This paper examines morphometry of MRI biomarkers derived from the network of temporal lobe structures including the amygdala, entorhinal cortex and hippocampus in subjects with preclinical Alzheimer's disease (AD). Based on template-centered population analysis, it is demonstrated that the structural markers of the amygdala, hippocampus and entorhinal cortex are statistically significantly different between controls and those with preclinical AD. Entorhinal cortex is the most strongly significant based on the linear effects model (p < .0001) for the high-dimensional vertex- and Laplacian-based markers corresponding to localized atrophy. The hippocampus also shows significant localized high-dimensional change (p < .0025) and the amygdala demonstrates more global change signaled by the strength of the low-dimensional volume markers. The analysis of the three structures also demonstrates that the volume measures are only weakly discriminating between preclinical and control groups, with the average atrophy rates of the volume of the entorhinal cortex higher than amygdala and hippocampus. The entorhinal cortex thickness also exhibits an atrophy rate nearly a factor of two higher in the ApoE4 positive group relative to the ApoE4 negative group providing weak discrimination between the two groups.

Genomic studies performed in cancer patients and tumor-derived cell lines have identified a high frequency of alterations in components of the mammalian switch/sucrose non-fermentable (mSWI/SNF or BAF) chromatin remodeling complex, including its core catalytic subunit, SMARCA4. Cells exhibiting loss of SMARCA4 rely on its paralog, SMARCA2, making SMARCA2 an attractive therapeutic target. Here we report the genomic profiling of solid tumors from 131,668 cancer patients, identifying 9434 patients with one or more SMARCA4 gene alterations. Homozygous SMARCA4 mutations were highly prevalent in certain tumor types, notably non-small cell lung cancer (NSCLC), and associated with reduced survival. The large sample size revealed previously uncharacterized hotspot missense mutations within the SMARCA4 helicase domain. Functional characterization of these mutations demonstrated markedly reduced remodeling activity. Surprisingly, a few SMARCA4 missense variants partially or fully rescued paralog dependency, underscoring that careful selection criteria must be employed to identify patients with inactivating, homozygous SMARCA4 missense mutations who may benefit from SMARCA2-targeted therapy.

This paper examines the diffeomorphometry of MRI derived structural markers for the amygdala, in subjects with symptomatic Alzheimer’s disease (AD). Using linear mixed-effects models we show differences between those with symptomatic AD and controls. Based on template centered population analysis, the distribution of statistically significant change is seen in both the volume and shape of the amygdala in subjects with symptomatic AD compared to controls. We find that high-dimensional vertex based markers are statistically more significantly discriminating (p<.00001) than lower-dimensional markers and volumes, consistent with comparable findings in presymptomatic Alzheimer’s disease. Using a high-field 7T atlas, significant atrophy was found to be centered in the basomedial and basolateral subregions, with no evidence of centromedial involvement.

One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms—Freesurfer and FSL—by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.

Volumetric studies suggest smaller amygdalae in subjects with schizophrenia (SZ) than with bipolar disorder (BP). We use morphometry to identify subregions of amygdala differentially affected in SZ and psychotic BP. Based on template centered population analysis, the shape of the amygdala in psychotic BP differs from SZ (pleft=0.044, pright=0.042). Using a high-field 7T atlas, the bilateral basolateral, basomedial and centromedial subregions and the right lateral subregion were significantly atrophied in SZ compared to psychotic BP (p<0.02). These results suggest that change in shape of amygdala may represent a morphologic feature distinguishing SZ from psychotic BP.