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. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Brain Imaging Behav. 2023 Oct 31;18(1):106–116. doi: 10.1007/s11682-023-00819-w

Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia

Edward Ofori 1, David E Vaillancourt 2, Maria T Greig-Custo 3, Warren Barker 3, Kevin Hanson 4, Steven T DeKosky 5, Cynthia S Garvan 6, Malek Adjouadi 7, Todd Golde 5,8, David A Loewenstein 8, Chad Stecher 1, Rylan Fowers 1, Ranjan Duara 3
PMCID: PMC11157151  NIHMSID: NIHMS1994461  PMID: 37903991

Abstract

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer’s disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer’s Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.

Keywords: Diffusion-weighted imaging, Cognitive decline, Individual differences, Random forest models

Introduction

In the past decade, significant progress has been made in defining Alzheimer’s disease (AD) prior to dementia and during the earliest dementia stages through the use of neuroimaging. Magnetic resonance imaging (MRI) has facilitated the determination of hippocampal and temporal volume to assess neurodegeneration, while 18 F-flurodeoxyglucse positron emission tomography (FDG-PET) has enabled the measurement of energy metabolism in cortex, additionally, new amyloid PET ligands allow for detection of beta-amyloid plaque deposition in the cortex (Navitsky et al., 2018). These technologies represent major advances that have revolutionized the assessment of dementia in research studies and clinical drug trials (Chen et al., 2011; Frisoni et al., 2017; Geuze et al., 2005; Jack et al., 2008). Alongside these innovations, measures of cerebrospinal fluid (CSF), have provided critical insights into the temporal dynamics of AD pathology (Jack et al., 2010).

Despite these key developments, there remains little understanding as to how these brain-based biomarkers differ by between Hispanic and non-Hispanic cohorts. This distinction is important, given the high prevalence of dementia rates among Hispanic individuals and disparities across tests of memory, executive function, and overall cognitive performance, even when controlling for factors such as age, sex, education, and socioeconomic status (Boone et al., 2007; Castora-Binkley et al., 2015; Guerrero-Berroa et al., 2014, 2016). Among Medicare recipients over the age of 65, 11.5% of Hispanic individuals received an AD diagnosis, compared with only 6.9% for non-Hispanic adults (Alzheimer’sAssociation, 2016). There is also evidence that MRI-based predictors and risk factors for AD may differ between Hispanic and non-Hispanic individuals (O’Bryant et al., 2013; Zahodne et al., 2015). As ongoing and future clinical trials develop targeted therapeutics, focused neuroimaging assays using amyloid PET and MRI are desperately needed to answer these unresolved and clinically-important questions.

For the current project, we calculated brain-based metrics for Hispanic and non-Hispanic individuals with MCI, AD, and healthy, cognitively normal older adults (CN) using volumetric MRI, amyloid PET, and an advanced MRI method, free-water diffusion MRI (FW). FW assess the unconstrained diffusion in the extracellular space, and reflects neurodegeneration and inflammation (Pasternak et al., 2012). This FW method has proven useful in assessing brain changes associated with progression of Parkinson’s disease (Burciu et al., 2017; Ofori et al., 2015b) and may provide insights into ethnic differences in cognition, healthy aging, and AD (Archer et al., 2021; Bergamino et al., 2022). Unlike traditional volumetric MRI, free-water imaging has revealed significant differences in a broader array of regions, including those critical to understanding the pathophysiology of AD (Chen et al., 2018; Wolk et al., 2017). This method’s ability to better capture the complex neurodegenerative processes in AD, particularly among Hispanic individuals, adds a new dimension to the existing arsenal of neuroimaging tools (Zahodne et al., 2015). We tested the hypothesis that Hispanic individuals with cognitive impairment have varying volumetric MRI, FW and amyloid PET imaging measures compared with non-Hispanic individuals, aiming to explore whether the group effect in cognition and diagnosis could be associated with differences in MRI-based metrics across ethnic groups.

Methods

Participants

We evaluated 108 participants across 3 groups including CN (n = 28; 57% Hispanic), MCI (n = 53; 60% Hispanic), specifically the amnestic subtype (aMCI), and AD (n = 27; 48% Hispanic). A demographic questionnaire that assessed family history and racial identification was administered to determine ethnicity. These participants were enrolled at Mount Sinai Medical Center as part of the 1Florida Alzheimer’s Disease Research Center (1Florida ADRC) research program. Consent for the study was obtained from all subjects and all study partners. Inclusion criteria for all men and women were: (1) 60 + years for CN, MCI groups, and dementia groups; (2) available study partner (personal or telephone contact); and (3) 6th-grade educational and reading comprehension level. Exclusion criteria included: (1) any significant sensory (visual and hearing) or motor deficits sufficient to interfere with testing; (2) history of clinical stroke; and (3) major medical or psychiatric illnesses, which might prevent participation in longitudinal studies. A geriatric psychiatrist or neurologist at the Wein Center conducted medical and neurological evaluation for Alzheimer’s Disease and Memory Disorders.

Cognitively normal group

Cognitively Normal (CN) healthy controls were individuals who are 60 + years of age, no evidence of a memory complaint preferably confirmed by an informant, Mini-Mental State Exam (MMSE) (Mungas et al., 1996) scores greater than 26, a Global Clinical Dementia Rating (Morris, 1993) (CDR) Scale of 0, and all memory and non-memory measures (e.g., Category Fluency (Binetti et al., 1995), Trails A and B (Reitan, 1958) 0, WAIS-IV Block Design subtest (Wechlser, 2008) were no lower than 1.0 SD below normal limits for age, education, and language group.

Amnestic MCI group

Individuals with aMCI were 65 + years of age and met Petersen’s criteria (2014) for MCI and evidenced all of the following: (a) subjective cognitive complains by the participant and/or collateral informant; (b) evidence by clinical evaluation or history of memory or other cognitive decline; (c) Global Clinical Dementia Rating scale of 0.5; below expected performance on delayed recall of the HVLT-R or delayed paragraph recall from the National Alzheimer’s Coordinating Center-Unified Data Set as measured by a score that is 1.5 SD or more blow the mean using age, education, and language-related norms.

Dementia group

Individuals were diagnosed with dementia using criteria a and b as described for the aMCI group and also evidence the following: (a) Global CDR score of 1.0; (b) below expected performance on the memory measures described above that scored 2.0 SD or more below the mean using age, education, and language-related norms.

Other neuropsychological assessments of memory and cognitive function included the: (1) Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), (2) Hopkins Verbal Learning Test-Revised (HVLT-R) (Benedict et al., 1998; Vanderploeg et al., 2000), (3) Color-Word Interference Condition of the Stroop (Stroop, 1935), and (4) the National Alzheimer’s Coordinating Center Multilingual Naming Test (NACC MINT) (Gollan et al., 2012). All neuropsychological tests employed in the current application have appropriate age, education and cultural/language normative data and have been translated and back-translated from English to Spanish to assure being clear and culturally fair (See Table 1 for means and SDs of demographic and clinical information) (Arango-Laspirlla et al., 2015a; 2015b; Pena-Casanova et al., 2009). All our non-Hispanic groups preferred testing in English whereas, in the Hispanic group greater than 50% received testing in Spanish. Proficient bilingual (Spanish/English) psychometricians performed testing.

Table 1.

Demographic and clinical information

N CN
 MCI
 AD
Non-Hispanic Hispanic Non-Hispanic Hispanic Non-Hispanic Hispanic
12 16 21 32 14 13
Age (SD) in years 71.3 (4.2) 66.3 (12.6) 71.9 (8.7) 74.4 (6.4) 69.3 (10.8) 72.2 (8.9)
Sex (% Female) 66% 75% 48% 63% 43% 54%
Education (Years) 16.7 (2.6) 15.3 (3.8) 15.6 (3.4) 14.8 (3.5) 14.9 (3.5) 11.6 (4.4)
CDR-SB 0.1 (0.3) 0.1 (0.2) 2.3 (1.2) 1.6 (1.2) 5.5 (2.3) 6.7 (2.7)
MMSE 29.1 (1.0) 29.1 (0.9) 26.2 (3.1) 27.2 (2.3) 22.1 (4.9) 20.2 (6.6)
MoCA 25.7 (2.0) 23.5 (3.5) 23.9 (10.6) 21.5 (3.1) 15.3 (5.5) 12.1 (5.6)
HVLT 26.0 (4.0) 23.2 (4.1) 23.9 (10.6) 19.3 (5.7) 11.0 (7.0) 10.3 (6.4)
NACC MINT 30.1 (2.0) 26.1 (2.4) 26.4 (6.2) 25.4 (3.9) 19.5 (7.9) 21.5 (7.5)
Stroop Color-Word 37. 8 (8.4) 34.6 (8.2) 29.3 (8.7) 26.0 (8.0) 15.8 (11.9) 16.4 (9.4)
Amyloid status (−/+) 11/0 13/0 11/8 18/6 5/7 4/7
ApoE e4 carrier (0/1+) 8/4 13/3 10/11 22/10 9/5 9/4
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Data are either count or mean (± SD). Abbreviations: AD = Alzheimer’s disease, ApoE = apolipoprotein, CDR-SB = clinical dementia rating-sum of boxes, CN = Cognitively Normal, HVLT = Hopkins Verbal Learning Test, F = female, ICV = Intracranial Volume, M = male, MCI = Mild Cognitive Impairment, MMSE = Mini-Mental State Examination, MoCA = Montreal Cognitive Assessment, NACC MINT = National Alzheimer’s Coordinating Center Multilingual Naming Test

Genetic testing. For genetic testing, samples were genotyped for APOE genotype using predesigned TaqMan SNP Genotyping Assays for SNPs rs7412 and rs429358 (Thermo Fisher Scientific, Massachusetts, USA) on the QuantStudio 7 Flex Real-Time PCR system (Applied Biosystems, California, USA) following the manufacturer’s protocol. The technologists and radiologist on site determined quality assurance for each sample. The genetic data were used in the analyses as a covariate due to the known influence of APOE E4 on AD risk (Reiman et al., 2009).

Data Acquisition

Volumetric and diffusion MRI

All protocols were performed using a 3T Siemens Magnetom Skyra with a 20-channel head/neck coil with software version E11. We collected T1, T2, and diffusion-weighted sequences with whole brain coverage. Structural MRI. Anatomical images were acquired in 176 contiguous axial slices at 1 × 1 × 1 mm resolution with a T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) repetition time (TR) = 1380 ms; echo time (TE) = 3.03 ms; flip angle = 9°. Diffusion MRI: The details of the echo planar diffusion protocol included: TR = 9000 ms, TE = 90 ms, b values: 0, 1000 s/mm2, diffusion gradient directions = 64, field of view (FOV) = 250 × 250, voxel size = 2 × 2 × 2 mm3, 64 slices, transverse slice orientation.

PET amyloid imaging

A Siemens Biograph 16 PET/CT scanner was used for these studies, and the 1Florida ADRC has been certified for use in ADNI-DOD (Weiner, 2017). For the florbetaben (FBB) scans, 300 MBq (± 10%) of 18 F-FBB were injected and images were acquired from 90 to 110 min after injection. For florbetapir, 10 mCi of 18 F-florbetapir were infused over 5 to 10 s, followed by an uptake phase of 50 min Scanning was begun 50 min post-injection and brain images acquired continuously for 20 min in four 5-minute frames. During the uptake phase, the participant was permitted to wait in a quiet room. The images were assessed immediately for technical validity. If considered inadequate, the participant would undergo an additional 20 min of continuous imaging, collected in four 5-minute frames.

Data analysis

Volumetric MRI analysis

Regional analysis was done on the 3D T1 MPRAGE imager with FreeSurfer version 6.0 (http://surfer.nmr.mgh.harvard.edu/) (Dale et al., 1999). This MRI software package is comprised of a suite of automated tools for segmentation, reconstruction, and derivation of regional volumes and surface-based rendering. FreeSurfer was used to derive 34 cortical regions based on Destrieux et al., 2010 automatic parcellation procedure (Destrieux et al., 2010). We employed the same software version, workstation, and operating system throughout the study, given known variability introduced by differences in these factors. Volumes are reported as a percentage of intracranial volume (ICV). We use regions segmented by FreeSurfer for regions of interest to calculate FW values.

Diffusion MRI analysis

All diffusion MRI data were processed using to previously published protocols: These includes eddy current and motion correction, rotate b vectors, skull strip, and quantify FW and free-water corrected fractional anisotropy (FWT). FW maps and FWT maps were calculated using a custom script written in MATLAB R2013a (The Mathworks, Natick, MA) code (Ofori et al., 2015a, b; Pasternak et al., 2009; Planetta et al., 2016). This code implemented a minimization procedure that fit a bitensor model, which quantifies the fractional volume of FW in each voxel. Free-water maps were registered to the T1 image in subject space using ANTs normalization (Avants et al., 2011).

Amyloid PET analysis

The scans were read at the Wien Center by Dr. Duara, who has received training on quantitative methods for PET scan binary read methodology to determine categorical amyloid status. Standard uptake value ratios (SUVRs) were generated for six standard bilateral brain regions (Precuneus/Posterior Cingulate, Anterior Cingulate, Orbitofrontal, Lateral Temporal, Medial Temporal, Occipital) as well as the Cerebellum, which served as the reference region. In addition, since two different amyloid ligands (florbetapir and florbetaben) were used for the study, we transposed the data to the Centiloid scale to allow direct comparisons (Rowe et al., 2017).

Statistical analyses

Measures of age, years of education, CDR-SB, MMSE, MoCA, HVLT, Stroop-Color Word, NACC-MINT and ICV were compared using one-way ANOVAs across the three groups (CN, MCI, and AD). Differences in sex, amyloid status (positive or negative), and ApoE ε4 carrier status were assessed with chi-square analyses. Mean values for the cortical region ROIs were calculated for FW and MANCOVAs was conducted, controlling for sex, age, education, and ApoE ε4 status, with group status and ethnicity as fixed factors. P-values were corrected for multiple comparisons controlling for false discovery rate (FDR). Post-hoc tests for the group status and group x ethnicity interactions were evaluated using Bonferroni pairwise comparisons. The level of significance was set at 0.05.

Regression analyses

To assess whether different cortical regions significantly predict cognitive scores by ethnicity, we used a forward selection regression model with significant cortical FW values found in the group x interaction analyses as independent variables and MOCA and MMSE scores as dependent variables for cognitive scores. The criteria for model selection of a predictor was based on based on the probability of F < 0.05 to include and greater than 0.10 to remove from the model.

Random forest models

To contrast the relative predictive value of FW versus cortical volumetric measures for dementia by ethnicity, random forest models were separately estimated among the Hispanic and non-Hispanic groups. First, models of a dementia diagnosis were fit using all of the available FW measures as predictors for the Hispanic and non-Hispanic groups. A second set of models were then separately fit among these two groups using all of the cortical volumetric measures. All of these continuous predictor variables (i.e., both FW and volumetric measures) were standardized to have mean 0 and standard deviation 1 for comparability, and the random forest models were averaged over 10,000 individual trees and using the default values for the other modeling parameters in the sklearn.ensemble. RandomForestClassifier algorithm in Python. To compare the performance of these different models for predicting dementia, the area under the receiver operating characteristic (ROC) curve was calculated, and the ROC curves were separately plotted for both the Hispanic and non-Hispanic groups using only the FW variables and then using only the volumetric measures. Finally, variable importance statistics were generated to show the relative importance of each predictor of dementia for each ethnic group.

Results

Demographics and clinical data

There were no significant differences among ethnic groups for age, sex, CDR-SB, MMSE, HVLT, Stroop Color-Word, or NACC MINT (Table 1). The only significant difference was found for years of education, with Hispanic (13.8 ± 0.5 yrs) individuals having less years of education than English (15.6 ± 0.5 yrs) individuals (F = 6.1, p = 0.015).

There were no significant differences for age, sex, or ApoE ε4 status across clinical severity. As expected, there were significant differences among groups for education (F = 3.4, p = 0.03), amyloid status (χ = 23.5,p < 0.0001), CDR-SB (F = 119.9, p < 0.0001), MMSE (F = 49.75, p < 0.0001), MoCA (F = 22.7, p < 0.0001), HVLT (F = 44.8, p < 0.0001, Stroop CW (F = 20.1, p < 0.0001), and NACC MINT (F = 11.4, p < 0.0001) scores across diagnostic groups (See Table 1). Post hoc tests revealed that the AD (13.4 ± 0.9 yrs) group had fewer years of education that the cognitively (15.9 ± 0.6 yrs) normal group (p = 0.031). There were more amyloid positive cases in the MCI and AD groups than in the CN group (p < 0.0001). There was no difference in ethnic or group x ethnicity effects for amyloid status. Post hoc tests revealed that CDR-SB scores significantly increased with increases in clinical severity, whereas MMSE, HVLT, and Stroop CW scores decreased with increases in clinical severity. For MoCA and NACC MINT scores, the cognitively normal group had significantly higher scores than the MCI and dementia groups. See Table 1 for detail.

Diffusion analyses

Significant group, ethnicity, and group x ethnicity effects were found for mean FW in the left and right hemispheres. For the group FW effects, most AD-signature regions had increased FW values across both hemispheres (Appendix Table 1). Middle and superior temporal gyrus, the banks of the STS, and the transverse temporal and insula cortices had significantly increased FW in the MCI group in comparison to the CN group. The group x ethnicity interaction revealed effects in the left bank of the superior temporal sulcus (F = 4.7, p = 0.012), the left inferior (F = 5.8, p = 0.001) and middle temporal (F = 4.4, p = 0.013) lobes, the left supramarginal gyrus (F = 4.6, p = 0.012), and the right entorhinal cortex (F = 3.3, p = 0.04; Fig. 1). Post hoc analyses revealed that these interactions were due mostly to the Hispanic AD group having greater FW values than the non-Hispanic AD group, with no differences between ethnicities for the CN and MCI groups. The right ERC also showed increased FW for the Hispanic CN group when compared to the non-Hispanic cognitively normal group (p = 0.048).

Fig. 1.

Fig. 1

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Distinct temporal gyri free-water at various stages of AD. The bar graphs indicate the levels of free-water for cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) in Hispanic and non-Hispanic Individuals. Asterisks(*) above standard error bars indicate significant differences (p < 0.05)

Volumetric analyses

Significant group effects for regional volume when accounting for ICV were found in the right hemisphere and left hemispheres (See Appendix Table 2). These were mostly due No ethnicity effect or ethnicity x group interactions were found across either hemisphere. The group effects for %ICV resulted in decreased %ICV regions in MCI and AD groups when compared to cognitive normal (p’s < 0.05).

Regression analyses

We found a linear relation between mean FW values in specific temporal lobe ROIs and the clinical cognition scores (Table 2). The stepwise linear regression model indicated that different regions influenced clinical cognition scores (i.e., total MMSE & MoCA) for both Hispanic and non-Hispanic individuals. For total MMSE scores, the linear regression model indicated that the left middle temporal gyrus FW value (Β1 = −24.7, p = 0.001) was significantly associated with MMSE total scores of non-Hispanic patients across the clinical spectrum (r = 0.44, p = 0.001; n = 56). The linear regression model for the Hispanic group revealed that the left inferior temporal gyrus (Β1 = −24.6, p = 0.048) and the left banks of the superior temporal sulcus (Β2 = −23.4, p = 0.025) were significantly associated with MMSE total scores of Hispanic patients across the clinical spectrum (r = 0.62, p = < 0.0001; n = 59) (Table 2). For total MoCA scores, the linear regression model indicated that the right entorhinal cortex (Β1 = −17.8, p = 0.023) was significantly associated with MoCA total scores across the clinical spectrum (r = 0.310, p = 0.023; n = 54) for the non-Hispanic group. For the Hispanic groups, the linear regression model revealed that the left banks of superior temporal sulcus (Β1 = −32.2, p = 0.017) and the left inferior temporal gyrus (Β2 = −34.3, p = 0.035) were significantly associated with MoCA total scores across the clinical spectrum (r = 0.66, p = 2 × 10−6; n = 50).

Table 2.

Regression analyses to predict cognitive scores for hispanics and non-hispanics

Regression Analyses MMSE
 p-value MOCA
 P-value
Predictors Model Fit Predictors Model Fit
Hispanic Left Banks of STS
Left Inferior Temporal Gyrus		 F = 17.47, r = 0.62 < 0.0001 Left Banks of STS
Left Inferior Temporal Gyrus		 F = 17.74, r = 0.66 < 0.0001
Non-Hispanic Left Middle Temporal Gyrus F = 12.87, r = 0.44 0.001 Right Entorhinal Cortex F = 5.53, r = 0.31 0.023
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MMSE = Mini Mental State Exam, MoCA = Montreal Cognitive Assessment, STS = Superior Temporal Sulcus

Random forests

The area under the receiver operating characteristic curve (AUC) for the random forest model using FW variables to associate with a dementia diagnosis among the Hispanic group was 0.85, while the AUC using the same FW variables was only 0.57 for the non-Hispanic group (See Fig. 2A). This indicates that FW variables were strong correlates of dementia for the Hispanic group, but performed poorly for the non-Hispanic group. The random forest models that used the volumetric measures performed better for the non-Hispanic group, with an AUC of 0.70, but slightly worse for the Hispanic group with an AUC of 0.82 (See Fig. 2B). To investigate what specific measures were most associated with dementia from each of these models, Appendix 3 Figures display the variable importance plots. Variable importance reflects the relative contribution of each variable to the model’s ability to accurately predict the outcome of interest. In the context of this study, it could represent how different brain regions or other factors contribute to the prediction of dementia status. The numeric values assigned to each variable represent a quantified measure of its importance, often calculated based on the increase in the model’s prediction error if the variable is permuted randomly. Higher values indicate that a variable is more critical for the model’s predictive accuracy. Understanding variable importance can help in interpreting the model and focusing on the most relevant factors that are associated with AD. These results show that FW Right Amygdala was the strongest correlate of dementia among the Hispanic group, while none of the FW variables were uniquely powerful correlates for the non-Hispanic group (See Appendix 3). Additionally, the Left Hemisphere Inferior Parietal ICV measure was the strongest predictor of dementia for Hispanics, and this volumetric measure was also one of the top predictors among non-Hispanics.

Fig. 2.

Fig. 2

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Receiver operating characteristic curve from random forest model predicting dementia using free-water diffusion MRI measures among hispanics (top) and non-hispanics (bottom). Note: The AUC is the area under the receiver operating characteristic curve

Discussion

MRI-based markers of cortical structures have helped characterize the regions vulnerable to neurodegeneration along the Alzheimer’s clinical continuum (Grundman et al., 2002; Meyer et al., 2005; Wolk et al., 2017). Understanding the rate of cognitive decline has potential prevention and public health implications. Factors such as socioeconomic status, education, and vascular health have been explored in relation to increased risk of cognitive decline between Hispanic and non-Hispanic individuals (Vega et al., 2017; Zahodne et al., 2015). The results from the current study do not suggest causality but rather indicate that differences in brain microstructure were observed between ethnic groups, which may be associated with the etiology of AD. We used FW imaging in cortical regions to identify specific temporal lobe regions (e.g., left banks of the superior temporal sulcus and left inferior temporal gyrus) that appear more susceptible to changes in Hispanic individuals, and found that increased FW in these regions correlates with cognitive performance in this group.

Comparing the results between free water and volumetric MRI diagnosis findings (see Appendix Table 1) in the context of Alzheimer’s disease (AD) reveals distinct and noteworthy differences. The free water findings across diagnostic group demonstrates significant diagnostic group differences in more regions, including the banks of the STS, entorhinal, fusiform, inferior temporal, isthmus cingulate, lateral occipital, lingual, medial orbitofrontal, middle temporal, parahippocampal, pericalcarine, posterior cingulate, precuneus, rostral anterior cingulate, superior temporal, and temporal pole. These regions only show significant differences for FW, highlighting the sensitivity of this method (Chen et al., 2018; Archer et al., 2021). In contrast, the volumetric findings reveals significant differences in a limited number of regions such as the caudal middle frontal, frontal pole, lateral orbitofrontal, pars opercularis, pars triangularis, and superior frontal, which are not observed with free water (Grundman et al., 2002). Furthermore, the effect sizes, as indicated by the F-statistics, appear more substantial in the free water table, suggesting a more robust differentiation between diagnostic groups (Wolk et al., 2017. In summary, free water MRI seems to offer a more sensitive measure for detecting changes associated with AD progression compared to volumetric MRI. The regional differences between these modalities are intriguing and call for further investigation and nuanced discussion (Meyer et al., 2005). Overall, the free water technique emerges as a more strongly supported method for understanding the complex neurodegenerative processes in AD, offering potential insights for both diagnosis and therapeutic interventions (Zahodne et al., 2015).

MRI predictors of cognitive performance differ across ethnic groups (Zahodne et al., 2015). Our results are consistent with extant literature suggesting that various AD-vulnerable regions show that may be dependent on clinical stage (Reas et al., 2018; Suarez-Gonzalez et al., 2016). We observed increased FW in specific regions for the AD group, but did not find differences in volumetric measures from cortical regions when controlling for intracranial volume across groups or ethnicities in these same regions. The FW increase observed in later stages of the AD continuum may be associated with factors such as tau aggregation and neurofibrillary tangles that are generated later in the course of the disease (Chen et al., 2018). It should be noted that these regions are usually subject to changes before the onset of clinical symptoms, and our study suggests that these regions may be more affected in Hispanic individuals.

Some studies have suggested that individuals with Hispanic origins may be at higher risk for vascular contributions to dementia. Evidence supporting these claims come through increased white matter hyperintensity volume estimates in individuals with vascular dementia and also been in revealed in studies with Hispanic Americans. One report indicated elevated HbA1c in the diabetes range among Hispanics is linked with increased WMH volume compared with non-Hispanic Whites. Further studies using advanced diffusion MRI metrics have reported impact to white matter structures and this is linked to cerebrovascular diseases (Ji et al., 2017; Vipin et al., 2019).

Educational attainment is related to prevalence of dementia (Cobb et al., 1995; Sharp & Gatz, 2011) and the degree to which the educational level influences the risk of developing dementia is not well understand. Reports have shown that a reduction in the risk for dementia in the US was associated in part to educational attainment (Kukull et al., 2002). This is in line with the cognitive reserve/resiliency theory that suggests high educational attainment could serve as a neuroprotective factor or at least increase or maintain cognitive performance to norms in the presence of pathological insults (Habeck et al., 2017; Kukull et al., 2002; Stern, 2012). The current study reported observed differences in education level for the Hispanic group when compared to the non-Hispanic group. We additionally found associations with regions in the temporal region and ethnicity. Further, the regions that correlated with cognitive performance for Hispanics were the same across the MMSE and the MOCA (i.e., the left banks of the superior temporal sulcus and inferior temporal lobe). Utilizing both MMSE and MoCA allows for a more comprehensive assessment. While MMSE is effective for general cognitive screening, MoCA provides a more nuanced evaluation of executive functions and other cognitive domains often affected in early Alzheimer’s. These regions found in the current study to differ due to ethnicity are important for language and semantic processing (Hurley et al., 2015; Visser et al., 2010). Language deficits are important in determining cognitive decline and are associated with AD with verbal fluency being a key indicator of the progression of dementia (Greenaway et al., 2006; Imamura et al., 1998). Scheff et al. (2011) report 36% synaptic loss in the inferior temporal gyrus aMCI and mild AD individuals and this loss is strongly associated with MMSE scores (Scheff et al., 2011). The findings from the current study revealing increased FW in the left inferior temporal gyrus may be indicative of changes in this region, possibly related to neuronal loss or microstructural events that would precede neuronal loss.

Although we reveal associations with Hispanic background, much of the current differences may related to socioeconomic status, which can be difficult to capture and can have great impacts on health in general. Being able to identify structures that may be more susceptible or that diffusion MRI may be more important in associating with dementia status in Hispanic Americans is a significant observation. Some of these factors may comingle with FW and are not ready to be examined in our current study.

While this study offers a novel and comprehensive approach to understanding the neuroimaging differences between Hispanic and non-Hispanic individuals in the context of dementia, several limitations must be acknowledged. First and foremost, the small sample size, particularly when conducting ethnic comparisons of dementia subjects (14 vs. 13), may limit the generalizability of the findings, introduce potential biases, and random forest training models may need larger samples for validation purposes (Castelo, 2023) (See Appendix 3). The etiology of dementia was not fully accounted for, with over 40% of dementia subjects being amyloid-negative, a factor that could significantly influence the interpretation of the results. This may be due to tau accumulation and more work needed understanding in the context of biological definition for AD. Additionally, prevalent vascular risk factors, which were alluded to but not included as covariates, may have confounded the observed associations. The insights gained from this study, however, provide a valuable foundation for continued investigation into the complex interplay between ethnicity, neurodegeneration, and cognitive decline, and underscore the need for larger, more diverse, and methodologically rigorous studies in this critical area of research.

Nonetheless, hispanic individuals with AD had significantly higher FW values in several temporo-parietal regions when compared to their non-Hispanic counterparts. This suggests that the AD process may differ or progress differently based on ethnicity. When controlling for education level, we still observed significant variation in specific regions in the temporo-parietal region associated with ethnic status. The left inferior temporal gyrus and banks of superior temporal sulcus were both significant correlates of clinical cognitive performance in this population. Additionally, when using supervised machine learning methods, the free-water from the right amygdala was found as a more important correlate of dementia for Hispanics than non-Hispanics. Our findings suggest that among the multifactorial risks for AD, Hispanic ethnicity is associated with significant neuroimaging changes and different cortical indicators of cognitive decline.

Supplementary Material

Appendix Tables and Figures

Acknowledgements

The authors would like to thank the participants and their families for their time and commitment to this research.

Funding

This work was supported by the National Institutes of Health (P50 AG047266, R01 NS052318, and T32 NS082168).

Footnotes

Declarations

Competing interests Edward Ofori – none, David E. Vaillancourt – Reports grants from NIH, NSF, Tyler’s Hope Foundation during the conduct of the study, and honoraria from NIH and Parkinson Foundation unrelated to the submitted work. He is a co-founder of Neuroimaging Solutions. Maria T. Greig-Custo – none, Warren Barker – none, Steven T. DeKosky – reports receiving consultation funds as member of the Neuroscience Advisory Board for Amgen; Chair of DSMB for Biogen; Chair, Medical Advisory Board for Cognition Therapeutics, Kevin Hanson – none, Cynthia Garvan – none, Malek Adjouadi – none, Todd Golde – receives grant support from NIH, Michael J. Fox Foundation, Ellison Medical Foundation, and Thome Foundation, David Loewenstein – none, Chad Stecher – none, Rylan Fowers – none, Ranjan Duara –receives grant support from Alzheimer’s Therapeutic Research Institute, Avid-Eli Lily & Company, Janssen Research & Development LLC, Merck & Company, Toyama Chemical Co., Ltd, and vTv Therapeutics LLC. Dr. Duara also serves as a consultant for Medical Learning Group.

Ethics approval The use of human subject data was approved by Mount Sinai and University of Florida IRB. All participants consented to the protocol and publish findings.

Data availability

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request to the 1Florida ADRC Data Core.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix Tables and Figures
NIHMS1994461-supplement-Appendix_Tables_and_Figures.docx (161.9KB, docx)

Data Availability Statement

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request to the 1Florida ADRC Data Core.

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