Memory load effect in auditory-verbal short-term memory task: EEG fractal and spectral analysis.

The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

Exploring histone deacetylases in type 2 diabetes mellitus: pathophysiological insights and therapeutic avenues.

Diabetes mellitus is a chronic disease that impairs metabolism, and its prevalence has reached an epidemic proportion globally. Most people affected are with type 2 diabetes mellitus (T2DM), which is caused by a decline in the numbers or functioning of pancreatic endocrine islet cells, specifically the ß-cells that release insulin in sufficient quantity to overcome any insulin resistance of the metabolic tissues. Genetic and epigenetic factors have been implicated as the main contributors to the T2DM. Epigenetic modifiers, histone deacetylases (HDACs), are enzymes that remove acetyl groups from histones and play an important role in a variety of molecular processes, including pancreatic cell destiny, insulin release, insulin production, insulin signalling, and glucose metabolism. HDACs also govern other regulatory processes related to diabetes, such as oxidative stress, inflammation, apoptosis, and fibrosis, revealed by network and functional analysis. This review explains the current understanding of the function of HDACs in diabetic pathophysiology, the inhibitory role of various HDAC inhibitors (HDACi), and their functional importance as biomarkers and possible therapeutic targets for T2DM. While their role in T2DM is still emerging, a better understanding of the role of HDACi may be relevant in improving insulin sensitivity, protecting ß-cells and reducing T2DM-associated complications, among others.

Cross-cultural differences through subjective cognition: illustration in translatology with the SSTIC-E in the UAE.

The development of appropriate and valid multicultural and multilingual instruments research is necessary due to a growing multicultural and multilingual society in the 21st century. We explored the use of a cognitive scale related to subjective complaints, focusing on the first step: a cross-cultural and semantic validation. This study presents the translation and cross-validation process of the "Subjective Scale to Investigate Cognition in Schizophrenia" (SSTICS) for the United Arab Emirates (UAE) region via different languages used in Dubaï/Abu Dhabi. This scale measures cognitive complaints and has been validated with psychosis and used in 20 clinical trials worldwide. It evaluates areas of the illness related to self-awareness focusing on memory dysfunction and deficits of attention, language, and praxis. We described the method of cross-cultural validation, with back-translation, semantic steps, and societal contexts. The use of the Subjective Scale to Investigate Cognition in Emirates (SSTIC-E) was explored with different samples of UAE Arabic-speaking subjects. First, a pilot sample mean SSTICS total score was 16.5 (SD:16.9); (p < 0.001). The SSTIC-E was then administered to 126 patients and 84 healthy control participants. The healthy group has a lower mean score of 22.55 (SD = 12.04) vs. 34.06 (SD = 15.19). The method was extended to nine other languages, namely, Pakistani/Urdu, Hindi, Marathi, Lithuanian, Serbian, German, Romanian, Sinhala, and Russian. The scales are provided in the article. The overall aim of the translation process should be to stay close to the original version of the instrument so that it is meaningful and easily understood by the target language population. However, for construct validity, some items must be adapted at the time of translation to ensure that the questioned cognitive domain is respected. For example, cooking, an executive function, does not have the same occurrence for an Emirati male, or remembering a prime minister's name, semantic memory, requires an electoral system to appoint the leader of a country. Translation methods and processes present many challenges but applying relevant and creative strategies to reduce errors is essential to achieve semantic validation. This study aims to measure personally experienced knowledge or attitudes; such language effects can be a thorny problem.

Risk of sexual dysfunctions in breastfeeding females: protocol for a systematic review and meta-analysis.

BACKGROUND: Epidemiological studies do not provide accurate statistics on the percentage of breastfeeding women experiencing sexual dysfunctions and restraining from sexual activity. The data vary between 40% and 83% in the first group and 20-50% in the second one. Despite excessive studies on contributors to intimacy changes, breast feeding received little attention from researchers. The relationship between lactation and postpartum sexual dysfunctions remains unclear. This systematic review and meta-analysis will synthesise available data and establish the link between breast feeding and sexuality problems. METHODS AND ANALYSIS: A comprehensive literature search will be performed in biomedical databases PubMed/Medline, Scopus, Web of Science, EMBASE and CINAHL. We will extract peer-reviewed original studies written in English, Arabic or Polish from 2000 to June 2023. We will also search for reports from international health organisations and local health authorities. The preliminary search was performed on 04 April 2023. The studies must provide data on dysfunction prevalence/incidence and the strength of the relationship between breast feeding and sexuality in generally healthy women. The Covidence software will be used to perform literature screening, data extraction and quality assessment of individual studies. We will use a random-effects model meta-analysis to calculate pooled weighted frequency measures and effect size. Between-study heterogeneity will be assessed with the I2 test. ETHICS AND DISSEMINATION: This meta-analysis does not require ethical approval because it synthesises data from previously published original studies. The final work will be published in a peer-reviewed journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42023411053.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Molecular hallmarks of long non-coding RNAs in aging and its significant effect on aging-associated diseases.

Aging is linked to the deterioration of many physical and cognitive abilities and is the leading risk factor for Alzheimer's disease. The growing aging population is a significant healthcare problem globally that researchers must investigate to better understand the underlying aging processes. Advances in microarrays and sequencing techniques have resulted in deeper analyses of diverse essential genomes (e.g., mouse, human, and rat) and their corresponding cell types, their organ-specific transcriptomes, and the tissue involved in aging. Traditional gene controllers such as DNA- and RNA-binding proteins significantly influence such programs, causing the need to sort out long non-coding RNAs, a new class of powerful gene regulatory elements. However, their functional significance in the aging process and senescence has yet to be investigated and identified. Several recent researchers have associated the initiation and development of senescence and aging in mammals with several well-reported and novel long non-coding RNAs. In this review article, we identified and analyzed the evolving functions of long non-coding RNAs in cellular processes, including cellular senescence, aging, and age-related pathogenesis, which are the major hallmarks of long non-coding RNAs in aging.

Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond.

BACKGROUND: Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age. OBJECTIVE: The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations. METHODS: We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology. RESULTS: (1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear. CONCLUSIONS: Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis.

BACKGROUND: The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. METHODS AND ANALYSIS: We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. ETHICS AND DISSEMINATION: The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42022354179.

Neuroimaging findings in a case of fluoxetine overdose.

Brain MRI and ¹8F-FDG PET/CT scans were performed in a patient who had survived a suicide attempt by fluoxetine overdose. The patient presented with the following clinical signs and symptoms, and neuroimaging findings: severe signs of serotonin toxicity, including comatose state, akinetic rigid syndrome and dysautonomia; bilateral globus pallidus changes consistent with extensive pallidal necrosis and subsequent reversible diffuse ischemic changes in white matter, with posterior predominance, involving the splenium of the corpus callosum on brain MRI; and marked hypometabolism in the frontal, parietal and temporal cortical regions as well as in both caudate nuclei on ¹8F-FDG PET/CT performed 37 days later. These findings suggest that acute severe serotonin toxicity can induce structural and long-standing functional changes in multiple cortical and subcortical brain regions that are associated with cognitive and extrapyramidal syndromes.

Brain Data in Pediatric Disorders of Consciousness: Special Considerations.

SUMMARY: The diagnosis and management of disorders of consciousness in children continue to present a clinical, research, and ethical challenge. Though the practice guidelines for diagnosis and management of disorders of consciousness in adults are supported by decades of empirical and pragmatic evidence, similar guidelines for infants and children are lacking. The maturing conscious experience and the limited behavioral repertoire to report consciousness in this age group restrict extrapolation from the adult literature. Equally challenging is the process of heightened structural and functional neuroplasticity in the developing brain, which adds a layer of complexity to the investigation of the neural correlates of consciousness in infants and children. This review discusses the clinical assessment of pediatric disorders of consciousness and delineates the diagnostic and prognostic utility of neurophysiological and neuroimaging correlates of consciousness. The potential relevance of these correlates for the developing brain based on existing theoretical models of consciousness in adults is outlined.

The effects of prefrontal vs. parietal cortex transcranial direct current stimulation on craving, inhibition, and measures of self-esteem.

While prefrontal cortex dysfunction has been implicated in high food cravings, other cortical regions, like the parietal cortex, are potentially also involved in regulating craving. This study explored the effects of stimulating the inferior parietal lobule (IPL) and dorsolateral prefrontal cortex (DLPFC) on food craving state and trait. Transcranial direct current stimulation (tDCS) was administered at 1.5 mA for 5 consecutive days. Participants received 20 min of IPL, DLPFC, or sham stimulation (SHAM) each day which consisted of two rounds of 10-min stimulation, divided by a 10-min mindfulness task break. In addition, we studied inhibition and subjective psychological aspects like body image and self-esteem state and trait. To decompose immediate and cumulative effects, we measured the following on days 1 and 5: inhibition through the Go/No-go task; and food craving, self-esteem, and body appreciation through a battery of questionnaires. We found that false alarm errors decreased in the participants receiving active stimulation in the DLPFC (DLPFC-group). In contrast, false alarm errors increased in participants receiving active stimulation in the IPL (IPL-group). At the same time, no change was found in the participants receiving SHAM (SHAM-group). There was a trending reduction in craving trait in all groups. Momentary craving was decreased in the DLPFC-group and increased in IPL-group, yet a statistical difference was not reached. According to time and baseline, self-esteem and body perception improved in the IPL-group. Furthermore, self-esteem trait significantly improved over time in the DLPFC-group and IPL-group. These preliminary results indicate that tDCS modulates inhibition in frontoparietal areas with opposite effects, enhancing it in DLPFC and impairing it in IPL. Moreover, craving is moderately linked to inhibition, self-esteem, and body appreciation which seem not to be affected by neuromodulation but may rely instead on broader regions as more complex constructs. Finally, the fractionated protocol can effectively influence inhibition with milder effects on other constructs.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.

Background: The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques. Objective: To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia. Materials and methods: We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia. Results: In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases. Conclusion: The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision.

Background: Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients. Objective: We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT. Materials and Methods: We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, H C O 3 - , K +, Na +, anion gap, C-reactive protein) served as ground truth. Results: Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant. Conclusion: The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Effective inhibition of substantia nigra by deep brain stimulation fails to suppress tonic epileptic seizures.

Experimental and clinical data suggest that high-frequency deep brain stimulation (DBS) of different subcortical structures can be used to control or modulate epileptic seizures. Recent studies showed that DBS of the substantia nigra reticulata (SNr) in rats has an anticonvulsant effect on forebrain clonic seizures. The aim of this study was to determine whether DBS of SNr could also suppress tonic epileptic seizures evoked in hindbrain structures. DBS with high frequency often mimics the effects of surgical ablation of a particular area of the brain. However, the optimal parameters of DBS stimulation to induce ablation-like effects on seizures are not well defined. Consequently, in the first experiment we examined the effects of different stimulation frequencies (80, 130, 260 and 390 Hz) on neuronal activation induced in SNr, using c-fos immunocytochemistry. The results showed that the stimulation of the SNr with 80 Hz has no inhibitory effect while stimulation with 130, 260 and 390 Hz produced a remarkable suppressive effect compared with the control unstimulated side. The aim of the second experiment was to determine whether bilateral inhibition of SNr with DBS could suppress tonic seizures induced by electric shock. Statistical analysis showed that the mean tonic seizure scores following SNr stimulation with either 130 or 260 Hz were not significantly different from scores following the application of the electrode without current. The data suggest that DBS of the SNr produces neuronal inhibition but fails to suppress tonic seizures. We conclude, therefore, that DBS of SNr with frequencies used in this study might not be effective for treatment of patients who suffer from tonic epileptic seizures.

Scaling analysis of bilateral hand tremor movements in essential tremor patients.

Recent evidence suggests that the dynamic-scaling behavior of the time-series of signals extracted from separate peaks of tremor spectra may reveal existence of multiple independent sources of tremor. Here, we have studied dynamic characteristics of the time-series of hand tremor movements in essential tremor (ET) patients using the detrended fluctuation analysis method. Hand accelerometry was recorded with (500 g) and without weight loading under postural conditions in 25 ET patients and 20 normal subjects. The time-series comprising peak-to-peak (PtP) intervals were extracted from regions around the first three main frequency components of power spectra (PwS) of the recorded tremors. The data were compared between the load and no-load condition on dominant (related to tremor severity) and non-dominant tremor side and with the normal (physiological) oscillations in healthy subjects. Our analysis shows that, in ET, the dynamic characteristics of the main frequency component of recorded tremors exhibit scaling behavior. Furthermore, they show that the two main components of ET tremor frequency spectra, otherwise indistinguishable without load, become significantly different after inertial loading and that they differ between the tremor sides (related to tremor severity). These results show that scaling, a time-domain analysis, helps revealing tremor features previously not revealed by frequency-domain analysis and suggest that distinct oscillatory central circuits may generate the tremor in ET patients.

Gestational diabetes: an evaluation of serum fructosamine as a screening test in a high-risk population.

AIM: To evaluate the value of serum fructosamine as a screening test for gestational diabetes mellitus (GDM). METHODS: 849 pregnant women underwent the one-step 75 g oral glucose tolerance test (OGTT) for universal screening of GDM. The fasting serum fructosamine (cFruc) was assessed using the area under the receiver operating characteristic curve (AUC). The GDM diagnostic criteria used were those of the American Diabetes Association; however, the cFruc performance was also evaluated using criteria of the World Health Organization, Australian (ADIPS), European (EASD) and International Association of Diabetes and Pregnancy Study Groups (IADPSG). RESULTS: 113 (13.3%) women had GDM. The AUC of the cFruc was 0.60 (95% CI 0.54-0.66). A cFruc threshold of 237 µmol/l achieved an acceptable sensitivity of 85.8% (95% CI 78.0-91.0%), but the associated specificity remained poor at 23.4% (95% CI 20.0-27.0%) with a positive predictive value of just 14.7%. Overall, over 4 out of 5 pregnant women, being over this cutoff, would need the confirmatory OGTT. No cFruc threshold reached acceptable likelihood ratios to rule-in or rule-out GDM. The AUC for cFruc remained unacceptable independent of the diagnostic criteria. CONCLUSIONS: Despite all the advances in technology, serum fructosamine is a poor test to screen for GDM.

Neurophysiological Predictors of Response to Medication in Parkinson's Disease.

Background: Although dopaminergic medication has been the foundation of Parkinson's disease (PD) therapy for decades, sensitive and specific therapeutic response biomarkers that allow for better treatment optimization are lacking. Objective: We tested whether the features of Transcranial Magnetic Stimulation-based neurophysiological measures taken off-medication are associated with dopaminergic medication-induced clinical effects. Method: Motor cortex excitability [short-latency intracortical inhibition (SICI), intracortical facilitation (ICF), short-latency afferent inhibition (SAI), and input-output (IO) curve], and plasticity [paired associative stimulation (PAS) protocol] neurophysiological measures were examined in 23 PD patients off-medication. Clinical features were quantified by the motor section of the Unified Parkinson's Disease Scale (total score and lateralized total, bradykinesia, and rigidity sub-scores), and the differences between measures off-medication and on-medication (following the usual morning dose), were determined. Total daily dopaminergic medication dose (expressed as levodopa equivalent daily dose-LEDD), was also determined. Results: SICI significantly correlated with changes in lateralized UPDRS motor and bradykinesia sub-scores, suggesting that patients with stronger basal intracortical inhibition benefit more from dopaminergic treatment than patients with weaker intracortical inhibition. Also, ICF significantly negatively correlated with LEDD, suggesting that patients with stronger intracortical facilitation require less dopaminergic medication to achieve optimal therapeutic benefit. Both associations were independent of disease severity and duration. Conclusions: The results suggest variability of (patho) physiological phenotypes related to intracortical inhibitory and facilitatory mechanisms determining clinical response to dopaminergic medication in PD. Measures of intracortical excitability may help predict patients' response to dopaminergic therapy, thus potentially providing a background for developing personalized therapy in PD.

Proportional Changes in Cognitive Subdomains During Normal Brain Aging.

Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline.

Background: Neuronal reactions and cognitive processes slow down during aging. The onset, rate, and extent of changes vary considerably from individual to individual. Assessing the changes throughout the lifespan is a challenging task. No existing test covers all domains, and batteries of tests are administered. The best strategy is to study each functional domain separately by applying different behavioral tasks whereby the tests reflect the conceptual structure of cognition. Such an approach has limitations that are described in the article. Objective: Our aim was to improve the diagnosis of early cognitive decline. We estimated the onset of cognitive decline in a healthy population, using behavioral tests, and predicted the age group of an individual. The comparison between the predicted ("cognitive") and chronological age will contribute to the early diagnosis of accelerated aging. Materials and Methods: We used publicly available datasets (POBA, SSCT) and Pearson correlation coefficients to assess the relationship between age and tests results, Kruskal-Wallis test to compare distribution, clustering methods to find an onset of cognitive decline, feature selection to enhance performance of the clustering algorithms, and classification methods to predict an age group from cognitive tests results. Results: The major results of the psychophysiological tests followed a U-shape function across the lifespan, which reflected the known inverted function of white matter volume changes. Optimal values were observed in those aged over 35 years, with a period of stability and accelerated decline after 55-60 years of age. The shape of the age-related variance of the performance of major cognitive tests was linear, which followed the trend of lifespan gray matter volume changes starting from adolescence. There was no significant sex difference in lifelong dynamics of major tests estimates. The performance of the classification model for identifying subject age groups was high. Conclusions: ML models can be designed and utilized as computer-aided detectors of neurocognitive decline. Our study demonstrated great promise for the utility of classification models to predict age-related changes. These findings encourage further explorations combining several tests from the cognitive and psychophysiological test battery to derive the most reliable set of tests toward the development of a highly-accurate ML model.