Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

ABSTRACT A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome wide association studies (GWAS). 142 measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10 -7 and p<10 -6 ). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication. Shen et al.

Relating Intrinsic Low-Frequency BOLD Cortical Oscillations to Cognition in Schizophrenia

The amplitude of low-frequency fluctuations (ALFF) in the blood oxygenation level-dependent (BOLD) signal during resting-state fMRI reflects the magnitude of local low-frequency BOLD oscillations, rather than interregional connectivity. ALFF is of interest to studies of cognition because fluctuations in spontaneous intrinsic brain activity relate to, and possibly even constrain, task-evoked brain responses in healthy people. Lower ALFF has been reported in schizophrenia, but the cognitive correlates of these reductions remain unknown. Here, we assess relationships between ALFF and attention and working memory in order to establish the functional relevance of intrinsic BOLD oscillatory power alterations with respect to specific cognitive impairments in schizophrenia. As part of the multisite FBIRN study, resting-state fMRI data were collected from schizophrenia subjects (SZ; n=168) and healthy controls (HC; n=166). Voxelwise fractional ALFF (fALFF), a normalized ALFF measure, was regressed on neuropsychological measures of sustained attention and working memory in SZ and HC to identify regions showing either common slopes across groups or slope differences between groups (all findings p<0.01 height, p<0.05 family-wise error cluster corrected). Poorer sustained attention was associated with smaller fALFF in the left superior frontal cortex and bilateral temporoparietal junction in both groups, with additional relationships in bilateral posterior parietal, posterior cingulate, dorsal anterior cingulate (ACC), and right dorsolateral prefrontal cortex (DLPFC) evident only in SZ. Poorer working memory was associated with smaller fALFF in bilateral ACC/mPFC, DLPFC, and posterior parietal cortex in both groups. Our findings indicate that smaller amplitudes of low-frequency BOLD oscillations during rest, measured by fALFF, were significantly associated with poorer cognitive performance, sometimes similarly in both groups and sometimes only in SZ, in regions known to subserve sustained attention and working memory. Taken together, these data suggest that the magnitude of resting-state BOLD oscillations shows promise as a biomarker of cognitive function in health and disease