Relevant bibliographies by topics / Neuroimaging biomarkers

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Neuroimaging biomarkers'

Author: Grafiati
Published: 15 January 2025

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Journal articles on the topic "Neuroimaging biomarkers"

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Hager, Brandon M., and Matcheri S. Keshavan. "Neuroimaging Biomarkers for Psychosis." Current Behavioral Neuroscience Reports 2, no. 2 (March 6, 2015): 102–11. http://dx.doi.org/10.1007/s40473-015-0035-4.

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Mishra, Asht Mangal, Harrison Bai, Alexandra Gribizis, and Hal Blumenfeld. "Neuroimaging biomarkers of epileptogenesis." Neuroscience Letters 497, no. 3 (June 2011): 194–204. http://dx.doi.org/10.1016/j.neulet.2011.01.076.

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Mackey, Sean, Henry T. Greely, and Katherine T. Martucci. "Neuroimaging-based pain biomarkers." PAIN Reports 4, no. 4 (2019): e762. http://dx.doi.org/10.1097/pr9.0000000000000762.

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Risacher, Shannon L. "Neuroimaging in Dementia." CONTINUUM: Lifelong Learning in Neurology 30, no. 6 (December 2024): 1761–89. https://doi.org/10.1212/con.0000000000001509.

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ABSTRACT OBJECTIVE This article captures the current literature regarding the use of neuroimaging measures to study neurodegenerative diseases, including early- and late-onset Alzheimer disease, vascular cognitive impairment, frontotemporal lobar degeneration disorders, dementia with Lewy bodies, and Parkinson disease dementia. In particular, the article highlights significant recent changes in novel therapeutics now available for the treatment of Alzheimer disease and in defining neurodegenerative disease using biological frameworks. Studies summarized include those using structural and functional MRI (fMRI) techniques, as well as metabolic and molecular emission tomography imaging (ie, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]). LATEST DEVELOPMENTS Neuroimaging measures are considered essential biomarkers for the detection and diagnosis of most neurodegenerative diseases. The recent approval of anti-amyloid antibody therapies has highlighted the importance of MRI and PET techniques in treatment eligibility and monitoring for associated side effects. Given the success of the initial biomarker-based classification system for Alzheimer disease (the amyloid, tau, neurodegeneration [A/T/N] framework), researchers in vascular cognitive impairment have created similar techniques for biomarker-based diagnosis. Further, the A/T/N framework for Alzheimer disease has been updated to include several pathologic targets for biomarker detection. ESSENTIAL POINTS Neurodegenerative diseases have a major health impact on millions of patients around the world. Neuroimaging biomarkers are rapidly becoming major diagnostic tools for the detection, monitoring, and treatment of neurodegenerative diseases. This article educates readers about the current literature surrounding the use of neuroimaging tools in neurodegenerative diseases along with recent important developments in the field.
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Russo, Antonio, Marcello Silvestro, Alessandro Tessitore, and Gioacchino Tedeschi. "Functional Neuroimaging Biomarkers in Migraine: Diagnostic, Prognostic and Therapeutic Implications." Current Medicinal Chemistry 26, no. 34 (December 12, 2019): 6236–52. http://dx.doi.org/10.2174/0929867325666180406115427.

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Background: In current migraine clinical practice, conventional neuroimaging examinations are often sought to exclude possible causes of secondary headaches or migraineassociated disorders. Contrariwise, although advanced Magnetic Resonance Imaging (MRI) has improved tremendously our understanding of human brain processes in migraine patients, to the state of the art they have not superseded the conventional neuroimaging techniques in the migraine clinical setting. Methods: A comprehensive review was conducted of PubMed citations by entering the keyword “marker” and/or “biomarker” combined with “migraine” and/or “headache”. Other keywords included “imaging” or “neuroimaging”, “structural” or “functional”. The only restriction was English-language publication. The abstracts of all articles meeting these criteria were reviewed, and the full text was retrieved and examined for relevant references. Results: Several authors tried to identify imaging biomarkers able to identify different migraine phenotypes or, even better, to follow-up the same migraine patients during the course of the disease, to predict the evolution into more severe phenotypes and, finally, the response to specific treatment. Conclusion: The identification of diagnostic, prognostic and therapeutic advanced neuroimaging biomarkers in the migraine clinical setting, in order to approach to patients in a more and more rational and “tailored” way, is extremely intriguing and futuristic. Unfortunately, reliable and robust neuroimaging biomarkers are still lacking for migraine, probably due to both not completely understood pathogenesis and clinical and neuroimaging heterogeneity. Although further longitudinal advanced neuroimaging studies, aimed to identify effective neuroimaging biomarkers, are needed, this review aims to collect the main and most recent works on this topic.
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Lai, Chien-Han. "Promising Neuroimaging Biomarkers in Depression." Psychiatry Investigation 16, no. 9 (September 25, 2019): 662–70. http://dx.doi.org/10.30773/pi.2019.07.25.2.

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Houenou, Josselin. "Neuroimaging biomarkers in bipolar disorder." Frontiers in Bioscience E4, no. 2 (2012): 593–606. http://dx.doi.org/10.2741/e402.

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van der Miesen, Maite M., Martin A. Lindquist, and Tor D. Wager. "Neuroimaging-based biomarkers for pain." PAIN Reports 4, no. 4 (2019): e751. http://dx.doi.org/10.1097/pr9.0000000000000751.

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Mok, Vincent. "Neuroimaging biomarkers in vascular dementia." Journal of the Neurological Sciences 455 (December 2023): 120937. http://dx.doi.org/10.1016/j.jns.2023.120937.

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Nestor, Peter. "Neuroimaging biomarkers in Alzheimer's disease." Journal of the Neurological Sciences 455 (December 2023): 120938. http://dx.doi.org/10.1016/j.jns.2023.120938.

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Dissertations / Theses on the topic "Neuroimaging biomarkers"

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Kawadler, J. M. "Neuroimaging biomarkers in paediatric sickle cell disease." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1464063/.

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Sickle Cell Disease (SCD) is a collection of genetic haemoglobinopathies, the most common and severe being homozygous sickle cell anaemia. In the UK, it has been estimated that 1 in 2000 children are born with SCD. The disease is characterised by chronic anaemia, recurrent pain crises and vascular occlusion. Neurologically, there is a high incidence of stroke in childhood, as well as cognitive dysfunction. Newborn screening programmes and preventative treatments have allowed a much longer lifespan; however recently, neurological research has shifted to characterising subtler aspects of brain development and functioning that may be critically important to the individual’s quality of life. This thesis overviews the neurological and neurocognitive complications of SCD, and how magnetic resonance imaging (MRI) can provide biomarkers for severity of disease. During the PhD, retrospective and prospective cognitive and MRI data were collected and analysed. Diagnostic clinical MRI sequences and advanced MRI sequences were applied, as well as a neuropsychological test battery aimed at intelligence and executive function. First, this thesis reviews the intelligence literature in SCD and includes previously unreported data, finding patients, regardless of abnormality seen on conventional MRI, have lowered full-scale intelligence quotient than controls. Then, to determine imaging biomarkers, volumetric differences and diffusion characteristics were identified. Patients were found to have decreased volumes of subcortical structures compared to controls, in groups corresponding to disease severity. Results from a three-year longitudinal clinical trial suggest evidence of atrophy in paediatric patients, with no apparent protective effect of treatment. Diffusion tensor imaging revealed reduced white matter integrity across the brain, correlating with recognised markers of disease severity (i.e. oxygen saturation and haemoglobin from a full blood count). Overall, the four experiments bridge a gap in the cognitive and neuroimaging literature of the extent of neurological injury in children with SCD.
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Santos, Santos Miguel Ángel. "Clinicopathologic correlations and neuroimaging biomarkers in primary progressive aphasia." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457508.

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Los trabajos incluidos en esta tesis van sobre el tema de la correlación clínico patológica en la enfermedad neurodegenerativa y más específicamente en la afasia primaria progresiva (APP). En el primer trabajo analizamos la proporción de positividad PET-amiloide en cada variante clínica para probar la hipótesis que clasificación según los criterios diagnósticos actuales (establecidos por consenso en 2011) de afasia primaria progresiva resultaría en grupos patológicamente homogéneos. Esta clasificación resulto ser altamente predictiva del estado del biomarcador de amiloide, en particular, la variante logopénica (vlAPP) se asoció a positividad de PET-amiloide en más del 95% de los casos. Además, todos los casos “amiloide discordantes (variante semántica [vsAPP] y variante no-fluente/agrammatica [vnfAPP])” con datos de autopsia disponible recibieron un diagnostico patológico primario de degeneración lobar frontotemporal (DLFT) con contribución secundaria de patología de enfermedad de Alzheimer (EA), sugiriendo que en casos de vsAPP y vnfAPP, un resultado PET-amiloide positivo puede ser más indicativo de patología mixta DLFT- EA que EA aislada. En el segundo trabajo identificamos rasgos clínicos y neuroanatómicos que pueden ayudar en la predicción en-vivo de patología subyacente en casos de vnfAPP que es la variante más heterogénea en los que respecta al diagnóstico patológico. VnfAPP-paralisis supranuclear progresiva se caracterizó por presentar con mayor disartria y predominio relativo de atrofia de sustancia blanca en la primera visita, y mayor velocidad de atrofia del tronco de encéfalo y aparición de signos clínicos de afectación del tronco de encéfalo en visitas posteriores. VnfAPP-degeneración corticobasal presento más déficit de comprensión de frases, de memoria de trabajo, y más atrofia de sustancia gris en la visita inicial, junto con progresión de atrofia en regiones corticales anteriores y mayor presencia de síntomas conductuales en visitas posteriores. En el tercer trabajo, cuantificamos y evaluamos la habilidad de diferentes medidas clínicas y neuroanatómicas para predecir qué casos de APP son presuntamente debidos a EA (usando PET-amiloide como marcador). Mediante un “data-driven” análisis, pudimos clasificar correctamente al 96% de los casos PET-amiloide negativos y 86% de los positivos. Las medidas de memoria visual y sintomatología conductual mostraron una capacidad predictiva similar a las medidas de lenguaje que más aportaron a la función discriminatoria, que fueron medidas del habla motora y repetición de frases, sugiriendo que medidas clínicas extra-lingüísticas tienen potencial para mejorar el diagnóstico en la APP. Finalmente, en el último trabajo también estudiamos la relación entre el depósito de amiloide (medido por PET-PiB) y la atrofia cerebral. Encontramos que en casos de afasia primaria progresiva generalmente asociados a EA (es decir, vlAPP), la atrofia cerebral era altamente asimétrica y predominante en áreas corticales de lenguaje mientras que la deposición de amiloide estaba distribuida difusamente y simétricamente a través de las áreas corticales de asociación de ambos hemisferios cerebrales, sugiriendo que otro factor está impulsando la progresión de atrofia cerebral.
The studies included in this thesis addressed the issue of clinicopathologic correlation in neurodegenerative disease and more specifically in primary progressive aphasia (PPA). In the first study we analyzed rates of amyloid PET positivity to test the hypothesis that classification according to the recently established consensus PPA variant diagnostic criteria would result in groups with largely homogeneous amyloid biomarker profiles. We found that the current classification scheme was highly predictive of amyloid biomarker status with logopenic variant (lvPPA) being associated to amyloid positivity in more than 95% of cases. Furthermore, the amyloid biomarker discordant cases (amyloid positive semantic variant [svPPA] and non-fluent/agrammatic variant [nfvPPA]) that had available autopsy data received a primary pathologic diagnosis of frontotemporal lobar degeneration (FTLD) with presence of contributing Alzheimer’s disease (AD) pathology, suggesting that cases of amyloid biomarker positive svPPA and nvfPPA might be more indicative of mixed FTLD – AD pathology than primary AD. In the second study we identified clinical and neuroimaging features that may help predict underlying pathology in nfvPPA which is the most pathologically heterogeneous of the PPA clinical variants. Greater dysarthria and relative predominance of white-matter atrophy at presentation and greater rate of brainstem atrophy and appearance of brainstem clinical signs at follow-up were characteristic of underlying nfvPPA-progressive supranuclear palsy. NfvPPA-corticobasal degeneration showed more impairment in sentence comprehension, verbal working memory, and greater grey matter atrophy at presentation along with spread of atrophy to anterior cortical structures and greater presence of behavioral symptoms at follow-up. The third study quantified and evaluated the ability of different cognitive and neuroimaging measures to predict which primary progressive aphasia patients have presumptive Alzheimer’s disease pathology (using amyloid-PET as a surrogate marker). A data-driven analysis was able to correctly classify 96% amyloid negative and 86% amyloid positive cases. We found that measures of visual memory and behavioral impairment show similar ability to predict amyloid-PET status as the best performing language measures, which were motor speech and sentence repetition suggesting non-language measures hold potential value for improving differential diagnosis. Finally, the last study also investigated the relationship between amyloid deposition measured by PET-PiB imaging and brain atrophy. We found that, within lvPPA (which is generally due to AD), grey-matter volume loss was highly asymmetric and predominant in language regions whereas amyloid deposition was diffuse throughout association cortices and symmetric between hemispheres suggesting another factor different from amyloid deposition is driving progression of brain atrophy.
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Rittman, Timothy. "Connectivity biomarkers in neurodegenerative tauopathies." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/248866.

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The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks. The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks.
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Adanyeguh, Isaac Mawusi. "Biomarkers Identification and Disease Modeling using Multimodal Neuroimaging Approaches in Polyglutamine Diseases." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066279/document.

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Les maladies par expansion de polyglutamines sont des maladies neurodégénératives dues à l’expansion du trinucléotide cytosine-adénine-guanine (CAG) dans les gènes correspondants codant pour une expansion d’homopolymère de glutamine dans les protéines mutées. Ce projet concerne les formes les plus courantes qui sont la maladie de Huntington (MH) et les ataxies spinocérébelleuses (SCA) types 1, 2, 3 et 7. Ce sont des maladies autosomiques dominantes, responsables de troubles graves de la motricité partageant des voies physiopathologiques communes, avec un effet notable sur la dysfonction métabolique. La disponibilité des tests génétiques et le fait que la plupart du temps la maladie débute à l’âge adulte offre la possibilité d’une intervention thérapeutique avant l’apparition de symptômes. Toutefois, les échelles cliniques ne sont pas assez sensibles et ne peuvent effectivement être utilisés pour évaluer les personnes au stade présymptomatique de la maladie. Les techniques d’imagerie par résonance magnétique (IRM) et de spectroscopie (SRM) sont des approches non invasives qui permettent de recueillir des informations pertinentes et sensibles. Ainsi, dans ce travail, nous présentons une combinaison de différentes techniques d’IRM et SRM afin d’identifier de robustes biomarqueurs de la MH et des SCA. Nous présentons aussi des approches thérapeutiques prometteuses dans la MH. De la même manière, nous voulons démontrer que des biomarqueurs d’imagerie sont plus sensibles que des échelles cliniques. Pour conclure, nous combinons des données multimodales – volumétrie, SRM, métabolomique et lipidomique – à partir de SCA dans un modèle qui explique mieux la pathologie
Mutations in different gene loci that lead to the encoding of the unstable and expanded glutamine-encoding cytosine-adenine-guanine (CAG) repeats results in the group of diseases known as the polyglutamine diseases. This project focuses on the most common forms which are Huntington disease (HD) and spinocerebellar ataxia (SCA) types 1, 2, 3 and 7. These are autosomal dominant diseases responsible for severe movement disorders and are thought to share common pathophysiological pathways with a major emphasis on metabolic dysfunction. The availability of genetic testing and their predominantly adult onset opens a window for therapeutic intervention before symptoms onset. However, current clinical scales are not sensitive and cannot effectively be used to evaluate individuals at the presymptomatic stage of the diseases. This prompts the need for biomarkers that are sensitive to macroscopic and microscopic changes that may occur prior to disease onset. Magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques present non-invasive approaches to extract pertinent information that otherwise would not be possible with clinical scales. In this work therefore, we present a combination of different MRI and MRS techniques to identify robust biomarkers in HD and SCA. We also present therapeutic approaches that hold promise in HD. Likewise, we show that imaging biomarkers have higher effect sizes than clinical scales. Finally, we combine multimodal data – volumetry, MRS, metabolomics and lipidomic – from SCA into a model that best explains the pathology
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Heise, Verena. "How can magnetoencephalography and magnetic resonance imaging improve our understanding of genetic susceptibility to Alzheimer's disease?" Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:a3c670f3-aef5-4f34-b983-37f21d0019ad.

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The Apolipoprotein E (APOE) ε4 allele is the best-established genetic risk factor for sporadic Alzheimer's disease (AD) while the ε2 allele confers a reduced risk compared with the most common ε3 allele. Neuroimaging studies using magnetic resonance imaging (MRI) have shown that APOE genotype affects brain structure and function. The aims of the research presented in this DPhil thesis were twofold: 1) to investigate the effect of APOE genotype on brain function in healthy adults using magnetoencephalography (MEG), which is a direct measure of neuronal activity and 2) to explore interactions between the AD risk factors APOE ε4 allele, age and female gender and their effects on brain structure and function using resting-state functional MRI and diffusion tensor imaging. MEG revealed similar neuronal activity at rest for APOE ε2 and ε4 carriers, i.e. those with opposite AD risk, indicating a more general effect on the functional architecture of the brain that is not directly linked to AD risk. However, differences between APOE ε2 and ε4 carriers became apparent when reactivity to stimuli was explored using the excellent temporal resolution of MEG. APOE ε4 carriers showed faster sensory pro- cessing and APOE genotype effects were found for functional networks associated with attention. In the second part of this project, female APOE ε4 carriers showed overall significantly reduced functional connectivity between the hippocampus and precuneus and a significant age-related decrease in connectivity of these regions. Increased vulnerability of this connection might be one reason for increased AD risk and interventions targeting hippocampal connectivity might be especially effective in at-risk populations. The research presented in this DPhil thesis showed a complex pattern of APOE genotype effects on brain structure and function. While global APOE genotype effects on functional and structural connectivity do not follow patterns of AD risk, more specific measures of connectivity and task-related brain function could be of use in the development of preclinical markers for AD development.
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Wilson, D. R. "Clinical relevance of neuroimaging biomarkers of small vessel disease in relation to intracranial haemorrhage." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10053154/.

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Introduction: Small vessel disease is the underlying cause of most spontaneous (non-traumatic) ICH. Cerebral imaging markers of small vessel disease, particularly cerebral microbleeds (CMBs) and white matter hyperintensities of presumed vascular origin (WMH) offer clinicians and researchers an opportunity to further understand the pathogenesis and risk of ICH in patients with stroke. In this thesis I present a portfolio of studies aimed to show the clinical relevance of neuroimaging biomarkers of small vessel disease in relation to intracerebral haemorrhage (ICH). Methods: I ascertained patients primarily through the Clinical Relevance Of Cerebral Microbleeds In Stroke (CROMIS-2) study, a multicentre prospective observational study recruiting patients with both ICH and patients with ischaemic stroke associated with atrial fibrillation (AF) from 79 centres throughout the UK and one in the Netherlands. Data was also collected locally from ICH patients seen in the UCL Hospital’s comprehensive stroke service, international collaborations and through the meta-analysis of published studies. Main findings: 1) CMBs are associated with an increased relative risk of subsequent ICH in patients with ischaemic stroke (primarily treated with antiplatelet drugs) and the ICH risk increases more steeply with CMB burden than does the risk of ischaemic stroke; 2) In patients with AF anticoagulated after recent ischaemic stroke or TIA, CMB presence is independently associated with symptomatic intracranial haemorrhage risk, improves the predictive ability of clinical risk scores, and can inform anticoagulation decisions; 3) The presence of cerebral small vessel disease is associated with a lower risk of a macrovascular cause of ICH; 4) Lobar ICH location (compared to non-lobar location) is associated with higher recurrent ICH risk and lower new ischaemic stroke risk; 5) The CHA2DS2VASC score has similar modest predictive value in estimating the risk of ischaemic stroke in patients with ICH and concurrent AF, but risk prediction was not improved by adding SVD presence. Conclusion: These studies confirm the clinical relevance of neuroimaging markers of small vessel disease in the diagnosis and prediction of intracranial haemorrhage and provide a framework for future research.
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CARLI, GIULIA. "Parkinson’s disease and dementia in the α-synuclein spectrum: the role of cognitive assessment and in vivo neuroimaging biomarkers." Doctoral thesis, Università Vita-Salute San Raffaele, 2021. http://hdl.handle.net/20.500.11768/122897.

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Parkinson’s disease (PD) is the neurological disease with the fastest growing rate. 1% of the world population over 60 years have a PD diagnosis. PD presents a complex and heterogenous clinical picture during the disease course, and dementia represents the most severe condition. This thesis investigates the neurobiological mechanisms and cognitive features of PD patients with a severe clinical phenotype – developing cognitive deterioration, reaching dementia condition. The studies included in this dissertation contributed to identifying risk factors, biomarkers, cognitive features, and sources of clinical variability of dementia in Lewy Bodies disorders (LBD) with multiple methodological approaches to neuroimaging data. Moreover, the cognitive picture of the LBD clinical spectrum has been explored by combining cross-sectional and longitudinal approaches. This dissertation provides new evidence on modifiable and non-modifiable risk factors that influence the development of severe phenotypes within LBD and those acting on the timing of dementia symptoms onset. Moreover, we identify valuable biomarker and cognitive marker candidates for dementia risk profiling since early preclinical stages.
La malattia di Parkinson è la malattia neurologica con il tasso di crescita più rapida. L'1% della popolazione mondiale oltre i 60 anni ha una diagnosi di Parkinson. Il morbo di Parkinson presenta un quadro clinico complesso ed eterogeneo durante il decorso della malattia, di cui la demenza rappresenta la condizione più grave. Questa tesi indaga i meccanismi neurobiologici e le caratteristiche cognitive dei pazienti affetti da Malattia di Parkinson con un grave fenotipo clinico – che sviluppano un deterioramento cognitivo raggiungendo la condizione di demenza. Gli studi inclusi in questo elaborato hanno contribuito a identificare fattori di rischio, biomarcatori, caratteristiche cognitive e fonti di variabilità clinica della demenza nei disturbi a corpi di Lewy (LBD) con molteplici approcci metodologici ai dati di neuroimaging. Inoltre, è stato esplorato il quadro cognitivo dello spettro clinico LBD combinando approcci cross-sectional e longitudinali. Questa tesi fornisce nuove evidenze sui fattori di rischio modificabili e non modificabili che influenzano lo sviluppo di fenotipi gravi all'interno della LBD e sui fattori che agiscono sui tempi di insorgenza dei sintomi della demenza. Identifica inoltre validi candidati biomarcatori e marcatori cognitivi per la profilazione del rischio di demenza sin dalle fasi precliniche.
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Wang, Chenyu. "Improving the specificity of quantitative neuroimaging biomarkers for monitoring disease progression and understanding disease mechanisms in multiple sclerosis with diffusion magnetic resonance imaging." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17939.

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Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of the central nervous system (CNS) that is associated with progressive neurodegeneration. To better understand the dynamics of disease progression in individuals with MS, and to personalise treatment strategies, the development of quantitative in-vivo biomarkers is critical. Magnetic resonance imaging (MRI) is an essential technique that has been successfully embedded in the formal diagnostic criteria for MS since 2001. Conventional MRI techniques facilitate the demonstration of lesion dissemination in both space and time. Furthermore, conventional MRI metrics derived from quantitative analysis can predict disability progression in clinical trials. However, these imaging metrics are often criticised for their weak correlations with clinical outcomes at the individual level; and lack of specificity for the underlying pathological process(es). Despite successes in large group studies, the transition of quantitative neuroimaging to clinical practice as a tool for both monitoring disease progression and guiding therapeutic strategy has progressed slowly. In this thesis, a refined analysis framework that improves the specificity and clinical validity of MRI metrics, is described and evaluated. Specifically, multi-modal approaches that integrate advanced diffusion imaging and conventional structural metrics are investigated; and a potential composite biomarker of MS disease progression is proposed.
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Pierrefeu, Amicie de. "Apprentissage automatique avec parcimonie structurée : application au phénotypage basé sur la neuroimagerie pour la schizophrénie." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS329/document.

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La schizophrénie est un trouble mental, chronique et invalidant caractérisé par divers symptômes tels que des hallucinations, des épisodes délirants ainsi que des déficiences dans les fonctions cognitives. Au fil des ans, l'Imagerie par Résonance Magnétique (IRM) a été de plus en plus utilisée pour mieux comprendre les anomalies structurelles et fonctionnelles inhérentes à ce trouble. Les progrès récents en apprentissage automatique et l'apparition de larges bases de données ouvrent maintenant la voie vers la découverte de biomarqueurs pour le diagnostic/ pronostic assisté par ordinateur. Compte tenu des limitations des algorithmes actuels à produire des signatures prédictives stables et interprétables, nous avons prolongé les approches classiques de régularisation avec des contraintes structurelles provenant de la structure spatiale du cerveau afin de: forcer la solution à adhérer aux hypothèses biologiques, produisant des solutions interprétables et plausibles. De telles contraintes structurelles ont été utilisées pour d'abord identifier une signature neuroanatomique de la schizophrénie et ensuite une signature fonctionnelle des hallucinations chez les patients atteints de schizophrénie
Schizophrenia is a disabling chronic mental disorder characterized by various symptoms such as hallucinations, delusions as well as impairments in high-order cognitive functions. Over the years, Magnetic Resonance Imaging (MRI) has been increasingly used to gain insights on the structural and functional abnormalities inherent to the disorder. Recent progress in machine learning together with the availability of large datasets now pave the way to capture complex relationships to make inferences at an individual level in the perspective of computer-aided diagnosis/prognosis or biomarkers discovery. Given the limitations of state-of-the-art sparse algorithms to produce stable and interpretable predictive signatures, we have pushed forward the regularization approaches extending classical algorithms with structural constraints issued from the known biological structure (spatial structure of the brain) in order to force the solution to adhere to biological priors, producing more plausible interpretable solutions. Such structured sparsity constraints have been leveraged to identify first, a neuroanatomical signature of schizophrenia and second a neuroimaging functional signature of hallucinations in patients with schizophrenia. Additionally, we also extended the popular PCA (Principal Component Analysis) with spatial regularization to identify interpretable patterns of the neuroimaging variability in either functional or anatomical meshes of the cortical surface
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Sendi, Shahbaz. "Biomarkers of major depressive disorder : a study of the interaction of genetic, neuroimaging and endocrine factors, and the effects of childhood adversity, in major depressive disorder." Thesis, King's College London (University of London), 2016. http://kclpure.kcl.ac.uk/portal/en/theses/biomarkers-of-major-depressive-disorder(743a993b-8c01-46be-8707-855dc01bc355).html.

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My thesis consisted of two studies. The first study was a part of a wider study; within this, we investigated the modulation of amygdala structure by the val66met BDNF (Brain Derived Neurotrophic Factor) polymorphism. Structural Magnetic Resonance Imaging (MRI) scans were obtained at 1.5 T in 87 Major Depressive Disorder (MDD) patients and 74 age, gender, and verbal IQ matched healthy controls. We used Freesurfer version 5.1.0 to examine the grey matter amygdala volume. In the second study, we investigated neuroendocrine abnormalities˗˗ Hypothalamus-Pituitary-Adrenal Axis (HPA) axis changes˗˗ in MDD and their relation to early life stress (ELS). In total 112 subjects took part, consisting of MDD patients with (n=28) and without (n=15) a history of ELS and healthy controls with (n=26) and without (n=43) a history of ELS. The cortisol awakening response (CAR) was used as an index of HPA axis activity. In both studies, the data were analyzed using Statistical Package for Social Science (SPSS version 22). In the first study, we did not find any modulatory effect of the val66met polymorphism on the grey matter of right and left amygdala volumes. In the second study, we showed that the CAR was most elevated in those who were both depressed and had a history of ELS, which supports the argument that the effects of early life stress and MDD on the HPA axis may be additive.
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Books on the topic "Neuroimaging biomarkers"

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Gordon, Brian A., Stephanie J. B. Vos, and Anne M. Fagan. Neuroimaging and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0052.

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Alzheimer’s disease is characterized by a long asymptomatic (preclinical) phase during which disease-related pathology accumulates in the absence of overt cognitive symptoms. The most prominent neuropathologies are extracellular amyloid plaques and intraneuronal neurofibrillary tangles. Until recently such pathology was observable only at autopsy. Now these, and other novel pathological markers, can be measured in living individuals using cerebrospinal fluid assays, blood tests, and neuroimaging techniques to track disease progression. Understanding changes in these biomarkers is critical for diagnosis, monitoring disease progression, and for the development of disease-modifying therapies. This chapter reviews the current scientific understanding regarding the use of biomarkers to assess Alzheimer’s disease pathology.
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Chen, Jiu, Rong Chen, and Yong Liu, eds. Neuroimaging Biomarkers and Cognition in Alzheimer’s disease Spectrum. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-713-9.

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Ritsner, Michael S. Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes : Volume II: Neuroanatomical and Neuroimaging Endophenotypes and Biomarkers. Springer Netherlands, 2010.

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Wu, Ping, Behrooz Hooshyar Yousefi, Wei Cheng, and Binbin Nie, eds. Biomarkers from Multi-tracer and Multi-modal Neuroimaging in Age-related Neurodegenerative Diseases. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-956-8.

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Warner, Matthew A., Carlos Marquez de la Plata, David S. Liebeskind, and Ramon Diaz-Arrastia. Imaging Assessment of Brain Injury. Edited by David L. Reich, Stephan Mayer, and Suzan Uysal. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190280253.003.0003.

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Imaging plays a pivotal role in discerning the extent and nature of brain injuries. Advances in neuroimaging techniques have improved sensitivity for detecting smaller lesions, improved the anatomical specificity of lesions in white matter, and improved the prognostic value of detected lesions. Novel quantitative methods allow measurements of hemorrhage and infarct volume in the acute phase of injury, and regional brain atrophy and functional disconnectedness months after injury. It is likely that the success of future clinical trials of neuroprotective therapies will be dependent on reliable and validated neuroimaging biomarkers of injury and recovery. This chapter describes neuroimaging modalities that are currently being utilized in clinical and experimental settings and their implications for the development and testing of neuroprotection strategies.
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Steele, Vaughn R., Vani Pariyadath, Rita Z. Goldstein, and Elliot A. Stein. Reward Circuitry and Drug Addiction. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0044.

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Addiction is a complex neuropsychiatric syndrome related to dysregulation of brain systems including the mesocorticolimbic dopamine reward circuit. Dysregulation of reward circuitry is related to each of the three cyclical stages in the disease model of addiction: maintenance, abstinence, and relapse. Parsing reward circuitry is confounded due to the anatomical complexity of cortico-basal ganglia-thalamocortical loops, forward and backward projections within the circuit, and interactions between neurotransmitter systems. We begin by introducing the neurobiology of the reward system, specifically highlighting nodes of the circuit beyond the basal ganglia, followed by a review of the current literature on reward circuitry dysregulation in addiction. Finally, we discuss biomarkers of addiction identified with neuroimaging that could help guide neuroprediction models and development of targets for effective new interventions, such as noninvasive brain stimulation. The neurocircuitry of reward, especially non-prototypical nodes, may hold essential keys to understanding and treating addiction.
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Perez, David L., and Valerie Voon. The Neurobiology of PNES and Other Functional Neurological Symptoms. Edited by Barbara A. Dworetzky and Gaston C. Baslet. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265045.003.0006.

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Patients with psychogenic nonepileptic seizures (PNES) and related functional neurological symptoms are highly prevalent yet poorly understood on a neurobiological level. Clinical and research efforts in PNES and other functional neurological symptoms have lagged behind clinical neuroscience advancements in other neuropsychiatric conditions, despite the high frequency with which clinicians encounter PNES and other functional neurological symptoms. In this chapter, systems-level neurobiological studies in PNES are reviewed. Specific emphasis is given to structural and functional neuroimaging, electrophysiology, autonomic, and neuroendocrine investigations. Early systems-level neurobiology research suggests that PNES may develop in the context of alterations within and across brain networks mediating emotion processing, regulation and expression, cognitive control, multimodal integration, and sensory-motor functions. An improved biological understanding of PNES may reduce the stigma associated with this neuropsychiatric disorder and aid the development of biologically informed treatments and biomarkers of treatment response in this population.
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Glannon, Walter. Psychiatric Neuroethics II. Edited by John Z. Sadler, K. W. M. Fulford, and Werdie (C W. ). van Staden. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780198732372.013.31.

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I discuss ethical issues relating to interventions other than intracranial surgery and psychopharmacology for psychiatric disorders. I question the distinction between “invasive” and “non-invasive” techniques applying electrical stimulation to the brain, arguing that this should be replaced by a distinction between more and less invasive techniques. I discuss electroconvulsive therapy (ECT); it can be a relatively safe and effective treatment for some patients with depression. I consider transcranial magnetic stimulation (TMS) and transcranial current stimulation (tCS); the classification of these techniques as non-invasive may lead to underestimation of their risks. I discuss how placebos can justifiably be prescribed non-deceptively and even deceptively in clinical settings. An analysis of neurofeedback as the neuromodulating technique most likely to promote autonomy/control for some conditions follows. Finally, I examine biomarkers identified through genetic screening and neuroimaging; they might contribute to more accurate prediction and diagnosis, more effective treatment, and possibly prevention of psychiatric disorders.
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Hilsabeck, Robin C., and Gayle Y. Ayers, eds. Dementia. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/med/9780197690024.001.0001.

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Abstract Adults aged 65 and older are the fastest-growing segment of the United States population. This increase in older adults will result in a greater number of individuals with age-related neurocognitive disorders such as Alzheimer’s, vascular disease, and frontotemporal lobular degeneration. The purpose of this book is to provide trainees and early career professionals, particularly in psychiatry, psychology, neurology, geriatrics, family medicine, and internal medicine, with the information necessary to care for the often complex clinical presentations of older adults with mild cognitive impairment and dementia. The book is organized into three sections: (I) Core Concepts, (II) Dementia Syndromes, and (III) Disease Management. The chapters in Section I focus on clinical knowledge and skills that can be applied across all syndromes, including conducting the diagnostic interview and neurologic examination, frequently used cognitive screening measures, neuropsychological assessment, neuroimaging, and developing differential diagnoses. Chapters in Section II review the most common syndromes: mild cognitive impairment, Alzheimer’s disease, vascular disease, frontotemporal lobular degeneration, Lewy body and Parkinson plus syndromes, rare and rapidly progressive dementias, and dementia due to other medical conditions that may present in older adults (e.g., traumatic brain injury, hypothyroidism). Each chapter in Section II covers epidemiology, neuropathology, biomarkers, genetics (as applicable), clinical signs and symptoms (e.g., cognitive, emotional/behavioral, and motor features), and provides at least one illustrative case. Section III presents the latest evidence-based interventions, from risk management to pharmacologic and nonpharmacologic approaches. Safety considerations, legal issues, palliative and advanced care planning, and attending to caregivers are also addressed.
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Miskowiak, Kamilla W., and Lars V. Kessing. Cognitive enhancement in bipolar disorder: current evidence and methodological considerations. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198748625.003.0026.

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Cognitive dysfunction is an emerging treatment target in bipolar disorder (BD). Numerous trials have assessed the efficacy of novel pharmacological and psychological treatments on cognition. Overall, the results are disappointing, possibly due to methodological challenges. A key issue is the lack of consensus on whether and how to screen for cognitive impairment and on how to assess efficacy. We suggest that screening for cognitive impairment is critical and should involve objective neuropsychological tests. We also recommend that the primary outcome is a composite of neuropsychological tests with socio-occupational function as co-primary or secondary outcome. Trials should include fully or partially remitted patients, ensure that concomitant medication is kept stable and that statistical methods include mixed models or similar ways to take account of missing values. Future treatment development should implement a ‘circuit-based’ neuroimaging biomarker model to examine neural target engagement. Interventions targeting multiple treatment modalities may also be beneficial.
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Book chapters on the topic "Neuroimaging biomarkers"

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Singleterry, Sydney, Damek Homiack, and Olusola Ajilore. "Functional Neuroimaging Biomarkers." In Biomarkers in Neuropsychiatry, 65–80. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43356-6_5.

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Tost, Heike, and Andreas Meyer-Lindenberg. "Neuroimaging Biomarkers in Schizophrenia." In Biomarkers for Psychiatric Disorders, 235–71. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-79251-4_11.

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Sanches, Marsal. "Structural Neuroimaging Biomarkers in Psychiatry." In Biomarkers in Neuropsychiatry, 55–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43356-6_4.

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Galderisi, Silvana, Giulia Maria Giordano, and Lynn E. DeLisi. "Neuroimaging: Diagnostic Boundaries and Biomarkers." In Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders, 1–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97307-4_1.

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Chong, M. S., and W. S. Lim. "Neuroimaging Biomarkers in Alzheimer's Disease." In The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, 3–15. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9831-4_1.

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Holmes, Sophie, and Sule Tinaz. "Neuroimaging Biomarkers in Parkinson’s Disease." In Advances in Neurobiology, 617–63. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69491-2_21.

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Masdeu, Joseph C., and Belen Pascual. "Neuroimaging Biomarkers in Alzheimer’s Disease and Related Disorders." In Biomarkers in Neuropsychiatry, 163–88. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43356-6_11.

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Franklin, Teresa R., Joel Mumma, Kanchana Jagannathan, Reagan R. Wetherill, and Anna Rose Childress. "Morphometric Biomarkers of Addiction and Treatment Response." In Neuroimaging and Psychosocial Addiction Treatment, 111–24. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137362650_8.

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Kim, Geon Ha, Jihye Hwang, and Jee Hyang Jeong. "Classical Neuroimaging Biomarkers of Vascular Cognitive Impairment." In Stroke Revisited, 99–112. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-1433-8_9.

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Wang, Lei, and John G. Csernansky. "Recent Advances in Neuroimaging Biomarkers of Schizophrenia." In Schizophrenia, 71–103. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0656-7_6.

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Conference papers on the topic "Neuroimaging biomarkers"

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van de Zande, Nadine A., Eidrees Ghariq, Jeroen HJM de Bresser, Raymund AC Roos, and Susanne T. de Bot. "E14 Neuroimaging biomarkers in Huntington’s disease." In EHDN 2022 Plenary Meeting, Bologna, Italy, Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jnnp-2022-ehdn.90.

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Ni, Yunjia, Trenton House, Ashton Huppert Steed, Alma Jukic, Richard Dortch, and Shawn Stevens. "Comprehensive Review of Strategic Neuroimaging Biomarkers in Vestibular Schwannoma." In 33rd Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2024. http://dx.doi.org/10.1055/s-0044-1780395.

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Laton, Jorne, Jeroen Van Schependom, Jeroen Gielen, Jeroen Decoster, Tim Moons, Jacques De Keyser, Marc De Hert, and Guy Nagels. "In search of biomarkers for schizophrenia using electroencephalography." In 2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2014. http://dx.doi.org/10.1109/prni.2014.6858527.

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Obertino, S., G. Roffo, C. Granziera, and G. Menegaz. "Infinite feature selection on shore-based biomarkers reveals connectivity modulation after stroke." In 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2016. http://dx.doi.org/10.1109/prni.2016.7552347.

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Kumar, Kuldeep, Christian Desrosiers, Ahmad Chaddad, and Matthew Toews. "Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899956.

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Acar, Evrim, Yuri Levin-Schwartz, Vince D. Calhoun, and Tulay Adali. "ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081286.

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Obafemi-Ajayi, Tayo, Khalid Al-Jabery, Lauren Salminen, David Laidlaw, Ryan Cabeen, Donald Wunsch, and Robert Paul. "Neuroimaging biomarkers of cognitive decline in healthy older adults via unified learning." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8280937.

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"USING MACHINE LEARNING FOR EARLY ALZHEIMER'S DETECTION IN COGNITIVE NEUROSCIENCE." In RAD Conference. RAD Centre, Niš, Serbia, 2024. http://dx.doi.org/10.21175/radproc.2024.01.

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Alzheimer's disease (AD) is a leading cause of dementia, with early detection crucial for effective intervention. Machine learning (ML) has emerged as a promising tool for identifying AD-related biomarkers in neuroimaging and cognitive assessments. We reviewed literature from peer-reviewed journals and conference proceedings using PubMed, focusing on studies employing ML for early AD detection through neuroimaging and cognitive data. ML techniques show significant promise in early AD detection. Key studies demonstrate high accuracy in distinguishing between AD, mild cognitive impairment (MCI), and healthy controls. Notable, methods include MRI- based biomarkers, computer-aided diagnosis systems, and various ML algorithms. ML techniques can enhance early AD detection, leading to improved patient outcomes. Despite the promising results, this study did not conduct a systematic review, and further research is needed to address data availability and refine feature selection for better accuracy and generalizability.
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Costa, Larissa Maria de Paula Rebouças da, Gabriel de Souza Torres, Kauan Alves Sousa Madruga, and Poliana Rafaela dos Santos. "Biomarkers in Alzheimer’s disease." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.670.

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Background: Alzheimer’s disease (AD) is the most common cause of dementia and cognitive dysfunction in old ages. AD is characterised by beta- amyloid (Aβ) plaques and neurofibrillary tangles of the hyper-phosphorylated Tau protein. It has an extensive preclinical stage, which emphasizes the importance of the biological components related to an early diagnostic: biomarkers. Objectives: After critical analysis of the selected literature, this review has the goal of describing the main biomarkers in AD and discussing different ways of detecting it. Methods: This review was elaborated after a literature review in the PubMed database, with 15 articles published between 2016 and 2021. The keywords were used with the boolean operator “AND”. Articles of meta-analysis, review and systematic review were selected. Results: It was found central biomarkers for the AD diagnostic, such as Tau and Aβ. The following tests were used: CSF puncture; blood tests; neuroimaging; saliva and mucosa samples. Aβ and Tau can be collected by CSF or PET-TC. Conclusions: Biomarkers play an important role in early AD diagnostic, even with limitations in the tests. The CSF and PET-TC are expensive methods, only used in atypical cases of AD. Reliable blood tests remain in development. In conclusion, there’s the need for more studies about alternative diagnostic tests, that are non-invasive and have low cost. Those developments can be beneficial for health plans, helping early diagnosis of AD.
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Liu, Qing, Defu Yang, Jingwen Zhang, Ziming Wei, Guorong Wu, and Minghan Chen. "Analyzing The Spatiotemporal Interaction And Propagation Of Atn Biomarkers In Alzheimer’s Disease Using Longitudinal Neuroimaging Data." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9434021.

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