Alzheimer’s disease (AD) has become a global health problem as human life span extends significantly. Since there is no cure for AD, finding a brain marker represents a high research priority for early diagnosis and prevention as well as disease progression monitoring. For that purpose, recent studies have increasingly examined functional brain activity regarding its inter-regional temporal coherence or power spectrum using resting state fMRI (rs-fMRI) [1, 2]. Entropy, a classical physical term, indicates the irregularity level of the temporal dynamics and has been introduced by our group and others into the rs-fMRI research [3, 4]. Brain entropy (BEN) has been shown to be stable in healthy brain, be related with aging [5], schizophrenia [6] and attention deficit hyperactivity disorder (ADHD) [7]. In this study, we aimed to test whether BEN can differentiate AD from matched controls and whether BEN is reflective AD disease severity.

17 AD patients (age 74.2 ± 8.0, age range 55 - 83, 9 males) and 19 age, education matched healthy human volunteers (age 59.5 ± 8.2, age range 51 - 80, 5 males) were recruited with signed consent forms approved by local IRB.

All MR images were acquired on a Siemens 3 Tesla Trio whole-body scanner (Erlangen, Germany). T1-weighted images were acquired using a 3D-MPRAGE sequence with: TR = 1620 ms, TE = 3 ms, flip angle = 15°, slice thickness = 1.0 mm. Resting-state fMRI images were acquired using a T2*-weighted gradient echo echo-planar-imaging (EPI) sequence with: TR = 2s, TE = 30ms, slice thickness = 3.3mm, 35 slices, FOV = 220x220 mm², matrix = 64x64 and 150 time points. During the resting-state scan, the subjects were instructed to lie still and keep eyes open.

Data preprocessing was performed with the standard resting state preprocessing pipeline [8] using FSL [9] and AFNI [10] software. Briefly, the rs-fMRI images were corrected for slice timing, corrected for motion. The images were subsequently smoothed (FWHM = 6mm) and high-pass filtered (0.009 Hz). CSF and white matter signals, as well as the motion parameters and their derivatives, were regressed out. Each subject’s brain entropy map was then calculated using BENtbx (https://cfn.upenn.edu/~zewang/software.html) [3].

Two sample T test was conducted to find the AD versus (vs) control BEN difference. A regression analysis between the BEN of AD patients and the Clinical Dementia Rating (CDR, scores varies from 0 to 3, 0 means normal, 3 means severe disease) was then carried out to test the correlation between BEN and AD severity. Age and gender were regressed out in the regression analysis. The statistical maps were thresholded with un-corrected p value of 0.005 and a spatial extent threshold of 100 voxels.

Figure 1 shows that AD presented significant lower BEN in anterior cingulate cortex (ACC), bilateral ventral medial prefrontal cortex (vmPFC), and right insular as compared to controls. In AD, BEN was negatively related to AD severity in a widespread of cerebral cortex as shown in Figure 2.

Entropy indicates the system irregularity and approximately the system complexity, which may reflect the functional capacity in a neurobiological system. ACC, vmPFC and right insular are associated with attention, emotional, societal, memory, salience processing, and inhibition function, which have been shown to be degraded in AD. Lower resting BEN in AD in those areas suggest a reduced functional capacity for either executing or initiating those related brain functions due to AD interference. The negative correlations between BEN and CDR in a widespread brain cortex suggest BEN as a sensitive marker for tracking disease severity.

While some of the cross-sectional BEN comparison results overlap with findings in previous rs-fMRI AD studies [1, 2], our results showed that BEN differentiates AD from controls mostly in the anterior part of the brain regarding the resting brain functional irregularity (functional capacity). Different from previous rs-fMRI studies, the observed widespread correlations of BEN to AD severity clearly showed the clinical relevance of BEN to AD, though a longitudinal study would be necessary in the future to further validate its sensitivity for tracking disease progression.

In summary, we found decreased BEN in AD compared to controls in brain areas associated with attention, emotional, societal, memory, salience processing, and inhibition function; BEN was predictive of disease severity in most of brain cortex. These results demonstrated BEN as a potential imaging biomarker for differentiating AD and predicting disease progression.