3,003 research outputs found

    Tau pathology in Alzheimer's disease and other dementias : translational approach from in vitro autoradiography to in vivo PET imaging

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    Tauopathies, including Alzheimer's disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP), are complex neurodegenerative disorders characterized by the pathological accumulation of tau proteins in the brain. These often overlapping disorders, with intricate pathologies and growing prevalence, lack definitive treatments, highlighting the necessity for advanced research. Positron emission tomography (PET) imaging aids in the diagnosis and monitoring of diseases, by providing in vivo insights into pathological features. This thesis focused on deciphering the binding properties and brain regional distribution of PET tracers for accurate disease differentiation. Spanning four studies, we aimed to bridge in vitro and in vivo PET data to investigate tau pathology and its association with dementia-related markers such as reactive astrogliosis, peripheral inflammation, and dopaminergic dysfunction. The 2nd generation tau PET tracers, 3H-MK6240 and 3H-PI2620, demonstrated high affinity and specificity in AD post-mortem brain tissues, especially in early-onset AD, compared to controls. 3H-PI2620, 3H-MK6240, and 3HRO948 displayed similar binding patterns in AD tissue, with multiple binding sites and equivalent high affinities (Papers I and II). 3H-PI2620 showed specificity in CBD and PSP tissues, in contrast to 3H-MK6240. However, differentiating CBD from PSP brains with 3H-PI2620 remained challenging in multiple brain regions, potentially due to complex tracer-target interactions (Papers II and III). Reactive astrogliosis PET tracers 3H-Deprenyl and 3H-BU99008 bound primarily to stable distinct high-affinity binding sites in AD, CBD and PSP, but also to transient binding sites, differing by brain region and condition. This pattern implied that these tracers may interact with similar or diverse subtypes or populations of astrocytes, expressing varying ratios of transient sites, which may vary depending on the brain location and the disease (Paper III). Using 3H-FEPE2I, we delineated a reduction in dopamine transporter (DAT) levels within the putamen across CBD, PSP and Parkinson's Disease (PD) brains. Concomitantly, elevated 3H-Raclopride binding reflected higher dopamine D2 receptor (D2R) levels in PSP and PD. Nonetheless, our observations underscored the heterogeneity inherent to these neurodegenerative pathologies, emphasizing the criticality of individual variability in neuropathological manifestations (Paper III). Lastly, we investigated late middle-aged cognitively unimpaired Hispanic individuals, in dichotomous groups of in vivo amyloid-β (Aβ) PET (18F-Florbetaben) and plasma neurofilament light (NfL) biomarkers. Our findings suggest that elevated plasma inflammation and tau burden as measured by 18FMK6240, can be detected at early preclinical stages of AD, offering potential for early diagnosis (Paper IV). This thesis underscored the importance of PET imaging in advancing our understanding of tauopathies. The innovative use of multiple PET tracers provided crucial insights into their potential use in clinics to distinguish pathological features of AD, CBD and PSP. The findings emphasized the need for more studies applying a multifaceted approach to studying and managing these complex neurodegenerative disorders, combining advanced imaging techniques with a broad spectrum of biological markers

    Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

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    ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096

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

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    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

    Hippocampal Volume and the Detection of Mild Cognitive Impairment in an Older Adult Population: Assessing Performance on Cognitive Screeners Administered In-Person and Electronically

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    The present study investigated how performance on in-person and electronic neuropsychological assessment measures predicted subcortical hippocampal volume and cognitive decline consistent with mild cognitive impairment. It was hypothesized that the Montreal Cognitive Assessment would display better predictive strength than the Cogstate Brief Battery when evaluating subcortical hippocampal volume measured via structural magnetic resonance imaging. It was further hypothesized that the Montreal Cognitive Assessment would be more sensitive to predicting group membership to the diagnostic classification of mild cognitive impairment compared to the Cogstate Brief Battery. The sample included 445 older adult participants selected from the Alzheimer’s Disease Neuroimaging Initiative 3. Participants met criteria for diagnostic classifications of cognitively normal and mild cognitive impairment and had undergone neuropsychological testing consisting of the Montreal Cognitive Assessment and Cogstate Brief Battery, as well as structural magnetic resonance imaging scans of the hippocampus at baseline testing. The learning/working memory composite from the Cogstate Brief Battery was the only substantial predictor for total subcortical hippocampal volume. When evaluating predictive strength relative to group membership of either cognitively normal or mild cognitive impairment, the Montreal Cognitive Assessment was the most substantial predictor of diagnostic classification, specifically mild cognitive impairment. The learning/working memory composite from the Cogstate Brief Battery was also a good predictor of group membership, though the Montreal Cognitive Assessment was observed to be more sensitive overall. The results of this study maintained the effectiveness of in-person neuropsychological assessment, while also supporting the use of electronic measures with older adults when evaluating cognitive status. The data also contributes additional information that is helpful in the early detection of progressive neurodegenerative diseases, such as Alzheimer’s disease

    Genetic and molecular biomarkers of Alzheimer's disease

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    [eng] Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Research in the past decade has led to major progress in understanding the genetic etiology of the disease; since I started my PhD (2019), nearly 20 genetic risk factors have been associated with late onset AD. Among them, the Ԑ4 allele of the APOE gene was the first identified, and remains the major genetic risk factor for AD. Despite extensive genetic research, a large part of the disease heritability remains elusive, the disease mechanisms incomprehensible, and targeted preventive interventions or pharmacological treatments for AD unavailable at the time. In this context, the overarching aim of the studies included in this thesis was to contribute to the knowledge of AD identifying new genetic risk factors and to better understand the role played by the APOE gene in the development of the disease. Such information would allow us to gain new insights into the molecular and biological mechanisms underlaying the disease and ultimately find new targets for treatment. This thesis provides evidence of the possible effectiveness of the use of a polygenic risk scores in a clinical setting for diagnosis of AD and actively improves the knowledge of the genetic factors associated with AD through genome-wide association studies

    Brain organoids as innovative tool for regenerative medicine

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    The introduction of the tridimensional (3D) organoids technology is revolutionizing the fields of developmental and stem cell biology and it is emerging as the latest frontier in regenerative medicine for the treatment of neurodegenerative disorders, such as epilepsy. The overall objective of my PhD thesis was to set the stage to develop functional hippocampal brain organoid that can be used for regenerative medicine to cure the Temporal Lobe epilepsy (TLE)

    Apraxias in the Diagnosis of Frontotemporal Dementia and Alzheimer’s Disease

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    Alzheimerin tauti alkaa tyypillisesti muistihäiriöllä, mutta työikäisillä alkuoire on usein jokin muu neuropsykologinen häiriö. Yksi näistä on apraksia, joka tarkoittaa aivovaurion aiheuttamaa puutosta tahdonalaisissa liikkeissä ja niiden ymmärtämisessä. Työikäisillä Alzheimerin tauti tunnistetaan epätyypillisyytensä vuoksi hitaammin kuin iäkkäillä potilailla ja sekoitetaan herkästi esimerkiksi otsa-ohimolohkorappeumien aiheuttamiin dementioihin tai psykiatrisiin oireyhtymiin. Apraksioiden yleisyydestä ja muodoista otsa-ohimolohkorappeumissa ei ole kokonaiskuvaa, joten niiden selvittäminen oli väitöstyön ensimmäinen tavoite. Toinen tavoite oli määrittää, millä tarkkuudella apraksioiden arviointiin kehitetty testi tunnistaa varhain alkavan Alzheimerin taudin. Systemaattisessa kirjallisuuskatsauksessa ilmeni, että kuhunkin otsa-ohimolohkorappeumien dementiamuotoon voi kehittyä oma apraksiaprofiilinsa: Käytösjohtoisessa tautimuodossa selkein löydös oli kasvoapraksia, joka auttoi erottelussa Alzheimerin taudista. Yläraaja-apraksia ilmeni hienovaraisempana. Sujumattomassa afasiassa raportoitiin sekä kasvo- että raaja-apraksiaa ja lisäksi puheapraksiaa. Semanttisessa dementiassa ei tyypillisesti havaittu mitään näistä vaan liikkeiden merkityksen ja esineiden käytön ymmärryksen häiriötä. Logopenisessä afasiassa ei ilmennyt kasvo-apraksiaa, ja raaja-apraksia oli samankaltainen kuin Alzheimerin taudissa. Dementia Apraxia Testin erottelutarkkuutta tutkittiin 50–70-vuotiaiden Alzheimer-potilaiden, psykiatristen potilaiden ja terveiden verrokkien välillä. Testin raajapistemäärä erotteli Alzheimer-potilaat terveistä herkkyydellä 92% ja tarkkuudella 100% (Youden-arvo .92). Potilasryhmien välillä erotteluherkkyys oli 83% ja -tarkkuus 100% (Youden-arvo .83). Kasvopistemäärä ja muistitestit olivat epätarkempia: ne erottelivat oikein 70–75% ja 66–78% potilaista. Otsa-ohimolohkorappeumien dementiamuotoihin näyttää siis kehittyvän apraksiaprofiileja, joiden kliininen pätevyys ja erotusdiagnostinen arvo on relevantti jatkotutkimuksen aihe. Varhain alkavan Alzheimerin taudin tunnistukseen Dementia Apraxia Test tuottaa lisähyötyä erityisesti eroteltaessa psykiatrisperäisistä muistihäiriöistä. Jatkossa tarvitaan tietoa testin toimivuudesta muissa etenevissä aivosairauksissa ja psykoosisairauksissa.Early dementia is challenging to diagnose in late middle age if the disease debuts with symptoms other than memory disturbance. Alzheimer’s disease, the most prevalent type of dementia, is commonly confused with psychiatric syndromes and frontotemporal dementia. One of the atypical symptoms of Alzheimer’s disease is apraxia, a deficit in voluntary action. Frontotemporal dementia may also involve apraxia, but there are no integrative data on the topic. This thesis synthesized current evidence on apraxias in frontotemporal dementia and explored whether an apraxia test could support the detection of Alzheimer’s disease in middle age. The systematic literature review suggested specific apraxia profiles for each of the four clinical variants of frontotemporal dementia. The behavioural variant involved early face apraxia, a feature that enabled differentiation from Alzheimer’s disease. Limb apraxia was present but subtler than in Alzheimer’s disease. The nonfluent variant typically showed remarkable face and limb apraxia, often in combination with apraxia of speech. The semantic variant showed preserved production of simple gestures but impaired understanding of tool use and gestures. The apraxia profile of the logopenic variant resembled that of Alzheimer’s disease, with remarkable limb apraxia and spared face praxis. The diagnostic accuracy of the Dementia Apraxia Test was estimated between samples of 50–70-year-old Alzheimer’s disease patients, psychiatric patients and healthy control participants. The limb praxis scale of the test distinguished between the Alzheimer’s disease group and the healthy participants, with 92% sensitivity and 100% specificity (Youden index .92). Between the Alzheimer’s disease and psychiatric groups, the limb scale reached 83% sensitivity and 100% specificity (Youden index 0.83). The face scale and memory tests were diagnostically less accurate, correctly classifying 70–75% and 66–78% of the patients, respectively. Apraxia profiles may thus support differentiation between early dementias, a hypothesis that requires future clinical validation. The Dementia Apraxia Test accurately detects Alzheimer’s disease in middle-aged populations and is especially recommended for clinical use to identify psychiatric aetiology in patients with memory disturbances. The test’s performance in other neurodegenerative diseases and psychotic conditions should be investigated next
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