Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion

Hippocampal shape across the healthy lifespan and its relationship with cognition

The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures

Background

The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conversion from mild cognitive impairment to Alzheimer disease, but predicting scores from clinical assessments (such as the Alzheimer’s Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention. Predicting clinical scores can be crucial in providing a nuanced prognosis and inferring symptomatic severity.

Methods

We predicted clinical scores at the individual level using a novel anatomically partitioned artificial neural network (APANN) model. The model combined input from 2 structural MRI measures relevant to the neurodegenerative patterns observed in Alzheimer disease: hippocampal segmentations and cortical thickness. We evaluated the performance of the APANN model with 10 rounds of 10-fold cross-validation in 3 experiments, using cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): ADNI1, ADNI2 and ADNI1 + 2.

Results

Pearson correlation and root mean square error between the actual and predicted scores on the Alzheimer’s Disease Assessment Scale (ADNI1: r = 0.60; ADNI2: r = 0.68; ADNI1 + 2: r = 0.63) and Mini Mental State Examination (ADNI1: r = 0.52; ADNI2: r = 0.55; ADNI1 + 2: r = 0.55) showed that APANN can accurately infer clinical severity from MRI data.

Limitations

To rigorously validate the model, we focused primarily on large cross-sectional baseline data sets with only proof-of-concept longitudinal results.

Conclusion

The APANN provides a highly robust and scalable framework for predicting clinical severity at the individual level using high-dimensional, multimodal neuroimaging data.

Heritability of hippocampal subfield volumes using a twin and non-twin siblings design

The hippocampus is composed of distinct subfields linked to diverse functions and disorders. The subfields can be mapped using high-resolution magnetic resonance images, and their volumes can potentially be used as quantitative phenotypes for genetic investigation of hippocampal function. We estimated the heritability of hippocampus subfield volumes of 465 subjects from the Human Connectome Project (twins and non-twin siblings) using two methods. The first used a univariate model to estimate heritability with and without adjustment for total brain volume (TBV) and ipsilateral hippocampal volume to determine if heritability was uniquely attributable to subfield volume rather than confounds that attributed to global volumes. We observed the right: subiculum, cornu ammonis 2/3, and cornu ammonis 4/dentate gyrus subfields had the highest significant heritability estimates after adjusting for ipsilateral hippocampal volume. In the second analysis, we used a bivariate model to investigate the shared heritability and genetic correlation of the subfield volumes with TBV and ipsilateral hippocampal volume. Genetic correlation demonstrates shared genetic architecture between phenotypes and shared heritability is what proportion of the genetic architecture of one trait is shared by the other. Highest genetic correlations were between subfield volumes and ipsilateral hippocampal volume than with TBV. The pattern was opposite for shared heritability suggesting that subfields share greater proportion of the genetic architecture with TBV than with ipsilateral hippocampal volume. The relationship between the genetic architecture of TBV, hippocampal volume, and of individual subfields should be accounted for when using hippocampal subfield volumes as quantitative phenotypes for imaging genetics studies. Hum Brain Mapp 38:4337-4352, 2017. © 2017 Wiley Periodicals, Inc.

Neuroanatomical and Symptomatic Sex Differences in Individuals at Clinical High Risk for Psychosis

Sex differences have been widely observed in clinical presentation, functional outcome and neuroanatomy in individuals with a first-episode of psychosis, and chronic patients suffering from schizophrenia. However, little is known about sex differences in the high-risk stages for psychosis. The present study investigated sex differences in cortical and subcortical neuroanatomy in individuals at clinical high risk (CHR) for psychosis and healthy controls (CTL), and the relationship between anatomy and clinical symptoms in males at CHR. Magnetic resonance images were collected in 26 individuals at CHR (13 men) and 29 CTLs (15 men) to determine total and regional brain volumes and morphology, cortical thickness, and surface area (SA). Clinical symptoms were assessed with the brief psychiatric rating scale. Significant sex-by-diagnosis interactions were observed with opposite directions of effect in male and female CHR subjects relative to their same-sex controls in multiple cortical and subcortical areas. The right postcentral, left superior parietal, inferior parietal supramarginal, and angular gyri [<5% false discovery rate (FDR)] were thicker in male and thinner in female CHR subjects compared with their same-sex CTLs. The same pattern was observed in the right superior parietal gyrus SA at the regional and vertex level. Using a recently developed surface-based morphology pipeline, we observed sex-specific shape differences in the left hippocampus (<5% FDR) and amygdala (<10% FDR). Negative symptom burden was significantly higher in male compared with female CHR subjects (p = 0.04) and was positively associated with areal expansion of the left amygdala in males (<5% FDR). Some limitations of the study include the sample size, and data acquisition at 1.5 T. This study demonstrates neuroanatomical sex differences in CHR subjects, which may be associated with variations in symptomatology in men and women with psychotic symptoms.