Alzheimer’s disease (AD) is characterized by the presence of both amyloid-beta (Aβ) and tau proteins that lead to neurodegeneration (Jack et al., 2015). The dominant view in the literature is that Aβ pathology triggers tau aggregation, strongly supported by evidence from autosomal dominant AD (Hardy, 2002). However, there have been important recent findings from sporadic AD that suggest that hyperphosphorylated tau proteins that are no longer governed by normal cellular removal mechanisms play a necessary and underappreciated role in AD progression (Musiek and Holtzman, 2015), with some authors even suggesting that this may be the primary step in the disease needed for subsequent AB aggregation (Arnsten et al., 2020; Braak and Del Tredici, 2015). In addition, a recent study found that tau-positron emission tomography (PET), but not β-amyloid–PET, could predict longitudinal brain atrophy in AD (La Joie et al., 2020). Perhaps for this reason, across animal and human studies, phosphorylated tau (Ptau), rather than Aβ, seems to better reflect cognitive deficits (Giannakopoulos et al., 2003; Hedden et al., 2013; Huber et al., 2018; Mitchell et al., 2002; Bennett et al., 2004; Schöll et al., 2016). However, for Ptau to become a potentially meaningful therapeutic target for preventing or slowing dementia progression, it is essential to better understand the mechanisms linking Ptau and cognitive deficits.

Both in vivo and in vitro findings support that abnormal tau appears to originate in the anterior medial temporal lobe (MTL) before spreading to anterior-temporal (AT) and posterior-medial (PM) regions of the cerebral cortex (Braak and Del Tredici, 2015; Cho et al., 2016). Cumulative evidence suggests that AD is a connectome disease (Acosta-Cabronero et al., 2010, Dai and He, 2014, Damoiseaux et al., 2009, Delbeuck et al., 2003, Rose et al., 2000), and abnormal tau spreads via neuronal connections (Clavaguera et al., 2009). MTL is strongly connected with both the AT and PM systems, with anterolateral entorhinal cortex showing preferential connectivity with anterior regions overlapping with the AT system and parahippocampal gyrus and posteromedial entorhinal cortex with posterior regions overlapping with the PM system (Maass et al., 2015; Navarro Schröder et al., 2015). The accumulation of abnormal tau in the MTL and subsequent spread to these cortical memory systems are likely to be particularly relevant for understanding memory deficits in AD, as these systems play dissociable roles in human memory: the AT system is specific for object-related stimuli (i.e., semantic memory) and the PM is specific for remembering scenes (Davachi et al., 2003; Epstein and Kanwisher, 1998; Wang et al., 2020). Taken together, these findings suggest that damage to the integrity of neural connections between the MTL, AT, and PM may be a mechanism by which Ptau accumulation leads to cognitive decline.

Several studies have examined the relationship between the functional (Berron et al., 2020; Cope et al., 2018; Franzmeier et al., 2020; Franzmeier et al., 2019; Maass et al., 2015; Ossenkoppele et al., 2019) and structural connectivity and tau pathology (Jacobs et al., 2018; Kim et al., 2019). Tau propagation from the entorhinal cortex to anterior and posterior brain regions that overlap with the AT and PM memory systems has been shown to relate to connectivity strength as measured by functional connectivity in cognitively normal adults, consistent with the idea that tau spreads via neuronal connections (Adams et al., 2019). Furthermore, tau pathology is related to worse performance in object memory tasks involving the AT system relative to scene memory tasks involving the PM system in cognitive normal older adults, also matching the proposed spread of the disease via neuronal connections, as tau burden was much higher in the AT system compared with the PM system in the early stages of AD (Arnsten et al., 2020; Maass et al., 2019). While single measures of functional and structural connectivity can give clues into the spread of Ptau, abnormalities in the longitudinal integrity of the structural connectivity may directly reflect loss of axonal integrity from tau pathology (Conturo et al., 1999). Especially during predementia AD stages when AD pathology is relatively mild, the loss of this integrity due to tau pathology might help explain how tau accumulation in the MTL leads to memory deficits. Synthesizing these separate lines of evidence, we suspect that the longitudinal integrity of the structural connections of the MTL may link Ptau accumulation and episodic memory decline in predementia AD syndromes.

Here, we capture the longitudinal integrity of the structural connectivity by calculating correlations across multiple time points to give a measure of stability. This novel concept has been first applied to functional connectivity (Kaufmann et al., 2018; Kaufmann et al., 2017). An emerging study suggests that a functional connectome stability measure is associated with cognition (average and rate of episodic memory decline) (Ousdal et al., 2020). Traditional univariate tract-wise analysis first derives a change score of each tract and then compares it across participants. Conversely, the stability measure calculates the pattern similarity between two time points across multiple connections within participants, in which subtle changes of individual connections may affect the stability score. The stability measure may therefore be suitable to capture subtle structural changes in the predementia AD stage.

We aimed to examine the relationship between structural stability, Ptau, and episodic memory. We extracted data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, focusing on participants with cerebrospinal fluid (CSF) Ptau collected at baseline, as well as diffusion tensor imaging (DTI) measure twice, 2 years apart with the baseline measure corresponding to Ptau measurement. In addition, we extracted episodic memory data at baseline (anchored by Ptau measurement) and throughout four additional years.

We had several hypotheses: first, based on the idea that the accumulation of Ptau in MTL in the predementia AD stages causes a loss of integrity in the axons that connect MTL to AT and PM systems, we hypothesized that higher CSF Ptau levels will be associated with decreased MTL-related structural stability. Specifically, we examined stability within the MTL, as well as in MTL-AT and MTL-PM connections. Second, given that tau tangles deposit early in the AT lobe (Arnsten et al., 2020; Braak and Del Tredici, 2015; Maass et al., 2019), we hypothesized that Ptau first affects the stability in the MTL and MTL-AT in the predementia stages, followed by MTL-PM as the disease progresses and tau pathology worsens and spreads to posterior brain regions. Finally, given that MTL and related structures play a central role in episodic memory, we hypothesized that MTL-related structural stability might be associated with tau-related episodic memory decline.

We extracted data from ADNI participants with CSF Ptau collected at baseline, DTI measure twice, 2 year apart, and longitudinal episodic memory data over 5 years. We defined three age- and education-matched groups based on baseline Ptau and cognitive impairment: Ptau negative cognitively unimpaired (CN Ptau–, n = 26), Ptau positive cognitively unimpaired (CN Ptau+, n = 18), and Ptau positive individuals with mild cognitive impairment (MCI Ptau+, n = 30). Group comparison of baseline characteristics is presented in Table 1. There was no significant difference between the CN Ptau– and CN Ptau+ groups, except for the Ptau pathology (p < 0.001). Compared with CN groups, MCI Ptau+ group had more APOEε4 carriers, higher Ptau levels, lower CSF Aβ level, greater neurodegeneration, as well as worse performance in episodic memory and global cognition (all ps < 0.05).

Longitudinal integrity of structural connections was measured as a stability index of correlations across baseline and 2 year follow-up for connections between the MTL and cortical memory systems (see Materials and methods for details). We identified regions of interests based on the Desikan-Killiany Atlas. Since the hippocampus and the surrounding hippocampal region including the parahippocampal cortex and entorhinal cortex are the primary regions supporting memory processing (Davachi et al., 2003), we consider them to belong to MTL memory network. Thus, the MTL system includes entorhinal, hippocampus, and parahippocampal gyrus in both hemispheres. In line with previous literature (Berron et al., 2020; Ranganath and Ritchey, 2012), the AT system includes bilateral inferior temporal, temporal polar, and lateral and medial orbitofrontal cortex, while the PM system includes bilateral posterior and isthmus cingulate, lateral occipital cortex, precuneus, and thalamus. Figure 1A shows the center of mass of regions of interests. For visualization purposes, we presented the connections generated between MTL-related structures in a representative participant in Figure 1B. The connections within MTL include the hippocampal cingulum bundle and fornix. The connections between MTL and AT largely overlap with the anterior segments of the inferior longitudinal fasciculus (ILF) and inferior fronto-occipital fasciculus (IFOF), while the connections between MTL and PM mainly involve the cingulum bundle and posterior segments of ILF and IFOF.

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Relationship between baseline CSF Ptau and MTL structural stability

We hypothesized that Ptau affects the stability in MTL and MTL-AT early in predementia AD stages (e.g., CN Ptau+) and with disease progression and increased tau pathology (e.g., MCI Ptau+), the stability in MTL-PM is also disrupted. To test this, one-tailed independent t-tests were conducted between (1) CN Ptau+ and CN Ptau–; (2) MCI Ptau+ and CN Ptau–; and (3) MCI Ptau+ and CN Ptau+. We expected to see disrupted stability in MTL and MTL-AT in CN Ptau+, compared with CN Ptau–. For MCI Ptau+, the disruption would extend to MTL-PM, compared to both CN Ptau– and CN Ptau+. One-tailed tests were used because our hypotheses were directional, expecting worse stability with disease progression.

In line with our hypothesis that MTL and MTL-AT stability is affected by Ptau early in the predementia AD stage, compared to CN Ptau–, structural stability was significantly lower in CN Ptau+ in MTL (t(42) = –2.11, p = 0.022) and MTL-AT (t(42) = –2.57, p = 0.007). Decreased structural stability was further found in MTL (t(54) = –2.61, p = 0.006) and MTL-AT (t(54) = –3.55, p < 0.001) in MCI Ptau+, compared to CN Ptau-. In line with our hypothesis that MTL-PM is affected later in disease progression, we found a significantly lower stability in MTL-PM in MCI Ptau+, compared to CN Ptau+ (t(46) = –5.12, p < 0.001) and CN Ptau– (t(54) = –3.89, p < 0.001, Figure 2A). These results suggested that Ptau affects the structural integrity of connections from the MTL in a way that follows the proposed spread of the disease via neuronal connections.

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Group comparisons.

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In addition, using the entire sample, Model 1 (Y stability = β10 + β11Ptau + ε₁) suggested that levels of Ptau significantly related to structural stability in MTL (B = –0.61, SE = 0.23, Wald’s χ² = 7.32, BH-adjusted p = 0.011, Figure 2B, left), MTL-AT (B = –0.59, SE = 0.21, Wald’s χ² = 7.73, BH-adjusted p = 0.011, Figure 2B, middle), and MTL-PM (B = –0.37, SE = 0.17, Wald’s χ² = 4.43, BH-adjusted p = 0.035, Figure 2B, right).

The effect of age, CSF Aβ pathology, and neurodegeneration on MTL structural stability in the whole sample

We examined whether structural stability could be directly affected by covariates (i.e., age, CSF Aβ, or neurodegeneration) in the entire sample with Model 2 (Y stability = β20 + β21Covariates + ε₂). We found no significant relationship between age and stability (all ps > 0.05, Figure 3A). We found a significant correlation between CSF Aβ and stability in MTL-PM (β = 0.40, SE = 0.16, Wald’s χ² = 6.04, BH-adjusted p = 0.042, Figure 3B). Neurodegeneration significantly related to stability within MTL (β = 0.58, SE = 0.26, Wald’s χ² = 4.97, BH-adjusted p = 0.039), MTL-AT (β = 0.49, SE = 0.25, Wald’s χ² = 3.89, BH-adjusted p = 0.048), and MTL-PM (β = 0.61, SE = 0.19, Wald’s χ² = 10.39, BH-adjusted p = 0.003, Figure 3C).

Figure 3

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The effect of covariates on the structural stability in the whole sample.

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The effect of age, CSF Aβ pathology, and neurodegeneration on the relationship between Ptau and the MTL structural stability in the whole sample

We further examined whether the relationship between Ptau and stability would be affected by covariates (i.e., age, Aβ, or neurodegeneration). We tested Ptau and each covariate’s interaction effect, controlling for their main effects, in predicting the structural stability in the whole sample with Model 3 (Y stability = β30 + β31Ptau + β32Covariate + β33Ptau× Covariates + ε₃). Results showed no significant interaction for Ptau × age, Ptau × CSF Aβ, or Ptau × neurodegeneration (Figure 4), suggesting that the relationship between Ptau and stability is not affected by age, CSF Aβ pathology, or neurodegeneration.

Figure 4

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The effect of covariates on the relationship between Ptau and structural stability.

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Relationship between MTL structural stability, Ptau, and memory over 5 years in the whole sample

Having determined that Ptau related to our measure of structural stability in a way that suggests it follows proposed models of disease progression, we next assessed whether structural stability predicts episodic memory. In line with our hypothesis that decreased stability in MTL-related structures leads to deficits in episodic memory, results from Model 4 (Y average episodic memory = β40 + β41 Stability + ε₄) suggested that stability in MTL (B = 0.60, SE = 0.24, Wald’s χ² = 6.23, BH-adjusted p = 0.020, Figure 5A, left), MTL-AT (B = 0.56, SE = 0.26, Wald’s χ² = 4.73, BH-adjusted p = 0.030, Figure 5A, middle), and MTL-PM (B = 0.95, SE = 0.31, Wald’s χ² = 9.29, BH-adjusted p = 0.006, Figure 5A, right) significantly predicted average episodic memory across 5 years. When we included the covariates (i.e., age, education, sex, neurodegeneration, and CSF Aβ), all results remained significant (MTL: B = 0.42, SE = 0.21, Wald’s χ² = 3.98, BH-adjusted p = 0.046; MTL-AT: B = 0.47, SE = 0.22, Wald’s χ² = 4.52, BH-adjusted p = 0.046; MTL-PM: B = 0.88, SE = 0.29, Wald’s χ² = 9.10, BH-adjusted p = 0.009). Furthermore, results from Model 4’ (Y rate of episodic memory change = β40’ + β41’ Stability + ε_4’) showed lower stability in MTL (B = 0.12, SE = 0.06, Wald’s χ² = 4.13, BH-adjusted p = 0.040, Figure 5B, left) and MTL-PM (B = 0.15, SE = 0.07, Wald’s χ² = 4.04, BH-adjusted p = 0.042, Figure 5B, right) related to greater decreased rate of episodic memory. We did not find any relationship between MTL-AT stability and rate of episodic memory change (Figure 5B, middle). Only the result in MTL remained significant after the covariates were controlled (B = 0.11, SE = 0.06, Wald’s χ² = 3.85, p = 0.050).

Figure 5

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MTL structural stability predicts episodic memory.

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To this point, our analyses have established that levels of Ptau significantly related to structural integrity in a manner that follows proposed disease progression, with Ptau in patients early in the disease relating to a loss of structural stability in MTL and MTL-AT connections, followed by MTL-PM connections over time as the disease progresses to MCI. We have also shown that decreased stability in the MTL, MTL-AT, and MTL-PM connections related to worse average episodic memory. Specifically, decreased stability within the MTL connections predicted future rate of memory decline. Our final analysis sought to formally test the hypothesis that this loss of structural stability in MTL provides a link between Ptau accumulation and memory decline. We expected to see stability as a significant moderator of the relationship between Ptau and episodic memory.

Results from Model 5 (Y average episodic memory = β50 + β51 Ptau + ε₅) suggested that Ptau at baseline predicted average episodic memory across 5 years (B = –1.09, SE = 0.49, Wald’s χ² = 4.85, p = 0.028). After adding stability and the interaction between stability and Ptau (Model 6: Y average episodic memory = β60+ β61Ptau + β62Stability + β63Ptau × Stability + ε₆), there was no significant interaction between Ptau and stability of MTL (B = 1.34, SE = 1.62, Wald’s χ² = 0.69, p = 0.41).

Results from Model 5’ (Y rate of episodic memory change = β50’ + β51’ Ptau + ε_5’) suggested that higher level of Ptau predicted greater decreased rate of memory (B = –0.38, SE = 0.11, Wald’s χ² = 11.34, p = 0.001). After adding stability and the interaction between stability and Ptau into the model (Model 6’: Y rate of episodic memory change = β60_’ + β61_’Ptau + β62_’Stability + β63_’Ptau × Stability + ε_6’), Ptau × Stability of MTL was significant (B = –0.01, SE = 0.01, Wald’s χ² = 5.28, p = 0.022). The interaction effect remained significant while accounting for covariates (B = –0.01, SE = 0.01, Wald’s χ² = 3.97, p = 0.046). Figure 6 displayed the associations between Ptau and rate of memory change depending on MTL stability. Participants were divided based on terciles of MTL stability. Stronger relationship between Ptau and rate of memory change was observed in those with higher MTL stability. All of these results suggested that the structural stability of the MTL links Ptau and episodic memory.

Figure 6

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MTL structural stability moderates the relationship between Ptau and memory change.

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In this study, we examined whether the presence of CSF Ptau at baseline was related to a loss of structural stability in a way that matched proposed disease progression and whether this loss of stability in connections important for episodic memory is related to the link between Ptau accumulation and cognitive decline. We did this by first examining the relationship between Ptau at baseline and longitudinal white matter stability across three groups (CN Ptau–, CN Ptau+, and MCI Ptau+). CN Ptau+ and MCI Ptau+ had decreased stability in MTL and MTL-AT compared to CN Ptau-, while MCI Ptau+ further showed decrease in MTL-PM stability relative to both CN Ptau– and CN Ptau+ groups, suggesting that Ptau relates to structural stability in a temporal pattern that matches its proposed spread from the MTL via neuronal connections. Furthermore, the stability in MTL, MTL-AT, and MTL-PM predicted average episodic memory over 5 years, suggesting that damage to these pathways specifically relates to episodic memory decline. Importantly, structural stability in MTL moderated the effect of Ptau on the rate of memory change, suggesting that structural stability in MTL may be a candidate as a mechanistic link between the accumulation of Ptau and cognitive decline.

Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

1. 1. Jack CR
   2. Bernstein MA
   3. Fox NC
   4. Thompson P
   5. Alexander G
   6. Harvey D
   7. Borowski B
   8. Britson PJ
   9. Whitwell LJ
   10. Ward C
   11. Dale AM

   (2008) ADNI

   ID adni. The Alzheimer's disease neuroimaging initiative.

   https://ida.loni.usc.edu/login.jsp?project=ADNI

1. 1. Acosta-Cabronero J
   2. Williams GB
   3. Pengas G
   4. Nestor PJ

   (2010) Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease

   Brain 133:529–539.

   https://doi.org/10.1093/brain/awp257

   • Google Scholar
2. 1. Adams JN
   2. Maass A
   3. Harrison TM
   4. Baker SL
   5. Jagust WJ

   (2019) Cortical tau deposition follows patterns of entorhinal functional connectivity in aging

   eLife 8:e49123.

   https://doi.org/10.7554/eLife.49132

   • Google Scholar
3. 1. Arnsten AFT
   2. Datta D
   3. Tredici KD
   4. Braak H

   (2020) Hypothesis: tau pathology is an initiating factor in sporadic alzheimer's disease

   Alzheimer's & Dementia 5:12192.

   https://doi.org/10.1002/alz.12192

   • Google Scholar
4. 1. Bennett DA
   2. Schneider JA
   3. Wilson RS
   4. Bienias JL
   5. Arnold SE

   (2004) Neurofibrillary tangles mediate the association of amyloid load with clinical alzheimer disease and level of cognitive function

   Archives of Neurology 61:378–384.

   https://doi.org/10.1001/archneur.61.3.378

   • Google Scholar
5. 1. Berron D
   2. van Westen D
   3. Ossenkoppele R
   4. Strandberg O
   5. Hansson O

   (2020) Medial temporal lobe connectivity and its associations with cognition in early Alzheimer's disease

   Brain 143:1233–1248.

   https://doi.org/10.1093/brain/awaa068

   • PubMed
   • Google Scholar
6. 1. Braak H
   2. Del Tredici K

   (2015) The preclinical phase of the pathological process underlying sporadic alzheimer's disease

   Brain 138:2814–2833.

   https://doi.org/10.1093/brain/awv236

   • PubMed
   • Google Scholar
7. 1. Cho H
   2. Choi JY
   3. Hwang MS
   4. Kim YJ
   5. Lee HM
   6. Lee HS
   7. Lee JH
   8. Ryu YH
   9. Lee MS
   10. Lyoo CH

   (2016) In vivo cortical spreading pattern of tau and amyloid in the alzheimer disease spectrum

   Annals of Neurology 80:247–258.

   https://doi.org/10.1002/ana.24711

   • PubMed
   • Google Scholar
8. 1. Clavaguera F
   2. Bolmont T
   3. Crowther RA
   4. Abramowski D
   5. Frank S
   6. Probst A
   7. Fraser G
   8. Stalder AK
   9. Beibel M
   10. Staufenbiel M
   11. Jucker M
   12. Goedert M
   13. Tolnay M

   (2009) Transmission and spreading of tauopathy in transgenic mouse brain

   Nature Cell Biology 11:909–913.

   https://doi.org/10.1038/ncb1901

   • PubMed
   • Google Scholar
9. 1. Conturo TE
   2. Lori NF
   3. Cull TS
   4. Akbudak E
   5. Snyder AZ
   6. Shimony JS
   7. McKinstry RC
   8. Burton H
   9. Raichle ME

   (1999) Tracking neuronal fiber pathways in the living human brain

   PNAS 96:10422–10427.

   https://doi.org/10.1073/pnas.96.18.10422

   • PubMed
   • Google Scholar
10. 1. Cope TE
    2. Rittman T
    3. Borchert RJ
    4. Jones PS
    5. Vatansever D
    6. Allinson K
    7. Passamonti L
    8. Vazquez Rodriguez P
    9. Bevan-Jones WR
    10. O'Brien JT
    11. Rowe JB

    (2018) Tau burden and the functional connectome in Alzheimer's disease and progressive supranuclear palsy

    Brain 141:550–567.

    https://doi.org/10.1093/brain/awx347

    • PubMed
    • Google Scholar
11. 1. Crane PK
    2. Carle A
    3. Gibbons LE
    4. Insel P
    5. Mackin RS
    6. Gross A
    7. Jones RN
    8. Mukherjee S
    9. Curtis SM
    10. Harvey D
    11. Weiner M
    12. Mungas D
    13. Alzheimer’s Disease Neuroimaging Initiative

    (2012) Development and assessment of a composite score for memory in the alzheimer's Disease Neuroimaging Initiative (ADNI)

    Brain Imaging and Behavior 6:502–516.

    https://doi.org/10.1007/s11682-012-9186-z

    • PubMed
    • Google Scholar
12. 1. Dai Z
    2. He Y

    (2014) Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease

    Neuroscience Bulletin 30:217–232.

    https://doi.org/10.1007/s12264-013-1421-0

    • Google Scholar
13. 1. Damoiseaux JS
    2. Smith SM
    3. Witter MP
    4. Sanz-Arigita EJ
    5. Barkhof F
    6. Scheltens P
    7. Stam CJ
    8. Zarei M
    9. Rombouts SA

    (2009) White matter tract integrity in aging and Alzheimer's disease

    Human Brain Mapping 30:1051–1059.

    https://doi.org/10.1002/hbm.20563

    • PubMed
    • Google Scholar
14. 1. Davachi L
    2. Mitchell JP
    3. Wagner AD

    (2003) Multiple routes to memory: distinct medial temporal lobe processes build item and source memories

    PNAS 100:2157–2162.

    https://doi.org/10.1073/pnas.0337195100

    • PubMed
    • Google Scholar
15. 1. Delbeuck X
    2. Van der Linden M
    3. Collette F

    (2003) Alzheimer's disease as a disconnection syndrome?

    Neuropsychology Review 13:79–92.

    https://doi.org/10.1023/a:1023832305702

    • PubMed
    • Google Scholar
16. 1. Desikan RS
    2. Ségonne F
    3. Fischl B
    4. Quinn BT
    5. Dickerson BC
    6. Blacker D
    7. Buckner RL
    8. Dale AM
    9. Maguire RP
    10. Hyman BT
    11. Albert MS
    12. Killiany RJ

    (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

    NeuroImage 31:968–980.

    https://doi.org/10.1016/j.neuroimage.2006.01.021

    • PubMed
    • Google Scholar
17. 1. Epstein R
    2. Kanwisher N

    (1998) A cortical representation of the local visual environment

    Nature 392:598–601.

    https://doi.org/10.1038/33402

    • PubMed
    • Google Scholar
18. 1. Fellgiebel A
    2. Dellani PR
    3. Greverus D
    4. Scheurich A
    5. Stoeter P
    6. Müller MJ

    (2006) Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the Hippocampus

    Psychiatry Research: Neuroimaging 146:283–287.

    https://doi.org/10.1016/j.pscychresns.2006.01.006

    • PubMed
    • Google Scholar
19. 1. Field D
    2. Minkler M

    (1988) Continuity and change in social support between young-old and old-old or very-old age

    Journal of Gerontology 43:P100–P106.

    https://doi.org/10.1093/geronj/43.4.P100

    • PubMed
    • Google Scholar
20. 1. Fjell AM
    2. Sneve MH
    3. Storsve AB
    4. Grydeland H
    5. Yendiki A
    6. Walhovd KB

    (2016) Brain events underlying episodic memory changes in aging: a longitudinal investigation of structural and functional connectivity

    Cerebral Cortex 26:1272–1286.

    https://doi.org/10.1093/cercor/bhv102

    • PubMed
    • Google Scholar
21. 1. Franzmeier N
    2. Rubinski A
    3. Neitzel J
    4. Kim Y
    5. Damm A
    6. Na DL
    7. Kim HJ
    8. Lyoo CH
    9. Cho H
    10. Finsterwalder S
    11. Duering M
    12. Seo SW
    13. Ewers M
    14. Alzheimer’s Disease Neuroimaging Initiative

    (2019) Functional connectivity associated with tau levels in ageing, alzheimer's, and small vessel disease

    Brain 142:1093–1107.

    https://doi.org/10.1093/brain/awz026

    • PubMed
    • Google Scholar
22. 1. Franzmeier N
    2. Neitzel J
    3. Rubinski A
    4. Smith R
    5. Strandberg O
    6. Ossenkoppele R
    7. Hansson O
    8. Ewers M
    9. Alzheimer’s Disease Neuroimaging Initiative (ADNI)

    (2020) Functional brain architecture is associated with the rate of tau accumulation in Alzheimer's disease

    Nature Communications 11:347.

    https://doi.org/10.1038/s41467-019-14159-1

    • PubMed
    • Google Scholar
23. 1. Giannakopoulos P
    2. Herrmann FR
    3. Bussière T
    4. Bouras C
    5. Kövari E
    6. Perl DP
    7. Morrison JH
    8. Gold G
    9. Hof PR

    (2003) Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease

    Neurology 60:1495–1500.

    https://doi.org/10.1212/01.WNL.0000063311.58879.01

    • PubMed
    • Google Scholar
24. 1. Girard G
    2. Whittingstall K
    3. Deriche R
    4. Descoteaux M

    (2014) Towards quantitative connectivity analysis: reducing tractography biases

    NeuroImage 98:266–278.

    https://doi.org/10.1016/j.neuroimage.2014.04.074

    • PubMed
    • Google Scholar
25. 1. Hansson O
    2. Mormino EC

    (2018) Is longitudinal tau PET ready for use in Alzheimer’s disease clinical trials?

    Brain 141:1241–1244.

    https://doi.org/10.1093/brain/awy065

    • Google Scholar
26. 1. Hardy J

    (2002) The amyloid hypothesis of Alzheimer's Disease: Progress and Problems on the Road to Therapeutics

    Science 297:353–356.

    https://doi.org/10.1126/science.1072994

    • Google Scholar
27. 1. Hedden T
    2. Oh H
    3. Younger AP
    4. Patel TA

    (2013) Meta-analysis of amyloid-cognition relations in cognitively normal older adults

    Neurology 80:1341–1348.

    https://doi.org/10.1212/WNL.0b013e31828ab35d

    • Google Scholar
28. 1. Huber CM
    2. Yee C
    3. May T
    4. Dhanala A
    5. Mitchell CS

    (2018) Cognitive decline in preclinical alzheimer's Disease: Amyloid-Beta versus Tauopathy

    Journal of Alzheimer's Disease 61:265–281.

    https://doi.org/10.3233/JAD-170490

    • PubMed
    • Google Scholar
29. 1. Jack CR
    2. Wiste HJ
    3. Weigand SD
    4. Knopman DS
    5. Mielke MM
    6. Vemuri P
    7. Lowe V
    8. Senjem ML
    9. Gunter JL
    10. Reyes D
    11. Machulda MM
    12. Roberts R
    13. Petersen RC

    (2015) Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings

    Brain 138:3747–3759.

    https://doi.org/10.1093/brain/awv283

    • PubMed
    • Google Scholar
30. 1. Jack CR
    2. Bennett DA
    3. Blennow K
    4. Carrillo MC
    5. Dunn B
    6. Haeberlein SB
    7. Holtzman DM
    8. Jagust W
    9. Jessen F
    10. Karlawish J
    11. Liu E
    12. Molinuevo JL
    13. Montine T
    14. Phelps C
    15. Rankin KP
    16. Rowe CC
    17. Scheltens P
    18. Siemers E
    19. Snyder HM
    20. Sperling R
    21. Contributors

    (2018) NIA-AA research framework: toward a biological definition of alzheimer's disease

    Alzheimer's & Dementia 14:535–562.

    https://doi.org/10.1016/j.jalz.2018.02.018

    • PubMed
    • Google Scholar
31. 1. Jacobs HIL
    2. Hedden T
    3. Schultz AP
    4. Sepulcre J
    5. Perea RD
    6. Amariglio RE
    7. Papp KV
    8. Rentz DM
    9. Sperling RA
    10. Johnson KA

    (2018) Structural tract alterations predict downstream tau accumulation in amyloid-positive older individuals

    Nature Neuroscience 21:424–431.

    https://doi.org/10.1038/s41593-018-0070-z

    • PubMed
    • Google Scholar
32. 1. Jin Y
    2. Huang C
    3. Daianu M
    4. Zhan L
    5. Dennis EL
    6. Reid RI
    7. Jack CR
    8. Zhu H
    9. Thompson PM
    10. Alzheimer's Disease Neuroimaging Initiative

    (2017) 3d tract-specific local and global analysis of white matter integrity in Alzheimer's disease

    Human Brain Mapping 38:1191–1207.

    https://doi.org/10.1002/hbm.23448

    • PubMed
    • Google Scholar
33. 1. Kaufmann T
    2. Alnæs D
    3. Doan NT
    4. Brandt CL
    5. Andreassen OA
    6. Westlye LT

    (2017) Delayed stabilization and individualization in connectome development are related to psychiatric disorders

    Nature Neuroscience 20:513–515.

    https://doi.org/10.1038/nn.4511

    • PubMed
    • Google Scholar
34. 1. Kaufmann T
    2. Alnæs D
    3. Brandt CL
    4. Bettella F
    5. Djurovic S
    6. Andreassen OA
    7. Westlye LT

    (2018) Stability of the brain functional connectome fingerprint in individuals with schizophrenia

    JAMA Psychiatry 75:749–751.

    https://doi.org/10.1001/jamapsychiatry.2018.0844

    • PubMed
    • Google Scholar
35. 1. Kim WH
    2. Racine AM
    3. Adluru N
    4. Hwang SJ
    5. Blennow K
    6. Zetterberg H
    7. Carlsson CM
    8. Asthana S
    9. Koscik RL
    10. Johnson SC
    11. Bendlin BB
    12. Singh V

    (2019) Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: a multi-resolution graph analysis

    NeuroImage: Clinical 21:101586.

    https://doi.org/10.1016/j.nicl.2018.10.024

    • PubMed
    • Google Scholar
36. 1. La Joie R
    2. Visani AV
    3. Baker SL
    4. Brown JA
    5. Bourakova V
    6. Cha J
    7. Chaudhary K
    8. Edwards L
    9. Iaccarino L
    10. Janabi M
    11. Lesman-Segev OH
    12. Miller ZA
    13. Perry DC
    14. O'Neil JP
    15. Pham J
    16. Rojas JC
    17. Rosen HJ
    18. Seeley WW
    19. Tsai RM
    20. Miller BL
    21. Jagust WJ
    22. Rabinovici GD

    (2020) Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

    Science Translational Medicine 12:eaau5732.

    https://doi.org/10.1126/scitranslmed.aau5732

    • PubMed
    • Google Scholar
37. 1. Lin F
    2. Ren P
    3. Wang X
    4. Anthony M
    5. Tadin D
    6. Heffner KL

    (2017) Cortical thickness is associated with altered autonomic function in cognitively impaired and non-impaired older adults

    The Journal of Physiology 595:6969–6978.

    https://doi.org/10.1113/JP274714

    • PubMed
    • Google Scholar
38. 1. Maass A
    2. Berron D
    3. Libby LA
    4. Ranganath C
    5. Düzel E

    (2015) Functional subregions of the human entorhinal cortex

    eLife 4:e06426.

    https://doi.org/10.7554/eLife.06426

    • Google Scholar
39. 1. Maass A
    2. Berron D
    3. Harrison TM
    4. Adams JN
    5. La Joie R
    6. Baker S
    7. Mellinger T
    8. Bell RK
    9. Swinnerton K
    10. Inglis B
    11. Rabinovici GD
    12. Düzel E
    13. Jagust WJ

    (2019) Alzheimer's pathology targets distinct memory networks in the ageing brain

    Brain 142:2492–2509.

    https://doi.org/10.1093/brain/awz154

    • PubMed
    • Google Scholar
40. 1. Mitchell TW
    2. Mufson EJ
    3. Schneider JA
    4. Cochran EJ
    5. Nissanov J
    6. Han LY
    7. Bienias JL
    8. Lee VM
    9. Trojanowski JQ
    10. Bennett DA
    11. Arnold SE

    (2002) Parahippocampal tau pathology in healthy aging, mild cognitive impairment, and early alzheimer's disease

    Annals of Neurology 51:182–189.

    https://doi.org/10.1002/ana.10086

    • PubMed
    • Google Scholar
41. 1. Müller MJ
    2. Greverus D
    3. Weibrich C
    4. Dellani PR
    5. Scheurich A
    6. Stoeter P
    7. Fellgiebel A

    (2007) Diagnostic utility of hippocampal size and mean diffusivity in Amnestic MCI

    Neurobiology of Aging 28:398–403.

    https://doi.org/10.1016/j.neurobiolaging.2006.01.009

    • PubMed
    • Google Scholar
42. 1. Musiek ES
    2. Holtzman DM

    (2015) Three dimensions of the amyloid hypothesis: time, space and 'wingmen'

    Nature Neuroscience 18:800–806.

    https://doi.org/10.1038/nn.4018

    • PubMed
    • Google Scholar
43. 1. Navarro Schröder T
    2. Haak KV
    3. Zaragoza Jimenez NI
    4. Beckmann CF
    5. Doeller CF

    (2015) Functional topography of the human entorhinal cortex

    eLife 4:e06738.

    https://doi.org/10.7554/eLife.06738

    • Google Scholar
44. 1. Ossenkoppele R
    2. Iaccarino L
    3. Schonhaut DR
    4. Brown JA
    5. La Joie R
    6. O'Neil JP
    7. Janabi M
    8. Baker SL
    9. Kramer JH
    10. Gorno-Tempini ML
    11. Miller BL
    12. Rosen HJ
    13. Seeley WW
    14. Jagust WJ
    15. Rabinovici GD

    (2019) Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

    NeuroImage: Clinical 23:101848.

    https://doi.org/10.1016/j.nicl.2019.101848

    • PubMed
    • Google Scholar
45. 1. Ousdal OT
    2. Kaufmann T
    3. Kolskår K
    4. Vik A
    5. Wehling E
    6. Lundervold AJ
    7. Lundervold A
    8. Westlye LT

    (2020) Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging

    Human Brain Mapping 41:697–709.

    https://doi.org/10.1002/hbm.24833

    • PubMed
    • Google Scholar
46. 1. Ranganath C
    2. Ritchey M

    (2012) Two cortical systems for memory-guided behaviour

    Nature Reviews Neuroscience 13:713–726.

    https://doi.org/10.1038/nrn3338

    • PubMed
    • Google Scholar
47. 1. Rose SE
    2. Chen F
    3. Chalk JB
    4. Zelaya FO
    5. Strugnell WE

    (2000) Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging

    Journal of Neurology 69:528–530.

    https://doi.org/10.1136/jnnp.69.4.528

    • Google Scholar
48. 1. Salami A
    2. Wåhlin A
    3. Kaboodvand N
    4. Lundquist A
    5. Nyberg L

    (2016) Longitudinal evidence for dissociation of anterior and posterior MTL Resting-State connectivity in aging: links to perfusion and memory

    Cerebral Cortex 26:3953–3963.

    https://doi.org/10.1093/cercor/bhw233

    • PubMed
    • Google Scholar
49. 1. Schöll M
    2. Lockhart SN
    3. Schonhaut DR
    4. O'Neil JP
    5. Janabi M
    6. Ossenkoppele R
    7. Baker SL
    8. Vogel JW
    9. Faria J
    10. Schwimmer HD
    11. Rabinovici GD
    12. Jagust WJ

    (2016) PET imaging of tau deposition in the aging human brain

    Neuron 89:971–982.

    https://doi.org/10.1016/j.neuron.2016.01.028

    • PubMed
    • Google Scholar
50. 1. Smith SM

    (2002) Fast robust automated brain extraction

    Human Brain Mapping 17:143–155.

    https://doi.org/10.1002/hbm.10062

    • PubMed
    • Google Scholar
51. 1. Staffaroni AM
    2. Brown JA
    3. Casaletto KB
    4. Elahi FM
    5. Deng J
    6. Neuhaus J
    7. Cobigo Y
    8. Mumford PS
    9. Walters S
    10. Saloner R
    11. Karydas A
    12. Coppola G
    13. Rosen HJ
    14. Miller BL
    15. Seeley WW
    16. Kramer JH

    (2018) The longitudinal trajectory of default mode network connectivity in healthy older adults varies as a function of age and is associated with changes in episodic memory and processing speed

    The Journal of Neuroscience 38:2809–2817.

    https://doi.org/10.1523/JNEUROSCI.3067-17.2018

    • PubMed
    • Google Scholar
52. 1. Suárez-Calvet M
    2. Morenas-Rodríguez E
    3. Kleinberger G
    4. Schlepckow K
    5. Araque Caballero MÁ
    6. Franzmeier N
    7. Capell A
    8. Fellerer K
    9. Nuscher B
    10. Eren E
    11. Levin J
    12. Deming Y
    13. Piccio L
    14. Karch CM
    15. Cruchaga C
    16. Shaw LM
    17. Trojanowski JQ
    18. Weiner M
    19. Ewers M
    20. Haass C
    21. Alzheimer’s Disease Neuroimaging Initiative

    (2019) Early increase of CSF sTREM2 in Alzheimer's disease is associated with tau related-neurodegeneration but not with amyloid-β pathology

    Molecular Neurodegeneration 14:1.

    https://doi.org/10.1186/s13024-018-0301-5

    • PubMed
    • Google Scholar
53. 1. Wang X
    2. Men W
    3. Gao J
    4. Caramazza A
    5. Bi Y

    (2020) Two forms of knowledge representations in the human brain

    Neuron 107:383–393.

    https://doi.org/10.1016/j.neuron.2020.04.010

    • PubMed
    • Google Scholar
54. Software

    1. Wang R
    2. Wedeen VJ

    (2007) TrackVis. Org, version 0.6.1

    Martinos Center for Biomedical Imaging, Massachusetts.

    http://trackvis.org/
55. 1. Xia M
    2. Wang J
    3. He Y

    (2013) BrainNet viewer: a network visualization tool for human brain connectomics

    PLOS ONE 8:e68910.

    https://doi.org/10.1371/journal.pone.0068910

    • PubMed
    • Google Scholar
56. 1. Yu J
    2. Lam CLM
    3. Lee TMC

    (2017) White matter microstructural abnormalities in Amnestic mild cognitive impairment: a meta-analysis of whole-brain and ROI-based studies

    Neuroscience & Biobehavioral Reviews 83:405–416.

    https://doi.org/10.1016/j.neubiorev.2017.10.026

    • PubMed
    • Google Scholar
57. 1. Zhang Z
    2. Descoteaux M
    3. Zhang J
    4. Girard G
    5. Chamberland M
    6. Dunson D
    7. Srivastava A
    8. Zhu H

    (2018) Mapping population-based structural connectomes

    NeuroImage 172:130–145.

    https://doi.org/10.1016/j.neuroimage.2017.12.064

    • PubMed
    • Google Scholar

Acceptance summary:

Chen and colleagues provide timely new insights into the link between CSF measures of phosphorylated tau, changes in brain structural connectivity over time, and cognitive decline in a sample of older adults who are cognitively normal or have mild cognitive impairment. The concept of "stability" used here provides a novel means by which changes in structural integrity can be assessed, and opens up new avenues to understand how tau impacts structural brain networks implicated in episodic memory decline in aging and Alzheimer's disease.

Decision letter after peer review:

Thank you for submitting your article "Longitudinal stability of medial temporal lobe connectivity explains tau-related memory decline" for consideration by eLife. Your article has been reviewed by Chris Baker as the Senior Editor, a Reviewing Editor, and three reviewers. The following individual involved in review of your submission has agreed to reveal their identity: Kaitlin Kassady (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

Chen et al., present a timely study exploring the link between CSF measures of Ptau, changes in brain structural connectivity across 2 years (i.e., "stability"), and cognitive decline in a sample of older adults who are either cognitively normal or have mild cognitive impairment. While the earliest changes in structural connectivity were found within the MTL, and between the MTL and AT system, MTL and MTL-PM stability was related to better average episodic memory and change in episodic memory over five years. The research question of how tau impacts specific structural networks is important to further understand episodic memory impairment in aging and Alzheimer's disease, and the concept of "stability" provides a novel means by which to assess changes in structural integrity.

Essential revisions:

1) Introduction:

a) The authors state that hyperphosphorylated tau proteins are "necessary for subsequent Aβ aggregation". However, the prevailing view in the field is that Aβ aggregation is not dependent on concurrent tau pathology (ex. autosomal dominant AD). Please amend.

b) The "PM" system is referred to as "posterior-MTL", but there are no MTL regions in their PM network. It would be helpful to use the terminology "posterior-medial" when referring to "PM", as well as citing Ranganath and Ritchey, 2012, which first introduced this framework.

d) In the Introduction the authors state that the "MTL is more strongly connected to the AT than PM systems", however this is not accurate. In the papers they cite (especially Navarro Schröder, 2015), some regions of the MTL (such as parahippocampal gyrus and posteromedial entorhinal cortex) are more strongly connected to the PM system.

e) I believe Berron et al., showed mostly reduced MTL-AT functional connectivity in CU Ab+ vs CU Ab- and while there are some reduced connections between the MTL-AT with increased p-tau levels, increasing p-tau levels are much more robustly associated with reduced MTL-PM connectivity in CU Ab+.

f) Maass et al., report findings on functional task-based activity rather than functional connectivity

g) In the Introduction, the findings from Schröder et al., and Maass et al., are interpreted as indicating stronger associations between the MTL and the AT system. I believe these two papers report functional subregions in the entorhinal cortex that are differentially connected with two parallel processing streams (in the MTL and the neocortex) and the hippocampus, which means that the MTL is strongly connected with both the AT and PM systems.

h) The hypotheses in the Introduction could be formulated more clearly. What for example is meant by "tau pathology worsens"? It might be beneficial for the reader to refer to the stereotypical spatial progression pattern of tau accumulation.

i) In the fourth paragraph of the Introduction it should read, "An emerging study suggests that a functional connectome stability measure is associated with cognition (average and rate of episodic memory decline)."

Materials and methods and Results:

a) The rationale for the specific ROIs for MTL, AT, and PM networks is not clear, particularly as some of their selections differ from previous work. For example, the parahippocampal gyrus is a key region of the PM network, yet is placed in the MTL network here. Additionally, including more non-MTL AT regions, such as inferior temporal gyrus, could be beneficial to make the number of AT and PM regions more equivalent (3 regions/36 connections vs. 6 regions/60 connections), as more regions may inherently make the PM network more stable. Please cite appropriate references.

b) Could the authors include a visualization of the streamlines/connections between bilateral regions in order to understand whether that overlaps mainly with the aforementioned tracts. It would further help if the authors could elaborate in more detail on the underlying structural tracts, where they believe the signal is coming from and show examples of streamlines between MTL regions.

c) I was curious why the authors did not look at a pure baseline measure of memory performance at the first time point as a cleaner cross-sectional measure. Could the authors elaborate on that?

d) In the GLM, the authors report that their stability measure was associated with age (Model 2). However, they do not include age as a covariate in all other models. The same is true for other important covariates such as sex and years of education given that they are potentially related to CSF p-Tau and memory performance.

e) The authors should assess the relationships in models 3-5 while considering the effects of sex, age, and education. For model 5, given that stability is associated with age, how can the authors be sure that the moderating effect is driven by stability and not (also) by age? Regarding model 5, could the authors perform a mediation analysis to specifically test whether there is a significant indirect effect of CSF p-Tau via the stability measure on memory performance and decline while accounting for continuous age, sex, education and amyloid? In this framework the authors could also examine to what extent stability explains the effect of tau on memory (partial vs. full mediation).

f) "Stability" is interpreted as a positive aspect of structural integrity. However, it is possible that subjects with low MD measures at baseline stayed low at follow-up, and thus were "stable" (highly correlated) but have weak connectivity and low white matter integrity? This could be particularly common in the MTL in the MCI group, as damage from Ptau may have already occurred. Similarly, were there any subjects who had higher MD at follow-up, who were then "unstable" but increasing in connectivity (biologically less likely)? A breakdown of these different groups (high-MD stable, low-MD stable, high-low MD instable, low-high MD instable) and their prevalence in the CN/MCI samples would be informative.

g) Regarding the relationship between MTL structural stability, Ptau, and memory over 5 years in the whole sample – did you also test whether MTL-AT predicted average episodic memory? According to Figure 4, it appears that you did. Please include this info in the main text.

h) It would be helpful to have a figure (perhaps in the supplement) that shows the average mean diffusivity (MD) matrices for each group at each time point. This would allow readers to better understand the level of variability and raw values of these connectivity measures.

Discussion:

a) The finding of a stronger relationship between more Ptau and faster rate of memory decline in those with higher MTL stability (Figure 5) seems to run counter to the other findings where "stability" reflects structural integrity. Could this be explained by the "high stability" group containing both low-low MD and high-high MD subjects, allowing a strong relationship with cognition to emerge? I would like to see some consideration of this point in the Discussion.

b) In the stability group comparisons results (Figure 2A), the contrast between CN Ptau+ and MCI Ptau+ is currently not performed. From the figure, it appears that there may be a significant difference between those groups only in MTL-PM. This missing result would support their other findings because it would suggest that lower stability in MTL-PM connections might be related to the expression of clinical symptoms. Please amend and discuss accordingly.

c) While there was no interaction between CSF Aβ or neurodegeneration with Ptau on stability, was there a direct correlation between continuous levels of CSF Aβ or neurodegeneration with stability? If not, it helps support the finding that stability is specifically related to Ptau. Further, how do you interpret the age group interaction between Ptau and stability?

d) It is not clear why MTL-AT stability would not relate to episodic memory (average and rate of change) but MTL and MTL-PM would? Please discuss.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Longitudinal stability of medial temporal lobe connectivity explains tau-related memory decline" for further consideration by eLife. Your revised article has been evaluated by Chris Baker as the Senior Editor and a Reviewing Editor.

The manuscript has been much improved but there is one remaining issue that needs to be addressed before acceptance, as outlined below:

The reviewers are very pleased with the manner in which you have addressed all of their concerns, however, they believe the Title warrants modification prior to publication. There was concern that the term "explains" is perhaps too strong given that there are many other factors that were not taken into account that might explain the effect demonstrated here. The reviewers have therefore suggested substituting "moderates" or "is associated with" rather than "explains".

Similarly, please ensure that any causal or overly strong claims are tempered in the Abstract and Discussion.

Essential revisions:

1) Introduction:

a) The authors state that hyperphosphorylated tau proteins are "necessary for subsequent Aβ aggregation". However, the prevailing view in the field is that Aβ aggregation is not dependent on concurrent tau pathology (ex. autosomal dominant AD). Please amend.

Thanks for pointing this out. We further clarified in the Introduction: “The dominant view in the literature is that Aβ pathology triggers tau aggregation, strongly supported by evidence from autosomal dominant AD (Hardy and Selkoe, 2002). However, there have been important recent findings from sporadic AD that suggest that hyperphosphorylated tau proteins that are no longer governed by normal cellular removal mechanisms play a necessary and underappreciated role in AD progression (Musiek and Holtzman, 2015), with some authors even suggesting this may be the primary step in the disease needed for subsequent AB aggregation (Arnsten et al., 2020, Braak and Del Tredici, 2015).”

b) The "PM" system is referred to as "posterior-MTL", but there are no MTL regions in their PM network. It would be helpful to use the terminology "posterior-medial" when referring to "PM", as well as citing Ranganath and Ritchey, 2012, which first introduced this framework.

The reviewer is completely correct. We have now used "posterior-medial" when referring to “PM” and cited Ranganath and Ritchey, 2012 in the Introduction.

d) In the Introduction the authors state that the "MTL is more strongly connected to the AT than PM systems", however this is not accurate. In the papers they cite (especially Navarro Schröder, 2015), some regions of the MTL (such as parahippocampal gyrus and posteromedial entorhinal cortex) are more strongly connected to the PM system.

The reviewer is correct. We further clarified in the introduction: “MTL is strongly connected with both the AT and PM systems, with anterolateral entorhinal cortex showing preferential connectivity with anterior regions overlapping with the AT system and parahippocampal gyrus and posteromedial entorhinal cortex with posterior regions overlapping with the PM system (Maass et al., 2015, Schröder et al., 2015)”.

e) I believe Berron et al., showed mostly reduced MTL-AT functional connectivity in CU Ab+ vs CU Ab- and while there are some reduced connections between the MTL-AT with increased p-tau levels, increasing p-tau levels are much more robustly associated with reduced MTL-PM connectivity in CU Ab+.

The reviewer is correct. To avoid confusion, we have taken this out from the Introduction.

f) Maass et al., report findings on functional task-based activity rather than functional connectivity.

g) In the Introduction, the findings from Schröder et al., and Maass et al., are interpreted as indicating stronger associations between the MTL and the AT system. I believe these two papers report functional subregions in the entorhinal cortex that are differentially connected with two parallel processing streams (in the MTL and the neocortex) and the hippocampus, which means that the MTL is strongly connected with both the AT and PM systems.

We totally agree with review. We now modified the sentence in the Introduction:

“MTL is strongly connected with both the AT and PM systems, with anterolateral entorhinal cortex showing preferential connectivity with anterior regions overlapping with the AT system and parahippocampal gyrus and posteromedial entorhinal cortex with posterior regions overlapping with the PM system (Maass et al., 2015, Schröder et al., 2015).”

“Furthermore, tau pathology is related to worse performance in object memory tasks involving AT system relative to scene memory tasks involving PM system in cognitive normal older adults, matching the proposed spread of the disease via neuronal connections, as tau burden was much higher in the AT system compared with PM system in the early stages of AD (Arnsten et al., 2020, Maass et al., 2019).”

h) The hypotheses in the Introduction could be formulated more clearly. What for example is meant by "tau pathology worsens"? It might be beneficial for the reader to refer to the stereotypical spatial progression pattern of tau accumulation.

We further clarified in the Introduction:

“Second, given that tau tangles deposit early in the anterior temporal lobe (Braak and Del Tredici 2015, Maass et al., 2019), we hypothesized that Ptau first affects stability in the MTL and MTL-AT in the predementia stages, followed by MTL-PM as the disease progresses and tau pathology worsens and spreads to posterior brain regions”.

i) In the fourth paragraph of the Introduction it should read, "An emerging study suggests that a functional connectome stability measure is associated with cognition (average and rate of episodic memory decline)."

Thanks for pointing this out. We have corrected it.

Materials and methods and Results:

a) The rationale for the specific ROIs for MTL, AT, and PM networks is not clear, particularly as some of their selections differ from previous work. For example, the parahippocampal gyrus is a key region of the PM network, yet is placed in the MTL network here. Additionally, including more non-MTL AT regions, such as inferior temporal gyrus, could be beneficial to make the number of AT and PM regions more equivalent (3 regions/36 connections vs. 6 regions/60 connections), as more regions may inherently make the PM network more stable. Please cite appropriate references.

This is a valid concern. Numerous studies have shown that medial temporal lobe (MTL) structures including the hippocampus and the surrounding hippocampal region consisting of the parahippocampal and entorhinal neocortical regions, are crucial for long-term memory. In terms of brain anatomy, parahippocampal gyrus receives input mainly from visuospatial association areas and provides major input to the entorhinal cortex, which in turn feeds directly into the hippocampus (Davachi et al., 2003). At the functional level, parahippocampal gyrus plays an important role in episodic memory relating to associative memory, source memory, and recollection (Aminoff et al., 2013). Given the anatomy and function of the parahippocampal gyrus, we consider it to belong to the MTL memory network.

We further clarified the rationale in the Results: “Since the hippocampus and the surrounding hippocampal region including the parahippocampal cortex and entorhinal cortex, are the primary regions supporting memory processing (Davachi et al., 2003), we consider them to belong to the MTL memory network. Thus, the MTL system includes entorhinal, hippocampus, and parahippocampal gyrus in both hemispheres.”

We included the bilateral inferior temporal gyrus into the non-MTL AT regions as the reviewer suggested and updated the results. We now added in the method: “In line with previous literature (Berron et a.,l 2020, Ranganath and Ritchey 2012), the AT system includes bilateral inferior temporal, temporal polar, lateral and medial orbitofrontal cortex, while the PM system includes bilateral posterior and isthmus cingulate, lateral occipital cortex, precuneus, and thalamus.”

b) Could the authors include a visualization of the streamlines/connections between bilateral regions in order to understand whether that overlaps mainly with the aforementioned tracts. It would further help if the authors could elaborate in more detail on the underlying structural tracts, where they believe the signal is coming from and show examples of streamlines between MTL regions.

Thanks for pointing this out. We have added the Figure 1B to visualize the streamlines between MTL related regions in a representative participant. We now added in the Results:

“For visualization purposes, we presented the connections generated between MTL related structures in a representative participant in Figure 1B. The connections within MTL include the hippocampal cingulum bundle and fornix. The connections between MTL and AT largely overlap with the anterior segments of the inferior longitudinal fasciculus (ILF) and inferior fronto-occipital fasciculus (IFOF), while the connections between MTL and PM mainly involve the cingulum bundle and posterior segments of ILF and IFOF.”

c) I was curious why the authors did not look at a pure baseline measure of memory performance at the first time point as a cleaner cross-sectional measure. Could the authors elaborate on that?

The stability measure is based on the information from both baseline and 2 year follow up. It’s more clinically meaningful to predict cognition in the future (up to four year follow-up) rather than the baseline collected prior to the stability measure. We now added in the Materials and methods:

“To assess the association between the longitudinal structural stability, baseline Ptau, and episodic memory, an average episodic memory score is calculated by averaging episodic memory over 5 years. Since we were interested in prediction of memory in the future, average memory score was used instead of baseline memory”.

d) In the GLM, the authors report that their stability measure was associated with age (Model 2). However, they do not include age as a covariate in all other models. The same is true for other important covariates such as sex and years of education given that they are potentially related to CSF p-Tau and memory performance.

Thanks for pointing this out. We have now examined relationships between the longitudinal structural stability, baseline Ptau, and episodic memory in the whole sample, including and excluding the covariates (age, education, sex, CSF A$\beta$, and neurodegeneration). Changes have been highlighted in yellow in the method and result sections.

e) The authors should assess the relationships in models 3-5 while considering the effects of sex, age, and education. For model 5, given that stability is associated with age, how can the authors be sure that the moderating effect is driven by stability and not (also) by age? Regarding model 5, could the authors perform a mediation analysis to specifically test whether there is a significant indirect effect of CSF p-Tau via the stability measure on memory performance and decline while accounting for continuous age, sex, education and amyloid? In this framework the authors could also examine to what extent stability explains the effect of tau on memory (partial vs. full mediation).

Thanks for pointing this out. We have now examined relationships between the longitudinal structural stability, baseline Ptau, and episodic memory in the whole sample, including and excluding the covariates (age, education, sex, CSF A$\beta$, and neurodegeneration). Changes have been highlighted in yellow in the method and result sections.

For model 5 (referred as model 6 in the revised manuscript), we now show that the moderating effect of MTL stability remained after the covariates were controlled (age, education, sex, CSF A$\beta$, and neurodegeneration).

We conducted the mediation analysis and didn’t find a significant indirect effect of CSF p-Tau via the stability measure on memory, or rate of memory change. Regardless of the lack of mediation effect, we did find a significant moderation effect of MTL stability on the relationship between tau and memory decline rate (see Model 6’ and Figure 6). To keep the paper more focused, we didn’t report these negative findings.

f) "Stability" is interpreted as a positive aspect of structural integrity. However, it is possible that subjects with low MD measures at baseline stayed low at follow-up, and thus were "stable" (highly correlated) but have weak connectivity and low white matter integrity? This could be particularly common in the MTL in the MCI group, as damage from Ptau may have already occurred. Similarly, were there any subjects who had higher MD at follow-up, who were then "unstable" but increasing in connectivity (biologically less likely)? A breakdown of these different groups (high-MD stable, low-MD stable, high-low MD instable, low-high MD instable) and their prevalence in the CN/MCI samples would be informative.

First, we want to clarify that the stability is a similarity measure, which reflects information in the patterns of MD across multiple connections, rather than only changes in mean MD. We agree with the reviewer that it’s possible that some subjects with low average MD at baseline and follow-up could show relatively high stability, while other could show low stability if their MD patterns changed across specific connections. We also want to clarify that, while it is possible that a loss of integrity causes poor stability due to an overall reduction in connections caused by pathology, it is also possible that pathology in certain connections could cause them to decrease connectivity, while other connections show corresponding increases as a result. Stability as a similarity measure is ambiguous to the mechanism by which pathology causes connections to change, but we believe that change in the overall pattern of connectivity in itself is meaningful.

To address the reviewer’s concern, we averaged the MD measures across all connections within MTL to get a mean MD score for MTL network. Then we used the median as a cutoff to dichotomize the mean MD score to “high” and “low”. Similarly, we dichotomized stability as “stable” vs. “instable”. The Table 2 in the manuscript displays the prevalence in the CN/MCI samples.

As we can see from Table 2, the prevalence of low-low stable individuals is actually not common in the MCI group (13.3%). In the Materials and methods, we now added:

“The traditional univariate analysis only considers the overall magnitude of the network (e.g., the average MD across all connections within MTL network). […] Thus, the traditional univariate analysis may not be as sensitive as the stability measure in detecting these subtle structural changes in the predementia AD stage.”

g) Regarding the relationship between MTL structural stability, Ptau, and memory over 5 years in the whole sample – did you also test whether MTL-AT predicted average episodic memory? According to Figure 4, it appears that you did. Please include this info in the main text.

Thanks for pointing this out. We did test whether MTL-AT predicted average episodic memory and we have now included this result in the main text and Figure 5 middle.

h) It would be helpful to have a figure (perhaps in the supplement) that shows the average mean diffusivity (MD) matrices for each group at each time point. This would allow readers to better understand the level of variability and raw values of these connectivity measures.

This is a good point. We have now added this figure in Figure 1—figure supplement 1.

Discussion:

a) The finding of a stronger relationship between more Ptau and faster rate of memory decline in those with higher MTL stability (Figure 5) seems to run counter to the other findings where "stability" reflects structural integrity. Could this be explained by the "high stability" group containing both low-low MD and high-high MD subjects, allowing a strong relationship with cognition to emerge? I would like to see some consideration of this point in the Discussion.

This is a valid concern. As can be seen in Table 2, the stable group contains 12 high-high MD and 15 low-low MD subjects, while the instable group contains 12 high-high MD and 9 low-low MD subjects. As described above, since stability is calculated using all connections, rather than mean MD, instability is not restricted to high-low or low-high MD subjects. We now added in the Discussion:

“It is possible that the observation of a stronger relationship between more Ptau and faster rate of memory decline in those with higher MTL stability might be driven by greater variance in this group, caused by the inclusion of both stable individuals with overall low connectivity as well as consistently high connectivity. To assess this, we looked at the number of spread of individuals across these groups based on their mean structural connectivity (Table 2). The stable group contains 12 high-high MD and 15 low-low MD subjects, while the instable group contains 12 high-high MD and 9 low-low MD subjects. While there are more low-low subjects in the stable group, the ratio of low-low to high-high is similar across these groups, suggesting increased variance is unlikely to drive the strong relationship with cognition in the high stability group.”

b) In the stability group comparisons results (Figure 2A), the contrast between CN Ptau+ and MCI Ptau+ is currently not performed. From the figure, it appears that there may be a significant difference between those groups only in MTL-PM. This missing result would support their other findings because it would suggest that lower stability in MTL-PM connections might be related to the expression of clinical symptoms. Please amend and discuss accordingly.

This is an excellent point. We have conducted the contrast between CN Ptau+ and MCI Ptau+ and updated the results and discussion accordingly.

In the Results, we modified the sentences: “In line with our hypothesis that MTL and MTL-AT stability is affected by Ptau early in the predementia AD stage, compared to CN Ptau-, structural stability was significantly lower in CN Ptau+ in MTL (t(42)=-2.11, p = 0.022) and MTL-AT (t(42)=-2.57, p = 0.007). Decreased structural stability was further found in MTL (t(54)=-2.61, p = 0.006) and MTL-AT (t(54)=-3.55, p < 0.001) in MCI Ptau+, compared to CN Ptau-. In line with our hypothesis that MTL-PM is affected later in disease progression, we found a significantly lower stability in MTL-PM in MCI Ptau+, compared to CN Ptau+ (t(46)=-5.12, p < 0.001) and CN Ptau- (t(54)=-3.89, p < 0.001, Figure 2A).”

In Discussion, we modified the sentence: “CN Ptau+ and MCI Ptau+ had decreased stability in MTL and MTL-AT compared to CN Ptau-, while MCI Ptau+ further showed decrease in MTL-PM stability relative to both CN Ptau- and CN Ptau-+ groups, suggesting that Ptau relates to structural stability in a temporal pattern that matches its proposed spread from the MTL via neuronal connections”.

c) While there was no interaction between CSF Aβ or neurodegeneration with Ptau on stability, was there a direct correlation between continuous levels of CSF Aβ or neurodegeneration with stability? If not, it helps support the finding that stability is specifically related to Ptau. Further, how do you interpret the age group interaction between Ptau and stability?

We have now examined the direct correlation between continuous levels of age, CSF Aβ or neurodegeneration with stability in model 2 and updated the result in the main text and Figure 3. In the Results, we added now:

“We examined whether structural stability could be directly affected by covariates (i.e., age, CSF Aβ or neurodegeneration) in the entire sample with Model 2 (Y stability = β20 + β21Covariates + ԑ₂). We found no significant relationship between age and stability (all ps > 0.05, Figure 3A). We found a significant correlation between CSF Aβ and stability in MTL-PM (β = 0.40, SE = 0.16, Wald’s χ2 = 6.04, BH-adjusted p = 0.042, Figure 3B). Neurodegeneration significantly related to stability within MTL (β = 0.58, SE = 0.26, Wald’s $\chi$² = 4.97, BH-adjusted p = 0.039), MTL-AT (β = 0.49, SE = 0.25, Wald’s $\chi$² = 3.89, BH-adjusted p = 0.048) and MTL-PM (β = 0.61, SE = 0.19, Wald’s $\chi$² = 10.39, BH-adjusted p = 0.003, Figure 3C).”

Regarding the age group interaction between Ptau and stability, after we included the inferior temporal cortex into AT regions, there was no significant age group interaction (Figure 4A).

d) It is not clear why MTL-AT stability would not relate to episodic memory (average and rate of change) but MTL and MTL-PM would? Please discuss.

After we included the inferior temporal cortex into the non-MTL AT regions as one reviewer suggested, MTL-AT stability predicted episodic memory (average episodic memory). We have updated the Results in the main text and Figure 5.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been much improved but there is one remaining issue that needs to be addressed before acceptance, as outlined below:

The reviewers are very pleased with the manner in which you have addressed all of their concerns, however, they believe the Title warrants modification prior to publication. There was concern that the term "explains" is perhaps too strong given that there are many other factors that were not taken into account that might explain the effect demonstrated here. The reviewers have therefore suggested substituting "moderates" or "is associated with" rather than "explains".

We totally agree. We now used "is associated with" instead of “explains”. The current title is “Longitudinal stability of medial temporal lobe connectivity is associated with tau-related memory decline”.

Similarly, please ensure that any causal or overly strong claims are tempered in the Abstract and Discussion.

We have altered the claims and made them less causal in the abstract, introduction and discussion:

In the Abstract, we wrote: “…this loss of stability in connections known to be important for memory moderated the relationship between Ptau accumulation and memory decline.”

In the Introduction, we wrote: “Finally, given that MTL and related structures play a central role in episodic memory, we hypothesized that MTL related structural stability might be associated with tau-related episodic memory decline.”

In the Discussion, we wrote: “… whether this loss of stability in connections important for episodic memory is related to the link between Ptau accumulation and cognitive decline.”

“Importantly, structural stability in MTL moderated the effect of Ptau on the rate of memory change, suggesting that structural stability in MTL may be a candidate as a mechanistic link between the accumulation of Ptau and cognitive decline.”

“A model showed that decreases in the stability of within MTL connections significantly predicted episodic memory decline, suggesting changes in these connections resulting from increased Ptau is associated with decreases in episodic memory.”

Author details

1. Quanjing Chen

   1. Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, United States
   2. Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   For correspondence

   quanjingchen@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4630-6817

2. Adam Turnbull

   1. Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, United States
   2. Department of Imaging Sciences, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States

   Contribution

   Formal analysis, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

3. Timothy M Baran

   1. Department of Imaging Sciences, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States
   2. Department of Biomedical Engineering, University of Rochester, Rochester, United States

   Contribution

   Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

4. Feng V Lin

   1. Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, United States
   2. Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States
   3. Department of Neuroscience, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States
   4. Department of Neurology, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, United States
   5. Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
   6. School of Medicine, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

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