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Neurology

Contains research objectives and reports on six research projects.U.S. Public Health Service (B-3055)U.S. Public Health Service (B-3090)Office of Naval Research (Nonr-1841 (70))Air Force (AF33(616)-7588)Air Force (AFAFOSR-155-63)Air Force (AFAFOSR-155-63)Army Chemical Corps (DA-18-108-405-Cml-942)National Science Foundation (Grant G-16526

Neurology

Contains reports on five research projects.United States Navy, Office of Naval Research (Nonr-609(39))United States Public Health Service (B-3055, B-3090)Unites States Air Force (Contract AF33(616)-7282)Unites States Air Force (Contract AF-33(616)-7588, Project: 61(8-7232); Task 71784))United States Army Chemical Corps (DA-18-108-405-Cml-942

Neurology

Contains reports on eleven research projects.U.S. Air Force (AF49(638)-1130)Army Chemical Corps (DA-18-108-405-Cml-942)U.S. Public Health Service (B-3055)National Science Foundation (Grant G-16526)U.S. Public Health Service (B-3090)U.S. Air Force (AF33(616)-7588)Office of Naval Research (Nonr-1841(70)

Table of Contents

Contains the table of contents

A Kinematic Study of Progressive Micrographia in Parkinson's Disease

Progressive micrographia is decrement in character size during writing and is commonly associated with Parkinson's disease (PD). This study has investigated the kinematic features of progressive micrographia during a repetitive writing task. Twenty-four PD patients with duration since diagnosis of <10 years and 24 age-matched controls wrote the letter “e” repeatedly. PD patients were studied in defined off states, with scoring of motor function on the Unified Parkinson's Disease Rating Scale Part III. A digital tablet captured x-y coordinates and ink-pen pressure. Customized software recorded the data and offline analysis derived the kinematic features of pen-tip movement. The average size of the first and the last five letters were compared, with progressive micrographia defined as >10% decrement in letter stroke length. The relationships between dimensional and kinematic features for the control subjects and for PD patients with and without progressive micrographia were studied. Differences between the initial and last letter repetitions within each group were assessed by Wilcoxon signed-rank test, and the Kruskal-Wallis test was applied to compare the three groups. There are five main conclusions from our findings: (i) 66% of PD patients who participated in this study exhibited progressive micrographia; (ii) handwriting kinematic features for all PD patients was significantly lower than controls (p < 0.05); (iii) patients with progressive micrographia lose the normal augmentation of writing speed and acceleration in the x axis with left-to-right writing and show decrement of pen-tip pressure (p = 0.034); (iv) kinematic and pen-tip pressure profiles suggest that progressive micrographia in PD reflects poorly sustained net force; and (v) although progressive micrographia resembles the sequence effect of general bradykinesia, we did not find a significant correlation with overall motor disability, nor with the aggregate UPDRS-III bradykinesia scores for the dominant arm

Koneoppimisen hyödyntäminen Parkinsonin taudin diagnosoinnissa

Parkinsonin tauti on yleinen ikääntyvien ihmisten pitkäaikainen parantumaton hermoston rappeumasairaus. Parkinsonin taudin potilaalle varhaisempi oikea diagnoosi on tärkeä, sillä parhaiten potilaan elämänlaatua voidaan ylläpitää, kun oikeanlainen hoito aloitetaan ajoissa. Tämänhetkiset diagnoosimenetelmät kärsivät epätarkkuudesta ja ovat myös kalliita. Apuna voisi olla koneoppimisen menetelmät. Koneoppimisen käyttö on lisääntynyt viime vuosina nopeasti, ja menetelmät ovat käytössä myös terveydenhuollossa. Työn tavoitteena on selvittää, mitä koneoppimisen menetelmiä voidaan soveltaa Parkinsonin taudin diagnosointiin, ja mistä oireista Parkinsonin tautia voidaan diagnosoida parhaiten näillä menetelmillä. Työssä tutkitaan tarkemmin puheen, käsialan ja kävelyliikkeen mittausdatoihin perustuvia koneoppimismalleja. Parhaat tulokset on saatu kävelyliikedataa käyttävillä malleilla, johtuen mahdollisesti kävelyoireiden kytkeytymisestä vahvasti perinteiseen Parkinsonin taudin diagnoosiin, jolloin oireita on tutkittu pitempään. Kaikkien mittauskohteiden vahvuutena on niiden matalat kustannukset ja helppous koehenkilölle. Puheen sekä kävelyliikkeen mittaamiseen voidaan hyödyntää älypuhelinta, jonka avulla myös puolijatkuva pitkäaikaisseuranta on mahdollista, esimerkiksi hoidon vasteen arvioimiseksi. Työssä tutkittujen koneoppimismenetelmien hyödyntäminen Parkinsonin taudin diagnosoinnissa on tarkempaa ja kustannustehokkaampaa kuin perinteiset menetelmät. Koneoppimismalleilla saavutettu tarkkuus on keskimääräisesti lääkärin arviota parempi. Menetelmien avulla myös objektiivinen hoidon vasteen arviointi olisi mahdollista. Mallien koulutukseen käytetyt otannat ovat kuitenkin suppeita, joka voi johtaa mallin puolueellisuuteen. Lisäksi johtuen tutkimuksien raportointistandardien puutteellisuudesta, malleista ei kaikkia tarpeellisia tietoja ole jaettu, jolloin on vaikeaa uudelleen toistaa tutkimukset. Koneoppimismenetelmillä on paljon potentiaalia olla tulevaisuudessa kliinisessä käytössä Parkinsonin taudin diagnosoinnin aputyökaluna terveydenhuollonammattilaisille. Perusteluina ovat aikaisempi, tarkempi ja kustannustehokkaampi diagnoosi, jonka avulla hoito voidaan aloittaa sairauden varhaisemmassa vaiheessa, mikä ylläpitää potilaan työkykyä ja elämänlaatua. Menetelmillä on mahdollista myös suorittaa pitkäaikaisseurantaa, jolla voidaan arvioida hoidon vastetta ja löytää uusia tehoavia hoitokeinoja. Tällä hetkellä kuitenkin tutkimuksien puolueellisuus ja huono uudelleentoistettavuus estävät mallien yleistämisen. Lisäksi kehitetyn koneoppimismallin pitää olla kokonaan jäljitettävä, jotta se täyttäisi lääketieteelliset standardit

Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease

Parkinson's disease is one of the most prevalent neurodegenerative conditions. Currently, there is no standard clinical tool available to diagnose PD. One of the research priorities is to come up with biomarkers which will improve the diagnostic process and can be used for the clinical test. At present, the only way to assess this disease is by visually observing the symptoms of the patient which is performed only by expert neurologists. As of now, there is no treatment to prevent the progression of PD. However, there is an elemental drug `Levodopa' (L-dopa) available to control the disease by increasing dopamine cells in the brain. It is important to detect PD and start treatment in the early stages as it helps to control the symptoms and significantly delays the development of motor complications. In this study fine motor symptoms handwriting has been studied. As a first objective I have conducted the experiments on the significant number of patients and age-matched control (112 Participants:56 PD and 56 controls), and thus completed the task of data collection. The system developed extracts the dynamic features of the handwriting/drawing, reports the possible strength of dynamic features providing a basis for automated analysis. The advantage of this approach is that patients are not required to follow complex commands, and the analysis can be fully automized. I anticipate that following appropriate clinical tests already planned, the system will be able to detect early disease symptoms remotely outside hospitals or clinics. It could also be used for self-evaluation by patients with neuromuscular and motor neuron disorders. This device can be used without compromising on the comfort level of Patients who may still prefer writing with an ink pen on plain paper. This study proposes a new feature `Composite Index of Speed and Pen-pressure' (CISP) to distinguish between different stages of Parkinson's disease. The experiment also demonstrated a method which can be used with guided spiral drawing to improve classification results to predict Parkinson's disease. Further, I recommend using a panel of writing tasks which might prove to be an effective biomarker for cell loss in the substantia nigra and the associated dopamine deficiency. Thus, models developed can be used in designing an automated application for predicting and monitoring Parkinson's diseas

Reports to the President

A compilation of annual reports including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

Computer analysis of handwriting dynamics during dopamimetic tests in Parkinson's disease

Studies of dynamic descriptors characteristic of Parkinsonian handwriting have clearly shown potential in identifying various stages of Parkinsonism. This study compares stroke-level analyses of handwritten samples during apomorphine and levodopa "challenge" tests. Six patients with Parkinsonism, diagnosed with or suspected of having Parkinson's Disease generated ordinary handwriting patterns before medication and once again at peak motor performance, after doses of apomorphine or levodopa had been administered. Results show that movement efficiency parameters in addition to standard dynamic handwriting parameters are indicative of positive responses to dopamimetic drugs. Furthermore, it is observed that parameters dependent on higher processing fend to be unaffected during dopamimetic drug cycles