Showing papers in "Human Brain Mapping in 2017"

Deep learning with convolutional neural networks for EEG decoding and visualization.

Abstract: Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc.

Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure

Abstract: Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra-network connectivity of multiple resting-state networks (RSNs); however, whether impairment is present in inter-network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic-clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting-state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE-GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, k-mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state-specific FNC disruptions were observed in IGE-GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE-GTCS from controls with an accuracy of 77.91% (P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE-GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957-973, 2017. © 2016 Wiley Periodicals, Inc.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Abstract: We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Implementation errors in the GingerALE Software: Description and recommendations.

Abstract: Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp 38:7–11, 2017. © 2016 Wiley Periodicals, Inc.

Toward an electrophysiological "sweet spot" for deep brain stimulation in the subthalamic nucleus.

Abstract: Enhanced beta-band activity recorded in patients suffering from Parkinson's Disease (PD) has been described as a potential physiomarker for disease severity. Beta power is suppressed by Levodopa intake and STN deep brain stimulation (DBS) and correlates with disease severity across patients. The aim of the present study was to explore the promising signature of the physiomarker in the spatial domain. Based on local field potential data acquired from 54 patients undergoing STN-DBS, power values within alpha, beta, low beta, and high beta bands were calculated. Values were projected into common stereotactic space after DBS lead localization. Recorded beta power values were significantly higher at posterior and dorsal lead positions, as well as in active compared with inactive pairs. The peak of activity in the beta band was situated within the sensorimotor functional zone of the nucleus. In contrast, higher alpha activity was found in a more ventromedial region, potentially corresponding to associative or premotor functional zones of the STN. Beta- and alpha-power peaks were then used as seeds in a fiber tracking experiment. Here, the beta-site received more input from primary motor cortex whereas the alpha-site was more strongly connected to premotor and prefrontal areas. The results summarize predominant spatial locations of frequency signatures recorded in STN-DBS patients in a probabilistic fashion. The site of predominant beta-activity may serve as an electrophysiologically determined target for optimal outcome in STN-DBS for PD in the future. Hum Brain Mapp 38:3377-3390, 2017. © 2017 Wiley Periodicals, Inc.

Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis.

Abstract: Previous literature about the structural characterization of the human cerebellum is related to the context of a specific pathology or focused in a restricted age range. In fact, studies about the cerebellum maturation across the lifespan are scarce and most of them considered the cerebellum as a whole without investigating each lobule. This lack of study can be explained by the lack of both accurate segmentation methods and data availability. Fortunately, during the last years, several cerebellum segmentation methods have been developed and many databases comprising subjects of different ages have been made publically available. This fact opens an opportunity window to obtain a more extensive analysis of the cerebellum maturation and aging. In this study, we have used a recent state-of-the-art cerebellum segmentation method called CERES and a large data set (N = 2,831 images) from healthy controls covering the entire lifespan to provide a model for 12 cerebellum structures (i.e., lobules I-II, III, IV, VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX, and X). We found that lobules have generally an evolution that follows a trajectory composed by a fast growth and a slow degeneration having sometimes a plateau for absolute volumes, and a decreasing tendency (faster in early ages) for normalized volumes. Special consideration is dedicated to Crus II, where slow degeneration appears to stabilize in elder ages for absolute volumes, and to lobule X, which does not present any fast growth during childhood in absolute volumes and shows a slow growth for normalized volumes.

Dynamic functional connectivity of neurocognitive networks in children

Abstract: The human brain is highly dynamic, supporting a remarkable range of cognitive abilities that emerge over the course of development. While flexible and dynamic coordination between neural systems is firmly established for children, our understanding of brain functional organization in early life has been built largely on the implicit assumption that functional connectivity (FC) is static. Understanding the nature of dynamic neural interactions during development is a critical issue for cognitive neuroscience, with implications for neurodevelopmental pathologies that involve anomalies in brain connectivity. In this work, FC dynamics of neurocognitive networks in a sample of 146 youth from varied sociodemographic backgrounds were delineated. Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Results revealed six dynamic FC states that re-occur over time and that complement, but significantly extend, measures of static FC. Moreover, the occurrence and amount of time spent in specific FC states are related to the content of self-generated thought during the scan. Additionally, some connections are more variable over time than are others, including those between inferior parietal lobe and precuneus. These regions contribute to multiple networks and likely play a role in adaptive processes in childhood. Age-related increases in temporal variability of FC among neurocognitive networks were also found. Taken together, these findings lay the groundwork for understanding how variation in the developing chronnectome is related to risk for neurodevelopmental disorders. Understanding how brain systems reconfigure with development should provide insight into the ontogeny of complex, flexible cognitive processes. Hum Brain Mapp 38:97-108, 2017. © 2016 Wiley Periodicals, Inc.

The neurobiology of emotion regulation in posttraumatic stress disorder: Amygdala downregulation via real-time fMRI neurofeedback.

Abstract: Amygdala dysregulation has been shown to be central to the pathophysiology of posttraumatic stress disorder (PTSD) representing a critical treatment target. Here, amygdala downregulation was targeted using real-time fMRI neurofeedback (rt-fMRI-nf) in patients with PTSD, allowing us to examine further the regulation of emotional states during symptom provocation. Patients (n = 10) completed three sessions of rt-fMRI-nf with the instruction to downregulate activation in the amygdala, while viewing personalized trauma words. Amygdala downregulation was assessed by contrasting (a) regulate trials, with (b) viewing trauma words and not attempting to regulate. Training was followed by one transfer run not involving neurofeedback. Generalized psychophysiological interaction (gPPI) and dynamic causal modeling (DCM) analyses were also computed to explore task-based functional connectivity and causal structure, respectively. It was found that PTSD patients were able to successfully downregulate both right and left amygdala activation, showing sustained effects within the transfer run. Increased activation in the dorsolateral and ventrolateral prefrontal cortex (PFC), regions related to emotion regulation, was observed during regulate as compared with view conditions. Importantly, activation in the PFC, rostral anterior cingulate cortex, and the insula, were negatively correlated to PTSD dissociative symptoms in the transfer run. Increased functional connectivity between the amygdala- and both the dorsolateral and dorsomedial PFC was found during regulate, as compared with view conditions during neurofeedback training. Finally, our DCM analysis exploring directional structure suggested that amygdala downregulation involves both top-down and bottom-up information flow with regard to observed PFC-amygdala connectivity. This is the first demonstration of successful downregulation of the amygdala using rt-fMRI-nf in PTSD, which was critically sustained in a subsequent transfer run without neurofeedback, and corresponded to increased connectivity with prefrontal regions involved in emotion regulation during the intervention. Hum Brain Mapp 38:541-560, 2017. © 2016 Wiley Periodicals, Inc.

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.

Abstract: Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12-25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331-5342, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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

Abstract: 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.

Cooperation in lovers: An fNIRS-based hyperscanning study

Abstract: This study investigated interactive exchange in lovers and the associated interpersonal brain synchronization (IBS) using functional near-infrared spectroscopy (fNIRS)-based hyperscanning. Three types of female-male dyads, lovers, friends, and strangers, performed a cooperation task during which brain activity was recorded in right frontoparietal regions. We measured better cooperative behavior in lover dyads compared with friend and stranger dyads. Lover dyads demonstrated increased IBS in right superior frontal cortex, which also covaried with their task performance. Granger causality analyses in lover dyads revealed stronger directional synchronization from females to males than from males to females, suggesting different roles for females and males during cooperation. Our study refines the theoretical explanation of romantic interaction between lovers. Hum Brain Mapp 38:831-841, 2017. © 2016 Wiley Periodicals, Inc.

Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion.

Abstract: Motion-contaminated T1-weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion-induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in-scanner head motion. The magnitude of head motion increased with age and exhibited within-participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age- and gender-matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Detecting large-scale networks in the human brain using high-density electroencephalography.

Abstract: Funding Information: - Chinese Scholarship Council. Grant Number: 201306180008 - Swiss National Science Foundation. Grant Number: 320030_146531 and P1EZP3_165207 - Seventh Framework Programme European Commission. Grant Number: PCIG12‐334039 - KU Leuven Special Research Fund. Grant Number: C16/15/070 Research Foundation Flanders (FWO). Grant Number: G0F76.16N and G0936.16N

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging.

Abstract: Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting-state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Structural and functional connectivity of the precuneus and thalamus to the default mode network.

Abstract: Neuroimaging studies have identified functional interactions between the thalamus, precuneus, and default mode network (DMN) in studies of consciousness. However, less is known about the structural connectivity of the precuneus and thalamus to regions within the DMN. We used diffusion tensor imaging (DTI) to parcellate the precuneus and thalamus based on their probabilistic white matter connectivity to each other and DMN regions of interest (ROIs) in 37 healthy subjects from the Human Connectome Database. We further assessed resting-state functional connectivity (RSFC) among the precuneus, thalamus, and DMN ROIs. The precuneus was found to have the greatest structural connectivity with the thalamus, where connection fractional anisotropy (FA) increased with age. The precuneus also showed significant structural connectivity to the hippocampus and middle pre-frontal cortex, but minimal connectivity to the angular gyrus and midcingulate cortex. In contrast, the precuneus exhibited significant RSFC with the thalamus and the strongest RSFC with the AG. Significant symmetrical structural connectivity was found between the thalamus and hippocampus, mPFC, sFG, and precuneus that followed known thalamocortical pathways, while thalamic RSFC was strongest with the precuneus and hippocampus. Overall, these findings reveal high levels of structural and functional connectivity linking the thalamus, precuneus, and DMN. Differences between structural and functional connectivity (such as between the precuneus and AG) may be interpreted to reflect dynamic shifts in RSFC for cortical hub-regions involved with consciousness, but could also reflect the limitations of DTI to detect superficial white matter tracts that connect cortico-cortical regions. Hum Brain Mapp 38:938-956, 2017. © 2016 Wiley Periodicals, Inc.

Specifying the brain anatomy underlying temporo-parietal junction activations for theory of mind: A review using probabilistic atlases from different imaging modalities.

Abstract: In this quantitative review, we specified the anatomical basis of brain activity reported in the Temporo-Parietal Junction (TPJ) in Theory of Mind (ToM) research. Using probabilistic brain atlases, we labeled TPJ peak coordinates reported in the literature. This was carried out for four different atlas modalities: (i) gyral-parcellation, (ii) sulco-gyral parcellation, (iii) cytoarchitectonic parcellation and (iv) connectivity-based parcellation. In addition, our review distinguished between two ToM task types (false belief and social animations) and a nonsocial task (attention reorienting). We estimated the mean probabilities of activation for each atlas label, and found that for all three task types part of TPJ activations fell into the same areas: (i) Angular Gyrus (AG) and Lateral Occpital Cortex (LOC) in terms of a gyral atlas, (ii) AG and Superior Temporal Sulcus (STS) in terms of a sulco-gyral atlas, (iii) areas PGa and PGp in terms of cytoarchitecture and (iv) area TPJp in terms of a connectivity-based parcellation atlas. Beside these commonalities, we also found that individual task types showed preferential activation for particular labels. Main findings for the right hemisphere were preferential activation for false belief tasks in AG/PGa, and in Supramarginal Gyrus (SMG)/PFm for attention reorienting. Social animations showed strongest selective activation in the left hemisphere, specifically in left Middle Temporal Gyrus (MTG). We discuss how our results (i.e., identified atlas structures) can provide a new reference for describing future findings, with the aim to integrate different labels and terminologies used for studying brain activity around the TPJ. Hum Brain Mapp 38:4788-4805, 2017. © 2017 Wiley Periodicals, Inc.

Gray matter asymmetries in aging and neurodegeneration: A review and meta‐analysis

Abstract: Inter-hemispheric asymmetries are a common phenomenon of the human brain. Some evidence suggests that neurodegeneration related to aging and disease may preferentially affect the left—usually language- and motor-dominant—hemisphere. Here, we used activation likelihood estimation meta-analysis to assess gray matter (GM) loss and its lateralization in healthy aging and in neurodegeneration, namely, mild cognitive impairment (MCI), Alzheimer's dementia (AD), Parkinson's disease (PD), and Huntington's disease (HD). This meta-analysis, comprising 159 voxel-based morphometry publications (enrolling 4,469 patients and 4,307 controls), revealed that GM decline appeared to be asymmetric at trend levels but provided no evidence for increased left-hemisphere vulnerability. Regions with asymmetric GM decline were located in areas primarily affected by neurodegeneration. In HD, the left putamen showed converging evidence for more pronounced atrophy, while no consistent pattern was found in PD. In MCI, the right hippocampus was more atrophic than its left counterpart, a pattern that reversed in AD. The stability of these findings was confirmed using permutation tests. However, due to the lenient threshold used in the asymmetry analysis, further work is needed to confirm our results and to provide a better understanding of the functional role of GM asymmetries, for instance in the context of cognitive reserve and compensation. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Evolution of deep gray matter volume across the human lifespan

Abstract: Magnetic resonance imaging of subcortical gray matter structures, which mediate behavior, cognition and the pathophysiology of several diseases, is crucial for establishing typical maturation patterns across the human lifespan. This single site study examines T1-weighted MPRAGE images of 3 healthy cohorts: (i) a cross-sectional cohort of 406 subjects aged 5-83 years; (ii) a longitudinal neurodevelopment cohort of 84 subjects scanned twice approximately 4 years apart, aged 5-27 years at first scan; and (iii) a longitudinal aging cohort of 55 subjects scanned twice approximately 3 years apart, aged 46-83 years at first scan. First scans from longitudinal subjects were included in the cross-sectional analysis. Age-dependent changes in thalamus, caudate, putamen, globus pallidus, nucleus accumbens, hippocampus, and amygdala volumes were tested with Poisson, quadratic, and linear models in the cross-sectional cohort, and quadratic and linear models in the longitudinal cohorts. Most deep gray matter structures best fit to Poisson regressions in the cross-sectional cohort and quadratic curves in the young longitudinal cohort, whereas the volume of all structures except the caudate and globus pallidus decreased linearly in the longitudinal aging cohort. Males had larger volumes than females for all subcortical structures, but sex differences in trajectories of change with age were not significant. Within subject analysis showed that 65%-80% of 13-17 year olds underwent a longitudinal decrease in volume between scans (∼4 years apart) for the putamen, globus pallidus, and hippocampus, suggesting unique developmental processes during adolescence. This lifespan study of healthy participants will form a basis for comparison to neurological and psychiatric disorders. Hum Brain Mapp 38:3771-3790, 2017. © 2017 Wiley Periodicals, Inc.

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms.

Abstract: Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting-state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting-state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting-state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting-state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, but also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI. Hum Brain Mapp 38:2226-2241, 2017. © 2017 Wiley Periodicals, Inc.

Impact of correction factors in human brain lesion-behavior inference.

Abstract: Statistical voxel-based lesion-behavior mapping (VLBM) in neurological patients with brain lesions is frequently used to examine the relationship between structure and function of the healthy human brain. Only recently, two simulation studies noted reduced anatomical validity of this method, observing the results of VLBM to be systematically misplaced by about 16 mm. However, both simulation studies differed from VLBM analyses of real data in that they lacked the proper use of two correction factors: lesion size and "sufficient lesion affection." In simulation experiments on a sample of 274 real stroke patients, we found that the use of these two correction factors reduced misplacement markedly compared to uncorrected VLBM. Apparently, the misplacement is due to physiological effects of brain lesion anatomy. Voxel-wise topographies of collateral damage in the real data were generated and used to compute a metric for the inter-voxel relation of brain damage. "Anatomical bias" vectors that were solely calculated from these inter-voxel relations in the patients' real anatomical data, successfully predicted the VLBM misplacement. The latter has the potential to help in the development of new VLBM methods that provide even higher anatomical validity than currently available by the proper use of correction factors. Hum Brain Mapp 38:1692-1701, 2017. © 2017 Wiley Periodicals, Inc.

Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders

Abstract: Functional magnetic resonance imaging (fMRI) studies in psychiatry use various tasks to identify case-control differences in the patterns of task-related brain activation. Differently activated regions are often ascribed disorder-specific functions in an attempt to link disease expression and brain function. We undertook a systematic meta-analysis of data from task-fMRI studies to examine the effect of diagnosis and study design on the spatial distribution and direction of case-control differences on brain activation. We mapped to atlas regions coordinates of case-control differences derived from 537 task-fMRI studies in schizophrenia, bipolar disorder, major depressive disorder, anxiety disorders, and obsessive compulsive disorder comprising observations derived from 21,427 participants. The fMRI tasks were classified according to the Research Domain Criteria (RDoC). We investigated whether diagnosis, RDoC domain or construct and use of regions-of-interest or whole-brain analyses influenced the neuroanatomical pattern of results. When considering all primary studies, we found an effect of diagnosis for the amygdala and caudate nucleus and an effect of RDoC domains and constructs for the amygdala, hippocampus, putamen and nucleus accumbens. In contrast, whole-brain studies did not identify any significant effect of diagnosis or RDoC domain or construct. These results resonate with prior reports of common brain structural and genetic underpinnings across these disorders and caution against attributing undue specificity to brain functional changes when forming explanatory models of psychiatric disorders. Hum Brain Mapp, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Tinnitus distress is linked to enhanced resting‐state functional connectivity from the limbic system to the auditory cortex

Abstract: The phantom sound of tinnitus is believed to be triggered by aberrant neural activity in the central auditory pathway, but since this debilitating condition is often associated with emotional distress and anxiety, these comorbidities likely arise from maladaptive functional connections to limbic structures such as the amygdala and hippocampus. To test this hypothesis, resting-state functional magnetic resonance imaging (fMRI) was used to identify aberrant effective connectivity of the amygdala and hippocampus in tinnitus patients and to determine the relationship with tinnitus characteristics. Chronic tinnitus patients (n = 26) and age-, sex-, and education-matched healthy controls (n = 23) were included. Both groups were comparable for hearing level. Granger causality analysis utilizing the amygdala and hippocampus as seed regions were used to investigate the directional connectivity and the relationship with tinnitus duration or distress. Relative to healthy controls, tinnitus patients demonstrated abnormal directional connectivity of the amygdala and hippocampus, including primary and association auditory cortex, and other non-auditory areas. Importantly, scores on the Tinnitus Handicap Questionnaires were positively correlated with increased connectivity from the left amygdala to left superior temporal gyrus (r = 0.570, P = 0.005), and from the right amygdala to right superior temporal gyrus (r = 0.487, P = 0.018). Moreover, enhanced effective connectivity from the right hippocampus to left transverse temporal gyrus was correlated with tinnitus duration (r = 0.452, P = 0.030). The results showed that tinnitus distress strongly correlates with enhanced effective connectivity that is directed from the amygdala to the auditory cortex. The longer the phantom sensation, the more likely acute tinnitus becomes permanently encoded by memory traces in the hippocampus. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

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

Abstract: 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.

Dynamic causal modeling in PTSD and its dissociative subtype: Bottom-up versus top-down processing within fear and emotion regulation circuitry.

Abstract: Objective Posttraumatic stress disorder (PTSD) is associated with decreased top-down emotion modulation from medial prefrontal cortex (mPFC) regions, a pathophysiology accompanied by hyperarousal and hyperactivation of the amygdala. By contrast, PTSD patients with the dissociative subtype (PTSD + DS) often exhibit increased mPFC top-down modulation and decreased amygdala activation associated with emotional detachment and hypoarousal. Crucially, PTSD and PTSD + DS display distinct functional connectivity within the PFC, amygdala complexes, and the periaqueductal gray (PAG), a region related to defensive responses/emotional coping. However, differences in directed connectivity between these regions have not been established in PTSD, PTSD + DS, or controls. Methods To examine directed (effective) connectivity among these nodes, as well as group differences, we conducted resting-state stochastic dynamic causal modeling (sDCM) pairwise analyses of coupling between the ventromedial (vm)PFC, the bilateral basolateral and centromedial (CMA) amygdala complexes, and the PAG, in 155 participants (PTSD [n = 62]; PTSD + DS [n = 41]; age-matched healthy trauma-unexposed controls [n = 52]). Results PTSD was characterized by a pattern of predominant bottom-up connectivity from the amygdala to the vmPFC and from the PAG to the vmPFC and amygdala. Conversely, PTSD + DS exhibited predominant top-down connectivity between all node pairs (from the vmPFC to the amygdala and PAG, and from the amygdala to the PAG). Interestingly, the PTSD + DS group displayed the strongest intrinsic inhibitory connections within the vmPFC. Conclusions These results suggest the contrasting symptom profiles of PTSD and its dissociative subtype (hyper- vs. hypo-emotionality, respectively) may be driven by complementary changes in directed connectivity corresponding to bottom-up defensive fear processing versus enhanced top-down regulation. Hum Brain Mapp 38:5551-5561, 2017. © 2017 Wiley Periodicals, Inc.

Test–retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated T1w/T2w image ratio

Abstract: In an age-heterogeneous sample of healthy adults, we examined test-retest reliability (with and without participant repositioning) of two popular MRI methods of estimating myelin content: modeling the short spin-spin (T2 ) relaxation component of multi-echo imaging data and computing the ratio of T1 -weighted and T2 -weighted images (T1 w/T2 w). Taking the myelin water fraction (MWF) index of myelin content derived from the multi-component T2 relaxation data as a standard, we evaluate the concurrent and differential validity of T1 w/T2 w ratio images. The results revealed high reliability of MWF and T1 w/T2 w ratio. However, we found significant correlations of low to moderate magnitude between MWF and the T1 w/T2 w ratio in only two of six examined regions of the cerebral white matter. Notably, significant correlations of the same or greater magnitude were observed for T1 w/T2 w ratio and the intermediate T2 relaxation time constant, which is believed to reflect differences in the mobility of water between the intracellular and extracellular compartments. We conclude that although both methods are highly reliable and thus well-suited for longitudinal studies, T1 w/T2 w ratio has low criterion validity and may be not an optimal index of subcortical myelin content. Hum Brain Mapp 38:1780-1790, 2017. © 2017 Wiley Periodicals, Inc.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.

Abstract: Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc.

Neural signatures of trust in reciprocity: A coordinate-based meta-analysis

Abstract: Trust in reciprocity (TR) is defined as the risky decision to invest valued resources in another party with the hope of mutual benefit. Several fMRI studies have investigated the neural correlates of TR in one-shot and multiround versions of the investment game (IG). However, an overall characterization of the underlying neural networks remains elusive. Here, a coordinate-based meta-analysis was employed (activation likelihood estimation method, 30 articles) to investigate consistent brain activations in each of the IG stages (i.e., the trust, reciprocity and feedback stage). Results showed consistent activations in the anterior insula (AI) during trust decisions in the one-shot IG and decisions to reciprocate in the multiround IG, likely related to representations of aversive feelings. Moreover, decisions to reciprocate also consistently engaged the intraparietal sulcus, probably involved in evaluations of the reciprocity options. On the contrary, trust decisions in the multiround IG consistently activated the ventral striatum, likely associated with reward prediction error signals. Finally, the dorsal striatum was found consistently recruited during the feedback stage of the multiround IG, likely related to reinforcement learning. In conclusion, our results indicate different neural networks underlying trust, reciprocity, and feedback learning. These findings suggest that although decisions to trust and reciprocate may elicit aversive feelings likely evoked by the uncertainty about the decision outcomes and the pressing requirements of social standards, multiple interactions allow people to build interpersonal trust for cooperation via a learning mechanism by which they arguably learn to distinguish trustworthy from untrustworthy partners. Hum Brain Mapp 38:1233-1248, 2017. © 2016 Wiley Periodicals, Inc.

Exosomal biomarkers of brain insulin resistance associated with regional atrophy in Alzheimer's disease.

Abstract: Brain insulin resistance (IR), which depends on insulin-receptor-substrate-1 (IRS-1) phosphorylation, is characteristic of Alzheimer's disease (AD). Previously, we demonstrated higher pSer312-IRS-1 (ineffective insulin signaling) and lower p-panTyr-IRS-1 (effective insulin signaling) in neural origin-enriched plasma exosomes of AD patients vs. controls. Here, we hypothesized that these exosomal biomarkers associate with brain atrophy in AD. We studied 24 subjects with biomarker-supported probable AD (low CSF Aβ42). Exosomes were isolated from plasma, enriched for neural origin using immunoprecipitation for L1CAM, and measured for pSer312- and p-panTyr-IRS-1 phosphotypes. MPRAGE images were segmented by brain tissue type and voxel-based morphometry (VBM) analysis for gray matter against pSer312- and p-panTyr-IRS-1 was conducted. Given the regionally variable brain expression of IRS-1, we used the Allen Brain Atlas to make spatial comparisons between VBM results and IRS-1 expression. Brain volume was positively associated with P-panTyr-IRS-1 and negatively associated with pSer312-IRS-1 in a strikingly similar regional pattern (bilateral parietal-occipital junction, R middle temporal gyrus). This volumetric association pattern was spatially correlated with Allen Human Brain atlas normal brain IRS-1 expression. Exosomal biomarkers of brain IR are thus associated with atrophy in AD as could be expected by their pathophysiological roles and do so in a pattern that reflects regional IRS-1 expression. Furthermore, neural-origin plasma exosomes may recover molecular signals from specific brain regions. Hum Brain Mapp, 2016. © 2017 Wiley Periodicals, Inc.

Intrinsic functional connectivity variance and state-specific under-connectivity in autism.

Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered brain connectivity. Previous neuroimaging research demonstrates inconsistent results, particularly in studies of functional connectivity in ASD. Typically, these inconsistent findings are results of studies using static measures of resting-state functional connectivity. Recent work has demonstrated that functional brain connections are dynamic, suggesting that static connectivity metrics fail to capture nuanced time-varying properties of functional connections in the brain. Here we used a dynamic functional connectivity approach to examine the differences in the strength and variance of dynamic functional connections between individuals with ASD and healthy controls (HCs). The variance of dynamic functional connections was defined as the respective standard deviations of the dynamic functional connectivity strength across time. We utilized a large multicenter dataset of 507 male subjects (209 with ASD and 298 HC, from 6 to 36 years old) from the Autism Brain Imaging Data Exchange (ABIDE) to identify six distinct whole-brain dynamic functional connectivity states. Analyses demonstrated greater variance of widespread long-range dynamic functional connections in ASD (P < 0.05, NBS method) and weaker dynamic functional connections in ASD (P < 0.05, NBS method) within specific whole-brain connectivity states. Hypervariant dynamic connections were also characterized by weaker connectivity strength in ASD compared with HC. Increased variance of dynamic functional connections was also related to ASD symptom severity (ADOS total score) (P < 0.05), and was most prominent in connections related to the medial superior frontal gyrus and temporal pole. These results demonstrate that greater intraindividual dynamic variance is a potential biomarker of ASD. Hum Brain Mapp 38:5740-5755, 2017. © 2017 Wiley Periodicals, Inc.

Angular Default Mode Network Connectivity across Working Memory Load

Abstract: Evelyn Trust (RUAG/018), Yousef Jameel Academic Program, National Institute for Health Research Cambridge Biomedical Centre (RCZB/004), National Institute for Health Research (Senior Investigator Award (RCZB/014)), Queens’ College Cambridge (Stephen Erskine Fellowship)