Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

Learning-based structurally-guided construction of resting-state functional correlation tensors

Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study. The novel FCT estimation method consists of three major steps: First, we estimate the initial FCT using a patch-based approach for BOLD signal correlation to improve the noise robustness. Second, by utilizing the relationship between functional and diffusion data, we employ a regression forest model to learn the mapping between the initial FCTs and the corresponding DTs using the training data. The learned forest can then be applied to predict the DTI-like tensors given the initial FCTs from the testing fMRI data. Third, we re-estimate the enhanced FCT by utilizing the DTI-like tensors as a feedback guidance to further improve FCT computation. We have demonstrated the utility of our enhanced FCTs in Alzheimer's disease (AD) diagnosis by identifying mild cognitive impairment (MCI) patients from normal subjects.

Evidence for Functional Networks within the Human Brain's White Matter

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry.SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Functional connectivity and activity of white matter in somatosensory pathways under tactile stimulations

Functional MRI has proven to be effective in detecting neural activity in brain cortices on the basis of blood oxygenation level dependent (BOLD) contrast, but has relatively poor sensitivity for detecting neural activity in white matter. To demonstrate that BOLD signals in white matter are detectable and contain information on neural activity, we stimulated the somatosensory system and examined distributions of BOLD signals in related white matter pathways. The temporal correlation profiles and frequency contents of BOLD signals were compared between stimulation and resting conditions, and between relevant white matter fibers and background regions, as well as between left and right side stimulations. Quantitative analyses show that, overall, MR signals from white matter fiber bundles in the somatosensory system exhibited significantly greater temporal correlations with the primary sensory cortex and greater signal power during tactile stimulations than in a resting state, and were stronger than corresponding measurements for background white matter both during stimulations and in a resting state. The temporal correlation and signal power under stimulation were found to be twice those observed from the same bundle in a resting state, and bore clear relations with the side of stimuli. These indicate that BOLD signals in white matter fibers encode neural activity related to their functional roles connecting cortical volumes, which are detectable with appropriate methods.

Anisotropic cerebral vascular architecture causes orientation dependency in cerebral blood flow and volume measured with dynamic susceptibility contrast magnetic resonance imaging

Measurements of cerebral perfusion using dynamic susceptibility contrast magnetic resonance imaging rely on the assumption of isotropic vascular architecture. However, a considerable fraction of vessels runs in parallel with white matter tracts. Here, we investigate the effects of tissue orientation on dynamic susceptibility contrast magnetic resonance imaging. Tissue orientation was measured using diffusion tensor imaging and dynamic susceptibility contrast was performed with gradient echo planar imaging. Perfusion parameters and the raw dynamic susceptibility contrast signals were correlated with tissue orientation. Additionally, numerical simulations were performed for a range of vascular volumes of both the isotropic vascular bed and anisotropic vessel components, as well as for a range of contrast agent concentrations. The effect of the contrast agent was much larger in white matter tissue perpendicular to the main magnetic field compared to white matter parallel to the main magnetic field. In addition, cerebral blood flow and cerebral blood volume were affected in the same way with angle-dependent variations of up to 130%. Mean transit time and time to maximum of the residual curve exhibited weak orientation dependency of 10%. Numerical simulations agreed with the measured data, showing that one-third of the white matter vascular volume is comprised of vessels running in parallel with the fibre tracts.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

Reliability of the depth-dependent high-resolution BOLD hemodynamic response in human visual cortex and vicinity

Functional magnetic resonance imaging (fMRI) often relies on a hemodynamic response function (HRF), the stereotypical blood oxygen level dependent (BOLD) response elicited by a brief (<4s) stimulus. Early measurements of the HRF used coarse spatial resolutions (≥3mm voxels) that would generally include contributions from white matter, gray matter, and the extra-pial compartment (the space between the pial surface and skull including pial blood vessels) within each voxel. To resolve these contributions, high-resolution fMRI (0.9-mm voxels) was performed at 3T in early visual cortex and its apposed white-matter and extra-pial compartments. The results characterized the depth dependence of the HRF and its reliability during nine fMRI sessions. Significant HRFs were observed in white-matter and extra-pial compartments as well as in gray matter. White-matter HRFs were faster and weaker than in the gray matter, while extra-pial HRFs were comparatively slower and stronger. Depth trends of the HRF peak amplitude were stable throughout a broad depth range that included all three compartments for each session. Across sessions, however, the depth trend of HRF peak amplitudes was stable only in the white matter and deep-intermediate gray matter, while there were strong session-to-session variations in the superficial gray matter and the extra-pial compartment. Thus, high-resolution fMRI can resolve significant and dynamically distinct HRFs in gray matter, white matter, and extra-pial compartments.

A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data

Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two-level SLIC were proposed. The cluster number varied in a wide range in order to generate parcellations with multiple granularities. The two SLIC approaches were compared with three state-of-the-art approaches under different evaluation metrics, which include spatial contiguity, functional homogeneity, and reproducibility. Both the group-to-group reproducibility and the group-to-subject reproducibility were evaluated in our study. The experimental results showed that the proposed approaches obtained relatively good overall clustering performances in different conditions that included different weighting functions, different sparsifying schemes, and several confounding factors. Therefore, the generated atlases are appropriate to be utilized as nodes for network analysis. The generated atlases and major source codes of this study have been made publicly available at http://www.nitrc.org/projects/slic/.

Tracking thoughts: Exploring the neural architecture of mental time travel during mind-wandering

The capacity to imagine situations that have already happened or fictitious events that may take place in the future is known as mental time travel (MTT). Studies have shown that MTT is an important aspect of spontaneous thought, yet we lack a clear understanding of how the neurocognitive architecture of the brain constrains this element of human cognition. Previous functional magnetic resonance imaging (MRI) studies have shown that MTT involves the coordination between multiple regions that include mesiotemporal structures such as the hippocampus, as well as prefrontal and parietal regions commonly associated with the default mode network (DMN). The current study used a multimodal neuroimaging approach to identify the structural and functional brain organisation that underlies individual differences in the capacity to spontaneously engage in MTT. Using regionally unconstrained diffusion tractography analysis, we found increased diffusion anisotropy in right lateralised temporo-limbic, corticospinal, inferior fronto-occipital tracts in participants who reported greater MTT. Probabilistic connectivity mapping revealed a significantly higher connection probability of the right hippocampus with these tracts. Resting-state functional MRI connectivity analysis using the right hippocampus as a seed region revealed greater functional coupling to the anterior regions of the DMN with increasing levels of MTT. These findings demonstrate that the interactions between the hippocampus and regions of the cortex underlie the capacity to engage in MTT, and support contemporary theoretical accounts that suggest that the integration of the hippocampus with the DMN provides the neurocognitive landscape that allows us to imagine distant times and places.

Differences in Resting State Functional Connectivity between Young Adult Endurance Athletes and Healthy Controls

Expertise and training in fine motor skills has been associated with changes in brain structure, function, and connectivity. Fewer studies have explored the neural effects of athletic activities that do not seem to rely on precise fine motor control (e.g., distance running). Here, we compared resting-state functional connectivity in a sample of adult male collegiate distance runners (n = 11; age = 21.3 ± 2.5) and a group of healthy age-matched non-athlete male controls (n = 11; age = 20.6 ± 1.1), to test the hypothesis that expertise in sustained aerobic motor behaviors affects resting state functional connectivity in young adults. Although generally considered an automated repetitive task, locomotion, especially at an elite level, likely engages multiple cognitive actions including planning, inhibition, monitoring, attentional switching and multi-tasking, and motor control. Here, we examined connectivity in three resting-state networks that link such executive functions with motor control: the default mode network (DMN), the frontoparietal network (FPN), and the motor network (MN). We found two key patterns of significant between-group differences in connectivity that are consistent with the hypothesized cognitive demands of elite endurance running. First, enhanced connectivity between the FPN and brain regions often associated with aspects of working memory and other executive functions (frontal cortex), suggest endurance running may stress executive cognitive functions in ways that increase connectivity in associated networks. Second, we found significant anti-correlations between the DMN and regions associated with motor control (paracentral area), somatosensory functions (post-central region), and visual association abilities (occipital cortex). DMN deactivation with task-positive regions has been shown to be generally beneficial for cognitive performance, suggesting anti-correlated regions observed here are engaged during running. For all between-group differences, there were significant associations between connectivity, self-reported physical activity, and estimates of maximum aerobic capacity, suggesting a dose-response relationship between engagement in endurance running and connectivity strength. Together these results suggest that differences in experience with endurance running are associated with differences in functional brain connectivity. High intensity aerobic activity that requires sustained, repetitive locomotor and navigational skills may stress cognitive domains in ways that lead to altered brain connectivity, which in turn has implications for understanding the beneficial role of exercise for brain and cognitive function over the lifespan.

Mapping white-matter functional organization at rest and during naturalistic visual perception

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts.

Noise contributions to the fMRI signal: An overview

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

White matter abnormalities of microstructure and physiological noise in schizophrenia

White matter abnormalities in schizophrenia have been revealed by many imaging techniques and analysis methods. One of the findings by diffusion tensor imaging is a decrease in fractional anisotropy (FA), which is an indicator of white matter integrity. On the other hand, elevation of metabolic rate in white matter was observed from positron emission tomography (PET) studies. In this report, we aim to compare the two structural and functional effects on the same subjects. Our comparison is based on the hypothesis that signal fluctuation in white matter is associated with white matter functional activity. We examined the variance of the signal in resting state fMRI and found significant differences between individuals with schizophrenia and non-psychiatric controls specifically in white matter tissue. Controls showed higher temporal signal-to-noise ratios clustered in regions including temporal, frontal, and parietal lobes, cerebellum, corpus callosum, superior longitudinal fasciculus, and other major white matter tracts. These regions with higher temporal signal-to-noise ratio agree well with those showing higher metabolic activity reported by studies using PET. The results suggest that individuals with schizophrenia tend to have higher functional activity in white matter in certain brain regions relative to healthy controls. Despite some overlaps, the distinct regions for physiological noise are different from those for FA derived from diffusion tensor imaging, and therefore provide a unique angle to explore potential mechanisms to white matter abnormality.

Effects of anesthesia on resting state BOLD signals in white matter of non-human primates

Resting state functional magnetic resonance imaging (rsfMRI) has been widely used to measure functional connectivity between cortical regions of the brain. However, there have been minimal reports of bold oxygenation level dependent (BOLD) signals in white matter, and even fewer attempts to detect resting state connectivity. Recently, there has been growing evidence that suggests that reliable detection of white matter BOLD signals may be possible. We have previously shown that nearest neighbor inter-voxel correlations of resting state BOLD signal fluctuations in white matter are anisotropic and can be represented by a functional correlation tensor, but the biophysical origins of these signal variations are not clear. We aimed to assess whether MRI signal fluctuations in white matter vary for different baseline levels of neural activity. We performed imaging studies on live squirrel monkeys under different levels of isoflurane anesthesia at 9.4T. We found 1) the fractional power (0.01-0.08Hz) in white matter was between 60 to 75% of the level in gray matter; 2) the power in both gray and white matter low frequencies decreased monotonically in similar manner with increasing levels of anesthesia; 3) the distribution of fractional anisotropy values of the functional tensors in white matter were significantly higher than those in gray matter; and 4) the functional tensor eigenvalues decreased with increasing level of anesthesia. Our results suggest that as anesthesia level changes baseline neural activity, white matter signal fluctuations behave similarly to those in gray matter, and functional tensors in white matter are affected in parallel.

Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks

Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network.

Evaluation of mouse embryos produced in vitro after electromagnetic waves exposure; Morphometric study

Introduction

Today, the use of electromagnetic waves in medical diagnostic devices, such as magnetic resonance imaging (MRI), has increased, and many of its biological effects have been reported. The aim of the present study was to assess the biological effects of 1.5 Tesla (T) magnetic resonance imaging (MRI) on fertility and reproductive parameters.

Methods

Eighty adult male and female NMRI mice (NMRI: Naval Medical Research Institute) of age 6–8 weeks were studied and randomly divided into two study and control groups. After confirmation of pregnancy, the mice in the study group were exposed to the MRI (1.5 T) machine’s waves over the next three weeks, once a week for 36 minutes. One day and thirty-five days after the last radiation, the mice were killed in order to do the in vitro fertilization (IVF) by neck beads’ displacement and the impact on the evolution of embryos, and its quality was studied. Data were analyzed using SPSS version 20 and the significance level of less than 0.05 was considered.

Results

Embryo morphometry showed that the total diameter and the cytoplasm diameter of the study group embryos suffered significant reduction compared to the control group, 1 day after the last irradiation (p < 0.05), but the diameter of the perivitelline space of this group’s embryos had a significant increase (p < 0.05). The qualitative results during 35 days after irradiation showed that morphologically parameters of the embryos in the study group had no significant differences from the control group.

Conclusion

Exposure to MRI irradiation can transiently disturb the development of mouse embryos and fertility, but these effects are reversible 35 days after the last irradiation.

Quantitative β mapping for calibrated fMRI

The metabolic and hemodynamic dependencies of the blood oxygenation level-dependent (BOLD) signal form the basis for calibrated fMRI, where the focus is on oxidative energy demanded by neural activity. An important part of calibrated fMRI is the power-law relationship between the BOLD signal and the deoxyhemoglobin concentration, which in turn is related to the ratio between oxidative demand (CMRO2) and blood flow (CBF). The power-law dependence between BOLD signal and deoxyhemoglobin concentration is signified by a scaling exponent β. Until recently most studies assumed a β value of 1.5, which is based on numerical simulations of the extravascular BOLD component. Since the basal value of CMRO2 and CBF can vary from subject-to-subject and/or region-to-region, a method to independently measure β in vivo should improve the accuracy of calibrated fMRI results. We describe a new method for β mapping through characterizing R2' - the most sensitive relaxation component of BOLD signal (i.e., the reversible magnetic susceptibility component that is predominantly of extravascular origin at high magnetic field) - as a function of intravascular magnetic susceptibility induced by an FDA-approved superparamagnetic contrast agent. In α-chloralose anesthetized rat brain, at 9.4 T, we measured β values of ~0.8 uniformly across large neocortical swathes, with lower magnitude and more heterogeneity in subcortical areas. Comparison of β maps in rats anesthetized with medetomidine and α-chloralose revealed that β is independent of neural activity levels at these resting states. We anticipate that this method for β mapping can help facilitate calibrated fMRI for clinical studies.

Visualizing functional pathways in the human brain using correlation tensors and magnetic resonance imaging

Functional magnetic resonance imaging usually detects changes in blood oxygenation level dependent (BOLD) signals from T2*-sensitive acquisitions, and is most effective in detecting activity in brain cortex which is irrigated by rich vasculature to meet high metabolic demands. We recently demonstrated that MRI signals from T2*-sensitive acquisitions in a resting state exhibit structure-specific temporal correlations along white matter tracts. In this report we validate our preliminary findings and introduce spatio-temporal functional correlation tensors to characterize the directional preferences of temporal correlations in MRI signals acquired at rest. The results bear a remarkable similarity to data obtained by diffusion tensor imaging but without any diffusion-encoding gradients. Just as in gray matter, temporal correlations in resting state signals may reflect intrinsic synchronizations of neural activity in white matter. Here we demonstrate that functional correlation tensors are able to visualize long range white matter tracts as well as short range sub-cortical fibers imaged at rest, and that evoked functional activities alter these structures and enhance the visualization of relevant neural circuitry. Furthermore, we explore the biophysical mechanisms underlying these phenomena by comparing pulse sequences, which suggest that white matter signal variations are consistent with hemodynamic (BOLD) changes associated with neural activity. These results suggest new ways to evaluate MRI signal changes within white matter.

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span

In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI dataset of 86 healthy adults (41 males) with age ranging from 19 to 85 years. We expect the complexity of the resting state fMRI signals measured to be consistent with the Goldberger/Lipsitz model for robustness where healthier (younger) and more robust systems exhibit more complexity in their physiological output and system complexity decrease with age. The mean whole brain fApEn demonstrated significant negative correlation (r = -0.472, p<0.001) with age. In comparison, SampEn produced a non-significant negative correlation (r = -0.099, p = 0.367). fApEn also demonstrated a significant (p < 0.05) negative correlation with age regionally (frontal, parietal, limbic, temporal and cerebellum parietal lobes). There was no significant correlation regionally between the SampEn maps and age. These results support the Goldberger/Lipsitz model for robustness and have shown that fApEn is potentially a sensitive new method for the complexity analysis of fMRI data.

Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

Social anhedonia (SA) is a debilitating characteristic of schizophrenia, a common feature in individuals at psychosis-risk, and a vulnerability for developing schizophrenia-spectrum disorders. Prior work (Hooker et al., 2014) revealed neural deficits in the ventral lateral prefrontal cortex (VLPFC) when processing positive social cues in a community sample of people with high SA. Lower VLPFC neural activity was related to more severe self-reported schizophrenia-spectrum symptoms as well as the exacerbation of symptoms after social stress. In the current study, psycho-physiological interaction (PPI) analysis was applied to further investigate the neural mechanisms mediated by the VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA exhibited reduced connectivity between the VLPFC and the motor cortex, the inferior parietal and the posterior temporal regions when viewing socially positive (relative to neutral) emotions. Across all participants, VLPFC connectivity correlated with behavioral and self-reported measures of attentional control, emotion management, and reward processing. Our results suggest that impairments to the VLPFC mediated neural circuitry underlie the cognitive and emotional deficits associated with social anhedonia, and may serve as neural targets for prevention and treatment of schizophrenia-spectrum disorders.

Abnormal White Matter Blood-Oxygen-Level-Dependent Signals in Chronic Mild Traumatic Brain Injury

Concussion, or mild traumatic brain injury (mTBI), can cause persistent behavioral symptoms and cognitive impairment, but it is unclear if this condition is associated with detectable structural or functional brain changes. At two sites, chronic mTBI human subjects with persistent post-concussive symptoms (three months to five years after injury) and age- and education-matched healthy human control subjects underwent extensive neuropsychological and visual tracking eye movement tests. At one site, patients and controls also performed the visual tracking tasks while blood-oxygen-level-dependent (BOLD) signals were measured with functional magnetic resonance imaging. Although neither neuropsychological nor visual tracking measures distinguished patients from controls at the level of individual subjects, abnormal BOLD signals were reliably detected in patients. The most consistent changes were localized in white matter regions: anterior internal capsule and superior longitudinal fasciculus. In contrast, BOLD signals were normal in cortical regions, such as the frontal eye field and intraparietal sulcus, that mediate oculomotor and attention functions necessary for visual tracking. The abnormal BOLD signals accurately differentiated chronic mTBI patients from healthy controls at the single-subject level, although they did not correlate with symptoms or neuropsychological performance. We conclude that subjects with persistent post-concussive symptoms can be identified years after their TBI using fMRI and an eye movement task despite showing normal structural MRI and DTI.

Functional topography of the corpus callosum investigated by DTI and fMRI

This short review examines the most recent functional studies of the topographic organization of the human corpus callosum, the main interhemispheric commissure. After a brief description of its anatomy, development, microstructure, and function, it examines and discusses the latest findings obtained using diffusion tensor imaging (DTI) and tractography (DTT) and functional magnetic resonance imaging (fMRI), three recently developed imaging techniques that have significantly expanded and refined our knowledge of the commissure. While DTI and DTT have been providing insights into its microstructure, integrity and level of myelination, fMRI has been the key technique in documenting the activation of white matter fibers, particularly in the corpus callosum. By combining DTT and fMRI it has been possible to describe the trajectory of the callosal fibers interconnecting the primary olfactory, gustatory, motor, somatic sensory, auditory and visual cortices at sites where the activation elicited by peripheral stimulation was detected by fMRI. These studies have demonstrated the presence of callosal fiber tracts that cross the commissure at the level of the genu, body, and splenium, at sites showing fMRI activation. Altogether such findings lend further support to the notion that the corpus callosum displays a functional topographic organization that can be explored with fMRI.