G.M. D’Souza1,2, N.W. Churchill2-4, D.X. Guan5, M.A. Khoury1,2, S.J. Graham6-8, S. Kumar1,9,10, C.E. Fischer1,2,9, T.A. Schweizer1-3,11
1. Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada, M5S 1A8; 2. Keenan Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada, M5B 1T8; 3. Neuroscience Research Program, St. Michael’s Hospital, Toronto, Ontario, Canada, M5B 1T8; 4. Physics Department, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3; 5. Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada, T2N 4N1; 6. Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada, M4N 3M5; 7. Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada, M4N 3M5; 8. Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, M5G 1L7; 9. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, M5S 1A1; 10. Centre for Addiction and Mental Health, Toronto, Ontario, Canada, M6R 1A1; 11. Division of Neurosurgery, Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada M5T 1P5
Corresponding Author: Tom A. Schweizer, Li Ka Shing, 209 Victoria Street, Toronto, Ontario, M5B 1T8, Canada. E-mail: Tom.Schweizer@unityhealth.to.
J Prev Alz Dis 2024;
Published online May 9, 2024, http://dx.doi.org/10.14283/jpad.2024.86
Abstract
BACKGROUND: There has been little direct examination of how traumatic brain injury (TBI) affects the rate of neurodegeneration for individuals with Alzheimer’s disease (AD).
METHODS: The study examined 89 cognitively normal adults (65 with and 24 without prior TBI) and 65 with AD (16 with and 49 without prior TBI). Cortical thickness was quantified from T1-weighted MRI scans at baseline and follow-up (mean interval 33.4 months). Partial least squares analysis was used to evaluate the effects of AD and TBI history on the longitudinal change in cortical thickness.
RESULTS: Significant group effects were identified throughout the frontal and temporal cortices. Comparison of the AD groups to their control cohorts showed greater relative atrophy for the AD cohort with prior TBI.
CONCLUSION: These results indicate that a history of TBI exacerbates longitudinal declines in cortical thickness among AD patients, providing new insights into the shared pathomechanisms between these neurological conditions.
Key words: Traumatic brain injury, dementia, Alzheimer’s disease, neurodegeneration, magnetic resonance imaging.
Abbreviations: AD: Alzheimer’s disease; ADRC: Alzheimer’s disease Research Centers; ANOVA: analysis of variance; BSR: bootstrap ratio; CBRAIN: Canadian Brain Imaging Research Platform; CDR: Clinical Dementia Rating; CI: confidence interval; CN: cognitively normal; FDR: false discovery rate; MRI: magnetic resonance imaging; NACC: National Alzheimer’s Coordinating Center; NIA-AA: National Institute of Aging–Alzheimer’s Association; NINCDS-ADRDA: National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association; PLS: partial least squares; ROI: region of interest; SE: standard error; TBI: traumatic brain injury.
Introduction
Traumatic brain injury (TBI) may have long-lasting effects on an individual’s brain health and wellbeing. There is accumulating evidence that in some patients with TBI, after the acute injury period and initial recovery, a series of evolving biochemical changes contribute to progressive neurodegeneration (1), often with lasting impairments in multiple cognitive domains (2). Furthermore, in retrospective epidemiological studies, TBI has been associated with an increased risk of Alzheimer’s disease (AD) and other dementias (3). Considering the high prevalence of TBI, with approximately 69 million cases each year (4), and the associated neurodegenerative risk, it is critical to objectively study long-term neurobiological changes associated with TBI. Such research is required for the development of more accurate models of neurodegenerative outcome, potentially leading to improved patient management.
The post-TBI pathophysiological mechanisms that are thought to lead to atrophy in the chronic phase of injury also play a role in AD pathology, suggesting common disease pathways. This includes diffuse axonal injury, persisting neuroinflammation, cerebrovascular dysfunction, phosphorylated tau protein, oxidative stress, and metabolic dysregulation (5, 6). Given the diversity of these shared pathophysiological mechanisms, a history of TBI likely accelerates the course of decline in brains of patients with AD, but direct evidence of this is limited (6). Magnetic resonance imaging (MRI) studies of patients in the ensuing months and years after TBI have demonstrated that progressive neurodegeneration of gray matter is associated with negative cognitive outcomes (7). Although mechanisms of TBI are heterogenous, the fronto-temporal regions are particularly vulnerable to mechanical forces due to their location; therefore, the neurobiological effects of TBI tend to be most prominent in these brain regions (8). Longitudinal MRI studies in AD also reveal reliable patterns of gray matter atrophy in distributed regions of the cerebral cortex including medial temporal and posterior temporo-parietal regions (9-13). While these studies provide evidence of overlapping cortical changes in gray matter following TBI and AD, there is a lack of neuroimaging studies combining these two patient populations. Longitudinal examinations of older adults with AD, with and without a history of TBI, can provide important insights into TBI-related differences in AD progression.
The present study extends prior works by examining the relationships between TBI history and longitudinal changes in cortical thickness in AD and non-AD cohorts from the National Alzheimer’s Coordinating Center (NACC), monitored over an average time span of 33 months. It was hypothesized that individuals with AD and a history of TBI would exhibit greater cortical thickness decreases in frontal and temporal regions than those with AD alone, when comparing to their respective matched non-AD controls, supporting the hypothesized role of TBI in accelerating gray matter loss in AD cohorts.
Methods
Participants
Clinical and MRI data were acquired from the NACC database from 13 AD Research Centers (ADRCs). The current study included 154 participants (n = 24 cognitively normal (CN), n = 49 with AD (AD), n = 65 cognitively normal with history of TBI (TBI), and n = 16 with AD and history of TBI (AD+TBI). The CN and TBI groups consisted of participants that did not meet established clinical criteria for AD or any other neurodegenerative disease throughout the course of enrollment in the NACC database. The AD and AD+TBI groups consisted of participants who received a clinical diagnosis of AD based on NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association) criteria (14) and NIA-AA (National Institute of Aging–Alzheimer’s Association) criteria (15). Diagnosis and cognitive status were determined by a clinician or consensus team. Participants in the TBI and AD+TBI groups had a reported history of TBI based on subject and co-participant report; any number, type, or severity of TBI reported during the clinic visit was coded as a “yes”, and absence of TBI history was coded as “no” in the NACC dataset. Participants were excluded from the study if their TBI history was unknown or not available. Participants whose clinical data and MRI data were not collected within 12 months of each other were also excluded. The sample was restricted to participants who completed a follow-up MRI visit at least one month after the baseline MRI visit. An additional exclusion criterion was if participants had inconsistencies in AD diagnosis or their reported TBI status across visits.
MRI acquisition and processing
T1-weighted images were collected at baseline and follow-up (mean time interval = 33.4 months) for all 154 participants in the study; this included multiple different acquisition protocols from several ADRCs. Reconstruction of the cortical surfaces of T1-weighted images was performed using the longitudinal stream in the FreeSurfer image analysis toolkit version 6.0 (https://surfer.nmr.mgh.harvard.edu/). Structural processing of the large collective MRI dataset was performed using the Canadian Brain Imaging Research Platform (CBRAIN), which is a web-based, distributed high-performance computing environment (16). All image segmentations were then visually inspected using the VisualQC program (https://github.com/raamana/visualqc) to identify processing errors, manually edit minor errors and exclude low quality scans from further analysis. The researcher conducting the quality assessments and manual editing procedure was blinded to the group and diagnosis of the participant. All images that were manually edited were re-processed through the longitudinal pipeline via CBRAIN. Cortical thickness values were extracted from the 68 regions of interest (ROI), according to the Desikan-Killiany atlas provided by FreeSurfer. The subset of these ROIs that spanned the frontal and temporal lobes, were selected for analysis in this study, based on the importance of these areas in both AD and TBI (Table 1). The change in cortical thickness for each ROI was calculated by subtracting the cortical thickness at baseline from the cortical thickness at the follow-up visit.
Table 1. Regions of Interest used in the Cortical Thickness Analysis
Statistical analysis
For categorical variables, Pearson’s Chi-square test was used to test for between-group differences, with post-hoc comparisons between all group pairs and Bonferroni adjustment of p-values to correct for multiple comparisons. For continuous variables, one-way analysis of variance (ANOVA) was used to test for group differences, with Tukey’s post hoc test used to compare demographic and clinical characteristics across groups. Descriptive statistics were calculated, and all data were visualized using the statistical software R, version 1.4.1717.
To test for multivariate relationships between study group and measures of cortical thickness over time, a partial least squares (PLS) correlation analysis was performed (17), relating study group (CN, AD, TBI, and AD+TBI) to cortical thickness change scores. To control for the effects of age at baseline and sex, these variables were regressed from the MRI data, with PLS analyses conducted on the residualized data. The PLS approach identifies weighted combinations of behavioural variables (i.e., study groups) and imaging variables (i.e., fronto-temporal ROIs) that produce a pair of latent variables with maximized covariance between the two variable sets. The variable weights (or “loadings”) are used to measure the importance of individual variables in the bivariate relationship. Significant variable loadings were identified by performing bootstrap resampling on participants (1000 iterations) to obtain corresponding bootstrap ratios (BSRs; z-scored statistics of effect) and empirical p-values (two-tailed, percentile-based), corrected for multiple comparisons at a false discovery rate (FDR) of 0.05. In the current dataset, up to 4 such covariance relationships (or “components”) may be identified; only component pairs with significant loadings at FDR = 0.05 were retained. All PLS analyses were conducted using in-house software developed for MATLAB.
Additionally, for each group, the absolute quantification of cortical thickness change across the significant fronto-temporal ROIs from the PLS analysis were calculated using one-sample tests with bootstrapping of the mean difference scores (1000 resamples) to obtain standardized effect sizes (mean, standard error (SE), and 95% confidence interval (CI)). Post-hoc pairwise comparisons of PLS salience weights between groups were calculated using a bootstrap resampling framework (1000 iterations) to generate BSRs. The difference of the BSRs was calculated to directly compare the relative effects sizes of AD and AD+TBI groups to their matched controls CN and TBI, respectively.
Results
Participant demographics
The demographic and clinical data are summarized for each of the study groups (CN, AD, TBI, and AD+TBI) within Table 2. Groups significantly differed in terms of age (p < .001), sex (p < .05), CDR Dementia Staging Instrument Global and Sum of Boxes scores at baseline and follow-up, and time between baseline and follow-up imaging (p < .05). Post hoc testing found that the mean age was significantly lower for AD+TBI compared to AD (mean difference = -10.59 years, 95% CI = [-17.88, -3.29], p < .01), for CN compared to AD (mean difference = -8.03 years, 95% CI = [-14.34, -1.71], p < .01), and for TBI compared to AD (mean difference = -13.05 years, 95% CI = [-17.84, -8.26], p < .001). The CDR Sum of Boxes and Global scores were not significantly different between the AD and AD+TBI groups and between the TBI and CN groups, although the AD and AD+TBI scores differed from those of the CN and TBI groups. The only exception to this was that for CDR Global scores at baseline, post hoc testing found that the mean CDR Global score was significantly lower for AD compared to AD+TBI (mean difference = -0.23, 95% CI = [-0.42, -0.04], p < .01). In addition, mean months between baseline and follow-up imaging was significantly higher for TBI relative to AD (mean difference = 10.88 months, 95% CI = [1.60, 20.15], p < .05).
Table 2. Demographic and Clinical Characteristics of the Study Sample
Abbreviations: AD=Alzheimer’s disease; CDR = Clinical Dementia Rating Scale; CN=cognitively normal; TBI=traumatic brain injury. Note: Data are expressed as mean ± SD or number of participants and percentage, as appropriate.
Relationship between study group and cortical thickness change
Figure 1 shows PLS analysis results evaluating the relationship between study group and cortical thickness change, after adjusting for age at baseline and sex. Only 1 component had significant variable loadings at an FDR threshold of 0.05. Significant variable loadings in this plot indicates reliable involvement of study groups and cortical ROIs in the underlying covariance relationship.
Figure 1. Partial least squares correlation analysis
Significant covariance relationships between study group (CN, AD, TBI, AD+TBI) and longitudinal changes in cortical thickness of fronto-temporal regions. Panel A shows frontal and temporal brain areas with significant loadings. All fronto-temporal areas were significant except the left caudal anterior cingulate, right caudal anterior cingulate, right paracentral, left frontal pole, and right transverse temporal. Colour bar represents the bootstrap ratio. Only component 1 showed significant reliable loadings at p = .05, with “*” indicating significant reliable loadings at FDR = 0.05 in panel B. Error bars denote bootstrapped standard error of the mean. Abbreviations: AD = Alzheimer’s disease; CN, cognitively normal; TBI, traumatic brain injury.
For component 1 (66% of covariance; Figure 1), significant effects of group on the longitudinal changes in cortical thickness were identified in a spatial pattern encompassing 39 of 44 fronto-temporal regions (Fig. 1A), with greatest effects in the temporal lobes (average BSR = 7.3), compared to the frontal lobes (average BSR = 3.3). These regions showed negative loadings for the AD (p = .001, BSR = -3.3) and AD+TBI groups (p < .001, BSR = -3.4), and positive loadings for the TBI (p < .001, BSR = 13.3) and CN groups (p = .548, BSR = 0.6) (Fig. 1B), indicating more pronounced declines in cortical thickness for the AD groups than the control groups, within the identified pattern of ROIs. The AD group showed a mean absolute decrease in cortical thickness from baseline to follow-up of -0.086mm (SE = 0.016, 95% CI = -0.119, -0.057; Fig. 2) while the AD+TBI group showed a greater decrease of -0.121mm (SE = 0.025, 95% CI -0.176, -0.079). Conversely, the TBI group showed a slight mean absolute increase in thickness of 0.006mm (SE = 0.007, 95% CI = -0.007, 0.021), while the CN group demonstrated a modest mean absolute decrease of -0.032mm (SE = 0.014, 95% CI -0.059, -0.003).
Figure 2. Fronto-temporal longitudinal cortical thickness change
Cortical thickness change, averaged across 39 significant fronto-temporal areas for component 1, per group. Crossbars denote the group mean and 95% confidence intervals. Abbreviations: AD = Alzheimer’s disease; CN, cognitively normal; TBI, traumatic brain injury.
Significant between-group differences of PLS salience weights were also identified, with significant pairwise differences of the mean between AD and TBI groups (p < .001, BSR = -7.2), CN and TBI groups (p < .001, BSR = -4.0), AD+TBI and CN groups (p = .004, BSR = -2.9), AD and CN groups (p = .009, BSR = -2.6), and AD+TBI and TBI groups (p < .001, BSR = -8.8). A second-order comparison of the relative effect size for the AD+TBI vs. TBI contrast, with respect to the AD vs. CN contrast, showed a significantly greater decrease for AD+TBI relative to their matched control group (p < .001, BSR = -6.2).
Discussion
There is a growing body of literature on the long-term brain changes related to TBI and associated risk of developing AD later in life (18–22). To date, however, differences in the progression of brain atrophy among AD patients with and without a history of TBI remains incompletely understood. In this longitudinal study, a significant relationship between study group and changes in cortical thickness was identified using multivariate PLS correlation analyses. The study findings partly support the hypothesis that TBI history is associated with greater cortical thinning in fronto-temporal regions over time among patients with AD, relative to their non-AD controls, supporting a faster course of disease progression.
The AD groups with and without TBI history more strongly expressed the pattern of longitudinal frontal and temporal atrophy, while the CN group displayed an intermediate pattern of atrophy and the TBI group expressed the atrophy the least, after adjusting for age at baseline and sex. The absolute change of cortical thickness from baseline to follow-up reveals differences in AD groups compared to matched non-AD controls, with the AD groups demonstrating greater cortical thinning. These findings are supported by prior research that examined the effects of AD on cortical tissue and reported progressive atrophy and reliable patterns of decrease throughout the disease course, compared to healthy control cohorts (12, 23). In the present study, the association between the CN group and longitudinal change in cortical thickness was nonsignificant. The lack of significant findings suggests that patterns of cortical thinning over time in healthy aging are heterogenous across participants. This is supported by previous literature on diffuse cortical thinning in aging, with reports of age effects on frontal regions and moderate age effects in temporal, parietal, and occipital regions (24–28). Taken together, the normal aging process can be distinguished from neurodegenerative effects of AD.
While the AD and AD+TBI groups demonstrated comparable average patterns of atrophy, the hypothesis of greater cortical thickness decreases in AD+TBI relative to TBI matched controls, compared to AD relative to CN matched controls was supported. A methodological strength of the present study was to include a matched group of controls with and without prior TBI; and our findings highlight the differential structural trajectory between AD patients with TBI and cognitively normal controls with TBI. Specifically, there appears to be a significant association between TBI and decreased fronto-temporal cortical thinning among cognitively normal controls. Thus, the apparent effects of AD are more pronounced among individuals with a history of TBI. The pattern of cortical thinning over an average of 33 months was widespread, with greater effects in key temporal regions that are most commonly affected in TBI. The findings are consistent with the literature, which highlights the vulnerability of fronto-temporal regions to acute injury during a TBI (29), and chronic tissue loss due to secondary injury processes (5, 30). These novel results contribute to the emerging area of research of TBI as a risk factor for accelerated neurodegeneration and dementia. Further research is needed to better understand which individuals are at risk for neurodegeneration and worse clinical outcomes.
The precise mechanisms underlying the accelerated cortical thinning in frontal and temporal lobes associated with TBI history are incompletely understood. However, given overlapping neuropathological outcomes in TBI and AD, it is plausible that complex pathology following TBI may co-occur with and exacerbate the effects of pathological aging (31). Alterations in cortical thickness in the chronic phase of injury may reflect the evolution of acute injury mechanisms (5, 30). For instance, diffuse axonal and microvascular injury may lead to persisting neuroinflammatory processes and impaired neurometabolism that further triggers neurodegeneration and contributes to lasting cognitive impairment (32–34). It is necessary to quantify the effects of TBI in AD progression among living patients, for early identification and intervention of at-risk individuals. These direct effects of TBI likely involve complex alterations in brain physiology, with metabolic, perfusion, inflammatory, and degenerative components (30, 35–39). Further research is warranted to comprehensively assess brain physiology in these cohorts using longitudinal multimodal neuroimaging. The complexity of TBI-related physiological changes is further exemplified in the comparison of TBI groups to their non-TBI counterparts. Cortical thickness values show less decline in the TBI group than controls, while decreases are comparable for AD groups with and without TBI. These results suggest TBI-specific processes that mitigate apparent age-related atrophy. It is presently unclear whether this reflects post-TBI neuropathological changes, e.g., glial scarring and neuroplasticity (40, 41), or lifestyle factors influencing both TBI risk and long-term brain physiology (42). Whatever the cause, it underscores the importance of accounting for TBI history, as these effects may otherwise confound estimates of longitudinal cortical atrophy among AD cohorts.
The results of the study must be interpreted within the context of some potential limitations. While the PLS models adjusted for the effects of age at baseline and sex, the results would have been strengthened by adjusting for other variables including cardiovascular and genetic risk factors of AD. As well, the study lacked neuropathology data for AD patients and a more detailed TBI history reporting (e.g., severity of TBI, number of TBIs, mechanism of injury, and recency of TBI), which may have helped to better characterize heterogeneities within these cohorts. These are important considerations that should be evaluated in future research. Sample sizes were also modest, particularly in the AD+TBI group, despite being drawn from one of the largest existing databases of AD neuroimaging. These issues are mitigated somewhat by the longitudinal design and use of matched control cohorts, nevertheless, further replication studies are needed. There is also variability contributed by the MRI data being collected at multiple NACC ADRCs, although the baseline and follow-up scan of each participant was performed at the same MRI scanner. A rigorous quality control procedure was also conducted to assess image quality and minimize bias in image processing and analysis. The current study focused on the relationship between study group and longitudinal cortical thickness change across two timepoints, however, future longitudinal studies with a larger sample, over a longer duration, including multiple timepoints would be informative. Furthermore, longitudinal studies that correlate clinical and cognitive sequelae with imaging measures among different TBI severities are needed for an improved understanding of progression of dementia among individuals with TBI.
Overall, this study further supports a relationship between history of TBI and accelerated fronto-temporal gray matter loss among AD patients, highlighting this unique cohort. These findings, together with the current understanding of mechanisms of chronic neurodegeneration, can be used to assess patient risk before AD onset, and to inform the development of interventions for improving long-term outcomes and quality of life of individuals with TBI. This study is a critical step toward disease characterization and monitoring of disease evolution, but further research is required to determine the impact of fronto-temporal atrophy on clinical outcomes in this cohort.
Acknowledgements: The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD).
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethical standards: The current study obtained ethical approval from the Research Ethics Board (REB) at St. Michael’s Hospital, Toronto, Ontario, Canada. All participants included in the National Alzheimer’s Coordinating Center (NACC) database provided written informed consent to an open access framework for their deidentified data to be shared beyond the NACC team of researchers.
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