Koga, Shunsuke

AbstractThis study aimed to assess and compare the burden of transactive response DNA‐binding protein of 43 kDa (TDP‐43) pathology and clinical features of amyotrophic lateral sclerosis (ALS) in three age groups. All cases were from the Mayo Clinic brain bank for neurodegenerative disorders and most were followed longitudinally in the ALS Clinic. Cases with moderate‐to‐severe Alzheimer's disease neuropathological change were excluded. The 55 cases included in the study were divided into three groups by age at death: 75 years or older (old‐ALS, n = 8), 64–74 years (middle‐ALS, n = 23), and 63 years or younger (young‐ALS, n = 24). Clinical features, including disease duration, initial symptoms, and ALS Cognitive Behavior Score (ALS‐CBS), were summarized. Sections of paraffin‐embedded tissue from the motor cortex, basal forebrain, medial temporal lobe, and middle frontal gyrus were processed for phospho‐TDP‐43 immunohistochemistry. The burden of TDP‐43 pathology was analyzed using digital image analysis. The TDP‐43 burden in the limbic system (i.e., amygdala, dentate gyrus and CA1 sector of the hippocampus, subiculum, and entorhinal cortex) was greater in old‐ALS than in young‐ALS and middle‐ALS. TDP‐43 burden in the middle frontal gyrus was sparse and did not differ between the three groups. The average of ALS‐CBS was not different between the three groups. The present study shows that the amygdala and hippocampus are vulnerable to TDP‐43 pathology in older patients with ALS. We discuss the evidence for and against this pathology being related to concurrent limbic‐predominant, age‐related TDP‐43 encephalopathy neuropathologic change.

Primary progressive aphasia (PPA) is a progressive language disorder often due to an underlying neurodegenerative disease. The most common pathologies associated with PPA include frontotemporal lobar degeneration (FTLD)‐tau, FTLD‐associated with transactivation response DNA‐binding protein of 43 kDa (TDP‐43) (FTLD‐TDP), and Alzheimer's disease (AD). Accumulating evidence has suggested that Lewy body disease (LBD) can also be associated with PPA. We herein report a 78‐year‐old Caucasian woman who initially presented with levodopa‐responsive parkinsonism at age 67 and later developed cognitive impairment, visual hallucinations, rapid eye movement sleep behavior disorder, and progressive aphasia, characterized by reduced spontaneous speech, word‐finding difficulty, and difficulties in writing and reading. 18Fluorodeoxyglucoase (FDG)‐positron emission tomography (PET) performed at the age of 73 years identified hypometabolism in the frontal (right > left), temporal (left > right), and parietal (left > right) lobes. Neuropathological assessment revealed diffuse LBD (DLBD), AD, and TDP‐43 stage 6 with prehippocampal sclerosis. Senile plaques were numerous, but only a few neurofibrillary tangles were present in the neocortex. The Braak neurofibrillary tangle stage was IV, and the Thal amyloid phase was 3. Lewy‐related pathology was severe in the neocortex, as well as limbic cortices, basal forebrain, amygdala, and brainstem. Compared to 166 DLBD cases with a clinical diagnosis of dementia with Lewy bodies (DLB), the Lewy body count of the patient in this report was highest in the inferior parietal cortex, followed by midfrontal and superior temporal cortices. The findings suggest that severe cortical LBD pathology has contributed to her progressive aphasia. Autopsy cases of LBD presenting as PPA have been reported, but patients with PD and autopsy‐proven DLBD who later developed progressive aphasia have not been reported. Our findings indicate that PD can be associated with progressive aphasia later in the disease course. Although uncommon, LBD should be considered as a differential diagnosis of progressive aphasia.

Abstract Aims Accumulating evidence suggests that patients with frontotemporal lobar degeneration (FTLD) can have pathologic accumulation of multiple proteins, including tau and TDP‐43. This study aimed to determine the frequency and characteristics of concurrent tau pathology in FTLD with TDP‐43 pathology (FTLD‐TDP). Methods The study included 146 autopsy‐confirmed cases of FTLD‐TDP and 55 cases of FTLD‐TDP with motor neuron disease (FTLD‐MND). Sections from the basal forebrain were screened for tau pathology with phosphorylated‐tau immunohistochemistry. For cases with tau pathology on the screening section, additional brain sections were studied to establish a diagnosis. Genetic analysis of C9orf72 , GRN and MAPT was performed on select cases. Results We found 72 cases (36%) with primary age‐related tauopathy (PART), 85 (42%) with ageing‐related tau astrogliopathy (ARTAG), 45 (22%) with argyrophilic grain disease (AGD) and 2 cases (1%) with corticobasal degeneration (CBD). Patients with ARTAG or AGD were significantly older than those without these comorbidities. One of the patients with FTLD‐TDP and CBD had C9orf72 mutation and relatively mild tau pathology, consistent with incidental CBD. Conclusion The coexistence of TDP‐43 and tau pathologies was relatively common, particularly PART and ARTAG. Although rare, patients with FTLD can have multiple neurodegenerative proteinopathies. The absence of TDP‐43‐positive astrocytic plaques may suggest that CBD and FTLD‐TDP were independent disease processes in the two patients with both tau and TDP‐43 pathologies. It remains to be determined if mixed cases represent a unique disease process or two concurrent disease processes in an individual.

Abstract Synucleinopathies are clinically and pathologically heterogeneous disorders characterized by pathologic aggregates of α-synuclein in neurons and glia, in the form of Lewy bodies, Lewy neurites, neuronal cytoplasmic inclusions, and glial cytoplasmic inclusions (GCIs). Synucleinopathies can be divided into two major disease entities: Lewy body disease (LBD) and multiple system atrophy (MSA). Common clinical presentations of LBD are Parkinson's disease (PD), PD with dementia (PDD), and dementia with Lewy bodies (DLB), while MSA has two major clinical subtypes, MSA with predominant cerebellar ataxia (MSA-C) and MSA with predominant parkinsonism (MSA-P). There are currently no disease-modifying therapies for the synucleinopathies, but elucidation of genetics and mechanisms of α-synuclein conversion to pathologic oligomers and insoluble fibrils offer hope for eventual therapies. It remains unclear how α-synuclein can be associated with distinct cellular pathologies (e.g., Lewy bodies and GCI) and what factors determine neuroanatomical and cell type vulnerability. Accumulating evidence from in vitro and in vivo experiments suggests that α-synuclein species derived from LBD and MSA are distinct "strains" having different seeding properties. Recent advancements in in vitro seeding assays, such as real-time quaking-induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA), not only demonstrate distinct seeding activity in the synucleinopathies, but also offer exciting opportunities for molecular diagnosis using readily accessible peripheral tissues. Cryogenic electron microscopy (cryo-EM) structural studies of α-synuclein derived from recombinant or brain-derived filaments provide new insight into mechanisms of seeding in synucleinopathies. In this review, we describe clinical, genetic and neuropathologic features of synucleinopathies, including a review of classification and staging schemes for LBD. We also review evidence supporting the existence of distinct α-synuclein strains in LBD and MSA.

Abstract Aims This study aimed to develop a deep learning‐based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau‐immunostained digital slide images. Methods We trained the YOLOv3 object detection algorithm to detect five tau lesion types: neuronal inclusions, neuritic plaques, tufted astrocytes, astrocytic plaques and coiled bodies. We used 2522 digital slide images of CP13‐immunostained slides of the motor cortex from 10 cases each of AD, PSP and CBD for training. Data augmentation was performed to increase the size of the training dataset. We next constructed random forest classifiers using the quantitative burdens of each tau lesion from motor cortex, caudate nucleus and superior frontal gyrus, ascertained from the object detection model. We split 120 cases (32 AD, 36 PSP, 31 CBD and 21 PiD) into training (90 cases) and test (30 cases) sets to train random forest classifiers. Results The resultant random forest classifier achieved an average test score of 0.97, indicating that 29 out of 30 cases were correctly diagnosed. A validation study using hold‐out datasets of CP13‐ and AT8‐stained slides from 50 cases (10 AD, 17 PSP, 13 CBD and 10 PiD) showed >92% (without data augmentation) and >95% (with data augmentation) diagnostic accuracy in both CP13‐ and AT8‐stained slides. Conclusion Our diagnostic model trained with CP13 also works for AT8; therefore, our diagnostic tool can be potentially used by other investigators and may assist medical decision‐making in neuropathological diagnoses of tauopathies.

AbstractAimsThis study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning‐based decision tree classifier.MethodsParaffin‐embedded sections of the temporal cortex, motor cortex, caudate nucleus, globus pallidus, subthalamic nucleus, substantia nigra, red nucleus, and midbrain tectum from 1020 PSP and 199 CBD cases were assessed by phospho‐tau immunohistochemistry. The severity of tau lesions (i.e., neurofibrillary tangle, coiled body, tufted astrocyte or astrocytic plaque, and tau threads) was semi‐quantitatively scored in each region. Hierarchical cluster analysis was performed using tau pathology scores. A decision tree classifier was made with tau pathology scores using 914 cases. Cross‐validation was done using 305 cases. An additional ten cases were used for a validation study.ResultsCluster analysis displayed two distinct clusters; the first cluster included only CBD, and the other cluster included all PSP and six CBD cases. We built a decision tree, which used only seven decision nodes. The scores of tau threads in the caudate nucleus were the most decisive factor for predicting CBD. In a cross‐validation, 302 out of 305 cases were correctly diagnosed. In the pilot validation study, three investigators made a correct diagnosis in all cases using the decision tree.ConclusionRegardless of the morphology of astrocytic tau lesions, semi‐quantitative tau pathology scores in select brain regions are sufficient to distinguish PSP and CBD. The decision tree simplifies neuropathologic differential diagnosis of PSP and CBD.