Nonlinear changes in delayed functional network topology in Alzheimer’s disease: relationship with amyloid and tau pathology

doi:10.21203/rs.3.rs-2746214/v1

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Research Article

Nonlinear changes in delayed functional network topology in Alzheimer’s disease: relationship with amyloid and tau pathology

https://doi.org/10.21203/rs.3.rs-2746214/v1

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Journal Publication

published 16 Jun, 2023

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Background

Alzheimer’s disease is a neurodegenerative disorder associated with the abnormal deposition of pathological processes, such as amyloid-ß and tau, which produces nonlinear changes in the functional connectivity patterns between different brain regions across the Alzheimer’s disease continuum. However, the mechanisms underlying these nonlinear changes remain largely unknown. Here, we address this question using a novel method based on temporal or delayed correlations and calculate new whole-brain functional networks to tackle these mechanisms.

Methods

To assess our method, we evaluated 166 individuals from the ADNI database, including amyloid-beta negative and positive cognitively normal subjects, patients with mild cognitive impairment, and patients with Alzheimer’s disease dementia. We used the clustering coefficient and the global efficiency to measure the functional network topology and assessed their relationship with amyloid and tau pathology measured by positron emission tomography, as well as cognitive performance using tests measuring memory and global cognition.

Results

Our study found nonlinear changes in the global efficiency, but not in the clustering coefficient, showing that the nonlinear changes in functional connectivity are due to an altered ability of brain regions to communicate with each other through direct paths. These changes were most prominent in early disease stages, while later stages of Alzheimer’s disease were associated with more widespread network disruptions characterized by additional clustering changes. Both network measures, i.e., global efficiency and clustering, showed quadratic associations with pathological amyloid and tau burden as well as cognitive decline.

Conclusions

This study suggests that global efficiency is a more sensitive indicator of network changes in Alzheimer’s disease when compared to clustering coefficient. Both network properties were associated with pathology and cognitive performance, demonstrating their relevance in clinical settings. Our findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization in Alzheimer’s disease, suggesting that it is the lack of direct connections what drives these functional changes.

Anti-symmetric correlations

delayed connectivity

directed connectivity

functional MRI

functional integration

nonlinear functional connectivity

functional segregation

network analysis

Alzheimer’s disease

While the exact mechanisms that determine the development of Alzheimer’s disease (AD) remain under debate, there is consistent evidence showing that the abnormal accumulation of amyloid-beta (Aß) and tau plays a crucial role (1). This led to the development of several Aß and tau biomarkers to track the progression of AD, which have shown that Aβ deposition is one of the earliest events, followed by tau deposition (1–3). These abnormalities have downstream effects on several physiological processes, including the communication between brain regions or functional connections (4,5).

In particular, emerging evidence suggests that early Aβ deposition is associated with increased brain activity in cognitively normal individuals with Aβ burden (6–8) as well as in individuals at the earliest stages of mild cognitive impairment (MCI) (9–11). This increased brain activity has been interpreted as a reflection of maladaptive, excitotoxic processes preceding neuronal loss, as a compensatory mechanism in response to the adverse effects of Aβ buildup on neuronal function, or as a protraction of increases in activation occurring in cognitively healthy individuals (12–14). As the disease progresses, these increases are followed by a decrease in brain activation, together with cognitive decline (11,14,15). This loss of connectivity has been related to the progressive loss of structure and neuronal death induced by tau pathology, which destabilizes axonal structure and disrupts axonal transport (5,16). Together these findings indicate a nonlinear trajectory of functional changes throughout the AD spectrum, with increases of neuronal hyperexcitability being followed by hypoconnectivity or loss of functional connections.

Although a few studies have assessed these functional nonlinear changes in connectivity on functional magnetic resonance imaging (fMRI) (5,13), the mechanisms underlying this nonlinearity remain largely unknown. Specifically, it is unclear whether these changes are due to alterations in the clusters of connections between neighboring areas or due to changes in the communication between distant brain regions. Moreover, previous studies assessing functional nonlinearity in AD are limited by the fact that they evaluated connectivity as a static phenomenon that does not change over time (17,18). This contrasts with emerging evidence showing that functional connectivity is a dynamic process that can be characterized by time delays between the activation of different brain regions (17,19–21). These time delays can be regarded as a measure of the “functional distance” between such regions, with those that are closely connected becoming co-activated after a shorter delay, whereas those that are more distantly connected getting activated after longer delays (19,21,22). Capturing this information from functional connectivity is crucial for a deeper understanding of brain function, as changes in temporal dynamics have been proposed to critically underly brain changes associated with healthy aging and dementia (23).

Here, we measured the delayed functional network organization in individuals across the AD continuum, including cognitively normal subjects, patients with MCI and patients with AD dementia. In addition, we evaluated the relationship between time-varying functional network measures with amyloid and tau pathology measured on positron emission tomography as well as cognitive functions. Network organization was assessed using the clustering coefficient, which reflects how well neighboring brain areas are connected to each other by forming clusters of connections (a property known as segregation), in addition to the global efficiency, which increases when brain regions are able to communicate efficiently through short and direct connections (a property known as integration) (18,24).

Our findings suggest that the nonlinear changes in functional connectivity across the AD continuum are probably due to alterations in the global efficiency of communication between distant brain areas. Nevertheless, both network measures, i.e., global efficiency and clustering coefficient, were found to be associated with high levels of Aβ and tau pathology as well as cognitive decline, highlighting their clinical value in predicting disease severity. These findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization that are currently being increasingly reported in AD and suggest that the direct connections between brain areas seem to be most vulnerable to this disorder.

2.1. Participants

We included 166 participants with functional MRI, amyloid-PET (¹⁸F-Florbetapir) and tau-PET (18F-Flortaucipir) scans from the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3). The inclusion/exclusion criteria for ADNI can be found at: http://www.adni-info.org/. In summary, participants had to be fluent in Spanish or English, be aged between 55 to 90 years, have completed a minimum of 6 years of education and do not present significant neurological disorders other than AD. The controls were included if they scored between 24 and 30 on the Mini-Mental State Examination (MMSE) and had a Clinical Dementia Rating-Sum of Boxes (CDR-SB) score of 0, with no depression, MCI, or dementia. MCI participants were selected based on the Peterson criteria (25) for amnestic MCI. AD participants met the National Institute for Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorder Association (NINDS/ADRDA) criteria for probable AD, scored between 18 and 26 on the MMSE and had a CDR-SB score ranging from 0.5 to 1.0. We only included participants with scores on the 13 item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog 13), delayed word recall (ADAS Q4) as well as the Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB) since these tests are the most commonly used in AD clinical trials (26,27).

ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. ADNI is conducted in accordance with the ethical standards of the institutional research committees and with the 1975 Helsinki declaration and its later amendments. Written informed consent was obtained from all subjects and/or authorized representatives and study partners. Ethical permits have been obtained at each participating site of ADNI and we have signed the data user agreements to analyze the data.

2.2. Group classification

We classified the participants based on clinical diagnosis and Aβ-PET levels using a cut-off > 1.11 based on earlier research showing that Aβ deposition is one of the earliest events in AD (3). This resulted in 81 Aβ-negative cognitively normal (CN Aβ-) individuals, 36 Aβ-positive cognitively normal (CN Aβ+) individuals, 31 Aβ-positive patients with mild cognitive impairment (MCI Aβ+) and 18 Aβ-positive patients with AD dementia (AD Aβ+). Individuals with MCI and AD without Aβ pathology were excluded since they are not considered to be part of the AD continuum and may have a non-AD disorder (1).

2.3. Image acquisition

All subjects underwent 3T MRI using T1-weighted, resting-state fMRI, ¹⁸F-florbetapir PET and ¹⁸F-flortaucipir PET imaging. T1-weighted imaging was performed using a sagittal 3D accelerated MPRAGE sequence with full head coverage, voxel size = 1x1x1 mm³, field of view = 208x240x256 mm³, repetition time = 2300 ms and inversion time = 900 ms. fMRI was conducted using an axial echo planar imaging sequence with voxel size = 3.4x3.4x3.4 mm³, field of view = 220x220x163 mm³, repetition time = 3000 ms, echo time = 30 ms and flip angle = 90 °. ¹⁸F-florbetapir PET scans were acquired in 4x5 minute frames, 50–70 minutes after the injection of 10 mCi dose on average. Finally, ¹⁸F-flortaucipir PET images were acquired following an injection of 10.0 ± 1.0 mCi dose of [¹⁸F]-AV1451. They were acquired for 30 minutes in 6 frames (5 minutes per frame), 75–105 minutes after the injection. More information about the MRI and PET acquisition methods is provided at: https://adni.loni.usc.edu/data-samples/data-types/.

2.4. Image preprocessing

Functional and structural MRI scans were pre-processed using a standardized pipeline implemented in fMRIPrep (28) (v20.2.4, https://fmriprep.org/en/stable/). The first two volumes of the functional scans were removed to account for steady state magnetization effects. Then, functional images were motion-corrected and adjusted for slice timing effects. Brain extraction and registration using a two-stage registration approach to 2 mm resolution MNI152 standard space was performed with Freesurfer (29) and ANTs (30). The resulting functional images additionally underwent motion correction using the Friston-24 head motion model (31) and nuisance regression for signals from the white matter and cerebrospinal fluid. Finally, volumes underwent high-pass filtering with a 0.01 Hz cutoff.

We used the scalar standard uptake value ratios (SUVR) obtained from the PET scans already preprocessed using the standard ADNI pipeline. A detailed description of the PET preprocessing methods is available at: https://adni.loni.usc.edu/methods/pet-analysis-method/pet-analysis/. In short, the five-minute PET frames were co-registered, averaged and co-registered to the T1-weighted MRI images of each participant. Finally, normalized SUVR maps were created by using the cerebellar grey matter as a reference region (32).

2.5. Calculation of whole-brain functional connectivity networks

We used the temporal delay between the activity time series of two regions to define the direction and strength of the functional connection between them. The process for calculating the connectivity strength using this method is illustrated in Fig. 1, for an example of five brain regions and their corresponding activation time series (Fig. 1a). First, we calculated the delayed correlation connectivity matrix, where each entry represents the Pearson’s correlation coefficient between the time series of the corresponding brain regions after a given time delay (Fig. 1b). This delay is expressed as the number of time steps (fMRI repetition times) by which one time series is shifted relative to the other while calculating the corresponding correlation coefficient. This shift also determines the direction of the interregional connection, with earlier activated brain regions being the source and the later activated brain regions being the end of the connection.

Since it is a square matrix, this delayed correlation matrix can be univocally split into the sum of a symmetric matrix (Fig. 1c) and an anti-symmetric matrix (Fig. 1d). The anti-symmetric matrix identifies the directed connections between brain areas, capturing the directionality of the functional network. We summarize the whole-brain directed functional connectivity of each participant using this anti-symmetric matrix, which represents a topological map of the functional network at different temporal delays. Networks calculated at short time delays represent the patterns of connectivity between brain regions that are topologically close and have strong, direct connections. On the contrary, networks calculated at higher delays capture the connectivity between regions via indirect paths of varying lengths. As the anti-symmetric correlations can evaluate functional connectivity over a wide range of temporal delays, they can be used to study the organization of brain activity patterns at different levels of topological connectivity (22).

2.6. Network construction

Using the anti-symmetric correlation method described above, we calculated the edges of a weighted connectivity network for each participant, with the network nodes corresponding to the 200 brain regions derived from the Craddock atlas (33). For each weighted network, we computed a set of binary networks by assigning a value of 1 for each correlation coefficient that was above a certain threshold and 0 otherwise. Since currently there is no consensus regarding which threshold should be used (24), we performed the thresholding taking into account a wide range of network densities (D_min=5% to D_max=50% in steps of 1%). We did not consider densities below 5% since most nodes were disconnected at such low densities. The negative correlation coefficients were also set to zero.

We calculated the area under the curve (AUC) to summarize the behavior of the network measures across the complete density range, since it is less affected by the thresholding process and does not require selecting a specific density for the correlation analysis (24). We estimated the AUC for each network measure by numerically integrating the measure values across the complete network density range. This resulted in a single numerical value for each network measure at each temporal delay that was then used in the between-group comparisons as well as in the linear modelling.

2.7. Network analysis

We used two network measures, namely the clustering coefficient and the global efficiency, to assess the global topology of the individual connectivity networks. The clustering coefficient (illustrated in Fig. 2a) is a measure of network segregation that is calculated as the fraction of closed triangles around a node and increases with the number of local connections. In contrast, the global efficiency (illustrated in Fig. 2g) reflects the level of network integration and it increases when the paths connecting any two nodes in the network become shorter. All graph measures were calculated using the Brain Analysis using Graph Theory software (34) (BRAPH, http://braph.org/).

2.8. Statistical analysis

The statistical significance of the differences between different groups was assessed by performing nonparametric permutation tests with 10 000 replicates, which were considered significant for a two-tailed test of the null hypothesis at p < 0.05. To assess whether the functional network measures were associated with pathology and cognition, we ran linear regression models across all Aß positive individuals using the global Aß and tau Braak stages I-II, III-IV and V-VI SUVR values, as well as cognitive test scores, as dependent variables. In these analyses we only included subjects with values within the 1.5 interquartile range (to avoid the influence of outliers) and included cognitive status as a covariate. We built a separate linear model for each temporal delay, including age, sex, education, cognitive status and the AUC values for the two network measures as independent variables. The best model was chosen as a combination of predictors that resulted in the minimum value of the Akaike information criterion (AIC). We report the best fitting model (full model and individual predictor significance as well as the adjusted R²) for each dependent variable that included the network measures as significant predictors. The significance of the overall model and the independent coefficients was evaluated by a F-test, which was considered significant at p < 0.05. These results were adjusted for multiple comparisons by applying false discovery rate (FDR) corrections at q < 0.05 using the Benjamini–Hochberg procedure (35) to control for the number of clinical tests, pathology and different temporal delays.

To identify the most relevant temporal delays for characterizing functional connectivity changes in AD, we calculated a representative network for each group by averaging the individual weighted connectivity matrices. The histograms of the connectivity weights in these group-average networks, included in Supplementary Fig. S1, show that all groups exhibit narrower strength distributions as the temporal delays increase. This agrees with a previous study (22) and suggests that using longer temporal delays results in networks containing many connections with similar functional strengths, making them unsuitable for our analysis as they fail to detect any changes in the directed functional flow. Therefore, we limited our analysis to temporal delays ranging from one to five.

3.1. The clustering of nearby connections is increased in later stages of AD

The clustering coefficient did not show any significant differences between CN Aß+ or MCI Aß+ compared to CN Aß- individuals (Fig. 2b-f). However, the AD Aß+ group showed a widespread decrease in the clustering coefficient at all temporal delays when compared to the CN Aß- group (Fig. 2b-f). The AD Aß+ group also had lower clustering coefficient at high temporal delays when compared to the other Aß+ groups. These decreases were most pronounced at temporal delays 4 and 5 in comparison to MCI Aß+ (Fig. 2e-f), and at delay 5 in comparison to CN Aß+ (Fig. 2f). More details about these comparisons are shown in Supplementary Table S1.

3.2. The efficiency of direct connections shows nonlinear changes across the AD continuum

The global efficiency of CN Aß+ individuals decreased at delay 3 compared to the CN Aß- group (Fig. 2j and Supplementary Table S2). Furthermore, the MCI Aß+ group showed increased functional integration at delays 4 and 5 when compared to both the CN Aß+ and CN Aß- groups (Fig. 2k and 2l, respectively). However, these increases were followed by an efficiency decrease in AD Aß+ at multiple delays when compared to the MCI Aß+ group (Fig. 2h,j,k,l).

3.3. Network measures show nonlinear association with Aß and tau pathology as well as cognition

To identify which measures were associated with brain pathology, we fitted separate linear models across the AD continuum (all Aβ + subjects), including global Aβ-PET and tau-PET Braak I-II, III-IV and V-VI SUVR values as dependent variables. The linear models tested for nonlinear associations of second order between pathological variables and network measures at each temporal delay separately, while controlling for age, sex, education and cognitive status (section Statistical analysis). Our results showed a quadratic relationship between Aβ-PET and network measures (Fig. 3), where higher Aβ-PET burden was associated with lower clustering coefficient at delay 1 (p-value = 0.003, R² = 0.095) and lower global efficiency at delay 5 (p-value = 0.014, R² = 0.083). Regarding tau-PET burden, which showed quadratic associations with clustering coefficient, we found that Braak stage I/II values were associated with lower clustering coefficient at delay 5 (p-value < 0.001, R² = 0.239), while Braak III/IV values were associated with lower clustering coefficient at delay 4 (p-value = 0.005, R² = 0.171).

Similar non-linear quadratic relationships were observed between several cognitive tests and network measures (Fig. 4). In particular, better mPACC performance was associated with increased global efficiency at delay 4 (p-value < 0.001, R² = 0.595). Higher ADASQ4 scores were associated with lower global efficiency at delay 1 (p-value < 0.001, R² = 0.515) as well as with lower clustering coefficient at delay 5 (p-value < 0.001, R² = 0.468), while worse ADAS 13 scores were associated with lower clustering coefficient at delay 2 (p-value < 0.001, R² = 0. 556). Finally, higher global efficiency values at delay 2 were linearly associated with lower ADASQ4 scores (p-value < 0.001, R² = 0.481).

Closely related to behavior and cognitive performance (36), functional connectivity reflects the relationships between the activation time series measured from different brain regions (18). These associations are commonly assessed using methods that assume that brain areas become activated at the same time (17). However, mounting evidence shows that the activation of brain regions is not always simultaneous and some regions become activated first and are followed by the later activation of others (19–22,37). Incorporating this information is necessary to better understand the functional connectivity patterns occurring in AD. Here, we adopt a novel approach, called anti-symmetric correlations, that integrates the information contained in the temporal delays between activation of different regions to characterize the direction and strength of the functional connections, and demonstrate that this approach can detect changes in the organization of functional networks at multiple timescales across different stages of AD. Our findings show that this organization follows a nonlinear trajectory across the AD spectrum. In particular, we observed this trajectory in the global efficiency, which started with decreases in Aß+ cognitively normal individuals, followed by increases in individuals with MCI, and ending with a strong decrease in individuals with AD. However, the clustering coefficient did not follow a similar pattern and instead showed widespread decreases only in individuals in late AD stages, suggesting that functional integration is a more sensitive indicator of network changes in AD when compared to functional segregation. The between-group differences indicated that the temporal delay needed to classify a group of individuals based on their clinical diagnosis varied depending on their disease stage across the AD spectrum. For example, shorter time delays were more sensitive to changes in the functional connectivity networks in the early stages of AD, while long time delays were necessary to detect the changes present in individuals at late disease stages. Finally, both functional integration and segregation measures were associated with amyloid and tau pathology, as well as cognition, demonstrating their relevance in clinical settings.

Studies have proposed that the abnormal accumulation of Aβ in the brain is one of the earliest events in AD, which promotes a cascade of downstream processes ultimately resulting in cognitive decline and dementia (1–3). Therefore, identifying the alterations occurring in cognitively normal individuals with Aβ burden is crucial to understand the mechanisms that initiate AD (38). Functional MRI could be useful to detect these early changes, since it is sensitive to early synaptic dysfunction due to accumulating protein aggregates even when neurodegeneration has not yet occurred (39). A possible mechanism for the disruption of synaptic transmission is the presence of soluble amyloid ß oligomers, which appear in the intracellular space well before any plaques can be detected and affect neural transmission on the pre- and postsynaptic side as well, eventually leading to dendritic and synaptic loss (40). These oligomers also seem to show specific effects on glutamatergic transmission by affecting mechanisms of long-term potentiation and depression (41). The delayed functional connectivity, of approximately seven seconds, might be more sensitive to such early changes in synaptic transmission as it can evaluate downstream, polysynaptic information transfer, where these effects possibly accumulate and appear in a more widespread manner.

Our delayed functional connectivity method showed that CN Aβ + individuals had an abnormal global network topology at delay 3, characterized by a lower global efficiency compared to cognitively normal individuals without Aβ burden. This decrease indicates a lower ability of the network of CN Aβ + individuals to facilitate a quick transfer of information across different brain areas, in agreement with earlier studies identifying a reduction in neuronal activity in predementia AD stages (42–44). By interpreting the temporal delay between brain regions as a sign of their topological proximity (19,21,22), our results indicate that the global efficiency decreases in the CN Aβ + group are related to the disruption in the communication between brain areas either connected directly or only through a few network connections. Such direct connections are typically established by the central regions in brain networks that play a vital role in facilitating whole-network communication (45). Therefore, our findings are in line with earlier studies showing that early functional alterations in AD occur due to the preferential spatial distribution of Aβ pathology across distant and central brain regions (13,44,46,47). Since similar changes were not observed at larger delays, our study demonstrates that measures assessing direct interregional connections and their organization, but not capturing network-wide effects, are the most sensitive to changes to whole-brain connectivity changes in preclinical AD and should be considered by future studies.

Regarding middle stages of the AD continuum, individuals with MCI had an increased global efficiency compared to the CN Aβ + group at high temporal delays. This indicates that there are network-wide changes in functional connectivity in the MCI group, which could occur as a consequence of the widespread regional Aβ pathology in individuals with MCI (3). This increased network integration could be seen as a compensatory mechanism in response to the continuing accumulation of amyloid-β (5,12,13), as similar results have been found in previous studies in individuals with MCI performing different cognitive tasks (9–11,48). However, this enhanced network integration could also have negative effects and impair the ability of the brain to process information (22,49), which could predict a faster cognitive decline in individuals with MCI (15,50).

Following the hyper-global efficiency in the MCI Aβ + group, anti-symmetric correlations detected a subsequent decrease in functional integration in individuals with AD dementia, in agreement with earlier studies that observed decline in functional connectivity in AD (9–11,51). Therefore, our findings indicate that these nonlinear trajectories are probably due to the compounded effects of amyloid and tau on the functional connectivity. In particular, studies have shown that the accumulation of tau and amyloid in the brain is enhanced by increased neural activity (52,53). Then, the hyperactivity that results from the initial amyloid deposition could lead to higher levels of tau, which could cause an ongoing cycle of tau and amyloid buildup (5). Consequently, having the high deposition levels of tau and amyloid observed in AD (1) could result in disruptions in the network organization of functional connections.

In contrast, between-group differences were harder to detect in the clustering coefficient. We found that the clustering coefficient was lower in individuals with AD dementia in comparison to both amyloid positive groups at high temporal delays and compared to Aβ- cognitively normal individuals at all temporal delays. The clustering coefficient measures the density of local connections and can be used as an indicator of a network's ability to perform specialized processing tasks (24). Combined with the reduced global efficiency observed in AD dementia patients across various temporal delays, these findings indicate a widespread disruption in the temporal organization of the functional connectivity networks in AD patients. This organization is usually called small-world and corresponds to a balance between locally clustered connections and high functional integration (24,34). Previous studies have shown that disruptions in this type of organization are common in AD (54–56), which results in decreased processing abilities of their networks to function normally and potentially explain the severe cognitive deficits.

Furthermore, the differences between the amyloid positive groups along the AD continuum were observed only at higher temporal delays. As such high delays can occur between brain regions that are topologically distant from each other (19,21,22), these differences suggest that an increase in amyloid burden is linked to more severe network-wide disruptions that hinder the communication between brain regions through indirect connections. This aligns with previous research that has found decreased long-distance functional connectivity in AD (54,57) and in line with the view of AD as a disconnection syndrome (58).

We assessed the clinical relevance of the network measures by testing their associations with the amount of brain pathology and scores on cognitive tests commonly used in clinical practice to assess AD patients. The network measures were associated with both global amyloid and tau Braak stages, following a nonlinear, inverted-U pattern, in agreement with earlier studies showing that network structure correlates with amyloid and tau pathology (59,60). Furthermore, high network values were linked to better performance on tests measuring memory and global cognition. This is in line with previous reports showing an association between higher cognitive performance and networks with strong functional specialization and integration properties (61,62). As these tests are frequently used in clinical trials to evaluate the effectiveness of antidementia treatments (26,27), our findings suggest that changes in directed network activation patterns could be a viable biomarker for tracking clinical progression in AD.

Our results should be interpreted in light of the limitation that they were obtained from cross-sectional functional MRI data. This cross-sectional design did not allow us to determine whether measures of functional segregation and integration can predict the progression of AD or the rate of amyloid or tau accumulation over time. Therefore, further studies are needed to assess these questions and examine the causal relations between these variables.

In this study we demonstrate that a functional connectivity method that uses temporal delays in the activation between brain regions can identify novel network changes in individuals at various stages of AD. Our results indicate that functional integration measures, which exhibit a nonlinear inverse-U trajectory across the AD spectrum, are more effective in detecting these differences than functional segregation measures, and could explain the results obtained by previous studies showing nonlinear functional changes in AD. Furthermore, both functional integration and segregation measures had nonlinear relationships with amyloid and tau burden in the brain, as well as measures of memory and general cognitive performance. These findings suggest that this method may provide a deeper understanding of functional connectivity changes in patients at different stages of AD, as well as help improve AD diagnosis with non-invasive imaging measures.

Alzheimer’s disease

Aß

Amyloid-beta

MCI

Mild Cognitive Impairment

fMRI

functional Magnetic Resonance Imaging

PET

Positron Emission Tomography

ADNI

Alzheimer’s Disease Neuroimaging Initiative

MMSE

Mini-Mental State Examination

CDR-SB

Clinical Dementia Rating-Sum of Boxes

ADAS-Cog 13

Alzheimer’s Disease Assessment Scale–Cognitive Subscale

ADAS Q4

Delayed Word Recall test

mPACCtrailsB

Modified Preclinical Alzheimer Cognitive Composite with Trails test

CN Aβ-

Aβ-negative cognitively normal

CN Aβ+

Aβ-positive cognitively normal

MCI Aβ+

Aβ-positive patients with mild cognitive impairment

AD Aβ+

Aβ-positive patients with AD dementia

SUVR

Standard Uptake Value Ratios

AUC

Area Under the Curve

Ethics approval

The ADNI study follows the ethical standards of the institutional research committees and adheres to the principles outlined in the 1975 Helsinki declaration and its later amendments. All study participants, authorized representatives, and study partners have provided written informed consent, and each participating site of ADNI has obtained the necessary ethical permits. More details can be found at adni.loni.usc.edu.

Consent for publication

All authors have approved the manuscript and agreed with its submission.

Availability of data and materials

The authors did not participate in data collection. The data used in the current study were obtained from ADNI, an open-access multicenter cohort, which requires application for access (https://adni.loni.usc.edu/data-samples/access-data/). The BRAPH software can be freely downloaded from: http://braph.org/.

Competing interests

All authors have declared no conflicts of interest.

Funding

Swedish Research Council; Swedish Alzheimer Foundation; Swedish Brain Foundation; Strategic Research Area Neuroscience (StratNeuro); Center for Medical Innovation (CIMED); Foundation for Geriatric Diseases at Karolinska Institutet; Gamla Tjänarinnor; Stohne’s Foundation; Lars Hierta Memorial Foundation.

Authors' contributions

MM: Conceptualization, methodology and formal analysis, preparation of figures, writing the original draft, review and editing, funding acquisition. DV: data preprocessing, methodology, review and editing. ACG: data preprocessing, preparation of figures, review and editing. TH: methodology, review and editing. GV: conceptualization, methodology, review and editing. JBP: supervision, conceptualization, methodology, writing the original draft, review and editing, funding acquisition.

Acknowledgement

Data collection and sharing of ADNI was funded by the National Institutes of Health Grant U01 AG024904 and Department of Defense award number W81XWH-12-2-0012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-LaRoche Ltd and its affiliated company Genentech, Inc.; GEHealthcare; Innogenetics, N.V.; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. Full acknowledgements list can be found at https://adni.loni.usc.edu/.

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Table 1

Characteristics of the sample.
	CN Aβ- (n = 81)	CN Aβ+ (n = 36)	MCI Aβ+ (n = 31)	AD Aβ+ (n = 18)	p-values
Age	75.5 (32.5)	78.0 (27.8)	79.5 (30.1)	84.5 (29.3)	< 0.001
Sex (M/F)	34 / 47	19 / 17	16 / 15	9 / 9	0.654
Education	18.0 (10.0)	16.0 (8.0)	16.0 (12.0)	15.0 (7.0)	0.017
Global Aβ SUVR	1.0 (0.2)	1.3 (0.6)	1.4 (1.2)	1.4 (0.8)	< 0.001
Braak I-II SUVR	1.1 (0.5)	1.1 (0.6)	1.3 (1.0)	1.5 (1.9)	< 0.001
Braak III-IV SUVR	1.1 (0.5)	1.2 (0.4)	1.2 (0.9)	1.4 (2.0)	< 0.001
Braak V-VI SUVR	1.0 (0.4)	1.0 (0.4)	1.1 (0.9)	1.1 (1.2)	< 0.001
ADAS 13	12.0 (21.7)	13.3 (27.3)	19.3 (27.0)	32.3 (26.7)	< 0.001
ADAS Q4	2.0 (6.0)	3.0 (7.0)	5.0 (7.0)	8.0 (5.0)	< 0.001
mPACC	1.5 (15.5)	-0.4 (19.0)	-3.9 (16. 9)	-14.9 (26.4)	< 0.001
Medians for each group are followed by range of values in parenthesis, except for sex. Comparisons between groups were performed using Kruskal-Wallis tests for continuous variables and with Chi-squared tests for binary variables. CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; Aβ, amyloid-β; ADAS 13, 13 item Alzheimer’s Disease Assessment Scale–Cognitive Subscale; ADAS Q4, delayed word recall itemof the Alzheimer’s Disease Assessment Scale–Cognitive Subscale; mPACC, Modified Preclinical Alzheimer Cognitive Composite; SUVR, standardized uptake value ratio.

No competing interests reported.

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Nonlinear changes in delayed functional network topology in Alzheimer’s disease: relationship with amyloid and tau pathology

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Abstract

Figures

1. Background

2. Methods

2.1. Participants

2.2. Group classification

2.3. Image acquisition

2.4. Image preprocessing

2.5. Calculation of whole-brain functional connectivity networks

2.6. Network construction

2.7. Network analysis

2.8. Statistical analysis

3. Results

3.1. The clustering of nearby connections is increased in later stages of AD

3.2. The efficiency of direct connections shows nonlinear changes across the AD continuum

3.3. Network measures show nonlinear association with Aß and tau pathology as well as cognition

4. Discussion

5. Conclusions

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