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Poza, J.

Publications and source records attributed to Poza, J..

2 recordsLinked to original sources

Neurophysiological excitation/inhibition imbalance in young adults burdened with childhood interpersonal trauma

Adverse childhood experiences, such as violence, abuse, and neglect, are increasingly recognized as significant modifiers of brain development. Here, we tested whether adults with histories of childhood trauma, but without psychiatric comorbidities exhibit altered excitation/inhibition (E/I) balance, as indicated by electroencephalography (EEG) signatures. Participants, divided into low- trauma and high-trauma groups, underwent two experimental conditions: eyes-closed resting-state recording and a reaction-time task with visual stimuli. From these data, we computed 1/f spectral slopes, a widely used electrophysiological marker of E/I balance; we complemented these analyses with a leaky integrate-and-fire (LIF) microcircuit model combined with a biophysically grounded forward-modeling approach to simulate realistic brain signals and derive E/I balance estimates. Group comparisons for both slopes and E/I estimates revealed significant resting-state differences, characterized by a shift toward increased neuronal excitation in the high-trauma group. The high- trauma group exhibited altered stimulus-related 1/f slope dynamics relative to the pre-stimulus baseline, reflecting attenuated neuronal inhibition. E/I ratio measures were not significantly correlated with participants transient affective states. Together, these findings suggest that childhood trauma is associated with enduring, trait-like alterations in cortical E/I balance that extend beyond affective state and manifest across both resting and task-related brain dynamics. Significance StatementChildhood trauma is a major risk factor for mental illness, yet its lasting effects on basic brain physiology remain poorly understood. Using electroencephalography combined with biophysically grounded neural circuit modeling, we show that young adults with a history of childhood interpersonal trauma exhibit a persistent shift toward cortical hyperexcitation at rest and a reduced ability to engage inhibitory control during cognitive processing. These effects occur even in individuals without psychiatric diagnoses and are independent of current anxiety or depression, indicating a trait-like neurophysiological footprint of early adversity. This excitation-inhibition imbalance may represent a transdiagnostic vulnerability linking childhood trauma to later psychopathology.

neuroscience↗

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation reflect the underlying network topologies at group level for the MEG signals, but they fail to adapt to the individual differentiating patterns revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

bioinformatics↗