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2 Nuclear Magnetic Resonance. (a) A net magnetization dipole moment vector M forms when spins are exposed to an external magnetic field B 0 . (b) The NMR signal is generated by exciting the ensemble of spins precessing along B 0 by exposing them to a radio-frequency pulse – the magnetization vector M spirals down to the transverse plane in the fixed frame of reference.
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Diffusion MRI (dMRI) is a unique modality of MRI which allows one to indirectly examine the microstructure and integrity of the cerebral white matter in vivo and non-invasively. Its success lies in its capacity to reconstruct the axonal connectivity of the neurons, albeit at a coarser resolution, without having to operate on the patient, which can...
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The main limitation of diffusion tractography for connectivity studies is that the reconstructed
tractograms are not truly quantitative. Microstructure imaging allows the estimation of more
quantitative features of the neuronal tissue, such as the axon diameter distribution, but this
analysis can only be performed voxelwise. Here, we extend a frame...
Tractography is a powerful technique capable of non-invasively reconstructing the structural connections in the brain using diffusion MRI images, but the validation of tractograms is challenging due to lack of ground truth. Owing to recent developments in mapping the mouse brain connectome, high-resolution tracer injection-based axonal projection m...
The human brain is a complex and organized network, where the connection between regions is not achieved with single neurons crisscrossing each other but rather millions of densely packed and well-ordered neurons. Reconstruction from diffusion MRI tractography is only an attempt to capture the full complexity of this network, at the macroscale. Thi...
Citations
... Diffusion (also known as a Brownian motion) is a phenomenon of the thermal random movement of water molecules, which can be quantified using diffusion weighted imaging (DWI) in vivo and to ultimately define the characterization of neural activity [16,17]. In a DWI sequence, a diffusion sensitization gradient is applied on both sides of the refocusing radiofrequency pulses. ...
Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease's progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.
... 20 It is difficult to recognize and estimate the micro-level brain network technically, but the macro-level scale brain network can be estimated by the structural and functional MR connectome using diffusion tensor MR tractography and function MRI. 21,22 By using a graph theory, complex network can be visualized by a "graph" consisting of nodes and edges. In structural MR connectivity of the brain, the node is a brain area and the edge is the number of streams in the MR tractography between two nodes. ...
Purpose: Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are representative disorders of dementia of the elderly and the neuroimaging has contributed to early diagnosis by estimation of alterations of brain volume, blood flow and metabolism. A brain network analysis by MR imaging (MR connectome) is a recently developed technique and can estimate the dysfunction of the brain network in AD and DLB. A graph theory which is a major technique of network analysis is useful for a group study to extract the feature of disorders, but is not necessarily suitable for the disorder differentiation at the individual level. In this investigation, we propose a deep learning technique as an alternative method of the graph analysis for recognition and classification of AD and DLB at the individual subject level.
Materials and Methods: Forty-eight brain structural connectivity data of 18 AD, 8 DLB and 22 healthy controls were applied to the machine learning consisting of a six-layer convolution neural network (CNN) model. Estimation of the deep learning model to classify AD, DLB and non-AD/DLB was performed using the 4-fold cross-validation method.
Results: The accuracy, average precision and recall of our CNN model were 0.73, 0.78 and 0.73, and the specificity precision and recall were 0.68 and 0.79 in AD, 0.94 and 0.65 in DLB and 0.73 and 0.75 in non-AD/DLB. The triangular probability map of the MR connectome revealed the probability of AD, DLB and non-AD/DLB in each subject.
Conclusion: Our preliminary investigation revealed the adaptation of deep learning to the MR connectome and proposed its utility in the differentiation of dementia disorders at the individual subject level.
... However, these techniques do not yet admit resolution of individual neuronal projections. Such mesoscale resolution can only currently be achieved with post-mortem brain tissue sections (Van Dijk et al. 2010;Castellanos et al. 2013;Fornito et al. 2016;Ghosh et al. 2013;Griffa et al. 2013;Jbabdi et al. 2013;Morgan et al. 2013). Nevertheless, using an inferred human brain connectome, Raj et al. (Raj, Kuceyeski, et al. 2012) developed a network diffusion model of disease progression in Alzheimer's disease, and used investigate the spread of cerebral pathology in neurodegenerative diseases. ...
Certain neuronal populations are selectively vulnerable to alpha -synuclein pathology in Parkinson’s Disease, yet the reasons for this selectivity are unclear. Pathology affects neuronal populations that are anatomically connected although the contribution of neuronal connectivity remains to be quantitatively explored. Herein, we simulate the contribution of the connectome alone to the spread of arbitrary aggregates using a computational model of temporal spread within an abstract representation of the mouse mesoscale connectome. Our simulations are compared with the neuron-to-neuron spread of alpha -synuclein that has been observed with in vivo spreading experiments in rats. We find that neuronal connectivity appears to be compatible with the spreading pattern of alpha -synuclein pathology however, it may be per se insufficient to determine the anatomical pattern of protein spreading observed in experimental animals, suggesting a role of selective vulnerability of neuronal pathways to alpha -synuclein diffusion, accumulation and pathology.
Graphical abstract
La presente investigación ha tenido como objetivo explorar el de modo en que el arte
contemporáneo, puede contribuir a la adaptación biológica de los seres humanos desde
la transición a la sostenibilidad de los complejos sistemas sociotécnicos.
Debido al hecho de que el cerebro siempre está preparando hipótesis a futuro y
conociendo en parte, como emerge la conciencia en él, nos preguntamos de qué forma
la ciencia en toda su complejidad y las intersecciones que emergen entre diferentes
disciplinas al analizarse contextos reales, el uso de las tecnologías y los diferentes
sistemas de innovación contribuyen y pueden mejorar la adaptación cognitiva de los
seres humanos ante posibles disrupciones sociales y medioambientales en los
ecosistemas. En concreto se ha querido conocer de qué modo podemos aportar
información de valor, a través de la pedagogía, el modelado de sistemas y la ciencia de
sistemas. Al estudiar las estructuras emergentes en la autoorganización social, que
intervienen en la continua transformación de los diferentes regímenes del paisaje
dominante o Statu Quo del metabolismo socioeconómico global. A través de una
triangulación de metodologías la investigación presupone la capacidad creativa de los
seres humanos para abstraer la realidad y percibirla como totalidades, desde las que
poner conciencia de nuestras interdependencias biológicas con los ecosistemas. Y situar
en el entorno de descubrimiento la creencia de que la clave de la sostenibilidad está en
la organización de lo vivo, en el código orgánico, en las dinámicas relacionales. Creemos
que el acceso al conocimiento es un fenómeno dinámico, espontáneo que se crea y se
destruye infinitamente, como lo es el fenómeno de la conciencia.
Al estudiar el contexto como agente crítico se ha elaborado una propuesta de
cambio a nivel institucional, destacando que es posible, a futuro, que la ciencias de la
complejidad integren el sistema valenciano de innovación, y advertimos de los posibles
riesgos de una pérdida de información al no observarse las interacciones entre las
partes, componentes o elementos que configuran el sistema de la ciencia, la tecnología
y de la innovación a una escala concreta, la UPV, en un caso particular la experiencia de
una estudiante predoctoral en formación. Se ha sugerido que, si la UPV quiere adaptarse
al contexto desde una perspectiva evolutiva, deberá incrementar su complejidad social.
Para partir de una posición justificada desde un punto de vista de método científico se
ha acotado el objeto de investigación y recurrido a la investigación de acción
participativa de realismo crítico y como marco de trabajo o enfoque, la perspectiva
multinivel, que estudia la complejidad de forma agregada.
Palabras clave: Adaptación, Cognición, Sostenibilidad, Evolución, Redes, Complejidad,
Realismo Crítico, Ciencia de datos, Innovación, Ecosistemas, Arte Contemporáneo
