Living on the edge: network neuroscience beyond nodes

Betzel, Richard F.; Faskowitz, Joshua; Sporns, Olaf

doi:10.1016/j.tics.2023.08.009

Cited by 10 publications

(4 citation statements)

References 136 publications

(178 reference statements)

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“…We accomplish this by analyzing the temporal dynamics of all EEG channels and constructing multi-dimensional shapes using embedded point cloud (EPC) data. Our findings reveal how the brain progresses through state space over time (Sizemore et al, 2019;Barabási et al, 2023;Betzel et al, 2023). The study aims to develop an inexpensive neuro-biomarker for monitoring cognitive interventions and subsequent dementia care remotely.…”

Section: Discussionmentioning

confidence: 90%

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Rutkowski,

Komendziński,

Otake-Matsuura

2024

Front. Aging Neurosci.

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IntroductionThe main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI.MethodsWe use topological data analysis (TDA) on multivariate EEG data to extract features that can be used for unsupervised clustering, subsequent machine learning-based classification, and cognitive score regression. We perform EEG experiments to evaluate conscious awareness in affective reminiscent photography settings.ResultsWe use EEG and interior photography to distinguish between healthy cognitive aging and MCI. Our clustering UMAP and random forest application accurately predict MCI stage and MoCA scores.DiscussionOur team has successfully implemented TDA feature extraction, MCI classification, and an initial regression of MoCA scores. However, our study has certain limitations due to a small sample size of only 23 participants and an unbalanced class distribution. To enhance the accuracy and validity of our results, future research should focus on expanding the sample size, ensuring gender balance, and extending the study to a cross-cultural context.

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Section: Discussionmentioning

confidence: 90%

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Rutkowski,

Komendziński,

Otake-Matsuura

2024

Front. Aging Neurosci.

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“…Co-fluctuation patterns calculated from the whole brain allow us to capture brain-wide interactions and do so at the level of single frames. This is a parameter-free method to capture dynamic functional interactions that does not involve calculating the correlation between regions within sliding windows of a selected size (Betzel et al, 2023; Tanner et al, 2023). These advances are especially useful for capturing processes such as surprise that tend to be transient (e.g., the surprising “happy birthday!” might elicit a strong but instantaneous response).…”

Section: Mainmentioning

confidence: 99%

Brain network dynamics predict moments of surprise across contexts

Zhang,

Rosenberg

2023

Preprint

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We experience surprise when reality conflicts with our expectations. When we encounter such expectation violations in psychological tasks and daily life, are we experiencing completely different forms of surprise? Or is surprise a fundamental psychological process with shared neural bases across contexts? To address this question, we identified a brain network model, the surprise edge-fluctuation-based predictive model (EFPM), whose regional interaction dynamics measured with functional magnetic resonance imaging (fMRI) predicted surprise in an adaptive learning task. The same model generalized to predict surprise as a separate group of individuals watched suspenseful basketball games. The surprise EFPM also uniquely predicts surprise, capturing expectation violations better than models built from other brain networks, fMRI measures, and behavioral metrics. These results suggest that shared neurocognitive processes underlie surprise across contexts and that distinct experiences can be translated into the common space of brain dynamics.

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“…We showed that events express subject-specific information 15 , 16 , can be used to approximate static FC and enhance brain-behavior associations 13 , 17 , and can be partitioned into clusters of repeating patterns 15 , 18 whose relative frequency may be linked to variation in endogenous hormone fluctuations 19 . We refer to studies that calculate and analyze edge time series or the correlation structure of edge time series (so-called edge FC 20 – 23 ) as edge-centric.…”

Section: Introductionmentioning

confidence: 99%

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Ragone,

Tanner,

et al. 2024

Commun Biol

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Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of “bursty” dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

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Living on the edge: network neuroscience beyond nodes

Cited by 10 publications

References 136 publications

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Brain network dynamics predict moments of surprise across contexts

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

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