Band 35, Heft 2

Alzheimer’s disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71–0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.

Breathing is a natural daily action that one cannot do without, and it sensitively and intensely changes under various situations. What if this essential act of breathing can impact our overall well-being? Recent studies have demonstrated that breathing oscillations couple with higher brain functions, i.e., perception, motor actions, and cognition. Moreover, the timing of breathing, a phase transition from exhalation to inhalation, modulates specific cortical activity and accuracy in cognitive tasks. To determine possible respiratory roles in attentional and memory processes and functional neural networks, we discussed how breathing interacts with the brain that are measured by electrophysiology and functional neuroimaging: (i) respiration-dependent modulation of mental health and cognition; (ii) respiratory rhythm generation and respiratory pontomedullary networks in the brainstem; (iii) respiration-dependent effects on specific brainstem regions and functional neural networks (e.g., glutamatergic PreBötzinger complex neurons, GABAergic parafacial neurons, adrenergic C1 neurons, parabrachial nucleus, locus coeruleus, temporoparietal junction, default-mode network, ventral attention network, and cingulo-opercular salience network); and (iv) a potential application of breathing manipulation in mental health care. These outlines and considerations of “brain–breath” interactions lead to a better understanding of the interoceptive and cognitive mechanisms that underlie brain–body interactions in health conditions and in stress-related and neuropsychiatric disorders.

Glioblastoma multiform (GBM) is the most common primary brain tumor with a poor prognosis and few therapeutic choices. In vivo, tumor models are useful for enhancing knowledge of underlying GBM pathology and developing more effective therapies/agents at the preclinical level, as they recapitulate human brain tumors. The C6 glioma cell line has been one of the most widely used cell lines in neuro-oncology research as they produce tumors that share the most similarities with human GBM regarding genetic, invasion, and expansion profiles and characteristics. This review provides an overview of the distinctive features and the different animal models produced by the C6 cell line. We also highlight specific applications of various C6 in vivo models according to the purpose of the study and offer some technical notes for more convenient/repeatable modeling. This work also includes novel findings discovered in our laboratory, which would further enhance the feasibility of the model in preclinical GBM investigations.

Social anxiety disorder (SAD) is a debilitating disorder, characterized by fear and anxiety in social situations. Evidence suggests that the levels of SAD are rising, in particularly after the COVID-19 pandemic. Serotonin and noradrenaline reuptake inhibitors and cognitive-behavioral therapy are effective treatments for SAD. Nevertheless, a significant number of patients do not respond well to these therapeutic options. During the last years, Cannabis and cannabinoid-containing products have been investigated for the treatment of different neuropsychiatric disorders. Nevertheless, their efficacy for the treatment of anxiety disorders is still a matter of debate. The purpose of this review was to investigate subjective, behavioral, and neurobiological effects of Cannabis and cannabinoids in social anxiety and SAD. A search in the PubMed database for articles published between the years of 2003–2023 was conducted. One hundred and seventeen (117) original studies were identified. After the exclusion criteria, eighteen (18) studies were selected. The studies investigated the effects of the cannabinoids Δ 9 -tetrahydrocannabinol (THC) and cannabidiol (CBD) in patients or healthy volunteers submitted to tasks that assessed social anxiety. Results showed that CBD decreases social anxiety, producing an inverted U-shaped curve, with anxiety measurements being reduced at intermediate doses administered orally (300–600 mg), but not at lower or higher doses. THC either reduces (lower doses, 6–7.5 mg) or increases (higher doses) social anxiety measurements. CBD attenuates the anxiogenic effects of THC. The effects of THC and CBD in anxiety are associated to the modulation of fronto-limbic regions. Further clinical trials, conducted with male and female patients and larger cohorts are still necessary to consolidate these results.

In this systematic review, we address the status of intracortical brain-computer interfaces (iBCIs) applied to the motor cortex to improve function in patients with impaired motor ability. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for Systematic Reviews. Risk Of Bias In Non-randomized Studies – of Interventions (ROBINS-I) and the Effective Public Health Practice Project (EPHPP) were used to assess bias and quality. Advances in iBCIs in the last two decades demonstrated the use of iBCI to activate limbs for functional tasks, achieve neural typing for communication, and other applications. However, the inconsistency of performance metrics employed by these studies suggests the need for standardization. Each study was a pilot clinical trial consisting of 1–4, majority male (64.28 %) participants, with most trials featuring participants treated for more than 12 months (55.55 %). The systems treated patients with various conditions: amyotrophic lateral sclerosis, stroke, spinocerebellar degeneration without cerebellar involvement, and spinal cord injury. All participants presented with tetraplegia at implantation and were implanted with microelectrode arrays via pneumatic insertion, with nearly all electrode locations solely at the precentral gyrus of the motor cortex (88.88 %). The development of iBCI devices using neural signals from the motor cortex to improve motor-impaired patients has enhanced the ability of these systems to return ability to their users. However, many milestones remain before these devices can prove their feasibility for recovery. This review summarizes the achievements and shortfalls of these systems and their respective trials.

Even though the number of studies aiming to improve comprehension of ADHD pathology has increased in recent years, there still is an urgent need for more effective studies, particularly in understanding adult ADHD, both at preclinical and clinical levels, due to the increasing evidence that adult ADHD is highly distinct and a different entity from childhood ADHD. This review paper outlines the symptoms, diagnostics, and neurobiological mechanisms of ADHD, with emphasis on how adult ADHD could be different from childhood-onset. Data show a difference in the environmental, genetic, epigenetic, and brain structural changes, when combined, could greatly impact the behavioral presentations and the severity of ADHD in adults. Furthermore, a crucial aspect in the quest to fully understand this disorder could be through longitudinal analysis. In this way, we will determine if and how the pathology and pharmacology of ADHD change with age. This goal could revolutionize our understanding of the disorder and address the weaknesses in the current clinical classification systems, improving the characterization and validity of ADHD diagnosis, specifically those in adults.