Correcting for Superficial Bias in 7T Gradient Echo fMRI

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.

The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred to as "Prenorm"). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo), or amplitude of low-frequency fluctuation (ALFF) in individual space, before the spatial normalization (referred to as "Postnorm") can be an improvement to avoid artifacts and increase the results' reliability. We utilized two datasets: (1) simulated images where temporal signal-to-noise ratio (tSNR) is kept a constant and (2) an empirical fMRI dataset with 50 healthy young subjects. For simulated images, the tSNR is constant as generated in individual space but increased after Prenorm and intersubject variability of tSNR was induced. In contrast, tSNR was kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo, and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to those of Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to those of Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF, and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intraclass correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.

The Impact of Whole Brain Global Functional Connectivity Density Following MECT in Major Depression: A Follow-Up Study

To explore the alteration of global functional connectivity density (gFCD) in depressive patients after modified electroconvulsive therapy (MECT) and analyze the relationship between gFCD and clinical outcome. Thirty-seven subjects were evaluated based on the diagnostic criteria of the International Classification of Diseases-10 (ICD-10), consisting of a depressive group (24 patients after follow-ups) and a healthy control group with 13 normal individuals. All participants received Hamilton Depression Scale (HAMD) scores and resting-state functional magnetic resonance imaging scans. The gFCD significantly increased in the posterior-middle insula, the supra-marginal gyrus and the dorsal medial prefrontal cortex (dmPFC) before MECT treatment compared to healthy controlled patients. The gFCD statistically expanded in the perigenual anterior cingulate cortex (pgACC), the orbitofrontal cortex bilaterally and the left-supra-marginal gyrus after MECT, and it decreased notably in the posterior insula. The gFCD in the pgACC and the right orbital frontal cortex of depressive group before MECT showed a positive correlation with HAMD scores with treatment. Conforming to the impact of gFCD in depressive patients after MECT, the aforementioned brain region may become an indicator of MECT effect.

Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations

Studies of resting state functional MRI (rs-fRMI) are increasingly focused on “dynamics”, or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.

A New Measure for Neural Compensation Is Positively Correlated With Working Memory and Gait Speed

Neuroimaging studies suggest that older adults may compensate for declines in brain function and cognition through reorganization of neural resources. A limitation of prior research is reliance on between-group comparisons of neural activation (e.g., younger vs. older), which cannot be used to assess compensatory ability quantitatively. It is also unclear about the relationship between compensatory ability with cognitive function or how other factors such as physical exercise modulates compensatory ability. Here, we proposed a data-driven method to semi-quantitatively measure neural compensation under a challenging cognitive task, and we then explored connections between neural compensation to cognitive engagement and cognitive reserve (CR). Functional and structural magnetic resonance imaging scans were acquired for 26 healthy older adults during a face-name memory task. Spatial independent component analysis (ICA) identified visual, attentional and left executive as core networks. Results show that the smaller the volumes of the gray matter (GM) structures within core networks, the more networks were needed to conduct the task (r = -0.408, p = 0.035). Therefore, the number of task-activated networks controlling for the GM volume within core networks was defined as a measure of neural compensatory ability. We found that compensatory ability correlated with working memory performance (r = 0.528, p = 0.035). Among subjects with good memory task performance, those with higher CR used fewer networks than subjects with lower CR. Among poor-performance subjects, those using more networks had higher CR. Our results indicated that using a high cognitive-demanding task to measure the number of activated neural networks could be a useful and sensitive measure of neural compensation in older adults.

Inferring the Dysconnection Syndrome in Schizophrenia: Interpretational Considerations on Methods for the Network Analyses of fMRI Data

Schizophrenia has long been considered one of the most intractable psychiatric conditions. Its etiology is likely polygenic, and its symptoms are hypothesized to result from complex aberrations in network-level neuronal activity. While easily identifiable by psychiatrists based on clear behavioral signs, the biological substrate of the disease remains poorly understood. Here, we discuss current trends and key concepts in the theoretical framework surrounding schizophrenia and critically discuss network approaches applied to neuroimaging data that can illuminate the correlates of the illness. We first consider a theoretical framework encompassing basic principles of brain function ranging from neural units toward perspectives of network function. Next, we outline the strengths and limitations of several fMRI-based analytic methodologies for assessing in vivo brain network function, including undirected and directed functional connectivity and effective connectivity. The underlying assumptions of each approach for modeling fMRI data are treated in some quantitative detail, allowing for assessment of the utility of each for generating inferences about brain networks relevant to schizophrenia. fMRI and the analyses of fMRI signals provide a limited, yet vibrant platform from which to test specific hypotheses about brain network dysfunction in schizophrenia. Carefully considered and applied connectivity measures have the power to illuminate loss or change of function at the network level, thus providing insight into the underlying neurobiology which gives rise to the emergent symptoms seen in the altered cognition and behavior of schizophrenia patients.

Discovering networks altered by potential threat ("anxiety") using quadratic discriminant analysis

Researchers have only recently begun using functional neuroimaging to explore the human response to periods of sustained anxious anticipation, namely potential threat. Here, we investigated brain responses acquired with functional MRI during an instructed threat of shock paradigm used to create sustained periods of aversive anticipation. In this re-analysis of previously published data, we employed quadratic discriminant analysis to classify the multivariate pattern of whole-brain functional connectivity and to identify connectivity changes during periods of potential threat. Our method identifies clusters with altered connectivity on a voxelwise basis, thus eschewing the need to define regions a priori. Classifier generalization was evaluated by testing on data from participants not used during training. Robust classification between threat and safe contexts was possible, and inspection of "diagnostic features" revealed altered functional connectivity involving the intraparietal sulcus, task-negative regions, striatum, and anterior cingulate cortex. We anticipate that the proposed method will prove useful to experimenters wishing to identify large-scale functional networks that distinguish between experimental conditions or groups.

Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations

Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

Identifying the integrated neural networks involved in capsaicin-induced pain using fMRI in awake TRPV1 knockout and wild-type rats

In the present study, we used functional MRI in awake rats to investigate the pain response that accompanies intradermal injection of capsaicin into the hindpaw. To this end, we used BOLD imaging together with a 3D segmented, annotated rat atlas and computational analysis to identify the integrated neural circuits involved in capsaicin-induced pain. The specificity of the pain response to capsaicin was tested in a transgenic model that contains a biallelic deletion of the gene encoding for the transient receptor potential cation channel subfamily V member 1 (TRPV1). Capsaicin is an exogenous ligand for the TRPV1 receptor, and in wild-type rats, activated the putative pain neural circuit. In addition, capsaicin-treated wild-type rats exhibited activation in brain regions comprising the Papez circuit and habenular system, systems that play important roles in the integration of emotional information, and learning and memory of aversive information, respectively. As expected, capsaicin administration to TRPV1-KO rats failed to elicit the robust BOLD activation pattern observed in wild-type controls. However, the intradermal injection of formalin elicited a significant activation of the putative pain pathway as represented by such areas as the anterior cingulate, somatosensory cortex, parabrachial nucleus, and periaqueductal gray. Notably, comparison of neural responses to capsaicin in wild-type vs. knock-out rats uncovered evidence that capsaicin may function in an antinociceptive capacity independent of TRPV1 signaling. Our data suggest that neuroimaging of pain in awake, conscious animals has the potential to inform the neurobiological basis of full and integrated perceptions of pain.