Dataset Information

Longitudinal Changes in Hippocampal Network Connectivity in Alzheimer's Disease.

ABSTRACT:

Objective

The hippocampus is connected to 2 distinct cortical brain networks, the posterior-medial and the anterior-temporal networks, involving different medial temporal lobe (MTL) subregions. The aim of this study was to assess the functional alterations of these 2 networks, their changes over time, and links to cognition in Alzheimer's disease.

Methods

We assessed MTL connectivity in 53 amyloid-β-positive patients with mild cognitive impairment and AD dementia and 68 healthy elderly controls, using resting-state functional magnetic resonance imaging, cross-sectionally and longitudinally. First, we compared the functional connectivity of the posterior-medial and anterior-temporal networks within the control group to highlight their specificities. Second, we compared the connectivity of these networks between groups, and between baseline and 18-month follow-up in patients. Third, we assessed the association in the connectivity changes between the 2 networks, and with cognitive performance.

Results

We found decreased connectivity in patients specifically between the hippocampus and the posterior-medial network, together with increased connectivity between several MTL subregions and the anterior-temporal network. Moreover, changes in the posterior-medial and anterior-temporal networks were interrelated such that decreased MTL-posterior-medial connectivity was associated with increased MTL-anterior-temporal connectivity. Finally, both MTL-posterior-medial decrease and MTL-anterior-temporal increase predicted cognitive decline.

Interpretation

Our findings demonstrate that longitudinal connectivity changes in the posterior-medial and anterior-temporal hippocampal networks are linked together and that they both contribute to cognitive decline in Alzheimer's disease. These results shed light on the critical role of the posterior-medial and anterior-temporal networks in Alzheimer's disease pathophysiology and clinical symptoms. ANN NEUROL 2021;90:391-406.

SUBMITTER: Dautricourt S

PROVIDER: S-EPMC9291910 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Longitudinal Changes in Hippocampal Network Connectivity in Alzheimer's Disease.

Dautricourt Sophie S de Flores Robin R Landeau Brigitte B Poisnel Géraldine G Vanhoutte Matthieu M Delcroix Nicolas N Eustache Francis F Vivien Denis D de la Sayette Vincent V Chételat Gaël G

Annals of neurology 20210806 3

<h4>Objective</h4>The hippocampus is connected to 2 distinct cortical brain networks, the posterior-medial and the anterior-temporal networks, involving different medial temporal lobe (MTL) subregions. The aim of this study was to assess the functional alterations of these 2 networks, their changes over time, and links to cognition in Alzheimer's disease.<h4>Methods</h4>We assessed MTL connectivity in 53 amyloid-β-positive patients with mild cognitive impairment and AD dementia and 68 healthy el ...[more]

PMID: 34279043

Similar Datasets

Project description:Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

Project description:Resting-state or intrinsic connectivity network functional magnetic resonance imaging provides a new tool for mapping large-scale neural network function and dysfunction. Recently, we showed that behavioural variant frontotemporal dementia and Alzheimer's disease cause atrophy within two major networks, an anterior 'Salience Network' (atrophied in behavioural variant frontotemporal dementia) and a posterior 'Default Mode Network' (atrophied in Alzheimer's disease). These networks exhibit an anti-correlated relationship with each other in the healthy brain. The two diseases also feature divergent symptom-deficit profiles, with behavioural variant frontotemporal dementia undermining social-emotional function and preserving or enhancing visuospatial skills, and Alzheimer's disease showing the inverse pattern. We hypothesized that these disorders would exert opposing connectivity effects within the Salience Network (disrupted in behavioural variant frontotemporal dementia but enhanced in Alzheimer's disease) and the Default Mode Network (disrupted in Alzheimer's disease but enhanced in behavioural variant frontotemporal dementia). With task-free functional magnetic resonance imaging, we tested these ideas in behavioural variant frontotemporal dementia, Alzheimer's disease and healthy age-matched controls (n = 12 per group), using independent component analyses to generate group-level network contrasts. As predicted, behavioural variant frontotemporal dementia attenuated Salience Network connectivity, most notably in frontoinsular, cingulate, striatal, thalamic and brainstem nodes, but enhanced connectivity within the Default Mode Network. Alzheimer's disease, in contrast, reduced Default Mode Network connectivity to posterior hippocampus, medial cingulo-parieto-occipital regions and the dorsal raphe nucleus, but intensified Salience Network connectivity. Specific regions of connectivity disruption within each targeted network predicted intrinsic connectivity enhancement within the reciprocal network. In behavioural variant frontotemporal dementia, clinical severity correlated with loss of right frontoinsular Salience Network connectivity and with biparietal Default Mode Network connectivity enhancement. Based on these results, we explored whether a combined index of Salience Network and Default Mode Network connectivity might discriminate between the three groups. Linear discriminant analysis achieved 92% clinical classification accuracy, including 100% separation of behavioural variant frontotemporal dementia and Alzheimer's disease. Patients whose clinical diagnoses were supported by molecular imaging, genetics, or pathology showed 100% separation using this method, including four diagnostically equivocal 'test' patients not used to train the algorithm. Overall, the findings suggest that behavioural variant frontotemporal dementia and Alzheimer's disease lead to divergent network connectivity patterns, consistent with known reciprocal network interactions and the strength and deficit profiles of the two disorders. Further developed, intrinsic connectivity network signatures may provide simple, inexpensive, and non-invasive biomarkers for dementia differential diagnosis and disease monitoring.

Project description:BackgroundAmyloid-beta (Aβ) has a dose-response relationship with cognition in healthy adults. Additionally, the levels of functional connectivity within and between brain networks have been associated with cognitive performance in healthy adults. Aiming to explore potential synergistic effects, we investigated the relationship of inter-network functional connectivity, Aβ burden, and memory decline among healthy individuals and individuals with preclinical, prodromal, or clinical Alzheimer's disease.MethodsIn this longitudinal cohort study (ADNI2), participants (55-88 years) were followed for a maximum of 5 years. We included cognitively healthy participants and patients with mild cognitive impairment (with or without elevated Aβ) or Alzheimer's disease. Associations between memory decline, Aβ burden, and connectivity between networks across the groups were investigated using linear and curvilinear mixed-effects models.ResultsWe found a synergistic relationships between inter-network functional connectivity and Aβ burden on memory decline. Dose-response relationships between Aβ and memory decline varied as a function of directionality of inter-network connectivity across groups. When inter-network correlations were negative, the curvilinear mixed-effects models revealed that higher Aβ burden was associated with greater memory decline in cognitively normal participants, but when inter-network correlations were positive, there was no association between the magnitude of Aβ burden and memory decline. Opposite patterns were observed in patients with mild cognitive impairment. Combining negative inter-network correlations with Aβ burden can reduce the required sample size by 88% for clinical trials aiming to slow down memory decline.ConclusionsThe direction of inter-network connectivity provides additional information about Aβ burden on the rate of expected memory decline, especially in the preclinical phase. These results may be valuable for optimizing patient selection and decreasing study times to assess efficacy in clinical trials.

Dataset Information

Longitudinal Changes in Hippocampal Network Connectivity in Alzheimer's Disease.

Objective

Methods

Results

Interpretation

Publications

Longitudinal Changes in Hippocampal Network Connectivity in Alzheimer's Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets