Alessandro Stefani

Correction: The body does not keep the score: trauma, predictive coding, and the restoration of metastability

Steven Kotler, Michael Mannino, Glenn Fox, Karl Friston

Memory formation as selection: an eligibility-based architecture

Athanasios Rizos

Pharmacologically altered androgens, brain activation and response inhibition in a stop-signal task in male heavy drinkers

Marlene Kundlacz, Rafat Boroumand-Jazi, Sabine Hoffmann, Christiane Mühle, Iris Reinhard, Matthias Reichl, Leonard Wenger, Haoye Tan, Patrick Bach, Sabine Vollstädt-Klein, Georg W. Alpers, Falk Kiefer, Bernd Lenz, Sarah Gerhardt

Heavy episodic drinking (HED) is highly prevalent in men and a major risk factor for Alcohol Use Disorder (AUD). Previous studies indicated an influence of testosterone (T) on AUD, and dihydrotestosterone (DHT) was linked to alcohol consumption, impulsivity, and response inhibition. As the role of androgens in AUD and the modulation thereof is not fully understood, this study investigated the effect of a pharmacological reduction in DHT by inhibition of the 5-alpha reductase II with finasteride on brain activation and response inhibition during a stop signal task. Fifty males with HED participated in a randomized, placebo-controlled, double blind, cross-over, functional magnetic resonance imaging (fMRI) study. Participants received either 5 mg finasteride or placebo, resulting in a 9.6% reduction of DHT levels. Full factorial fMRI models and linear mixed models were used to estimate whole-brain effects of DHT and testosterone following finasteride and placebo on neural correlates of behavioral control ( N = 47 analyzable participants). While no medication effects were observed on behavioral or neural levels, lower levels of DHT and higher levels of testosterone were related to stronger neural activation in temporal regions during response inhibition for successful or unsuccessful stop-trails, respectively. However, no significant influence of hormone levels on behavioral data of the stop signal task was observed. Although the precise mechanisms underlying the effects of pharmacologically altered DHT levels on neural correlates of response inhibition remain unresolved, both DHT and testosterone appear to be specifically associated with neural activation during response inhibition. These preliminary findings may provide a rational for future studies investigating pharmaceutical modulation of androgens as a potential therapeutic approach for problem drinking, and potentially AUD. https://drks.de/search/de/trial/DRKS00020569 , identifier DRKS00020569.

A biophysical framework for conscious state transitions based on intrinsic neuronal properties: the dynamic contextual element memory (DCEM) model

Takayoshi Tsubo

Unlike artificial systems that execute externally specified commands, the brain constructs contextual representations to guide behaviors that satisfy innate and learned desires generated by the Affective Valuation System (AVS). However, the neural mechanisms by which contextual representations are formed, selected, maintained, and replaced to generate conscious state transitions remain unknown. Here, we propose the Dynamic Contextual Element Memory (DCEM) model as a biophysical framework within the dorsolateral prefrontal cortex. The DCEM is conceived as a dynamic contextual memory consisting of numerous logical Contextual Elements (CEs), each implemented by multiple physical cortical columns. AVS signals initiate contextual exploration in the DCEM and guide contextual selection, maintenance, and transition. Recurrent interactions between the DCEM and the parietal and temporal association cortices generate γ-band contextual candidates from sensory and memory information. Physical columns of candidates selected by Contextual Element Decision Neurons (CEDeN) in the mediodorsal thalamus receive synchronized feedback and form a coherent β-band Contextual Element Loop (CE Loop) representing a single logical Contextual Element. Outputs from DCEM Layer 5 redistribute the selected context throughout the association cortices, allowing AVS to shape candidates. CE Loops that lose CEDeN support following re-evaluation of behavioral outcomes or new information fail to maintain synchrony and disappear. We therefore propose that conscious state transitions occur through the formation, maintenance, disappearance, and replacement of CE Loops. This model was naturally derived by extending the intrinsic biophysical properties of neurons identified in our studies to cortical networks while integrating accumulated findings from modern observational techniques, neuroanatomy, neurophysiology, and neuroscience.

Music-evoked EEG signals for mental health assessment using two- and three-class classification

Abdelkader Nasreddine Belkacem, Abdelhadi Hireche, Nour Faris Ali, Mohamed Atef, Abdalla A. R. M. Hamid, Ibrahim M. Elfadel, Herbert F. Jelinek

Electroencephalography (EEG), a non-invasive method of measuring brain activity, has become a valuable tool in mental health research for examining neural activity associated with various psychological disorders and neurofeedback therapies, including music therapy. However, the use of EEG under music and no-music conditions for mental health screening remains insufficiently validated. In this preliminary study, we implemented an experimental protocol in which 48 participants underwent EEG recordings during both music and no-music sessions with identical relaxation instructions. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) scale were also included. Five complementary EEG features were extracted for each mental fatigue class: power spectral density (PSD) across different frequency bands, band powers, alpha-theta ratio, theta-beta ratio, and channel coherence. Binary classification (mental fatigue) and multi-output binary classification (anxiety, depression, and stress components) were implemented using a deep residual multi-layer perceptron (MLP), a feedforward neural network with residual skip connections. Leave-Subject-Out cross-validation ensured robust generalization, with all metrics computed at the subject level rather than epoch level. For binary classification, the model achieved 72.92% accuracy in the music condition and 58.33% accuracy in the no-music condition, with notably high recall (96.67%) in the music condition. Multi-output classification in the music condition showed stress with high accuracy (93.75%) but low AUC (35.56%), reflecting the high prevalence of the stressed class (85.7%), while anxiety and depression presented greater challenges (54.17% and 62.50% accuracy, respectively). These preliminary results suggest that music may modulate brain activity patterns in ways that enhance the discriminability of mental fatigue states, although the fixed condition order means order and habituation effects cannot be ruled out.