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Valderrama, C. E.

Publications and source records attributed to Valderrama, C. E..

2 recordsLinked to original sources
bioRxiv2025

Genetic Interaction of APOE-E-4 and PICALM rs3851179 is Associated With Dynamic Functional Connectivity in Healthy Middle-Aged Adults at Risk for Alzheimer's Disease

ObjectiveThis study investigates whether dynamic functional connectivity (dFC) dwell-time patterns derived from resting-state fMRI (rs-fMRI) can distinguish Alzheimers disease (AD) genetic risk profiles, specifically the APOE-{varepsilon}4 (A+) and PICALM rs3851179 (P+) variants, in cognitively healthy, middle-aged adults. ApproachWe estimated recurring dFC clusters from rs-fMRI data and quantified the dwell-time (total duration spent in specific connectivity states) for three cohorts: not-at-risk, A+P-, and A+P+. To evaluate the utility of these temporal features, group differences in dwell-time profiles were assessed, and logistic regression with permutation testing was employed to classify genotypes based on dFC patterns. Main resultsIndividuals in at-risk groups (A+P- and A+P+) exhibited significantly reduced dwell-time in left-hemisphere hubs compared to the not-at-risk group, aligning with known left-hemisphere vulnerability in early AD progression. The logistic regression models achieved above-chance discrimination of genotypes, with permutation tests confirming a significant trend when distinguishing not-at-risk individuals from the combined at-risk cohorts. SignificanceThese findings suggest that the temporal dFC features are sensitive to subtle functional brain alterations linked to AD genetic risk before clinical symptoms appear. Dwell-time features represent a promising physiological marker for early risk stratification and warrant further validation in larger longitudinal datasets. Our code is available at https://github.com/Shyamal-Dharia/APOE-PICALM-dFC-dwell-time.git.

neuroscience↗
bioRxiv2025

Automatic detection of nociceptive pain levels using frequency bands from electroencephalographic (EEG) signals

Pain is considered an unpleasant but vital experience for every living being, it is extremely complex and subjective because it is composed of different variables related to their experiences. Their history, biological sex, socio/cultural context, mood, and hormonal changes can affect their perception. Nociceptive pain is more linked to tissue damage or stimulus, and this will always have a reaction that goes from its activation in the nociceptors in the peripheral nerves of the living being to the central nervous system. The most common way to assess pain today is to apply numerical scales or "How much pain do you feel?" questionnaires, which are usually falsifiable and unreliable. Therefore, this work seeks to make use of biosignals such as Electroencephalography (EEG) to identify and evaluate pain at different levels. This nociceptive pain is generated by applying a laser on the back of the hand, which consists of three different intensities. It has been possible to differentiate between two levels of pain (high pain and low pain) with 83% accuracy using information from the power of frequency bands of the brain signal. Indicating that there are differences in the powers of the frequency bands as pain increases.

neuroscience↗