May the 4c’s Be with You: An Overview of Complexity-Inspired Frameworks for Analyzing Resting-State Neuroimaging Data

Hancock, Fran; Rosas, Fernando; Mediano, Pedro A. M.; Luppi, A.; Cabral, Joana; Dipasquale, Ottavia; Turkheim, Federico

doi:10.20944/preprints202203.0097.v1

Cited by 6 publications

(6 citation statements)

References 129 publications

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“…Theoretical work indicates that computation and information dynamics in complex systems display special features at critical points [12][13][14][15][16]). Building on these insights, criticality has been suggested as a useful principle to account for the brain's inherent complexity that is required to guide behavior in rich environments by processing various sources of information [2,[17][18][19][20][21][22][23]. Furthermore, it has been found that systems in nature often tune themselves into a critical state-a principle known as 'self-organized criticality' [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

Ruffini

Damiani²,

Lozano-Soldevilla³

et al. 2023

PLoS Comput Biol

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A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create “archetype” Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than the placebo condition (p = 9 × 10−5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity—especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.

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Section: Introductionmentioning

confidence: 99%

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

Ruffini

Damiani²,

Lozano-Soldevilla³

et al. 2023

PLoS Comput Biol

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“…This reduction could impact older adults' ability to retrieve semantic representations in a goal-directed manner, leading to difficulties suppressing irrelevant semantic associations during LP. Accordingly, our findings can be formalized as a novel model titled SENECA (Synergistic, Economical, Nonlinear, Emergent, Cognitive Aging), integrating connectomic (SE) and cognitive (CA) dimensions within a complex system perspective (NE) (Hancock et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Modeling the Neurocognitive Dynamics of Language across the Lifespan

Guichet¹,

Banjac²,

Mermillod³

et al. 2023

Preprint

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Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed in language performances. Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this cognitive variability. At the cognitive level, our findings suggest that language processing is not an isolated function but is modulated throughout an individual's lifespan by the extent of inter-cognitive synergy between language, long-term memory, and executive functions. At the cerebral level, we show that the coupling between DMN (Default Mode Network) and FPN (Fronto-Parietal Network) regions may be the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related cognitive decline in language production, fluid processing, and verbal fluency. Notably, the dynamic of cognitive resilience provided by this synergistic coupling seems to follow the Seneca effect typically found in complex systems: a slow rise followed by an abrupt decline. Specifically, we argue that reducing the DMN-FPN coupling could trigger accelerated cognitive decline around age 50. We summarize our findings in a novel SENECA model (Synergistic, Economical, Nonlinear, Emergent, Cognitive Aging), integrating connectomic and cognitive perspectives within a complex system perspective. Our study furthers our understanding of the dedifferentiation-compensation mechanisms that uphold inter-cognitive functioning during healthy aging and suggests that the fifth decade of life could transition towards less synergistic processing.

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“…The mathematical form of entropy was first derived by Boltzmann in 1872 in his seminal work on statistical mechanics (Perrot, 1998) and later re-derived independently by Shannon in 1942 in the context of information theory (Shannon, 2001). Since then, entropy has been adopted as a measure of the information processing capacity of neural systems (Bergström and Nevanlinna, 1972;Borst and Theunissen, 1999;Hancock et al, 2022;Keshmiri, 2020;Saxe et al, 2018;Strong et al, 1998;Wang et al, 2014;Yuan et al, 2003). Furthermore, entropy is used to quantify the repertoire of brain states (Ahmed et al, 2011;Monteforte and Wolf, 2010;Song and Zhang, 2016;Tagliazucchi et al, 2014;Vivot et al, 2020), the effect of disease (Drachman, 2006;Gómez and Hornero, 2010;Jara et al, 2021;Kannathal et al, 2005;Morabito et al, 2012;Sharanreddy and Kulkarni, 2013;Takahashi, 2013;Thomas et al, 2015;van Drongelen et al, 2003;Wang et al, 2017;Wu et al, 2017;Zhou et al, 2016), and more recently the influence of psychedelics (Carhart-Harris, 2018;Carhart-Harris et al, 2014;Herzog et al, 2020;Lebedev et al, 2016;Viol et al, 2017).…”

Section: Introductionmentioning

confidence: 99%