer ratio (MTR) imaging, and magnetic resonance spectroscopic imaging (MRSI) were performed in mild traumatic brain injury (mTBI) patients with negative computed tomographic scans and in an orthopedic-injured (OI) group without concomitant injury to the brain. The OI group served as a comparison group for mTBI. MRI scans were performed both in the acute phase of injury (~24 h) and at follow-up (~90 days). DTI data was analyzed using tract based spatial statistics (TBSS). Global and regional atrophies were calculated using tensor-based morphometry (TBM). MTR values were calculated using the standard method. MRSI was analyzed using LC Model. At the initial scan, the mean diffusivity (MD) was significantly higher in the mTBI cohort relative to the comparison group in several white matter (WM) regions that included internal capsule, external capsule, superior corona radiata, anterior corona radiata, posterior corona radiata, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, forceps major and forceps minor of the corpus callosum, superior longitudinal fasciculus, and corticospinal tract in the right hemisphere. TBSS analysis failed to detect significant differences in any DTI measures between the initial and follow-up scans either in the mTBI or OI group. No significant differences were found in MRSI, MTR or morphometry between the mTBI and OI cohorts either at the initial or follow-up scans with or without family wise error (FWE) correction. Our study suggests that a number of WM tracts are affected in mTBI in the acute phase of injury and that these changes disappear by 90 days. This study also suggests that none of the MRI-modalities used in this study, with the exception of DTI, is sensitive in detecting changes in the acute phase of mTBI.

What dementia reveals about proverb interpretation and its neuroanatomical correlates

OBJECTIVE

Neuropsychologists frequently include proverb interpretation as a measure of executive abilities. A concrete interpretation of proverbs, however, may reflect semantic impairments from anterior temporal lobes, rather than executive dysfunction from frontal lobes. The investigation of proverb interpretation among patients with different dementias with varying degrees of temporal and frontal dysfunction may clarify the underlying brain-behavior mechanisms for abstraction from proverbs. We propose that patients with behavioral variant frontotemporal dementia (bvFTD), who are characteristically more impaired on proverb interpretation than those with Alzheimer's disease (AD), are disproportionately impaired because of anterior temporal-mediated semantic deficits.

METHODS

Eleven patients with bvFTD and 10 with AD completed the Delis-Kaplan Executive Function System (D-KEFS) Proverbs Test and a series of neuropsychological measures of executive and semantic functions. The analysis included both raw and age-adjusted normed data for multiple choice responses on the D-KEFS Proverbs Test using independent samples t-tests. Tensor-based morphometry (TBM) applied to 3D T1-weighted MRI scans mapped the association between regional brain volume and proverb performance. Computations of mean Jacobian values within select regions of interest provided a numeric summary of regional volume, and voxel-wise regression yielded 3D statistical maps of the association between tissue volume and proverb scores.

RESULTS

The patients with bvFTD were significantly worse than those with AD in proverb interpretation. The worse performance of the bvFTD patients involved a greater number of concrete responses to common, familiar proverbs, but not to uncommon, unfamiliar ones. These concrete responses to common proverbs correlated with semantic measures, whereas concrete responses to uncommon proverbs correlated with executive functions. After controlling for dementia diagnosis, TBM analyses indicated significant correlations between impaired proverb interpretation and the anterior temporal lobe region (left>right).

CONCLUSIONS

Among two dementia groups, those with bvFTD, demonstrated a greater number of concrete responses to common proverbs compared to those with AD, and this performance correlated with semantic deficits and the volume of the left anterior lobe, the hub of semantic knowledge. The findings of this study suggest that common proverb interpretation is greatly influenced by semantic dysfunction and that the use of proverbs for testing executive functions needs to include the interpretation of unfamiliar proverbs.