Functional Neuroimaging in Mild Cognitive Impairment and Alzheimer's Disease

Functional neuroimaging has transformed our understanding of early brain changes in the Alzheimer’s disease continuum, spanning subjective decline, mild cognitive impairment (MCI) and overt dementia. Non-invasive methods such as functional MRI (fMRI) and positron emission tomography (PET) now reveal network-level disruptions long before clinical diagnosis. Resting-state fMRI consistently shows diminished connectivity within the default mode network alongside compensatory hyperconnectivity in frontoparietal circuits. Task-based paradigms further expose altered recruitment of prefrontal and parietal regions during memory and executive tasks. PET imaging of amyloid-β and tau burden correlates with functional alterations, linking molecular pathology to synaptic dysfunction. Emerging computational whole-brain models integrate multimodal data to simulate how toxic protein deposition drives changes in neuronal excitability and electrophysiological biomarkers. Together, these advances provide mechanistic insight into early network disintegration, support the development of sensitive diagnostic markers and inform targeted interventions aimed at preserving cognitive function.

Research from Nature Portfolio

Recent studies have introduced personalised whole-brain computational models that fuse resting-state fMRI with amyloid-β and tau PET in individuals across the Alzheimer’s spectrum. By parameterising neuronal populations with Wilson-Cowan oscillators, these models uncover synergistic influences of amyloid and tau on network excitability and accurately predict hallmark electrophysiological alterations—enhanced theta band power and reduced alpha rhythms—that arise with disease progression. Subject-specific excitability measures derived from these simulations also predict plasma biomarkers of neurodegeneration and grey matter atrophy, providing a unified framework that links molecular pathology, large-scale network dynamics and cognitive decline. Such integrative modelling offers a novel route to mechanistic understanding and may accelerate the design of early therapeutic strategies.

Functional Neuroimaging in Mild Cognitive Impairment and Alzheimer's Disease publication trend

The graph below shows the total number of articles in functional neuroimaging in mild cognitive impairment and alzheimer's disease across all publications each year (not limited to Nature Index journals).

Technical terms

Functional magnetic resonance imaging (fMRI): A non-invasive technique that measures blood oxygen level–dependent (BOLD) signals to infer neural activity and connectivity.

Positron emission tomography (PET): An imaging modality using radiotracers to quantify molecular markers such as amyloid-β and tau in the living brain.

Default mode network (DMN): A set of interconnected brain regions—posterior cingulate, medial prefrontal and inferior parietal cortices—active at rest and disrupted early in Alzheimer’s disease.

Frontoparietal network (FPN): A large-scale network involving dorsolateral prefrontal and parietal regions that underpins executive control and shows compensatory changes in MCI.

Structure–function coupling: The degree to which anatomical connections (assessed by diffusion MRI) align with functional connectivity patterns (assessed by fMRI).

Amplitude of low-frequency fluctuations (ALFF): A measure of the intensity of spontaneous BOLD signal oscillations in the 0.01–0.1 Hz band, used to index regional neural activity at rest.

References

1. Effects of computerized cognitive training on structure‒function coupling and topology of multiple brain networks in people with mild cognitive impairment: a randomized controlled trial. Alzheimer's Research & Therapy (2023).
2. Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer’s disease. Communications Biology (2024).
3. Functional MRI-specific alterations in frontoparietal network in mild cognitive impairment: an ALE meta-analysis. Frontiers in Aging Neuroscience (2023).