Analysis of surface folding patterns of diccols using the GPU-Optimized geodesic field estimate

Localization of cortical regions of interests (ROIs) in the human brain via analysis of Diffusion Tensor Imaging (DTI) data plays a pivotal role in basic and clinical neuroscience. In recent studies, 358 common cortical landmarks in the human brain, termed as Dense Indi- vidualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), have been identified. Each of these DICCCOL sites has been observed to possess fiber connection patterns that are consistent across individuals and populations and can be regarded as predictive of brain function. However, the regularity and variability of the cortical surface fold patterns at these DICCCOL sites have, thus far, not been investigated. This paper presents a novel approach, based on intrinsic surface geometry, for quantitative analysis of the regularity and variability of the cortical surface folding patterns with respect to the structural neural connectivity of the human brain. In particular, the Geodesic Field Estimate (GFE) is used to infer the relationship between the structural and connectional DTI features and the complex surface geometry of the human brain. A parallel algorithm, well suited for implementation on Graphics Processing Units (GPUs), is also proposed for efficient computation of the shortest geodesic paths between all cortical surface point pairs. Based on experimental results, a mathematical model for the morphological variability and regularity of the cortical folding patterns in the vicinity of the DICCCOL sites is proposed. It is envisioned that this model could be potentially applied in several human brain image registration and brain mapping applications

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity–based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work

Anatomy-Guided Dense Individualized and Common Connectivity-Based Cortical Landmarks (A-DICCCOL)

Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science

Longitudinal development of cortical thickness, folding, and fiber density networks in the first 2 years of life: Longitudinal Development of Cortical Thickness, Folding, and Fiber Density Networks

Quantitatively characterizing the development of cortical anatomical networks during the early stage of life plays an important role in revealing the relationship between cortical structural connection and high-level functional development. The development of correlation networks of cortical-thickness, cortical folding, and fiber-density is systematically analyzed in this article to study the relationship between different anatomical properties during the first 2 years of life. Specifically, longitudinal MR images of 73 healthy subjects from birth to 2 year old are used. For each subject at each time point, its measures of cortical thickness, cortical folding, and fiber density are projected to its cortical surface that has been partitioned into 78 cortical regions. Then, the correlation matrices for cortical thickness, cortical folding, and fiber density at each time point can be constructed, respectively, by computing the inter-regional Pearson correlation coefficient (of any pair of ROIs) across all 73 subjects. Finally, the presence/ absence pattern (i.e., binary pattern) of the connection network is constructed from each inter-regional correlation matrix, and its statistical and anatomical properties are adopted to analyze the longitudinal development of anatomical networks. The results show that the development of anatomical network could be characterized differently by using different anatomical properties (i.e., using cortical thickness, cortical folding, or fiber density)

Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer’s disease (AD) and its prodromal stage—mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods

Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks: Identification of Infants at High-Risk for ASD

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review

La estimación de redes de conectividad cerebral mediante señal fMRI para la caracterización del envejecimiento sano y patológico (deterioro cognitivo leve)

[spa] Los datos obtenidos a partir de señal fMRI son cada vez más utilizados en diferentes disciplinas, entre las cuales se encuentra la psicología. El hecho de tratarse de una herramienta no invasiva que proporciona información de las características del cerebro y su actividad hacen de estas imágenes un complemento muy valioso tanto en la práctica profesional como en la investigación de diferentes ámbitos. Sin embargo, su análisis resulta ciertamente más complejo de lo que podría parecer: utilizar esta herramienta requiere de equipos multidisciplinares que puedan hacer frente a todo lo que supone el análisis de este tipo de información. Los estudios con señal fMRI pueden realizarse en un amplio abanico de poblaciones diferentes, desde participantes sanos hasta participantes con diferentes patologías, tanto físicas como psicológicas. Una de estas poblaciones es la que engloba la tercera edad: participantes mayores que pueden sufrir algún tipo de deterioro de diferente gravedad, mientras que otros se encuentran cognitivamente preservados. Entre los participantes con deterioro cognitivo encontramos una categoría diagnóstica de interés creciente: el deterioro cognitivo leve. Las personas con este diagnóstico realizan sus actividades de la vida diaria casi con total independencia, aunque empiezan a presentar un deterioro relativamente reconocible. La mayoría sufren de deterioro en la memoria, aunque en algunos casos también se ven afectadas otras capacidades como el lenguaje. Se suele definir como un paso entre el envejecimiento normal y la demencia, aunque algunas de estas personas mantienen el diagnóstico durante años y no llegan a aumentar su deterioro. Así pues, la investigación en esta temática es cada vez más necesaria para entender tanto las características de esta patología como su diferenciación de la población sana del mismo rango de edad. La presente tesis doctoral tiene como objetivo estudiar la conectividad cerebral a través de fMRI en personas con deterioro cognitivo leve, así como personas con las capacidades cognitivas preservadas. Para ello será necesario identificar las técnicas de análisis más utilizadas para estos datos en la actualidad, estudiar la conectividad funcional en envejecimiento sano y también en envejecimiento con deterioro, procurando proporcionar herramientas que solventen las problemáticas estadísticas más habituales en este ámbito. Se han desarrollado tres estudios para alcanzar los objetivos propuestos. El Estudio 1 consta de una revisión bibliográfica de la literatura científica acerca del estudio del deterioro cognitivo leve a través de fMRI. El Estudio 2 se centra en la descripción de la conectividad funcional en envejecimiento sano para discernir sus principales características de patrones de conectividad, así como sus diferencias entre grupos de edades. Finalmente, el Estudio 3 compara los patrones de conectividad de 10 personas diagnosticadas de MCI con 10 participantes sanos apareados en sexo, edad y grado de escolaridad. En relación directa con los objetivos de la presente tesis, así como con los resultados obtenidos en los tres estudios llevados a cabo, se han podido extraer tres conclusiones principales. En primer lugar, quedó verificada la amplia variedad de aproximaciones estadísticas que se emplean actualmente para el estudio de la patología MCI a través de datos fMRI. Los objetivos de las investigaciones consultadas seguían líneas comunes o similares, aunque los resultados clínicos a los que se llegaba necesitan, en general, de más estudios para profundizar en la temática y poder generalizar los resultados obtenidos. Se ha constatado la necesidad de incluir los detalles relacionados con los análisis de conectividad funcional, puesto que en numerosos casos se ha echado de menos valiosa información para que puedan ser reproducidos o replicados. En segundo lugar, se ha verificado la existencia de cambios en los patrones de conectividad funcional en envejecimiento sano. El número e intensidad de conexiones entre regiones disminuía de forma progresiva a medida que aumentaba la edad de los participantes, teniendo en cuenta que se trabajaba con los participantes clasificados en grupos de edad, mostrando el declive más agudo en los participantes entre 75 y 79 años. Sin embargo, los participantes a partir de 80 años mostraron un pico más elevado en este sentido, probablemente relacionado con mecanismos de compensación o por teoría de supervivencia. Por último, el estudio de las diferencias en patrones de conectividad cerebral de los pacientes con MCI respecto a controles sanos permitió subrayar la existencia de dichas diversidades, aunque en muchos casos fueran relativamente sutiles. El aumento de la conectividad funcional que experimentan algunas regiones de los pacientes MCI quedó constatado, a la vez que la disminución de la conectividad en otras. La clasificación de las regiones cerebrales en clústeres permite ver diferencias en función del grupo, especialmente en relación a las regiones de la DMN anterior. Por todo lo expuesto, será importante profundizar en el estudio de los patrones de conectividad tanto en envejecimiento sano como patológico. Por un lado, es importante estudiar los grupos de edad entre 75 y 79 años de participantes sanos, así como a partir de 80 años, para entender los mecanismos que producen las diferencias tan abruptas entre ellos. Por otro lado, se deberán investigar la distribución de las regiones de la DMN anterior en pacientes MCI a partir del análisis de clústeres para confirmar su posible uso como biomarcador y facilitar así el diagnóstico de dicha patología.[eng] Nowadays, fMRI data is often used in many disciplines, among which we can find Psychology. Nevertheless, its statistical analysis could be more complex of what it could seem. The election of the statistical approach could be very different comparing different studies and are often related with the group resources. fMRI studies could be applied in a wide variety of populations, from healthy participants to people diagnosed by different pathologies, both physical and psychological. One of those populations is the elderly: old age participants that have more probabilities to develop any impairment of different gravity, while others are full cognitive preserved. Among the participants with cognitive impairment, we find a diagnostic category of increasing interest: mild cognitive impairment (MCI). People with this diagnostic can deal with daily activities with almost total independence, although they start to present some impairment that could be recognizable. Most of them suffer of memory impairment, even though they could have affectation in other abilities as language. It is usually defined as an intermediate step between normal aging and dementia, in spite of some of the people maintain this diagnostic during many years and never progress in more impairment. Therefore, research in this topic is everyday more necessary, keeping in mind the increasing of life expectancy, to understand the characteristics of this pathology as well as its differences with healthy population in the same age. The present doctoral thesis aims to study brain connectivity patterns with fMRI in people diagnosed of mild cognitive impairment just as well as healthy elderly individuals. To get this objective it will be necessary to identify the currently most popular analysis techniques for this type of data, as well as to study functional connectivity in healthy aging and impaired aging, trying to provide tools to resolve the most common statistical issues in this area. According to the objectives of our research, and directly to the results obtained, three main conclusions could be extracted. In the first place, it is confirmed that many statistical approaches are used to study MCI pathology with fMRI data. The objectives of the consulted publications were similar, although the clinical results needed, in general, more studies to obtain deep information in the topic and to generalize the results. The need to include all details related to functional connectivity analysis was verified, because in numerous articles there was a lack of information that permit reproduce or replicate the study. In the second place, the existence of changes in functional connectivity patterns in healthy aging was validated. The number and intensity of connections between regions shows a progressive diminution with age, showing the most noticeable decline in participants between 75 and 79 years old. However, participants with 80 or more show more connectivity, probably related with compensatory mechanisms. Lastly, the study of the differences in connectivity patterns between MCI patients and healthy controls permitted to emphasize the existence of differences, even though in some cases were subtle. Some brain regions showed an increase of connectivity, while others showed a decrease, in MCI patients in comparison to healthy controls. The classification provided by the cluster analysis allows to confirm the differences between groups, especially in the anterior DMN regions. With all of this, it is important to delve into the study of functional connectivity patterns as much in healthy and pathological aging. On one hand, it is important to study age groups from 75 to 79 years old and after 80, to understand the mechanisms that produce so many differences between them. On the other hand, the distribution of the anterior DMN regions in cluster analysis should be more investigated to confirm its possible use as a biomarker to facilitate the MCI diagnostic