Surface based electrode localization and standardized regions of interest for intracranial EEG

Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi‐automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface‐based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring‐based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface‐based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.     

Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis

In this paper we describe a method for functional connectivity analysis of fMRI data between given brain regions-of-interest (ROIs). The method relies on nonnegativity constrained- and spatially regularized multiset canonical correlation analysis (CCA), and assigns weights to the fMRI signals of the ROIs so that their representative signals become simultaneously maximally correlated. The different pairwise correlations between the representative signals of the ROIs are combined using the maxvar approach for multiset CCA, which has been shown to be equivalent to the generalized eigenvector formulation of CCA. The eigenvector in the maxvar approach gives an indication of the relative importance of each ROI in obtaining a maximal overall correlation, and hence, can be interpreted as a functional connectivity pattern of the ROIs. The successive canonical correlations define subsequent functional connectivity patterns, in decreasing order of importance. We apply our method on synthetic data and real fMRI data and show its advantages compared to unconstrained CCA and to PCA. Furthermore, since the representative signals for the ROIs are optimized for maximal correlation they are also ideally suited for further effective connectivity analyses, to assess the information flows between the ROIs in the brain.     

Increased cortico-subcortical functional connectivity in schizophrenia

It has been widely reported that structural and functional connectivities are disturbed in cortical networks in schizophrenia (SZ). However, much less is known about the structural and functional connectivities between cortical and subcortical regions in SZ. Here, diffusion tensor imaging (DTI) data was used to identify consistent cortico-subcortical structural connection patterns across SZ patients and controls, and thus 13 common cortical Regions of Interest (ROIs) were determined. DTI and resting state fMRI (R-fMRI) datasets were used to assess the structural and functional connectivities between the 13 cortical ROIs and 12 subcortical regions in 8 SZ patients and 10 normal controls. It was found that there are significantly increased functional connectivities for 7 cortico-subcortical connections between the 13 cortical ROIs and 12 subcortical regions. Among most of these connections, the functional connectivity strength was doubled in SZ in comparison to controls. The cortical ROIs with functional hyper-connectivities to subcortical regions are localized in frontal and parietal lobes. However, no significant difference in the structural connectivity between these cortical and subcortical regions was found between SZ and controls. Additional analysis results showed 4 significantly increased and 2 significantly decreased cortico-cortical connections. Our study results suggest the functional hyper-connectivity between cortical and subcortical regions, adding further evidence to literature findings that SZ is a disorder of connectivity between components of large-scale brain networks. The result of either increased or decreased functional connectivities among cortical ROIs exhibits the complex pattern of disturbance of brain networks in SZ.     

Complexity of brain activity and connectivity in functional neuroimaging

Understanding the complexity of human brain dynamics and brain connectivity across the repertoire of functional neuroimaging and various conditions, is of paramount importance. Novel measures should be designed tailored to the input focusing on multichannel activity and dynamic functional brain connectivity (DFBC). Here, we defined a novel complexity index (CI) from the field of symbolic dynamics that quantifies patterns of different words up to a length from a symbolic sequence. The CI characterizes the complexity of the brain activity. We analysed DFBC by adopting the sliding window approach using imaginary part of phase locking value (iPLV) for EEG/ECoG/MEG and wavelet coherence (WC) for fMRI. Both intra and cross‐frequency couplings (CFC) namely phase‐to‐amplitude were estimated using iPLV/WC at every snapshot of the DFBC. Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions‐of‐interest (ROI). The spatiotemporal probability distribution of DICM were reported to reveal the most prominent coupling modes per condition and modality. Finally, a novel flexibility index is defined that quantifies the transition of DICM per pair of ROIs between consecutive time windows. The whole methodology was demonstrated using four neuroimaging datasets (EEG/ECoG/MEG/fMRI). Finally, we succeeded to totally discriminate healthy controls from schizophrenic using FI and dynamic reconfiguration of DICM. Anaesthesia independently of the drug caused a global decreased of complexity in all frequency bands with the exception in δ and alters the dynamic reconfiguration of DICM. CI and DICM of MEG/fMRI resting‐state recordings in two spatial scales were high reliable.     

Variability in Stroop Task Performance and Functional Activation among a Small Brain-Injured Group

This study evaluated the effects of moderate to severe brain injury on cognitive task performance and cortical activation. Five participants completed a Stroop task while undergoing functional magnetic resonance imaging (fMRI) at two time points post-injury. Results revealed activation within regions typically activated during a Stroop task (the region of interest: ROI), though variability among participants was evident. Regions outside of the ROI were activated among all participants, to a greater degree than was present within the ROIs. This finding may indicate that recruitment of outside regions was necessary for successful task completion at both time points, and may suggest functional plasticity in cognitive task completion.     

A whole brain fMRI atlas generated via spatially constrained spectral clustering

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto‐architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data‐driven method for generating an ROI atlas by parcellating whole brain resting‐state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade‐offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard‐Oxford, Eickoff‐Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/ . Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc     

静息状态下海洛因成瘾者伏核功能连接的fMRI研究

目的 利用静息态功能磁共振成像(fMRI)技术分析海洛因成瘾者静息状态下与伏核有功能连接的脑区,以探讨海洛因成瘾者"奖赏系统"的组成.方法 选择安徽省戒毒所自2009年6月至2010年3月收治的自愿接受戒毒的海洛因成瘾患者15例作为成瘾组,同期健康体检者15例为对照组,进行静息态fMRI扫描后选取左、右侧伏核为感兴趣区(ROI)进行静息态脑功能连接分析,确定受试者静息状态下与双侧伏核有功能连接的相应脑区.结果 静息状态下成瘾组中与伏核有功能连接的脑区包括双侧丘脑、基底节区、海马、中脑以及对侧伏核等脑区(左侧伏核还与前扣带回有显著的功能连接);而对照组中与伏核有功能连接的脑区仅为海马和对侧伏核,而且激活程度明显小于成瘾组.结论 静息态下与伏核有功能连接的脑区构成了成瘾的"奖赏系统";静息态fMRI技术有助于了解与海洛因成瘾相关脑区之间的功能联系.     

Functional connectivity corresponding to the tonotopic differentiation of the human auditory cortex

Recent research has demonstrated that resting‐state functional connectivity (RS‐FC) within the human auditory cortex (HAC) is frequency‐selective, but whether RS‐FC between the HAC and other brain areas is differentiated by frequency remains unclear. Three types of data were collected in this study, including resting‐state functional magnetic resonance imaging (fMRI) data, task‐based fMRI data using six pure tone stimuli (200, 400, 800, 1,600, 3,200, and 6,400 Hz), and structural imaging data. We first used task‐based fMRI to identify frequency‐selective cortical regions in the HAC. Six regions of interest (ROIs) were defined based on the responses of 50 participants to the six pure tone stimuli. Then, these ROIs were used as seeds to determine RS‐FC between the HAC and other brain regions. The results showed that there was RS‐FC between the HAC and brain regions that included the superior temporal gyrus, dorsolateral prefrontal cortex (DL‐PFC), parietal cortex, occipital lobe, and subcortical structures. Importantly, significant differences in FC were observed among most of the brain regions that showed RS‐FC with the HAC. Specifically, there was stronger RS‐FC between (1) low‐frequency (200 and 400 Hz) regions and brain regions including the premotor cortex, somatosensory/‐association cortex, and DL‐PFC; (2) intermediate‐frequency (800 and 1,600 Hz) regions and brain regions including the anterior/posterior superior temporal sulcus, supramarginal gyrus, and inferior frontal cortex; (3) intermediate/low‐frequency regions and vision‐related regions; (4) high‐frequency (3,200 and 6,400 Hz) regions and the anterior cingulate cortex or left DL‐PFC. These findings demonstrate that RS‐FC between the HAC and other brain areas is frequency selective.     

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.     

Variability in Stroop task performance and functional activation among a small brain-injured group

This study evaluated the effects of moderate to severe brain injury on cognitive task performance and cortical activation. Five participants completed a Stroop task while undergoing functional magnetic resonance imaging (fMRI) at two time points post- injury. Results revealed activation within regions typically activated during a Stroop task (the region of interest: ROI), though variability among participants was evident. Regions outside of the ROI were activated among all participants, to a greater degree than was present within the ROIs. This finding may indicate that recruitment of outside regions was necessary for successful task completion at both time points, and may suggest functional plasticity in cognitive task completion.     

Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest

Cortical functional connectivity, as indicated by the concurrent spontaneous activity of spatially segregated regions, is being studied increasingly because it may determine the reaction of the brain to external stimuli and task requirements and it is reportedly altered in many neurological and psychiatric disorders. In functional magnetic resonance imaging (fMRI), such functional connectivity is investigated commonly by correlating the time course of a chosen "seed voxel" with the remaining voxel time courses in a voxel-by-voxel manner. This approach is biased by the actual choice of the seed voxel, however, because it only shows functional connectivity for the chosen brain region while ignoring other potentially interesting patterns of coactivation. We used spatial independent component analysis (sICA) to assess cortical functional connectivity maps from resting state data. SICA does not depend on any chosen temporal profile of local brain activity. We hypothesized that sICA would be able to find functionally connected brain regions within sensory and motor regions in the absence of task-related brain activity. We also investigated functional connectivity patterns of several parietal regions including the superior parietal cortex and the posterior cingulate gyrus. The components of interest were selected in an automated fashion using predefined anatomical volumes of interest. SICA yielded connectivity maps of bilateral auditory, motor and visual cortices. Moreover, it showed that prefrontal and parietal areas are also functionally connected within and between hemispheres during the resting state. These connectivity maps showed an extremely high degree of consistency in spatial, temporal, and frequency parameters within and between subjects. These results are discussed in the context of the recent debate on the functional relevance of fluctuations of neural activity in the resting state.     

Test–retest reliability of a finger-tapping fMRI task in a healthy population

Measuring brain activity during functional MRI (fMRI) tasks is one of the main tools to identify brain biomarkers of disease or neural substrates associated with specific symptoms. However, identifying correct biomarkers relies on reliable measures. Recently, poor reliability was reported for task-based fMRI measures. The present study aimed to demonstrate the reliability of a finger-tapping fMRI task across two sessions in healthy participants. Thirty-one right-handed healthy participants aged 18–60 years took part in two MRI sessions 3 weeks apart during which we acquired finger-tapping task-fMRI. We examined the overlap of activations between sessions using Dice similarity coefficients, assessing their location and extent. Then, we compared amplitudes calculating intraclass correlation coefficients (ICCs) in three sets of regions of interest (ROIs) in the motor network: literature-based ROIs (10-mm-radius spheres centred on peaks of an activation likelihood estimation), anatomical ROIs (regions as defined in an atlas) and ROIs based on conjunction analyses (superthreshold voxels in both sessions). Finger tapping consistently activated expected regions, for example, left primary sensorimotor cortices, premotor area and right cerebellum. We found good-to-excellent overlap of activations for most contrasts (Dice coefficients: .54–.82). Across time, ICCs showed large variability in all ROI sets (.04–.91). However, ICCs in most ROIs indicated fair-to-good reliability (mean = .52). The least specific contrast consistently yielded the best reliability. Overall, the finger-tapping task showed good spatial overlap and fair reliability of amplitudes on group level. Although caution is warranted in interpreting correlations of activations with other variables, identification of activated regions in response to a task and their between-group comparisons are still valid and important modes of analysis in neuroimaging to find population tendencies and differences.     

Functional changes of the cortical motor system in hereditary spastic paraparesis

Background – Hereditary spastic paraparesis (HSP) is a heterogeneous group of disorders characterized by progressive bilateral lower limb spasticity. Functional imaging studies in patients with corticospinal tract involvement have shown reorganization of motor circuitry. Our study investigates functional changes in sensorimotor brain areas in patients with HSP. Methods – Twelve subjects with HSP and 12 healthy subjects were studied. Functional magnetic resonance imaging (fMRI) was used to measure brain activation during right‐hand finger tapping. Image analysis was performed using general linear model and regions of interest (ROI)‐based approach. Weighted laterality indices (wLI) and anterior/posterior indicies (wAI and wPI) were calculated for predefined ROIs. Results and discussion – Comparing patients and controls at the same finger‐tapping rate (1.8 Hz), there was increased fMRI activation in patients’ bilateral posterior parietal cortex and left primary sensorimotor cortex. No differences were found when comparing patients and controls at 80% of their individual maximum tapping rates. wLI of the primary sensorimotor cortex was significantly lower in patients. Subjects with HSP also showed a relative increase in the activation of the posterior parietal and premotor areas compared with that of the primary sensorimotor cortex. Our findings demonstrate an altered pattern of cortical activation in subjects with HSP during motor task. The increased activation probably reflects reorganization of the cortical motor system.     

Mapping the functional and structural connectivity of the scene network

The recognition and perception of places has been linked to a network of scene-selective regions in the human brain. While previous studies have focussed on functional connectivity between scene-selective regions themselves, less is known about their connectivity with other cortical and subcortical regions in the brain. Here, we determine the functional and structural connectivity profile of the scene network. We used fMRI to examine functional connectivity between scene regions and across the whole brain during rest and movie-watching. Connectivity within the scene network revealed a bias between posterior and anterior scene regions implicated in perceptual and mnemonic aspects of scene perception respectively. Differences between posterior and anterior scene regions were also evident in the connectivity with cortical and subcortical regions across the brain. For example, the Occipital Place Area (OPA) and posterior Parahippocampal Place Area (PPA) showed greater connectivity with visual and dorsal attention networks, while anterior PPA and Retrosplenial Complex showed preferential connectivity with default mode and frontoparietal control networks and the hippocampus. We further measured the structural connectivity of the scene network using diffusion tractography. This indicated both similarities and differences with the functional connectivity, highlighting biases between posterior and anterior regions, but also between ventral and dorsal scene regions. Finally, we quantified the structural connectivity between the scene network and major white matter tracts throughout the brain. These findings provide a map of the functional and structural connectivity of scene-selective regions to each other and the rest of the brain.     

The Functional Relevance of Task-State Functional Connectivity

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping—an approach for building empirically derived network models—to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.     

Network asymmetry of motor areas revealed by resting-state functional magnetic resonance imaging

There are ample functional magnetic resonance imaging (fMRI) studies on functional brain asymmetries, and the asymmetry of cerebral network in the resting state may be crucial to brain function organization. In this paper, a unified schema of voxel-wise functional connectivity and asymmetry analysis was presented and the network asymmetry of motor areas was studied. Twelve healthy male subjects with mean age 29.8 ± 6.4 were studied. Functional network in the resting state was described by using functional connectivity magnetic resonance imaging (fcMRI) analysis. Motor areas were selected as regions of interest (ROIs). Network asymmetry, including intra- and inter-network asymmetries, was formulated and analyzed. The intra-network asymmetry was defined as the difference between the left and right part of a particular functional network. The inter-network asymmetry was defined as the difference between the networks for a specific ROI in the left hemisphere and its homotopic ROI in the right hemisphere. Primary motor area (M1), primary sensory area (S1) and premotor area (PMA) exhibited higher functional correlation with the right parietal-temporal-occipital circuit and the middle frontal gyrus than they did with the left hemisphere. Right S1 and right PMA exhibited higher functional correlation with the ipsilateral precentral and supramarginal areas. There exist the large-scale hierarchical network asymmetries of the motor areas in the resting state. These asymmetries imply the right hemisphere dominance for predictive motor coding based on spatial attention and higher sensory processing load for the motor performance of non-dominant hemisphere.     

Functional connectivity with the anterior cingulate is associated with extraversion during the emotional Stroop task

Previous research has investigated the association of personality traits with brain activation in response to emotional stimuli. Our current research efforts are directed at understanding the temporal dynamics of networks of structures associated with particular personality traits, and gain insights into the functional contributions of more narrowly defined trait-facets that comprise these personality traits. To begin this process, we conducted a functional magnetic resonance imaging (fMRI) study using an emotional attention task (emotional Stroop paradigm) and addressed the question whether individual differences in extraversion and its lower-level facets were associated with differences in activation, and in functional connectivity, of the anterior cingulate (AC) cortex. We replicated our earlier finding that extraversion was associated with increased AC activation to positive, relative to neutral, word stimuli, but now show that distinct facets of extraversion can account for this association. When analyzing for functional connectivity, we found that greater extraversion across individuals was associated with greater functional connectivity between the AC and the inferior parietal lobule, and that this association could also be accounted for by distinct facets of extraversion. Our data suggest that extraversion and some of its lower-level facets are associated with individual differences across a network of structures believed to be critical in cognitive and affective processing.     

A few thoughts on brain ROIs

Quantitative mapping of structural and functional connectivities in the human brain via non-invasive neuroimaging offers an exciting and unique opportunity to understand brain architecture. Because connectivity alterations are widely reported in a variety of brain diseases, assessment of structural and functional connectivities has emerged as a fundamental research area in clinical neuroscience. A fundamental question arises when attempting to map structural and functional connectivities: how to define and localize the best possible Regions of Interests (ROIs) for brain connectivity mapping? Essentially, when mapping brain connectivities, ROIs provide the structural substrates for measuring connectivities within individual brains and for pooling data across populations. Thus, identification of reliable, reproducible and accurate ROIs is critically important for the success of brain connectivity mapping. This paper discusses several major challenges in defining optimal brain ROIs from our perspective and presents a few thoughts on how to deal with those challenges based on recent research work done in our group.     

Functional imaging localization of complex organic hallucinations

Background: fMRI of mental phenomena is quite difficult to perform because lack of patient’s cooperation or because the symptoms are stable. In some exceptional cases, however, fMRI and DTI are capable to provide insights on the anatomy of organic hallucinations. Methods: In this report we describe a 14-year-old boy with a left fronto-dorsal tumor who experienced chronic complex brief, frequent and repetitive complex visual and auditory hallucinations. His clinical picture included multiple and severe social and mood problems. During a presurgical fMRI mapping the patient complained of having the visual and auditory hallucinations. A block-design FMRI paradigm was obtained from the event timecourse. Deterministic DTI of the brain was obtained seeding the lesion as ROI. The patient underwent surgery and electrocorticography of the lesional area. Results: The fMRI of the hallucinations showed activation in the left inferior frontal gyrus (IFG) and the peri-lesional area. The tractography of the tumor revealed structural aberrant connectivity to occipital and temporal areas in addition to the expected connectivity with the IFG via the aslant fasciculus and homotopic contralateral areas. Intraoperative EEG demonstrated epileptic discharges in the tumor and neighboring areas. After resection, the patient’s hallucinations stopped completely. He regained his normal social life and recover his normal mood. He remained asymptomatic for 90 days. Afterwards, hallucinations reappeared but with less intensity. Conclusions: To our knowledge, this is the first reported case of combined functional and structural connectivity imaging demonstrating brain regions participating in a network involved in the generation of complex auditory and visual hallucinations.