Parcellation Schemes and Statistical Tests to Detect Active Regions on the Cortical Surface

International audienceActivation detection in functional Magnetic Resonance Imaging (fMRI) datasets is usually performed by thresholding activation maps in the brain volume or, better, on the cortical surface. However, basing the analysis on a site-by-site statistical decision may be detrimental both to the interpretation of the results and to the sensitivity of the analysis, because a perfect point-to-point correspondence of brain surfaces from multiple subjects cannot be guaranteed in practice. In this paper, we propose a new approach that first defines anatomical regions such as cortical gyri outlined on the cortical surface, and then segments these regions into functionally homogeneous structures using a parcellation procedure that includes an explicit between-subject variability model, i.e. random effects. We show that random effects inference can be performed in this framework. Our procedure allows an exact control of the specificity using permutation techniques, and we show that the sensitivity of this approach is higher than the sensitivity of voxel- or cluster-level random effects tests performed on the cortical surface

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination.

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled dataset of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p<0.05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high-dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA-based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC

White matter impairment in the speech network of individuals with autism spectrum disorder

AbstractImpairments in language and communication are core features of Autism Spectrum Disorder (ASD), and a substantial percentage of children with ASD do not develop speech. ASD is often characterized as a disorder of brain connectivity, and a number of studies have identified white matter impairments in affected individuals. The current study investigated white matter integrity in the speech network of high-functioning adults with ASD. Diffusion tensor imaging (DTI) scans were collected from 18 participants with ASD and 18 neurotypical participants. Probabilistic tractography was used to estimate the connection strength between ventral premotor cortex (vPMC), a cortical region responsible for speech motor planning, and five other cortical regions in the network of areas involved in speech production. We found a weaker connection between the left vPMC and the supplementary motor area in the ASD group. This pathway has been hypothesized to underlie the initiation of speech motor programs. Our results indicate that a key pathway in the speech production network is impaired in ASD, and that this impairment can occur even in the presence of normal language abilities. Therapies that result in normalization of this pathway may hold particular promise for improving speech output in ASD

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Network organization of sensory-biased and multi-sensory working memory and attention in human cortex with fMRI

The ability to attentively filter sensory information and manipulate it in working memory is critical for our ability to interact with the world. Although primary and secondary sensory cortical areas have been well-studied, frontal lobe contributions to sensory attention and working memory remain under-investigated. This dissertation investigates the topography and network organization of sensory-biased and multi-sensory regions in the healthy human brain using functional magnetic resonance imaging (fMRI). First, this research developed a series of functional connectivity analyses of data from the Human Connectome Project to validate and extend recently localized auditory-biased network structures, transverse gyrus intersecting the precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS), and visual-biased network structures, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), in lateral frontal cortex (LFC). Results replicated the original findings and extended them by revealing five additional bilateral LFC regions, including middle inferior frontal sulcus (midIFS) and frontal operculum (FO), differentially connected to either the visual- or auditory-biased networks. Due to inter-subject anatomical variability, identification of sPCS, tgPCS, iPCS and cIFS depends critically on within-subject analyses. Next, this work demonstrated that an individual’s unique pattern of resting-state functional connectivity can accurately identify their specific pattern of working memory (WM) and attention task activation in LFC using “connectome fingerprinting” (CF). CF predictions were superior to group-average predictions and matched the accuracy of within-subject task-based functional localization. This research developed and validated methods that use intrinsic functional connectivity information to perform functional brain analyses on highly idiosyncratic brain regions. Finally, a combined auditory, tactile and visual WM study revealed the joint organization of sensory-biased and multi-sensory regions within individual subjects. Hypothesized visual-biased midIFS and auditory-biased FO regions were functionally confirmed for the first time. Several bilateral tactile-biased regions, premotor dorsal, premotor ventral, anterior middle frontal gyrus, middle insula, postcentral sulcus, posterior middle temporal gyrus and pre-supplemental motor area, abutting previously described visual- and auditory-biased regions were identified. Several multi-sensory WM regions, recruited in each stimulus modality, were observed to partially overlap with visual-biased regions. Intrinsic functional connectivity analyses revealed that regions segregate into networks largely based upon their modality preferences.2020-06-14T00:00:00

An empirical comparison of surface-based and volume-based group studies in neuroimaging

International audienceBeing able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random effects (RFX) and mixed-effects analyses (MFX). We consider different schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We find that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this effect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps

Functional Brain Organization in Space and Time

The brain is a network functionally organized at many spatial and temporal scales. To understand how the brain processes information, controls behavior and dynamically adapts to an ever-changing environment, it is critical to have a comprehensive description of the constituent elements of this network and how relationships between these elements may change over time. Decades of lesion studies, anatomical tract-tracing, and electrophysiological recording have given insight into this functional organization. Recently, however, resting state functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for whole-brain non-invasive measurement of spontaneous neural activity in humans, giving ready access to macroscopic scales of functional organization previously much more difficult to obtain. This thesis aims to harness the unique combination of spatial and temporal resolution provided by functional MRI to explore the spatial and temporal properties of the functional organization of the brain. First, we establish an approach for defining cortical areas using transitions in correlated patterns of spontaneous BOLD activity (Chapter 2). We then propose and apply measures of internal and external validity to evaluate the credibility of the areal parcellation generated by this technique (Chapter 3). In chapter 4, we extend the study of functional brain organization to a highly sampled individual. We describe the idiosyncratic areal and systems-level organization of the individual relative to a standard group-average description. Further, we develop a model describing the reliability of BOLD correlation estimates across days that accounts for relevant sources of variability. Finally, in Chapter 5, we examine whether BOLD correlations meaningfully vary over the course of single resting-state scans