Browsing by Author "Silva, Nuno A. da"
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- Involvement of the cerebellum in structural connectivity enhancement in episodic migrainePublication . Matoso, Ana; Fouto, Ana R.; Esteves, Inês; Ruiz-Tagle, Amparo; Caetano, Gina; Silva, Nuno A. da; Vilela, Pedro; Gil-Gouveia, Raquel; Nunes, Rita G.; Figueiredo, PatríciaBackground: The pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls. Methods: To that end, 14 migraine patients and 15 healthy controls were recruited (all female), and diffusion-weighted and T1-weighted MRI data were acquired. The structural connectome was estimated for each participant based on two different whole-brain parcellations, including cortical and subcortical regions as well as the cerebellum. The structural connectivity patterns, as well as global and local graph theory metrics, were compared between patients and controls, for each of the two parcellations, using network-based statistics and a generalized linear model (GLM), respectively. We also compared the number of connectome streamlines within specific white matter tracts using a GLM. Results: We found increased structural connectivity in migraine patients relative to healthy controls with a distinct involvement of cerebellar regions, using both parcellations. Specifically, the node degree of the posterior lobe of the cerebellum was greater in patients than in controls and patients presented a higher number of streamlines within the anterior limb of the internal capsule. Moreover, the connectomes of patients exhibited greater global efficiency and shorter characteristic path length, which correlated with the age onset of migraine. Conclusions: A distinctive pattern of heightened structural connectivity and enhanced global efficiency in migraine patients compared to controls was identified, which distinctively involves the cerebellum. These findings provide evidence for increased integration within structural brain networks in migraine and underscore the significance of the cerebellum in migraine pathophysiology.
- Uncovering longitudinal changes in the brain functional connectome along the migraine cycle: a multilevel clinical connectome fingerprinting frameworkPublication . Esteves, Inês; Fouto, Ana R.; Ruiz-Tagle, Amparo; Caetano, Gina; Nunes, Rita G.; Silva, Nuno A. da; Vilela, Pedro; Martins, Isabel Pavão; Gil-Gouveia, Raquel; Caballero-Gaudes, César; Figueiredo, PatríciaChanges in large-scale brain networks have been reported in migraine patients, but it remains unclear how these manifest in the various phases of the migraine cycle. Case-control fMRI studies spanning the entire migraine cycle are lacking, precluding a complete assessment of brain functional connectivity in migraine. Such studies are essential for understanding the inherent changes in the brain of migraine patients as well as transient changes along the cycle. Here, we leverage the concept of functional connectome (FC) fingerprinting, whereby individual subjects may be identified based on their FC, to investigate changes in FC and its stability across different phases of the migraine cycle.
- Zero-shot learning for clinical phenotyping: comparing LLMs and rule-based methodsPublication . Neves, Bernardo; Moreira, José Maria; Gonçalves, Simão; Cerejo, Jorge; Silva, Nuno A. da; Leite, Francisca; Silva, Mário J.Background: Phenotyping, the process of systematically identifying and classifying conditions within clinical data, is a crucial first step in any data science work involving Electronic Health Records (EHRs). Traditional approaches require extensive manual annotation efforts and face challenges with scalability. Methods: We investigated the use of Large Language Models (LLMs) for zero-shot phenotyping of 20 prevalent chronic conditions based on synthetic patient summaries generated from real structured EHRs codes. We evaluated the performance of multiple LLMs, including GPT-4o, GPT-3.5, and LLaMA 3 models with 8-billion, 70-billion, and 405-billion parameters, comparing them against traditional rule-based methods. For the analysis we used a dataset of 1,000 patients from Hospital da Luz Lisboa. Results: GPT-4o outperformed both traditional rule-based methods and alternative LLMs, achieving superior recall (0.97) and macro-F1 score (0.92). Rule-based phenotyping, while highly precise (0.92), showed lower recall (0.36). The integration of rule-based methods with LLMs optimized phenotyping accuracy by targeting manual annotation efforts on discordant cases. Conclusion: Zero-shot learning with LLMs, particularly GPT-4o, offers a powerful and efficient approach for phenotyping chronic conditions from EHRs, significantly reducing the need for extensive labeled datasets while maintaining high accuracy and interpretability.