Personalizing functional Magnetic Resonance Protocols for Studying Neural Substrates of Motor Deficits in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder characterized by a large number of motor and non-motor deficits, which significantly contribute to reduced quality of life. Despite the definition of the broad spectrum of clinical characteristics, mechanisms triggering illness, the nature of its progression and a character of therapeutic effects still remain unknown. The enormous advances in magnetic resonance imaging (MRI) in the last decades have significantly affected the research attempts to uncover the functional and structural abnormalities in PD and have helped to develop and monitor various treatment strategies, of which dopamine replacement strategies, mainly in form of levodopa, has been the gold standard since the late seventies and eighties. Motor, task-related functional MRI (fMRI) has been extensively used to assess the pathological state of the motor circuitry in PD. Several studies employed motor paradigms and fMRI to review the functional brain responses of participants to levodopa treatment. Interestingly, they provided conflicting results. Wide spectrum of symptoms, variability and asymmetry of the disease presentation, several treatment approaches and their divergent outcomes make PD enormously heterogeneous. In this work we hypothesized that not considering the disease heterogeneity might have been an adequate cause for the discrepant results in aforementioned studies. We show that not accounting for the disease variability might indeed compromise the results and invalidate the consequent interpretations. Accordingly, we propose and formalize a statistical approach to account for the intra and inter subject variability. This might help to minimize this bias in future motor fMRI studies revealing the functional brain dysfunction and contribute to the understanding of still unknown pathophysiological mechanisms underlying PD

The Influence of Dopamine Replacement on Movement Impairments During Bimanual Coordination in Parkinson’s Disease (PD)

The purpose of the current thesis was to investigate the influence of dopamine replacement on performance during bimanual coordination in individuals with Parkinson’s disease (PD) There has been conflicting research on the cause of movement impairments such as coordination deficits, slowed switching and upper limb freezing that occur during coordinated movements It is unclear whether decreased function of the dopaminergic system after withdrawal from dopamine replacement is responsible for these deficits Healthy age-matched control participants were compared to PD participants in two experiments to determine the movement impairments that occurred during three-dimensional wrist flexion-extension bimanual coordination as a result of PD. In addition, individuals with PD were compared without (‘off’) and with (‘on’) dopamine replacement in both experiments to determine whether modulation of the dopaminergic system influenced coordinated movements. In Experiment 1, continuous bimanual coordination was performed in m-phase (simultaneous wrist flexion and extension) and anti-phase (flexion of one wrist while extending other wrist) with movements externally paced with increasing across seven cycle frequencies (0.75 to 2 Hz). Visual feedback was also manipulated in one of three sensory conditions no vision, normal vision or augmented vision. Visual feedback, phase and cycle frequency manipulation was performed to determine whether other deficits (e.g. sensory and/or attentional deficits) may influence coordinated movements Despite reduced amplitude of movements in both limbs of individuals with PD (PD ‘off’), coordination deficits were not observed in PD compared to healthy control participants. In addition, there was an increased occurrence of upper limb freezing (ULF) when cycle frequency demand was greater Dopamine replacement did increase the amplitude of movements in individuals with PD but did not influence coordination performance or the occurrence of ULF. In Experiment 2, coordinated movements were initiated in either m-phase or antiphase and participants were required to voluntarily switch to the other phase pattern when an auditory cue was presented Trials were performed at one of two cycle frequencies (1 or 2 Hz) and one of two sensory conditions (no vision or normal vision) to determine whether other deficits (e.g. sensory and/or attentional deficits) may influence coordinated movement. In addition, a separate block of trials were performed in anti-phase coordination with an auditory cue that did not require a switch Non-switching trials were included to investigate whether the presence of a distracting cue could evoke ULF comparable to when switching between movements was required PD ‘off’ participants demonstrated slower switching, more delayed responses and deficits in coordination performance when compared to healthy control participants. The increased demand of cycle frequency particularly when initiating anti-phase coordination, after voluntary switching and with the presence of the auditory cue without switching contributed to a large occurrence of ULF in individuals with PD. Dopamine replacement improved the ability to switch between phase patterns but had no overall influence on coordination performance or the occurrence of ULF. Overall, the results of the current thesis demonstrated that dopamine replacement can improve motor symptoms during coordinated movements (e g hypometna and bradykinesia) but does not contribute to coordination performance or ULF in individuals with PD. As a consequence, it was concluded that coordination deficits and ULF are not caused by the dysfunctional dopaminergic system but rather associated to secondary impairment caused by PD. The movement impairments caused by secondary dysfunction of PD were proposed to be associated with increased attentional demands and possible executive dysfunction related to fronto-stnatal pathways that cannot be modulated by dopamine replacement. Thus, treatment of complex movement impairments such as coordination deficits and ULF may benefit from rehabilitation or non-dopamine therapies that focus on the global dysfunction caused by PD

Neural activity inspired asymmetric basis function TV-NARX model for the identification of time-varying dynamic systems

Inspired by the unique neuronal activities, a new time-varying nonlinear autoregressive with exogenous input (TV-NARX) model is proposed for modelling nonstationary processes. The NARX nonlinear process mimics the action potential initiation and the time-varying parameters are approximated with a series of postsynaptic current like asymmetric basis functions to mimic the ion channels of the inter-neuron propagation. In the model, the time-varying parameters of the process terms are sparsely represented as the superposition of a series of asymmetric alpha basis functions in an over-complete frame. Combining the alpha basis functions with the model process terms, the system identification of the TV-NARX model from observed input and output can equivalently be treated as the system identification of a corresponding time-invariant system. The locally regularised orthogonal forward regression (LROFR) algorithm is then employed to detect the sparse model structure and estimate the associated coefficients. The excellent performance in both numerical studies and modelling of real physiological signals showed that the TV-NARX model with asymmetric basis function is more powerful and efficient in tracking both smooth trends and capturing the abrupt changes in the time-varying parameters than its symmetric counterparts

Machine Learning in Tremor Analysis: Critique and Directions

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Neuromagnetic investigations of mechanisms and effects of STN-DBS and medication in Parkinson's disease

Parkinson’s disease (PD) is a neurodegenerative disorder cardinally marked by motor symptoms, but also sensory symptoms and several other non-motor symptoms. PD patients are typically treated with dopaminergic medication for several years. Many patients eventually experience bouts of periods where medication might not be able to effectively control symptoms as well as experience side-effects of long-term dopaminergic treatments. Deep brain stimulation (DBS) is an option as the next therapeutic recourse for such patients. DBS treatment essentially involves placement of stimulating electrodes in the subthalamic nucleus (STN) or the globus pallidus internum (GPi) along with an implanted pulse generator (IPG) in the sub-clavicular space. STN-DBS alleviates motor symptoms and leads to substantial improvements in quality of life for PD patients. Although DBS is known to improve several classes of symptoms, the effect mechanism of DBS is still not clear. While there is a lack of electrophysiological investigation of sensory processing and the effects of treatments in PD altogether, the electrophysiological studies of the cortical dynamics during motor tasks and at rest lack consensus.We recorded magnetoencephalography (MEG) and electromyography (EMG) from PD patients in three studies: (i) at rest, (ii) during median nerve stimulation, and (iii) while performing phasic contractions (hand gripping). The three studies focused on cortical oscillatory dynamics at rest, during somatosensory processing and during movement, respectively. The measurements were conducted in DBS-treated, untreated (DBS washout) and dopaminergic-medicated states. While both treatments (DBS and dopaminergic medication) ameliorated motor symptoms similarly in all studies, they showed differentiated effects on: (i) increased sensorimotor cortical low-gamma spectral power (31-45 Hz) (but no changes in beta power (13-30 Hz)) at rest only during DBS, (ii) somatosensory processing with higher gamma augmentation (31-45 Hz, 20-60 ms) in the dopaminergic-medicated state compared to DBS-treated and untreated states, and (iii) hand gripping with increased motor-related beta corticomuscular coherence (CMC, 13-30 Hz) during dopaminergic medication in contrast to increased gamma power (31-45 Hz) during DBS.Firstly, we infer from the three studies that DBS and dopaminergic medication employ partially different anatomo-functional pathways and functional strategies when improving PD symptoms. Secondly, we suggest that treatments act on pathological oscillatory dynamics differently at cortical and sub-cortical levels and may do so through more sophisticated mechanisms than mere suppression of the pathological spectral power in a particular band. And thirdly, we urge exploring effect mechanisms of PD treatments beyond the motor system. The effects of dopaminergic medication on early somatosensory processing has opened the door for exploring the effects of treatments and studying their mechanisms using electrophysiology, especially in higher order sensory deficits. Integration of such research findings into a holistic view on mechanisms of treatments could pave way for better disease management paradigms. 

Syväaivostimulaation aiheuttamat muutokset aivokuoritoiminnassa edenneessä Parkinsonin taudissa : MEG-tutkimus

The aims of this PhD research were to study the feasibility of magnetoencephalography (MEG) measurements in patients with advanced Parkinson's disease (PD) treated with deep brain stimulation (DBS) of the subthalamic nucleus (STN), and to cast light onto the effects of DBS on the electrical activity of the brain. The sensitivity of MEG to magnetic fields makes the measurements of patients using electrical devices challenging because of confounding artifacts. One aim was to investigate how an artifact suppression method called spatiotemporal signal space separation (tSSS) works in the suppression of DBS artifacts. The measured MEG signals included evoked responses, spontaneous brain activity and cortico-muscular coherence (CMC) in DBS-treated PD patients. A total of 24 PD patients were included in the studies. The effect of STN-DBS was measured with DBS first being on and then turned off. CMC and corticokinematic coherence (CKC) were studied in 10 healthy subjects. The motor task for CMC and CKC was to hold the dorsiflexion of the wrist. For CMC recordings, MEG was recorded simultaneously with surface EMG over the activated extensor carpi radialis muscle. The healthy volunteers had an accelerometer sensor attached to their index fingernail for the CKC measurements. The DBS artifact reduction by tSSS enabled a reliable MEG signal analysis for most patients. The effects of DBS on the brain's electrical activity varied considerably between patients. AEF N100m amplitude increased in the right hemisphere for ipsilateral stimulation during DBS. When the DBS was on and eyes open, the source strength of the sensorimotor spontaneous MEG in the 6 10 Hz and 12 20 Hz bands correlated with the UPDRS rigidity measures. No systematic increase in CMC in the 13 25 Hz band during DBS was observed. The patients that had CMC also had the best UPDRS motor scores. The source locations of CMC and CKC in healthy subjects were overlapping and their spectral profiles resembled each other. tSSS is useful in the artifact suppression in the MEG measurements of DBS patients. Despite this, the MEG measurements of DBS patients are still challenging and need exact planning of the experimental setups and caution in the data analysis. DBS modifies cortical electrical activity, and some of these modifications correlate with the clinical improvement in PD. The accelerometer device may turn out to be a good alternative to EMG for coherence calculations even during hold tasks.Edenneessä Parkinsonin taudissa, jossa lääkehoidolla ei enää saada toivottua tehoa potilaan oireisiin, syväaivostimulaatio lievittää olennaisesti potilaiden motorisia oireita. Aivojen tyvitumakkeisiin laitetaan elektrodit, joiden kautta kohdetumakkeisiin johdetaan suuritaajuista sähköstimulaatiota. Hoidon perimmäistä vaikutusmekanismia ei vielä täysin tunneta. Magnetoenkefalografialla (MEG) voidaan tutkia aivojen sähköistä toimintaa magneettikenttiä mittaamalla. Aivojen sähkövirtojen tuottamat magneettikentät ovat hyvin heikkoja ja aivoissa sijaitseva sähköelektrodi virtalähteineen aiheuttaa merkittäviä häiriöitä MEG-mittauksiin. Tässä työssä on käytetty tSSS-menetelmää artefaktien poistamiseen aivosignaalista. Sen avulla olemme pystyneet tutkimaan syväaivostimulaation aikaansaamia muutoksia aivojen sähköisessä toiminnassa. Olemme tutkineet edennyttä Parkinsonin tautia sairastavia potilaita, joilla on syväaivostimulaattori subtalaamisissa tumakkeissa. Potilaat on mitattu MEG:llä aivostimulaattorin ollessa päällä ja poiskytkettynä. Aivojen herätevasteet ääniärsykkeille voimistuivat oikealla aivopuoliskolla ääniärsykkeen tullessa potilaiden oikeaan korvaan kun stimulaattori oli päällä. Tuntoärsykkeisiin tai aivovasteen nopeuteen stimulaatio ei mittauksissamme vaikuttanut. Syväaivostimulaatio ei systemaattisesti muuttanut aivokuoren lepotoimintaa potilaiden ollessa levossa silmät auki tai kiinni. Stimulaation aikana aivokuoren myyrytmin lähdevoimakkuus korreloi merkitsevästi potilaiden raajajäykkyyden kanssa. Aivojen ja lihaksen välistä kommunikaatiota mitattiin MEG:n ja lihaksen aktiivisuutta mittaavan EMG:n välisellä koherenssilla. Syväaivostimulaatiolla ei ollut johdonmukaista vaikutusta tähän koherenssiin. Potilailla, joilla esiintyi koherenssia beetakaistalla, oli vähemmän Parkinsonoireita kuin potilailla joilla koherenssia ei esiintynyt. Koherenssia tutkittiin yksityiskohtaisemmin terveiltä koehenkilöiltä samanaikaisesti sekä lihaksen ja MEG:n väliltä että sormeen kiinnitetyn liikeanturin ja MEG:n väliltä. Lihas- ja liikeanturisignaalien kanssa koherentit aivosignaalit paikantuivat samoille aivoalueille. Väitöskirjatyössä tSSS osoittautui toimivaksi keinoksi vähentää stimulaation aiheuttamia artefakteja mitatusta MEG-signaalista. Syväaivostimulaatio vaikuttaa aivokuoren toimintaan ja osa näistä muutoksista korreloi potilaiden oireiden kanssa. Kiihtyvyysanturi saattaa jatkossa olla vaihtoehto EMG:lle koherenssimittauksissa myös staattisen lihasaktivaation aikana

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Acoustic and videoendoscopic techniques to improve voice assessment via relative fundamental frequency

Quantitative measures of laryngeal muscle tension are needed to improve assessment and track clinical progress. Although relative fundamental frequency (RFF) shows promise as an acoustic estimate of laryngeal muscle tension, it is not yet transferable to the clinic. The purpose of this work was to refine algorithmic estimation of RFF, as well as to enhance the knowledge surrounding the physiological underpinnings of RFF. The first study used a large database of voice samples collected from 227 speakers with voice disorders and 256 typical speakers to evaluate the effects of fundamental frequency estimation techniques and voice sample characteristics on algorithmic RFF estimation. By refining fundamental frequency estimation using the Auditory Sawtooth Waveform Inspired Pitch Estimator—Prime (Auditory-SWIPE′) algorithm and accounting for sample characteristics via the acoustic measure, pitch strength, algorithmic errors related to the accuracy and precision of RFF were reduced by 88.4% and 17.3%, respectively. The second study sought to characterize the physiological factors influencing acoustic outputs of RFF estimation. A group of 53 speakers with voice disorders and 69 typical speakers each produced the utterance, /ifi/, while simultaneous recordings were collected using a microphone and flexible nasendoscope. Acoustic features calculated via the microphone signal were examined in reference to the physiological initiation and termination of vocal fold vibration. The features that corresponded with these transitions were then implemented into the RFF algorithm, leading to significant improvements in the precision of the RFF algorithm to reflect the underlying physiological mechanisms for voicing offsets (p < .001, V = .60) and onsets (p < .001, V = .54) when compared to manual RFF estimation. The third study further elucidated the physiological underpinnings of RFF by examining the contribution of vocal fold abduction to RFF during intervocalic voicing offsets. Vocal fold abductory patterns were compared to RFF values in a subset of speakers from the second study, comprising young adults, older adults, and older adults with Parkinson’s disease. Abductory patterns were not significantly different among the three groups; however, vocal fold abduction was observed to play a significant role in measures of RFF at voicing offset. By improving algorithmic estimation and elucidating aspects of the underlying physiology affecting RFF, this work adds to the utility of RFF for use in conjunction with current clinical techniques to assess laryngeal muscle tension.2021-09-29T00:00:00

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses

Voice tremor in Parkinson's disease (PD) :identification, characterisation and relationship with speech, voice and disease variables

Phd ThesisVoice tremor is associated with Parkinson’s disease (PD), however little is known about the precise characteristics of PD voice tremor, optimum methods of evaluation or possible relationships with other speech, voice, and disease variables. The question of possible differences between voice tremor in people with PD (pwPD) and neurologically healthy ageing people has not been addressed. Thirty pwPD ‘off-medication’ and twenty eight age-sex matched neurologically healthy controls were evaluated for voice tremor features using acoustic measurement, auditory perceptual voice rating, and nasendoscopic vocal tract examination. Speech intelligibility, severity of voice impairment, voice disability and disease variables (duration, disability, motor symptom severity, phenotype) were measured and examined for relationship with acoustic voice tremor measures. Results showed that pwPD were more likely to show greater auditory perceived voice instability and a greater magnitude of frequency and amplitude tremor in comparison to controls, however without statistical significance. PwPD had a higher rate of amplitude tremor than controls (p<0.05). Judged from ‘silent’ video recordings of nasendoscopic examination, pwPD had a greater amount of tremor in the palate, tongue, and global larynx (vertical dimension) than controls during rest breathing, sustained /s/, /a/ and /i/ (p<0.05). Acoustic voice tremor did not relate significantly to other speech and voice variables. PwPD had a significantly higher voice disability than controls (p<0.05), though this was independent of voice tremor. The magnitude of frequency tremor was positively associated with disease duration (p<0.05). A lower rate of amplitude tremor was associated with an increase in motor symptoms severity (p<0.05). Acoustic voice tremor did not relate in any significant way to PD disability or phenotype. ii PD voice tremor is characterised by auditory perceived instability and tremor, a mean amplitude tremor of 4.94 Hz, and tremor in vocal tract structures. Acoustic analysis and nasendoscopy proved valuable adjunctive tools for characterising voice tremor. Voice tremor is not present in all people with PD, but does appear to increase with disease duration. However pwPD examined here represent a relatively mild group with relatively short disease duration. Further work will look at people with more severe disease symptomatology and longer duration