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

Situating the default-mode network along a principal gradient of macroscale cortical organization

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input

Unravelling The Subfields Of The Hippocampal Head Using 7-Tesla Structural MRI

Probing the functions of human hippocampal subfields is a promising area of research in cognitive neuroscience. However, defining subfield borders in Magnetic Resonance Imaging (MRI) is challenging. Here, we present a user-guided, semi-automated protocol for segmenting hippocampal subfields on T2-weighted images obtained with 7-Tesla MRI. The protocol takes advantage of extant knowledge about regularities in hippocampal morphology and ontogeny that have not been systematically considered in prior related work. An image feature known as the hippocampal ‘dark band’ facilitates tracking of subfield continuities, allowing for unfolding and segmentation of convoluted hippocampal tissue. Initial results suggest that this protocol offers sufficient precision and flexibility to accommodate inter-individual differences in morphology and produces segmentations that have improved accuracy and detail compared to other prominent protocols, with similar inter-rater reliability. We anticipate that this protocol will allow for improved anatomical precision in future research on hippocampal subfields in health and neurological disease

Unravelling the Intrinsic Functional Organization of the Human Lateral Frontal Cortex: A Parcellation Scheme Based on Resting State fMRI

Human and nonhuman primates exhibit flexible behavior. Functional, anatomical, and lesion studies indicate that the lateral frontal cortex (LFC) plays a pivotal role in such behavior. LFC consists of distinct subregions exhibiting distinct connectivity patterns that possibly relate to functional specializations. Inference about the border of each subregion in the human brain is performed with the aid of macroscopic landmarks and/or cytoarchitectonic parcellations extrapolated in a stereotaxic system. However, the high interindividual variability, the limited availability of cytoarchitectonic probabilistic maps, and the absence of robust functional localizers render the in vivo delineation and examination of the LFC subregions challenging. In this study, we use resting state fMRI for the in vivo parcellation of the human LFC on a subjectwise and data-driven manner. This approach succeeds in uncovering neuroanatomically realistic subregions, with potential anatomical substrates includingBA46, 44, 45, 9 and related (sub)divisions. Ventral LFC subregions exhibit different functional connectivity (FC), which can account for different contributions in the language domain, while more dorsal adjacent subregions mark a transition to visuospatial/sensorimotor networks. Dorsal LFC subregions participate in known large-scale networks obeying an external/internal information processing dichotomy. Furthermore, we traced “families” of LFC subregions organized along the dorsal–ventral and anterior–posterior axis with distinct functional networks also encompassing specialized cingulate divisions. Similarities with the connectivity of macaque candidate homologs were observed, such as the premotor affiliation of presumed BA 46. The current findings partially support dominant LFC models

Lateral–Medial Dissociation in Orbitofrontal Cortex–Hypothalamus Connectivity

The orbitofrontal cortex (OFC) is involved in cognitive functions, and is also closely related to autonomic functions. The OFC is densely connected with the hypothalamus, a heterogeneous structure controlling autonomic functions that can be divided into two major parts: the lateral and the medial. Resting-state functional connectivity has allowed us to parcellate the cerebral cortex into putative functional areas based on the changes in the spatial pattern of connectivity in the cerebral cortex when a seed point is moved from one voxel to another. In the present high spatial-resolution fMRI study, we investigate the connectivity-based organization of the OFC with reference to the hypothalamus. The OFC was parcellated using resting-state functional connectivity in an individual subject approach, and then the functional connectivity was examined between the parcellated areas in the OFC and the lateral/medial hypothalamus. We found a functional double dissociation in the OFC: the lateral OFC (the lateral orbital gyrus) was more likely connected with the lateral hypothalamus, whereas the medial OFC (the medial orbital and rectal gyri) was more likely connected with the medial hypothalamus. These results demonstrate the fundamental heterogeneity of the OFC, and suggest a potential neural basis of the OFC-hypothalamic functional interaction

The aging frontal lobe in health and disease : a structural magnetic resonance imaging study

Cortical and subcortical regions of the brain decrease in volume in normal as well as pathological aging. Previous studies indicate that certain parts of the brain, like the prefrontal cortex, may be particularly vulnerable to age-related processes which are manifested by significant volume loss in this region. Cortical volume loss may be further enhanced by different kinds of pathology in the brain. The purpose of this study was to further investigate regional volumetric changes of the frontal lobe in normal aging and in aging patients with dementia. In study I-III patients with frontotemporal lobar degeneration (FTLD), Alzheimer’s disease (AD) and healthy controls are investigated. Cortical atrophy is related to clinical symptoms (study I), discussed in relation to gross morphology and cytoarchitecture (study II), and compared with the atrophy in the hippocampus (study III). In study IV a large number of normal elderly participants are investigated. Age-related volume loss in the limbic system (the dorsal anterior cingulate cortex and the hippocampus) is compared with atrophy of a region of the prefrontal cortex (the orbitofrontal cortex). Volumetric data of frontal and temporal cortical regions and the hippocampus was acquired by manual delineation on structural magnetic resonance images. Results of study I and III reveal that the clinical symptoms displayed by the subtypes of FTLD are commonly reflected in a specific pattern of atrophy in frontotemporal cortices as well as in the hippocampus. Study II suggests that the surface morphology of sulci and gyri may be unreliable landmarks for cyto-architectonic regions of the frontal cortex. Study IV finally indicates that a common characteristic of limbic regions may be that age-related volume loss is delayed in comparison to regions of the prefrontal cortex. Results also suggest that the dorsal anterior cingulate is more resistant to age-related volume loss than hippocampus, which implies that age-related volume loss occurs at different rates for different regions also within the limbic system

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the localizer to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. Author summary No two individuals are alike. The size, shape, position, and connectivity patterns of brain functional regions can vary drastically between individuals. While interindividual differences in functional organization are well recognized, to date, standard procedures for functional neuroimaging research still rely on aligning different subjects' data to a nominal average brain based on global brain morphology. We developed an approach to reliably identify homologous functional regions in each individual and demonstrated that aligning data based on these homologous functional regions can significantly improve the study of resting state functional connectivity, task-fMRI activations, and brain-behavior associations. Moreover, we showed that individual differences in size, position, and connectivity of brain functional regions are dissociable, and each can provide nonredundant information in explaining human behavior