Maturation of the language network: from inter- to intrahemispheric connectivities

Language development must go hand-in-hand with brain maturation. Little is known about how the brain develops to serve language processing, in particular, the processing of complex syntax, a capacity unique to humans. Behavioral reports indicate that the ability to process complex syntax is not yet adult-like by the age of seven years. Here, we apply a novel method to demonstrate that the basic neural basis of language, as revealed by low frequency fluctuation stemming from functional MRI data, differs between six-year-old children and adults in crucial aspects. Although the classical language regions are actively in place by the age of six, the functional connectivity between these regions clearly is not. In contrast to adults who show strong connectivities between frontal and temporal language regions within the left hemisphere, children's default language network is characterized by a strong functional interhemispheric connectivity, mainly between the superior temporal regions. These data indicate a functional reorganization of the neural network underlying language development towards a system that allows a close interplay between frontal and temporal regions within the left hemisphere.

Long-term effects of motor training on resting-state networks and underlying brain structure

Acquired motor skills are coded in fronto-parietal brain networks, but how these networks evolve through motor training is unclear. On the one hand, increased functional connectivity has been shown immediately after a training session; on the other hand, training-induced structural changes are visible only after several weeks. Based on known associations between functional and structural network development during human ontogeny, we hypothesised that learning a challenging motor task leads to long-lasting changes in functional resting-state networks and the corresponding cortical and sub-cortical brain structures. Using longitudinal functional and structural MRI at multiple time points, we demonstrate increased fronto-parietal network connectivity one week after two brief motor training sessions in a dynamic balancing task, although subjects were engaged in their regular daily activities during the week. Repeated training sessions over six consecutive weeks progressively modulate these changes in accordance with individual performance improvements. Multimodal correlation analyses showed an association between structural grey matter alterations and functional connectivity changes in prefrontal and supplementary-motor areas. These coincident changes were most prominent in the first three weeks of training. In contrast, changes in fronto-parietal functional connectivity and the underlying white matter fibre structure developed gradually during the six weeks. Our results demonstrate a tight correlation between training-induced functional and structural brain plasticity on the systems level and suggest a functional relevance of intrinsic brain activity for morphological adaptation in the human brain.

Microstructural Parcellation of the Human Cerebral Cortex - From Brodmann's Post-Mortem Map to in vivo Mapping with High-Field Magnetic Resonance Imaging

The year 2009 marked the 100th anniversary of the publication of the famous brain map of Korbinian Brodmann. Although a “classic” guide to microanatomical parcellation of the cerebral cortex, it is – from today's state-of-the-art neuroimaging perspective – problematic to use Brodmann's map as a structural guide to functional units in the cortex. In this article we discuss some of the reasons, especially the problematic compatibility of the “post-mortem world” of microstructural brain maps with the “in vivo world” of neuroimaging. We conclude with some prospects for the future of in vivo structural brain mapping: a new approach which has the enormous potential to make direct correlations between microstructure and function in living human brains: “in vivo Brodmann mapping” with high-field magnetic resonance imaging.

Exploring functional relations between brain regions from fMRI meta-analysis data: comments on Ramsey, Spirtes, and Glymour

In this paper, we address the critical assessment of Ramsey et al. of our method for learning partially directed graphs from meta-analysis imaging data (Neumann et al., 2010). We argue that our method provides valid and interpretable results when applied to data representing a single experimental paradigm. Simulations further suggest that, despite theoretical limitations, the application of our method to mixed probability distributions yields reliable results with error rates at acceptable levels. Finally, we discuss the nature of meta-analysis data and the notion of causality in the context of functional neuroimaging.

Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain

Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.

Learning partially directed functional networks from meta-analysis imaging data

We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.

Setting the frame: the human brain activates a basic low-frequency network for language processing

Low-frequency fluctuations (LFFs) are a major source of variation in fMRI data. This has been established in numerous experiments-particularly in the resting state. Here we investigate LFFs in a task-dependent setting. We hypothesized that LFFs may contain information about cognitive networks that are specific to the overall task domain without being time locked to stimulus onsets. We analyzed data of 6 fMRI experiments, 4 of which belonged to the language domain. After regressing out specifics of the experimental design and low-pass filtering (<0.1 Hz), we found that the 4 language experiments produced a correlational pattern that was not present in the 2 nonlanguage studies. Specifically, a region in the posterior part of the left superior temporal sulcus/gyrus was consistently correlated with both the left Brodmann's area 44 and the left frontal operculum in all 4 language studies, whereas this correlation was not found in the 2 other experiments. This finding indicates the existence of a basic network that acts as a general framework for language processing. In contrast to networks obtained by a conventional conjunction analysis of activation maps, this network is independent of experimental specifics such as stimulus onsets and exists in the low-frequency range.

Neural activations at the junction of the inferior frontal sulcus and the inferior precentral sulcus: interindividual variability, reliability, and association with sulcal morphology

The sulcal morphology of the human frontal lobe is highly variable. Although the structural images usually acquired in functional magnetic resonance imaging studies provide information about this interindividual variability, this information is only rarely used to relate structure and function. Here, we investigated the spatial relationship between posterior frontolateral activations in a task-switching paradigm and the junction of the inferior frontal sulcus and the inferior precentral sulcus (inferior frontal junction, IFJ) on an individual-subject basis. Results show that, although variable in terms of stereotaxic coordinates, the posterior frontolateral activations observed in task-switching are consistently and reliably located at the IFJ in the brains of individual participants. The IFJ shares such consistent localization with other nonprimary areas as motion-sensitive area V5/MT and the frontal eye field. Building on tension-based models of morphogenesis, this structure-function correspondence might indicate that the cytoarchitectonic area underlying activations of the IFJ develops at early stages of cortical folding.

Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics

PURPOSE

To evaluate logical expressions over different effects in data analyses using the general linear model (GLM) and to evaluate logical expressions over different posterior probability maps (PPMs).

MATERIALS AND METHODS

In functional magnetic resonance imaging (fMRI) data analysis, the GLM was applied to estimate unknown regression parameters. Based on the GLM, Bayesian statistics can be used to determine the probability of conjunction, disjunction, implication, or any other arbitrary logical expression over different effects or contrast. For second-level inferences, PPMs from individual sessions or subjects are utilized. These PPMs can be combined to a logical expression and its probability can be computed. The methods proposed in this article are applied to data from a STROOP experiment and the methods are compared to conjunction analysis approaches for test-statistics.

RESULTS

The combination of Bayesian statistics with propositional logic provides a new approach for data analyses in fMRI. Two different methods are introduced for propositional logic: the first for analyses using the GLM and the second for common inferences about different probability maps.

CONCLUSION

The methods introduced extend the idea of conjunction analysis to a full propositional logic and adapt it from test-statistics to Bayesian statistics. The new approaches allow inferences that are not possible with known standard methods in fMRI.

Model-based clustering of meta-analytic functional imaging data

We present a method for the analysis of meta-analytic functional imaging data. It is based on Activation Likelihood Estimation (ALE) and subsequent model-based clustering using Gaussian mixture models, expectation-maximization (EM) for model fitting, and the Bayesian Information Criterion (BIC) for model selection. Our method facilitates the clustering of activation maxima from previously performed imaging experiments in a hierarchical fashion. Regions with a high concentration of activation coordinates are first identified using ALE. Activation coordinates within these regions are then subjected to model-based clustering for a more detailed cluster analysis. We demonstrate the usefulness of the method in a meta-analysis of 26 fMRI studies investigating the well-known Stroop paradigm.

Detecting groups of coherent voxels in functional MRI data using spectral analysis and replicator dynamics

PURPOSE

To investigate the relationship between functional MRI (fMRI) time series in the human brain, combining fMRI spectral analysis and replicator dynamics.

MATERIALS AND METHODS

Simulated and real fMRI time courses were investigated using the bivariate spectral coherence. Coherence values were placed in coherence matrices encoding the relationship between the time courses. Groups of maximally coherent voxels were detected using replicator dynamics. Results were compared to a former approach called number of coherent voxels (NCV).

RESULTS

NCV critically depends on a threshold that has to be chosen in advance. The lower this threshold, the larger the detected group. Using higher NCV thresholds in our simulations, the method did not detect all voxels that were constructed to have a high coherence among each other. In contrast, the replicator process found the whole group in all simulations.

CONCLUSION

The application of replicator dynamics to spectral matrices is a reliable method for detecting groups of maximally coherent voxels. A replicator process is able to determine groups of voxels with the property that each voxel in the group exhibits a high coherence with every other group member. In contrast to the NCV approach, this method is parameter-free and does not require the a priori selection of a reference voxel.

Using non-negative matrix factorization for single-trial analysis of fMRI data

The analysis of single trials of an fMRI experiment is difficult because the BOLD response has a poor signal to noise ratio and is sometimes even inconsistent across trials. We propose to use non-negative matrix factorization (NMF) as a new technique for analyzing single trials. NMF yields a matrix decomposition that is useful in this context because it elicits the intrinsic structure of the single-trial data. The results of the NMF analysis are then processed further using clustering techniques. In addition to analyzing single trials in one brain region, the method is also suitable for investigating interdependencies between trials across brain regions. The method even allows to analyze the effect that a trial has on a subsequent trial in a different region at a significant temporal offset. This distinguishes the present method from other methods that require interdependencies between brain regions to occur nearly simultaneously. The method was applied to fMRI data and found to be a viable technique that may be superior to other matrix decomposition methods for this particular problem domain.

Characterization of cortical thickness and ventricular width in normal aging: a morphometric study at 3 Tesla

PURPOSE

To describe loss of cortical thickness and ventricular enlargement during normal aging in a sample of 525 neurologically and psychiatrically inconspicuous subjects (17-68 years old).

MATERIALS AND METHODS

An automated segmentation algorithm was applied to assess cortical thickness and compared with conventional measurements of ventricular indices (ventricular body index (VBI), anterior horn index (AHI), and third ventricular width) as performed in clinical practice. Regression analysis was performed to elucidate the relationship between a decrease of the cortical mantle and increase in ventricular width with aging.

RESULTS

Cortical thickness decreases with age (r = -0.49, P < 0.01; r = -0.502 in male and r = -0.461 in female subjects). Regarding the ventricular indices, we found a significant correlation with age for both the whole sample and the subdivision by gender. Cortical thickness and ventricular width are closely correlated (r = -0.43 in women, r = -0.468 in men, P < 0.001 each). The bandwidth of variance scales up with aging in all parameters. The results are discussed in terms of the underlying mechanisms of normal aging.

CONCLUSION

Our findings suggest that a decrease in cortical thickness and increase in ventricular width occur with normal aging. The enlargement of the third ventricle correlates the most strongly with age.

The parcellation of cortical areas using replicator dynamics in fMRI

In this paper, we show that replicator dynamics can be used as an exploratory analysis tool to detect subregions of cortical areas on the basis of the similarity between fMRI time series. As similarity measure, we propose to use canonical correlation, a multivariate extension to the typically employed Pearson's correlation coefficient. We applied the replicator process to data obtained from two different experimental paradigms in the search for subregions within the left lateral frontal cortex (LFC). In both cases, the replicator process resulted in a parcellation that corresponds to a recently suggested subdivision of the LFC in anterior-posterior direction. Most notably, these results were very consistent when compared across different measurements of a single subject and across a group of subjects.

Investigating cortical variability using a generic gyral model

In this paper, we present a systematic investigation of the variability of the human cortical folding using a generic gyral model (GGM). The GGM consists of a fixed number of vertices that can be registered non-linearly to an individual anatomy so that for each individual we have a clearly defined set of landmarks that is spread across the cortex. This allows us to obtain a regionalized estimation of intersubject variability. Since the GGM is stratified into different levels of depth, it also allows us to estimate variability as a function of depth. As another application of a polygonal line representation underlying the generic gyral model, we present a cortical parcellation scheme that can be used to regionalize cortical measurements.

Magnetic resonance imaging of the human frontal cortex reveals differential anterior-posterior variability of sulcal basins

MRI data of 100 healthy human brains were analyzed to establish a neuroanatomical map of the most frequently occurring 'sulcal basins' of the human frontal cortex. Sulcal basins are defined to be concavities in the white matter surface constituting/representing components of entire sulci. We determined their volume, depth, and interindividual variability. The sulcal basins were found to fall into two groups, on average, eight anterior basins in the prefrontal and premotor region and four posterior ones in the motor region of the frontal lobe. Compared to posterior basins, anterior basins are characterized by lower volume and depth. Furthermore, they showed greater interindividual variability in volume, depth, and occurrence. Our results indicate the existence of a mechanism for cortical folding which shows a greater flexibility in the phylogenetically younger, anterior prefrontal areas.

Meta-analysis of functional imaging data using replicator dynamics

Despite the rapidly growing number of meta-analyses in functional neuroimaging, the field lacks formal mathematical tools for the quantitative and qualitative evaluation of meta-analytic data. We propose to use replicator dynamics in the meta-analysis of functional imaging data to address an important aspect of neuroimaging research, the search for functional networks of cortical areas that underlie a specific cognitive task. The replicator process requires as input only a list of activation locations, and it results in a network of locations that jointly show significant activation in most studies included in the meta-analysis. These locations are likely to play a critical role in solving the investigated cognitive task. Our method was applied to a meta-analysis of the Stroop interference task using data provided by the publicly accessible database BrainMap DBJ.

The correlation between blood oxygenation level-dependent signal strength and latency

PURPOSE

To investigate the relationship between signal strength and latency of the blood oxygenation level-dependent (BOLD) signal.

MATERIALS AND METHODS

Several correlation analyses were performed on data obtained in a functional magnetic resonance imaging (fMRI) experiment, where subjects were presented with a simple visual stimulus. The BOLD signal strength was correlated with both the phase shift of the spectral density matrix and time-to-peak calculated from trial-averaged time courses. Correlation coefficients were calculated for visual stimuli of 2, 6, and 15 seconds in duration.

RESULTS

Analyzing all functional runs for the same subject separately, i.e., including for each run all significantly activated voxels, we observed that correlations between phase shift and signal strength, as well as between time-to-peak and signal strength, decreased with increasing stimulus length. However, when analyses were restricted to voxels found activated in all functional runs, we observed similar correlations between BOLD signal strength and latency in all runs, independent of the length of stimulation. This result was again obtained for both latency measures: the spectral density phase shift and time-to-peak.

CONCLUSION

For both latency measures, phase shift and time-to-peak, a high correlation between BOLD signal strength and latency was observed. We have shown that this correlation is independent of the length of visual stimulation. Thus, the correlation between BOLD signal strength and latency seems to be an inherent property of the BOLD response that is independent of the length of stimulation and can be observed using different methods for determining signal latency.

Morphometry demonstrates loss of cortical thickness in cerebral microangiopathy

OBJECTIVE

To evaluate the role of MR morphometry in the characterization of cerebral microangiopathy (CMA) in relation to clinical and neuropsychological impairment.

SUBJECTS AND METHODS

3D MR images of 27 patients and 27 age-matched controls were morphometrically analysed for regional thickness. The normalized values were related to the patients' clinical and neuropsychological scores. The patients were categorised according to the amount of structural MR signal changes. A ventricle index reflecting internal atrophy was related to MR morphology and cortical thickness as an indicator for external atrophy.

RESULTS

Cortical thickness was significantly reduced in the patients group (3.03 mm +/- 0.26 vs. 3.22 mm +/-0.13 in controls, p=0.001). The severest loss of cortical thickness occurred in severe CMA. Internal and external atrophy evolved in parallel and both showed a significant relationship with structural MR-abnormalities (p<0.05; r=-0.7; r=0.67; r=-0.74, respectively). Neuropsychological performance correlated strongly with the loss of cortical thickness.

CONCLUSIONS

Cortical thickness was identified as the most sensitive parameter to characterize CMA. A strong correlation was found of morphometric parameters to the severity of CMA based on a score derived from T2-weighted MRI. The degree of cortical atrophy was directly related to the degree of neuropsychological impairment. Our findings suggest that the cortical thickness is a valid marker in the structural and clinical characterization of CMA.

Revisiting the role of Broca's area in sentence processing: syntactic integration versus syntactic working memory

Most previous neuroimaging studies of sentence processing have associated Broca's area with syntactic processing; however, the exact nature of the processes subserved by this brain region is yet not well understood. Although some authors suggest that Brodmann area (BA) 44 of the left inferior frontal gyrus (i.e., Broca's area) is relevant for syntactic integration processes, others claim that it is associated with working memory mechanisms relevant for language processing. To dissociate these two possible functions, the present study investigated hemodynamic responses elicited while participants processed German indirect wh-questions. Activation increases were observed in left BA 44 together with superior temporal areas and right hemispheric homologues for sentences with noncanonical word order, in which a verb argument was dislocated from its canonical position over a relatively long distance. In these sentences, syntactic working memory load was assumed to be greatest. In contrast, no activation increase was elicited by object-initial as opposed to subject-initial sentences that did not differ with respect to working memory costs but with respect to syntactic integration costs. These data strongly suggest that Broca's area plays a critical role in syntactic working memory during online sentence comprehension.