At the moment, when a patient presents with clinical symptoms of Parkinson’s or Alzheimer's disease there is very little the clinical community can do other than  manage the symptoms of what are inevitable degenerative conditions.  Alterations in the neural state (or states) that eventually will produce functional and behavioural consequences had been set in motion perhaps 20 years earlier, and provoked the search for proper biomarkers in asymptomatic individuals. The reorganisation of functional brain networks, either due to slow changes in the relative contribution/wiring of brain areas (plasticity) or due to fast modulation of their causal interactions (effective connectivity), mask the early stages of neurodegeneration, until alterations cannot be compensated.  Until recently, approaches to brain function in health and disease mostly did not take into account the fact that the brain is highly dynamic, and that studying brain function as a series of static states may not be the most effective way to encounter early biomarkers for neurodegeneration. Rather, the clues might lay in the path or trajectory by which functional brain networks evolve and transition over time or their dwell time irregularities. Methodologically, estimating the evolution of the non-stationary and non-linear brain is challenging. Most solutions apply a fixed predetermined moving window, over which functional connectivity is estimated, thus missing the explicit modelling of the dynamical regimes, across multiple temporal length-scales, over which the brain networks are assumed to switch, from few multi-seconds to seconds. The assumption here is that early stages of neurodegeneration could be detected and differentiated from normal ageing as alterations in the dynamical repertoires that the brain networks explore, either at rest or during task performance. These alterations would be reflected either in terms of the probability of the brain switching between network configurations, the duration it spends in particular configurations, or in terms of the sequence or path of configurations.
In this talk I will discuss advantages and disadvantages of current tools and present a conceptual framework for modelling brain dynamics that bridges between bottom-up data driven models and top-down biophysical models.