Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD.

Synaptic vesicles (SVs) release neurotransmitters at specialized active zones, but release sites and organizing principles for the other major secretory pathway, neuropeptide/neuromodulator release from dense-core vesicles (DCVs), remain elusive. We identify dynamins, yeast Vps1 orthologs, as DCV fusion site organizers in mammalian neurons. Genetic or pharmacological inactivation of all three dynamins strongly impaired DCV exocytosis, while SV exocytosis remained unaffected. Wild-type dynamin restored normal exocytosis but not guanosine triphosphatase–deficient or membrane-binding mutants that cause neurodevelopmental syndromes. During prolonged stimulation, repeated use of the same DCV fusion location was impaired in dynamin 1-3 triple knockout neurons. The syntaxin-1 staining efficiency, but not its expression level, was reduced. αSNAP (α–soluble N-ethylmaleimide–sensitive factor attachment protein) expression restored this. We conclude that mammalian dynamins organize DCV fusion sites, downstream of αSNAP, by regulating the equilibrium between fusogenic and non-fusogenic syntaxin-1 promoting its availability for SNARE (SNAP receptor) complex formation and DCV exocytosis.

Objective. Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Methods. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (‘SCD’) and 18 subjects with mild cognitive impairment (‘MCI’). Local activity was characterized by permutation entropy (PE). Network interactions were studied using the inverted Joint Permutation Entropy (JPEinv), corrected for volume conduction. Results. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta-band entropy. Between-group differences were widespread across brain regions. ROC analysis of classification of MCI versus SCD subjects revealed that a linear regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power based models (76.9% [60.4–93.3%]). Conclusion. Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD, and should be explored in larger studies.