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Much is known about the development of the whole amygdala, but less is known about its structurally and functionally diverse subregions. One notable distinguishing feature is their wide range of androgen and estrogen receptor densities. Given the rise in pubertal hormones during adolescence, sex steroid levels as well as receptor sensitivity could influence age-related subregion volumes. Therefore, our goal was to evaluate the associations between the total amygdala and its subregion volumes in relation to sex hormones – estradiol and free testosterone (FT) – as a function of age and genetic differences in androgen receptor (AR) sensitivity in a sample of 297 adolescents (46% female). In males, we found small effects of FT-by-age interactions in the total amygdala, portions of the basolateral complex, and the cortical and medial nuclei (CMN), with the CMN effects being moderated by AR sensitivity. For females, small effects were seen with increased genetic AR sensitivity relating to smaller basolateral complexes. However, none of these small effects passed multiple comparisons. Future larger studies are necessary to replicate these small, yet possibly meaningful effects of FT-by-age associations and modulation by AR sensitivity on amygdala development to ultimately determine if they contribute to known sex differences in emotional neurodevelopment.

Highlights • Amygdala subregions play distinct functionally and structurally roles. • Novel analyses of amygdala subregion volumes in adolescents. • Males show age associations with specific amygdala subregions. • Age seems to be a better predictor compared to puberty in males. • Neither age nor puberty relate to amygdala subregion volumes in females., Total amygdala volumes develop in association with sex and puberty, and postmortem studies find neuronal numbers increase in a nuclei specific fashion across development. Thus, amygdala subregions and composition may evolve with age. Our goal was to examine if amygdala subregion absolute volumes and/or relative proportion varies as a function of age, sex, or puberty in a large sample of typically developing adolescents (N = 408, 43 % female, 10–17 years). Utilizing the in vivo CIT168 atlas, we quantified 9 subregions and implemented Generalized Additive Mixed Models to capture potential non-linear associations with age and pubertal status between sexes. Only males showed significant age associations with the basolateral ventral and paralaminar subdivision (BLVPL), central nucleus (CEN), and amygdala transition area (ATA). Again, only males showed relative differences in the proportion of the BLVPL, CEN, ATA, along with lateral (LA) and amygdalostriatal transition area (ASTA), with age. Using a best-fit modeling approach, age, and not puberty, was found to drive these associations. The results suggest that amygdala subregions show unique variations with age in males across adolescence. Future research is warranted to determine if our findings may contribute to sex differences in mental health that emerge across adolescence.

Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress towards identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique - Correlation Explanation (CorEx) - to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer’s disease (AD). We examined 829 participants (Mage: 75.3 ± 6.9 years; 350 women and 479 men) from the Alzheimer’s Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a one-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis. Analyses revealed latent factors based on data-driven combinations of plasma markers and brain metrics, that were aligned with established biological pathways in AD. These factors were able to improve disease prediction along the trajectory from normal cognition and MCI to AD, with an area under the receiver operating curve of up to 99%, and prediction accuracy of up to 89.9% on independent “held out” testing data. Further, the most important latent factors that predicted AD consisted of a novel set of variables that are essential for cardiovascular, immune, and bioenergetic functions. Collectively, these results demonstrate the strength of unsupervised network measures in the detection and prediction of AD.

In network analysis, the so-called “rich club” describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in-depth diffusion imaging study to investigate the rich club along with other organizational changes in the brain's anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early-onset Alzheimer's disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion-weighted magnetic resonance imaging (MRI), and based on whole-brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brain's connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm = 5.7 × 10−3, 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi-squared P = 1.4 × 10−4)—predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm = 6 × 10−4), but especially more peripheral alterations (chi-squared P = 6.5 × 10−3), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients. Hum Brain Mapp, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

© 2016 The Authors The goal of this work was to assess statistical power to detect treatment effects in Alzheimer's disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer's Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid–/ApoE4– group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.

Great advances have been made in functional Magnetic Resonance Imaging (fMRI) studies, including the use of longitudinal design to more accurately identify changes in brain development across childhood and adolescence. While longitudinal fMRI studies are necessary for our understanding of typical and atypical patterns of brain development, the variability observed in fMRI blood-oxygen-level dependent (BOLD) signal and its test-retest reliability in developing populations remain a concern. Here we review the current state of test-retest reliability for child and adolescent fMRI studies (ages 5–18 years) as indexed by intraclass correlation coefficients (ICC). In addition to highlighting ways to improve fMRI test-retest reliability in developmental cognitive neuroscience research, we hope to open a platform for dialogue regarding longitudinal fMRI study designs, analyses, and reporting of results. Keywords: fMRI, Test-retest reliability, Intraclass correlation, Development

Our understanding of network breakdown in Alzheimer’s disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE-4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE-4 risk gene in people with AD. Each additional copy of the APOE-4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized “disconnection syndrome”.

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.

The medial temporal lobe is implicated as a key brain region involved in the pathogenesis of Alzheimer's disease (AD) and consequent memory loss. Tau tangle aggregation in this region may develop concurrently with cortical Aβ deposition in preclinical AD, but the pathological relationship between tau and Aβ remains unclear. We used task-free fMRI with a focus on the medical temporal lobe, together with Aβ PET imaging, in cognitively normal elderly human participants. We found that cortical Aβ load was related to disrupted intrinsic functional connectivity of the perirhinal cortex, which is typically the first brain region affected by tauopathies in AD. There was no concurrent association of cortical Aβ load with cognitive performance or brain atrophy. These findings suggest that dysfunction in the medial temporal lobe may represent a very early sign of preclinical AD and may predict future memory loss.