Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Cortical Cartography: Mapping Functional Areas Across the Human Brain with Resting State Functional Connectivity MRI

Human behavior and cognition are largely supported by the cerebral cortex, a structure organized at many physical scales ranging from individual neurons up to distributed systems of multiple interconnected “functional areas”. Each functional area possesses a unique combination of inputs, outputs, and internal structure, and is thought to make a distinct contribution to information processing. Thus, the study of each area\u27s normal function, developmental trajectory, and modified responses following loss or injury may greatly enhance our understanding of cognition. Indeed, one of the: often unsaid) overarching goals of functional neuroimaging is to use differential activity between conditions to identify specific information processing operations reflected in these functional areas. Unfortunately, delineating a complete collection of functional areas in any mammal, let alone non-invasively in humans, is not straightforward and currently incomplete. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity: rs-fcMRI), are especially promising as a way to delineate functional areas since they localize differences in patterns of correlated activity across large expanses of cortex. Presented here is the exploration, development, initial application, and first order validation of rs-fcMRI mapping, the non-invasive delineation of putative functional areas and boundaries across the cortical surface in individual humans using rs-fcMRI. rs-fcMRI ‘contour’ maps can be created in individual subjects which delineate sharp transitions and stable locations in correlation patterns. Several of the strongest and most resilient of these features can be consistently detected both across time within subject, are comparable across subject, independent cohort, and scanner, and appear to represent known functional-anatomical divisions. An initial validation of rs-fcMRI mapping against task-related activity, finds consistency with task-related fMRI results for two separate tasks in two groups of subjects, as well as in individual data. These results provide a proof-of-concept for using rs-fcMRI mapping to describe a putative distribution of functional areas and boundaries within single individuals, as well as to potentially improve functional neuroimaging studies in basic, translational, and clinical settings through the independent delineation of functional areas that can be compared across subjects, groups, and studies

Validating a new methodology for optical probe design and image registration in fNIRS studies

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing

Brain covariance selection: better individual functional connectivity models using population prior

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver : Canada (2010

Surface-based versus volume-based fMRI group analysis: a case study

International audienceBeing able to detect reliably functional activity in a popula- tion of subjects is crucial in human brain mapping, both for the under- standing of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common 3D 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 3D. Nevertheless, few as- sessments 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 e ects (RFX) and mixed-e ects analyses (MFX). We nd that, using a voxel-level thresholding, and to some extent, cluster-level thresholding, the surface-based approach generally detects more, but smaller active regions than the corresponding volume-based approach for both RFX and MFX procedures, and that surface-based supra-threshold regions are more reproducible by bootstrap

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

Modulating perceptual complexity and load reveals degradation of the visual working memory network in ageing

Previous neuroimaging studies have reported a posterior to anterior shift of activation in ageing (PASA). Here, we explore the nature of this shift by modulating load (1,2 or 3 items) and perceptual complexity in two variants of a visual working memory task (VWM): a ‘simple’ color and a ‘complex’ shape change detection task. Functional near-infrared spectroscopy (fNIRS) was used to record changes in activation in younger (N=24) and older adults (N=24). Older adults exhibited PASA by showing lesser activation in the posterior cortex and greater activation in the anterior cortex when compared to younger adults. Further, they showed reduced accuracy at loads 2 and 3 for the simple task and across all loads for the complex task. Activation in the posterior and anterior cortices was modulated differently for younger and older adults. In older adults, increasing load in the simple task was accompanied by decreasing activation in the posterior cortex and lack of modulation in the anterior cortex, suggesting the inability to encode and/or maintain representations without much aid from higher-order centers. In the complex task, older adults recruited verbal working memory areas in the posterior cortex, suggesting that they used adaptive strategies such as labelling the shape stimuli. This was accompanied by reduced activation in the anterior cortex reflecting the inability to exert top-down modulation to typical VWM areas in the posterior cortex to improve behavioral performance

Validating an image-based fNIRS approach with fMRI and a working memory task

In the current study, we extend a previous methodological pipeline by adding a novel image reconstruction approach to move functional near-infrared (fNIRS) signals from channel-space on the surface of the head to voxel-space within the brain volume. We validate this methodology by comparing voxel-wise fNIRS results to functional magnetic resonance imaging (fMRI) results from a visual working memory (VWM) task using two approaches. In the first approach, significant voxel-wise correlations were observed between fNIRS and fMRI measures for all experimental conditions across brain regions in the fronto-parieto-temporal cortices. In the second approach, we conducted separate multi-factorial ANOVAs on fNIRS and fMRI measures and then examined the correspondence between main and interaction effects within common regions of interest. Both fMRI and fNIRS showed similar trends in activation within the VWM network when the number of items held in working memory increases. These results validate the image-based fNIRS approach