Spontaneous brain activity persists and transitions between brain states, such as from wakefulness to sleep, from development to ageing, or it may transition to pathological states such as coma. Nevertheless, a consensual definition of brain state remains elusive, and the best way of measuring its dynamical complexity is unknown. Here, we propose whole-brain computational frameworks combined with neuroimaging data to characterize brain states in health and disease. We show that such states exhibit ...
Spontaneous brain activity persists and transitions between brain states, such as from wakefulness to sleep, from development to ageing, or it may transition to pathological states such as coma. Nevertheless, a consensual definition of brain state remains elusive, and the best way of measuring its dynamical complexity is unknown. Here, we propose whole-brain computational frameworks combined with neuroimaging data to characterize brain states in health and disease. We show that such states exhibit unique complex dynamics across spacetime scales. Furthermore, we show that whole-brain models can be fitted to such states to study in silico the capacity of brain areas to promote a transition, e.g., from disease to health. Finally, we show that perturbations of this model can measure the brain’s reactivity in different conscious and unconscious states. In the long term, these methods may open new ways for clinical interventions to rebalance brain disorders.
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L’activitat cerebral espont`ània persisteix i transiciona entre estats cerebrals, com ara de la vigília al son, del desenvolupament a l’envelliment, o pot transicionar a estats patològics com el coma. Tanmateix, una definición consensuada d’estat cerebral segueix sent esquiva, i es desconeix la millor manera de mesurar la seva complexitat dinàmica. Aquí, proposem marcs computacionals de tot el cervell per caracteritzar estats cerebrals en la salut i la malaltia. Mostrem que aquests estats presenten ...
L’activitat cerebral espont`ània persisteix i transiciona entre estats cerebrals, com ara de la vigília al son, del desenvolupament a l’envelliment, o pot transicionar a estats patològics com el coma. Tanmateix, una definición consensuada d’estat cerebral segueix sent esquiva, i es desconeix la millor manera de mesurar la seva complexitat dinàmica. Aquí, proposem marcs computacionals de tot el cervell per caracteritzar estats cerebrals en la salut i la malaltia. Mostrem que aquests estats presenten dinàmiques complexes úniques a través d’escales espacio-temporals. Addicionalment, ajustem models de tot el cervell als estats cerebrals per estudiar in silico la capacitat de les àrees cerebrals per promoure una transició, per exemple,
de la malaltia a la salut. Finalment, mostrem que les pertorbacions d’aquest model poden mesurar la reactivitat del cervell en estats conscients i inconscients. A llarg termini, aquests mètodes poden obrir noves vies per reequilibrar els transtorns cerebrals.
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