The 3D Structural Architecture of the Human Hand Area Is Nontopographic

The functional topography of the human primary somatosensory cortex hand area is a widely studied model system to understand sensory organization and plasticity. It is so far unclear whether the underlying 3D structural architecture also shows a topographic organization. We used 7 Tesla (7T) magnetic resonance imaging (MRI) data to quantify layer-specific myelin, iron, and mineralization in relation to population receptive field maps of individual finger representations in Brodman area 3b (BA 3b) of human S1 in female and male younger adults. This 3D description allowed us to identify a characteristic profile of layer-specific myelin and iron deposition in the BA 3b hand area, but revealed an absence of structural differences, an absence of low-myelin borders, and high similarity of 3D microstructure profiles between individual fingers. However, structural differences and borders were detected between the hand and face areas. We conclude that the 3D structural architecture of the human hand area is nontopographic, unlike in some monkey species, which suggests a high degree of flexibility for functional finger organization and a new perspective on human topographic plasticity.SIGNIFICANCE STATEMENT Using ultra-high-field MRI, we provide the first comprehensive in vivo description of the 3D structural architecture of the human BA 3b hand area in relation to functional population receptive field maps. High similarity of precise finger-specific 3D profiles, together with an absence of structural differences and an absence of low-myelin borders between individual fingers, reveals the 3D structural architecture of the human hand area to be nontopographic. This suggests reduced structural limitations to cortical plasticity and reorganization and allows for shared representational features across fingers.

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

BACKGROUND

Stroke is one of the most frequent diseases, and half of the stroke survivors are left with permanent impairment. Prediction of individual outcome is still difficult. Many but not all patients with stroke improve by approximately 1.7 times the initial impairment, that has been termed proportional recovery rule. The present study aims at identifying factors predicting motor outcome after stroke more accurately than before, and observe associations of rehabilitation treatment with outcome.

METHODS

The study is designed as a multi-centre prospective clinical observational trial. An extensive primary data set of clinical, neuroimaging, electrophysiological, and laboratory data will be collected within 96 h of stroke onset from patients with relevant upper extremity deficit, as indexed by a Fugl-Meyer-Upper Extremity (FM-UE) score ≤ 50. At least 200 patients will be recruited. Clinical scores will include the FM-UE score (range 0-66, unimpaired function is indicated by a score of 66), Action Research Arm Test, modified Rankin Scale, Barthel Index and Stroke-Specific Quality of Life Scale. Follow-up clinical scores and applied types and amount of rehabilitation treatment will be documented in the rehabilitation hospitals. Final follow-up clinical scoring will be performed 90 days after the stroke event. The primary endpoint is the change in FM-UE defined as 90 days FM-UE minus initial FM-UE, divided by initial FM-UE impairment. Changes in the other clinical scores serve as secondary endpoints. Machine learning methods will be employed to analyze the data and predict primary and secondary endpoints based on the primary data set and the different rehabilitation treatments.

DISCUSSION

If successful, outcome and relation to rehabilitation treatment in patients with acute motor stroke will be predictable more reliably than currently possible, leading to personalized neurorehabilitation. An important regulatory aspect of this trial is the first-time implementation of systematic patient data transfer between emergency and rehabilitation hospitals, which are divided institutions in Germany.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov ( NCT04688970 ) on 30 December 2020.

Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on 'connectome fingerprinting'. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Brainglance: Visualizing Group Level MRI Data at One Glance

The vast majority of studies using functional magnetic resonance imaging (fMRI) are analyzed on the group level. Standard group-level analyses, however, come with severe drawbacks: First, they assume functional homogeneity within the group, building on the idea that we use our brains in similar ways. Second, group-level analyses require spatial warping and substantial smoothing to accommodate for anatomical variability across subjects. Such procedures massively distort the underlying fMRI data, which hampers the spatial specificity. Taken together, group statistics capture the effective overlap, rendering the modeling of individual deviations impossible – a major source of false positivity and negativity. The alternative analysis approach is to leave the data in the native subject space, but this makes comparison across individuals difficult. Here, we propose a new framework for visualizing group-level information, better preserving the information of individual subjects. Our proposal is to limit the use of invasive data procedures such as spatial smoothing and warping and rather extract regional information from the individuals. This information is then visualized for all subjects and brain areas at one glance – hence we term the method brainglance. Additionally, our method incorporates a means for clustering individuals to further identify common traits. We showcase our method on two publicly available data sets and discuss our findings.

Enhanced North Pacific deep-ocean stratification by stronger intermediate water formation during Heinrich Stadial 1

The deglacial history of CO₂ release from the deep North Pacific remains unresolved. This is due to conflicting indications about subarctic Pacific ventilation changes based on various marine proxies, especially for Heinrich Stadial 1 (HS-1) when a rapid atmospheric CO₂ rise occurs. Here, we use a complex Earth System Model to investigate the deglacial North Pacific overturning and its control on ocean stratification. Our results show an enhanced intermediate-to-deep ocean stratification coeval with intensified North Pacific Intermediate Water (NPIW) formation during HS-1, compared to the Last Glacial Maximum. The stronger NPIW formation causes lower salinities and higher temperatures at intermediate depths. By lowering NPIW densities, this enlarges vertical density gradient and thus enhances intermediate-to-deep ocean stratification during HS-1. Physically, this process prevents the North Pacific deep waters from a better communication with the upper oceans, thus prolongs the existing isolation of glacial Pacific abyssal carbons during HS-1.

LISA improves statistical analysis for fMRI

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

North Pacific freshwater events linked to changes in glacial ocean circulation

There is compelling evidence that episodic deposition of large volumes of freshwater into the oceans strongly influenced global ocean circulation and climate variability during glacial periods^1,2. In the North Atlantic region, episodes of massive freshwater discharge to the North Atlantic Ocean were related to distinct cold periods known as Heinrich Stadials^1-3. By contrast, the freshwater history of the North Pacific region remains unclear, giving rise to persistent debates about the existence and possible magnitude of climate links between the North Pacific and North Atlantic oceans during Heinrich Stadials^4,5. Here we find that there was a strong connection between changes in North Atlantic circulation during Heinrich Stadials and injections of freshwater from the North American Cordilleran Ice Sheet to the northeastern North Pacific. Our record of diatom δ¹⁸O (a measure of the ratio of the stable oxygen isotopes ¹⁸O and ¹⁶O) over the past 50,000 years shows a decrease in surface seawater δ¹⁸O of two to three per thousand, corresponding to a decline in salinity of roughly two to four practical salinity units. This coincided with enhanced deposition of ice-rafted debris and a slight cooling of the sea surface in the northeastern North Pacific during Heinrich Stadials 1 and 4, but not during Heinrich Stadial 3. Furthermore, results from our isotope-enabled model⁶ suggest that warming of the eastern Equatorial Pacific during Heinrich Stadials was crucial for transmitting the North Atlantic signal to the northeastern North Pacific, where the associated subsurface warming resulted in a discernible freshwater discharge from the Cordilleran Ice Sheet during Heinrich Stadials 1 and 4. However, enhanced background cooling across the northern high latitudes during Heinrich Stadial 3-the coldest period in the past 50,000 years⁷-prevented subsurface warming of the northeastern North Pacific and thus increased freshwater discharge from the Cordilleran Ice Sheet. In combination, our results show that nonlinear ocean-atmosphere background interactions played a complex role in the dynamics linking the freshwater discharge responses of the North Atlantic and North Pacific during glacial periods.

The auditory cortex hosts network nodes influential for emotion processing: An fMRI study on music-evoked fear and joy

Sound is a potent elicitor of emotions. Auditory core, belt and parabelt regions have anatomical connections to a large array of limbic and paralimbic structures which are involved in the generation of affective activity. However, little is known about the functional role of auditory cortical regions in emotion processing. Using functional magnetic resonance imaging and music stimuli that evoke joy or fear, our study reveals that anterior and posterior regions of auditory association cortex have emotion-characteristic functional connectivity with limbic/paralimbic (insula, cingulate cortex, and striatum), somatosensory, visual, motor-related, and attentional structures. We found that these regions have remarkably high emotion-characteristic eigenvector centrality, revealing that they have influential positions within emotion-processing brain networks with “small-world” properties. By contrast, primary auditory fields showed surprisingly strong emotion-characteristic functional connectivity with intra-auditory regions. Our findings demonstrate that the auditory cortex hosts regions that are influential within networks underlying the affective processing of auditory information. We anticipate our results to incite research specifying the role of the auditory cortex—and sensory systems in general—in emotion processing, beyond the traditional view that sensory cortices have merely perceptual functions.

Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

OBJECTIVE

Electroencephalographic (EEG) brain-computer interfaces (BCIs) hold promise in restoring communication for patients with completely locked-in stage amyotrophic lateral sclerosis (ALS). However, these patients cannot use existing EEG-based BCIs, arguably because such systems rely on brain processes that are impaired in the late stages of ALS. In this work, we introduce a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS.

APPROACH

We trained two ALS patients via EEG-based neurofeedback to use self-regulation of theta or gamma oscillations in the precuneus for basic communication. Because there is a tight connection between the precuneus and consciousness, precuneus oscillations are arguably generated by high-level cognitive processes, which are less likely to be affected by ALS than processes linked to the peripheral nervous system.

MAIN RESULTS

Both patients learned to self-regulate their precuneus oscillations and achieved stable online decoding accuracy over the course of disease progression. One patient achieved a mean online decoding accuracy in a binary decision task of 70.55% across 26 training sessions, and the other patient achieved 59.44% across 16 training sessions. We provide empirical evidence that these oscillations were cortical in nature and originated from the intersection of the precuneus, cuneus, and posterior cingulate.

SIGNIFICANCE

Our results establish that ALS patients can employ self-regulation of precuneus oscillations for communication. Such a BCI is likely to be available to ALS patients as long as their consciousness supports communication.

Interoceptive awareness changes the posterior insula functional connectivity profile

Interoceptive awareness describes the ability to consciously perceive inner bodily signals, such as one's own heartbeat. The right anterior insula is assumed to mediate this ability. The role of the posterior insula, particularly posterior-to-anterior insula signal flows is less clear in this respect. We scanned 27 healthy people with either high or low interoceptive awareness using 3T fMRI, while they either monitored their own heartbeats, or external tones, respectively. We used a combination of network centrality and bivariate connectivity analyses to characterize changes in cortical signal flows between the posterior insula and the anterior insula during interoceptive awareness or exteroceptive awareness, respectively. We show that heartbeat monitoring was accompanied by reduced network centrality of the right posterior insula, and decreased functional connectivity strengths between the right posterior insula and the right mid and anterior insula. In addition, decreased signal flows between the right posterior insula and the bilateral anterior cingulate cortices, and the bilateral orbitofrontal cortices were observed during interoceptive awareness. Functional connectivity changes were only shown by people with high interoceptive awareness, and occurred specifically within the low-frequency range (i.e., <0.1 Hz). Both groups did not differ in their functional connectivity profiles during rest. Our results show for the first time that interoceptive awareness changes intra-insula signal flows in the low-frequency range. We speculate that the selective inhibition of slow signal progression along the posterior-to-anterior insula pathway during interoceptive awareness allows the salient and noiseless detection of one's own heartbeat.

Deficient approaches to human neuroimaging

Functional magnetic resonance imaging (fMRI) is the workhorse of imaging-based human cognitive neuroscience. The use of fMRI is ever-increasing; within the last 4 years more fMRI studies have been published than in the previous 17 years. This large body of research has mainly focused on the functional localization of condition- or stimulus-dependent changes in the blood-oxygenation-level dependent signal. In recent years, however, many aspects of the commonly practiced analysis frameworks and methodologies have been critically reassessed. Here we summarize these critiques, providing an overview of the major conceptual and practical deficiencies in widely used brain-mapping approaches, and exemplify some of these issues by the use of imaging data and simulations. In particular, we discuss the inherent pitfalls and shortcomings of methodologies for statistical parametric mapping. Our critique emphasizes recent reports of excessively high numbers of both false positive and false negative findings in fMRI brain mapping. We outline our view regarding the broader scientific implications of these methodological considerations and briefly discuss possible solutions.

Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI

Network studies of large-scale brain connectivity have demonstrated that highly connected areas, or “hubs,” are a key feature of human functional and structural brain organization. We use resting-state functional MRI data and connectivity clustering to identify multi-network hubs and show that while hubs can belong to multiple networks their degree of integration into these different networks varies dynamically over time. The extent of the network variation was related to the connectedness of the hub. In addition, we found that these network dynamics were inversely related to positive self-generated thoughts reported by individuals and were further decreased with older age. Moreover, the left caudate varied its degree of participation between a default mode subnetwork and a limbic network. This variation was predictive of individual differences in the reports of past-related thoughts. These results support an association between ongoing thought processes and network dynamics and offer a new approach to investigate the brain dynamics underlying mental experience.

Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping

Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a non-parametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain

Functional connectivity, a flourishing new area of research in human neuroscience, carries a substantial challenge for visualization: while the end points of connectivity are known, the precise path between them is not. Although a large body of work already exists on the visualization of anatomical connectivity, the functional counterpart lacks similar development. To optimize the clarity of whole-brain and complex connectivity patterns in three-dimensional brain space, we develop mean-shift edge bundling, which reveals the multitude of connections as derived from correlations in the brain activity of cortical regions.

Exenatide-induced reduction in energy intake is associated with increase in hypothalamic connectivity

OBJECTIVE

Glucagon-like peptide-1 receptor agonists such as exenatide are known to influence neural activity in the hypothalamus of animals and to reduce energy intake. In humans, however, significant weight loss has been observed in only a subgroup of patients. Why only some individuals respond with weight loss and others do not remains unclear. In this functional magnetic resonance imaging (fMRI) study, we investigated differences in hypothalamic connectivity between "responders" (reduction in energy intake after exenatide infusion) and "nonresponders."

RESEARCH DESIGN AND METHODS

We performed a randomized, double-blinded, placebo-controlled, cross-over fMRI study with intravenous administration of exenatide in obese male volunteers. During brain scanning with continuous exenatide or placebo administration, participants rated food and nonfood images. After each scanning session, energy intake was measured using an ad libitum buffet. Functional hypothalamic connectivity was assessed by eigenvector centrality mapping, a measure of connectedness throughout the brain.

RESULTS

Responders showed significantly higher connectedness of the hypothalamus, which was specific for the food pictures condition, in the exenatide condition compared with placebo. Nonresponders did not show any significant exenatide-induced changes in hypothalamic connectedness.

CONCLUSIONS

Our results demonstrate a central hypothalamic effect of peripherally administered exenatide that occurred only in the group that showed an exenatide-dependent anorexigenic effect. These findings indicate that the hypothalamic response seems to be the crucial factor for the effect of exenatide on energy intake.

Response to commentaries on our paper: Critical comments on dynamic causal modelling

First of all, we would like to state that we are pleased that our paper has spawned a vivid debate about the validity of DCM. Given that DCM has been around for so many years, we think that this was long overdue. In the following, we would like to respond to the comments by Friston et al. and Breakspear.

Connectivity concordance mapping: a new tool for model-free analysis of 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. Here, we propose a new analysis method called Connectivity Concordance Mapping (CCM). The main idea is to assign a label to each voxel based on the reproducibility of its whole-brain pattern of connectivity. Specifically, we compute the correlations of time courses of each voxel with every other voxel for each measurement. Voxels whose correlation pattern is consistent across measurements receive high values. The result of a CCM analysis is thus a voxel-wise map of concordance values. Regions of high inter-subject concordance can be assumed to be functionally consistent, and may thus be of specific interest for further analysis. Here we present two fMRI studies to demonstrate the possible applications of the algorithm. The first is a eyes-open/eyes-closed paradigm designed to highlight the potential of the method in a relatively simple domain. The second study is a longitudinal repeated measurement of a patient following stroke. Longitudinal clinical studies such as this may represent the most interesting domain of applications for this algorithm.

Perception of words and pitch patterns in song and speech

This functional magnetic resonance imaging study examines shared and distinct cortical areas involved in the auditory perception of song and speech at the level of their underlying constituents: words and pitch patterns. Univariate and multivariate analyses were performed to isolate the neural correlates of the word- and pitch-based discrimination between song and speech, corrected for rhythmic differences in both. Therefore, six conditions, arranged in a subtractive hierarchy were created: sung sentences including words, pitch and rhythm; hummed speech prosody and song melody containing only pitch patterns and rhythm; and as a control the pure musical or speech rhythm. Systematic contrasts between these balanced conditions following their hierarchical organization showed a great overlap between song and speech at all levels in the bilateral temporal lobe, but suggested a differential role of the inferior frontal gyrus (IFG) and intraparietal sulcus (IPS) in processing song and speech. While the left IFG coded for spoken words and showed predominance over the right IFG in prosodic pitch processing, an opposite lateralization was found for pitch in song. The IPS showed sensitivity to discrete pitch relations in song as opposed to the gliding pitch in speech. Finally, the superior temporal gyrus and premotor cortex coded for general differences between words and pitch patterns, irrespective of whether they were sung or spoken. Thus, song and speech share many features which are reflected in a fundamental similarity of brain areas involved in their perception. However, fine-grained acoustic differences on word and pitch level are reflected in the IPS and the lateralized activity of the IFG.

Critical comments on dynamic causal modelling

Dynamic causal modelling (DCM) (Friston et al., 2003) is a technique designed to investigate the influence between brain areas using time series data obtained by EEG/MEG or functional magnetic resonance imaging (fMRI). The basic idea is to fit various models to time series data, and select one of those models using Bayesian model comparison. Here, we present a critical evaluation of DCM in which we show that DCM can be challenged on several grounds. We will discuss three main points relating to combinatorial explosion, the validity of the model selection procedure, and problems with respect to model validation.

A software tool for interactive exploration of intrinsic functional connectivity opens new perspectives for brain surgery

BACKGROUND

Functional connectivity analysis of resting-state functional magnetic resonance imaging data (fcrs-fMRI) has been shown to be a robust non-invasive method for localization of functional networks (without using specific tasks) and to be promising for presurgical planning. However, in order to transfer the approach to everyday clinical practice, fcrs-fMRI needs to be further validated and made easily accessible to neurosurgeons. This paper addresses the latter by presenting a software tool designed for neurosurgeons for analyzing and visualizing fcrs-fMRI data.

METHODS

A prototypical interactive visualization tool was developed to enable neurosurgeons to explore functional connectivity data and evaluate its usability. The implementation builds upon LIPSIA, an established software package for the assessment of functional neuroimaging data, and integrates the selection of a region-of-interest with the computation and visualization of functionally connected areas. The tool was used to explore data from a healthy participant and eight brain lesion patients. The usability of the software was evaluated with four neurosurgeons previously unacquainted with the methodology, who were asked to identify prominent, large-scale cortical networks.

FINDINGS

With this novel tool, previously published findings, such as tumor displacement of the sensorimotor cortex and other disturbances of functional networks, were reproduced. The neurosurgeons were able to consistently obtain results similar to the results of an expert, with the exception of the language network. Immediate feedback helped to pinpoint functional networks quickly and intuitively, with even inexperienced users requiring less than 3 min per network.

CONCLUSIONS

Although fcrs-fMRI is a nascent method still undergoing evaluation with respect to established standards, the interactive software is nonetheless a promising tool for non-invasive exploration of individual functional connectivity networks in neurosurgical practice, both for well-known networks and for those less typically addressed.

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.