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The brainstem controls sub-cortical and cortical activity and influences the processing of incoming information. The goal of this study was to characterize age related alterations of brainstem-brain interactions during different brain states detected by dynamic analysis of task-free fMRI. 79 young (20-40 years) and 51 older adults (55-80 years) were studied. Internal brainstem structures were segmented using a new multi-contrast segmentation approach. Brain and brainstem gray matter segmentations were warped onto a population template. The ICV-corrected Jacobian determinants were converted into z-score maps and the means from 420 cortical/subcortical/brainstem rois extracted. The fMRI was preprocessed in SPM12/Conn18 and the BOLD signal from 420 cortical/subcortical/brainstem rois extracted. A dynamic task-free analysis approach based on hierarchical cluster analysis was used to identify 15 brain states that were characterized using graph analysis (strength, diversity, modularity). Kruskal-Wallis tests and Spearman correlations were used for statistical analysis. One brain state (cluster 21) occurred more often in older adults (p=0.008). It was characterized by a lower mean modular strength and brainstem-cortical strength in older adults compared to younger adults. Global age related gray matter differences were positively correlated with brain state 21’s modular strength. Furthermore, brain state 21 duration was negatively correlated with working memory (r = -0.28, p=0.002). The findings suggest an age related weakening of the within and between network synchronization at the brainstem level during brain state 21 in older adults that negatively affects cortical and subcortical synchronization and working memory performance.

Objective Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population. Methods The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach. Results Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19). Conclusions T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry., Highlights • Comparison of 4 automated and 1 manual subfield labeling technique in common data set. • Subfield labeling approaches perform better than whole hippocampal approaches. • High resolution T2 based approaches perform better than T1 based approaches. • Different high res T2 approaches have different strengths/weaknesses.

Background MR Imaging has shown atrophy in brainstem regions that were linked to autonomic dysfunction in epilepsy patients. The brainstem projects to and modulates the activation state of several wide-spread cortical/subcortical regions. The goal was to investigate 1. Impact of brainstem atrophy on gray matter connectivity of cortical/subcortical structures and autonomic control. 2. Impact on the modulation of cortical/subcortical functional connectivity. Methods 11 controls and 18 patients with non-lesional focal epilepsy (FE) underwent heart rate variability (HRV) measurements and a 3 T MRI (T1 in all subjects, task-free fMRI in 7 controls/ 12 FE). The brainstem was extracted, and atrophy assessed using deformation-based-morphometry. The age-corrected z-scores of the mean Jacobian determinants were extracted from 71 5x5x5 mm grids placed in brainstem regions associated with autonomic function. Cortical and non-brainstem subcortical gray matter atrophy was assessed with voxel-based-morphometry and mean age corrected z-scores of the modulated gray matter volumes extracted from 380 cortical/subcortical rois. The profile similarity index was used to characterize the impact of brainstem atrophy on gray matter connectivity. The fMRI was preprocessed in SPM12/Conn17 and the BOLD signal extracted from 398 ROIs (16 brainstem). A dynamic task-free analysis approach was used to identify activation states. Connectivity HRV relationship were assessed with Spearman rank correlations. Results HRV was negatively correlated with reduced brainstem right hippocampus/parahippocampus gray matter connectivity in controls (p, Highlights • Brainstem and cortical/subcortical gray matter (gm) connectivity is impaired in FE. • FE is associated with an abnormal brain activation state in the interictal state. • The severity of the gm impairment and of the abnormal brain state are correlated. • GM connectivity impairment and abnormal brain activity affect HRV.