Correspondence between Resting-State Activity and Brain Gene Expression

Abstract

The relationship between functional brain activity and gene expression has not been fully explored in the human brain. Here, we identify significant correlations between gene expression in the brain and functional activity by comparing fractional amplitude of low-frequency fluctuations (fALFF) from two independent human fMRI resting-state datasets to regional cortical gene expression from a newly generated RNA-seq dataset and two additional gene expression datasets to obtain robust and reproducible correlations. We find significantly more genes correlated with fALFF than expected by chance and identify specific genes correlated with the imaging signals in multiple expression datasets in the default mode network. Together, these data support a population-level relationship between regional steady-state brain gene expression and resting-state brain activity.

Figure 1

Schematic of data analysis and results. (A) Significant correlations were investigated between gene expression and fMRI index (fALFF) among different brain regions. Brodmann areas from our RNA-seq dataset are indicated in the fALFF schematic. (B) Three gene expression studies and two resting state fMRI datasets were used. Significantly correlated genes were identified using a t-value, p < 0.005. Enrichment or significance was determined based on consistency in two out of three expression datasets. Example scatterplots for two different genes show the relationship between gene expression and fALFF: the colors represent the 5 DMN cortical regions used in our RNA-seq study and each of the five points represents one of the 5 samples in the region.

Figure 2

Significantly more genes than expected are correlated in expression to fALFF. The arrow indicates the observed number of correlated genes in each dataset ((A) our RNA-seq, (B) Kang et al., and (C) Brainspan). Bar plots indicate the number of correlated genes in the randomized dataset. Fold enrichment is 5, 4 and 7 for our RNA-seq, Kang et al. and Brainspan respectively. The complete sets of all possible permutations were carried out 120 (our RNA-seq) or 720 (Kang and Brainspan datasets) and the empirical significance levels are indicated.

Figure 3

Replication of fALFF-correlated genes using two fMRI datasets. Pearson correlations between fMRI datasets using (A) our RNA-seq data, (B) Kang et al., (C) Brainspan and (D) an average of all three expression datasets. Red points indicate fALFF-correlated genes significant in only the fMRI_1 dataset. p < 1e-10 for all the correlations. Blue points indicate fALFF-correlated genes significant in only the fMRI_2 dataset. Black points indicate fALFF-correlated genes significant in both fMRI_1 and fMRI_2.

Figure 4

Expression of individual genes is correlated with fALFF signals. Heatmap view of the fALFF-correlated genes. The colors for the genes indicate relative expression levels while the colors for the regions indicate fALFF values. Yellow indicates smaller values in the fALFF data or gene expression and purple indicates higher values. There are five measurements for each correlation as we used five independent brains in each region for our RNA-seq. fALFF values from fMRI_1 are presented and the order of the normalized fALFF values is the same in both fMRI datasets.