Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson\u27s Patients

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease\u27s symptoms, with the help of various therapies. In the case of Parkinson\u27s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist\u27s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient \u27well-being\u27 scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naive Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD

Application of MRI Connectivity in Stereotactic Functional Neurosurgery

This thesis examines potential applications of advanced MRI-connectivity studies in stereotactic functional neurosurgery. Several new analysis methodologies are employed to: (1) build predictive models of DBS surgery outcome; (2) refine the surgical target and (3) help build a better understanding of the pathogenesis of the treated conditions and the mechanism of action of DBS therapy. The experimental component is divided into three main parts focusing on the following pathologies: (1) Parkinson’s disease (PD), (2) tremor and (3) trigeminal autonomic cephalalgias (TAC). Section I: In the first experiment (chapter 3), resting state fMRI was used to find radiological biomarkers predictive of response to L-DOPA in 19 patients undergoing subthalamic nucleus (STN) DBS for PD. A greater improvement in UPDRS-III scores following L-DOPA administration was characterized by higher resting state functional connectivity (fcMRI) between the prefrontal cortex and the striatum (p=0.001) and lower fcMRI between the pallidum (p=0.001), subthalamic nucleus (p=0.003) and the paracentral lobule. In the second experiment (chapter 4), structural (diffusion) connectivity was used to map out the influence of the hyperdirect pathways on outcome and identify the therapeutic ‘sweet spots’ in twenty PD patients undergoing STN-DBS. Clusters corresponding to maximum improvement in symptoms were in the posterior, superior and lateral portion of the STN. Greater connectivity to the primary motor area, supplementary motor area and prefrontal cortex was predictive of higher improvement in tremor, bradykinesia and rigidity, and rigidity respectively. The third experiment (chapter 5) examined pyramidal tract (PT) activation in 20 PD patients with STN-DBS. Volume of tissue activation (VTA) around DBS contacts were modelled in relation to the PT. VTA/ PT overlap predicted EMG activation thresholds. Sections II: Pilot data suggest that probabilistic tractography techniques can be used to segment the ventrolateral (VL) and ventroposterior (VP) thalamus based on cortical and cerebellar connectivity in nine patients who underwent thalamic DBS for tremor (chapter 6). The thalamic area, best representing the ventrointermedialis nucleus (VIM), was connected to the contralateral dentate cerebellar nucleus. Streamlines corresponding to the dentato-rubro-thalamic tract (DRT) connected M1 to the contralateral dentate nucleus via the dentato-thalamic area. Good response was seen when the active contact’s VTA was in the thalamic area with the highest connectivity to the contralateral dentate nucleus. Section III: The efficacy and safety of DBS in the ventral tegmental area (VTa) in the treatment of chronic cluster headache (CH) and short lasting unilateral neuralgiform headache attacks (SUNA) were examined (chapters 7 and 8). The optimum stimulation site within the VTa that best controls symptoms was explored (chapter 9). The average responders’ deep brain stimulation activation volume lay on the trigemino-hypothalamic tract, connecting the trigeminal system and other nociceptive brainstem nuclei, with the hypothalamus, and the prefrontal and mesial temporal areas

Body sensor networks: smart monitoring solutions after reconstructive surgery

Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery. A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage. An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively. The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Quantification of Physical Activity and Sleep Behaviors with Wearable Sensors : Analysis of a large-scale real-world heart rate variability dataset

Puettavia mittalaitteita, kuten älykelloja, voidaan käyttää arjessa oman terveydentilan, fyysisen kunnon, terveyskäyttäytymisen sekä hyvinvoinnin seuraamiseen. Puettavien mittalaitteiden käyttö on nykyisin suosittua, ja kuluttajat mittaavat niillä yleensä liikuntaa ja unta. Puettavien mittalaitteiden keräämä mittausaineisto on esimerkki arkielämän aineistoista (real-world data), jotka voivat tarjota käytännönläheisiä havaintoja terveydestä ja hyvinvoinnista. Arkielämässä kerättyjen aineistojen hyödyntäminen tutkimustarkoituksiin on kuitenkin haastavaa, sillä kuluttajat käyttävät puettavia mittalaitteita vapaaehtoisesti arkielämän olosuhteissa. Siksi aineiston käsittelyssä on otettava huomioon aineiston keräyksen kontrolloimattomat tutkimusasetelmien ulkopuoliset olosuhteet, jotka aiheuttavat mittausaineistoon tyypillisesti epätarkkuutta ja puutteellisuutta sekä otospopulaation valikoituneisuutta. Puettavien mittalaitteiden tuottamille jatkuva-aikaisille aineistoille ei myöskään toistaiseksi ole vakiintuneita käsittelytapoja. Näiden tekijöiden vuoksi puettavien mittalaitteiden keräämiä aineistoja käytetään nykyisin vielä vain vähän tutkimuksissa, vaikka ne voivat tarjota uusia havaintoja terveyskäyttäytymisestä ja hyvinvoinnista. Väitöstyössä hyödynnetään puettavan sydämen sykevälivaihtelua mittaavan laitteen tuottamaa arkielämän suurta aineistoa määrittämään liikuntaan ja uneen liittyvää käyttäytymistä. Liikunta ja uni ovat tärkeitä terveyskäyttäytymisen tekijöitä, ja väitöstyössä tutkitaan erityisesti liikunnan määrittämisen menetelmiä, liikuntakäyttäytymisen ajallista vaihtelua, sekä liikunnan, alkoholin nauttimisen ja muiden elämäntapojen vaikutusta uneen. Lisäksi väitöstyön tavoitteena on arvioida puettavien mittalaitteiden tuottamien suurten arkielämän aineistojen ja niiden hyödyntämisen soveltuvuutta tieteellisen tutkimukseen sekä osoittaa näiden aineistojen tarjoamia uusia havaintoja ja näkökulmia terveydestä ja hyvinvoinnista. Väitöstutkimuksen aineistona käytettiin 52 273 suomalaisen työntekijän tunnisteettomia arkielämässä tehtyjä sydämen sykevälivaihtelun mittauksia, jotka oli alun perin tehty osana terveyttä edistävää ja ennaltaehkäisevää terveydenhuoltoa. Aineisto on kerätty Firstbeat Technologies Oy:n toimesta, joka kehittää ja tarjoaa sykevälivaihtelun analyysimenetelmiä liikunnan, stressin ja palautumisen arviointiin. Aineisto sisälsi kolmipäiväisiä jatkuva-aikaisia mittauksia sydämen sykevälivaihtelusta sekä itseraportointeja nautitusta alkoholin määrästä sekä työ- että nukkumisajoista. Väitöstyössä liikunnan määrittämisessä hyödynnettiin sykevälivaihteluun perustuvaa hapenoton arviota. Unta arvioitiin autonomisen hermoston säätelyn kautta käyttäen perinteisiä sykevälivaihtelumuuttujia sekä uudenlaisia sykevälivaihteluun perustuvia palautumismuuttujia. Väitöstyön tulokset pohjautuvat sekä perinteisiin tilastollisiin että koneoppimisen menetelmiin. Liikuntakäyttäytymisessä havaittiin ajallista vaihtelua: liikunnan määrä oli korkein viikonloppuisin sekä alkuvuonna. Kun liikuntaa arvioitiin absoluuttisella hapenotolla, liikunnan määrä oli korkeampi miehillä kuin naisilla, ja nuoremmilla kuin vanhemmilla sekä normaalipainoisilla kuin lihavilla henkilöillä. Toisaalta kun liikunnan määrää arvioitiin ottaen huomioon henkilöiden kuntotaso, erot liikunnan määrässä henkilöiden välillä pieneni huomattavasti. Lisäksi liikuntakäyttäytymisellä havaittiin olevan yhteys uneen. Päivällä harrastettu liikunta näytti heikentävän autonomisen hermoston parasympaattista säätelyä unen aikana, mutta säännöllinen liikunta näytti lisäävän parasympaattista säätelyä ja palautumista unen aikana. Unen aikaisen autonomisen hermoston säätelyn kannalta tärkein tekijä oli kuitenkin päivän aikana nautittu alkoholi. Jo 1–2 alkoholiannosta heikensi autonomisen hermoston parasympaattista säätelyä unen aikana ja tämä säätely heikkeni sitä enemmän, mitä useampia alkoholiannoksia päivän aikana nautittiin. Painoon suhteutettu, sama alkoholimäärä näytti vaikuttavan autonomisen hermoston säätelyyn enemmän nuoremmilla kuin vanhemmilla henkilöillä, mutta samalla tavalla sekä paljon että vähän liikuntaa harrastavilla henkilöillä, ja sekä miehillä että naisilla. Monet väitöstyön tulokset tukevat aiempia tutkimustuloksia, kuten esimerkiksi havainnot suuremmasta liikunta-aktiivisuudesta viikonloppuisin, miesten, nuorten ja normaalipainoisten suuremmasta liikuntamäärästä absoluuttisella hapenottomäärällä mitattuna, sekä liikunnan ja alkoholin yhteydestä autonomisen hermoston säätelyyn unen aikana. Toisaalta väitöstyössä havaittiin esimerkiksi myös alkoholin nauttimisen ja henkilön taustatekijöiden yhteisvaikutuksia autonomisen hermoston säätelyyn, joita ei ole voitu aiemmin tutkia pienten tutkimuspopulaatioiden vuoksi. Kokonaisuudessaan väitöstyö osoittaa, että puettavien mittalaitteiden tuottamat arkielämän aineistot soveltuvat tieteelliseen tutkimukseen ja tulokset tukevat aiempia tutkimustuloksia, mutta tarjoavat myös uusia havaintoja sekä näkemyksiä. Tosielämän tieto voikin parantaa terveyskäyttäytymisen ja hyvinvoinnin tuntemusta, erityisesti niiltä osin, joihin perinteiset tutkimusasetelmat eivät sovellu. Käytännössä tosielämän havaintoja ja tietoa voidaan käyttää havainnollistamaan käyttäytymisen vaikutusta terveyteen ja hyvinvointiin, sekä tukemaan terveyskäyttäytymisen muutosta entistä henkilökohtaisemmin ja kohdennetummin.Wearable monitoring devices, such as smartwatches, are used for monitoring personal health, fitness, health behaviors and well-being in daily life. Nowadays, wearable devices are popular and many consumers use them, in particular, to record their physical activity and sleep. Data recorded with wearable devices is an example of real-world data that can provide practical observations and insights on health and wellness, but its analyses pose challenges for research. Consumers conduct continuous recordings with wearable devices in non-research settings. Hence, any analysis of wearable real-world monitoring data must take into account the limitations and inaccuracies of the data, as well as sampling biases and incomplete representativeness of the population that arise from the uncontrolled data collection setting. To date, there are no well-established methods for analyzing health behaviors and well-being from continuous wearable monitoring data. Consequently, real-world health monitoring data is not commonly used for research although it could provide valuable observations and insights on health behaviors and well-being. This thesis work aims at analyzing a large-scale real-world dataset of wearable heart rate variability (HRV) recordings to quantify the behaviors of physical activity (PA) and sleep that are one of the most important health behaviors. Specifically, the thesis focuses on the quantification methods and temporal patterns of PA behavior, as well as the associations that PA, alcohol intake and other lifestyles have with sleep. In addition, this thesis work aims to evaluate the feasibility to use real-world wearable monitoring data with applicable analysis methodologies for scientific research, and to demonstrate the observations and data-driven hypotheses that the results provide. The study material was an anonymized real-world HRV monitoring dataset of 52,273 Finnish employees, which was gathered and prepared by Firstbeat Technologies Oy (Jyväskylä, Finland), a Finnish company providing and developing HRV analytics for stress, recovery and exercise. The dataset included three-day continuous HRV recordings performed in free-living settings combined with self- reports of alcohol intake, work and sleep times. The recordings were originally performed for a routine wellness program (Firstbeat Lifestyle Assessment) provided for the employees by their employers as a part of preventive occupational healthcare and health promotion program. For the analysis of this thesis, PA behavior was quantified from the recordings using an HRV-based estimate of the oxygen uptake. Sleep was quantified by the regulation of the autonomic nervous system (ANS) using traditional HRV parameters and novel HRV-based indices of recovery. Both statistical and machine- learning methods were employed in the analysis for the thesis results. Temporal variations in PA behavior were observed: the amount of PA was highest at the weekends and at the beginning of the year. The amount of PA quantified by the absolute oxygen consumption was higher for men than for women, and higher for younger than older subjects, and also higher for individuals of normal weight than obese. However, PA levels were more similar between the subjects when their physical fitness level was considered in quantifying PA. Moreover, PA behavior was associated with sleep. After a day including PA, the parasympathetic regulation of the ANS and recovery during sleep were diminished, but regular PA seemed to increase parasympathetic regulation of the ANS and aid recovery during sleep. The most important predictor for ANS regulation during sleep was, however, acute alcohol intake. Acute alcohol intake dose-dependently diminished the parasympathetic regulation of the ANS and recovery during sleep, an effect that was already observable after only 1–2 standardized units of alcohol. Moreover, the same alcohol intake, normalized by the body weight, seemed to affect the ANS regulation more in younger subjects than in the older ones, but was similar for both sedentary and physically active subjects, as well as for both men and women. Many of the results obtained in this thesis accord with the findings of previous studies, such as the higher PA level on weekends, the higher amount of absolute intensity PA in men, younger and normal weight subjects, and the relationship of PA and alcohol intake with the ANS regulation during sleep. On the other hand, the results of this thesis provide new observations, for example, about the interaction between alcohol intake and subject’s background characteristics that could not have been studied before due to the limited and homogenous study populations. In conclusion, the results of this thesis demonstrates that real-world wearable monitoring data can be feasible for scientific research and its results not only supports the findings of existing studies but also provides new observations, insights and data-driven hypotheses. The real-world evidence facilitates our understanding of aspects of health behaviors and wellness that cannot be studied in the more traditional, controlled research settings. These real-world insights can be further used for designing more personalized and targeted health interventions and as tools for promoting health and well-being

Using Statistics, Computational Modelling and Artificial Intelligence Methods to Study and Strengthen the Link between Kinematic Impacts and mTBIs

Mild traumatic brain injuries (mTBIs) are frequently occurring, yet poorly understood, injuries in sports (e.g., ice hockey) and other physical recreation activities where head impacts occur. Helmets are essential pieces of equipment used to protect participants’ heads from mTBIs. Evaluating the performance of helmets to prevent mTBIs using simulations on anatomically accurate computational head finite element models is critically important for advancing the development of safer helmets. Advancing the level of detail in, and access to, such models, and their continued validation through state-of-the-art brain imaging methods and traditional head injury assessment procedures, is also essential to improve safety. The significant research contributions in this thesis involve evaluating the decrease in blunt impact-induced brain axon fiber tract strains that various helmets provide by studying outputs of existing finite element brain models and implementing open-source artificial intelligence technology to create a novel pipeline for predicting such strains

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing