Participants
Data used in this study were obtained from our in-home database: Nanjing Brain Hospital-Alzheimer’s Disease Spectrum Neuroimaging Project (NBH-ADsnp) (Nanjing, China), which is constantly being updated. Relevant information of NBH-ADsnp is summarized in SI Methods S.2.
Network discovery of altered HIPsub related to SCD
A total of 99 elderly individuals participated in this study. Of the subjects, we excluded 2 healthy controls (CN) and 4 SCD subjects due to excessive head movement (see quality assurance section below), and incomplete or missing MRI data. The final analyses included 55 CN and 38 SCD eligible subjects. SI Methods S.2 shows detailed inclusion and exclusion criteria.
Network validation of altered HIPsub related to SCD with rTMS
A total of 20 SCD subjects participated in the clinical trial (No. ChiCTR2000034533) in this study from the NBH-ADsnp database.
We used rTMS (or sham) with 5-day of once-daily to modulate the precuneus of SCD subjects for 2 weeks in a sham-controlled design. Clinical measures, neuropsychological assessments, and MRI data were collected at baseline (pre-rTMS or sham intervention) and at the end of 2 weeks of rTMS or sham. A total of 20 SCD subjects were enrolled in the study, of which 16 SCD subjects were randomly divided into real rTMS (8 SCD) or sham (8 SCD), and 13 SCD subjects (8 SCD for real rTMS, 5 SCD for sham rTMS) completed the trial of 2 weeks of rTMS.
Neuropsychological assessment
Neuropsychological assessments are summarized in SI MethodsS.3. This study performed a standardized clinical interview and comprehensive neuropsychological assessment to evaluate general cognitive function, executive function, information processing speed, episodic memory, and visuo-spatial function.
MRI data acquisition
Detailed MRI data acquisition parameters in NBH-ADsnp are summarized in SI MethodsS.4.
fMRI data preprocessing
In this study, MATLAB2015b and DPABI software [27] were used to preprocess all fMRI data. The image processing procedure was performed as described in a previous study [28] and is summarized inSI Methods S.5.
Quality assurance (QA)
Brain atrophy effect
Given that significant hippocampal GM atrophies in SCD subjects have been reported [12, 29], the anatomical differences between groups may affect these differences on the FCs of HIPsub. To clarify this issue, we computed global intracranial volumes (ITV) based on native GM, WM, and CSF in CN and SCD subjects by using in-home MATLAB codes. Furthermore, we used ITV as an additional covariate when general linear model (GLM) analysis is used to investigate the differences on the network connectivity of HIPsub between CN and SCD subjects.
Head motion effect
In this study, we used three approaches to control the head motion effect both at the individual and at group levels. Firstly, we excluded SCD subjects with excessive head motion (cumulative translation or rotation > 3.0 mm or 3.0°). Then we used a Friston 24-parameter model to regress out head motion effects from the realigned data [30]. Secondly, we performed a ‘scrubbing’ procedure to scrub frames (volumes) with an excessively high whole-brain root mean square (RMS) signal change over time in the preprocessed fMRI data for each individual [31-33]. Furthermore, we regressed out all volumes with a framewise displacement (FD) greater than 0.2 mm as nuisance covariates, and discarded any scan with 50% of volumes removed as described in a previous study [34]. Overall, we excluded 1 CN because of excessive head movement. No significant differences were observed in the head motion parameters between qualified CN and SCD subjects (Table 1).
Strict multiple comparison correction strategy
To ensure the reproducibility, test–retest reliability, and replicability on the fMRI metrics, we performed a strict multiple comparison correction [35], that is, statistical maps were thresholded using the permutation test with Threshold-Free Cluster Enhancement (TFCE) [36] and the false discovery rate (FDR), as implemented in DPABI [27]. For cluster-extent permutation tests, voxel thresholds of two-tailed p < 0.02 (Z > 2.3) were set. Finally, we set a two-tailed p < 0.05 threshold (1,000 permutations in FDR evaluation).
Definition of hippocampal subregions
Our definition of HIPsub referred to recent studies from Robinson et al. [13] and Bai et al.[37], who used coactivation-based parcellation to reveal a subspecialization in the hippocampus by a data-driven method. We only selected the left HIPsub as regions of interest (ROI) (Fig. S2) based on the recent study published by Bai and colleagues [37]. The left hippocampus was defined as three subregions (HIPe, HIPc, and HIPp).
Functional connectivity analyses
First, we extracted the average time courses for all voxels within each HIPsub as the reference time course. Second, we performed voxelwise cross-correlation analysis between the averaged time courses of all voxels within the seed HIPsub region and each voxel in the remainder of the whole brain within the group-specific GM mask. Finally, we performed a Fisher's z-transform analysis to enhance the normality of the correlation coefficients.
rTMS protocol
rTMS was used to stimulate the precuneus of all aMCI participants using a Magstim Rapid2 magnetic stimulator with a 70-mm figure-8-shaped coil. We used the Pz site of the 10–20 electroencephalogram system to locate the precuneus, and the tip of the intersection of the two coil loops was placed at the Pz site to stimulate the precuneus [23].
rTMS was applied, using trains of 1000 stimuli at a frequency of 20 Hz and at an intensity of 100% of the motor threshold (MT). We defined the MT as the lowest intensity producing motor evoked potentials of greater than 50 μV in at least 5 out of 10 trials in the relaxed first dorsal interosseous (FDI) muscle of the contralateral (right) hand [38]. Participants received 25 sessions of either rTMS or sham stimulation over the precuneus. Each daily stimulation session consisted of a stimulation of 42 s duration with an interval of 28 s. The entire session lasted approximately 30 minutes each daily. We performed the sham rTMS blocks with the coil held close to the precuneus, but angled away.
TMS protocol adverse events
The participants did not report any adverse events during the rTMS trial.
Statistical Analysis
Demographics and neuropsychological data
We performed two-sample t-test and chi-square tests to assess differences in demographic data, clinical, cognitive performance, ITV, and head rotation parameters between SCD and CN subjects (p < 0.05).
Network discovery of altered HIPsub related to SCD
To characterize the HIPsub network FC patterns at a group level, we performed a random-effect analysis using one-sample t-tests in the spatial maps of FC in CN and SCD subjects with a stringently threshold of p < 0.001 using the permutation test with TFCE and the family-wise error (FWE) correction together with a cluster extent k > 100 voxels (2700 mm3).
We performed GLM analysis to investigate the differences in the FCs of HIPsub between SCD subjects and CN before rTMS treatment after controlling for age, sex, education, ITV, and mean FD (TFCE-FDR-corrected p < 0.05 and cluster size > 405 mm3). Then we made masks based on brain regions showing differences in the FCs of HIPsub in SCD compared to CN. These masks were used for the analysis of pre- v.s. post-rTMS (pre-sham- v.s. post-sham-rTMS) fMRI data from study #2 (i.e., network validation of altered HIPsub related to SCD). These findings identified network connectivity of altered HIPsub related to SCD, which can explain the changes that are related to SCD during rTMS treatment.
Pattern classification based on the altered HIPsub GM and FC
To further identify GM and network connectivity of altered HIPsub as closely related to SCD patients, we applied a support vector machine (SVM) approach using the alterations in the identified ROIs as a biomarker to test how well this could distinguish SCD patients from CN subjects. A leave-one-out cross-validation (LOOCV) strategy was used to assess the generalization of this SVM classifier and to assess its accuracy, sensitivity, and specificity. These findings identified GM and network connectivity of altered HIPsub related to SCD patients, which can explain the changes that are related to SCD during rTMS treatment.
Network validation of altered HIPsub related to SCD with rTMS
To empirically validate altered HIPsub network connectivity related to SCD, we used paired t-tests to calculate the changes in network FC of HIPsub pre- v.s. post-rTMS (or pre-sham- v.s. post-sham-rTMS) in SCD subjects after controlling for age, sex, education, and GM.
Sham v.s. real rTMS comparison
Of the 13 SCD subjects with full clinical assessments, usable sMRI, and fMRI scan data at baseline and 2 weeks of post-rTMS (or sham), 8 subjects had been randomized to real rTMS and 5 subjects received the sham rTMS. We performed a two-sample t-test to investigate the differences in the changes in FC of HIPsub between pre-post real rTMS and pre-post sham rTMS. Pre-real-rTMS (or sham-rTMS) maps were subtracted from post-real-rTMS (or sham-rTMS) maps to generate maps of FC changes for each subject.
Non-parametric statistics
To improve the statistical power with a low sample size, we performed a re-sampling method of stationary 10,000 bootstrap samplings to obtain significance in demographic data, clinical characteristics, cognitive performance, and FC of HIPsub between baseline assessment and at 2 weeks of post-rTMS (sham rTMS) for all statistical analyses (i.e., chi-square test, two-sample t-test, Pearson correlation, and paired-sample t-test).
Study approval
This study was approved by the responsible Human Participants Ethics Committee of the Affiliated Brain Hospital of Nanjing Medical University (No. 2018-KY010-01 and No. 2020-KY010-02) (Nanjing, China). Written informed consent was received from participants prior to inclusion in the study.