Biology subjects

Chandaria, S.

Publications and source records attributed to Chandaria, S..

3 recordsLinked to original sources

bioRxiv2025

Quantum-like dynamics in the human brain

Emerging new research indicates evidence of quantum-like (QL) probability laws, including interference effects, in non-quantum physical systems using coupled oscillators. This can produce QL states which can compute in a QL fashion. Given the success of using coupled oscillators for human whole-brain modelling, we investigate the possibility of QL dynamics in the human brain. Here, we investigate how the special topology of human brain anatomy together with QL bits can promote the rich dynamic repertoire necessary for human advanced cognition. We systematically changed the level of QL processing in a whole-brain model. We found the QL regime provided the best whole-brain model fit to large-scale human empirical neuroimaging data. Extraordinarily, at this optimum point we found significantly lower energy consumption than for the non-QL networks. Mechanistically, this implies that the significantly larger whole-brain spectral gap for QL networks offers a backbone to the functional metastability needed to provide the necessary dynamical regime for efficient computation. The underlying QL spectral gaps amplify through interference the metastability and richness of repertoire of the human brain. Overall, we found that the special topology of the human brain promotes QL information processing.

neuroscience↗

bioRxiv2025

The cost of cognition: Measuring the energy consumption of non-equilibrium computation

In biological systems, survival is predicated on an animal being able to perform computations quickly on a minimal energy budget. What is the energy consumption of non-equilibrium brain computation, i.e., what is the cost of cognition? Previous literature has estimated the metabolic cost using neuroimaging measures of glucose consumption but complementary to these findings, here we directly estimate the computational costs by combining the new field of stochastic thermodynamics with whole-brain modelling. We developed the COCO (COst of COgnition) framework using an analytical expression quantifying the links between energy cost, non-equilibrium and information processing for any given brain state measured with neuroimaging. Importantly, this key relationship also holds at the level of individual brain regions. We used this to quantify the benefits of information processing on the highly anatomically, interconnected hierarchical systems of the brain. Crucially, in empirical neuroimaging data we demonstrate that the human brain uses significantly less energy overall than other mammals (including non-human primates and mice), suggestive of an evolutionary optimisation of the effectiveness of computation. Focusing on the cost of cognition, using large-scale human neuroimaging data of 970 healthy human participants, we show that the resting state uses significantly less energy that seven different cognitive tasks. Furthermore, different kinds of tasks require different amounts of non-equilibrium, information processing and energy consumption. We found that tasks requiring more distributed computation also use more energy. Overall, these results directly quantify the cost of cognition, i.e., the non-equilibrium and energetic demands of information processing, allowing a deeper understanding of how the brain compute in a way that is far more energy efficient than current generations of digital computers and artificial intelligence.

neuroscience↗

bioRxiv2023

The meditative brain: State and trait changes in harmonic complexity for long-term mindfulness meditators

Meditation is an ancient practice that is shown to yield benefits for cognition, emotion regulation and human flourishing. In the last two decades, there has been a surge of interest in extracting the neural correlates of meditation, in particular of mindfulness meditation. Yet, these efforts have been mostly limited to the analysis of certain regions or networks of interest and a clear understanding of meditation-induced changes in the whole-brain dynamics has been lacking. Here, we investigate meditation-induced changes in brain dynamics using a novel connectome-specific harmonic decomposition method. Specifically, utilising the connectome harmonics as brain states - elementary building blocks of complex brain dynamics - we study the immediate (state) and long-term (trait) effects of mindfulness meditation in terms of the energy, power and complexity of the repertoire of these harmonic brain states. Our results reveal increased power, energy and complexity of the connectome harmonic repertoire and demonstrate that meditation alters brain dynamics in a frequency selective manner. Remarkably, the frequency-specific alterations observed in meditation are reversed in resting state in group-wise comparison revealing for the first time the long-term (trait) changes induced by meditation. These findings also provide evidence for the entropic brain hypothesis in meditation and provide a novel understanding of state and trait changes in brain dynamics induced by mindfulness meditation revealing the unique connectome harmonic signatures of the meditative brain.

neuroscience↗