The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson's Disease (PD) or Essential Tremors (ET). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3~Hz and 6~Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to as ET, vary between 4~Hz and 12~Hz. We simulated and used these signals as reference signals in adaptive algorithms, Filtered-x Least Mean Square (Fx-LMS), Filtered-x Normalized Least Mean Square (Fx-NLMS), and a hybrid Fx-LMS\&NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS\&LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we have used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

Tremor is a prevalent symptom associated with multiple conditions, including Essential Tremor (ET), Parkinson’s disease (PD), multiple sclerosis (MS), stroke, and trauma. The surgical management of tremor evolved from stereotactic lesions to deep-brain stimulation (DBS), which allowed safe and reversible interference with specific neural networks. This paper reviews the current literature on DBS for tremor, starting with a detailed discussion of current tremor targets (Vim, Raprl, caudal Zi, Vo and STN) and continuing with a discussion of results obtained when performing DBS in the various aforementioned tremor syndromes. Future directions for DBS research are then briefly discussed.

Multidisciplinary neurotechnology holds the promise of understanding and non-invasively treating neurodegenerative diseases. In this preclinical trial on Parkinson's disease (PD), we combined neuroscience together with the nascent field of medical virtual reality and generated several important observations. First, we established the Oculus Rift virtual reality system as a potent measurement device for parkinsonian involuntary hand tremors (IHT). Interestingly, we determined changes in rotation were the most sensitive marker of PD IHT. Secondly, we determined parkinsonian tremors can be abolished in VR with algorithms that remove tremors from patients' digital hands. We also found that PD patients were interested in and were readily able to use VR hardware and software. Together these data suggest PD patients can enter VR and be asymptotic of PD IHT. Importantly, VR is an open-medium where patients can perform actions, activities, and functions that positively impact their real lives - for instance, one can sign tax return documents in VR and have them printed on real paper or directly e-sign via internet to government tax agencies. Lastly, we generated a technical framework wherein movements in the real world can be measured side-by-side with those in virtual reality. With this framework, we observed anecdotal evidence of parkinsonian tremors being reduced in real life when our algorithms abolished digital hand tremors in VR.

A method for studying properties of the earth's surface tremor, measured by means of GPS, is proposed. Four characteristics of tremor are considered: the entropy of the distribution of wavelet coefficients, the Donoho-Johnston wavelet index, and two estimates of the spectral slope. The anomalous areas of tremor are determined by estimating the probability densities of extreme values of the studied properties. The criteria for abnormal tremor behavior are based on the proximity to, or the difference between, tremor properties and white noise. The greatest deviation from the properties of white noise is characterized by entropy minima and spectral slope and DJ index maxima. This behavior of the tremor is called "active". The "passive" tremor behavior is characterized by the maximum proximity to the properties of white noise. The principal components approach provides weighted averaged density maps of these 2 variants of extreme distributions of parameters in a sliding time window of 3 years are considered. Singular points are the points of maximum average densities. The method is applied to the analysis of daily time series from a GPS network in the West USA in 2009-2022. It turned out that singular points of tremor form well-defined clusters.

A method for studying properties of the earth's surface tremor, measured by means of GPS, is proposed. Four characteristics of tremor are considered: the entropy of the distribution of wavelet coefficients, the Donoho-Johnston wavelet index, and two estimates of the spectral slope. The anomalous areas of tremor are determined by estimating the probability densities of extreme values of the studied properties. The criteria for abnormal tremor behavior are based on the proximity to, or the difference between, tremor properties and white noise. The greatest deviation from the properties of white noise is characterized by entropy minima and spectral slope and DJ index maxima. This behavior of the tremor is called "active". The "passive" tremor behavior is characterized by the maximum proximity to the properties of white noise. The principal components approach provides weighted averaged density maps of these 2 variants of extreme distributions of parameters in a sliding time window of 3 years are considered. Singular points are the points of maximum average densities. The method is applied to the analysis of daily time series from a GPS network in the West USA in 2009-2022. It turned out that singular points of tremor form well-defined clusters.

The X-linked FMR1 gene contains a non-coding trinucleotide repeat in its 5’ region that in normal, healthy individuals contains 20-44 copies. Large expansions of this region (>200 copies) cause fragile X syndrome (FXS), but expansions of 55-199 copies (referred to as premutation alleles) predispose carriers to a neurodegenerative disease called fragile X-associated tremor/ataxia syndrome (FXTAS). The cytopathological mechanisms underlying FXTAS are poorly understood, but abnormalities in mitochondrial function are believed to play a role. We previously reported that lymphoblastoid cell lines (LCLs, or lymphoblasts) of premutation carriers have elevated mitochondrial respiratory activities. In the carriers, especially those not clinically affected with FXTAS, AMPK activity was shown to be elevated. In the FXTAS patients, however, it was negatively correlated with brain white matter lesions, suggesting a protective role in the molecular mechanisms. Here we report an enlarged and extended study of mitochondrial function and associated cellular stress-signalling pathways in lymphoblasts isolated from male and female premutation carriers, regardless of their clinical status, and healthy controls. The results confirmed the elevation of AMPK and mitochondrial respiratory activities and reduction of reactive O2 species (ROS) levels in premutation cells and revealed for the first time that TORC1 activities are reduced. Extensive correlation, multiple regression and Principal Components analysis revealed the best fitting statistical explanations of these changes in terms of the other variables measured. These suggested which variables might be the most “proximal” regulators of the others in the extensive network of known causal interactions amongst the measured parameters of mitochondrial function and cellular stress signalling. In the resulting model, the premutation alleles activate AMPK and inhibit both TORC1 and ROS production, the reduced TORC1 activity contributes to activation of AMPK activation and of nonmitochondrial metabolism, and the higher AMPK activity results in elevated catabolic metabolism, mitochondrial respiration and ATP steady state levels. In addition the results suggest a separate CGG repeat number-dependent elevation of TORC1 activity that is insufficient to overcome the inhibition of TORC1 in premutation cells, but may presage the previously reported activation of TORC1 in FXS cells.

Spinocerebellar ataxia type 2 (SCA2) is caused by polyglutamine expansion in Ataxin-2 (ATXN2). This factor binds RNA/proteins to modify metabolism after stress, and to control calcium (Ca2+) homeostasis after stimuli, thus exerting crucial neuroprotection for cerebellar ataxias and corticospinal motor neuron degeneration. Our Atxn2-CAG100-Knock-In mouse faithfully models features observed in patients at pre-onset, early and terminal stages. Here, its cerebellar global RNA profiling revealed downregulation of signaling cascades to precede motor deficits. Validation work at mRNA/protein level defined alterations that were independent of constant physiological ATXN2 functions, but specific for RNA/aggregation toxicity, and progressive across the short lifespan. Earliest changes were detected at 3 months among Ca2+ channels/transporters (Itpr1, Ryr3, Atp2a2, Atp2a3, Trpc3), IP3 metabolism (Plcg1, Inpp5a, Itpka), and Ca2+-Calmodulin dependent kinases (Camk2a, Camk4). CaMKIV–Sam68 control over alternative splicing of Nrxn1, an adhesion component of glutamatergic synapses between granule and Purkinje neurons, was found affected. Systematic screening of pre/post-synapse components, with dendrite morphology assessment, suggested early impairment of CamKIIα abundance together with weakening of parallel fiber connectivity. These data reveal molecular changes due to ATXN2 pathology, impacting communication and excitability of cerebellar neurons. Discovery of such risk versus progression markers improves the assessment of pre-symptomatic treatments in SCA2 and related disorders.