Brazilian Institute of Neuroscience and Neurotechnology

Background:Inner-Speech (IS) based Brain-Computer Interfaces (BCIs) offer potential communication solutions for individuals with disabilities by decoding brain signals generated during speech imagination. While most IS-BCI systems rely on time-frequency EEG features, this study investigates functional connectivity-specifically, motif synchronization (MS)-to determine whether interactions between brain regions improve the discrimination of imagined words.Methods: We analyzed Electroencephalography (EEG) data from the 'Thinking Out Loud' dataset by Nietoet al2022, involving 10 participants mentally simulating four Spanish words ('Up,' 'Down,' 'Left,' 'Right'). Connectivity matrices were derived using the MS method, and node metrics (strength, PageRank) were calculated across 11 frequency bands. A two-stage classification framework was implemented: a support vector machine (SVM) ranked features by discriminatory power, followed by 5-fold cross-validation to identify optimal feature combinations.Results:The proposed approach achieved an average classification accuracy of 43.7%, comparable to previously reported results on the same dataset.Conclusions:Motif synchronization-based functional connectivity and graph metrics provide informative features for imagined speech BCIs by capturing cross-regional brain interactions. Further work is needed to improve robustness and generalization across sessions and subjects.

Protein structure prediction, a fundamental challenge emerging from the protein folding problem, forms the basis of modern computational biology. This field addresses the critical question of how the amino acids sequence determines its three-dimensional structure, a relationship critical to understanding biological function. Over the last four decades, methodologies have evolved from template-based modeling (TBM) and free modeling (FM) to advanced hybrid and end-to-end deep learning approaches. TBM explores sequence homology and threading to predict structures based on a known template, while FM applies physics-based principles to navigate the rugged energy landscape that governs protein folding, predicting de novo stable native conformations. Recent breakthrough methods in protein structure prediction include hybrid methods that integrate physics, bioinformatics, and machine learning, as well as end-to-end methods such as AlphaFold2 and RoseTTAFold, which have revolutionized the field by using neural networks to directly predict atomic coordinates from sequences, achieving near-experimental accuracy. Protein language models further advance the field by learning sequence-structure-function relationships directly from amino acid sequences, bypassing the need for multiple-sequence alignments. These innovations address the sequence-structure-function paradigm and find applications in drug discovery, enzyme engineering, and disease research. This chapter explores the principles, advances, and transformative impact of these methodologies on the structural biology field.

ABSTRACTSchizophrenia spectrum disorders (SSD) are marked by cognitive deficits and clinical symptoms linked to disrupted neural oscillations. While changes in spectral power are well documented in SSD, many studies have not clearly separated rhythmic (periodic) from the nonrhythmic (aperiodic) brain activity. This study examined both periodic and aperiodic resting‐state EEG components in SSD, recorded from 152 healthy controls and 97 SSD participants. EEG features (periodic power, bandwidth, center frequency; aperiodic exponent and offset) were extracted from global scalp averages and frontoinsular regions, including the dorsal anterior cingulate cortex (dACC), right anterior insula (R‐INS), and left anterior insula (L‐INS). At the scalp level, SSD individuals exhibited a global increase in theta power, along with a decreased alpha center frequency. Aperiodic activity showed increased exponent and offset in SSD. In frontoinsular regions, increased theta power was observed in the dACC, R‐INS, and L‐INS, along with lower alpha center frequency in L‐INS. No significant differences were found for aperiodic activity in these regions. Increased frontoinsular theta power, especially in the dACC, was associated with worse cognitive performance, particularly global cognition and working memory. These findings highlight the importance of separating periodic and aperiodic EEG activity in SSD, suggesting that periodic alterations, particularly in frontoinsular theta oscillations, may underlie cognitive dysfunction in SSD.

Resting-state functional connectivity (rsFC) is a fundamental aspect of neuroscience and has been extensively researched, particularly for cognitive and clinical purposes. In the past, we developed methods to characterize rsFC networks using functional near-infrared spectroscopy (fNIRS). However, the question of whether fNIRS is sensitive to changes in rsFC as a function of age hasn’t been addressed yet. In this work, we examined the different connectivity profiles obtained with fNIRS during rest in 57 healthy participants. We divided the individuals into two groups: young (N=26, 18 to 30 yo) and older adults (N=31, 50 to 77 yo). Functional connectivity was assessed using Pearson correlation coefficients and graph theory metrics. Across the group, we observed significant differences in global network properties between young and old adults, with the latter group exhibiting higher connection density, leading to higher clustering and global efficiency. These results are consistent with other neuroimaging techniques (EEG, fMRI), reinforcing the ability of fNIRS to probe rsFC reliably.

Precision medicine (PM) stands on the brink of revolutionizing medical practice throughout the world, holding significant potential for enhancing patient outcomes. However, its practical implementation, particularly in resource-limited countries, is not without challenges. The success of PM largely hinges on the availability of extensive datasets, including genetic and genomic information. This paper delves into the PM landscape and the current state of genetic and genomic testing in Brazil. We also shed light on the unique challenges posed by the country's diverse population and discuss ongoing initiatives to tackle these obstacles.

Background: Bipolar disorder (BD) is a severe psychiatric illness characterized by a chronic course and recurrent episodes, including residual depressive symptoms even during euthymic phases. These symptoms, although not meeting criteria for a depressive episode, are linked to relapse risk and impaired social functioning. This study aims to assess whether Virtual Reality Cognitive Remediation Training reduces depressive symptoms below the clinical threshold in individuals with BD. Methods: This post hoc analysis focuses on the secondary outcome (PHQ9) of a randomized-controlled trial. Participants were recruited from the Center of Liaison Psychiatry and Psychosomatics in Italy. The experimental group received Virtual Reality Cognitive Remediation, while the control group received standard treatment Results: Data from 39 individuals in the experimental group and 25 in the control group were analyzed. A greater reduction in PHQ-9 scores (>9) was observed in the experimental group (71.8% to 48.7%) compared to the control group. Significant improvements in total PHQ-9 scores and specific symptoms were noted in the experimental group compared to the control group. Conclusions: The study highlights the significant impact of virtual reality intervention on reducing depressive symptoms in bipolar disorder. This promising outcome underscores the potential preventive role of cognitive stimulation in relapse prevention. The intervention could offer valuable benefits for both treatment and prevention strategies in bipolar disorder.