Multivariate Granger causality analysis of fMRI data

This article describes the combination of multivariate Granger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data

BACKGROUND

Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN).

METHODOLOGY/PRINCIPLE FINDINGS

A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network.

CONCLUSIONS/SIGNIFICANCE

Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries.

Novel functions of nanos in downregulating mitosis and transcription during the development of the Drosophila germline

It has previously been shown that germ cells in embryos derived from nos mutant mothers do not migrate to the primitive gonad and prematurely express several germline-specific markers. In the studies reported here, we have traced these defects back to the syncytial blastoderm stage. We show that pole cells in nos embryos fail to establish/maintain transcriptional quiescence; the sex determination gene Sex-lethal (Sxl) and the segmentation genes fushi tarazu and even-skipped are ectopically activated in nos- germ cells. We show that nos- germ cells are unable to attenuate the cell cycle and instead continue dividing. Unexpectedly, removal of the Sxl gene in the zygote mitigates both the migration and mitotic defects of nos- germ cells. Supporting the conclusion that Sxl is an important target for nos repression, ectopic, premature expression of Sxl protein in germ cells disrupts migration and stimulates mitotic activity.

Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis

Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of Granger causality analysis. Finally, we discuss our previous work on the less-appreciated interactions between instantaneous and causal relationships and the utility and interpretation of Granger causality results obtained from task versus resting state (e.g., ability of causal relationships to provide a mode of connectivity between regions that are instantaneously dissociated in resting state). We conclude by discussing future directions in this area.

Effective connectivity during haptic perception: a study using Granger causality analysis of functional magnetic resonance imaging data

Although it is accepted that visual cortical areas are recruited during touch, it remains uncertain whether this depends on top-down inputs mediating visual imagery or engagement of modality-independent representations by bottom-up somatosensory inputs. Here we addressed this by examining effective connectivity in humans during haptic perception of shape and texture with the right hand. Multivariate Granger causality analysis of functional magnetic resonance imaging (fMRI) data was conducted on a network of regions that were shape- or texture-selective. A novel network reduction procedure was employed to eliminate connections that did not contribute significantly to overall connectivity. Effective connectivity during haptic perception was found to involve a variety of interactions between areas generally regarded as somatosensory, multisensory, visual and motor, emphasizing flexible cooperation between different brain regions rather than rigid functional separation. The left postcentral sulcus (PCS), left precentral gyrus and right posterior insula were important sources of connections in the network. Bottom-up somatosensory inputs from the left PCS and right posterior insula fed into visual cortical areas, both the shape-selective right lateral occipital complex (LOC) and the texture-selective right medial occipital cortex (probable V2). In addition, top-down inputs from left postero-supero-medial parietal cortex influenced the right LOC. Thus, there is strong evidence for the bottom-up somatosensory inputs predicted by models of visual cortical areas as multisensory processors and suggestive evidence for top-down parietal (but not prefrontal) inputs that could mediate visual imagery. This is consistent with modality-independent representations accessible through both bottom-up sensory inputs and top-down processes such as visual imagery.

Activation and effective connectivity changes following explicit-memory training for face-name pairs in patients with mild cognitive impairment: a pilot study

BACKGROUND

Mild cognitive impairment (MCI) is often a precursor to Alzheimer disease. Little research has examined the efficacy of cognitive rehabilitation in patients with MCI, and the relevant neural mechanisms have not been explored. The authors previously showed the behavioral efficacy of cognitive rehabilitation using mnemonic strategies for face-name associations in patients with MCI. Here, the authors used functional magnetic resonance imaging (fMRI) to test whether there were training-specific changes in activation and connectivity within memory-related areas.

METHODS

A total of 6 patients with amnestic, multidomain MCI underwent pretraining and posttraining fMRI scans, during which they encoded 90 novel face-name pairs and completed a 4-choice recognition memory test immediately after scanning. Patients were taught mnemonic strategies for half the face-name pairs during 3 intervening training sessions.

RESULTS

Training-specific effects comprised significantly increased activation within a widespread cerebral cortical network involving medial frontal, parietal, and occipital regions; the left frontal operculum and angular gyrus; and regions in the left lateral temporal cortex. Increased activation common to trained and untrained stimuli was found in a separate network involving inferior frontal, lateral parietal, and occipital cortical regions. Effective connectivity analysis using multivariate, correlation-purged Granger causality analysis revealed generally increased connectivity after training, particularly involving the middle temporal gyrus and foci in the occipital cortex and the precuneus.

CONCLUSION

The authors' findings suggest that the effectiveness of explicit-memory training in patients with MCI is associated with training-specific increases in activation and connectivity in a distributed neural system that includes areas involved in explicit memory.

Art for reward's sake: visual art recruits the ventral striatum

A recent study showed that people evaluate products more positively when they are physically associated with art images than similar non-art images. Neuroimaging studies of visual art have investigated artistic style and esthetic preference but not brain responses attributable specifically to the artistic status of images. Here we tested the hypothesis that the artistic status of images engages reward circuitry, using event-related functional magnetic resonance imaging (fMRI) during viewing of art and non-art images matched for content. Subjects made animacy judgments in response to each image. Relative to non-art images, art images activated, on both subject- and item-wise analyses, reward-related regions: the ventral striatum, hypothalamus and orbitofrontal cortex. Neither response times nor ratings of familiarity or esthetic preference for art images correlated significantly with activity that was selective for art images, suggesting that these variables were not responsible for the art-selective activations. Investigation of effective connectivity, using time-varying, wavelet-based, correlation-purged Granger causality analyses, further showed that the ventral striatum was driven by visual cortical regions when viewing art images but not non-art images, and was not driven by regions that correlated with esthetic preference for either art or non-art images. These findings are consistent with our hypothesis, leading us to propose that the appeal of visual art involves activation of reward circuitry based on artistic status alone and independently of its hedonic value.

Identification of neural connectivity signatures of autism using machine learning

Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Dual pathways for haptic and visual perception of spatial and texture information

Segregation of information flow along a dorsally directed pathway for processing object location and a ventrally directed pathway for processing object identity is well established in the visual and auditory systems, but is less clear in the somatosensory system. We hypothesized that segregation of location vs. identity information in touch would be evident if texture is the relevant property for stimulus identity, given the salience of texture for touch. Here, we used functional magnetic resonance imaging (fMRI) to investigate whether the pathways for haptic and visual processing of location and texture are segregated, and the extent of bisensory convergence. Haptic texture-selectivity was found in the parietal operculum and posterior visual cortex bilaterally, and in parts of left inferior frontal cortex. There was bisensory texture-selectivity at some of these sites in posterior visual and left inferior frontal cortex. Connectivity analyses demonstrated, in each modality, flow of information from unisensory non-selective areas to modality-specific texture-selective areas and further to bisensory texture-selective areas. Location-selectivity was mostly bisensory, occurring in dorsal areas, including the frontal eye fields and multiple regions around the intraparietal sulcus bilaterally. Many of these regions received input from unisensory areas in both modalities. Together with earlier studies, the activation and connectivity analyses of the present study establish that somatosensory processing flows into segregated pathways for location and object identity information. The location-selective somatosensory pathway converges with its visual counterpart in dorsal frontoparietal cortex, while the texture-selective somatosensory pathway runs through the parietal operculum before converging with its visual counterpart in visual and frontal cortex. Both segregation of sensory processing according to object property and multisensory convergence appear to be universal organizing principles.

Dynamic brain connectivity is a better predictor of PTSD than static connectivity

Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.

Behavioral relevance of the dynamics of the functional brain connectome

While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.

Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data

Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Toward this end, an international competition was conducted for classifying ADHD using functional magnetic resonance imaging data acquired from multiple sites worldwide. Here, we consider the data from this competition as an example to illustrate the utility of fully connected cascade (FCC) artificial neural network (ANN) architecture for performing classification. We employed various directional and nondirectional brain connectivity-based methods to extract discriminative features which gave better classification accuracy compared to raw data. Our accuracy for distinguishing ADHD from healthy subjects was close to 90% and between the ADHD subtypes was close to 95%. Further, we show that, if properly used, FCC ANN performs very well compared to other classifiers such as support vector machines in terms of accuracy, irrespective of the feature used. Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD.

Posteromedial parietal cortical activity and inputs predict tactile spatial acuity

We used functional magnetic resonance imaging (fMRI) to investigate the neural circuitry underlying tactile spatial acuity at the human finger pad. Stimuli were linear, three-dot arrays, applied to the immobilized right index finger pad using a computer-controlled, MRI-compatible, pneumatic stimulator. Activity specific for spatial processing was isolated by contrasting discrimination of left-right offsets of the central dot in the array with discrimination of the duration of stimulation by an array without a spatial offset. This contrast revealed activity in a distributed frontoparietal cortical network, within which the levels of activity in right posteromedial parietal cortical foci [right posterior intraparietal sulcus (pIPS) and right precuneus] significantly predicted individual acuity thresholds. Connectivity patterns were assessed using both bivariate analysis of Granger causality with the right pIPS as a reference region and multivariate analysis of Granger causality for a selected set of regions. The strength of inputs into the right pIPS was significantly greater in subjects with better acuity than those with poorer acuity. In the better group, the paths predicting acuity converged from the left postcentral sulcus and right frontal eye field onto the right pIPS and were selective for the spatial task, and their weights predicted the level of right pIPS activity. We propose that the optimal strategy for fine tactile spatial discrimination involves interaction in the pIPS of a top-down control signal, possibly attentional, with somatosensory cortical inputs, reflecting either visualization of the spatial configurations of tactile stimuli or engagement of modality-independent circuits specialized for fine spatial processing.

Altered local coherence in the default mode network due to sevoflurane anesthesia

Recently we introduced a robust measure, integrated local correlation (ILC), of local connectivity in the brain using fMRI data which reflects the temporal correlation of brain activity in every voxel neighborhood. The current work studies ILC in fMRI data obtained in the absence and presence of sevoflurane anesthesia (0%, 2%, and 1% end-tidal concentration, respectively) administered to healthy volunteers. ILC was determined specifically in regions of the default mode network (DMN) to address local changes in each state. In addition, a potential confound in analyses based on correlations due to signal-to-noise variations was addressed by wavelet denoising. This accommodated decreases in signal power commonly seen during anesthesia without artificially reducing derived correlations. Results showed that ILC was significantly reduced in the entire DMN during 2% sevoflurane yet recovered in the posterior and anterior cingulate cortices as well as inferior parietal cortex during 1% sevoflurane. By contrast, ILC remained attenuated prefrontally in the 1% condition, which indicates uncoupling of the frontal areas of DMN during light anesthesia. These results confirm widespread anesthetic-induced cortical suppression but also demonstrate that the local connectivity of the prefrontal cortex is rapidly reduced by sevoflurane. It remains to be seen whether these alterations arise locally as a direct consequence of anesthetic action on local neurons or are driven by distant changes in oscillations and activity elsewhere in the brain.

Hedgehog signaling in germ cell migration

The primitive gonad of the Drosophila embryo is formed from two cell types, the somatic gonad precursor cells (SGPs) and the germ cells, which originate at distant sites. To reach the SGPs the germ cells must undergo a complex series of cell movements. While there is evidence that attractive and repulsive signals guide germ cell migration through the embryo, the molecular identity of these instructive molecules has remained elusive. Here, we present evidence suggesting that hedgehog (hh) may serve as such an attractive guidance cue. Misexpression of hh in the soma induces germ cells to migrate to inappropriate locations. Conversely, cell-autonomous components of the hh pathway appear to be required in the germline for proper germ cell migration.

Object familiarity modulates effective connectivity during haptic shape perception

In the preceding paper (Lacey, S., Flueckiger, P., Stilla, R., Lava, M., Sathian, K., 2009a. Object familiarity modulates involvement of visual imagery in haptic shape perception), we showed that the activations evoked by visual imagery overlapped more extensively, and their magnitudes were more correlated, with those evoked during haptic shape perception of familiar, compared to unfamiliar, objects. Here we used task-specific analyses of functional and effective connectivity to provide convergent evidence. These analyses showed that the visual imagery and familiar haptic shape tasks activated similar networks, whereas the unfamiliar haptic shape task activated a different network. Multivariate Granger causality analyses of effective connectivity, in both a conventional form and one purged of zero-lag correlations, showed that the visual imagery and familiar haptic shape networks involved top-down paths from prefrontal cortex into the lateral occipital complex (LOC), whereas the unfamiliar haptic shape network was characterized by bottom-up, somatosensory inputs into the LOC. We conclude that shape representations in the LOC are flexibly accessible, either top-down or bottom-up, according to task demands, and that visual imagery is more involved in LOC activation during haptic shape perception when objects are familiar, compared to unfamiliar.

Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild-traumatic brain injury and posttraumatic stress disorder

OBJECTIVES

Military service members risk acquiring posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI), with high comorbidity. Owing to overlapping symptomatology in chronic mTBI or postconcussion syndrome (PCS) and PTSD, it is difficult to assess the etiology of a patient's condition without objective measures. Using resting-state functional MRI in a novel framework, we tested the hypothesis that their neural signatures are characterized by functionally hyperconnected brain regions which are less variable over time. Additionally, we predicted that such connectivities possessed the highest ability in predicting the diagnostic membership of a novel subject (top-predictors) in addition to being statistically significant.

METHODS

U.S. Army Soldiers (N = 87) with PTSD and comorbid PCS + PTSD were recruited along with combat controls. Static and dynamic functional connectivities were evaluated. Group differences were obtained in accordance with our hypothesis. Machine learning classification (MLC) was employed to determine top predictors.

RESULTS

From whole-brain connectivity, we identified the hippocampus-striatum connectivity to be significantly altered in accordance with our hypothesis. Diffusion tractography revealed compromised white-matter integrity between aforementioned regions only in the PCS + PTSD group, suggesting a structural etiology for the PCS + PTSD group rather than being an extreme subset of PTSD. Employing MLC, connectivities provided worst-case accuracy of 84% (9% more than psychological measures). Additionally, the hippocampus-striatum connectivities were found to be top predictors and thus a potential biomarker of PTSD/mTBI.

CONCLUSIONS

PTSD/mTBI are associated with hippocampal-striatal hyperconnectivity from which it is difficult to disengage, leading to a habit-like response toward episodic traumatic memories, which fits well with behavioral manifestations of combat-related PTSD/mTBI. Hum Brain Mapp 38:2843-2864, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Neural processing underlying tactile microspatial discrimination in the blind: a functional magnetic resonance imaging study

Although blindness alters neocortical processing of non-visual tasks, previous studies do not allow clear conclusions about purely perceptual tasks. We used functional magnetic resonance imaging (fMRI) to examine the neural processing underlying tactile microspatial discrimination in the blind. Activity during the tactile microspatial task was contrasted against that during a tactile temporal discrimination task. The spatially selective network included frontoparietal and visual cortical regions. Activation magnitudes in left primary somatosensory cortex and in visual cortical foci predicted acuity thresholds. Effective connectivity was investigated using multivariate Granger causality analyses. Bilateral primary somatosensory cortical foci and a left inferior temporal focus were important sources of connections. Visual cortical regions interacted mainly with one another and with somatosensory cortical regions. Among a set of distributed cortical regions exhibiting greater spatial selectivity in early blind compared to late blind individuals, the age of complete blindness was predicted by activity in a subset of frontoparietal regions and by the weight of a path from the right lateral occipital complex to right occipitopolar cortex. Thus, many aspects of neural processing during tactile microspatial discrimination differ between the blind and sighted, with some of the key differences reflecting visual cortical engagement in the blind.

Effective connectivity during episodic memory retrieval in schizophrenia participants before and after antipsychotic medication

BACKGROUND

Impairment in episodic memory is one of the most robust findings in schizophrenia. Disruptions of fronto-temporal functional connectivity that could explain some aspects of these deficits have been reported. Recent work has identified abnormal hippocampal function in unmedicated patients with schizophrenia (SZ), such as increased metabolism and glutamate content that are not always seen in medicated SZ. For these reasons, we hypothesized that altered fronto-temporal connectivity might originate from the hippocampus and might be partially restored by antipsychotic medication.

METHODS

Granger causality methods were used to evaluate the effective connectivity between frontal and temporal regions in 21 unmedicated SZ and 20 matched healthy controls (HC) during performance of an episodic memory retrieval task. In 16 SZ, effective connectivity between these regions was evaluated before and after 1-week of antipsychotic treatment.

RESULTS

In HC, significant effective connectivity originating from the right hippocampus to frontal regions was identified. Compared to HC, unmedicated SZ showed significant altered fronto-temporal effective connectivity, including reduced right hippocampal to right medial frontal connectivity. After 1-week of antipsychotic treatment, connectivity more closely resembled the patterns observed in HC, including increased effective connectivity from the right hippocampus to frontal regions.

CONCLUSIONS

These results support the notion that memory disruption in schizophrenia might originate from hippocampal dysfunction and that medication restores some aspects of fronto-temporal dysconnectivity. Patterns of fronto-temporal connectivity could provide valuable biomarkers to identify new treatments for the symptoms of schizophrenia, including memory deficits.

Probiotics for preterm neonates: what will it take to change clinical practice?

Necrotizing enterocolitis (NEC) is a major cause of mortality (25%) and morbidity including recurrent sepsis, dependence on parenteral nutrition, need for surgery, and survival with short bowel syndrome in preterm very low birth weight infants. Mortality (45-100%) and morbidity including the risk of long-term neurodevelopmental impairment are higher in extremely preterm infants needing surgery for NEC. Systematic reviews of randomized controlled trials (RCT) indicate that probiotics significantly reduce the risk of NEC (RR 0.39; 95% CI 0.29-0.52; p < 0.00001) and all-cause mortality (RR 0.52; 95% CI 0.40-0.69; p < 0.00001) while facilitating enteral feeds in preterm infants. At present, data from 25 RCT (∼5,000 neonates) and reports on routine use (∼3,000 neonates) indicates that significant adverse effects of probiotics are rare. Despite the robust evidence, there is still reluctance in incorporating routine probiotic prophylaxis in clinical practice. If the goal is to have zero tolerance for NEC, then probiotic prophylaxis must be adopted as soon as possible. Current gaps in knowledge can be addressed by continued research while providing routine probiotic supplementation. We believe that the concept of evidence-based practice of medicine has been stretched too far in this case. Trial sequential analysis has already shown that the evidence for probiotic supplementation was conclusive after 10 trials. Results of the ongoing trials are unlikely to change the conclusions of the systematic reviews significantly. Currently we are at trial number 25; how many more trials do we need? What will it take to change clinical practice?

Integrated local correlation: a new measure of local coherence in fMRI data

This article introduces the measure of integrated local correlation (ILC) for assessing local coherence in the brain using functional magnetic resonance imaging (fMRI) data and characterizes the measure in terms of reproducibility, the effect of physiological noise, and the dependence on image resolution. The coupling of local neuronal processes influences coherence in a voxel's neighborhood. ILC is defined, for each voxel, as the integration of its spatial correlation function. This integrated measure does not require the specification of a neighborhood and, as demonstrated by experimental data, is effectively independent of image resolution. Respiratory and cardiac fluctuations do not considerably alter the ILC value except in isolated areas in and surrounding large vessels. With resting-state fMRI data, ILC was demonstrated to be tissue-specific, higher in gray matter than white matter, and reproducible across consecutive runs in healthy individuals. Within the gray matter, ILC was found to be higher in the default mode network, particularly the posterior and anterior cingulate cortices. Comparing ILC maps obtained from resting state and continuous motor task data, we observed reduced local coherence in the default mode network during the task. Finally, we compared ILC and regional homogeneity by examining their ability to discriminate between gray and white matters in resting state data and found ILC to be more sensitive.

Diffusion of responsibility attenuates altruistic punishment: A functional magnetic resonance imaging effective connectivity study

Humans altruistically punish violators of social norms to enforce cooperation and pro-social behaviors. However, such altruistic behaviors diminish when others are present, due to a diffusion of responsibility. We investigated the neural signatures underlying the modulations of diffusion of responsibility on altruistic punishment, conjoining a third-party punishment task with event-related functional magnetic resonance imaging and multivariate Granger causality mapping. In our study, participants acted as impartial third-party decision-makers and decided how to punish norm violations under two different social contexts: alone (i.e., full responsibility) or in the presence of putative other third-party decision makers (i.e., diffused responsibility). Our behavioral results demonstrated that the diffusion of responsibility served as a mediator of context-dependent punishment. In the presence of putative others, participants who felt less responsible also punished less severely in response to norm violations. Our neural results revealed that underlying this behavioral effect was a network of interconnected brain regions. For unfair relative to fair splits, the presence of others led to attenuated responses in brain regions implicated in signaling norm violations (e.g., AI) and to increased responses in brain regions implicated in calculating values of norm violations (e.g., vmPFC, precuneus) and mentalizing about others (dmPFC). The dmPFC acted as the driver of the punishment network, modulating target regions, such as AI, vmPFC, and precuneus, to adjust altruistic punishment behavior. Our results uncovered the neural basis of the influence of diffusion of responsibility on altruistic punishment and highlighted the role of the mentalizing network in this important phenomenon. Hum Brain Mapp 37:663-677, 2016. © 2015 Wiley Periodicals, Inc.

Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of FMRI

Effective connectivity in brain networks can be studied using Granger causality analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does "leak" into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.