Background:From the clinical and neurobiological perspective of cortico subcortical loop concepts, Alzheimer's disease (AD) is associated with progressive deterioration of cognitive, emotional, and motor functions, which can be assessed subclinically using digital motion tracking technology in an electromagnetic digitizing tablet technology. Graphomotor analysis provides a source of sensitive digital biomarkers with potential applications in the early diagnosis and monitoring of AD. Methods:A case-control study was conducted including 72 patients with clinically diagnosed Alzheimer's disease (AD) and 44 cognitively healthy controls. Participants performed three graphomotor tasks (signature, free drawing, and the Trail Making Test A/B) using a digitizing tablet. Temporal, kinematic, and pressure-related parameters were recorded, including execution time, path length, mean and maximum velocity, pressure variability on the surface, and total harmonic distortion indices. Temporal measures reflected the duration and timing structure of task execution, kinematic measures described movement speed and variability, and pressure-related measures quantified the force applied by the pen and its stability during graphomotor performance. Statistical analysis was performed using the Mann-Whitney U test, and diagnostic accuracy was evaluated with receiver operating characteristic (ROC) analyses. Findings:Patients with Alzheimer's disease exhibited significantly longer execution times, reduced mean and maximum velocities, and greater variability of velocity and pressure compared with healthy controls. In the signature task, kinematic measures showed the strongest discriminatory power, whereas in the drawing task, pressure variability emerged as the most sensitive parameter. In the Trail Making Test A, patients with AD performed significantly worse than controls across temporal, kinematic, and pressure domains, and the majority were unable to complete part B. ROC analyses demonstrated that execution time in TMT A (AUC 0.79; 95% CI 0.71-0.86), maximum velocity in TMT B (0.77; 0.68-0.85), and pressure variability (0.78; 0.69-0.85) achieved the highest diagnostic discriminatory value. Interpretation:Digital analysis of biomechanical signals from graphomotor tasks indicates significantly reduced motor control and diminished visuospatial-executive functioning in individuals with Alzheimer's disease (AD) compared with healthy controls. Patients with AD demonstrated longer task execution times, decreased movement velocity, and greater variability of pressure relative to the control group. These findings suggest that tablet-derived graphomotor parameters may serve as candidate digital markers of motor and executive dysfunction in Alzheimer's disease. Further external validation and longitudinal studies are needed before their use as diagnostic biomarkers can be recommended.

Alcohol Use Disorder (AUD) is associated with widespread neurophysiological dysregulation, yet accessible and objective biomarkers for early risk identification remain limited. This preliminary study investigates electroencephalographic (EEG) frequency-domain and event-related potential features as candidate group-associated markers in AUD, with a focus on band power reduction and P300 event-related potential attenuation. Using the Begleiter EEG Database (UCI Machine Learning Repository, 1995), comprising 77 individuals with AUD and 45 healthy controls and recorded from 64 channels, we performed power spectral density estimation via Welch’s method and extracted band power across five frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz). Band power was computed at the trial level and averaged across trials at the subject level. Statistical comparisons using the Mann–Whitney U test with Benjamini–Hochberg FDR correction revealed significant band power reductions in the AUD group across frontal, central, and parietal channels, with the largest effects observed in parietal delta (P3: Hedges’ g=−0.97, q<0.0001; P4: g=−0.95, q<0.0001) and central theta (C4: g=−0.97, q<0.0001). P300 amplitude was significantly attenuated at parietal sites in S2 match (target) trials (Pz: g=−0.75, p=0.0002), consistent with hypothesised dopaminergic dysregulation of attentional processing networks. A Gaussian Naive Bayes classifier using the eight analysis channels selected for classification achieved a cross-validated ROC AUC = 0.713±0.088 [95% CI: 0.526, 0.833] under repeated stratified cross-validation, confirmed above chance by a permutation test (p=0.001). The complete analytical pipeline was executed on consumer-grade hardware without GPU acceleration, in line with Green AI principles. Topographic analysis indicated a spatially consistent parietal-dominant pattern of band power reduction. These findings suggest that task-evoked EEG frequency-domain and P300 features are systematically associated with AUD group membership and may warrant further investigation as candidate markers. The cross-sectional design of this study precludes conclusions regarding early risk identification, screening utility, or causal dopaminergic mechanisms; longitudinal validation in larger cohorts is required.

This paper presents a preliminary study delving into the application of machine learning-based methods for optimizing parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Oxidative stress observed in schizophrenia and other psychiatric disorders can induce neuronal damage and modulate intracellular signaling, ultimately leading to neuronal death by apoptosis or necrosis. The aim of this study was to estimate in vitro the possible antioxidant properties of curcumin, the natural polyphenolic antioxidant, and its protective effects against lipid peroxidation induced by the atypical antipsychotic Ziprasidone. Curcumin (5 µg/mL, 12.5 µg/mL, 25 µg/mL, 50 µg/mL) was added to human plasma and incubated for 1 and 24 h, alone and in the presence of Ziprasidone (40 ng/mL, 139 ng/mL, 250 ng/mL). Control plasma samples were incubated for 1 and 24 h. The concentration of thiobarbituric acid-reactive substances (TBARSs; lipid peroxidation marker) was determined by the spectrophotometric method according to Rice-Evans. Curcumin at the tested concentrations significantly inhibited lipid peroxidation in human plasma by about 60%. Ziprasidone (40 ng/mL, 139 ng/mL, 250 ng/mL) significantly increased TBARS levels, but in the presence of the studied curcumin concentrations, its pro-oxidative effects were reduced by about 56%. Our results confirm that Ziprasidone in vitro may induce lipid peroxidation in human plasma, whereas curcumin protects against lipid peroxidation in human plasma caused by the antipsychotic Ziprasidone.

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

Relationships between protective enzymatic and non-enzymatic pro-antioxidant mechanisms and addictive substances use disorders (SUDs) are analyzed here, based on the results of previous research, as well as on the basis of our current own studies. This review introduces new aspects of comparative analysis of associations of pro-antixidant and neurobiological effects in patients taking psychoactive substances and complements very limited knowledge about relationships with SUDs from different regions, mainly Europe. In view of the few studies on relations between antioxidants and neurobiological processes acting in patients taking psychoactive substances, this review is important from the point of view of showing the state of knowledge, directions of diagnosis and treatment, and further research needed explanation.We found significant correlations between chemical elements, pro-antioxidative mechanisms, and lipoperoxidation in the development of disorders associated with use of addictive substances, therefore elements that show most relations (Pr, Na, Mn, Y, Sc, La, Cr, Al, Ca, Sb, Cd, Pb, As, Hg, Ni) may be significant factors shaping SUDs. The action of pro-antioxidant defense and lipid peroxidation depends on the pro-antioxidative activity of ions. We explain the strongest correlations between Mg and Sb, and lipoperoxidation in addicts, which proves their stimulating effect on lipoperoxidation and on the induction of oxidative stress. We discussed which mechanisms and neurobiological processes change susceptibility to SUDs.The innovation of this review is to show that addicted people have lower activity of dismutases and peroxidases than healthy ones, which indicates disorders of antioxidant system and depletion of enzymes after long-term tolerance of stressors. We explain higher level of catalases, reductases, ceruloplasmin, bilirubin, retinol, α-tocopherol and uric acid of addicts.In view of poorly understood factors affecting addiction, analysis of interactions allows for more effective understanding of pathogenetic mechanisms leading to formation of addiction and development the initiation of directed, more effective treatment (pharmacological, hormonal) and may be helpful in the diagnosis of psychoactive changes.

Knowledge about determinants of addiction in people taking addictive substances is poor and needs to be supplemented. The novelty of this paper consists in the analysis of innovative aspects of current research about relationships between determinants of addiction in Polish patients taking addictive substances and rare available data regarding the relationships between these factors from studies from recent years from other environments, mainly in Europe, and on the development of genetic determinants of physiological responses. We try to explain the role of the microelements Mn, Fe, Cu, Co, Zn, Cr, Ni, Tl, Se, Al, B, Mo, V, Sn, Sb, Ag, Sr, and Ba, the toxic metals Cd, Hg, As, and Pb, and the rare earth elements Sc, La, Ce, Pr, Eu, Gd, and Nd as factors that may shape the development of addiction to addictive substances or drugs. The interactions between factors (gene polymorphism, especially ANKK1 (TaqI A), ANKK1 (Taq1 A-CT), DRD2 (TaqI B, DRD2 Taq1 B-GA, DRD2 Taq1 B-AA, DRD2-141C Ins/Del), and OPRM1 (A118G)) in patients addicted to addictive substances and consumption of vegetables, consumption of dairy products, exposure to harmful factors, and their relationships with physiological responses, which confirm the importance of internal factors as determinants of addiction, are analyzed, taking into account gender and region. The innovation of this review is to show that the homozygous TT mutant of the ANKK1 TaqI A polymorphism rs 1800497 may be a factor in increased risk of opioid dependence. We identify a variation in the functioning of the immune system in addicted patients from different environments as a result of the interaction of polymorphisms.

Huntington's disease (HD) is a rare, incurable neurodegenerative disorder where fast and non-invasive diagnosis targeting patients' condition plays a crucial role. In modern medicine, various scientific areas are being combined, such as computing, medicine and biomedical engineering. This survey is focused on the most recent image processing methods applied not only for the purpose of diagnosing HD but also for the assessment of its progression severity, in order to contribute to the effort to prolong life of and to improve its quality.

Long-acting buprenorphine formulations have been recently marketed for the Opioid Agonist Treatment (OAT) of opioid use disorder (OUD) associated with medical, social, and psychological support. Their duration of action ranges from one week up to 6 months. The non-medical use of opioids is increasing with a parallel rise in lethal overdoses. Methadone and buprenorphine are the standard treatment for opioid dependence. Methadone Maintenance Treatment (MMT) is widely recognized as one of the most effective ways of reducing the risks of overdose, crime, and transmission of HIV (Human Immunodeficiency Virus) in people who use opioids; however, its effectiveness has been hindered by low rates of uptake and retention in treatment. Furthermore, both methadone and buprenorphine are widely diverted and misused. Thus, a crucial aspect of treating OUD is facilitating patients’ access to treatment while minimizing substance-related harm and improving quality of life. The newly developed long-acting buprenorphine formulations represent a significant change in the paradigm of OUD treatment, allowing an approach individualized to patients’ needs. Strengths of this individualized approach are improved adherence (lack of peaks and troughs in blood concentrations) and a reduced stigma since the patient doesn’t need to attend their clinic daily or nearly daily, thus facilitating social and occupational integrations as the quality of life. However, less frequent attendance at the clinic should not affect the patient–physician relationship. Therefore, teleconsulting or digital therapeutic services should be developed in parallel. In addition, diversion and intravenous misuse of buprenorphine are unlikely due to the characteristics of these formulations. These features make this approach of interest for treating OUD in particular settings, such as subjects staying or when released from prison or those receiving long-term residential treatment for OUD in the therapeutic communities. The long-lasting formulations of buprenorphine can positively impact the OUD treatment and suggest future medical and logistic developments to maximize their personalized management and impact.

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

We can obtain valuable information about the human brain using functional Near Infrared Spectroscopy (fNIRS). This paper describes the theoretical basis associated with this neuroimaging method through a custom-made prototype of a single-channel fNIRS device. The optodes were soldered to a milled Printed Circuit Board (PCB) and enclosed in a 3D printed housing. Using this fNIRS device, we performed a preliminary study to measure emotional responses from participants. Our results suggest that fNIRS allows for accurate measurement of emotions evoked by positive and negative images.

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

The main aim of this work was to determine the impact of COMT and DRD2 gene polymorphisms together with temperament and character traits on alcohol craving severity alcohol-dependent persons. The sample comprised of 89 men and 16 women (aged 38±7). For the sake of psychological assessment various analytic methods have been applied like the Short Alcohol Dependence Data Questionnaire (SADD), Penn Alcohol Craving Scale (PACS) or Temperament and Character Inventory (TCI) test. The SNP polymorphism of the analyzed genes was determined by Real Time PCR test. The results showed, that the COMT polymorphismmay have an indirected relationship with the intensity and changes in alcohol craving during abstinence. The DRD2 receptor gene polymorphisms are related with the intensity of alcohol craving. It seems that the character traits like “self-targeting”, including “self-acceptance”, are more closely related to the severity of alcohol craving and polymorphic changes in the DRD2 receptor than temperamental traits. Although this is a pilot study the obtained results appeared to be promising and clearly indicate the link betweengene polymorphisms alcohol craving and its severity.

Evidence suggests that both opioid addicted and gambling addicted individuals are characterized by higher levels of risky behavior in comparison to healthy people. It has been shown that the administration of substitution drugs can reduce cravings for opioids and the risky decisions made by individuals addicted to opioids. Although it is suggested that the neurobiological foundations of addiction are similar, it is possible that risk behaviors in opioid addicts may differ in detail from those addicted to gambling. The aim of this work was to compare the level of risk behavior in individuals addicted to opioid, with that of individuals addicted to gambling, using the Iowa Gambling Task (IGT). The score and response time during the task were measured. It was also observed, in the basis of the whole IGT test, that individuals addicted to gambling make riskier decisions in comparison to healthy individuals from the control group but less riskier decisions in comparison to individuals addicted to opioids, before administration of methadone and without any statistically significant difference after administration of methadone—as there has been growing evidence that methadone administration is strongly associated with a significant decrease in risky behavior.

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

The aim of this paper is to analyze the neurobiology of addiction and consider if it can contribute to a better understanding of free will. In the first part, we study the role of cortico-subcortical loops mechanism in addiction. Empirical data show that addiction leads to the structural remodeling of the striatum. These changes making the striatum more difficult to inhibit by the neocortex, which disrupts the ability to make decisions. In the second part, we introduce six criteria of voluntary abilities (based on a literature review) and integrate this view with cortico-subcortical loops conception. We suggest that the cortico-subcortical loops mechanism can play a relevant role in inducing spontaneous actions.