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Advancements in Neurotechnology: Pioneering Brain Monitoring and Stimulation for Enhanced Treatment and Understanding.
ACS Med Chem Lett
January 2025
Usona Institute, Fitchburg, Wisconsin 53711-5300, United States.
This Patent Highlight explores recent innovations in neuroscience and neurotechnology, particularly in brain monitoring and stimulation. It examines four essential patents: novel psychoplastogens for neuronal growth, techniques for transferring emotional states, and advanced systems for self-guided neural diagnostics and treatment. The discussion extends to deep brain stimulation (DBS) for motor and memory disorders, enhanced brain function monitoring through electroencephalography (EEG), and the role of artificial intelligence in personalizing treatment strategies.
View Article and Find Full Text PDFInfluence of early-life stress on hippocampal synaptic and network properties.
Front Neural Circuits
January 2025
Federal Center of Brain Research and Neurotechnologies, Moscow, Russia.
According to the World Health Organization, the number of people suffering from depressive disorders worldwide is approaching 350 million. The consequences of depressive disorders include considerable worsening of the quality of life, which frequently leads to social isolation. One of the key factors which may cause depression in adulthood is early life stress, in particular, insufficient maternal care during infancy.
View Article and Find Full Text PDFAdvanced AI techniques for classifying Alzheimer's disease and mild cognitive impairment.
Front Aging Neurosci
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Service de Chimie Clinique CHUV, Lausanne, Switzerland.
Background: Alzheimer's disease and mild cognitive impairment are often difficult to differentiate due to their progressive nature and overlapping symptoms. The lack of reliable biomarkers further complicates early diagnosis. As the global population ages, the incidence of cognitive disorders increases, making the need for accurate diagnosis critical.
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Machine learning classification meets migraine: recommendations for study evaluation.
J Headache Pain
December 2024
Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino ICOT, Latina, Italy.
The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility.
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