Lin4Neuro: a customized Linux distribution ready for neuroimaging analysis

<p>Abstract</p> <p>Background</p> <p>A variety of neuroimaging software packages have been released from various laboratories worldwide, and many researchers use these packages in combination. Though most of these software packages are freely available, some people find them difficult to install and configure because they are mostly based on UNIX-like operating systems. We developed a live USB-bootable Linux package named "Lin4Neuro." This system includes popular neuroimaging analysis tools. The user interface is customized so that even Windows users can use it intuitively.</p> <p>Results</p> <p>The boot time of this system was only around 40 seconds. We performed a benchmark test of inhomogeneity correction on 10 subjects of three-dimensional T1-weighted MRI scans. The processing speed of USB-booted Lin4Neuro was as fast as that of the package installed on the hard disk drive. We also installed Lin4Neuro on a virtualization software package that emulates the Linux environment on a Windows-based operation system. Although the processing speed was slower than that under other conditions, it remained comparable.</p> <p>Conclusions</p> <p>With Lin4Neuro in one's hand, one can access neuroimaging software packages easily, and immediately focus on analyzing data. Lin4Neuro can be a good primer for beginners of neuroimaging analysis or students who are interested in neuroimaging analysis. It also provides a practical means of sharing analysis environments across sites.</p

Early Diagnosis of Mild Cognitive Impairment with 2-Dimensional Convolutional Neural Network Classification of Magnetic Resonance Images

We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnosis (CAD) framework, to assist clinicians in the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Our framework is based on a Convolutional Neural Network (CNN) trained and tested on functional Magnetic Resonance Images datasets. We contribute to the literature on AI-CAD frameworks for AD by using a 2D CNN for early diagnosis of MCI. Contrary to current efforts, we do not attempt to provide an AI-CAD framework that will replace clinicians, but one that can work in synergy with them. Our framework is cheaper and faster as it relies on small datasets without the need of high-performance computing infrastructures. Our work contributes to the literature on digital transformation of healthcare, health Information Systems, and NeuroIS, while it opens novel avenues for further research on the topic

The Impact of Lesion In-Painting and Registration Methods on Voxel-Based Morphometry in Detecting Regional Cerebral Gray Matter Atrophy in Multiple Sclerosis

Background and Purpose: VBM has been widely used to study GM atrophy in MS. MS lesions lead to segmentation and registration errors that may affect the reliability of VBM results. Improved segmentation and registration have been demonstrated by WM LI before segmentation. DARTEL appears to improve registration versus the USM. Our aim was to compare the performance of VBM-DARTEL versus VBM-USM and the effect of LI in the regional analysis of GM atrophy in MS. Materials and Methods: 3T T1 MR imaging scans were acquired from 26 patients with RRMS and 28 age-matched NC. LI replaced WM lesions with normal-appearing WM intensities before image segmentation. VBM analysis was performed in SPM8 by using DARTEL and USM with and without LI, allowing the comparison of 4 VBM methods (DARTEL + LI, DARTEL − LI, USM + LI, and USM − LI). Accuracy of VBM was assessed by using NMI, CC, and a simulation analysis. Results: Overall, DARTEL + LI yielded the most accurate GM maps among the 4 methods (highest NMI and CC, P < .001). DARTEL + LI showed significant GM loss in the bilateral thalami and caudate nuclei in patients with RRMS versus NC. The other 3 methods overestimated the number of regions of GM loss in RRMS versus NC. LI improved the accuracy of both VBM methods. Simulated data suggested the accuracy of the results provided from patient MR imaging analysis. Conclusions: We introduce a pipeline that shows promise in limiting segmentation and registration errors in VBM analysis in MS

Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy.

Abnormalities of tau protein are central to the pathogenesis of progressive supranuclear palsy, whereas haplotype variation of the tau gene MAPT influences the risk of Parkinson disease and Parkinson's disease dementia. We assessed whether regional MAPT expression might be associated with selective vulnerability of global brain networks to neurodegenerative pathology. Using task-free functional magnetic resonance imaging in progressive supranuclear palsy, Parkinson disease, and healthy subjects (n = 128), we examined functional brain networks and measured the connection strength between 471 gray matter regions. We obtained MAPT and SNCA microarray expression data in healthy subjects from the Allen brain atlas. Regional connectivity varied according to the normal expression of MAPT. The regional expression of MAPT correlated with the proportionate loss of regional connectivity in Parkinson's disease. Executive cognition was impaired in proportion to the loss of hub connectivity. These effects were not seen with SNCA, suggesting that alpha-synuclein pathology is not mediated through global network properties. The results establish a link between regional MAPT expression and selective vulnerability of functional brain networks to neurodegeneration.Medical Research Council (Grant IDs: G1100464, MR/K020706/1, G0700503), Wellcome Trust (Grant ID: 103838), National Institute for Health Research Cambridge Biomedical Research Centre, Beverley Sackler fellowship scheme, NARSAD Young Investigator Award, Isaac Newton TrustThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neurobiolaging.2016.09.00

A Computed Tomography-Based Spatial Normalization for the Analysis of [18F] Fluorodeoxyglucose Positron Emission Tomography of the Brain

OBJECTIVE: We developed a new computed tomography (CT)-based spatial normalization method and CT template to demonstrate its usefulness in spatial normalization of positron emission tomography (PET) images with [(18)F] fluorodeoxyglucose (FDG) PET studies in healthy controls. MATERIALS AND METHODS: Seventy healthy controls underwent brain CT scan (120 KeV, 180 mAs, and 3 mm of thickness) and [(18)F] FDG PET scans using a PET/CT scanner. T1-weighted magnetic resonance (MR) images were acquired for all subjects. By averaging skull-stripped and spatially-normalized MR and CT images, we created skull-stripped MR and CT templates for spatial normalization. The skull-stripped MR and CT images were spatially normalized to each structural template. PET images were spatially normalized by applying spatial transformation parameters to normalize skull-stripped MR and CT images. A conventional perfusion PET template was used for PET-based spatial normalization. Regional standardized uptake values (SUV) measured by overlaying the template volume of interest (VOI) were compared to those measured with FreeSurfer-generated VOI (FSVOI). RESULTS: All three spatial normalization methods underestimated regional SUV values by 0.3-20% compared to those measured with FSVOI. The CT-based method showed slightly greater underestimation bias. Regional SUV values derived from all three spatial normalization methods were correlated significantly (p < 0.0001) with those measured with FSVOI. CONCLUSION: CT-based spatial normalization may be an alternative method for structure-based spatial normalization of [(18)F] FDG PET when MR imaging is unavailable. Therefore, it is useful for PET/CT studies with various radiotracers whose uptake is expected to be limited to specific brain regions or highly variable within study population.ope

Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging

AbstractResults from studies that have examined age-related changes in gray matter based on structural MRI scans have not always been consistent. Reasons for this variability likely include small or unevenly-distributed samples, different methods for tissue class segmentation and spatial normalization, and the use of different statistical models. Particularly relevant to the latter is the method of adjusting for global (total) gray matter when making inferences about regionally-specific changes. In the current study, we use voxel-based morphometry (VBM) to explore the impact of these methodological choices in assessing age-related changes in gray matter volume in a sample of 420 adults evenly distributed between the ages of 18–77years. At a broad level, we replicate previous findings, showing age-related gray matter decline in nearly all parts of the brain, with particularly rapid decline in inferior regions of frontal cortex (e.g., insula and left inferior frontal gyrus) and the central sulcus. Segmentation was improved by increasing the number of tissue classes and using less age-biased templates, and registration was improved by using a diffeomorphic flow-based algorithm (DARTEL) rather than a “constrained warp” approach. Importantly, different approaches to adjusting for global effects – not adjusting, Local Covariation, Global Scaling, and Local Scaling – significantly affected regionally-specific estimates of age-related decline, as demonstrated by ranking age effects across anatomical ROIs. Split-half cross-validation showed that, on average, Local Covariation explained a greater proportion of age-related variance across these ROIs than did Global Scaling. Nonetheless, the appropriate choice for global adjustment depends on one's assumptions and specific research questions. More generally, these results emphasize the importance of being explicit about the assumptions underlying key methodological choices made in VBM analyses and the inferences that follow

Understanding the Relevance of Extended Amygdala Reactivity to Dispositional Negativity

Elevated dispositional negativity (DN; i.e., neuroticism/negative emotionality) is associated with a range of deleterious outcomes, including mental illness. Yet, DN’s neurobiology remains incompletely understood. Prior work suggests that DN reflects heightened threat-elicited reactivity in the extended amygdala (EAc), a circuit encompassing the central nucleus (Ce) and the bed nucleus of the stria terminalis (BST), and that this association may be intensified for uncertain threat. We utilized a multi-trait, multi-occasion DN composite and neuroimaging assays of threat anticipation and perception to demonstrate that individuals with elevated DN show heightened BST activation during threat anticipation. Analyses revealed that DN is uniquely predicted by BST reactivity to uncertain threat. DN was unrelated to Ce activation during threat anticipation or EAc activation during ‘threatening’-face presentation. Follow-up analyses revealed that the threat paradigms are not interchangeable probes of EAc function. These observations lay the foundation for future studies necessary to determine causation and improve interventions

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

Correlatos neuroanatómicos del déficit de memoria episódica en personas mayores con deterioro cognitivo leve

Programa Oficial de Postgrado en NeurocienciasLa enfermedad de Alzheimer (EA) es la forma más común de demencia y, en su forma esporádica, afecta sobre todo a personas mayores de 65 años. En España, un 8,2% de la población sufre esta enfermedad, cifra que podría triplicarse en el año 2050. Aunque la sintomatologia se asocia con un metabolismo alterado de las proteínas beta-amiloide y tau, aún se desconocen cuáles son los factores que desencadenan tales alteraciones. Nos enfrentamos por tanto a una enfermedad sin tratamiento eficaz y de carácter terminal. Las evidencias sugieren que cuanto más temprana sea la intervención terapéutica más probabilidades habrá de ralentizar la progresión de la enfermedad, por lo que la búsqueda de biomarcadores tempranos se ha convertido en un reto para la neurociencia contemporánea. Tanto los pacientes diagnosticados con EA como las personas mayores no dementes que presentan deterioro cognitivo leve (DCL), estadio considerado como la fase prodrómica, muestran cambios anatómicos en diferentes regiones del lóbulo temporal medial (LTM), lo cual explicaría la pérdida gradual de la memoria episódica, muy especialmente de la memoria asociativa. Pero esta relación no siempre es evidente, como ocurre con las atrofias localizadas en la corteza entorrinal y en la capa CA1 del hipocampo. Inspirados por estos resultados, el presente trabajo tiene un triple objetivo. En primer lugar, determinar en personas mayores con DCL de tipo amnésico (DCLa) la magnitud del deterioro de la memoria asociativa y el grado de reversibilidad cuando se introducen aspectos que facilitan la codificación y consolidación de nuevas asociaciones, como ocurre con la congruencia semántica del contexto en el que se codifican los eventos estimulares. En segundo lugar, determinar si las alteraciones de la memoria asociativa guardan relación con la integridad anatómica de diferentes estructuras del LTM como son la corteza entorrinal, subiculum, Cornu Ammonis (CA) y giro dentado. Y por último, evaluar el impacto del genotipo ApoE4 sobre dicha relación, por ser este el principal factor de riesgo genético para desarrollar la EA. Los resultados han puesto de manifiesto que las personas con DCLa, y muy especialmente las portadoras del genotipo ApoE4, muestran una menor capacidad para establecer y/o recuperar nuevas asociaciones así como para beneficiarse de la congruencia semántica durante la codificación. Este déficit en la memoria asociativa correlaciona con cambios de volumen que afectan fundamentalmente a la corteza entorrinal, a la región CA1 del hipocampo y a la transición CA1-CA2, mientras que la incapacidad para beneficiarse del contexto semántico durante la codificación correlaciona con cambios de volumen en CA. Las diferencias de grupo en lo que a estas relaciones se refiere son independientes del genotipo ApoE. Estos resultados son congruentes con la idea de que el fenotipo cognitivo de la EA guarda una estrecha relación con la distribución topográfica de las lesiones cerebrales que anteceden al diagnóstico de la enfermedad. Además, abren nuevas perspectivas para mejorar nuestro conocimiento sobre los daños cerebrales que caracterizan a las fases prodrómicas de la EA, aspecto que podría tener implicaciones prácticas para el diagnóstico temprano de esta patología neurodegenerativa.Universidad Pablo de Olavide. Departamento de Fisiología, Anatomía y Biología Celula