The accumulation of amyloid-β and tau deposits has been implicated in the genesis of Alzheimer’s disease (Jack et al., 2010; Jack et al., 2013; Jack et al., 2018; Jack and Holtzman, 2013). These deposits appear and spread in the brain according to a characteristic spatial pattern: whereas amyloid-β deposits appear first in neocortical regions and spread to limbic and subcortical areas (Thal et al., 2002); tau deposits appear in the transentorhinal cortex and spread to paralimbic and neocortical areas (Braak and Braak, 1991). The relationship between these different and temporally dissociated spatial patterns is currently unclear but could provide an important insight into how they accumulate in the brain and contribute to cognitive decline in Alzheimer’s disease.

Multimodal brain imaging offers a unique opportunity to explore the interrelation and overlap between distinct molecular pathologies and their functional consequences in vivo (Drzezga et al., 2011). Studies using positron emission tomography (PET) have shown that amyloid-β deposition occurs in highly connected brain areas, including the posterior cingulate, precuneus, anterior cingulate and medial orbitofrontal gyri (Buckner et al., 2005; Buckner et al., 2009; Jagust and Mormino, 2011; Mormino et al., 2011) already in asymptomatic stages (Palmqvist et al., 2017). In contrast, tau PET studies found that tau deposition occurs mainly in medial and inferior temporal areas, with partial involvement of the parietal and occipital lobes in patients with mild cognitive impairment and Alzheimer’s disease dementia (Villemagne et al., 2015; Cho et al., 2016a; Johnson et al., 2016; Schöll et al., 2016; Mattsson et al., 2017). These results suggest that amyloid-β and tau may target areas that belong to different functional networks (Hansson et al., 2017).

To our knowledge, the exact relationship between the spatial patterns of amyloid-β PET, tau PET and functional MRI within the same individuals is unknown, or between these patterns and white matter integrity. In addition, although a few studies have looked into the spatial patterns of amyloid-β and tau, none of them studied both of these patterns across the Alzheimer’s disease continuum (Jack et al., 2018) in individuals that are cognitively normal as well as patients with mild cognitive impairment and dementia (Myers et al., 2014; Brier et al., 2016; Jones et al., 2017; Sepulcre et al., 2017a; Hoenig et al., 2018). This is important for several reasons, including the fact that amyloid-β and tau deposits accumulate in different brain regions with disease progression; thus, if different disease stages are not included, we might not be able to provide a complete overview of their spatial extent and topography or assess their full clinical value.

The aim of this study is to characterize the spatial networks associated with amyloid-β and tau accumulation across different stages of Alzheimer’s disease. We applied a multivariate approach based on joint independent component analyses to identify linked amyloid-β and tau networks and assessed their relationship with resting-state functional MRI networks and white matter integrity in addition to cognition and gray matter atrophy. Based on previous evidence showing a strong link between amyloid-β pathology and functional connectivity (Buckner et al., 2009; Jagust and Mormino, 2011; Myers et al., 2014; Elman et al., 2014), we predicted there would be a greater spatial overlap between the amyloid-β and functional MRI networks compared to tau. Moreover, based on previously reported associations between tau pathology, white matter degeneration, gray matter atrophy and cognitive decline (Villemagne et al., 2015; Bejanin et al., 2017; Aschenbrenner et al., 2018; Jacobs et al., 2018; Sintini et al., 2019; Strain et al., 2018), we expected to find a stronger correlation between the tau networks, white matter integrity, gray matter volume and cognitive impairment.

One hundred seventeen individuals were included with ¹⁸F-Flutemetamol PET, ¹⁸F-Flortaucipir PET, structural T1-weighted MRI and neuropsychological data. This sample consisted of 26 cognitively normal subjects who were amyloid-β negative, in addition to 34 cognitively normal subjects, 21 patients with mild cognitive impairment and 36 patients with Alzheimer’s disease dementia that were all amyloid-β positive (Figure 1). A subsample of this cohort also underwent resting-state functional MRI and diffusion tensor imaging (Figure 1). Amyloid-β positivity was established using a composite cortical region normalized by the whole cerebellum, brain stem and eroded subcortical white matter (Landau et al., 2015) on ¹⁸F-Flutemetamol PET images with a cut-off of >0.693.

Figure 1

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The characteristics of the sample and differences between groups can be found in Table 1. As expected, there were no significant differences in cognition between amyloid-β negative and amyloid-β positive controls, but patients with mild cognitive impairment and Alzheimer’s disease dementia showed worse scores in several cognitive tests.

Amyloid-β and tau networks

The optimal number of components identified in the joint independent component analysis of ¹⁸F-Flutemetamol and ¹⁸F-Flortaucipir data was eleven using the minimum description length criterion (Li et al., 2007). This criterion selects the best hypothesis (a model and its parameters) for a given set of data as the one that leads to the best compression of the data. This method is provided within the Fusion ICA toolbox (Calhoun et al., 2006), which we used to perform the joint independent component analysis. The total variance explained by the amyloid-β components was 94.4% and the total variance explained by the tau components was 87.4%. Two of the independent components (IC 1, IC 9) were excluded from the analyses since they mainly included white matter areas for ¹⁸F-Flutemetamol data or off-target binding regions such as the basal ganglia for ¹⁸F-Flortaucipir data. The excluded components can be found in Figure 2—figure supplement 1. The remaining amyloid-β and tau components had major clusters in the gray matter and are described below.

Three of the components showed similar spatial patterns for both amyloid-β and tau, and consisted of the left inferior occipital cortex (IC 2), the superior frontal gyrus (IC 5), and medial brain regions such as the precuneus and posterior cingulate (IC 6) (Figure 2). The other components included different areas for amyloid-β and tau, such as the medial fronto-parietal and temporal areas (IC 3), middle frontal and insular areas (IC 4), right parietal and fronto-parietal areas (IC 7), the anterior cingulate and hippocampus (IC 8), the posterior parietal and right inferior occipital areas (IC 9), and the lateral temporal and middle frontal areas (IC 11), respectively (Figure 2).

Figure 2 with 4 supplements see all

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When the ¹⁸F-Flutemetamol SUVRs of each amyloid-β network were compared between groups, as expected, we found significant differences in all networks in the amyloid-β positive groups compared to amyloid-β negative controls (Table 2). Moreover, patients with mild cognitive impairment showed significantly higher ¹⁸F-Flutemetamol SUVRs in almost all networks compared to amyloid-β positive controls. Patients with Alzheimer’s disease dementia showed significantly higher ¹⁸F-Flutemetamol SUVRs in all networks compared to amyloid-β positive controls, and in the right parietal (IC 7) and lateral temporal (IC 11) networks compared to patients with mild cognitive impairment (Table 2).

Table 2

	Cognitively normal (CN)		Mild cognitive impairment (MCI)	Alzheimer’s disease (AD)	CN β− vs CN β+	CN β− vs MCI	CN β− vs AD	CN β+ vs MCI	CN β+ vs AD	MCI vs AD
	amyloid-β−	amyloid-β+	All amyloid-β+	All amyloid-β+	P value	P value	P value	P value	P value	P value
amyloid-β networks
IC 2: Left Inferior Occipital	0.71 (0.04)	0.92 (0.14)	1.10 (0.10)	1.13 (0.13)	<0.001	<0.001	<0.001	<0.001	<0.001	0.287
IC 3: Medial Fronto-Parietal	0.67 (0.04)	0.98 (0.16)	1.15 (0.13)	1.19 (0.14)	<0.001	<0.001	<0.001	<0.001	<0.001	0.244
IC 4: Middle Frontal	0.74 (0.06)	0.89 (0.12)	0.94 (0.11)	1.02 (0.16)	<0.001	<0.001	<0.001	0.159	0.004	0.048
IC 5: Superior Frontal	0.68 (0.05)	0.91 (0.14)	1.02 (0.11)	1.08 (0.16)	<0.001	<0.001	<0.001	0.014	<0.001	0.101
IC 6: Medial	0.64 (0.04)	0.92 (0.14)	1.07 (0.13)	1.13 (0.14)	<0.001	<0.001	<0.001	<0.001	<0.001	0.112
IC 7: Right Parietal	0.69 (0.06)	0.88 (0.14)	1.00 (0.11)	1.11 (0.12)	<0.001	<0.001	<0.001	0.003	<0.001	0.001
IC 8: Anterior Cingulate	0.67 (0.05)	0.99 (0.17)	1.16 (0.14)	1.23 (0.16)	<0.001	<0.001	<0.001	0.003	<0.001	0.080
IC 10: Posterior Parietal	0.67 (0.05)	0.95 (0.17)	1.11 (0.16)	1.15 (0.15)	<0.001	<0.001	<0.001	0.002	<0.001	0.226
IC 11: Lateral Temporal	0.70 (0.04)	0.95 (0.17)	1.09 (0.14)	1.18 (0.16)	<0.001	<0.001	<0.001	0.006	<0.001	0.033
tau networks
IC 2: Left Inferior Occipital	1.15 (0.07)	1.21 (0.10)	1.76 (0.62)	2.03 (0.58)	0.112	<0.001	<0.001	<0.001	<0.001	0.061
IC 3: Temporal	1.15 (0.06)	1.22 (0.13)	1.65 (0.42)	1.83 (0.34)	0.020	<0.001	<0.001	<0.001	<0.001	0.057
IC 4: Insular	1.00 (0.07)	1.02 (0.07)	1.17 (0.20)	1.39 (0.48)	0.151	0.030	<0.001	0.115	<0.001	0.010
IC 5: Superior Frontal	1.11 (0.08)	1.14 (0.07)	1.32 (0.34)	1.58 (0.48)	0.267	0.038	<0.001	0.060	<0.001	0.014
IC 6: Posterior Medial	1.05 (0.06)	1.09 (0.07)	1.30 (0.34)	1.57 (0.57)	0.157	0.019	<0.001	0.066	<0.001	0.015
IC 7 Fronto-Parietal	1.13 (0.07)	1.20 (0.13)	1.63 (0.60)	1.97 (0.60)	0.498	0.001	<0.001	0.002	<0.001	0.020
IC 8: Hippo-campus	1.11 (0.06)	1.18 (0.12)	1.46 (0.30)	1.67 (0.39)	0.018	<0.001	<0.001	0.002	<0.001	0.012
IC 10: Right Inferior Occipital	1.12 (0.07)	1.16 (0.08)	1.57 (0.57)	1.91 (0.68)	0.143	0.001	<0.001	0.004	<0.001	0.034
IC 11: Middle Frontal	1.07 (0.07)	1.10 (0.08)	1.39 (0.34)	1.71 (0.51)	0.188	0.001	<0.001	0.003	<0.001	0.003

Interestingly, consistent with the earliest stages of tau deposition, we found higher ¹⁸F-Flortaucipir SUVRs in the temporal (IC 3) and hippocampal (IC 8) tau networks in amyloid-β positive compared to amyloid-β negative controls. Patients with mild cognitive impairment had significantly higher ¹⁸F-Flortaucipir SUVRs in almost all networks compared to controls that were amyloid-β negative or amyloid-β positive (Table 2). Patients with Alzheimer’s disease had significantly higher ¹⁸F-Flortaucipir SUVRs in all networks compared to both amyloid-β negative and positive controls, and in almost all networks compared to patients with mild cognitive impairment (Table 2).

The SUVRs of the amyloid-β networks were positively correlated with the SUVRs of all tau networks (p<0.001).

Overlap with functional MRI networks

The amyloid-β and tau patterns showed a fair to moderate overlap with the well-established functional networks provided by Biswal et al. (2010) (Figure 3). For instance, the amyloid-β medial fronto-parietal, medial, and posterior parietal networks overlapped moderately well with the posterior default-mode functional network (dice coefficients: 0.41, 0.24, 0.34), whereas the amyloid-β anterior cingulate network showed a fair overlap with the anterior cingulate functional network (dice coefficient: 0.30). The other amyloid-β networks showed a poor overlap with the rest of functional MRI networks, as can be observed in Figure 3.

Figure 3

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In contrast to amyloid-β, the tau networks overlapped moderately well with a wider range of functional networks that did not include anterior or posterior parts of the default-mode network. These networks were the extra-striate visual network (dice coefficients: 0.33, 0.30), the primary visual network (dice coefficient: 0.41), the limbic network (dice coefficient: 0.39) the somatosensory network (dice coefficient: 0.48); the language network (dice coefficient: 0.37) and the right fronto-parietal network (dice coefficient: 0.24).

These findings indicate that the patterns of amyloid-β and tau accumulation show a moderate spatial affinity with distinct functional networks, with the amyloid-β patterns overlapping mainly with the default-mode network, whereas the tau patterns overlap fairly well with several different resting-state networks.

Association with the integrity of white matter tracts

In contrast to the amyloid-β networks, which were not significantly associated with any white matter measures, the tau SUVRs correlated with fractional anisotropy in several white matter tracts. These tracts included the hippocampal cingulum tract, the inferior fronto-occipital fasciculus, the inferior longitudinal fasciculus, the superior longitudinal fasciculus, and the uncinate fasciculus (Table 3, Figure 4).

Figure 4

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Association between the tau networks and fractional anisotropy of white matter tracts.

Values for the tau networks and fractional anisotropy of white matter tracts that were significantly correlated with each other, after regressing out the effects of age, sex and presence of cognitive impairment.

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Table 3

	Fractional anisotropy
	ATR	CST	CCT	HCT	FMAJ	FMIN	IFO	ILF	SLF	SLFt	UNC
amyloid-β IC 2	−0.016 (0.904)	0.081 (0.527)	0.014 (0.915)	0.031 (0.810)	0.011 (0.930)	0.079 (0.540)	−0.093 (0.467)	−0.073 (0.569)	−0.111 (0.386)	−0.123 (0.336)	−0.047 (0.717)
amyloid-β IC 3	−0.162 (0.205)	0.133 (0.298)	0.027 (0.832)	−0.052 (0.685)	−0.094 (0.462)	−0.091 (0.478)	−0.180 (0.159)	−0.105 (0.411)	−0.158 (0.217)	−0.211 (0.097)	−0.073 (0.572)
amyloid-β IC 4	−0.121 (0.347)	0.026 (0.841)	−0.043 (0.740)	−0.107 (0.404)	−0.112 (0.384)	−0.110 (0.393)	−0.166 (0.195)	−0.076 (0.552)	−0.130 (0.310)	−0.187 (0.142)	−0.042 (0.743)
amyloid-β IC 5	−0.025 (0.848)	0.089 (0.488)	0.006 (0.964)	−0.027 (0.836)	−0.073 (0.568)	0.018 (0.890)	−0.085 (0.507)	−0.002 (0.989)	−0.036 (0.781)	−0.125 (0.331)	0.014 (0.911)
amyloid-β IC 6	−0.177 (0.166)	0.085 (0.509)	0.033 (0.799)	−0.009 (0.947)	−0.123 (0.338)	−0.072 (0.575)	−0.164 (0.199)	−0.080 (0.533)	−0.123 (0.337)	−0.194 (0.128)	−0.061 (0.636)
amyloid-β IC 7	0.007 (0.957)	0.004 (0.978)	−0.071 (0.582)	−0.046 (0.719)	−0.062 (0.630)	0.022 (0.863)	−0.072 (0.576)	−0.031 (0.813)	−0.018 (0.890)	−0.064 (0.617)	−0.051 (0.692)
amyloid-β IC 8	−0.017 (0.896)	0.065 (0.611)	0.024 (0.852)	0.045 (0.725)	−0.003 (0.979)	0.052 (0.687)	−0.037 (0.774)	0.024 (0.851)	−0.003 (0.981)	−0.073 (0.572)	0.029 (0.824)
amyloid-β IC 10	−0.170 (0.183)	0.082 (0.525)	0.038 (0.765)	−0.062 (0.627)	−0.152 (0.234)	−0.072 (0.576)	−0.175 (0.170)	−0.082 (0.524)	−0.140 (0.275)	−0.175 (0.170)	−0.039 (0.760)
amyloid-β IC 11	0.040 (0.757)	0.064 (0.618)	−0.040 (0.760)	−0.045 (0.724)	−0.020 (0.874)	0.096 (0.456)	−0.083 (0.517)	−0.087 (0.497)	−0.070 (0.585)	−0.093 (0.467)	−0.046 (0.718)
tau IC 2	−0.214 (0.093)	−0.123 (0.336)	−0.291 (0.021)	−0.364 (0.003)	−0.283 (0.025)	−0.170 (0.184)	−0.307 (0.014)	−0.429 (<0.001)	−0.339 (0.007)	−0.259 (0.041)	−0.280 (0.026)
tau IC 3	−0.207 (0.104)	−0.092 (0.476)	−0.286 (0.023)	−0.398 (0.001)	−0.251 (0.047)	−0.234 (0.065)	−0.301 (0.017)	−0.362 (0.004)	−0.257 (0.042)	−0.283 (0.025)	−0.292 (0.020)
tau IC 4	−0.222 (0.081)	−0.305 (0.015)	−0.309 (0.014)	−0.348 (0.005)	−0.292 (0.020)	−0.165 (0.198)	−0.339 (0.007)	−0.459 (<0.001)	−0.378 (0.002)	−0.297 (0.018)	−0.303 (0.016)
tau IC 5	−0.266 (0.035)	−0.245 (0.053)	−0.370 (0.003)	−0.361 (0.004)	−0.271 (0.032)	−0.288 (0.022)	−0.395 (0.001)	−0.430 (<0.001)	−0.386 (0.002)	−0.286 (0.023)	−0.398 (0.001)
tau IC 6	−0.224 (0.077)	−0.176 (0.167)	−0.239 (0.059)	−0.309 (0.014)	−0.281 (0.026)	−0.150 (0.241)	−0.292 (0.020)	−0.393 (0.001)	−0.330 (0.008)	−0.214 (0.093)	−0.225 (0.077)
tau IC 7	−0.226 (0.075)	−0.194 (0.127)	−0.352 (0.005)	−0.424 (<0.001)	−0.337 (0.007)	−0.232 (0.068)	−0.375 (0.003)	−0.448 (<0.001)	−0.370 (0.003)	−0.272 (0.031)	−0.324 (0.010)
tau IC 8	−0.064 (0.619)	−0.094 (0.464)	−0.245 (0.053)	−0.323 (0.010)	−0.234 (0.065)	−0.049 (0.702)	−0.222 (0.080)	−0.319 (0.011)	−0.215 (0.091)	−0.171 (0.180)	−0.179 (0.160)
tau IC 10	−0.258 (0.041)	−0.121 (0.344)	−0.212 (0.096)	−0.304 (0.016)	−0.324 (0.010)	−0.136 (0.287)	−0.311 (0.013)	−0.427 (<0.001)	−0.350 (0.005)	−0.250 (0.048)	−0.223 (0.081)
tau IC 11	−0.166 (0.192)	−0.188 (0.140)	−0.300 (0.017)	−0.306 (0.015)	−0.208 (0.102)	−0.145 (0.257)	−0.310 (0.013)	−0.392 (0.002)	−0.347 (0.005)	−0.271 (0.032)	−0.299 (0.017)
	Mean Diffusivity
	ATR	CST	CCT	HCT	FMAJ	FMIN	IFO	ILF	SLF	SLFt	UNC
amyloid-β IC 2	0.031 (0.808)	−0.156 (0.223)	−0.061 (0.636)	0.011 (0.931)	−0.132 (0.302)	0.048 (0.712)	−0.072 (0.573)	0.011 (0.930)	−0.042 (0.742)	0.087 (0.500)	0.078 (0.543)
amyloid-β IC 3	0.053 (0.679)	−0.362 (0.004)	−0.154 (0.227)	0.020 (0.879)	−0.216 (0.089)	−0.051 (0.691)	−0.108 (0.398)	−0.028 (0.829)	−0.168 (0.188)	0.082 (0.525)	0.065 (0.613)
amyloid-β IC 4	0.067 (0.601)	−0.143 (0.263)	−0.029 (0.824)	0.092 (0.476)	0.043 (0.739)	−0.055 (0.669)	0.039 (0.763)	0.088 (0.495)	0.011 (0.935)	0.101 (0.433)	0.034 (0.793)
amyloid-β IC 5	0.029 (0.820)	−0.120 (0.348)	0.001 (0.995)	0.014 (0.915)	0.003 (0.983)	−0.029 (0.823)	−0.018 (0.886)	0.024 (0.850)	−0.057 (0.656)	0.059 (0.645)	0.029 (0.823)
amyloid-β IC 6	0.128 (0.318)	−0.278 (0.028)	−0.085 (0.508)	0.088 (0.493)	−0.125 (0.331)	0.021 (0.869)	−0.016 (0.899)	0.050 (0.698)	−0.083 (0.519)	0.148 (0.249)	0.122 (0.342)
amyloid-β IC 7	0.081 (0.530)	−0.088 (0.493)	0.085 (0.507)	0.096 (0.456)	−0.057 (0.655)	0.108 (0.401)	0.066 (0.605)	0.141 (0.272)	0.106 (0.409)	0.108 (0.398)	0.118 (0.358)
amyloid-β IC 8	0.011 (0.932)	−0.115 (0.368)	−0.046 (0.718)	−0.005 (0.972)	0.037 (0.771)	−0.009 (0.943)	−0.038 (0.767)	0.025 (0.847)	−0.085 (0.507)	0.009 (0.944)	0.015 (0.907)
amyloid-β IC 10	0.088 (0.494)	−0.310 (0.013)	−0.112 (0.381)	0.057 (0.657)	−0.211 (0.097)	−0.056 (0.661)	−0.068 (0.595)	−0.025 (0.844)	−0.142 (0.266)	0.107 (0.403)	0.063 (0.625)
amyloid-β IC 11	0.049 (0.703)	−0.096 (0.453)	0.034 (0.791)	0.023 (0.858)	−0.238 (0.060)	0.125 (0.329)	−0.053 (0.679)	0.037 (0.771)	0.068 (0.598)	0.102 (0.428)	0.134 (0.296)
tau IC 2	0.266 (0.035)	0.039 (0.760)	0.324 (0.010)	0.353 (0.005)	−0.011 (0.930)	0.244 (0.054)	0.271 (0.032)	0.307 (0.015)	0.183 (0.152)	0.432 (<0.001)	0.303 (0.016)
tau IC 3	0.219 (0.085)	0.016 (0.900)	0.289 (0.022)	0.354 (0.004)	0.041 (0.748)	0.080 (0.532)	0.250 (0.048)	0.331 (0.008)	0.171 (0.181)	0.351 (0.005)	0.183 (0.150)
tau IC 4	0.172 (0.177)	0.054 (0.673)	0.280 (0.026)	0.222 (0.081)	0.016 (0.899)	0.164 (0.201)	0.236 (0.062)	0.243 (0.055)	0.093 (0.471)	0.303 (0.016)	0.259 (0.040)
tau IC 5	0.228 (0.072)	0.195 (0.126)	0.387 (0.002)	0.260 (0.039)	0.072 (0.576)	0.186 (0.145)	0.362 (0.004)	0.348 (0.005)	0.262 (0.038)	0.436 (<0.001)	0.337 (0.007)
tau IC 6	0.226 (0.075)	−0.006 (0.960)	0.261 (0.039)	0.265 (0.036)	−0.074 (0.565)	0.210 (0.099)	0.242 (0.056)	0.245 (0.054)	0.116 (0.365)	0.382 (0.002)	0.298 (0.018)
tau IC 7	0.261 (0.039)	0.083 (0.519)	0.418 (<0.001)	0.371 (0.003)	0.086 (0.501)	0.243 (0.055)	0.371 (0.003)	0.403 (0.001)	0.288 (0.022)	0.463 (<0.001)	0.319 (0.011)
tau IC8	0.107 (0.405)	0.084 (0.514)	0.311 (0.013)	0.253 (0.046)	0.025 (0.848)	0.148 (0.248)	0.202 (0.112)	0.232 (0.067)	0.158 (0.216)	0.281 (0.026)	0.174 (0.172)
tau IC 10	0.252 (0.046)	−0.054 (0.672)	0.302 (0.016)	0.327 (0.009)	−0.052 (0.685)	0.212 (0.096)	0.225 (0.076)	0.239 (0.059)	0.074 (0.563)	0.388 (0.002)	0.285 (0.024)
tau IC 11	0.193 (0.129)	0.056 (0.663)	0.314 (0.012)	0.258 (0.042)	−0.075 (0.557)	0.259 (0.040)	0.226 (0.075)	0.299 (0.017)	0.199 (0.118)	0.431 (<0.001)	0.350 (0.005)

Moreover, we found several significant correlations between the tau network SUVRs and mean diffusivity in the cingulate cingulum tract, the hippocampal cingulum tract, the inferior fronto-occipital fasciculus, the inferior longitudinal fasciculus, and the superior longitudinal temporal fasciculus (Table 3, Figure 5).

Figure 5

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Figure 5—source data 1

Association between the tau networks and mean diffusivity of white matter tracts.

Values for the tau networks and mean diffusivity of white matter tracts that were significantly correlated with each other, after regressing out the effects of age, sex and presence of cognitive impairment.

https://cdn.elifesciences.org/articles/50830/elife-50830-fig5-data1-v2.xlsx

Download elife-50830-fig5-data1-v2.xlsx

The interplay between amyloid-β and tau has been extensively studied in Alzheimer’s disease (Ittner and Götz, 2011). There is clear evidence that these proteins start accumulating several years before the appearance of clinical symptoms and spread between brain areas with disease progression (Braak and Braak, 1998; Thal et al., 2002; Hyman et al., 2012; Jack et al., 2014; Braak and Del Tredici, 2015; Jansen et al., 2015). In line with this, neuropathological evidence has shown that amyloid-β and tau have different spatial affinities and target distinct brain regions (Braak and Braak, 1991). In this study, we identified these different spatial patterns in vivo and assessed how they relate to functional and anatomical brain connectivity. Our results showed that amyloid-β and tau have distinct accumulation patterns but also overlap in a few brain regions. Moreover, both of them showed a fair to moderate overlap with distinct functional MRI networks, but only the tau patterns were associated with white matter integrity. These findings indicate that amyloid-β and tau deposits not only have distinct spatial patterns but their accumulation might have a different impact on functional and anatomical brain connectivity.

The recent development of tau PET tracers has provided us the unique opportunity to combine them with amyloid-β PET tracers and assess their specific contributions to Alzheimer’s disease pathogenesis. Most of the studies carried out so far have focused on the accumulation of amyloid-β and tau PET in independent voxels or brain regions (Cho et al., 2016a; Cho et al., 2016b; Johnson et al., 2016; Schöll et al., 2016; Schöll et al., 2017; Ossenkoppele et al., 2016; Wang et al., 2016; Mattsson et al., 2017; Mattsson et al., 2018), despite the fact that their accumulation in these voxels and regions are presumably not independent. In the current study, we were able to capture these dependencies in amyloid-β and tau by applying an unsupervised method based on joint independent component analysis. As a result, we identified a number of linked amyloid-β and tau networks that resembled, to a large extent, the neuropathological stages describing the spread of plaques and tangles in Alzheimer’s disease (Braak and Braak, 1991). For instance, for amyloid-β we identified a medial, anterior cingulate, posterior parietal and medial fronto-parietal networks that matched early amyloid stages (Palmqvist et al., 2017) in addition to five neocortical networks that matched more advanced amyloid stages (Thal et al., 2002). Regarding tau, we identified temporal, hippocampal, insular and neocortical networks, which agreed well with the spread of tangles from medial temporal to limbic and neocortical regions (Braak and Braak, 1991; Hyman et al., 2012). These findings suggest that our amyloid-β and tau networks reflected the accumulation of pathological changes occurring in postmortem studies in patients at early and advanced stages of Alzheimer’s disease. This was further confirmed by our group analyses, which showed significant differences in amyloid-β and tau burden not only between patients and controls (which has already been shown by previous studies: Cho et al., 2016a; Johnson et al., 2016; Schöll et al., 2016; Wang et al., 2016), but also between amyloid-β positive and negative controls, and between patients with Alzheimer’s disease and patients with mild cognitive impairment. Moreover, the associations we found between the amyloid-β and tau networks with cognition indicate that they also have an important value in characterizing clinical impairment. For amyloid-β, there were significant correlations between global cognition and memory decline, whereas for tau there were additional correlations with executive and visuospatial impairment. Of note, we did not find any significant associations between cognition and global amyloid-β PET, one of the most used markers of amyloid pathology in Alzheimer’s disease. This provides further support to the use of amyloid-β networks like the ones identified in this study since they offer greater sensitivity to clinical impairment compared to conventional amyloid measures.

Amyloid-β and tau pathologies have been consistently described as accumulating across interconnected brain networks in Alzheimer’s disease (Seeley et al., 2009; Zhou et al., 2012). A number of studies using functional MRI have supported this by showing that the regions of the default-mode network could be important hubs through which the disease progresses in the brain (Buckner et al., 2009; Hedden et al., 2009; Sperling et al., 2009; Sheline et al., 2010; Drzezga et al., 2011; Mormino et al., 2011; Sheline and Raichle, 2013). Our findings indicate that the amyloid-β patterns overlapped moderately well with the anterior and posterior hubs of the default-mode network, in line with previous studies showing that the brain regions showing highest amyloid-β accumulation coincide with the regions of this functional network like the precuneus and posterior cingulate (Buckner et al., 2005; Buckner et al., 2009; Palmqvist et al., 2017). In contrast, the tau patterns overlapped fairly or moderately well with a wider range of functional networks such as the visual, limbic, somatosensory, language and fronto-parietal networks. These findings suggest that tau could have a higher spatial affinity with several functional networks, which could potentially explain why it correlates with impairment in more cognitive domains in contrast to amyloid-β. Thus, as previously suggested (Blennow and Hampel, 2003), tau could be a better stage marker that reflects the progression of Alzheimer’s disease through different brain regions and clinical stages.

Interestingly, in this study we found that the accumulation of amyloid-β and tau converged in three brain areas such as the left inferior occipital cortex, superior frontal gyri and the precuneus. The focus on amyloid-β and tau has shifted from studying their separate toxic roles to understanding their possible interactions. There are different studies showing that amyloid-β exacerbates the toxic effects of tau and that tau can also mediate amyloid-β toxicity (Ittner and Götz, 2011). Here, we suggest that the precuneus, superior frontal and left inferior occipital gyri might be vulnerable to the combined toxic effects of amyloid-β and tau since they were present in both amyloid-β and tau network analyses. In a previous study, it was also found that amyloid-β and tau converge in a few brain regions such as the precuneus (Sepulcre et al., 2016), in line with our findings.

Another interesting finding of our joint independent component analyses is that the temporal tau network was linked to the medial fronto-parietal amyloid-β network, which in turn overlapped moderately well with the resting-state default-mode network. Since the accumulation of tau in temporal regions and alterations of the default-mode network are both important imaging markers that can be observed since early stages of Alzheimer’s disease (Sperling et al., 2009; Sepulcre et al., 2017a; Sepulcre et al., 2017b), it is interesting to see that they both came out as connected components in our analyses, which could potentially reflect the interaction between the two proteinopathies in early disease stages. Future studies using longitudinal PET imaging are needed to further explore this finding and assess whether the link between these components changes over time or predicts clinical progression.

Our correlation analyses with gray matter volumes and white matter integrity suggest that, compared to amyloid-β, tau accumulation in Alzheimer’s disease is more closely associated with brain atrophy and white matter degeneration. Several studies have shown that tau pathology increases both locally and distally to brain regions with synaptic connections (Ahmed et al., 2014; Khan et al., 2014; Hu et al., 2016), and is responsible for neuronal loss (Gómez-Isla et al., 1997; Giannakopoulos et al., 2003) and axonal degeneration (de Calignon et al., 2012). Our findings are in line with previous studies since we found that different tau networks were associated with lower fractional anisotropy and higher mean diffusivity in several white matter tracts, some of which were spatially close to the tau patterns (such as the temporal tau pattern and the cingulum hippocampal tract), whereas others were distant (such as the fronto-parietal tau pattern and the inferior occipito-frontal fasciculus).

We would like to highlight that a large part of the analyses carried out in the current study were based on multiple correlation analyses in all amyloid-β positive individuals. By performing these analyses, we aimed to evaluate the strength of the relationship between different imaging and clinical markers across the Alzheimer’s disease continuum. According to the amyloid cascade hypothesis proposed by Jack et al. (2013), individuals that are cognitively normal progress to mild cognitive impairment and Alzheimer’s disease due to increasing amyloid accumulation, tau accumulation and MRI changes. In addition, according to the recent NIA-AA criteria (Jack et al., 2018), AD should be regarded as a continuum rather than three distinct clinically defined entities (cognitively normal, mild cognitive impairment, dementia). By carrying out correlation analyses across all amyloid-β positive subjects, our goal was to obtain information about how different PET, MRI and clinical markers behave with disease progression, in line with the recent NIA-AA guidelines for AD (Jack et al., 2018). However, it should be noted that these analyses do not provide information about causality or temporal ordering between variables. Thus, our analyses do not allow drawing definitive conclusions regarding the spread of amyloid-β and tau across different brain networks.

Moreover, there are a few other issues that should be recognized in our study. First of all, although we acquired different imaging sequences from the same individuals, the functional MRI and diffusion tensor imaging scans were only available for a subsample of subjects in our study. Our secondary analyses (Figure 2—figure supplement 4) showed that the PET networks identified in this subsample were similar to the ones in the whole cohort but it would still have been better to have the same number of subjects for all imaging modalities. Secondly, our study is cross-sectional so our findings need to be replicated in longitudinal studies to assess whether the amyloid-β and tau networks can predict connectivity changes and cognitive decline over time. The weak correlations we found between amyloid-β and tau SUVRs with the signals of functional MRI networks was unexpected and suggests that perhaps our network analyses are not well suited to study these type of associations.

Despite these limitations, in this study we provide key evidence that amyloid-β and tau accumulate across distinct spatial networks that are clinically meaningful and closely associated with cognitive decline. In addition, we provide important clues on their potential overlap with different functional networks and their association with anatomical brain connectivity as well as gray matter atrophy. In line with the ongoing debate on the underlying mechanisms of Alzheimer’s disease (Seeley et al., 2009; Zhou et al., 2012), our findings shed some light on which networks are preferentially targeted by amyloid-β and tau as well as their clinical value in distinguishing different disease stages and predicting cognitive decline.

Source data files have been provided for Figures 4 and 5. The source data for the rest of the analyses performed in this study can be requested from Prof. Oskar Hansson (Oskar.Hansson@med.lu.se), after signing a material transfer agreement from Lund University that ensures that the data will only be used for the sole purpose of replicating procedures and results presented in the article and as long as data transfer is in agreement with EU legislation on the general data protection regulation and decisions by the Ethical Review Board of Sweden and Region Skåne.

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Author details

1. Joana B Pereira

   1. Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
   2. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology

   For correspondence

   joana.pereira@ki.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4604-2711

2. Rik Ossenkoppele

   1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
   2. Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

3. Sebastian Palmqvist

   1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
   2. Department of Neurology, Skåne University Hospital, Lund, Sweden

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

4. Tor Olof Strandberg

   Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden

   Contribution

   Data curation

   Competing interests

   No competing interests declared

5. Ruben Smith

   1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
   2. Department of Neurology, Skåne University Hospital, Lund, Sweden

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

6. Eric Westman

   Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

7. Oskar Hansson

   1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
   2. Memory Clinic, Skåne University Hospital, Malmö, Sweden

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Project administration

   For correspondence

   oskar.hansson@med.lu.se

   Competing interests

   has acquired research support (for the institution) from Roche, GE Healthcare, Biogen, AVID Radiopharmaceuticals, Fujirebio, and Euroimmun. In the past 2 years, he has received consultancy/speaker fees (paid to the institution) from Biogen, Roche, and Fujirebio

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