Aberrant brain language network in schizophrenia spectrum disorder: a systematic review of its relation to language signs beyond symptoms.

Background: Language disturbances are a core feature of schizophrenia, often studied as a formal thought disorder. The neurobiology of language in schizophrenia has been addressed within the same framework, that language and thought are equivalents considering symptoms and not signs. This review aims to systematically examine published peer-reviewed studies that employed neuroimaging techniques to investigate aberrant brain-language networks in individuals with schizophrenia in relation to linguistic signs. Methods: We employed a language model for automatic data extraction. We selected our studies according to the PRISMA recommendations, and we conducted the quality assessment of the selected studies according to the STROBE guidance. Results: We analyzed the findings from 37 studies, categorizing them based on patient characteristics, brain measures, and language task types. The inferior frontal gyrus (IFG) and superior temporal gyrus (STG) exhibited the most significant differences among these studies and paradigms. Conclusions: We propose guidelines for future research in this field based on our analysis. It is crucial to investigate larger networks involved in language processing, and language models with brain metrics must be integrated to enhance our understanding of the relationship between language and brain abnormalities in schizophrenia.

Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Exposure to high-altitude hypoxia, with reduced oxygen levels, can replicate brain function changes akin to aging and Alzheimer's disease. In our work, we propose high-altitude hypoxia as a possible reversible model of human brain aging. We gathered EEG data at high altitude and sea level, investigating the impact of hypoxia on brainwave patterns and connectivity. Our findings revealed that high-altitude exposure led to slower and noisier brain oscillations and produced altered brain connectivity, resembling some remarkable changes seen in the aging process. Intriguingly, these changes were linked to age, even when hypoxia's effects were considered. Our research unveils how high-altitude conditions emulate brain patterns associated with aging and neurodegenerative conditions, providing valuable insights into the understanding of both normal and impaired brain function.

The effect of a cognitive training therapy based on stimulation of brain oscillations in patients with mild cognitive impairment in a Chilean sample: study protocol for a phase IIb, 2 × 3 mixed factorial, double-blind randomised controlled trial.

BACKGROUND: The ageing population has increased the prevalence of disabling and high-cost diseases, such as dementia and mild cognitive impairment (MCI). The latter can be considered a prodromal phase of some dementias and a critical stage for interventions to postpone the impairment of functionality. Working memory (WM) is a pivotal cognitive function, representing the fundamental element of executive functions. This project proposes an intervention protocol to enhance WM in these users, combining cognitive training with transcranial electrical stimulation of alternating current (tACS). This technique has been suggested to enhance the neuronal plasticity needed for cognitive processes involving oscillatory patterns. WM stands to benefit significantly from this approach, given its well-defined electrophysiological oscillations. Therefore, tACS could potentially boost WM in patients with neurodegenerative diseases. METHODS: This study is a phase IIb randomised, double-blind clinical trial with a 3-month follow-up period. The study participants will be 62 participants diagnosed with MCI, aged over 60, from Valparaíso, Chile. Participants will receive an intervention combining twelve cognitive training sessions with tACS. Participants will receive either tACS or placebo stimulation in eight out of twelve training sessions. Sessions will occur twice weekly over 6 weeks. The primary outcomes will be electroencephalographic measurements through the prefrontal theta oscillatory activity, while the secondary effects will be cognitive assessments of WM. The participants will be evaluated before, immediately after, and 3 months after the end of the intervention. DISCUSSION: The outcomes of this trial will add empirical evidence about the benefits and feasibility of an intervention that combines cognitive training with non-invasive brain stimulation. The objective is to contribute tools for optimal cognitive treatment in patients with MCI. To enhance WM capacity, postpone the impairment of functionality, and obtain a better quality of life. TRIAL REGISTRATION: ClinicalTrials.gov NCT05291208. Registered on 28 February 2022. ISRCTN87597719 retrospectively registered on 15 September 2023.

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization.

Introduction: Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods: We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results: Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion: Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.