Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator.

Deep brain stimulation (DBS) for Parkinson\u27s disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

Impulsivity Relates to Relative Preservation of Mesolimbic Connectivity in Patients with Parkinson Disease.

IntroductionThe relationship between Parkinson Disease (PD) pathology, dopamine replacement therapy (DRT), and impulse control disorder (ICD) development is still incompletely understood. Given the sensorimotor-lateral substantia nigra (SN) selective degeneration associated with PD, we posit that a relative sparing of the limbic-medial SN in the context of DRT drives impulsive, reward-seeking behavior in PD patients with recent history of severe impulsivity.MethodsImpulsive and control participants were selected from a consecutive list of PD patients receiving pre-operative deep brain stimulation (DBS) planning scans including 3T structural MRI and 64 direction diffusion tensor imaging (DTI). Using previously identified substantia nigra (SN) subsegment network connectivity profiles to develop classification targets, split-hemisphere target-based SN segmentation with probabilistic tractography was performed. The relative subsegment volumes and strength of connectivity between the SN and the limbic, associative, and motor network targets were compared.ResultsOur results show that there is greater probability of connectivity between the SN and limbic network targets relative to motor and associative network targets in PD patients with recent history of severe impulsivity as compared to PD patients without impulsivity (P = 0.0075). We did not observe relative volumetric subsegment differences across groups.ConclusionFirstly, our results suggest that fine-grained, atlas-derived classification targets may be used in PD to parcellate and classify functionally distinct subsegments of the SN, with the apparent preservation of previously reported topographical limbic-medial SN, associative-ventral SN, and sensorimotor-lateral SN orientation. We suggest that relative, as opposed to absolute, degeneration amongst SN-associated dopaminergic networks relates to the impulsivity phenotype in PD

TWNK in Parkinson's Disease: A Movement Disorder and Mitochondrial Disease Center Perspective Study

Background: Parkinsonian features have been described in patients harboring variants in nuclear genes encoding for proteins involved in mitochondrial DNA maintenance, such as TWNK. Objectives: The aim was to screen for TWNK variants in an Italian cohort of Parkinson's disease (PD) patients and to assess the occurrence of parkinsonism in patients presenting with TWNK-related autosomal dominant progressive external ophthalmoplegia (TWNK-adPEO). Methods: Genomic DNA of 263 consecutively collected PD patients who underwent diagnostic genetic testing was analyzed with a targeted custom gene panel including TWNK, as well as genes causative of monogenic PD. Genetic and clinical data of 18 TWNK-adPEO patients with parkinsonism were retrospectively analyzed. Results: Six of 263 PD patients (2%), presenting either with isolated PD (n = 4) or in combination with bilateral ptosis (n = 2), carried TWNK likely pathogenic variants. Among 18 TWNK-adPEO patients, 5 (28%) had parkinsonism. Conclusions: We show candidate TWNK variants occurring in PD without PEO. This finding will require further confirmatory studies. © 2022 Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society

Pathophysiological mechanisms in Parkinson`s Disease and Dystonia – converging aetiologies

In this thesis I used a range of experimental approaches including genetics, enzyme activity measurements, histology and imaging to explore converging pathophysiological mechanisms of Parkinson`s disease and dystonia, two conditions with frequent clinical overlap. First, based on a combined retro- and prospective cohort of patients, using a combination of lysosomal enzyme activity measurements in peripheral blood and brain samples, as well as a target gene approach, I provide first evidence of reduced levels of enzyme activity in Glucocerebrosidase and the presence of GBA mutations, indicating lysosomal abnormality, in a relevant proportion of patients with dystonia of previously unknown origin. Second, based on a retrospective cohort of patients, I detail that a relevant proportion of genetically confirmed mitochondrial disease patients present with a movement disorder phenotype - predominantly dystonia and parkinsonism. Analysing volumetric MRI data, I describe a patterned cerebellar atrophy in these particular patients. This also includes the first cases of isolated dystonia due to mitochondrial disease, adding the latter as a potential aetiology for dystonia of unknown origin. Third, I used a combination of post-GWAS population genetic approaches and tissue-based experiments to explore in how far the strong association between advancing age and Parkinson ́s disease is mediated via telomere length. Although the initial finding of an association between genetically determined telomere length and PD risk did not replicate in independent cohorts, I provide evidence that telomere length in human putamen physiologically shortens with advancing age and 3 is regulated differently than in other brain regions. This is unique in the human brain, implying a particular age-related vulnerability of the striatum, part of the nigro-striatal network, crucially involved in PD pathophysiology. I conclude by discussing the above findings in light of the current literature, expand on their relevance and possible direction of future experiments

Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future

Anatomical texture patterns identify cerebellar distinctions between essential tremor and Parkinson's disease

Voxel-based morphometry is an established technique to study focal structural brain differences in neurologic disease. More recently, texture-based analysis methods have enabled a pattern-based assessment of group differences, at the patch level rather than at the voxel level, allowing a more sensitive localization of structural differences between patient populations. In this study, we propose a texture-based approach to identify structural differences between the cerebellum of patients with Parkinson's disease (n =???280) and essential tremor (n =???109). We analyzed anatomical differences of the cerebellum among patients using two features: T1-weighted MRI intensity, and a texture-based similarity feature. Our results show anatomical differences between groups that are localized to the inferior part of the cerebellar cortex. Both the T1-weighted intensity and texture showed differences in lobules VIII and IX, vermis VIII and IX, and middle peduncle, but the texture analysis revealed additional differences in the dentate nucleus, lobules VI and VII, vermis VI and VII. This comparison emphasizes how T1-weighted intensity and texture-based methods can provide a complementary anatomical structure analysis. While texture-based similarity shows high sensitivity for gray matter differences, T1-weighted intensity shows sensitivity for the detection of white matter differences

Basal ganglia function in parkinsonism and dystonia

Parkinsonism and dystonia are movement disorders linked with abnormal function of the basal ganglia. The most common cause of parkinsonism, Parkinson’s disease (PD), is caused by loss of dopaminergic neurons in the nigrostriatal tract, leading to dopamine depletion in the striatum. The pathophysiology in dystonia is largely unknown, although a major role of the basal ganglia has been suspected. Both syndromes can be treated with deep brain stimulation (DBS) in specific targets in the basal ganglia. The aim of this thesis was to study the function of the basal ganglia in parkinsonism and dystonia using single-photon emission computed tomography (SPECT) and positron emission tomography (PET). The work in this thesis was broadly divided into two sets of experiments. The basal ganglia function of patients with PD, non-degenerative parkinsonism and healthy controls were evaluated using dopamine transporter (DAT) imaging. In the other experiment, basal ganglia function in dystonia was investigated in patients with cervical dystonia undergoing globus pallidus interna (GPi) DBS using 18F-fluoro-deoxyglucose-positron emission tomography (FDG-PET). The results of this thesis showed that DAT binding does not predict the number of preserved neurons in the striatum in PD. Moreover, patients with a non-degenerative condition seemed to have higher DAT binding compared to healthy controls. Bupropion, even with a recommended wash-out time, caused clearly abnormal DAT binding in a patient without a neurodegenerative disorder affecting the dopamine system. In cervical dystonia, GPi-DBS increased glucose metabolism at the stimulation site and in other basal ganglia structures as well as in the primary sensorimotor cortex. Metabolic changes in the cortical regions, including the primary sensorimotor cortex and the supplementary motor area (SMA), correlated with acute and long-term therapeutic benefits, respectively. The symptoms returned gradually to the preoperative level after cessation of treatment. The results of this thesis indicate that DAT imaging reflects dopamine function of the striatum rather than neuron count. Moreover, DAT binding is affected by several factors that should be controlled for in both clinical work and in research settings. The findings also suggest that dystonia involves brain regions outside the basal ganglia, which may play a critical role in motor symptoms of dystonia and contribute to slow neuroplastic changes associated with DBS.Tyvitumakkeiden toiminta parkinsonismissa ja dystoniassa Parkinsonismi ja dystonia ovat neurologisia liikehäiriösairauksia, jotka yhdistetään tyvitumakkeiden eli aivojen liikehäiriökeskuksen toimintahäiriöihin. Yleisimmässä parkinsonismissa, Parkinsonin taudissa, aivojen striatumin ja mustatumakkeen dopamiinisolut tuhoutuvat. Dystonian etiologia on sen sijaan edelleen epäselvä, mutta sen on epäilty johtuvan tyvitumakkeiden poikkeavasta toiminnasta. Molempien sairauksien vaikeita muotoja voidaan hoitaa tyvitumakealueelle kohdennettavalla syväaivostimulaattorilla. Tässä tutkimuksessa tyvitumakkeiden toimintaa tutkittiin isotooppikuvantamisella parkinsonismissa ja dystoniassa. Väitöskirjassa tutkittiin Parkinsonin tautia sairastavia henkilöitä, oireisia ja dopamiinitoiminnaltaan terveitä henkilöitä sekä terveitä ja oireettomia henkilöitä dopamiinitransportterikuvantamisella. Lisäksi syväaivostimulaatiohoitoa saavia dystoniapotilaita tutkittiin aivojen sokeriaineenvaihduntaa kuvaavalla PET-tutkimuksella. Tulokset osoittivat, että aivojen dopamiinitransportterisitoutuminen ei ennusta säilyneiden hermosolujen määrää. Sitoutumisarvot saattavat myös olla korkeampia dopamiinitoiminnallaan terveillä, mutta oireisilla potilailla kuin terveillä ja oireettomilla henkilöillä. Lisäksi bupropion saattaa aiheuttaa virheellisiä tuloksia dopamiinikuvantamiseen. Syväaivostimulaattori lisää dystoniapotilalla aivojen sokeriaineenvaihduntaa stimulaatiokohdassa ja lisäksi viereisissä rakenteissa tyvitumakealueella sekä aivokuorella tunto- ja liikeaivokuorella. Oirekuvan nopea korjaantuminen korreloi aineenvaihdunnan lisääntymiseen tunto- ja liikeaivokuorella ja pitkäaikainen hoitovaste lisääntymiseen suplementaarisella motorisella aivokuorella. Oirekuvan hitaampaa palautumista kahden vuorokauden hoitotauon aikana ennusti nuori ikä. Tulokset osoittavat, että tyvitumakkeiden dopamiinikuvantamisessa tulosten tulkinta kliinisessä työssä ja tutkimuksessa ei ole johtopäätösten kannalta yksiselitteistä ja tulee tehdä mahdolliset virhelähteet huomioiden. Dystoniassa myös tyvitumakkeen ulkopuoliset aivoalueet saattavat olla tärkeitä oirekuvan synnyssä sekä hoitovasteen kehittymisessä syväaivostimulaatiohoidossa

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Neuropsychiatric and cognitive symptoms in Parkinson’s disease: the contribution to subtype classification, to differential diagnosis, their clinical and instrumental correlations

Il piano di ricerca è volto ad approfondire il contributo dei sintomi neuropsichiatrici e cognitivi nelle diverse fasi della Malattia di Parkinson (MP). In particolare, l’argomento di studio è focalizzato sull’analisi dei sintomi cognitivi e neuropsichiatrici nella MP, affrontando queste tematiche anche mediante l’utilizzo di tecniche di neuroimaging, in pazienti drug-naïve, in fase precoce di malattia ed in fase avanzata. Nei pazienti drug-naïve, la ricerca è stata finalizzata alla caratterizzazione dei sintomi neuropsichiatrici e cognitivi nei sottotipi motori (i.e., tremorigeni vs acinetico-rigidi) e rispetto alla lateralità di esordio degli stessi (i.e., lateralità destra vs lateralità sinistra). Nei pazienti in fase precoce di malattia, è stato indagato il contributo dei sintomi neuropsichiatrici e cognitivi nella diagnosi differenziale tra MP e Paralisi Sopranucleare Progressiva (PSP) in pazienti valutati entro i 24 mesi dall’esordio motorio, finestra temporale in cui spesso si assiste ad un overlapping dei sintomi motori. Nei pazienti in fase avanzata di malattia, la ricerca è stata finalizzata alla caratterizzazione, mediante i sintomi neuropsichiatrici e cognitivi, del Gioco D’Azzardo Patologico (gambling) rispetto agli altri tipi di Disturbi del controllo degli Impulsi (ICDs). Ancora nell’ambito dell’ICDs, è stato sviluppato uno studio di neuroimaging, volto ad identificare i correlati morfostrutturali (spessori corticali e volumi dei nuclei sottocorticali) di tali disturbi. Infine, si sono identificati i sintomi neuropsichiatrici e cognitivi che possono impedire l’esecuzione di un esame di Risonanza Magnetica (RM), al fine, in ambito clinico, di preparare adeguatamente all’esame i pazienti più a rischio di mancato svolgimento e con l’intento di indagare, in ambito di ricerca, la reale rappresentatività campionaria dei pazienti inseriti in studi di RM