The role of neuroimaging in the diagnosis of the atypical parkinsonian syndromes in clinical practice

Atypical parkinsonian disorders (APD) are a heterogenous group of neurodegenerative diseases such as: progressive supranuclear palsy (PSP), multiple system atrophy (MSA), cortico-basal degeneration (CBD) and dementia with Lewy bodies (DLB). In all of them core symptoms of parkinsonian syndrome are accompanied by many additional clinical features not typical for idiopathic Parkinson's disease (PD) like rapid progression, gaze palsy, apraxia, ataxia, early cognitive decline, dysautonomia and usually poor response to levodopa therapy. In the absence of reliably validated biomarkers the diagnosis is still challenging and mainly based on clinical criteria. However, robust data emerging from routine magnetic resonance imaging (MRI) as well as from many advanced MRI techniques such as: diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), voxel-based morphometry (VBM), susceptibility-weighted imaging (SWI) may help in differential diagnosis. The main aim of this review is to summarize briefly the most important and acknowledged radiological findings of conventional MRI due to its availability in standard clinical settings. Nevertheless, we present shortly other methods of structural (like TCS – transcranial sonography) and functional imaging (like SPECT – single photon emission computed tomography or PET – positron emission tomography) as well as some selected advanced MRI techniques and their potential future applications in supportive role in distinguishing APD

Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy

Background: Structural connectivity is a promising methodology to detect patterns of neural network dysfunction in neurodegenerative diseases. This approach has not been tested in progressive supranuclear palsy (PSP). Objectives: The aim of this study is reconstructing the structural connectome to characterize and detect the pathways of degeneration in PSP patients compared with healthy controls and their correlation with clinical features. The second objective is to assess the potential of structural connectivity measures to distinguish between PSP patients and healthy controls at the single-subject level. Methods: Twenty healthy controls and 19 PSP patients underwent diffusion-weighted MRI with a 3T scanner. Structural connectivity, represented by number of streamlines, was derived from probabilistic tractography. Global and local network metrics were calculated based on graph theory. Results: Reduced numbers of streamlines were predominantly found in connections between frontal areas and deep gray matter (DGM) structures in PSP compared with controls. Significant changes in structural connectivity correlated with clinical features in PSP patients. An abnormal small-world architecture was detected in the subnetwork comprising the frontal lobe and DGM structures in PSP patients. The classification procedure achieved an overall accuracy of 82.23% with 94.74% sensitivity and 70% specificity. Conclusion: Our findings suggest that modelling the brain as a structural connectome is a useful method to detect changes in the organization and topology of white matter tracts in PSP patients. Secondly, measures of structural connectivity have the potential to correctly discriminate between PSP patients and healthy controls

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

Cognitive and Brain Imaging Changes in Parkinsonism

The present thesis comprises three main parts: one theoretical and two experimental. The first part, composed of two chapters, will introduce the clinical and neuropathological features underlying parkinsonian disorders, namely in Parkinson’s disease (PD) as well as in atypical parkinsonisms — multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) (Chapter 1). In this regard, PD and MSA are defined as synucleinopathies due to the presence of synuclein aggregates; while PSP that is characterized by tau protein accumulations, is part of tauopathies. Further, Chapter 2 will provide an overview of the cognitive dysfunctions characterizing these disorders, as well as evidence on the biological mechanisms and structural changes underlying cognitive alterations. The second and third parts are composed by studies I conducted during my doctoral research. Namely, in Chapter 3, I report results of my studies on cognitive screening instruments most sensitive in detecting cognitive alterations in atypical parkinsonisms compared to PD. In the following study, I characterized the progression of cognitive decline in these disorders (Chapter 4). Finally, I investigated with magnetic resonance imaging the structural changes underlying cognitive alterations in PD (Chapter 5), and MSA (Chapter 6). I conclude this thesis by discussing the clinical consequences of these cognitive and imaging findings (Chapter 7). PART I - Theoretical background Chapter 1: Parkinsonian disorders Parkinsonian disorders are characterized by different underlying pathologies. In PD and MSA there are synuclein aggregates respectively in dopamine neurons or in glial cells, while PSP patients present pathological aggregation of the tau-protein, resulting in neurofibrillary tangles formation (Daniel, de Bruin, & Lees, 1995; Dickson, 1999). Clinical manifestations depend by the characteristics of protein aggregation and by the extent of disease spread to cortical and subcortical regions (Halliday, Holton, Revesz, & Dickson, 2011). Thus, the present chapter will overview the underlying pathology of PD, MSA and PSP; and it will describe the different clinical features; and lastly review the most recent diagnostic criteria (e.g., Gelb, Oliver, & Gilman, 1999; Gilman et al., 2008; Höglinger et al., 2017). Chapter 2: Cognitive features and their underlying mechanisms in parkinsonian disorders Non-motor symptoms represent a crucial part of the parkinsonian disorders spectrum; and cognitive dysfunctions, including dementia, are probably the most relevant, since they affect functional independence of patients, increase caregiver burden as well as wield a considerable socioeconomic impact (Keranen et al., 2003; McCrone et al., 2011; Vossius, Larsen, Janvin, & Aarsland, 2011). The first part of this chapter will provide an overview on cognitive dysfunctions in PD, MSA, and PSP. Moreover, the clinical criteria for the diagnosis of mild cognitive impairment and dementia in PD will be reported (Dubois et al., 2007; Emre et al., 2007; Litvan et al., 2012), while so far there are no available criteria to assess cognitive syndromes in PSP and MSA. Lastly, the second and third parts of this chapter will review the evidence on biological mechanisms and structural changes underlying cognitive alterations in these disorders. PART II - Studies on cognitive manifestations in parkinsonian disorders Chapter 3: Montreal Cognitive Assessment and Mini-Mental State Examination performance in progressive supranuclear palsy, multiple system atrophy and Parkinson’s disease There is general agreement that cognitive dysfunctions are common in PD as well as in other parkinsonian disorders (Aarsland et al., 2017; Brown et al., 2010; Gerstenecker, 2017). Brief screening cognitive scales can be adopted in routine care, to support the clinician in the diagnostic process (Marras, Troster, Kulisevsky, & Stebbins, 2014). The Mini-Mental State Examination (MMSE) is the most widely used (Folstein, Folstein, & McHugh, 1975) although MMSE is relatively insensitive in detecting cognitive deficits in parkinsonian disorders mainly because it does not investigate the fronto-executive domain (Hoops et al., 2009). Conversely, the Montreal Cognitive Assessment (MoCA), another brief cognitive screening tool widely used with PD patients (Nasreddine et al., 2005), showed high sensitivity and specificity in the assessment of cognitive dysfunctions in PD (Gill, Freshman, Blender, & Ravina, 2008; Hoops et al., 2009; Zadikoff et al., 2008), as well as also in several neurodegenerative conditions such as Alzheimer’s disease, dementia with Lewy bodies (DLB) and Huntington’s disease (Biundo et al., 2016b; Hoops et al., 2009; Nasreddine et al., 2005; Videnovic et al., 2010). However, MoCA has been poorly investigated in atypical parkinsonisms — especially in PSP and MSA (Kawahara et al., 2015). Thus, this study’s main aim was to determine if MoCA is more sensitive than the commonly used MMSE in detecting cognitive abnormalities in patients with probable PSP and MSA, compared to PD. In this multicenter study across three European institutions, MMSE and MoCA were administered to 130 patients: 35 MSA, 30 PSP and 65 age, and education and sex matched-PD. We assessed between-group differences for MMSE, MoCA, and their subitems and calculated receiver operating characteristic (ROC) curves. Our results show that the mean MMSE is higher than the mean MoCA score in each patient group: MSA (27.7 ± 2.4 vs. 22.9 ± 3.0, p<0.0001), PSP (26.0 ± 2.9 vs. 18.2 ± 3.9, p<0.0001), and PD (27.3 ± 2.0 vs. 22.3 ± 3.5, p<0.0001). Furthermore, MoCA total score as well as its letter fluency subitem differentiates PSP from MSA and PD with high specificity and moderate sensitivity. Namely, a cut-off score of seven words or less per minute would support a diagnosis of PSP (PSP vs. PD: 86% specificity, 70% sensitivity; PSP vs. MSA: 71% specificity, 70% sensitivity). On the contrary, MMSE presented a ceiling effect for most subitems, except for the ‘bisecting pentagons’, with PSP performing worse than MSA and PD patients. These findings suggest that PSP and MSA, similar to PD patients, may present normal performance on MMSE, but reduced performance on MoCA. To conclude, MoCA is more sensitive than MMSE in detecting cognitive dysfunctions in atypical parkinsonisms, and together with its verbal fluency subitem can be a valuable test to support PSP diagnosis. Chapter 4: Prospective assessment of cognitive dysfunctions in parkinsonian disorders Clinical and research evidence suggests cognitive impairments in parkinsonian disorders are progressive. However, there are only a few longitudinal studies in the literature that investigated cognitive progression in PSP and MSA compared to PD (Dubois & Pillon, 2005; Rittman et al., 2013; Soliveri, 2000). In addition, previous studies are based on brief cognitive screening scales or on neuropsychological assessments that do not extensively investigate the full spectrum of cognitive abilities across the five cognitive domains (i.e., attention/working-memory, executive, memory, visuospatial and language). Furthermore, even though clinical criteria for mild cognitive impairment (MCI) and dementia in PD have been formulated (Dubois et al., 2007; Litvan et al., 2012), it remains to be investigated whether similar criteria might be applied also for atypical parkinsonisms (Marras et al., 2014). Based on these observations, the aims of the present study were to: i) assess the severity of cognitive dysfunctions in PSP and MSA patients using PD-criteria for cognitive statuses (i.e., MCI or dementia); ii) investigate the sensitivity of two widely used cognitive screening instruments, the MMSE and MoCA, in differentiating MSA, PSP and PD global cognitive profile; iii) characterize the progression of cognitive decline on the five cognitive domains and behavioral features; and to compare the 15-month follow-up profile across the parkinsonian diseases. Our sample included 18 patients with PSP, 12 MSA; and 30 PD patients, matched for age, education and sex. They were evaluated at baseline and at a mean of 15-month follow-up. Demographic and clinical variables were collected. From the cognitive standpoint, I selected a comprehensive neuropsychological battery specifically designed to target cognitive deficits in PD, according to Level II criteria (Dubois et al., 2007; Litvan et al., 2012; Marras et al., 2014). Thus, I applied these criteria also to MSA and PSP since there are no published criteria for atypical parkinsonisms. Statistical non-parametric analyses were used. I found PSP patients had more severe cognitive decline compared to PD and MSA. Namely, after 15-month follow-up, we noted a marked decline in the executive and language domains in the PSP group. Baseline and follow-up evaluations agreed, showing that PSP had a worse performance than PD and MSA patients: especially, in the Stroop test, verbal fluencies (semantic and phonemic) and MoCA. Assessing the severity of cognitive deficits, I found different percentages of cognitive status (i.e., normal cognition vs. MCI vs. dementia) among the three groups. In particular, the percentage of patients with dementia was higher in PSP compared to MSA (33% vs. no patients with dementia) even if disease duration was similar. Among MSA and PSP patients with multidomain MCI at baseline only PSP converted to dementia at follow-up. Then, although the disease duration was longer for PD patients compared with PSP, the proportion of patients who converted to dementia was lower in the PD group compared to PSP (7% vs. 16%), despite both groups having had similar baseline severity of MCI. Overall, these results suggest more rapid and severe cognitive decline in PSP while MSA patients generally have milder deficits. MoCA showed higher sensitivity than MMSE in detecting cognitive changes, especially in PSP. But MoCA was less sensitive than MMSE in detecting cognitive decline at 15-month in PD, suggesting that MMSE is better if one wants to track cognitive changes in PD. Neuropsychiatric features are more common in PSP than PD patients, especially apathy with accompanying low levels of anxiety and depression. Lastly, analysis of subitems revealed that PSP patients had a ‘clinically significant’ worsening after 15-month in the attentive/executive subitems (Trial Making Test part B and Clock drawing). But it has been observed that some patients also improved in specific subtasks at the follow-up. This improvement could be related to their higher medication dose (although the dopaminergic treatment was not significantly different between the baseline and follow-up). However, noteworthy alterations in performance have been seen for subitems sensitive to motor conditions (such as drawing figures and linking circles with a pen), which could affect cognitive outcome, leading to higher performance at follow-up. These limits of MoCA and MMSE scale have already been reported in PD patients (Biundo et al., 2016b; Hu et al., 2014), and maybe are more pronounced in atypical parkinsonisms. Taken together, these findings show that PSP patients were markedly impaired in comparison to the other parkinsonian disorders (MSA and PD) and six years after first symptoms, 33 percent of patients have dementia. This severe progression is possibly associated with the distribution of tau pathology that involves also cortical structures. On the contrary, the pattern of cognitive impairment in MSA is less severe, possibly due to the predominance of subcortical pathology with cortical involvement occurring only secondary to these abnormalities. Thus, these findings recommend using cognitive assessment to help differential diagnosis in atypical parkinsonisms, and to monitor disease progression. PART III - Neuroimaging studies of synucleinopathies Chapter 5: Amyloid depositions affect cognitive and motor manifestations in Parkinson’s disease Cognitive deficits, particularly executive problems, can be observed early in PD (Aarsland, Bronnick, Larsen, Tysnes, & Alves, 2009). Dysfunction of the frontostriatal dopaminergic system may influence the presence of executive and attention problems (Bruck, Aalto, Nurmi, Bergman, & Rinne, 2005), but so far, evidence from dopamine transporter (DAT) imaging are inconsistent (Delgado-Alvarado, Gago, Navalpotro-Gomez, Jimenez-Urbieta, & Rodriguez-Oroz, 2016). In this regard, the neuropathology underlying cognitive impairment in PD is heterogeneous (Irwin, Lee, & Trojanowski, 2013; Kehagia, Barker, & Robbins, 2010) and amyloid deposit involvement with synuclein pathology remains poorly defined, particularly in the disease’s early stages. Thus, this study’s aims were to investigate the interplay between amyloid depositions in frontostriatal pathways, striatal dopaminergic deficit and brain atrophy rates; and their contribution to cognitive defects (i.e., fronto-executive functions) in early-PD. A multicenter cohort of 33 PD patients from the Parkinson's Progression Markers Initiative underwent [18F]florbetaben positron emission tomography (PET) amyloid, [123I]FP-CIT (see Abbreviations List) single-photon emission computed tomography (SPECT), structural magnetic resonance imaging (MRI), clinical and selective cognitive evaluations. Our results showed that high amyloid levels were associated with reduced dopaminergic deficits in the dorsal striatum (as compared to low amyloid levels), increased brain atrophy in frontal and occipital regions and a tendency to show more frequent cognitive impairment in global cognition (as assessed by MoCA) and fronto-executive tests. Of note, amyloid depositions in frontostriatal regions were inversely correlated with cognitive performance. Overall, our findings suggest that early-PD patients with amyloid burden have higher brain atrophy rates and may experience more cognitive dysfunctions (i.e., executive) and motor impairment as compared to amyloid negative subjects. In this regard, our results seem to be aligned with a recent neuropathological hypothesis that considers synaptic axonal damage and dysfunction as the PD key feature (Tagliaferro & Burke, 2016). Indeed, dopaminergic system neurons are particularly vulnerable to synuclein pathology due to their axonal characteristics — long, thin and unmyelinated. This is also confirmed by imaging studies with DAT-binding PET (Caminiti et al., 2017), suggesting that synuclein aggregations in PD can affect synaptic function, and thus signal transmission from the disease’s very early stages. Our findings suggested a possible interaction between synuclein and the coincident amyloid pathology, wherein amyloid burden may facilitate the spread of synuclein (i.e., Lewy bodies) (Toledo et al., 2016), and we speculate that this interaction can further contribute to axonal vulnerability. Thus, consistently with this hypothesis, we conclude that possibly amyloid depositions act synergistically with synuclein pathology and affect PD clinical manifestations. Chapter 6: Brain structural profile of multiple system atrophy patients with cognitive impairment In contrast to other synucleinopathies (e.g., PD and DLB), presence of dementia is considered a non-supporting feature for MSA diagnosis (Gilman et al., 2008), however there is growing evidence that MSA patients can experience cognitive impairment ranging from executive dysfunctions to multiple-domain cognitive deficits, and in a few cases, also dementia (Gerstenecker, 2017). MMSE is a commonly used global cognitive scale and recently a large multicenter study has suggested using a cutoff score below 27 to increase the MMSE sensitivity in identifying cognitive dysfunctions in MSA (Auzou et al., 2015). Underlying mechanisms of cognitive impairment in MSA are still not understood, and in this regard evidence from MRI studies suggested a discrete cortical and subcortical contribution to explain cognitive deficits (Kim et al., 2015; Lee et al., 2016a), even though these findings were based on a relatively small number of patients at various disease stages as well as being single-center. Thus, the aim of our multicenter study was to better characterize the anatomical changes associated with cognitive impairment in MSA and to further investigate the cortical and subcortical structural differences in comparison to a sample of healthy subjects. We examined retrospectively 72 probable MSA patients and based on the MMSE threshold below 27, we defined 50 MSA as cognitively normal (MSA-NC) and 22 with cognitive impairment (MSA-CI). We directly compared the MSA subgroup, and further compared them to 36 healthy subjects using gray- and white-matter voxel-based morphometry and fully automated subcortical segmentation. Compared to healthy subjects, MSA patients showed widespread cortical (i.e., bilateral frontal, occipito-temporal, and parietal areas), subcortical, and white matter alterations. However, the direct comparison MSA-CI showed only focal volume reduction in the left dorsolateral prefrontal cortex compared with MSA-NC. These findings suggest only a marginal contribution of cortical pathology to cognitive deficits in MSA. Hence, we suggest that cognitive alterations are driven by focal frontostriatal degeneration that is in line with the concept of ‘subcortical cognitive impairment’

Differential diagnosis of parkinsonism based on deep metabolic imaging indices.

The clinical presentations of early idiopathic Parkinson's disease (PD) substantially overlap with those of atypical parkinsonian syndromes like multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1275 parkinsonian patients and 863 non-parkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3D deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices, which was blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI, and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8% for the diagnosis of PD, MSA, and PSP in the blind test cohort. In the German cohort, They resulted in sensitivities of 94.1%, 82.4%, 82.1%, and specificities of 84.0%, 99.9%, 94.1% respectively. Employing the PET scans independently achieved comparable performance to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show potential to provide an early and accurate differential diagnosis for parkinsonism and is robust when dealing with discrepancies between populations and imaging acquisitions

The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism

Introduction: For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems. Methods: For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types. Results: In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone. Conclusion: We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.ope

Fronto-striatal contributions to cognition and behaviour: Investigations in neurodegeneration

Alterations to fronto-striatal neural circuitry are the hallmark of many neurodegenerative conditions, giving rise to significant cognitive and behavioural symptoms. This thesis explores fronto-striatal atrophic change in two such conditions, Parkinson’s disease (PD) and behavioural variant frontotemporal dementia (bvFTD). This is a critical area of interest in PD where the role of atrophy in non-motor symptoms, as opposed to dopamine-mediated functional changes, is only beginning to be uncovered. In contrast, cognitive and behavioural decline in bvFTD has long been associated with cortical atrophy, but the contribution of striatal atrophic change is less established. Fronto-striatal atrophy in the conditions is investigated for its role in an array of cognitive and behavioural symptoms. In each study reported, patients have undergone either caregiver questionnaires, neuropsychological testing or novel experimental tasks, to assess 1) neuropsychiatric symptoms (Chapter 2, Publications I and II); 2) learning deficits (Chapter 3, Publication III); and 3) social decision-making (Chapter 4, Publication IV). Behavioural measures are related to fronto-striatal atrophy via voxel-based morphometry, a technique for neuroimaging analysis that enables quantification of local grey matter volume. This analysis was approached firstly at the group level to determine the extent of fronto-striatal grey matter loss in patients with respect to age-matched controls, before being correlated with specific cognitive/behavioural scores. Broadly, the results show that either distinct, or combined, regional fronto-striatal atrophy was related to cognition and behaviour in PD and bvFTD. More specially, these findings highlight a role for fronto-striatal atrophy in both the cognitive and everyday manifestations of neuropsychiatric dysfunction in PD, and in specific learning deficits. These findings have important implications for understanding the pathophysiology of those symptoms in PD, and represent a critical consideration in the future development of therapeutic interventions. In bvFTD these novel findings reveal a role for the striatum in complex cognition and behaviour, emphasising this as an important region for characterising symptoms in the disease, which may assist in diagnosis. Together, the findings provide important insights into the cognitive and behavioural symptoms in neurodegenerative disease, which at present remain incompletely understood and difficult to treat

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

The role of tau and neuroinflammation in Progressive Supranuclear Palsy and Alzheimer's Disease

The ability to assess the distribution and extent of tau pathology and neuroinflammation in Progressive Supranuclear Palsy (PSP) and Alzheimer’s disease (AD) ‘in vivo’ and to validate it using post mortem data is critical to develop reliable biomarkers for these disorders, and for tracking the effects of clinical trials using disease-modifying therapies. The main aim of my PhD was therefore to assess the utility of the PET radiotracers [18F]AV1451 and [11C](R)-PK11195 to compare the distribution and intensity of tau pathology and neuroinflammation respectively, in PSP and AD. I use both in vivo scanning and post mortem tissue from archival cases in the Cambridge Brain Bank. For the in vivo studies, I investigated whether tau accumulation and neuroinflammation relate to clinical measures of disease severity and cognitive impairment. My PhD was undertaken as part of the Neuroimaging of Inflammation in MemoRy and Other Disorders (NIMROD). Nineteen people with PSP-Richardson’s syndrome, 15 patients with amnestic Alzheimer’s disease and biomarker-positive mild cognitive impairment (MCI+) and 13 age- and sex-matched controls participated. Post mortem brain tissue from three subjects (one PSP case, one AD patient, and one control with similar age) were included. Autoradiographic and immunohistochemical analyses were conducted in different cases from those included in the [18F]AV1451 and [11C](R)-PK11195 PET in vivo studies. [18F]AV1451 PET imaging revealed distinct patterns of tau binding in AD and its prodromal state of MCI+, in comparison to PSP. The clinical syndromes of AD and MCI+ were associated with increased [18F]AV1451 BPND in widely distributed subcortical and cortical areas that have been consistently implicated in the pathogenesis and progression of AD (e.g. hippocampus, amygdala as well as frontal, parietal, temporal, and occipital cortices). Conversely, PSP was associated with a pattern of increased [18F]AV1451 uptake in the basal ganglia, midbrain, and dentate nucleus of the cerebellum, consistent with the pathophysiology of the disease. Despite this potential of [18F]AV1451 as a tau biomarker, caution in the interpretation of its binding targets is indicated by the neuropathological and autoradiographic data. In particular, while [18F]AV1451 strongly bound to AD-related tau pathology, non-specific binding of the same tracer can be found in PSP patients and controls. Furthermore, [18F]AV1451 uptake was not correlated with disease severity in the clinical groups. [11C](R)-PK11195 PET imaging revealed distinct patterns of inflammation in AD and PSP, and mirrored the pathological distribution for each disease seen by [11C](R)-PK11195 post mortem phosphor screen autoradiography and immunohistochemistry. In AD, microglial activation was observed in prefrontal cortex, and medial temporal lobe. In PSP microglial activation was observed in midbrain and basal ganglia. In both clinical cohorts, disease severity correlated with neuroinflammation in the regions most closely associated with principal neuropathological markers including tau aggregates

Synaptic loss in the primary tauopathies of Progressive Supranuclear Palsy and Corticobasal Degeneration

In this thesis I address the debilitating symptom of cognitive dysfunction in the primary tauopathies of Progressive Supranuclear Palsy (PSP) and Corticobasal Degeneration (CBD). Both PSP and CBD are associated with an accumulation of 4-repeat tau in cortical and subcortical areas. As well as movement disorders, they impair cognitive function, even where there is minimal atrophy. Neurophysiological studies have also identified electrophysiological changes associated with cognitive dysfunction, in areas without atrophy. I propose that synaptic loss prior to cell loss contributes to these effects of disease. Chapter two summarises my cohort and principal methods. I quantify synaptic density in vivo with dynamic [11C]UCB-J PET, and molecular pathology with [18F]AV1451 PET. Brain structural changes are quantified by MRI. Disease severity and cognition are assessed with the PSP rating scale, and neuropsychological tests. Patients with CBD are negative on amyloid-imaging ([11C]PiB PET) to exclude those with Alzheimer’s pathology. In chapter three, [11C]UCB-J PET reveals widespread loss of synapses in PSP and CBD including areas with minimal atrophy. The loss of synapses correlated with cognition and disease severity. In chapter four, I test whether presynaptic changes (from [11C]UCB-J PET) are correlated with postsynaptic abnormalities (i.e. changes to postsynaptic dendritic microstructural integrity quantified by MRI using the Neurite Orientation and Dispersion Index, NODDI). In accordance with in vitro and animal models, I confirm that loss of dendritic complexity is tightly coupled with presynaptic density, over and above the effects of atrophy. In chapter five, I test the relationship between the molecular pathology in primary tauopathies (tau burden) and synaptic loss, using [18F]AV-1451 and [11C]UCB-J PET. The use of the “tau” ligand [18F]AV-1451 has become controversial in PSP. With due consideration to the caveats, I report that brain regions with a higher synaptic density have higher [18F]AV-1451 binding, consistent with the hypothesis of connectivity-based progression of tauopathy. I further show that accrual of pathology in any given area is associated with loss of synapses, consistent with synaptic injury from tauopathy. I conclude my thesis in chapter 6, by discussing and highlighting the importance of synaptic density in primary tauopathies. The findings are relevant to other neurodegenerative disorders, and support early interventional studies targeting synaptic maintenance and restoration.Association of British Neurologists - Patrick Berthoud Charitable Trus