Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

DESIGN, SYNTHESIS, AND PHARMACOLOGICAL EVALUATION OF A SERIES OF NOVEL, GUANIDINE AND AMIDINE-CONTAINING NEONICOTINOID-LIKE ANALOGS OF NICOTINE: SUBTYPE-SELECTIVE INTERACTIONS AT NEURONAL NICOTINIC-ACETYLCHOLINE RECEPTOR.

The current project examined the ability of a novel series of guandine and amidine-containing nicotine analogs to interact with several native and recombinantlyexpressed mammalian neuronal nicotinic-acetylcholine receptor (nAChR) subtypes. Rational drug design methods and parallel organic synthesis was used to generate a library of guanidine-containing nicotine (NIC) analogs (AH compounds). A smaller series of amidine-containing nicotine analogs (JC compounds) were also synthesized. In total, \u3e150 compounds were examined. Compounds were first assayed for affinity in a high-throughput [3H]epibatidine radioligand-binding screen. Lead compounds were evaluated in subtype-selective binding experiments to probe for affinity at the α4β2* and α7* neuronal nAChRs. Several compounds were identified which possess affinity and selectivity for the α4β2* subtype [AH-132 (Ki=27nm) and JC-3-9 (Ki=11nM)]. Schild analysis of binding suggests a complex one-site binding interaction at the desensitized high-affinity nAChR. Whole-cell functional fluorescence (FLIPR) assays revealed mixed subtype pharmacology. AH-compounds were identified which act as activators and inhibitors at nAChR subtypes, while lead JC-compounds were found which possess full agonist activity at α4β2* and α3β4* subtypes. Compounds were identified as partial agonists, full agonists and inhibitors of multiple nAChR subtypes. Several SAR-based, ligand-receptor pharmacophore models were developed to guide future ligand design. Second-generation lead compounds were identified

Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning

This study proposes a new approach that investigates differences in topological characteristics of visual networks, which are constructed using fMRI BOLD time-series corresponding to visual datasets of COCO, ImageNet, and SUN. A publicly available BOLD5000 dataset is utilized that contains fMRI scans while viewing 5254 images of diverse complexities. The objective of this study is to examine how network topology differs in response to distinct visual stimuli from these visual datasets. To achieve this, 0- and 1-dimensional persistence diagrams are computed for each visual network representing COCO, ImageNet, and SUN. For extracting suitable features from topological persistence diagrams, K-means clustering is executed. The extracted K-means cluster features are fed to a novel deep-hybrid model that yields accuracy in the range of 90%-95% in classifying these visual networks. To understand vision, this type of visual network categorization across visual datasets is important as it captures differences in BOLD signals while perceiving images with different contexts and complexities. Furthermore, distinctive topological patterns of visual network associated with each dataset, as revealed from this study, could potentially lead to the development of future neuroimaging biomarkers for diagnosing visual processing disorders like visual agnosia or prosopagnosia, and tracking changes in visual cognition over time

Using stratified medicine to understand, diagnose, and treat neuropathic pain

Neuropathic pain (NeuP) is defined as pain arising from a lesion or disease of the somatosensory nervous system. NeuP is common, affecting approximately 6-8% of the general population and currently treatment is inadequate due to both poor drug efficacy and tolerability. Many different types of injury can cause neuropathic pain including genetic (e.g. SCN9A gain of function variants), metabolic (e.g. diabetic polyneuropathy), infective (e.g. HIV associated neuropathy, hepatitis), traumatic and toxic (e.g. chemotherapy induced neuropathy) causes. Such injurious events can impact on anatomically distinct regions of the somatosensory nervous system ranging from the terminals of nociceptive afferents (in small fiber neuropathy) to the thalamus (in post-stroke pain). Classification of neuropathic pain using etiology and location remains an important aspect of routine clinical practice; however, pain medicine is coming to the realization that we need more precision in this classification. The hope is that improved classification will lead to better understanding of risk, prognosis and optimal treatment of NeuP

The psychiatric and neural effects of L-type calcium channel antagonism: pharmacoepidemiology and experimental medicine studies

L-type calcium channel (LTCC) antagonists are used to manage cardiovascular conditions. However, several factors suggest they may also have therapeutic potential in psychiatry. First, there is evidence for calcium signalling abnormalities in bipolar disorder (BD). Second, there is some evidence, albeit very inconclusive, that LTCC antagonists may have beneficial effects in BD. Third, calcium channel genes are involved in a number of psychiatric conditions. In particular, genome-wide association studies (GWAS) have consistently identified CACNA1C as a gene associated with psychiatric disorders including BD. CACNA1C codes for the CaV1.2 alpha subunit, the primary target of LTCC antagonists, and the genomic data have given new impetus to studying whether and how these drugs affect psychiatric or neural phenotypes. This study used two complementary approaches to investigate this issue. Using a federated network of electronic health records (EHRs), the first part of this thesis aimed to explore the association between LTCC antagonism and psychiatric disorder. Analyses compared LTCC antagonists with other antihypertensives in matched cohorts of patients. Findings demonstrated LTCC antagonists were associated with lower incidence of first-onset psychiatric disorder compared to beta blockers and diuretics, but higher incidence compared to angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs). Follow-up analyses specifically compared brain-penetrant LTCC antagonists with non-penetrant variants (amlodipine and verapamil/diltiazem) and with ARBs. These findings demonstrated that brain-penetrant LTCC antagonists were associated with overall lower incidence of first-onset neuropsychiatric disorder compared to amlodipine, verapamil/diltiazem, and ARBs. However, benefits varied across individual disorders, and indications of residual confounding between groups undermined the interpretation of some of the findings. The second part of this thesis aimed to examine the broader effects of LTCC antagonism on human brain and behaviour through an exploratory experimental medicine study. The Oxford Study of Calcium Channel Antagonism, Cognition, Mood Instability and Sleep (OxCaMS) compared the effect of 14 days’ nicardipine (a brain-penetrant LTCC antagonist) with placebo across various parameters, including measures of mood, cognition, and neural activity, using a randomised, double-blind design. While there was no evidence of an effect of LTCC antagonism on mood instability, behavioural and neural findings suggested LTCC antagonism may shift emotional processing in line with an antidepressant effect. Cognitive evidence indicated that, compared to placebo, LTCC antagonism reduced negative bias through changes in the perception of sad and angry faces, while neural evidence suggested that LTCC antagonism decreased amygdala activity in response to fear. However, neural findings were based on small voxel clusters, and therefore further research is warranted to assess LTCC antagonist effects in the brain. In summary, these findings offer insights into the possible associations between LTCC antagonists and neuropsychiatric disorder, as well as the effects of these drugs on mood, cognitive function, and neural activity. Several lines of evidence support the potential of brain-selective LTCC antagonists in psychiatry. However further research is required to fully clarify the therapeutic possibilities of LTCC antagonism in the future

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices