Individual Functional ROI Optimization Via Maximization of Group-Wise Consistency of Structural and Functional Profiles

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.     

A post-processing/region of interest (ROI) method for discriminating patterns of activity in statistical maps of fMRI data

To combine functional neuroimaging studies across subjects, anatomical and functional data are typically either transformed to a common space or averaged across regions of interest (ROIs). However, if there are (1) anatomical variations within the subject pool (as in clinical or aging populations), (2) non-Gaussian distributions of task-related activity within a typical ROI or, (3) more ROIs than subjects, neither spatial transformation of the data to a common space nor averaging across all subjects' ROIs is suitable for standard discriminant analysis. To solve these problems, we describe a post-processing method that uses voxel-based statistics representing task-related activity (pooled within ROIs) to establish combinations of ROIs that maximally differentiate tasks across all subjects. The method involves randomized resampling from multiple ROIs within each subject, multivariate linear discriminant analysis across all subjects and validation with bootstrapping techniques. When applied to experimental data from healthy subjects performing two motor tasks, the method detected some brain regions, including the supplementary motor area (SMA), that participated in a distributed network differentially active between tasks. However there was not a significant difference in SMA activity when this region was examined in isolation. We suggest this method is a practical means to combine voxel-based statistics within anatomically defined ROIs across subjects.     

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     

Evidencing a place for the hippocampus within the core scene processing network

Functional neuroimaging studies have identified several “core” brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene‐sensitivity in the HC relative to these other “core” regions. Here, we characterised the frequency and consistency of individual scene‐preferential responses within these regions by analysing a large dataset (n = 51) in which participants performed a one‐back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently‐defined anatomical ROIs to individual‐level functional data across different voxel‐wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene‐related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap “peaks” were close to those identified in group‐level analyses (particularly for TOS and HC), inter‐individual consistency varied across regions and statistical thresholds. The inter‐regional and inter‐individual variability revealed by these analyses has implications for how scene‐sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779–3794, 2016. © 2016 Wiley Periodicals, Inc .     

Data‐driven approaches for tau‐PET imaging biomarkers in Alzheimer's disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions‐of‐interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post‐mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data‐driven method to identify spatial patterns of tau‐PET distribution, and to compare these patterns to previously published “pathology‐driven” ROIs. Tau‐PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [¹⁸F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [¹⁸F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [¹⁸F]AV1451 uptake in the data‐driven clusters, and in 35 previously published pathology‐driven ROIs, was extracted from ADNI [¹⁸F]AV1451 scans. We performed linear models comparing [¹⁸F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data‐driven ROIs consistently demonstrated the strongest or near‐strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data‐driven, one pathology‐driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [¹⁸F]AV1451‐PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.     

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.     

High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)

BackgroundTranscutaneous auricular Vagus Nerve Stimulation (taVNS) applies low-intensity electrical current to the ear with the intention of activating the auricular branch of the Vagus nerve. The sensitivity and selectivity of stimulation applied to the ear depends on current flow pattern produced by a given electrode montage (size and placement).ObjectiveWe compare different electrodes designs for taVNS considering both the predicted peak electric fields (sensitivity) and their spatial distribution (selectivity).MethodsBased on optimized high-resolution (0.47 mm) T1 and T2 weighted MRI, we developed an anatomical model of the left ear and the surrounding head tissues including brain, CSF/meninges, skull, muscle, blood vessels, fat, cartilage, and skin. The ear was further segmented into 6 regions of interest (ROI) based on various nerve densities: cavum concha, cymba concha, crus of helix, tragus, antitragus, and earlobe. A range of taVNS electrode montages were reproduced spanning varied electrodes sizes and placements over the tragus, cymba concha, earlobe, cavum concha, and crus of helix. Electric field across the ear (from superficial skin to cartilage) for each montage at 1 mA or 2 mA taVNS, assuming an activation threshold of 6.15 V/m, 12.3 V/m or 24.6 V/m was predicted using a Finite element method (FEM). Finally, considering every ROI, we calculated the sensitivity and selectivity of each montage.ResultsCurrent flow patterns through the ear were highly specific to the electrode montage. Electric field was maximal at the ear regions directly under the electrodes, and for a given total current, increases with decreasing electrode size. Depending on the applied current and nerves threshold, activation may also occur in the regions between multiple anterior surface electrodes. Each considered montage was selective for one or two regions of interest. For example, electrodes across the tragus restricted significant electric field to the tragus. Stimulation across the earlobe restricted significant electric field to the earlobe and the antitragus. Because of this relative selectivity, use of control ear montages in experimental studies, support testing of targeting. Relative targeting was robust across assumptions of activation threshold and tissue properties.DiscussionComputational models provide additional insight on how details in electrode shape and placement impact sensitivity (how much current is needed) and selectivity (spatial distribution), thereby supporting analysis of existing approaches and optimization of new devices. Our result suggest taVNS current patterns and relative target are robust across individuals, though (variance in) axon morphology was not represented.     

Functional connectivity maps based on hippocampal and thalamic dynamics may account for the default‐mode network

The default‐mode network (DMN) has been reported to comprise a set of inter‐connected transmodal cortical areas, including the posterior cingulate cortex (PCC), medial prefrontal cortex, posterior inferior parietal lobule, lateral temporal region and others. However, the subcortical constituents of the DMN are still not clear. This study aimed to examine whether the correlation maps derived from subcortical structures may also account for neural pattern of the DMN. Structural magnetic resonance imaging (MRI) and resting‐state functional MRI scans of 36 subjects were selected from the Rockland sample (Nathan Kline Institute). The hippocampus and thalamus were chosen as subcortical regions of interest (ROIs). Each ROI was partitioned into composite modules which in turn provided simplified and representative dynamics of blood‐oxygen‐level‐dependent (BOLD) signals. PCC‐seeded and ROI‐based correlation maps were compared by conjunction analyses and paired t‐tests (corrected P < 0.05). Our results unveiled that the hippocampus‐, thalamus‐ and PCC‐centred correlation patterns actually overlapped to a substantial degree. Integrating the signals in the thalamus and hippocampus altogether fully explained the PCC‐seeded DMN. Supplementary analyses based on the BOLD dynamics in several subcortical nuclei (caudate, putamen and globus pallidus) were dissimilar to the DMN. The DMN derived from the ROI/seed‐based approach may represent combined limbic and region‐specific informatics (and their closely interacting neural substrates). The possible causes for previous methods of task‐induced deactivation and seed‐based correlation that failed to depict the holistic limbic picture are discussed. The neocortical manifestation of DMN may reflect the limbic information in the transmodal brain regions.     

MR atlas of the baboon brain for functional neuroimaging

Mathematical co-registration of functional image data (e.g., positron emission tomography, PET) to anatomical magnetic resonance (MR) imaging data allows for objective associations between function and anatomy. Baboons are often used as non-human primate models for functional neuroimaging studies. In this work, a digital MR-based high-resolution atlas of the baboon brain was generated and evaluated for PET. The atlas was generated from six SPGR-MR datasets (centered at mid-sagittal line, AC-PC orientation) that were transformed into the space of one representative MR, averaged and resampled into PET space. The atlas was evaluated by comparing blood flow and dopamine receptor and serotonin transporter binding measures determined using regions-of-interest (ROIs) generated on each individual co-registered MR (ROI(i)) and the atlas-defined ROI template (ROI(ATLAS)). Common ROIs applied to all data included frontal cortex, temporal cortex, thalamus, caudate, putamen and cerebellum. High correlations (r(2)>0.87) were found between the ROI(i) and ROI(ATLAS) data for all radiotracers (linear regression across ROIs for each baboon). The average regression slope values ranged from 0.95 to 1.02 across radiotracers. Lastly, use of the atlas for statistical parametric mapping (SPM) of [15O]water data yielded good agreement with previous ROI(i) results. Overall, the digital MR-based atlas allowed for automatic co-registration, proved useful across a range of PET Studies, and is accessible electronically via the Internet.     

Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI‐based morphological information. Classification performance is further improved by integrating the correlative information with ROI‐based information via multi‐kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non‐converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high‐order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD. Hum Brain Mapp 34:3411–3425, 2013. © 2012 Wiley Periodicals, Inc.     

Analysis of a distributed neural system involved in spatial information, novelty, and memory processing

Perceiving a complex visual scene and encoding it into memory involves a hierarchical distributed network of brain regions, most notably the hippocampus (HIPP), parahippocampal gyrus (PHG), lingual gyrus (LNG), and inferior frontal gyrus (IFG). Lesion and imaging studies in humans have suggested that these regions are involved in spatial information processing as well as novelty and memory encoding; however, the relative contributions of these regions of interest (ROIs) are poorly understood. This study investigated regional dissociations in spatial information and novelty processing in the context of memory encoding using a 2 x 2 factorial design with factors Novelty (novel vs. repeated) and Stimulus (viewing scenes with rich vs. poor spatial information). Greater activation was observed in the right than left hemisphere; however, hemispheric effects did not differ across regions, novelty, or stimulus type. Significant novelty effects were observed in all four regions. A significant ROI x Stimulus interaction was observed - spatial information processing effects were largest effects in the LNG, significant in the PHG and HIPP and nonsignificant in the IFG. Novelty processing was stimulus dependent in the LNG and stimulus independent in the PHG, HIPP, and IFG. Analysis of the profile of Novelty x Stimulus interaction across ROIs provided evidence for a hierarchical independence in novelty processing characterized by increased dissociation from spatial information processing. Despite these differences in spatial information processing, memory performance for novel scenes with rich and poor spatial information was not significantly different. Memory performance was inversely correlated with right IFG activation, suggesting the involvement of this region in strategically flawed encoding effort. Stepwise regression analysis revealed that memory encoding accounted for only a small fraction of the variance (< 16%) in medial temporal lobe activation. The implications of these results for spatial information, novelty, and memory processing in each stage of the distributed network are discussed.     

Novel noninvasive identification of patient‐specific epileptic networks in focal epilepsies: Linking single‐photon emission computed tomography perfusion during seizures with resting‐state magnetoencephalography dynamics

Single‐photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug‐resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase‐locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper‐, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network.     

Analyzing brain networks with PCA and conditional Granger causality

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series.     

Unification of sentence processing via ear and eye: An fMRI study

We present new evidence based on fMRI for the existence and neural architecture of an abstract supramodal language system that can integrate linguistic inputs arising from different modalities such that speech and print each activate a common code. Working with sentence material, our aim was to find out where the putative supramodal system is located and how it responds to comprehension challenges. To probe these questions we examined BOLD activity in experienced readers while they performed a semantic categorization task with matched written or spoken sentences that were either well-formed or contained anomalies of syntactic form or pragmatic content. On whole-brain scans, both anomalies increased net activity over non-anomalous baseline sentences, chiefly at left frontal and temporal regions of heteromodal cortex. The anomaly-sensitive sites correspond approximately to those that previous studies ( [Constable et?al., 2004] and [Michael et?al., 2001] ) have found to be sensitive to other differences in sentence complexity (object relative minus subject relative). Regions of interest (ROIs) were defined by peak response to anomaly averaging over modality conditions. Each anomaly-sensitive ROI showed the same pattern of response across sentence types in each modality. Voxel-by-voxel exploration over the whole brain based on a cosine similarity measure of common function confirmed the specificity of supramodal zones.     

Exploring the relation between brain response to speech at 6-months and language outcomes at 24-months in infants at high and low risk for autism spectrum disorder: A preliminary functional near-infrared spectroscopy study

Infants at high familial risk for autism spectrum disorder (ASD) are at increased risk for language impairments. Studies have demonstrated that atypical brain response to speech is related to language impairments in this population, but few have examined this relation longitudinally. We used functional near-infrared spectroscopy (fNIRS) to investigate the neural correlates of speech processing in 6-month-old infants at high (HRA) and low risk (LRA) for autism. We also assessed the relation between brain response to speech at 6-months and verbal developmental quotient (VDQ) scores at 24-months. LRA infants exhibited greater brain response to speech in bilateral anterior regions of interest (ROIs) compared to posterior ROIs, while HRA infants exhibited similar brain response across all ROIs. Compared to LRA infants, HRA+ infants who were later diagnosed with ASD had reduced brain response in bilateral anterior ROIs, while HRA- infants who were not later diagnosed with ASD had increased brain response in right posterior ROI. Greater brain response in left anterior ROI predicted VDQ scores for LRA infants only. Findings highlight the importance of studying HRA+ and HRA- infants separately, and implicate a different, more distributed neural system for speech processing in HRA infants that is not related to language functioning.     

ROI and phobias: The effect of ROI approach on an ALE meta‐analysis of specific phobias

About 90% of fMRI findings on specific phobias (SP) include analysis of region of interest (ROI). This approach characterized by higher sensitivity may produce inflated results, particularly when findings are aggregated in meta‐analytic maps. Here, we conducted a systematic review and activation likelihood estimation (ALE) meta‐analysis on SP, testing the impact of the inclusion of ROI‐based studies. ALE meta‐analyses were carried out either including ROI‐based results or focusing on whole‐brain voxelwise studies exclusively. To assess the risk of bias in the neuroimaging field, we modified the Newcastle–Ottawa Scale (NOS) and measured the reliability of fMRI findings. Of the 31 selected investigations (564 patients and 485 controls) one‐third did not motivate ROI selection: five studies did not report an explicit rationale, whereas four did not cite any specific reference in this regard. Analyses including ROI‐based studies revealed differences between phobics and healthy subjects in several regions of the limbic circuit. However, when focusing on whole‐brain analysis, only the anterior midcingulate cortex differentiated SP from controls. Notably, 13 studies were labeled with low risk of bias according to the adapted NOS. The inclusion of ROI‐based results artificially inflates group differences in fMRI meta‐analyses. Moreover, a priori, well‐motivated selection of ROIs is desirable to improve quality and reproducibility in SP neuroimaging studies. Lastly, the use of modified NOS may represent a valuable way to assess and evaluate biases in fMRI studies: “low risk” of bias was reported for less than half of the included studies, indicating the need for better practices in fMRI.     

Reliability of drawing regions of interest for positron emission tomographic data.

We determined interrater reliability for two raters who independently used a standard protocol to draw regions of interest (ROIs) on F-18 fluorodeoxyglucose positron emission tomographic data acquired from eight patients with mild to moderate memory impairment. Intraclass correlation coefficients for region sizes (total pixel number) varied among anatomical ROIs (RIs from 0.324 to 0.935). However, correlations calculated from relative metabolic rates (normalized average counts) were consistently high (RI > or = 0.954). The raters also showed high agreement for region recognition on each image plan (kappa = 0.978). These results suggest that rater disagreements in ROI margins have minimal impact on average count densities used to calculate metabolic rates.     

Reproducibility of brain‐cognition relationships using three cortical surface‐based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T₁‐weighted images were processed using three different surface‐based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT‐cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT‐cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples. Hum Brain Mapp 36:3227–3245, 2015. © 2015 Wiley Periodicals, Inc.     

Spatial-temporal current source correlations and cortical connectivity.

The purpose of this study was to explore spatial-temporal correlations between 3-dimensional current density estimates using Low Resolution Electromagnetic Tomography (LORETA). The electroencephalogram (EEG) was recorded from 19 scalp locations from 97 subjects. LORETA current density was computed for 2,394 gray matter pixels. The gray matter pixels were grouped into 33 left hemisphere and 33 right hemisphere regions of interest (ROIs) based on groupings of Brodmann areas. The average source current density in a given region of interest (ROI) was computed for each 2 second epoch of EEG and then a Pearson product correlation coefficient was computed over the time series of successive 2 second epochs of current density between all pairwise combinations of ROIs during the resting eyes-closed EEG session. Rhythmic changes in source correlation as a function of distance were present in all regions of interest. Also, maximum correlations at certain frequencies were present independent of distance. The occipital regions exhibited the highest short distance correlations and the frontal regions exhibited the highest long distance correlations. In general, the right hemisphere exhibited higher intra-hemispheric source correlations than the left hemisphere especially in the temporal, parietal and occipital cortex. The strongest left vs. right hemisphere differences were in the alpha frequency band (8-12 Hz) and in the gamma frequency band (37-40 Hz). The pattern of spatial frequencies in different cortical lobules is consistent with differences in neural packing density and the operation of 'U' shaped fiber systems. The general conclusions were: 1--the higher the packing density then the greater the intra-cortical connection contribution to LORETA source correlations, 2--spatial frequencies are primarily due to intra-cortical 'U' shaped fiber connections and long distance fiber connections, 3--posterior and temporal cortical intra-hemispheric coupling is generally stronger in the right hemisphere than in the left hemisphere.     

A majority rule approach for region-of-interest-guided streamline fiber tractography

Hand-drawn gray matter regions of interest (ROI) are often used to guide the estimation of white matter tractography, obtained from diffusion-weighted magnetic resonance imaging (DWI), in healthy and in patient populations. However, such ROIs are vulnerable to rater bias of the individual segmenting the ROIs, scan variability, and individual differences in neuroanatomy. In this report, a “majority rule” approach is introduced for ROI segmentation used to guide streamline tractography in white matter structures. DWI of one healthy participant was acquired in ten separate sessions using a 3 T scanner over the course of a month. Four raters identified ROIs within the left hemisphere [Cerebral Peduncle (CPED); Internal Capsule (IC); Hand Portion of the Motor Cortex, or Hand Bump, (HB)] using a group-established standard operating procedure for ROI definition to guide the estimation of streamline tracts within the corticospinal tract (CST). Each rater traced the ROIs twice for each scan session. The overlap of each rater’s two ROIs was used to define a representative ROI for each rater. These ROIs were combined to create a “majority rules” ROI, in which the rule requires that each voxel is selected by at least three of four raters. Reproducibility for ROIs and CST segmentations were analyzed with the Dice Similarity Coefficient (DSC). Intra-rater reliability for each ROI was high (DSCs ≥ 0.83). Inter-rater reliability was moderate to adequate (DSC range 0.54–0.75; lowest for IC). Using intersected majority rules ROIs, the resulting CST showed improved overlap (DSC = 0.82) in the estimated streamline tracks for the ten sessions. Despite high intra-rater reliability, there was lower inter-rater reliability consistent with the expectation of rater bias. Employing the majority rules method improved reliability in the overlap of the CST.