Participants
101 Norwegian-speaking participants were screened, scanned, and tested, yet only participants stratified into SOMI subgroups (0-4) were included in the analysis, resulting in 91 participants with a mean age of 71.55 years of age (SD = 9.55), with descriptive statistics summarized in Table 2. Written informed consent was signed by each participant, and for those with clinical diagnoses of Mild Cognitive Impairment or Alzheimer’s disease, participants were accompanied by next-of-kin who provided additional informed consent.
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)
Data is divided into stages of objective memory impairment (SOMI, 0-4. Handedness is listed as those who are right-handed. Medication type listed in terms of affirmative use of medication type (percentage of those who replied yes to using medication).
All participants were enrolled in the Alzheimer’s and Music Therapy study (ALMUTH) (44), which was conducted at the University of Bergen in Norway. In this study, we included participants who (1) have Norwegian as their first language, (2) are currently living at home, (3) show subjective memory decline as measured using items on the SCD-Q questionnaire, (4) are able to provide written informed consent, and (5) can be accompanied by a caregiver if they have been previously diagnosed with Alzheimer’s disease or Mild Cognitive Impairment.
Exclusion criteria included (1) incidence of Major Depressive Disorder, Bipolar Disorder, Schizophrenia, Psychotic symptoms, (2) history of traumatic brain injury, (3) neurological disease (e.g., Multiple Sclerosis, Epilepsy), (4) sensory disorders (including deafness or severe auditory impairments), (5) physical immobility or a diagnosis of Parkinson’s disease (6) vascular disorders (i.e., history of heart disease, heart attack, heart surgery, or stroke), (7) other dementia types (e.g., Lewy body dementia, Frontotemporal dementia, Vascular dementia), (8) claustrophobia (9) participants with ferromagnetic metal in the soft tissue of the body or any other counter-indication for MRI scanning, (10) currently living in a residential aged care facility, and (11) participants meeting criteria for SOMI stage 5, known as atypical SOMI.
16 of 91 participants were included before the ALMUTH study was adapted to include participants with milder forms of memory impairment and the inclusion of additional test measures like the FCSRT to the test battery (45);(44). Thus, those 16 participants without an FCSRT who were diagnosed by a physician with Alzheimer’s disease or Mild Cognitive Impairment were categorized as SOMI-4, specifically since they did not have prior vascular-related illness nor the presence of other dementia subtypes. Further, 10 participants (9.90 % of total sample N = 101) could not be classified into SOMI stages as their retrieval was impaired while their memory storage remained intact, with FR scores under 20 and TR scores above 46 (refer to Table 1 for SOMI categorization).
Participants (n = 91) had a mean age of 71.55 years (SD = 9.55, range 42-85), 14.23 years of education (SD = 3.25), 53.26% were women, and 97.80 % were right-handed. The mean FCSRT results FR was 24.77 (SD = 9.94), and the mean TR was 44.24 (SD = 9.86).
Neuropsychological assessments
Ethics approval was given by the Regional Committees for Medical and Health Research Ethics (REC-WEST: reference number 2018/206). Participants were assessed by study team members, who were either psychologists or researchers following a standardized procedure. Neuropsychological testing included the administration of the subjective cognitive decline questionnaire (SCD-Q; (46) which is divided into MyCog (independently answered by participant) and TheirCog (independently answered by next-of-kin), the Consortium to establish a registry for Alzheimer’s disease (CERAD) world list test (WLT; (47)), instrumental and physical activities of daily living questionnaires (I-ADL and P-ADL; (48), geriatric depression scale (GDS; (49), mini-mental state examination (MMSE-Norwegian revised version; (50);(51), the Free and Cued Selective Reminding Task (FCSRT, (52); (53), the Delis-Kaplan Executive functioning system (D-KEFS) word color interference Stroop test (54), and a measure of physical status using the Short physical performance battery, (SPPB; (55). Once testing was completed, qualified participants underwent an MR scan, which included a T1-weighted structural and DTI acquisition.
Free and cued selective reminding task (FCSRT)
We categorized participants into stages of objective memory impairment (SOMI) using the results from the picture version of the Free and Cued Selective Reminding task (with immediate recall) (pFCSRT + IR) (52);(53). The FCSRT begins with an encoding phase in which participants are shown four sets of four images each (e.g., owl, grapes) paired with their respective category cues (e.g., animal, fruit). Participants are immediately tested on their immediate cued recall of these four sets of items, which can be regarded as a controlled learning condition. The task consists of three trial runs with a backward counting task between each trial to divert attention. The FCSRT measures consist of free recall, FR, which are the freely recalled items without the use of a category cue; cued recall (CR), which is the cued recalled items using a category cue; and the sum of both FR and CR which sums to total recall (TR). There are a total of three trial runs, which include a 30-second non-memory related task between each trial run, subsequently followed by a delayed free and cued recall examination after a 20-30 minute pause.
Staging of objective memory impairment (SOMI)
The first stage (SOMI-0) is regarded as a cognitively normal stage where there is an absence of memory impairment (i.e., both retrieval and storage are intact), followed by subtle (SOMI-1) and moderate retrieval impairments (SOMI-2). This decline is then followed by subtle storage impairments (SOMI-3) and then significant storage impairments that are deemed compatible with dementia (SOMI-4). In the current study, we included participants with SOMI stages between 0-4 and excluded those with atypical SOMI-5, who could not be classified using the SOMI system (See Table 1 for categorizations into SOMI stages).
Brain data
MRI acquisitions
All participants were scanned using a 3-T GE Discovery MR750 scanner (General Electric Medical Systems, Milwaukee, WI, United States) with a 32-channel head coil. To acquire T1-weighted anatomical images, we used Sagittal 3D T1-weighted fast spoiled gradient-echo (FSPGR) sequence [repetition time/echo time (TR/TE)] = 6.9/3.0 ms; slice thickness = 1.0 mm; in-plane field of view (FOV) = 25.6 x 25.6 cm2; 256 by 256 matrix size; flip angle = 12°, inversion time (TI) = 450 ms. The total scan time was nine minutes.
Diffusion imaging data were acquired using a single-shot spin-echo echo-planar imaging sequence with the following parameters: 60 axial slices of 2.4 mm without gap; FOV = 220 by 220 by 220 mm3; acquisition matrix size of 128 by 128, reconstructed voxel size of 1.72 by 1.72 by 2.4 mm3; TR = 14000 ms; TE = 93 ms; flip angle = 90°; with diffusion sensitizing gradients (b-value) b = 1000 s/mm2 along 30 non-colinear diffusion directions, including 6 non-diffusion-weighted b = 0 s/mm2 scans).. The total scan time was eight minutes.
During the structural scans, participants were instructed to view nature landscapes while listening to instrumental music selected by the researchers between six genres (i.e., classical, jazz, world, pop, rock, and folk).
Image preprocessing of T1-weighted images
All MR images were converted from Dicom to Nifti format using dcm2niix (Chris Rorden, version v1.0.20170724, https://www.nitrc.org/projects/mricrogl/) in MRIcroGL and then further defaced for anonymity using the mri_deface function (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface) in FreeSurfer (version 7.3.2; https://surfer.nmr.mgh.harvard.edu/). Images were visually inspected to ensure proper defacing. T1-weighted images were preprocessed using the fully automated recon-all pipeline, which included normalization, removal of non-brain tissues, Talairach transformations, segmentation, and meshing of grey and white matter boundaries (56). Cortical parcellations were conducted using the Dessikan-Kiliany atlas (57), which separates cortical regions into 34 regions per hemisphere. Whole-brain “aseg” subcortical segmentation statistics were extracted for further analysis of hippocampal-based brain reserve.
Hippocampal-based brain reserve (HBBR)
HBBR was calculated through the summation of bilateral hippocampal volumes and division by total estimated intracranial volume (58). These values were then standardized and further orthogonalized to the age of the participants using the spm_orth function in SPM12 (http://www.fil.ion.ucl.ac.uk/spm).
Image preprocessing of diffusion images and whole-brain tractography
Image preprocessing of diffusion images was conducted in ExploreDTI v4.8.6 (59) using Matlab (version R2022a), and scripts were automated via the ExploreDTI GUI interface.
Preprocessing of diffusion images was conducted with the following data processing steps: (i) corrections for signal intensity drift and Gibbs ringing artifacts, (ii) corrections for subject motion and eddy current-induced geometrical distortions (60), and susceptibility distortions on co-registered T1-weighted images (iii) B-matrices were adjusted with appropriate reorientations and signal intensities were modulated using a Jacobian determinant of the spatial transformation (60); (61). Diffusion tensor estimation used recursively reweighted linear regression (62); (63), and whole-brain deterministic tractography was then performed based on the following parameters: FA tracking threshold of 0.2, angle threshold of 30°, step size of 1 mm, seed points sampled at a uniform resolution of 2 x 2 x 2 mm3, and a fiber length range of 50-500 mm.
Regions of interest (ROIs) for DTI analysis
ROIs were selected based on the Papez circuit. The Papez circuit has been linked to early stages of degeneration in AD and has been validated as a preclinical circuit of interest (64). The Papez circuit involves the hippocampus, fornix, mammillary bodies, mammillothalamic tract, anterior thalamic nuclei, cingulate cortex, and parahippocampal cortex (43).
Using the Desikan-Killiany atlas (57), eight bilateral ROIs (16 in total) were computed, which included the hippocampus, parahippocampal cortex, thalamus proper, entorhinal cortex, and cingulate cortex (caudal anterior cingulate, rostral anterior cingulate, isthmus cingulate, and posterior cingulate).
Network analysis
For network analysis, we used the brain connectivity toolbox (BCT) (65) to obtain graph metrics. In order to construct the individual DTI-based connectivity matrices, the automated anatomical labeling atlas (AAL; (66) was used to subdivide the brain into 90 regions. The AAL atlas is a commonly used atlas that can be used to derive nodes in a graph theoretical analysis, with FA values extracted from all the fibers connecting each pair of regions as edges. The cerebellum was excluded in the network analysis for a total of 90 nodes (78 cortical and 12 sub-cortical). Using phase-aligned signal separation (PASS) filters in ExploreDTI, the matrices were weighted by the number of FA streamlines connecting node i to node j, which were then aggregated into a 90 by 90 connectivity matrix for each subject.
All self-connections were excluded from the analysis by setting the values of the connectivity matrix, CM diagonal, to 0. For metrics that required connection lengths, lengths were also calculated using the weight_conversion.m (“lengths”) function in the BCT toolbox.
Graph theoretical metrics were calculated using the BCT toolbox, and calculations were based on all 90 nodes that were defined using the AAL atlas (66). Measures of global connectivity, such as global efficiency, characteristic path length, and nodal density, were determined across all 90 nodes. For cross-nodal analyses using ROIs of the Papez circuit, we included six bilateral ROIs (12 in total) in our analyses: hippocampus, parahippocampal cortex (which includes the entorhinal cortices), thalamus proper, anterior cingulum, middle cingulum, and posterior cingulum. Local nodal topological metrics of interest included clustering coefficient, betweenness centrality, eigenvector centrality, and nodal degree.
Statistical analysis
All statistical analyses were conducted using SPSS software (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp) and JASP (JASP Team (2023) JASP (Version 0.17.3)). The effect of SOMI stage on HBBR was investigated using an analysis of covariance (ANCOVA), with a 2-tailed significance level set at p < 0.05. The dependent variable was HBBR, whereas SOMI stage and gender were considered as fixed factors, and years of education as a covariate. Post-hoc Bonferroni tests were applied to the ANCOVA results at a 0.05 level of significance (2-tailed) in order to explore the associations between regions of the Papez circuit and SOMI stages across DTI metrics (FA and MD) and both global and local nodal metrics of structural connectivity. For correlation analyses, non-parametric Spearman’s rho correlations were employed (p = 0.05 level, 2-tailed). The non-parametric Kruskal-Wallis test was used to test statistically significant differences between ordinal independent variables (SOMI stages) and global graph metrics (global efficiency, characteristic path length, and nodal density). In this test, SOMI stages served as the independent grouping variable, and the global metrics were the dependent variables.