Bernstein, Matt A.

PurposeTo describe a model‐based reconstruction strategy for routine magnetic resonance imaging that accounts for gradient nonlinearity (GNL) during rather than after transformation to the image domain, and demonstrate that this approach reduces the spatial resolution loss that occurs during strictly image‐domain GNL‐correction.MethodsAfter reviewing conventional GNL‐correction methods, we propose a generic signal model for GNL‐affected magnetic resonance imaging acquisitions, discuss how it incorporates into contemporary image reconstruction platforms, and describe efficient nonuniform fast Fourier transform‐based computational routines for these. The impact of GNL‐correction on spatial resolution by the conventional and proposed approaches is investigated on phantom data acquired at varying offsets from gradient isocenter, as well as on fully sampled and (retrospectively) undersampled in vivo acquisitions.ResultsPhantom results demonstrate that resolution loss that occurs during GNL‐correction is significantly less for the proposed strategy than for the standard approach at distances >10 cm from isocenter with a 35 cm field‐of‐view gradient coil. The in vivo results suggest that the proposed strategy better preserves fine anatomical detail than retrospective GNL‐correction while offering comparable geometric correction.ConclusionAccounting for GNL during image reconstruction allows geometric distortion to be corrected with less spatial resolution loss than is typically observed with the conventional image domain correction strategy. Magn Reson Med 74:1019–1031, 2015. © 2014 Wiley Periodicals, Inc.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

AbstractThe Alzheimer's Disease Neuroimaging Initiative (ADNI) three‐dimensional T1‐weighted magnetic resonance imaging (MRI) acquisitions provide a rich data set for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI‐1 have been created, comprising screening visits, 1‐year completers (subjects who all have screening, 6‐ and 12‐month scans), 2‐year annual completers (screening, 1‐year and 2‐year scans), 2‐year completers (screening, 6‐months, 1‐year, 18‐months [mild cognitive impaired (MCI) only], and 2‐year scans), and complete visits (screening, 6‐month, 1‐year, 18‐month [MCI only], 2‐year, and 3‐year [normal and MCI only] scans). As the ADNI‐GO/ADNI‐2 data become available, updated standard analysis sets will be posted regularly.