Map-based B0 shimming for single voxel brain spectroscopy at 7T.

While B0 shimming is an important requirement for in vivo brain spectroscopy, for single voxel spectroscopy (SVS), the role for advanced shim methods has been questioned. Specifically, with the small spatial dimensions of the voxel, the extent to which inhomogeneities higher than second order exist and the ability of higher order shims to correct them is controversial. To assess this, we acquired SVS from two loci of neurophysiological interest, the rostral prefrontal cortex (rPFC; 8 cc) and hippocampus (Hc; 9 cc). The rPFC voxel was placed using SUsceptibility Managed Optimization (SUMO) and an initial B0 map that covers the entire cerebrum to cerebellum. In each location, we compared map-based shimming (Bolero) with projection-based shimming (FAST(EST)MAP). We also compared vendor-provided spherical harmonic first- and second-order shims with additional third- and fourth-order shim hardware. The 7T SVS acquisition used stimulated echo acquisition mode (STEAM) TR/TM/TE of 6 s/20 ms/8 ms, a tissue water acquisition for concentration reference, and LCModel for spectral analysis. In the rPFC (n = 7 subjects), Bolero shimming with first- and second-order shims reduced the residual inhomogeneity σ B 0 from 9.8 ± 4.5 Hz with FAST(EST)MAP to 6.5 ± 2.0 Hz. The addition of third- and fourth-order shims further reduced σ B 0 to 4.0 ± 0.8 Hz. In the Hc (n = 7 subjects), FAST(EST)MAP, Bolero with first- and second-order shims, and Bolero with first- to fourth-order shims achieved σ B 0 values of 8.6 ± 1.9, 5.6 ± 1.0, and 4.6 ± 0.9 Hz, respectively. The spectral linewidth, Δ v σ B 0 , was estimated with a Voigt lineshape using σ B 0 and T2 = 130 ms. Δ v σ B 0 significantly correlated with the Cramer-Rao lower bounds and concentrations of several metabolites, including glutamate and glutamine in the rPFC. In both loci, if the B0 distribution is well described by a Gaussian model, the variance of the metabolite concentrations is reduced, consistent with the LCModel fit based on a unimodal lineshape. Overall, the use of the high order and map-based B0 shim methods improved the accuracy and consistency of spectroscopic data.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease.

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.