1. Neuroscience
Download icon

Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer’s disease

  1. Alexa Pichet Binette  Is a corresponding author
  2. Guillaume Theaud
  3. François Rheault
  4. Maggie Roy
  5. D Louis Collins
  6. Johannes Levin
  7. Hiroshi Mori
  8. Jae Hong Lee
  9. Martin Rhys Farlow
  10. Peter Schofield
  11. Jasmeer P Chhatwal
  12. Colin L Masters
  13. Tammie Benzinger
  14. John Morris
  15. Randall Bateman
  16. John CS Breitner
  17. Judes Poirier
  18. Julie Gonneaud
  19. Maxime Descoteaux
  20. Sylvia Villeneuve  Is a corresponding author
  21. DIAN Study Group
  22. PREVENT-AD Research Group
  1. Department of Psychiatry, Faculty of Medicine, McGill University, Canada
  2. Douglas Mental Health University Institute, Canada
  3. Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada
  4. Electrical Engineering, Vanderbilt University, United States
  5. McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada
  6. Department of Neurology, Ludwig-Maximilians-Universität München, Germany
  7. German Center for Neurodegenerative Diseases (DZNE), Germany
  8. Department of Clinical Neuroscience, Osaka City University Medical School, Japan
  9. Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea
  10. Department of Neurology, Indiana University, United States
  11. Neuroscience Research Australia, Australia
  12. School of Medical Sciences, UNSW Sydney, Australia
  13. Harvard Medical School, Massachusetts General Hospital, United States
  14. The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia
  15. Knight Alzheimer Disease Research Center, Washington University School of Medicine, United States
  16. Department of Neurology, Washington University School of Medicine, United States
  17. Normandie Univ, UNICAEN, INSERM, U1237, Institut Blood and Brain @ Caen-Normandie, Cyceron, France
Research Article
  • Cited 0
  • Views 631
  • Annotations
Cite this article as: eLife 2021;10:e62929 doi: 10.7554/eLife.62929

Abstract

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer’s disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.

Introduction

The progression of Alzheimer’s disease (AD) includes a long asymptomatic phase, during which accumulating pathology is accompanied by various brain changes (Jack et al., 2013; Sperling et al., 2011). Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of the disease (Duyckaerts et al., 2009), start to accumulate decades before signs of cognitive impairment (Bateman et al., 2012; Jansen et al., 2015). Positron emission tomography (PET) can image both proteins in vivo (Johnson et al., 2016; Klunk et al., 2004; Schöll et al., 2016), and thus help in identifying the earliest brain changes associated with such pathologies. Aβ- and tau-PET tracer accumulate in distinct patterns of deposition that follows canonical brain networks/organization. Aβ develops a widespread pattern of deposition that recapitulates a default mode network-like pattern, accumulating early in the frontal and parietal lobes (Mattsson et al., 2019; Villeneuve et al., 2015). Tau accumulates in a more localized pattern that can start in the locus coeruleus before being detectable by tau-PET scans, followed by the medial temporal lobe in the preclinical phase of the disease, and spreading to the lateral temporal lobe and the rest of the brain in late stages (Braak and Braak, 1991; Braak et al., 2011). A prominent view is that pathology accumulates in functionally and/or structurally connected regions (Franzmeier et al., 2019; Seeley et al., 2009; Sepulcre et al., 2017; Vogel et al., 2020). Many studies have highlighted associations between Aβ- and tau-PET and brain functional activity early in the course of the disease (Berron et al., 2020; Jones et al., 2017; Mormino et al., 2011; Sepulcre et al., 2017). However, relations between pathology and white matter (WM) microstructure, as assessed by diffusion magnetic resonance imaging (MRI), remain elusive in preclinical AD. While WM degeneration is clearly apparent in the late symptomatic stages, how WM microstructure is affected early on in the disease process is less clear (Sachdev et al., 2013). Whole-brain diffusion MRI tractograms can represent the brain’s WM architecture, but these are difficult to reconstruct because of extensive crossing of WM fibers and the complexity of tracking algorithms (Rheault et al., 2020). Recent advances in modeling and available algorithms have facilitated robust extraction of WM bundles with automated methods, thereby allowing their more precise investigation. As well, more specific measures have become available for analysis of WM (Dyrby et al., 2014). In particular, free-water (FW)-corrected diffusion tensor measures may offer better estimates of WM microstructure, yielding tissue-based fractional anisotropy and diffusivities after removing the FW contribution to each voxel (Pasternak et al., 2009).

We investigated diffusion-based measures of WM microstructure in bundles that connect cortical regions vulnerable to Aβ and tau deposition. We hypothesized that such bundles would show lower fractional anisotropy and higher diffusivity with more pathology as proxy of WM degeneration. We sought to expand upon the few studies linking preclinical AD pathology and WM microstructure and focused on bundles (defined a priori) connecting brain regions targeted early by AD pathology, notably the cingulum bundle (Jacobs et al., 2018). The latter is a large association bundle under the cingulate gyri that connects anterior to posterior cingulate regions and curves further into the parahippocampal gyri of the temporal lobe. This bundle is typically affected in symptomatic AD dementia (Bubb et al., 2018; Jacobs et al., 2018; Kantarci et al., 2017; Roy et al., 2020; Wen et al., 2019), and given its location, could be preferentially affected by Aβ, particularly in its anterior segment. Also of interest is the uncinate fasciculus, reported to be affected at the stage of mild cognitive impairment (Mito et al., 2018; Roy et al., 2020). This bundle connects parts of the limbic system, such as the hippocampus and amygdala, with the orbitofrontal cortex (Von Der Heide et al., 2013), brain regions thought to be key regions for tau and Aβ propagation, respectively (van der Kant et al., 2020). Our objective was to investigate associations between the microstructure in those bundles of interest and AD pathology in two cohorts of cognitively normal individuals at risk of AD, older adults at increased risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD (ADAD).

Results

Approach and participants

Using state-of-the-art methods in diffusion MRI modeling, tractography, and tractometry, we aimed to better understand the associations between WM microstructure of key bundles in preclinical AD and deposition of Aβ and tau as measured by PET. We reasoned that the preclinical stage of AD should be the ideal point at which to study these questions, given that this is a period during which AD pathology is spreading but overall brain structure and function remain largely preserved. We therefore studied the preclinical stage of both late-onset sporadic AD and ADAD. Sporadic AD is the most common form of dementia, is multifactorial, and occurs most often in late life. ADAD is the rarer form of AD, caused by fully penetrant genetic mutations in PSEN1, PSEN2, or APP, that leads to Aβ accumulation up to 20 years prior to symptom onset (Bateman et al., 2012) and to onset of cognitive symptoms often in the 40s and early 50s. ADAD is considered a ‘purer’ form of preclinical AD since most mutation carriers do not exhibit age-associated co-pathologies. We studied a subset of 126 asymptomatic individuals at high risk of sporadic AD from the PRe-symptomatic EValuation of Experimental or Novel Treatments for AD (PREVENT-AD) cohort (Breitner et al., 2016) and 81 ADAD presymptomatic mutation carriers from the Dominantly Inherited Alzheimer’s Network (DIAN) cohort (Morris et al., 2012). PREVENT-AD enrolls cognitively normal older adults at risk of sporadic AD given their parental or multiple-sibling family history of the disease. At the time of study, participants were on average 67.3 years of age, predominantly female, and highly educated (Table 1). Based on a threshold established previously using global cortical Aβ burden (McSweeney et al., 2020), 19% of the participants were considered Aβ-positive. We also considered the same proportion of participants with the highest entorhinal tau uptake to be tau-positive. DIAN enrolls adults from families with ADAD. Our focus was on presymptomatic mutation carriers, but analyses were also conducted in mutation non-carriers in order to rule out false-positive associations that could be due to off-target binding properties of the PET tracer. Mutation carriers were on average 34.5 years of age, while non-carriers were slightly older. Both groups had more than 50% female and were highly educated. For the DIAN cohort, we had access to Aβ-PET only, with 43% of the mutation carriers and none of the non-carriers classified as Aβ-positive (Su et al., 2013).

Table 1
Demographics.
PREVENT-AD
(n = 126)
DIAN mutation carriers (n = 81)DIAN mutation non-carriers (n = 96)
Age (years)67.3 ± 4.8 (68–83)34.5 ± 9.9 (18–61)39.3 ± 11.7 (19–69)
Sex F:M (%F)94:32 (75%)42:39 (52%)56:40 (58%)
APOE4 carriers (%)50 (40%)24 (30%)26 (27%)
Education (years)15.2 ± 3.3 (7–24)15.2 ± 3.0 (10–24)15.1 ± 2.7 (10–26)
Handedness (n, % right-handed)114 (90%)69 (85%)82 (85%)
Systolic blood pressure129.0 ± 13.8 (100–164)122.5 ± 10.2 (95–155)123.5 ± 17.1 (90–190)
Diastolic blood pressure74.0 ± 8.1 (60–96)75.2 ± 8.8 (55–104)77.1 ± 10.1 (60–110)
Global Aβ SUVR*1.3 ± 0.3 (1.0–2.8)1.6 ± 0.7 (0.8–3.7)1.0 ± 0.1 (0.9–1.3)
Aβ-positive (%)24 (19%)35 (43%)0 (0%)
Entorhinal tau SUVR1.1 ± 0.1 (0.7–1.6)NANA
Mini-Mental State Examination28.8 ± 1.2 (24–30)29.0 ± 1.3 (24–30)29.2 ± 1.2 (25–30)
Estimated years to symptom onset−5.7 ± 7.6 (−20.8 to 16.8)−13.6 ± 8.3 (−31.5 to 11.8)−7.4 ± 12.5 (−28.8 to 21.4)
  1. Values represent Mean ± SD (range). Participants with at least one ε4 allele were considered APOE4 positive. The Mini-Mental State Evaluation was administered at the same time as PET.

    * Note that NAV4694 was used in PREVENT-AD and PIB was used in DIAN.

  2. † Estimated years to symptom onset was calculated as the parent’s age at dementia onset minus the age of the participant; four missing values in PREVENT-AD.

    Aβ: beta-amyloid; APOE: apolipoprotein E; SUVR: standardized uptake value ratio; PET: positron emission tomography.

Methodology overview

We extracted FW-corrected diffusion tensor measures in bundles of interest. We reconstructed each individual’s whole-brain tractogram using high angular resolution diffusion imaging and fiber orientation distribution functions (fODFs), and employed automated tools to isolate the anterior cingulum, the posterior cingulum, and the uncinate fasciculus (Garyfallidis et al., 2018; Rheault, 2020; Wassermann et al., 2016). Tractometry then generated bundle-specific quantification of five WM properties (Cousineau et al., 2017; Rheault et al., 2017). These were tissue fractional anisotropy (FAT), mean diffusivity (MDT), axial diffusivity (ADT), and radial diffusivity (RDT). In each, ‘T’ represents tissue in these FW-corrected diffusion tensor measures. We also report the FW index, which is thought to reflect a measure of neuroinflammation (Pasternak et al., 2009). To investigate the relationships with AD pathology, we focused on typical measures of Aβ- and tau-PET, which is a global cortical Aβ burden (in PREVENT-AD and DIAN) and entorhinal tau tracer uptake (in PREVENT-AD only).

We first evaluated the partial correlations between WM microstructure and pathology at the whole group level, controlling for age, sex, and bundle volume. We then repeated these analyses while restricting them to participants with (Aβ-positive or tau-positive) and without (Aβ-negative or tau-negative) pathology. The analysis conducted in Aβ-positive or tau-positive groups was especially important for the PREVENT-AD cohort since participants free from pathology might never develop AD. In complementary analyses, to investigate whether the associations would be independent from gray matter (GM) neurodegeneration, we further added GM volume in brain regions connected by our bundles of interest as covariates in the regression models. Lastly, we evaluated whether similar associations could be detected with typical diffusion tensor measures, that is, FA, MD, AD, and RD (not corrected for FW).

An overview of the processing steps is shown in Figure 1 and can be summarized as follows: in three a priori WM bundles of interest extracted in the left and right hemisphere from each participant’s tractogram, we evaluated associations between five related microstructure measures and AD pathology measured with PET (global cortical Aβ and entorhinal tau). We analyzed all five WM microstructure measures to detect whether a consistent pattern of associations across measures emerges rather than focusing on one given measure.

Overview of the processing steps.

PREVENT-AD and DIAN participants were processed following the same pipeline. Whole-brain tractogram was reconstructed using the TractoFlow Atlas-Based Segmentation pipeline, and automated bundle extraction tools were used to extract the bundles of interest in each hemisphere. Free-water-corrected tensor measures were calculated for each bundle. Associations between white matter microstructure and global Aβ and entorhinal tau PET were then investigated. Aβ: beta-amyloid; PET: positron emission tomography; SUVR: standardized uptake value ratio.

Associations in the uncinate fasciculus and posterior cingulum in PREVENT-AD Aβ-positive and tau-positive groups

In PREVENT-AD, at the level of the whole group, there were no associations between global cortical Aβ or entorhinal tau burden with any of the WM microstructure measures across the three bundles of interest (Figure 2—source data 1, Figure 3—source data 1). Associations were detected only in the participants considered as Aβ-positive or tau-positive. In the Aβ-positive group, controlling for age, sex, and bundle volume, lower FAT, higher MDT, and higher RDT in the left posterior cingulum and in the uncinate fasciculus were related to greater cortical Aβ burden (Figure 2, Figure 2—source data 1). Similar associations were present in the right uncinate fasciculus at trend level (p=0.06 for FAT, MDT, and RDT). Further, in the posterior cingulum, tau-positive participants displayed the same pattern of associations aforementioned; in this group, lower FAT, higher MDT, and higher RDT in the left posterior cingulum related to greater entorhinal tau-PET tracer binding (Figure 3, Figure 3—source data 1). Associations in the right posterior cingulum in tau-positive participants were trend level (p=0.06 for FAT, MDT, and RDT).

Associations between diffusion measures and Aβ burden in Aβ-positive PREVENT-AD participants.

(A–C) Bivariate associations between FAT and global cortical Aβ in each bundle in the left hemisphere to show examples of raw values in PREVENT-AD. Data are represented for the full sample, with Aβ-positive in orange (our group of interest) and Aβ-negative in gray. (D–F) Partial correlations between diffusion measures (average diffusion measure in the bundle) and global cortical Aβ-PET controlling for age, sex, and bundle volume (divided by total intracranial volume) were performed in PREVENT Aβ-positive participants. Partial correlation coefficient for each diffusion measure in the right and left bundles is reported as bar graphs. Black asterisks highlight that associations are significant in both hemispheres, otherwise the color of the symbol matches the hemisphere where the association is significant. *p=0.05; ** 0.05 > p > 0.001; +p=0.06. See also Figure 2—source data 1. Aβ: beta-amyloid; FAT: tissue fractional anisotropy; MDT: tissue mean diffusivity; ADT: tissue axial diffusivity; RDT: tissue radial diffusivity; FW: free-water index; PET: positron emission tomography.

Figure 2—source data 1

Associations between microstructure and beta-amyloid–positron emission tomography (Aβ-PET) in PREVENT-AD.

https://cdn.elifesciences.org/articles/62929/elife-62929-fig2-data1-v2.docx
Associations between diffusion measures and entorhinal tau burden in tau-positive PREVENT-AD participants.

(A–C) Bivariate associations between FAT and entorhinal tau in each bundle in the left hemisphere to show examples of raw values in PREVENT-AD. Data are represented for the full sample, with tau-positive in blue (our group of interest) and tau-negative in gray. (D–F) Partial correlations between diffusion measures (average diffusion measure in the bundle) and entorhinal tau-PET controlling for age, sex, and bundle volume (divided by total intracranial volume) were performed in PREVENT tau-positive participants. Partial correlation coefficient for each diffusion measure in the right and left bundles is reported as bar graphs. The color of the symbol on the bar graphs matches the hemisphere where the association is significant. *p=0.05; ** 0.05 > p > 0.001; +p=0.06. See also Figure 3—source data 1. FAT: tissue fractional anisotropy; MDT: tissue mean diffusivity; ADT: tissue axial diffusivity; RDT: tissue radial diffusivity; FW: free-water index; PET: positron emission tomography.

Figure 3—source data 1

Associations between microstructure and tau-positron emission tomography (tau-PET) in PREVENT-AD.

https://cdn.elifesciences.org/articles/62929/elife-62929-fig3-data1-v2.docx

In the anterior cingulum, there were no associations between WM measures and pathology in either Aβ-positive or tau-positive participants (Figures 2D3D). No association was found in the Aβ- and tau-negative groups.

Associations in anterior and posterior cingulum in DIAN mutation carriers

In DIAN mutation carriers, associations were found at the group level between global Aβ burden and WM microstructure in the anterior cingulum, following the same pattern of associations as in PREVENT-AD. As such, lower FAT, higher MDT, and higher RDT related to greater cortical Aβ across all mutation carriers (partial R = −0.27 for FAT and 0.28 for MDT and RDT, p=0.02; Figure 4—source data 1), but associations were higher when restricted to the Aβ-positive participants (Figure 4A–D). Associations in Aβ-positive participants were also found in the right posterior cingulum (Figure 4B–E). Focusing only on the Aβ-negative group, microstructure measures in the posterior cingulum were associated with global cortical Aβ in the same directions as in the Aβ-positive group (partial R = −0.31 for FAT and 0.31 for MDT and RDT, p=0.01). Of note, we did not find any associations between bundle microstructure and pathology in mutation non-carriers.

Associations between diffusion measures and Aβ burden in Aβ-positive DIAN mutation carriers.

(A–C) Bivariate associations between FAT and global cortical Aβ in each bundle in the left hemisphere to show examples of raw values in DIAN. Data are represented for the full sample, with Aβ-positive in red (our group of interest) and Aβ-negative in gray. (D–F) Partial correlations between diffusion measures (average diffusion measure in the bundle) and global cortical Aβ-PET controlling for age, sex, and bundle volume (divided by total intracranial volume) were performed in DIAN Aβ-positive participants. Partial correlation coefficient for each diffusion measure in the right and left bundles is reported as bar graphs. Black asterisks highlight that associations are significant in both hemispheres, otherwise the color of the symbol matches the hemisphere where the association is significant *p=0.05; ** 0.05 > p > 0.001. See also Figure 4—source data 1. Aβ: beta-amyloid; FAT: tissue fractional anisotropy; MDT: tissue mean diffusivity; ADT: tissue axial diffusivity; RDT: tissue radial diffusivity; FW: free-water index; PET: positron emission tomography.

Figure 4—source data 1

Associations between microstructure and beta-amyloid–positron emission tomography (Aβ-PET) in DIAN.

https://cdn.elifesciences.org/articles/62929/elife-62929-fig4-data1-v2.docx

Effect of GM atrophy on microstructure-pathology associations

We further wanted to evaluate whether significant associations between bundle microstructure and pathology in Aβ-positive or tau-positive participants were affected by GM atrophy. To do so, we added GM volume specifically in brain regions connected by the bundle of interest as an additional covariate. Partial correlations were thus controlled for age, sex, bundle volume, and GM volume. GM volume of the following regions were considered: the anterior and posterior cingulate cortex for the anterior cingulum, the precuneus and the parahippocampal gyrus for the posterior cingulum, and the medial orbitofrontal cortex and the parahippocampal gyrus for the uncinate fasciculus. In both PREVENT-AD and DIAN, further adjusting for GM volume did not change the significance of the microstructure measures in neither the anterior cingulum nor the uncinate fasciculus. The only bundle where atrophy changed the original associations was the posterior cingulum, with GM volume of the precuneus as a covariate. In both PREVENT-AD and DIAN, in models including volume of the precuneus, microstructure properties were no longer related to pathology. When volume of the parahippocampal gyrus was a covariate in the models of the posterior cingulum, microstructure associations were unchanged compared to the initial analyses, with the exception of the ones with Aβ burden in PREVENT-AD (the contribution of diffusion measures became marginal, changing from p=0.05 to p=0.06). In complementary analyses, we evaluated whether GM volume related to Aβ- and tau-PET controlling for age and sex. The main significant associations were in the right precuneus or posterior cingulate in PREVENT-AD and in the right posterior cingulate in DIAN (Table 2).

Table 2
Associations between gray matter volume and Aβ- and tau-PET in PREVENT-AD and DIAN.
PREVENT-AD
Aβ-positive
PREVENT-AD
Tau-positive
DIAN
Aβ-positive
Rpartialp-valueRpartialp-valueRpartialp-value
Left hemisphere
Anterior cingulate−0.2390.2710.0320.891−0.2070.248
Posterior cingulate−0.1160.598−0.1560.5−0.2520.156
Precuneus−0.2650.221−0.4390.047−0.3070.082
Parahippocampal gyrus0.1140.605−0.0730.7530.0790.661
Medial orbitofrontal cortex−0.4680.024−0.1970.392−0.1740.334
Right hemisphere
Anterior cingulate−0.3350.118−0.0850.713−0.1330.461
Posterior cingulate−0.3420.111−0.5460.01−0.3520.045
Precuneus−0.4680.024−0.4910.024−0.2870.105
Parahippocampal gyrus0.0140.948−0.2130.3550.160.373
Medial orbitofrontal cortex−0.3580.093−0.2370.3010.0140.94
  1. Rpartial and p-values from regression models investigating associations between gray matter volume (divided by total intracranial volume; independent variable) and pathology (dependent variable) in Aβ-positive or tau-positive participants in PREVENT-AD and DIAN. Models included age and sex as covariates.

    Aβ: beta-amyloid; PET: positron emission tomography.

Importance of advanced FW measures to these results

To evaluate the sensitivity of FW-corrected measures over the typical tensor measures, we tested whether similar associations with pathology exist with FA, MD, AD, and RD (i.e., not corrected for FW). Except for FA, which gave similar results to FAT, MD, RD, and AD were not associated with pathology in any bundle (Table 3), suggesting that FW-corrected measures capture subtle WM microstructure alterations not always detectable with more classical diffusion tensor imaging (DTI) measures.

Table 3
Associations between typical tensor measures and Aβ- and tau-PET in PREVENT-AD and DIAN.
PREVENT-AD
Aβ-positive
Anterior cingulumPosterior cingulumUncinate fasciculus
Rpartialp-valueRpartialp-valueRpartialp-value
Left hemisphere
FA−0.1280.571−0.4290.046−0.5260.012
MD0.0220.9230.180.4240.320.147
AD−0.1060.637−0.3150.153−0.3050.168
RD0.0810.7210.3050.1680.4480.037
Right hemisphere
FA−0.0210.927−0.30.175−0.5660.006
MD−0.0480.8310.1310.560.0780.729
AD−0.0670.766−0.1020.651−0.3810.08
RD−0.0190.9310.2210.3220.3440.116
Tau-positive
Anterior cingulumPosterior cingulumUncinate fasciculus
Rpartialp-valueRpartialp-valueRpartialp-value
Left hemisphere
FA−0.4650.039−0.5230.0180.1080.65
MD0.5110.0210.3960.0840.0540.821
AD0.1120.6390.0820.7310.2060.384
RD0.5460.0130.4650.039−0.0140.953
Right hemisphere
FA−0.4610.041−0.4870.029−0.3990.081
MD0.4160.0680.3720.1060.2110.372
AD0.0120.9590.1570.508−0.0110.965
RD0.4950.0270.4440.050.3110.182
DIAN
Aβ-positive
Anterior cingulumPosterior cingulumUncinate fasciculus
Rpartialp-valueRpartialp-valueRpartialp-value
Left hemisphere
FA−0.3730.035−0.1920.293−0.0310.868
MD0.150.413−0.0510.783−0.0150.935
AD−0.1960.283−0.2260.213−0.0670.716
RD0.3180.0760.0490.790.0210.91
Right hemisphere
FA−0.4520.009−0.40.023−0.350.049
MD0.1220.505−0.0020.9910.1080.558
AD−0.2390.188−0.2190.229−0.0980.595
RD0.3350.0610.1460.4260.2090.251
  1. Rpartial and p-values from regression models investigating associations between each tensor measure (average diffusion measure in the bundle; independent variable) and pathology (dependent variable) in PREVENT-AD Aβ-positive or tau-positive participants and DIAN Aβ-positive participants. Models included age, sex, bundle volume (divided by total intracranial volume) as covariates. 

    Aβ: beta-amyloid; FA: fractional anisotropy; MD: mean diffusivity; AD: axial diffusivity; RD: radial diffusivity; PET: positron emission tomography.

Discussion

The notion that AD pathology accumulates in connected regions in the brain has foundations in rodent models (Ahmed et al., 2014; Jucker and Walker, 2018; Palop and Mucke, 2010), and it is gaining credence in human neuroimaging studies. It is striking how pathology deposit in structurally or functionally connected regions (Franzmeier et al., 2020; Seeley et al., 2009; Sepulcre et al., 2016; Vogel et al., 2020). However, there is limited evidence on how WM microstructure in bundles linking those key pathology regions is affected in the early phases of AD. Combining Aβ- and tau-PET with recent advanced diffusion imaging analyses, we investigated WM microstructure in bundles (selected a priori) that connect key AD brain regions with Aβ and tau deposition. Our aim here was not to test the spreading hypothesis per se, but, assuming that this hypothesis is correct, to focus on local effects of microstructure alterations and the presence of AD pathology in the preclinical stage of the disease. We investigated diffusion–PET associations in a cohort of asymptomatic older adults at risk of AD dementia due to their family history of sporadic AD and presymptomatic ADAD mutation carriers. In both cohorts, we found lower FAT, higher MDT, and higher RDT were related to greater pathology. In PREVENT-AD, associations were found in the uncinate fasciculus and the posterior cingulum, whereas in DIAN associations were found in the anterior and posterior segments of the cingulum. Furthermore, in the PREVENT-AD the associations were restricted to participants with significant AD pathology. These results suggest that significant levels of Aβ- and tau-PET tracer binding are associated with WM neurodegeneration both in the preclinical phase of sporadic AD and ADAD.

Our ‘bundle-specific’ approach through tractography and tractometry suggests topographical relationships between pathology and WM microstructural alterations in the early stage of AD and complements the typical approach of voxel-wise analyses (Harrison et al., 2020; Zhang et al., 2019). Using more precise tissue measures with FW corrected as opposed to classical diffusion tensor measures strengthened our findings, further highlighting the relevance of novel methods. Most of the models proposing a cascade of events over the course of AD have not included WM alterations (Iturria-Medina et al., 2016; Jack et al., 2013). One exception being a recent model in ADAD that included diffusivity, with higher MD being detectable 5–10 years prior to symptom onset (Araque Caballero et al., 2018). Although the current study design precludes us from staging when microstructure starts to change, our findings suggest that WM degeneration already occurs with early pathology accumulation prior to symptom onset both in the sporadic and the autosomal dominant forms of AD.

The observed associations follow the classical pattern of degeneration that is characterized by lower anisotropy and higher diffusivity, representing loss of coherence in the WM microstructure with AD progression (Badea et al., 2016; Caso et al., 2016; Sexton et al., 2011). This pattern of WM degeneration develops invariably along the AD spectrum (Amlien and Fjell, 2014; Pereira et al., 2019), with changes often becoming detectable only in the mild cognitive impairment and dementia stages (Mito et al., 2018; Song et al., 2018; Wang et al., 2019; Wen et al., 2019), and rarely in Aβ-positive cognitively normal participants (Rieckmann et al., 2016; Vipin et al., 2019). Our results further emphasize that in presymptomatic populations associations start to be detectable in individuals with high amount of pathology. In effect, most of the microstructure-pathology associations were restricted to the Aβ-positive or tau-positive participants. We should note that in the asymptomatic stage there is also evidence of WM alterations opposing the typical degeneration pattern, suggesting a possible biphasic relationship over the course of the disease (Fortea et al., 2010; Montal et al., 2018; Wearn et al., 2020). For instance, hypertrophy, glial activation, neuronal or glial swelling have been attributed higher anisotropy and lower diffusivity in the asymptomatic phase (Fortea et al., 2010; Montal et al., 2018). The biomarker status (i.e., Aβ-positive or negative) might be important to disentangle such early processes (Dong et al., 2020; Racine et al., 2014). Not dichotomizing by pathology status might obscure some associations in the early disease stages, as shown here.

The bundle that was consistently affected in participants with high pathology in both cohorts was the posterior cingulum, a key bundle in AD (Agosta et al., 2011; Caso et al., 2016; Zhuang et al., 2012). The posterior cingulum is certainly altered in the symptomatic stage, and diffusivity in this bundle has also shown to be related to tau accumulation in preclinical individuals (Jacobs et al., 2018). In the PREVENT-AD cohort, the posterior segment of the bundle was the only region where tau-positive participants presented WM degeneration with greater entorhinal tau. In DIAN, although we did not have tau-PET, we hypothesize that the associations found in Aβ-positive in the posterior cingulum would be present with tau since mutation carriers harbor elevated tau binding in the precuneus (Gordon et al., 2019). In an attempt to explore whether associations were independent of atrophy in brain regions connected the bundles of interest, we also controlled for GM volume in such regions. In both cohorts, the precuneus is the only region where, when added as a covariate, microstructure was no longer related to pathology. Such finding might suggest that this critical region in AD pathophysiology might already be further along the degeneration process, with white and GM being affected. Our results both in preclinical sporadic and ADAD corroborate the idea that the precuneus/posterior cingulum, more largely part of the posterior default mode network or posterior-medial system, is a critical area in the cascading events of AD (Berron et al., 2020; Jones et al., 2016).

In DIAN, the other bundle where Aβ and WM measures were related was the anterior cingulum, another bundle connecting key regions where Aβ accumulates. In line with these results, similar associations have been found in DIAN using CSF Aβ (Finsterwalder et al., 2020). On the other hand, in PREVENT-AD, the strongest associations with Aβ were detected in the uncinate fasciculus. This bundle has an interesting anatomy, connecting regions at the intersection of both Aβ (frontal lobe) and tau (temporal lobe) deposition patterns in sporadic AD. We speculate that the particular localization of the uncinate fasciculus with regards to Aβ and tau deposition might confer early vulnerability to pathological insults. Further, the orbitofrontal cortex is not only a region where Aβ pathology accumulates early but is also a highly plastic late-developing region, typically affected in aging (Fjell et al., 2014; Pichet Binette et al., 2020). This might in part explain why the uncinate fasciculus is preferentially affected in preclinical sporadic AD compared to the younger mutation carriers of ADAD.

The direct investigation of WM fiber bundles and their microstructure was possible due to recent advances in diffusion imaging modeling, tractography, bundle extraction, and tractometry quantification. However, there are several limitations to these techniques and to our study. First, there are no common standards (yet) to extract predefined bundles from tractograms, and bundles with high curvature are more challenging to extract. To mitigate this challenge, we mostly relied on algorithms that use priors to help generate fuller bundles. We also used automated algorithms to increase reproducibility and performed rigorous visual inspection to make sure all algorithms yielded comparable bundles. The diffusion sequence, similar in both cohorts, relied on only one b-value, and future acquisitions with multiple b-values could further improve capturing fine-grained changes (Pines et al., 2020). We should also note that the PREVENT-AD cohort does not present highly elevated levels of tau, hence the deliberate choice of focusing on the proportion of participants with the highest levels rather than applying a definite cut-off. The sample size might not be huge, but we replicate all main findings in our two groups of interest. Both cohorts are also followed over time on cognition and imaging, so future longitudinal studies can help clarify the sequence of events between pathology and WM changes in the preclinical and early symptomatic stages.

Overall, we used state-of-the-art analytical techniques to study associations between WM microstructure and Aβ- and tau-PET in key bundles affected in AD in the PREVENT-AD cohort of cognitively normal older adults whose strong family history of AD suggests an increased risk of subsequent dementia (Cupples et al., 2004; Devi et al., 2000) and in presymptomatic mutation carriers from the DIAN cohort. We highlighted the vulnerability of WM bundles to early presence of Aβ and tau proteins. More generally, the topography of our results aligns with the concept of retrogenesis, postulating that late-myelinated fibers, from temporal and neocortical regions, are affected first in the disease course and less resistant to neurodegeneration (Alves et al., 2015; Bartzokis, 2004; Bartzokis, 2011). As more studies highlight that WM changes might precede changes in GM (Caso et al., 2016; Sachdev et al., 2013), further investigations of WM microstructure in the early stages of AD will help understand better the complex pathogenesis of the disease.

Materials and methods

Participants

PREVENT-AD

Request a detailed protocol

We studied cognitively unimpaired participants at risk of sporadic AD dementia from the PREVENT-AD study. PREVENT-AD is a longitudinal study that started in 2012 (Breitner et al., 2016) and enrolled 386 participants. Inclusion criteria were as follows: (1) having intact cognition; (2) having a parent or two siblings diagnosed with AD-like dementia, and therefore being at increased risk of sporadic AD; (3) being above 60 years of age, or between 55 and 59 if fewer than 15 years from their affected family member’s age at symptom onset; and (4) being free of major neurological and psychiatric diseases. Overall participants presented low vascular risk factors and about 28% took anti-hypertensive drugs (Köbe et al., 2020). Intact cognition was based on the Montreal Cognitive Assessment, a Clinical Dementia Rating of 0, and a standardized neuropsychological evaluation using the Repeatable Battery for the Assessment of Neuropsychological Status (Randolph et al., 1998). The cognitive status of individuals with questionable neuropsychological status was reviewed in consensus meetings of neuropsychologists (including SV) and/or psychiatrists. Annual visits include neuropsychological testing and an MRI session. Since 2017, Aβ and tau PET scans were integrated to the study protocol for interested participants. The present study includes participants who had structural and diffusion-weighted MRI and who underwent PET, for a total of 126 participants. All participants included in the current study were cognitively normal at the time they underwent neuroimaging. All underwent diffusion MRI an average of 1.1 ± 0.8 years prior to PET imaging (one completed MRI 5 years prior to PET, but results were unchanged when this participant was removed from analyses).

DIAN

Request a detailed protocol

The DIAN study group enrolls individuals over 18 years old with a family history of ADAD. We had access to the DIAN data-freeze 11 of November 2016, from which we selected participants who were cognitively normal as evidenced by Clinical Dementia Rating (Morris, 1993) of 0, and who underwent both Aβ-PET and diffusion MRI. Out of the 302 participants with a baseline visit with imaging, 201 underwent diffusion MRI with 64 directions (we excluded 12 participants who only had diffusion MRI with 32 directions), and from those, 177 had Aβ-PET and all demographics available. The final sample thus comprised 81 mutation carriers (49 PSEN1 mutation carriers, 15 PSEN2 mutation carriers, and 17 APP mutation carriers) and 96 mutation non-carriers. Less than 1% of mutation carriers and 14% of non-carriers were categorized as having hypertension.

Image acquisition

Magnetic resonance imaging

Request a detailed protocol

PREVENT-AD is a single-site study. All MRI images were acquired on a Magnetom Tim Trio 3 Tesla (Siemens) scanner at the Douglas Mental Health University Institute prior to PET imaging. Structural scans were acquired yearly, and thus we selected the closest scan prior to PET (average time between PET and structural MRI: 8 ± 4 months). Diffusion-weighted MRI was not acquired every year, and again the diffusion scan closest to PET was chosen for analysis (average time between PET and diffusion-weighted MRI: 1.1 ± 0.8 years). DIAN is a multisite study, and the imaging protocols (MRI and PET) were unified across the different study sites. MRI was also acquired on Siemens 3T scanners (BioGraph mMR PET-MR or Trio). All imaging data were selected from the baseline visit of every participant.

In both studies, the T1-weighted structural image was acquired using a MPRAGE sequence similar to the Alzheimer Disease Neuroimaging Initiative protocol (TR = 2300 ms; TE = 2.98 ms; FA = 9°; FoV = 256 mm; slice thickness = 1 mm; 160–170 slices). In both cohorts, diffusion-weighted MRI consisted of one b0 image and 64 diffusion-weighted volumes acquired with a b-value of 1000 s/mm2. The PREVENT-AD sequence parameters were the following: TR = 9300 ms, TE = 92 ms, FoV = 130 mm, 2 mm voxels. The DIAN sequence parameters were the following: TR = 11500 ms, T = 87 ms, 2.5 mm voxels.

Positron emission tomography

Request a detailed protocol

In PREVENT-AD, PET was performed using [18F]NAV4694 to assess Aβ burden and flortaucipir ([18F]AV1451) to assess tau deposition. PET scanning took place at the McConnell Brain Imaging Centre at the Montreal Neurological Institute using a brain-dedicated PET Siemens/CT high-resolution research tomograph (HRRT) on two consecutive days. Aβ scans were acquired 40–70 min post-injection (≈6 mCi) and tau scans 80–100 min post-injection (≈10 mCi). All scans were completed between March 2017 and April 2019.

In DIAN, PET was performed using Pittsburgh compound B ([11C]PIB) to assess Aβ deposition either with full dynamic or an acquisition 40–70 min post-injection (≈15 mCi).

Positron emission tomography processing

Request a detailed protocol

PREVENT-AD PET scans were processed using a standard pipeline (see https://github.com/villeneuvelab/vlpp for more details; Bedetti, 2019). Briefly, Aβ- and tau-PET images were realigned, averaged, and registered to the T1-weighted scan of each participant, which had been segmented with the Desikan–Killiany atlas using FreeSurfer version 5.3 (Desikan et al., 2006). The same structural scan was used in the diffusion and the PET pipelines. PET images were then masked to remove the scalp and cerebrospinal fluid to reduce contamination by non-grey and non-WM voxels. Standardized uptake value ratios (SUVR) images were obtained using the whole cerebellum as reference region for Aβ-PET (Jagust et al., 2015) and the inferior cerebellar GM for tau-PET (Baker et al., 2017). A global Aβ burden was calculated from the average bilateral SUVR of medial and lateral frontal, parietal, and temporal regions, and as described previously, participants with an average global Aβ > 1.37 SUVR were considered Aβ-positive (McSweeney et al., 2020). For tau, we focused on the average bilateral tau uptake in the entorhinal cortex as it is among the earliest cortical region to be affected over the course of AD (Braak and Braak, 1991; Maass et al., 2017). Given that there is no consensus yet as to how to define tau-positivity (Villemagne et al., 2021) and that the presence of Aβ is needed to facilitate the accumulation of tau (Jack et al., 2019), we considered the same proportion of Aβ-positive and tau-positive participants in PREVENT-AD. As such, participants in the top 20% of tau uptake in the entorhinal cortex were considered tau-positive. In the tau-positive group, 60% of participants were also amyloid-positive.

DIAN PET scans were processed by the DIAN image processing core and made available after extensive quality control. Briefly, PET images were registered to the structural image that had been processed with FreeSurfer 5.3. PET images were converted to regional SUVR using the cerebellar GM as reference region (Su et al., 2013), and a regional spread function-based approach for partial volume correction was applied (Su et al., 2015). A global Aβ burden was calculated from averaging the SUVR of four cortical regions (prefrontal, gyrus rectus, lateral temporal, and precuneus) typically used in the DIAN study group (Morris et al., 2010). Participants with a global Aβ SUVR above 1.42 were considered Aβ-positive, as established previously (Mishra et al., 2018; Schultz et al., 2020; Su et al., 2019).

Diffusion MRI processing

An overview of the processing steps is displayed in Figure 1.

Preprocessing steps

Request a detailed protocol

The diffusion-weighted images were processed using the TractoFlow Atlas-Based Segmentation (TractoFlow-ABS) pipeline. TractoFlow-ABS is an extension of the recent TractoFlow pipeline (Theaud et al., 2020a) publicly available for academic research purposes (https://github.com/scilus/tractoflow-ABSTheaud, 2020b) that uses Nextflow (Di Tommaso et al., 2017) and Singularity (Kurtzer et al., 2017) to ensure efficient and reproducible diffusion processing. All major processing steps are performed through this pipeline, from preprocessing of the structural and diffusion images to tractography. The pipeline computes typical DTI maps, fODF, and a whole-brain tractogram. The pipeline calls different functions from various neuroimaging software, namely FSL (Jenkinson et al., 2012), MRtrix3 (Tournier et al., 2019), ANTs (Avants et al., 2011), and DIPY (Garyfallidis et al., 2014). For a detailed description of the different steps, see Theaud et al., 2020a.

Diffusion measures

Request a detailed protocol

After the preprocessing steps, different diffusion measures can be generated as part of TractoFlow-ABS. The following DTI measures were computed using DIPY: FA, MD, RD, and AD. Along with typical DTI modeling, fODFs were also computed using constrained spherical deconvolution (Descoteaux et al., 2007; Tournier et al., 2007) and the fiber response function from the group average.

We also generated FW-corrected DTI measures, which were the main diffusion measures of interest in this study. FW correction has been proposed as a way to remove the contamination of water from the tissue properties by modeling the isotropic diffusion of the FW component (Pasternak et al., 2009). FW modeling was performed using the accelerated microstructure imaging via convex optimization (Daducci et al., 2015) to calculate FW index and FW-corrected measures, namely FAT, MDT, ADT, and RDT. Processing was done using the freely available FreeWater pipeline (https://github.com/scilus/freewater_flow, Bore, 2020). Removing the contribution of FW is thought to better represent the tissue microstructure (hence the subscript T for tissue) and might be more sensitive than the non-corrected measures (Albi et al., 2017; Chad et al., 2018; Pasternak et al., 2012).

Tractography

Request a detailed protocol

The last step of the pipeline is tractography. This is where TractoFlow and TractoFlow-ABS differ. The former uses a more sophisticated algorithm, particle filtering tractography, that takes into account anatomical information to reduce tractography biases (Girard et al., 2014). Such an algorithm requires probabilistic maps of GM, WM and cerebrospinal fluid to add additional constraints for tracking. However, with aging, probabilistic maps in ‘bottleneck’ areas of WM fibers, for example, where the uncinate fasciculus bends, show poorer distinction between GM and WM voxels. Furthermore, increasing WM hyperintensities and general atrophy with aging also complicates the use of more advanced algorithms. As a result, the performance of particle filtering tractography was affected and failed to generate bundles suitable for analysis. Instead, as implemented in TractoFlow-ABS, we opted for local tracking with a probabilistic algorithm to reconstruct whole-brain tractograms. The inputs for tracking were the fODF image for directions and a WM mask for seeding. The mask was computed by joining the WM and the subcortical masks from the structural image that had been segmented with the Desikan–Killiany atlas in FreeSurfer version 5.3 (Desikan et al., 2006). For tracking, seeding was initiated in voxels from the WM mask with 10 seeds per voxel. The tractograms had between 2 and 3 million streamlines.

White matter bundles extraction

Request a detailed protocol

From the tractogram, we extracted different bundles of interest. We focused on bundles connecting the main brain region where Aβ and tau accumulate in the early phase of AD, namely the uncinate fasciculus, the anterior cingulum, and the posterior cingulum. To extract the uncinate fasciculus and the anterior cingulum, we used RecoBundlesX (Rheault, 2020), an automated algorithm to segment the tractograms into different bundles. This algorithm is an improved and more stable version of RecoBundles (Garyfallidis et al., 2018). Briefly, the method is based on shape priors to detect similarity in streamlines. Taking the whole-brain tractogram and templates from the bundles of interest as inputs, RecoBundlesX extracts bundles based on the shape of the streamlines from the templates. The difference between RecoBundles and RecoBundlesX resides in that the latter can take multiple templates as inputs and multiple parameters, which refines which streamlines are included or excluded from the final bundle. RecoBundlesX is typically run 80 times and the output is the conjunction of the multiple runs, yielding more robust bundles. RecoBundlesX does not include templates for the posterior cingulum, and thus we used TractQuerier (Wassermann et al., 2016) for this bundle. This method works with customizable queries to extract bundles based on anatomical definitions. Using inclusion and exclusion regions of interest based on the FreeSurfer parcellation, we implemented a query specifically for the posterior cingulum, as used in another recent study (Roy et al., 2020), which can be found in Supplementary Material.

After extracting all bundles, there were inspected visually in MI-Brain (https://www.imeka.ca/fr/mi-brain/) to make sure the shape, location, and size were adequate.

Bundle-specific quantification with tractometry

Request a detailed protocol

The last step required to put together the different WM measures and bundles of interest was tractometry (Cousineau et al., 2017). Tractometry is a way to extract the measures of interest specifically in each bundle. It takes as input the maps of all microstructure measures and the bundles in which we want to extract them. In our case, we extracted the average tissue measures (FAT, MDT, RDT, ADT, and FW index) for each bundle (uncinate fasciculus, cingulum, posterior cingulum). For complementary analyses, we also extracted typical tensor measures (average FA, MD, RD, and AD) in each bundle. The overall approach, done entirely in native space, has the advantage of generating bundles specific to each individual.

Statistical analysis

Request a detailed protocol

Partial correlations were performed to evaluate the relationships between Aβ- or tau-PET and the different microstructure measures in each bundle, controlling for age, sex, and bundle volume. In primary analyses, the diffusion measures investigated as independent variables were FAT, MDT, RDT, ADT, and FW index. Analyses were performed separately in left and right bundles for Aβ and tau. The dependent variables were global cortical Aβ and entorhinal tau SUVR. We display bivariate associations between diffusion and PET measures to show the raw data, but we based the results on the partial correlation coefficient of the diffusion measure, controlling for age, sex, and bundle volume (divided by total intracranial volume). Models were first performed at the whole-group level and then specifically in the Aβ-positive or tau-positive groups versus the Aβ- or tau-negative groups. We reasoned that the participants harboring pathology (and thus being in the preclinical stage of the disease) would be the most likely to show WM degeneration. We repeated the analyses including either APOE ε4 status or handedness as a covariate in the models. Since the results were mainly unchanged, these data are not presented. In the bundles where associations were found between pathology and microstructure, we further controlled for GM volume (divided by total intracranial volume) of cortical regions connected by the given bundle to evaluate whether associations were also influenced by atrophy. For the uncinate fasciculus, GM regions of interest were the medial orbitofrontal cortex and the parahippocampal gyrus; for the cingulum, regions were the anterior and posterior cingulate; and for the posterior cingulum, regions were precuneus and parahippocampal gyrus. We also performed similar analyses with the typical tensor measures (FA, MD, AD, and RD) to evaluate whether the FW-corrected measures were more sensitive. Associations with a p-value < 0.05 were considered significant. Analyses were conducted using SPSS version 27 (IBM, NY, USA) and R version 3.6.3 (R Development Core Team, 2020).

Appendix 1

Supplementary material

import FreeSurfer.qry

#Posterior cingulumPosterior_Cg.side = only(isthmuscingulate.side or posteriorcingulate.side and (entorhinal.side or fusiform.side or parahippocampal.side or precuneus.side or lingual.side or amygdala.side))

Appendix 2

DIAN study group

Last nameFirst nameInstitutionAffiliationCoreRoleEmail address
AllegriRicardoFLENIFLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia)N/APIrallegri@fleni.org.ar
BatemanRandyWUWashington University in St. Louis School of MedicineAdminCore leader/PI/chairbatemanr@wustl.edu
BecharaJacobSydneyNeuroscience Research AustraliaN/ASite leaderj.bechara@neura.edu.au
BenzingerTammieWUWashington University in St. Louis School of MedicineImagingCore leaderbenzingert@wustl.edu
BermanSarahPittUniversity of PittsburghN/APIbermans@upmc.edu
BodgeCourtneyButlerBrown University-Butler HospitalN/ASite coordinatorCbodge@Butler.org
BrandonSusanWUWashington University in St. Louis School of MedicineAdmin/clinicalCore personnelbrandons@wustl.edu
BrooksWilliam (Bill)SydneyNeuroscience Research AustraliaN/ASite coordinatorw.brooks@NeuRA.edu.au
BuckJillIUIndiana UniversityN/ASite coordinatorjilmbuck@iu.edu
BucklesVirginiaWUWashington University in St. Louis School of MedicineAdminCore personnelbucklesv@wustl.edu
CheaSochendaMayoMayo Clinic JacksonvilleN/ASite coordinatorchea.sochenda@mayo.edu
ChhatwalJasmeerBWHBrigham and Women’s Hospital–Massachusetts General HospitalN/APIChhatwal.Jasmeer@mgh.harvard.edu
ChremPatricioFLENIFLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia)N/ASite coordinatorpchremmendez@fleni.org.ar
ChuiHelenaUSCUniversity of Southern CaliforniaN/APIhelena.chui@med.usc.edu
CincoJakeUCLUniversity College LondonN/ASite coordinatorjcinco@nhs.net
CruchagaCarlosWUWashington University in St. Louis School of MedicineGeneticsCore co-leadercruchagac@wustl.edu
DonahueTamaraWUWashington University in St. Louis School of MedicineN/ASite coordinatortammie@wustl.edu
DouglasJaneUCLUniversity College LondonN/ASite coordinatorjdouglas@dementia.ion.ucl.ac.uk
EdigoNoeliaFLENIFLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia)N/ASite coordinatornegido@fleni.org.ar
Erekin-TanerNiluferMayoMayo Clinic JacksonvilleN/Asub-Itaner.nilufer@mayo.edu
FaganAnneWUWashington University in St. Louis School of MedicineBiomarkerCore leaderfagana@wustl.edu
FarlowMartyIUIndiana UniversityN/APImfarlow@iupui.edu
FitzpatrickColleenBWHBrigham and Women's Hospital-MassachusettsN/ASite co-coordinatorcdfitzpatrick@bwh.harvard.edu
FlynnGigiWUWashington University in St. Louis School of MedicineAdmin/ClinicalCore personnelflynng@wustl.edu
FoxNickUCLUniversity College LondonN/APInfox@dementia.ion.ucl.ac.uk
FranklinErinWUWashington University in St. Louis School of MedicineNeuropathCore coordinatorefranklin@wustl.edu
FujiiHisakoJapanOsaka City UniversityN/AAssistant/coord
hfujii@med.osaka-cu.ac.jp
GantCortaigaWUWashington University in St. Louis School of MedicineAdmin/ClinicalCore personnelcortaiga.gant@wustl.edu
GardenerSamanthaPerthEdith Cowan University, PerthN/ASite coordinators.gardener@ecu.edu.au
GhettiBernardinoIUIndiana UniversityN/Asub-Ibghetti@iupui.edu
GoateAlisonIcahn NYIcahn School of Medicine at Mount SinaiGeneticsCore co-leaderalison.goate@mssm.edu
GoldmanJillCUColumbia UniversityN/AGenetics ethicsJG2673@cumc.columbia.edu
GordonBrianWUWashington University in St. Louis School of MedicineImagingCore personnelbagordon@wustl.edu
Graff-RadfordNeillMayoMayo Clinic JacksonvilleN/APIgraffradford.neill@mayo.edu
GrayJuliaWUWashington University in St. Louis School of MedicineBiomarkerCore personnelgray@wustl.edu
GrovesAlexanderWUWashington University in St. Louis School of MedicineBiomarkerCore coordinatoramgroves@wustl.edu
HassenstabJasonWUWashington University in St. Louis School of MedicineClinicalCore personnelhassenstabj@wustl.edu
Hoechst- SwisherLauraWUWashington University in St. Louis School of MedicineAdmin/clinicalCore coordinatorgoodl@wustl.edu
HoltzmanDavidWUWashington University in St. Louis School of MedicineN/AAssociate directorholtzman@wustl.edu
HornbeckRussWUWashington University in St. Louis School of MedicineImagingCore coordinatorruss@wustl.edu
Houeland DiBariSiriMunichGerman Center for Neurodegenerative Diseases (DZNE) MunichN/ASite coordinatorSiri.HouelandDiBari@dzne.de
IkeuchiTakeshiNiigataNiigata UniversityN/ASite leaderikeuchi@bri.niigata-u.ac.jp
IkonomovicSnezanaPittUniversity of PittsburghN/ASite coordinatorikonomovics@upmc.edu
JackCliffordMayoMayo Clinic JacksonvilleMRI QCVendor MRI QCjack.clifford@mayo.edu
JeromeGinaWUWashington University in St. Louis School of MedicineBiomarkerCore coordinatorginajerome@wustl.edu
JuckerMathiasTubingenGerman Center for Neurodegnerative Diseases (DZNE) TubingenN/APImathias.jucker@uni-tuebingen.de
KarchCelesteWUWashington University in St. Louis School of MedicineAdministrativeCore personnelkarchc@wustl.edu
KasugaKensakuNiigataNiigata UniversityN/ASite coordinatorken39@bri.niigata-u.ac.jp
KawarabayashiTakeshiHirosakiHirosaki UniversityN/ACliniciantkawara@hirosaki-u.ac.jp
KlunkWilliam (Bill)PittUniversity of PittsburghN/Asub-Iklunkwe@gmail.com
KoeppeRobertU of MichiganUniversity of MichiganPET QCVendor PET QCkoeppe@umich.edu
Kuder-BulettaElkeTubingenGerman Center for Neurodegnerative Diseases (DZNE) TubingenN/ASite coordinatorelke.buletta@med.uni-tuebingen.de
LaskeChristophTubingenGerman Center for Neurodegnerative Diseases (DZNE) TubingenN/Asub-Ichristoph.laske@med.uni-tuebingen.de
LeeJae-HongKoreaAsan Medical CenterN/APIjhlee@amc.seoul.kr
LevinJohannesMunichGerman Center for Neurodegnerative Diseases (DZNE) MunichN/APIJohannes.Levin@med.uni-muenchen.de
MartinsRalphPerthEdith Cowan UniversityN/APIr.martins@ecu.edu.au
MasonNeal ScottUPMCUniversity of Pittsburgh Medical CenterPIB QCVendor PIB QCmasonss@upmc.edu
MastersColinMelbUniversity of MelbourneN/API – formerc.masters@unimelb.edu.au
Maue-DreyfusDeniseWUWashington University in St. Louis School of MedicineClinicalCore personneldmdreyfu@wustl.edu
McDadeEricWUWashington University in St. Louis School of MedicineClinicalCore leader assocericmcdade@wustl.edu
MoriHiroshiJapanOsaka City UniversityN/APImori@med.osaka-cu.ac.jp
MorrisJohnWUWashington University in St. Louis School of MedicineClinicalCore leaderjcmorris@wustl.edu
NagamatsuAkemTokyoTokyo UniversityN/ASite coordinatorakm77-tky@umin.ac.jp
NeimeyerKatieCUColumbia UniversityN/ASite coordinatorkn2416@cumc.columbia.edu
NobleJamesCUColumbia UniversityN/APIjn2054@columbia.edu
NortonJoanneWUWashington University in St. Louis School of MedicineGeneticsCore coordinatornortonj@wustl.edu
PerrinRichardWUWashington University in St. Louis School of MedicineNeuropathCore leaderrperrin@wustl.edu
RaichleMarcWUWashington University in St. Louis School of MedicineImagingCore personnelmraichle@wustl.edu
RentonAlanIcahn NYIcahn School of Medicine at Mount SinaiGeneticsCore personnelalan.renton@mssm.edu
RingmanJohnUSCUniversity of Southern CaliforniaN/Asub-Ijohn.ringman@med.usc.edu
RohJee HoonKoreaAsan Medical CenterN/Asub-Iroh@amc.seoul.kr
SallowayStephenButlerBrown University-Butler HospitalN/APISSalloway@Butler.org
SchofieldPeterSydneyNeuroscience Research AustraliaN/APIp.schofield@neura.edu.au
ShimadaHiroyukiOsakaOsaka City UniversityN/ASite leaderh.shimada@med.osaka-cu.ac.jp
SigurdsonWendyWUWashington University in St. Louis School of MedicineN/ASite coordinatorsigurdsonw@wustl.edu
SohrabiHamidPerthEdith Cowan UniversityN/ASite coordinatorh.sohrabi@ecu.edu.au
SparksPaigeBWHBrigham and Women's Hospital-MassachusettsN/ASite coordinatorkpsparks@bwh.harvard.edu
SuzukiKazushiTokyoTokyo UniversityN/ASite leaderkazusuzuki-tky@umin.ac.jp
TaddeiKevinPerthEdith Cowan UniversityN/ASite coordinatork.taddei@ecu.edu.au
WangPeterWUWashington University in St. Louis School of MedicineBiostatCore coordinatorguoqiao@wustl.edu
XiongChengjieWUWashington University in St. Louis School of MedicineBiostatCore leaderchengjie@wustl.edu
XuXiongWUWashington University in St. Louis School of MedicineBiostatCore personnelxxu@wustl.edu
LeveyAllanEmoryEmory University School of MedicineN/AProject leaderalevey@emory.edu

Data availability

All raw imaging data from PREVENT-AD is openly available to researchers on the data repository https://registeredpreventad.loris.ca/.

The following data sets were generated

References

    1. Jansen WJ
    2. Ossenkoppele R
    3. Knol DL
    4. Tijms BM
    5. Scheltens P
    6. Verhey FR
    7. Visser PJ
    8. Aalten P
    9. Aarsland D
    10. Alcolea D
    11. Alexander M
    12. Almdahl IS
    13. Arnold SE
    14. Baldeiras I
    15. Barthel H
    16. van Berckel BN
    17. Bibeau K
    18. Blennow K
    19. Brooks DJ
    20. van Buchem MA
    21. Camus V
    22. Cavedo E
    23. Chen K
    24. Chetelat G
    25. Cohen AD
    26. Drzezga A
    27. Engelborghs S
    28. Fagan AM
    29. Fladby T
    30. Fleisher AS
    31. van der Flier WM
    32. Ford L
    33. Förster S
    34. Fortea J
    35. Foskett N
    36. Frederiksen KS
    37. Freund-Levi Y
    38. Frisoni GB
    39. Froelich L
    40. Gabryelewicz T
    41. Gill KD
    42. Gkatzima O
    43. Gómez-Tortosa E
    44. Gordon MF
    45. Grimmer T
    46. Hampel H
    47. Hausner L
    48. Hellwig S
    49. Herukka SK
    50. Hildebrandt H
    51. Ishihara L
    52. Ivanoiu A
    53. Jagust WJ
    54. Johannsen P
    55. Kandimalla R
    56. Kapaki E
    57. Klimkowicz-Mrowiec A
    58. Klunk WE
    59. Köhler S
    60. Koglin N
    61. Kornhuber J
    62. Kramberger MG
    63. Van Laere K
    64. Landau SM
    65. Lee DY
    66. de Leon M
    67. Lisetti V
    68. Lleó A
    69. Madsen K
    70. Maier W
    71. Marcusson J
    72. Mattsson N
    73. de Mendonça A
    74. Meulenbroek O
    75. Meyer PT
    76. Mintun MA
    77. Mok V
    78. Molinuevo JL
    79. Møllergård HM
    80. Morris JC
    81. Mroczko B
    82. Van der Mussele S
    83. Na DL
    84. Newberg A
    85. Nordberg A
    86. Nordlund A
    87. Novak GP
    88. Paraskevas GP
    89. Parnetti L
    90. Perera G
    91. Peters O
    92. Popp J
    93. Prabhakar S
    94. Rabinovici GD
    95. Ramakers IH
    96. Rami L
    97. Resende de Oliveira C
    98. Rinne JO
    99. Rodrigue KM
    100. Rodríguez-Rodríguez E
    101. Roe CM
    102. Rot U
    103. Rowe CC
    104. Rüther E
    105. Sabri O
    106. Sanchez-Juan P
    107. Santana I
    108. Sarazin M
    109. Schröder J
    110. Schütte C
    111. Seo SW
    112. Soetewey F
    113. Soininen H
    114. Spiru L
    115. Struyfs H
    116. Teunissen CE
    117. Tsolaki M
    118. Vandenberghe R
    119. Verbeek MM
    120. Villemagne VL
    121. Vos SJ
    122. van Waalwijk van Doorn LJ
    123. Waldemar G
    124. Wallin A
    125. Wallin ÅK
    126. Wiltfang J
    127. Wolk DA
    128. Zboch M
    129. Zetterberg H
    130. Amyloid Biomarker Study Group
    (2015) Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis
    Jama 313:1924–1938.
    https://doi.org/10.1001/jama.2015.4668
  1. Book
    1. Rheault F
    (2020)
    Analyse Et Reconstruction De Faisceaux De La Matière Blanche. in Computer Science
    Université de Sherbrooke.

Decision letter

  1. Tamar R Makin
    Senior Editor; University College London, United Kingdom
  2. Morgan Barense
    Reviewing Editor; University of Toronto, Canada
  3. Arun Bokde
    Reviewer; Trinity College Dublin, Ireland
  4. Alfie Wearn
    Reviewer; University of Bristol, United Kingdom
  5. Hakon Grydeland
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This paper implements a collection of innovative and versatile neuroimaging techniques to investigate how alterations in white matter microstructure affects the presence of Alzheimer's Disease (AD) pathology in the preclinical disease. In large two large cohorts of older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD, significant levels of AD pathology were associated with white matter neurodegeneration. These results suggest that microstructural changes accompany the accumulation of AD pathology in the early preclinical stage of the disease.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Bundle-specific white matter microstructure associations with Alzheimer's disease pathology at the connecting endpoints" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Arun Bokde (Reviewer #1); Alfie Wearn (Reviewer #2); Hakon Grydeland (Reviewer #3).

Our decision has been reached after a very extensive discussion between the reviewers and editors. Based on this discussion and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

As you will find below, all three reviewers provided very positive technical reviews – there was a strong consensus that this is a well-executed study. The reviewers highlighted the large cohort of participants, the innovative and versatile use of neuroimaging techniques, and in particular the water-corrected diffusion and tau-PET measures, and the careful analysis. While we acknowledge these methodological strengths, we found it difficult to agree on the validity of the interpretation of the findings, considering the unexpected directionality of the results. In addition, we felt that without additional proof-of-concept (e.g. longitudinal study), the current experimental design does not provide sufficient evidence for an early brain pathology marker. Although these two key issues preclude us from accepting the paper for publication with eLife, it was agreed that the study provide a clear advancement relative to other studies looking at the relationship between different imaging domains in AD. As such, the present findings should be particularly valuable for an audience interested in white-matter pathology in neurodegenerative diseases. We hope you will find the highly supportive comments below helpful in your path towards publication.

Reviewer #1:

The manuscript reports the results of a study examining the linear correlation between white matter tracts and AD- related pathology in the grey matter regions connected by the white matter tracts. The integrity of the tracts were measured using FA, MD, AD, RD (corrected for free water) and free water index (FW) and apparent fiber density (AFD). The white matter tracts examine were the cingulum (main and posterior branch), uncinate fasciculus, and fornix. The population studies were older healthy subjects at risk (based on family history) for developing AD. The AD related pathology were tau and amyloid measured using PET. The study was very well done, and it addresses key questions in regards the p-clinical phase of AD.

a. It would be very helpful to reader to understand the distribution of the global ABeta SUVR and temporal tau SUVR – given that studies dichotomise study participants based on high and low deposition, it would help readers better understand context of the results. The mean and range given in table 1 is not enough.

b. Related to previous question, I would suggest that the same graphs be made for the ROIs at the end of the tracts – again it would help a reader understand the context of the study.

c. I am surprised that APOE e4 allele was not included as a covariate in the statistical model. Why not? Given that APOE increases risk of developing AD, it would seem to be a relevant parameter. Amyloid positivity has been shown to be associated with age, sex and APOE e4 status.

d. The negative results of the posterior cingulate and yet statistically significant results for the uncinate fasciculus are an interesting contrast. Both tracts connect regions with presumably high Β and high tau deposition. Have there been studies that have compared the amyloid deposition in posterior cingulate cortex and anterior cingulate/anterior frontal regions? It might be supportive of the idea that posterior cingulate is further along the disease progression compared to the anterior frontal regions. Having the data plots as described in (a) and (b) could help in supporting the points made in the discussion.

Reviewer #2:

Here authors show interesting, seemingly counter-intuitive, associations between key Alzheimer's pathological hallmarks (Aβ and tau) and free-water corrected diffusion measures in a large cohort of cognitively healthy older adults with family history of Alzheimer's. They show direct associations between amyloid (and tau in some cases) and increased FA and decreased MD/RD in key white matter bundle cortical endpoints. Whilst for some tracts this association is only just 'statistically significant' at p<0.05, results for the uncinate fasciculus are very convincing. Overall, this paper is an interesting, well-written and potentially highly impactful piece of work with robust methodology, in which the authors should take pride.

I have no major concerns to raise regarding this paper. However, I will mention for the authors' interest, that the principle of a biphasic change in quantitative MRI measures (initial decrease due to water mobility restriction, followed by later increase associated in symptomatic phase) is one discussed in our recently published paper (rdcu.be/b62Yp). A linear change across the course of the disease (which the authors here say would be impossible to detect in slowly progressing individuals) may be brought about by studying the changing and increasing distribution width, rather than averaging across a region of interest. I am not suggesting the authors change their analyses to reflect this, it is merely food for thought, or worth a mention in the paper as an avenue of future research.

I hate to be 'that reviewer' demanding citation of their own work and would not mention it if it were not directly relevant, so I will leave it at the authors' discretion whether they include this or not.

Reviewer #3:

This work started from the notion that Alzheimer's disease (AD) pathology spreads through connected regions, and investigated whether the level of AD pathology in specific regions relates to the integrity of the fiber bundles connecting them, in 126 elderly with normal cognition at risk of AD. Specifically, AD pathology was quantified by β-amyloid (Aβ) and tau protein levels from positron emission tomography (PET). Three fiber bundles, the cingulum, the fornix, and the uncinate fasciculus, were a priori selected, and six measures were derived from free-water corrected diffusion tensor imaging. The authors hypothesized that Aβ levels would relate to the integrity of (i) the (anterior) cingulum, and (ii) the uncinate, and (iii) that tau levels to would relate to fornix integrity. The direction of the relations was not specified. The authors find support for particularly the second hypothesis (Aβ levels and the uncinate), but also for the first (Aβ levels and anterior cingulum). They also find relations between tau levels and uncinate integrity, and Aβ levels and right fornix integrity. The relations were consistently in a direction the authors refer to as "unanticipated", that is, more restricted diffusion with the presence of pathology. The authors conclude that the result "suggests more restricted diffusion in bundles vulnerable to preclinical AD pathology».

The work addresses important topics (early detection and spreading of AD pathology) of great interest to people from several disciplines. The sample is interesting with both regional Aβ and tau measurements, and the imaging processing methods used are advanced. The paper is clearly written and nicely illustrated.

My main concern relates to the main conclusion of "more restricted diffusion in bundles vulnerable to preclinical AD pathology". Although this result is discussed as "unanticipated", I think the centrality of this point makes more scrutiny warranted.

1. Direction of relationship. The authors state that "[..]the directionality of the observed pattern of association opposes the classical pattern of degeneration. The classical degeneration pattern accompanying disease progression is characterized by lower anisotropy and higher diffusivity, representing loss of coherence in the white matter microstructure with AD progression", and further: "[..] more restricted diffusion with the presence of pathology was unanticipated [..]".

Indeed, there results were unanticipated based on the literature, as highlighted by the authors. As this is the central point of the work, I believe it is important to do additional analyses to try and enlighten the results and the suggestion of a biphasic relation. I understand that the authors have done a lot of work already, but here are some fairly simple and not too time-consuming suggestions which might be informative (please feel free to ignore these suggestions and instead follow other paths to show the reader more results to evaluate the unexpected direction of the relations):

i. A simple start could be to assess the relationship with age, how strong this relationship is, and what the residuals look like when regressing out age (and bundle volume).

ii. As the authors mention, a reduction in crossing fibers might lead to "more restricted diffusion" but be a sign of deterioration. Analyses undertaken to assess this point would be valuable. For instance, one could test if the relations are similar in regions of the bundles where there are little crossing fibers and in regions with more crossing fibers.

iii. The authors state that "[…] we estimated that 20% of the participants would be considered Aβ-positive". Were a majority of these also tau-positive? If so (or if participants exist in the larger PREVENT-AD sample that were not "cognitively normal at the time they underwent diffusion-weighted MRI»), creating a group of high AD pathology, is the relations between Aβ/tau and diffusivity similar in this group of high Aβ and tau compared to a similar-sized (and, if possible) age-matched group with (very) low Aβ and tau levels?

2. Hypotheses. As mentioned, the authors state in the Discussion that directionality of the observed pattern of association was unanticipated. I am therefore somewhat surprised that the directionally of the hypothesized relations were not included in the hypotheses presented in the Introduction. I think it would increase the readability of the Results section if this point was made explicit earlier in the text, and the non-expected direction mentioned in the Results.

3. Number of tests. The author state that "Associations with a p-value < 0.05 were considered significant, but we also report associations that would survive false-discovery rate (FDR) correction for each bundle with q-value of 0.05, accounting for 6 tests (i.e. the number of diffusion measures assessed per bundle).". I find this somewhat problematic (at least without further justification). First, I think the authors should only considered corrected p-values significant. Second, these 6 measures are tested per hemisphere, and across at least 3 fiber bundles (for cingulum, it seems the authors have done separate analyses for the anterior and posterior part), making the total number of tests higher. Correcting for the number of diffusion measures per bundle might be too strict, but I think the total number to correct for should be higher than 6. Whether any correction has been applied is also difficult to grasp while reading the Result section, as it seems like p-values are not FDR-corrected in Tables 2 and 3 (mentioned only in Table 4). I think the total number of bundles assessed, and the correction should be made explicit when introducing Figure 2 and Table 2.

https://doi.org/10.7554/eLife.62929.sa1

Author response

[Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]

All three reviewers provided very positive technical reviews – there was a strong consensus that this is a well-executed study. The reviewers highlighted the large cohort of participants, the innovative and versatile use of neuroimaging techniques, and in particular the water-corrected diffusion and tau-PET measures, and the careful analysis. While we acknowledge these methodological strengths, we found it difficult to agree on the validity of the interpretation of the findings, considering the unexpected directionality of the results. In addition, we felt that without additional proof-of-concept (e.g. longitudinal study), the current experimental design does not provide sufficient evidence for an early brain pathology marker.

We agree with the editors and reviewers that additional proof-of-concept was needed to provide sufficient evidence to support our interpretation of the results considering their unexpected directionality. To respond to this concern, we did a large amount of additional processing and analyses, including testing a new cohort. We also restricted some of the analyses to individuals with significant amount of pathology (amyloid- or tau- positive individuals) as suggested by reviewers. All these steps resulted in major changes to the original manuscript which are described below.

First, we included a replication cohort with presymptomatic mutation carriers of autosomal dominant Alzheimer’s disease (ADAD) from the DIAN cohort. A major advantage of the DIAN cohort is that everyone with the genetic mutation will develop AD, which will not be the case of all PREVENT-AD participants. Individuals who are mutation carriers start accumulating amyloid one to two decades before symptom onset and will develop AD dementia often already in midlife.

As suggested by Reviewer 1 and 3 we also evaluated whether the pattern of associations differed in the participants considered amyloid- or tau-positive vs the negative participants, a typical way to categorize participants in AD research. This analysis is especially important for the PREVENT-AD cohort given that the participants who do not harbor pathology might never develop AD dementia. In individuals with significant pathology (amyloid- or tau-positive participants), we found associations depicting the typical white matter neurodegeneration pattern: lower FAT and higher MDT were associated with more amyloid or tau burden while we found no association between white matter measures and AD pathology in individual with no or very low levels of pathology. This pattern of associations, that significantly changed the main results of the paper, was found consistently in the PREVENT-AD and DIAN cohorts.

Finally, instead of extracting the amyloid and tau PET SUVR values at the voxels of gray matter endpoints of the bundles, we now use more conventional assessment of amyloid and tau PET burden. We took this decision to reduce the possible partial volume effect of white matter contamination in the amyloid and tau PET values. It also simplifies the scope of the paper. Doing so we do not see the unexpected associations between the pathology and white matter integrity at the group level.

The overall rational and methodology of the paper, highlighted as strengths by the

reviewers, were unchanged, but the focus in this revised manuscript is now on participants with significant pathology. The main analyses are now performed in two independent cohorts. We also made the paper more focused (only free-water corrected measures, three bundles of interest, global measure of pathology instead of the cortical endpoints) and included secondary information in supplementary material. We are confident that we have addressed the issues raised by the editorial board and the referees. You will find below a point-by-point response addressing all comments.

Reviewer #1:

The manuscript reports the results of a study examining the linear correlation between white matter tracts and AD- related pathology in the grey matter regions connected by the white matter tracts. The integrity of the tracts were measured using FA, MD, AD, RD (corrected for free water) and free water index (FW) and apparent fiber density (AFD). The white matter tracts examine were the cingulum (main and posterior branch), uncinate fasciculus, and fornix. The population studies were older healthy subjects at risk (based on family history) for developing AD. The AD related pathology were tau and amyloid measured using PET. The study was very well done, and it addresses key questions in regards the p-clinical phase of AD.

We thank the reviewer for the positive assessment of our manuscript.

a. It would be very helpful to reader to understand the distribution of the global ABeta SUVR and temporal tau SUVR – given that studies dichotomise study participants based on high and low deposition, it would help readers better understand context of the results. The mean and range given in table 1 is not enough.

We clarified this aspect in the manuscript. According to a conservative threshold of global amyloid (SUVR of 1.37 [McSweeney, Pichet Binette et al., Neurology, 2020]), 19% (24/126) of the Prevent-AD participants are considered amyloid-positive. There is no consensus yet as to how to establish a threshold of tau-positivity across cohorts (Villemagne et al., J Nuclear Medicine, 2021). Given that our cohort is all asymptomatic, we focused on tau in the entorhinal cortex as it is one of the earliest regions where we can detect high tau-PET tracer retention. We considered the top 20% of participants with the highest entorhinal tau-PET signal as “tau-positive” given how closely elevated tau signal is associated with amyloid-positivity (Jack et al., Brain, 2019).

We added another preclinical cohort to the current study, participants carrying the genetic mutation causing autosomal dominant AD (DIAN cohort), in which 43% of participants (35/81) are amyloid-positive. We did not have access to tau-PET in the DIAN cohort.

This information has been added to Table 1 (page 7) and the Participants overview in the Results section 2.1 (p. 6):

“Based on a threshold established previously using global cortical Aβ burden (McSweeney et al., 2020), 19% of the participants were considered Aβ-positive. We also considered the same proportion of participants with the highest entorhinal tau uptake to be tau-positive.

For the DIAN cohort we had access to Aβ-PET only, and as per DIAN PET processing protocol, 43% of the mutation carriers, and none of the non-carriers were Aβ-positive (Su et al., 2013).“

Further, in line with this comment and as per Reviewer 3 suggestions, we refined our analyses to focus on the amyloid-positive (or tau-positive) groups, given that this group is more likely to have pathology and harbour white matter changes. These new analyses revealed a consistent pattern of associations both in Prevent-AD and DIAN, such that participants with the highest amount of pathology present the typical diffusion pattern of neurodegeneration. For instance, in the amyloid-positive participants, lower free-water corrected FA and higher free-water corrected MD correlated with greater amyloid burden. In Prevent-AD such a pattern was found with amyloid in the uncinate fasciculus and with tau in the posterior cingulum. In DIAN, such a pattern was found with amyloid in the anterior and posterior cingulum.

Please see the updated Results section for all details.

b. Related to previous question, I would suggest that the same graphs be made for the ROIs at the end of the tracts – again it would help a reader understand the context of the study.

We thank the reviewer for the suggestion, and we opted to simplify the amyloid and tau measures that were investigated. Initially we were extracting amyloid and tau specifically at the voxels where the streamlines of the bundles ended, and we realized it was confusing in addition to being prone to white matter signal contamination. In this revised version, we look at associations with a global score of amyloid SUVR and tau SUVR in the entorhinal cortex in the whole sample and in amyloid-positive and tau-positive individuals. Given how widespread amyloid is already in the preclinical stage of the disease, the regional approach did not provide additional information than a global score. For tau, given how restricted tau deposition is in the preclinical stage, the entorhinal cortex is the best region to capture participants with elevated tau. We believe this change makes the paper clearer since we are investigating associations between white matter measures and the same pathology measure across bundles. We revised the text and figures accordingly.

Here is the description of the statistical analysis that were performed in the Methods section 4.7 (page 31):

“Linear regression models were performed to evaluate the relationships between Aβ or tau and the different microstructure measures in each bundle. In primary analyses, the diffusion measures investigated as independent variables were FAT, MDT, RDT, ADT, and FW index. Regression models were performed separately for Aβ and tau in the left and right bundles separately. Age, sex, and bundle volume (divided by total intracranial volume) were included as covariates in each regression model. The dependent variable were global cortical Aβ and entorhinal tau SUVR. Models were first performed at the whole-group level and specifically in the Aβ-positive or tau-positive groups versus the Aβ- or tau-negative groups.”

c. I am surprised that APOE e4 allele was not included as a covariate in the statistical model. Why not? Given that APOE increases risk of developing AD, it would seem to be a relevant parameter. Amyloid positivity has been shown to be associated with age, sex and APOE e4 status.

We decided to keep the models as simple as possible given the low number of participants by groups, particularly now that the paper focused on the participants with significant pathology. However, we acknowledge that is an important parameter and we verified that our associations remained when adding APOE e4 as a covariate.

In Prevent-AD, the vast majority of significant associations between pathology and white matter microstructure remained when we further controlled for APOE e4 status. The only exception is with the model of the posterior cingulum with amyloid burden. These original associations, with a p-value of 0.05, where no longer significant when controlling for APOE e4 status.

In DIAN, given that the familial genetic mutation is causing amyloid deposition, APOE e4 is expected to play a lesser role. In effect, all significant associations where virtually unchanged when APOE e4 was included in the statistical models.

We added the following sentence in the manuscript: “We repeated the analyses including APOE e4 as a covariate in the models. Since the results were mainly unchanged, these data are not presented.” on page 32.

d. The negative results of the posterior cingulate and yet statistically significant results for the uncinate fasciculus are an interesting contrast. Both tracts connect regions with presumably high Β and high tau deposition. Have there been studies that have compared the amyloid deposition in posterior cingulate cortex and anterior cingulate/anterior frontal regions? It might be supportive of the idea that posterior cingulate is further along the disease progression compared to the anterior frontal regions. Having the data plots as described in (a) and (b) could help in supporting the points made in the discussion.

We thank the reviewer for the interesting suggestion. There are not many studies looking at regional amyloid deposition and to investigate this further, we used the same rational to dichotomize participants at the regional level as we used on the global amyloid level. That is, we fitted a two-distribution Gaussian mixture models on the amyloid load in the anterior and posterior cingulate to evaluate how many participants would be considered positive in each region. Given that tracer retention varies by region (depending on its location, partial volume effect, etc.), this approach is preferable than comparing the SUVR values in both regions. In Prevent-AD, 19% of participants would be considered positive in the anterior cingulate vs. 29% of positive in the posterior cingulate.

We believe the updated results also strengthen the idea that the posterior cingulum is further along the disease progression. With the new scheme of analyses, the uncinate fasciculus is still the bundle where we see the strongest associations in amyloid-positive participants in PREVENT-AD. However, in the posterior cingulum, we see similar associations in the tau-positive group (see Figure 2 and 3). As highlighted by the reviewer, both tracts connect regions with presumably high amyloid and tau, as opposed to the anterior cingulum that would be a tract more related to amyloid only. Given that tau accumulates after amyloid has deposited in the brain and that associations in the posterior cingulum are detected in high tau-positive participants, it might suggest that this tract is further along the disease progression.

By contrast, in DIAN, the anterior and posterior cingulum are both showing degeneration with greater amyloid burden, unlike in the uncinate fasciculus (see Figure 4). This regional difference is in line with the progression of pathology in ADAD, where tau is accumulating predominantly in the precuneus and less in temporal regions like in sporadic AD (Gordon et al., Brain, 2019). Of course, we would need tau-PET to provide a definite answer.

We updated the Discussion (page 20) to better reflect the new findings:

“The bundle that was consistently affected in participants with high pathology in both cohorts was the posterior cingulum, a key bundle in AD (Agosta et al., 2011; Caso et al., 2016; Zhuang et al., 2012). The posterior cingulum is certainly altered in the symptomatic stage, and diffusivity in this bundle has also shown to be related to tau accumulation in preclinical individuals (Jacobs et al., 2018). In the PREVENT-AD cohort, this posterior segment of the bundle was the only region where tau-positive participants presented microstructure degeneration with greater entorhinal tau. In DIAN, although we did not have tau-PET, we hypothesize that the associations found in Aβ-positive in the posterior cingulum would also be present with tau, since mutation carriers harbour elevated tau binding in the precuneus (Gordon et al., 2019). Our results both in preclinical sporadic and autosomal dominant AD corroborate the idea that the posterior cingulum, more largely part of the posterior default mode network or posterior-medial system, is a critical area in the cascading events of AD (Berron et al., 2020; Jones et al., 2016).”

Reviewer #2:

Here authors show interesting, seemingly counter-intuitive, associations between key Alzheimer's pathological hallmarks (Aβ and tau) and free-water corrected diffusion measures in a large cohort of cognitively healthy older adults with family history of Alzheimer's. They show direct associations between amyloid (and tau in some cases) and increased FA and decreased MD/RD in key white matter bundle cortical endpoints. Whilst for some tracts this association is only just 'statistically significant' at p<0.05, results for the uncinate fasciculus are very convincing. Overall, this paper is an interesting, well-written and potentially highly impactful piece of work with robust methodology, in which the authors should take pride.

I have no major concerns to raise regarding this paper. However, I will mention for the authors' interest, that the principle of a biphasic change in quantitative MRI measures (initial decrease due to water mobility restriction, followed by later increase associated in symptomatic phase) is one discussed in our recently published paper (rdcu.be/b62Yp). A linear change across the course of the disease (which the authors here say would be impossible to detect in slowly progressing individuals) may be brought about by studying the changing and increasing distribution width, rather than averaging across a region of interest. I am not suggesting the authors change their analyses to reflect this, it is merely food for thought, or worth a mention in the paper as an avenue of future research.

I hate to be 'that reviewer' demanding citation of their own work and would not mention it if it were not directly relevant, so I will leave it at the authors' discretion whether they include this or not.

We thank the reviewer for the positive assessment of our manuscript. According to the editors’ and other reviewers’ comments, we did major revisions to the manuscript to ensure the counterintuitive results were robust. When focussing specifically on the participants with high pathology, we found that this group exhibited the typical neurodegeneration pattern. We also replicate this finding in an additional cohort, which we added to the revised manuscript. We still believe this biphasic phase probably exists in AD since our first “unexpected results” have been found by others in the early phase of the disease and therefore we kept this notion in the discussion. However, the initial results have not been kept in the text given that the analyses have changed and the initial results were not replicated in DIAN. The overall goal and methodology of the manuscript remain the same, but please see the updated Results and Methods sections to assess this revised version.

We updated the Discussion to better reflect the new findings on page 19-20: “Our results further emphasize that in presymptomatic populations, associations start to be detectable in individuals with high amount of pathology. In effect, most of the microstructure-pathology associations were restricted to the Aβ-positive our tau-positive participants. We should note that in the asymptomatic stage, there is also evidence of white matter alterations opposing the typical degeneration pattern, suggesting a possible biphasic relationship over the course of the disease (Fortea et al., 2010; Montal et al., 2018; Wearn et al., 2020).”

Reviewer #3:

This work started from the notion that Alzheimer's disease (AD) pathology spreads through connected regions, and investigated whether the level of AD pathology in specific regions relates to the integrity of the fiber bundles connecting them, in 126 elderly with normal cognition at risk of AD. Specifically, AD pathology was quantified by β-amyloid (Aβ) and tau protein levels from positron emission tomography (PET). Three fiber bundles, the cingulum, the fornix, and the uncinate fasciculus, were a priori selected, and six measures were derived from free-water corrected diffusion tensor imaging. The authors hypothesized that Aβ levels would relate to the integrity of (i) the (anterior) cingulum, and (ii) the uncinate, and (iii) that tau levels to would relate to fornix integrity. The direction of the relations was not specified. The authors find support for particularly the second hypothesis (Aβ levels and the uncinate), but also for the first (Aβ levels and anterior cingulum). They also find relations between tau levels and uncinate integrity, and Aβ levels and right fornix integrity. The relations were consistently in a direction the authors refer to as "unanticipated", that is, more restricted diffusion with the presence of pathology. The authors conclude that the result "suggests more restricted diffusion in bundles vulnerable to preclinical AD pathology».

The work addresses important topics (early detection and spreading of AD pathology) of great interest to people from several disciplines. The sample is interesting with both regional Aβ and tau measurements, and the imaging processing methods used are advanced. The paper is clearly written and nicely illustrated.

My main concern relates to the main conclusion of "more restricted diffusion in bundles vulnerable to preclinical AD pathology". Although this result is discussed as "unanticipated", I think the centrality of this point makes more scrutiny warranted.

We thank the reviewer for the constructive feedback on the manuscript. The concern about the unanticipated results was also shared by the editors and we agree that the pattern of more restricted diffusion needed further investigation that would ideally have included longitudinal data. We have longitudinal data in the PREVENT-AD for a subset of the participants and we looked at two additional timepoints. The number of participants were however lower in these other time points and one was performed on an upgraded version of our MRI scanner and an updated diffusion sequence. Overall, we could not replicate the results suggesting more restricted diffusion (most of the associations were not significant in the other time points). We therefore decided to pre-process and analyze a new cohort of individuals with ADAD. Based on your suggestions and one from Reviewer 1, we now focus more on the participants with significant pathology in which we find higher FA and lower MD with greater AD pathology in both cohorts.

The biphasic hypothesis is nevertheless kept in the discussion since the change in the reported results of this study highlight that the population selected, even in asymptomatic individuals, can have a main influence on the results.

1. Direction of relationship. The authors state that "[..]the directionality of the observed pattern of association opposes the classical pattern of degeneration. The classical degeneration pattern accompanying disease progression is characterized by lower anisotropy and higher diffusivity, representing loss of coherence in the white matter microstructure with AD progression", and further: "[..] more restricted diffusion with the presence of pathology was unanticipated [..]".

Indeed, there results were unanticipated based on the literature, as highlighted by the authors. As this is the central point of the work, I believe it is important to do additional analyses to try and enlighten the results and the suggestion of a biphasic relation. I understand that the authors have done a lot of work already, but here are some fairly simple and not too time-consuming suggestions which might be informative (please feel free to ignore these suggestions and instead follow other paths to show the reader more results to evaluate the unexpected direction of the relations):

i. A simple start could be to assess the relationship with age, how strong this relationship is, and what the residuals look like when regressing out age (and bundle volume).

ii. As the authors mention, a reduction in crossing fibers might lead to "more restricted diffusion" but be a sign of deterioration. Analyses undertaken to assess this point would be valuable. For instance, one could test if the relations are similar in regions of the bundles where there are little crossing fibers and in regions with more crossing fibers.

iii. The authors state that "[…] we estimated that 20% of the participants would be considered Aβ-positive". Were a majority of these also tau-positive? If so (or if participants exist in the larger PREVENT-AD sample that were not "cognitively normal at the time they underwent diffusion-weighted MRI»), creating a group of high AD pathology, is the relations between Aβ/tau and diffusivity similar in this group of high Aβ and tau compared to a similar-sized (and, if possible) age-matched group with (very) low Aβ and tau levels?

We agreed that we needed additional proof-of-concept of the “unanticipated” results. We specifically investigated point iii both in our Prevent-AD cohort and in a second cohort of pre-symptomatic individuals, i.e mutation carriers of autosomal dominant AD from the DIAN cohort. We looked at associations in the amyloid-positive group vs the amyloid-negative group. For tau (available in Prevent-AD only) there is no consensus yet on how to establish a clear cut-off of positivity. Here we considered the top 20% of participants with highest entorhinal tau SUVR to be tau-positive, based on the fact that amyloid is needed for tau to start spreading.

This dichotomization based on the amount of pathology revealed a different pattern of associations. There were no associations in the negative participants between white matter measures and pathology. However, in the positive participants, there were associations following the typical pattern of neurodegeneration, such that lower FAT and higher MDT is associated with greater pathology burden. Such associations were found in the amyloid-positive (Figure 2) and tau-positive (Figure 3) group in Prevent-AD and in the amyloid-positive group in DIAN (Figure 4). We believe this a more appropriate analysis of the data compared to the previous version and sincerely thank the reviewer for the suggestion. The figures have been updated in the revised manuscript as well as the Results section. Please see the revised manuscript for all details.

Additionally, regarding point ii, we extracted a measure called “number of fiber orientations” (NuFo) for each bundle, which represented the number of orientations in each voxel. This measure was not related to the amount of pathology, suggesting that

we did not find evidence for reduced crossing fibers being related to greater amyloid or tau burden.

2. Hypotheses. As mentioned, the authors state in the Discussion that directionality of the observed pattern of association was unanticipated. I am therefore somewhat surprised that the directionally of the hypothesized relations were not included in the hypotheses presented in the Introduction. I think it would increase the readability of the Results section if this point was made explicit earlier in the text, and the non-expected direction mentioned in the Results.

We updated the Introduction to mention that we expect the direction of diffusion

measures to match the pattern of white matter degeneration.

The Introduction (p. 4) has been updated as follows: “We hypothesized that such bundles would show lower fractional anisotropy and higher diffusivity with more pathology as proxy of white matter degeneration.”

3. Number of tests. The author state that "Associations with a p-value < 0.05 were considered significant, but we also report associations that would survive false-discovery rate (FDR) correction for each bundle with q-value of 0.05, accounting for 6 tests (i.e. the number of diffusion measures assessed per bundle).". I find this somewhat problematic (at least without further justification). First, I think the authors should only considered corrected p-values significant. Second, these 6 measures are tested per hemisphere, and across at least 3 fiber bundles (for cingulum, it seems the authors have done separate analyses for the anterior and posterior part), making the total number of tests higher. Correcting for the number of diffusion measures per bundle might be too strict, but I think the total number to correct for should be higher than 6. Whether any correction has been applied is also difficult to grasp while reading the Result section, as it seems like p-values are not FDR-corrected in Tables 2 and 3 (mentioned only in Table 4). I think the total number of bundles assessed, and the correction should be made explicit when introducing Figure 2 and Table 2.

We clarified the analyses that were conducted in the revised version and we also simplified the analytical plan, so it is easier to follow.

First, we simplified the amyloid and tau measurements we assess, taking a global score rather than the cortical endpoints of each bundle. As mentioned by the reviewer, we assess white matter measures in three bundles (anterior cingulum, posterior cingulum and uncinate fasciculus) in both hemispheres. Regarding the white matter measures of interest, it is true that we assess many of them, but the fact that there is a consistent pattern of associations across measures suggests biological association rather than sparse false positive associations. The measures are related to one another and thus we see it as a strength that the diffusion measures go in the same direction across all bundles. Still, to make the manuscript more focussed, we now consider the five free-water corrected diffusion measures and we no longer include the apparent fiber density. We should note again that the apparent fiber density was considered as a further validation of the results given that it is a measure of FA robust to crossing fibers, and not an additional comparison. Similarly, we consider looking both in left and right hemisphere a way to assess whether there is a laterality effect to our results and not independent comparisons. Finally, we added a new independent cohort in which we replicated the main results.

We agree with the reviewer that we should have stated our number of analyses and rationale more explicitly. We thus updated the Results section 2.2 Methodology Overview (p. 8) as follows:

“An overview of the processing steps is shown in Figure 1 and can be summarized as follows: in three a priori white matter bundles of interest extracted in the left and right hemisphere from each participant’s tractogram, we evaluated associations between five related microstructure measures and AD pathology (global cortical Aβ and entorhinal tau). We present all five microstructure measures in order to detect whether a consistent pattern of associations across measures emerges rather than focussing on one given measure.”

Given that we are transparent in our analytical plan and that report all correlation coefficients and p-values, the reader can now assess the strength of the results and consider the number of comparisons they judge appropriate to perform a Bonferroni or FDR correction. For this reason, we decided to report uncorrected p-values.

https://doi.org/10.7554/eLife.62929.sa2

Article and author information

Author details

  1. Alexa Pichet Binette

    1. Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada
    2. Douglas Mental Health University Institute, Montreal, Canada
    Contribution
    Conceptualization, Formal analysis, Visualization, Writing - original draft
    For correspondence
    alexa.pichetbinette@mail.mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5218-3337
  2. Guillaume Theaud

    Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
    Contribution
    Software, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  3. François Rheault

    Electrical Engineering, Vanderbilt University, Nashville, United States
    Contribution
    Software, Methodology
    Competing interests
    No competing interests declared
  4. Maggie Roy

    Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
    Contribution
    Conceptualization, Writing - review and editing
    Competing interests
    No competing interests declared
  5. D Louis Collins

    McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
    Contribution
    Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8432-7021
  6. Johannes Levin

    1. Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
    2. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    Contribution
    Resources, Project administration
    Competing interests
    reports speaker fees from Bayer Vital and Roche, consulting fees from Axon Neuroscience, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers, non-financial support from Abbvie and compensation for duty as part-time CMO from MODAG, outside the submitted work
  7. Hiroshi Mori

    Department of Clinical Neuroscience, Osaka City University Medical School, Osaka, Japan
    Contribution
    Resources, Project administration
    Competing interests
    No competing interests declared
  8. Jae Hong Lee

    Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
    Contribution
    Resources, Project administration
    Competing interests
    No competing interests declared
  9. Martin Rhys Farlow

    Department of Neurology, Indiana University, Bloomington, United States
    Contribution
    Resources, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Peter Schofield

    1. Neuroscience Research Australia, Sydney, Australia
    2. School of Medical Sciences, UNSW Sydney, Sydney, Australia
    Contribution
    Resources, Project administration
    Competing interests
    No competing interests declared
  11. Jasmeer P Chhatwal

    Harvard Medical School, Massachusetts General Hospital, Boston, United States
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Colin L Masters

    The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
    Contribution
    Resources, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Tammie Benzinger

    1. Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States
    2. Department of Neurology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  14. John Morris

    1. Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States
    2. Department of Neurology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Randall Bateman

    1. Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States
    2. Department of Neurology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  16. John CS Breitner

    1. Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada
    2. Douglas Mental Health University Institute, Montreal, Canada
    Contribution
    Conceptualization, Resources, Data curation, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  17. Judes Poirier

    1. Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada
    2. Douglas Mental Health University Institute, Montreal, Canada
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  18. Julie Gonneaud

    1. Douglas Mental Health University Institute, Montreal, Canada
    2. Normandie Univ, UNICAEN, INSERM, U1237, Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
    Contribution
    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  19. Maxime Descoteaux

    Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    is the co-founder of Imeka Solution Inc
  20. Sylvia Villeneuve

    1. Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada
    2. Douglas Mental Health University Institute, Montreal, Canada
    3. McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing
    For correspondence
    sylvia.villeneuve@mcgill.ca
    Competing interests
    No competing interests declared
  21. DIAN Study Group

    Contribution
    Resources
  22. PREVENT-AD Research Group

    Contribution
    Resources

Funding

Canadian Institutes of Health Research (PJT-162091)

  • Sylvia Villeneuve

Canadian Institutes of Health Research (PJT-148963)

  • Sylvia Villeneuve

Jean-Louis Lévesque Foundation

  • Judes Poirier

Douglas Foundation

  • John CS Breitner

Canada Foundation for Innovation

  • Sylvia Villeneuve

NIA (UF1AG032438)

  • DIAN Study Group

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We wish to acknowledge the staff of PREVENT-AD as well as of the Brain Imaging Centre of the Douglas Mental Health University Institute and of the PET unit of the McConnell Brain Imaging Centre of the Montreal Neurological Institute, and members of the SCIL lab. A full listing of members of the PREVENT-AD Research Group can be found at https://preventad.loris.ca/acknowledgements/acknowledgements.php?date=[2020-06-30]. We would also like to acknowledge the participants of the PREVENT-AD cohort for dedicating their time and energy to helping us collect these data. Thank you to the Neuroinformatics Chair of the Université de Sherbrooke for supporting neuroscience research. Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer’s Network (DIAN, UF1AG032438) funded by the National Institute on Aging (NIA), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI). This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study.

Ethics

Human subjects: The study was approved by the ethics committee of the Faculty of Medicine of McGill University and of the Douglas Mental Health University Institute. Informed consent was obtained from all PREVENT-AD and DIAN participants prior to enrolling in the respective studies. We had access to the DIAN data with approval from DIAN leaders (data request DIAN-D1624).

Senior Editor

  1. Tamar R Makin, University College London, United Kingdom

Reviewing Editor

  1. Morgan Barense, University of Toronto, Canada

Reviewers

  1. Arun Bokde, Trinity College Dublin, Ireland
  2. Alfie Wearn, University of Bristol, United Kingdom
  3. Hakon Grydeland

Publication history

  1. Received: September 9, 2020
  2. Accepted: May 12, 2021
  3. Accepted Manuscript published: May 13, 2021 (version 1)
  4. Version of Record published: June 1, 2021 (version 2)

Copyright

© 2021, Pichet Binette et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 631
    Page views
  • 122
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Xiaoxuan Jia et al.
    Research Article

    Temporal continuity of object identity is a feature of natural visual input, and is potentially exploited -- in an unsupervised manner -- by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here we investigated whether plasticity of individual IT neurons underlies human core-object-recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed -- at least in part -- by naturally occurring unsupervised temporal contiguity experience.

    1. Neuroscience
    Nick Taubert et al.
    Research Article

    Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.