Neurodegenerative disorders (NDDs) are increasingly being acknowledged as conditions characterized by network collapse rather than neuronal loss. Studies have shown that pathological proteins, such as β-amyloid (Aβ), tau, and α-synuclein, disseminate across structural and functional brain connections, utilizing highly interconnected hub areas characterized by increased metabolic demand. Progress in connectomics and network control theory indicates that hub susceptibility and modified network topology are fundamental to the development of Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS). Moreover, multimodal biomarkers, including diffusion and functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography/magnetoencephalography (EEG/MEG), and digital phenotyping, facilitate the early identification of network dysfunction and allow classification into physiologically relevant subgroups. Electrophysiological assessments of oscillatory synchrony and complexity provide dynamic and economical indications of cortical hyperexcitability and network failure. The integration of digital and wearable technologies allows for an ongoing ecologically accurate assessment of cognitive and motor deterioration. Furthermore, therapeutic approaches are transitioning towards network-level restoration, encompassing neuromodulation (transcranial magnetic stimulation, deep brain stimulation, and closed-loop stimulation), polypharmacology, and adaptive cognitive or digital therapies aimed at reestablishing connections and plasticity. In conjunction with machine learning and in-silico modeling, individualized network “fingerprints” have been developed as a basis for connectomic precision medicine. This network-centric architecture allows for proactive, personalized, and potentially preventative strategies to decelerate or cease the progression of neurodegeneration.
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Rohan Gupta (2026) studied this question.
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