Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuropsychopharmacology, Год журнала: 2024, Номер 50(1), С. 67 - 84

Опубликована: Сен. 6, 2024

Язык: Английский

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Dynamic switching between brain networks predicts creative ability

Communications Biology, Год журнала: 2025, Номер 8(1)

Опубликована: Янв. 15, 2025

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding functional connectivity between the brain's Default Mode (DMN) Executive Control (ECN) Networks. Here, we conduct large-scale, multi-center examination of this hypothesis. Employing meta-analytic network neuroscience approach, analyze resting-state fMRI creative task performance across 10 independent samples Austria, Canada, China, Japan, United States (N = 2433)—constituting largest most ethnically diverse creativity study date. Using time-resolved analysis, investigate relationship (i.e., divergent thinking ability) dynamic switching DMN ECN. We find that creativity, but not general intelligence, can be reliably predicted by number DMN-ECN switches. Importantly, identify an inverted-U degree balance switching, suggesting optimal requires balanced brain dynamics. Furthermore, task-fMRI validation 31) demonstrates higher during idea generation (compared control condition) replicates relationship. Therefore, provide robust evidence datasets tied capacity dynamically switch networks supporting controlled cognition. Robust

Язык: Английский

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Distinct Structural Connectivity Patterns Associated with Variations in Language Lateralisation

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Abstract Hemispheric asymmetries in white matter tracts are proposed key determinants of language lateralisation, yet evidence healthy individuals remains inconsistent. This suggests that simple tractography techniques might not be sensitive enough to identify dominance. Significant insights into the functional organization human brain may achieved by considering networks and connectivity, providing more information about discrepancies people with different hemispheric In this study, we examined 285 participants compare their structural connectomes at whole-brain level determine responsible for three lateralisation groups (typical, atypical strongly atypical). Probabilistic generated tractograms, fibres were filtered according anatomical Boolean guidelines. Connectivity matrices nodes corresponding supramodal sentence areas atlas edges weighted fractional anisotropy (FA) using graph theory network-based statistic (NBS) approaches. We demonstrated both (bilateral) (right-lateralised) characterised heightened interhemispheric temporal connectivity. Post-hoc analyses showed exhibited increased temporo-frontal while had enhanced frontal connectivity but lacked connections. These patterns diverge from traditional models dominance, suggesting a reliance on integrated bilateral atypically lateralised individuals. reflects distinct neural mechanisms underlying organisation, departing developmental trajectory typical offering cognitive flexibility clinical applications.

Язык: Английский

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Bias in data-driven replicability analysis of univariate brain-wide association studies

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 19, 2025

Abstract Recent studies have used big neuroimaging datasets to answer an important question: how many subjects are required for reproducible brain-wide association studies? These data-driven approaches could be considered a framework testing the reproducibility of several models and measures. Here we test part this framework, namely estimates statistical errors univariate brain-behaviour associations obtained from resampling large with replacement. We demonstrate that reported largely consequence bias introduced by random effects when sampling replacement close full sample size. show future meta-analyses can avoid these biases only up 10% discuss implications reproducing mass-univariate requires tens-of-thousands participants, urging researchers adopt other methodological approaches.

Язык: Английский

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Learning from missteps: Potential of transcranial electrical stimulation in neuropsychological rehabilitation

Journal of Neuropsychology, Год журнала: 2025, Номер unknown

Опубликована: Март 23, 2025

Abstract Transcranial electrical stimulation (tES) holds promise for neuropsychological rehabilitation by leveraging the brain's inherent plasticity to enhance cognitive and motor functions. However, early results have been variable due oversimplified approaches. This manuscript explores potential complexities of tES, particularly focusing on a protocol defined transcranial direct current as reference model all tES protocols, emphasising need precision in tailoring parameters individual characteristics. By integrating intrinsic (i.e. neuro‐physiological system state) extrinsic factors experimental set up), highlighting critical role state‐dependent effects synergy with tasks, we aim refine protocols. approach not only addresses complexity brain (as its but also highlights importance collaborative research data sharing understand underlying mechanisms tES‐induced changes optimising therapeutic efficacy. Emphasising integration targeted tasks clearer hypotheses, this work underscores more effective neurorehabilitation strategies.

Язык: Английский

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Multimodal brain imaging of insomnia, depression and anxiety symptoms indicates transdiagnostic commonalities and differences

Nature Mental Health, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

Язык: Английский

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Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity

Journal of Computational Science, Год журнала: 2024, Номер 82, С. 102416 - 102416

Опубликована: Авг. 12, 2024

The brain is a complex system with functional and structural networks. Different neuroimaging methods have been developed to explore these networks, but each method has its own unique strengths limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) near-infrared spectroscopy (fNIRS) gained interest, understanding how information derived from modalities related other remains an exciting open question. multilayer network model emerged as promising approach integrate different sources data. In this study, we investigated hemodynamic electrophysiological data captured by fNIRS EEG compare topologies modality, examining vary between resting state (RS) task-related conditions. Additionally, adopted evaluate benefits of combining multiple compared using single modality in capturing characteristic functioning. A small-world structure was observed rest, right motor imagery, left imagery tasks both modalities. We found that captures faster changes neural activity, thus providing more precise estimation timing transfer regions RS. provides insights into slower responses associated longer-lasting sustained processes cognitive tasks. outperformed unimodal analyses, offering richer function. Complementarity observed, particularly during tasks, well certain level redundancy complementarity multimodal approach, which depends specific state. Overall, results highlight differences capture topology RS emphasize value integrating for comprehensive view connectivity

Язык: Английский

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Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis

NeuroImage Clinical, Год журнала: 2025, Номер unknown, С. 103785 - 103785

Опубликована: Апрель 1, 2025

Understanding complex brain-behaviour relationships in psychiatric and neurological conditions is crucial for advancing clinical insights. This review explores the current landscape of network estimation methods context functional MRI (fMRI) based neuroscience, focusing on static undirected analysis. We focused papers published a single year (2022) characterised what we consider fundamental building blocks analysis: sample size, association type, edge inclusion strategy, weights, modelling level, confounding factors. found that most common across all included studies (n = 191) were use pairwise correlations to estimate associations between brain regions (79.6 %), weighted networks (95.3 at individual level (86.9 %). Importantly, substantial number lacked comprehensive reporting their methodological choices, hindering synthesis research findings within field. underscores critical need careful consideration transparent fMRI methodologies advance our understanding relationships. By facilitating integration neuroscience psychometrics, aim significantly enhance these intricate connections.

Язык: Английский

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Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Frontiers in Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Янв. 24, 2024

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate cortical thickness-based structural topological network T2DM patients without mild (MCI). Fifty-six 59 healthy controls underwent neuropsychological assessments sagittal 3-dimensional T1-weighted magnetic resonance imaging. Then, we combined graph theoretical analysis explore abnormalities covariance networks patients. Correlation analyses were performed relationship between altered parameters cognitive/clinical variables. exhibited significantly lower clustering coefficient (C) local efficiency (Elocal) values showed nodal property disorders occipital cortical, inferior temporal, frontal regions, precuneus, precentral insular gyri. Moreover, multiple nodes correlated findings tests Thus, while MCI relatively normal global network, organization was disordered. impaired ventral visual pathway involved neural mechanism enriched characteristics gray matter structure

Язык: Английский

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Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome

eLife, Год журнала: 2023, Номер 12

Опубликована: Март 28, 2023

Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance nature of differences between two networks an open problem, such analysis has not been extensively applied nanoscale connectomes. Here, we investigate this problem via a case study on bilateral symmetry larval

Язык: Английский

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