Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual‐vendor reproducibility and repeatability study

Multicenter clinical and quantitative magnetic resonance imaging (qMRI) studies require a high degree of reproducibility across different sites and scanner manufacturers, as well as time points. We therefore implemented a multiparameter mapping (MPM) protocol based on vendor's product sequences and demonstrate its repeatability and reproducibility for whole‐brain coverage. Within ~20 min, four MPM metrics (magnetization transfer saturation [MT], proton density [PD], longitudinal [R1], and effective transverse [R2*] relaxation rates) were measured using an optimized 1 mm isotropic resolution protocol on six 3 T MRI scanners from two different vendors. The same five healthy participants underwent two scanning sessions, on the same scanner, at each site. MPM metrics were calculated using the hMRI‐toolbox. To account for different MT pulses used by each vendor, we linearly scaled the MT values to harmonize them across vendors. To determine longitudinal repeatability and inter‐site comparability, the intra‐site (i.e., scan‐rescan experiment) coefficient of variation (CoV), inter‐site CoV, and bias across sites were estimated. For MT, R1, and PD, the intra‐ and inter‐site CoV was between 4 and 10% across sites and scan time points for intracranial gray and white matter. A higher intra‐site CoV (16%) was observed in R2* maps. The inter‐site bias was below 5% for all parameters. In conclusion, the MPM protocol yielded reliable quantitative maps at high resolution with a short acquisition time. The high reproducibility of MPM metrics across sites and scan time points combined with its tissue microstructure sensitivity facilitates longitudinal multicenter imaging studies targeting microstructural changes, for example, as a quantitative MRI biomarker for interventional clinical trials.     

Test‐retest reliability of the default mode network in a multi‐centric fMRI study of healthy elderly: Effects of data‐driven physiological noise correction techniques

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within‐site test‐retest reliability and the across‐site reproducibility consistency of DMN‐derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue‐based regression, PESTICA and FSL‐FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z‐scores and, albeit less markedly, the cluster‐size in the DMN; in particular, FSL‐FIX tended to increase the DMN z‐scores compared to others. Within‐site test‐retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5–11% for DMN z‐scores and cluster‐size reliability. DMN pattern overlap was in the range 60–65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL‐FIX and Tissue‐based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z‐scores relative to NPC. Overall these findings support the use of rPNC methods like tissue‐based or FSL‐FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114–2132, 2016. © 2016 Wiley Periodicals, Inc.     

Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in‐scanner motion on morphological analysis of structural MRI is relatively under‐studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in‐scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend‐level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non‐overlapping sets of structural MRI scans, convergent evidence showed that in‐scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non‐sedated humans. Hum Brain Mapp 37:2385–2397, 2016. © 2016 Wiley Periodicals, Inc .     

Test–retest reliability of freesurfer measurements within and between sites: Effects of visual approval process

In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test–retest reliability of FreeSurfer‐derived cortical measures in four groups of subjects—those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test–retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan–Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects’ results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI‐derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution. Hum Brain Mapp 36:3472–3485, 2015. © 2015 Wiley Periodicals, Inc.     

Reliability of single and paired‐pulse transcranial magnetic stimulation in the vastus lateralis muscle

Introduction: Transcranial magnetic stimulation (TMS) is an important tool to examine neurological pathologies, movement disorders, and central nervous system responses to exercise, fatigue, and training. The reliability has not been examined in a functional locomotor knee extensor muscle. Methods: Within‐ (n = 10) and between‐day (n = 16) reliability of single and paired‐paired pulse TMS was examined from the active vastus lateralis. Results: Motor evoked potential amplitude and cortical silent period duration showed good within‐ and between‐day reliability (intraclass correlation coefficient [ICC] ≥ 0.82). Short‐ and long‐interval intracortical inhibition (SICI and LICI, respectively) demonstrated good within‐day reliability (ICC ≥ 0.84). SICI had moderate to good between‐day reliability (ICC ≥ 0.67), but LICI was not repeatable (ICC = 0.47). Intracortical facilitation showed moderate to good within‐day reliability (ICC ≥ 0.73) but poor to moderate reliability between days (ICC ≥ 0.51). Conclusions: TMS can reliably assess cortical function in a knee extensor muscle. This may be useful to examine neurological disorders that affect locomotion. Muscle Nerve 52: 605–615, 2015     

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

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 38:472–492, 2017. © 2016 Wiley Periodicals, Inc.     

Mitigating site effects in covariance for machine learning in neuroimaging data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer''s Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group.     

Automated subcortical segmentation using FIRST: Test–retest reliability,interscanner reliability,and comparison to manual segmentation

Multiple techniques exist for the automated segmentation of magnetic resonance images (MRIs). The validity of these techniques can be assessed by evaluating test–retest reliability, interscanner reliability, and consistency with manual segmentation. We evaluate these measures for the FSL/FIRST subcortical segmentation tool. We retrospectively analyzed 190 MRI scans from 87 subjects with mood or anxiety disorders and healthy volunteers scanned multiple times on different platforms (N = 56) and/or the same platform (N = 45, groups overlap), and 146 scans from subjects who underwent both high‐resolution and whole brain imaging in a single session, for comparison with manual segmentation of the hippocampus. The thalamus, caudate, putamen, hippocampus, and pallidum were reliably segmented in different sessions on the same scanner (Intraclass correlation coefficient (ICC) > 0.83 scanners and diagnostic groups pooled). In these regions, the range of between platform reliabilities were lower (0.527 < ICC < 0.953), although values below 0.7 were due to systematic differences between platforms or low reliability in the hippocampus between eight‐ and single‐channel coil platforms. Accumbens and amygdala segmentations were generally unreliable within and between scanning platforms. ICC values for hippocampal volumes between automated and manual segmentations were acceptable (ICC > 0.7, groups pooled), and both methods detected significant differences between genders. In addition, FIRST segmentations were consistent with manual segmentations (in a subset of images; N = 20) in the left caudate and bilateral putamen. This retrospective analysis assesses realistic performance of the algorithm in conditions like those found in multisite trials or meta‐analyses. In addition, the inclusion of psychiatric patients establishes reliability in subjects exhibiting volumetric abnormalities, validating patient studies. Hum Brain Mapp 34:2313–2329, 2013. © 2012 Wiley Periodicals, Inc.     

Reproducibility of R‐fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes

Concerns regarding reproducibility of resting‐state functional magnetic resonance imaging (R‐fMRI) findings have been raised. Little is known about how to operationally define R‐fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test–retest reliability and replicability, on widely used R‐fMRI metrics in both between‐subject contrasts of sex differences and within‐subject comparisons of eyes‐open and eyes‐closed (EOEC) conditions. We noted permutation test with Threshold‐Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family‐wise error rate (under 5%) and test–retest reliability/replicability (e.g., 0.68 for test–retest reliability and 0.25 for replicability of amplitude of low‐frequency fluctuations (ALFF) for between‐subject sex differences, 0.49 for replicability of ALFF for within‐subject EOEC differences). Although R‐fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between‐subject sex differences, < 0.5 for within‐subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect “true” effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R‐fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300–318, 2018. © 2017 Wiley Periodicals, Inc.     

Longitudinal reliability of tract‐based spatial statistics in diffusion tensor imaging

Relatively little is known about reliability of longitudinal diffusion‐tensor imaging (DTI) measurements despite growing interest in using DTI to track change in white matter structure. The purpose of this study is to quantify within‐ and between session scan‐rescan reliability of DTI‐derived measures that are commonly used to describe the characteristics of neural white matter in the context of neural plasticity research. DTI data were acquired from 16 cognitively healthy older adults (mean age 68.4). We used the Tract‐Based Spatial Statistics (TBSS) approach implemented in FSL, evaluating how different DTI preprocessing choices affect reliability indices. Test‐Retest reliability, quantified as ICC averaged across the voxels of the TBSS skeleton, ranged from 0.524 to 0.798 depending on the specific DTI‐derived measure and the applied preprocessing steps. The two main preprocessing steps that we found to improve TBSS reliability were (a) the use of a common individual template and (b) smoothing DTI data using a 1‐voxel median filter. Overall our data indicate that small choices in the preprocessing pipeline have a significant effect on test‐retest reliability, therefore influencing the power to detect change within a longitudinal study. Furthermore, differences in the data processing pipeline limit the comparability of results across studies. Hum Brain Mapp 35:4544–4555, 2014. © 2014 Wiley Periodicals, Inc.     

Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study: Clarification and implications for statistical power

In this commentary, we clarify the meaning of the generalizability‐theory‐based coefficients reported in our multisite reliability study of fMRI measures of regional brain activation during an emotion processing task (Gee et al., Human Brain Mapping 2015;36:2558–2579). While the original paper reported generalizability and dependability coefficients based on the design of our traveling subjects study (in which each subject was scanned twice at each of eight sites), those coefficients are of limited applicability outside of the reliability study context. Here we report generalizability and dependability coefficients that represent the reliability one can expect for a multisite study, in which a given subject is scanned once on a scanner drawn randomly from the pool of available scanners (i.e., analogous to the more typical multisite study design). We also characterize the implications of a multisite versus single‐site study design for statistical power, including Figure 1 that shows sample size requirements to detect activation in two key nodes of the emotion processing circuitry given observed differences in reliability of measurement between single‐site and multisite designs.     

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes—eigenmodes—of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.     

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

The cortex is organised into broadly hierarchical functional systems with distinct neuroanatomical characteristics reflected by macroscopic measures of cortical morphology. Diffusion‐weighted magnetic resonance imaging allows the delineation of areal connectivity, changes to which reflect the ongoing maturation of white matter tracts. These developmental processes are intrinsically linked with timing coincident with the development of cognitive function. In this study, we use a data‐driven multivariate approach, nonnegative matrix factorisation, to define cortical regions that co‐vary together across a large paediatric cohort (n = 456) and are associated with specific subnetworks of cortical connectivity. We find that age between 3 and 21 years is associated with accelerated cortical thinning in frontoparietal regions, whereas relative thinning of primary motor and sensory regions is slower. Together, the subject‐specific weights of the derived set of cortical components can be combined to predict chronological age. Structural connectivity networks reveal a relative increase in strength in connection within, as opposed to between hemispheres that vary in line with cortical changes. We confirm our findings in an independent sample.     

Performance characteristics of scintigraphic colon transit measurement in health and irritable bowel syndrome and relationship to bowel functions

Background The inter‐ and intra‐subject variations of scintigraphy, which are used to identify colonic transit disturbances in irritable bowel syndrome (IBS), are unclear. The relationship between colonic transit and bowel functions is incompletely understood. To assess inter‐ and intra‐subject variations of scintigraphic colonic transit measurements in 86 IBS patients and 17 healthy subjects and to quantify the relationship between colonic transit and bowel symptoms in 147 IBS patients and 46 healthy subjects. Methods Data from participants with multiple colonic transit measurements were analysed. Primary end points were colonic filling at 6 h (CF6h) and geometric center (GC) at 24 and 48 h for colonic transit. Bowel functions were assessed by daily stool diaries. Key Results Inter‐ and intra‐subject variations were greater for small intestinal than colonic transit. Overall, inter‐ and intra‐subject variations were relatively narrow for colonic transit (both GC24h and GC48h, with lower COV at 48 h); there was little intra‐subject variation in health and IBS‐constipation over a period of ≤3 weeks and over 2.0 years (median, range 0.1, 11.0 years). Significant intra‐individual differences in GC24h were observed only in IBS‐D patients. Colonic transit was significantly associated with stool form (accounting for 19–27% of the variance), frequency (19%), and ease of stool passage (12%). Conclusions & Inferences Despite inter‐subject variation in scintigraphic colonic transit results, the intra‐subject measurements are reproducible over time in healthy volunteers and patients with IBS; significant changes in colonic transit at 24 h were observed only in IBS‐D. Colonic transit is associated with stool form, frequency and ease of passage.     

Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer's disease and mild cognitive impairment

Studies have found non‐negligible differences in cortical thickness estimates across versions of software that are used for processing and quantifying MRI‐based cortical measurements, and issues have arisen regarding these differences, as obtained estimates could potentially affect the validity of the results. However, more critical for diagnostic classification than absolute thickness estimates across versions is the inter‐subject stability. We aimed to investigate the effect of change in software version on classification of older persons in groups of healthy, mild cognitive impairment and Alzheimer's Disease. Using MRI samples of 100 older normal controls, 100 with mild cognitive impairment and 100 Alzheimer's Disease patients obtained from the Alzheimer's Disease Neuroimaging Initiative database, we performed a standard reconstruction processing using the FreeSurfer image analysis suite versions 4.1.0, 4.5.0 and 5.1.0. Pair‐wise comparisons of cortical thickness between FreeSurfer versions revealed significant differences, ranging from 1.6% (4.1.0 vs. 4.5.0) to 5.8% (4.1.0 vs. 5.1.0) across the cortical mantle. However, change of version had very little effect on detectable differences in cortical thickness between diagnostic groups, and there were little differences in accuracy between versions when using entorhinal thickness for diagnostic classification. This lead us to conclude that differences in absolute thickness estimates across software versions in this case did not imply lacking validity, that classification results appeared reliable across software versions, and that classification results obtained in studies using different FreeSurfer versions can be reliably compared. Hum Brain Mapp 37:1831–1841, 2016. © 2016 Wiley Periodicals, Inc .     

Video‐evoked fMRI BOLD responses are highly consistent across different data acquisition sites

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here, we characterize the consistency of brain responses across independent samples of participants viewing the same videos in functional magnetic resonance imaging (fMRI) scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video‐evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant‐level variability rather than scanner‐ or data‐ related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.     

Sample size estimates for well‐powered cross‐sectional cortical thickness studies

Introduction : Cortical thickness mapping is a widely used method for the analysis of neuroanatomical differences between subject groups. We applied power analysis methods over a range of image processing parameters to derive a model that allows researchers to calculate the number of subjects required to ensure a well‐powered cross‐sectional cortical thickness study. Methods : 0.9‐mm isotropic T₁‐weighted 3D MPRAGE MRI scans from 98 controls (53 females, age 29.1 ± 9.7 years) were processed using Freesurfer 5.0. Power analyses were carried out using vertex‐wise variance estimates from the coregistered cortical thickness maps, systematically varying processing parameters. A genetic programming approach was used to derive a model describing the relationship between sample size and processing parameters. The model was validated on four Alzheimer's Disease Neuroimaging Initiative control datasets (mean 126.5 subjects/site, age 76.6 ± 5.0 years). Results : Approximately 50 subjects per group are required to detect a 0.25‐mm thickness difference; less than 10 subjects per group are required for differences of 1 mm (two‐sided test, 10 mm smoothing, α = 0.05). Sample size estimates were heterogeneous over the cortical surface. The model yielded sample size predictions within 2–6% of that determined experimentally using independent data from four other datasets. Fitting parameters of the model to data from each site reduced the estimation error to less than 2%. Conclusions : The derived model provides a simple tool for researchers to calculate how many subjects should be included in a well‐powered cortical thickness analysis. Hum Brain Mapp 34:3000–3009, 2013. © 2012 Wiley Periodicals, Inc.     

Automated segmentation of the human hippocampus along its longitudinal axis

The human hippocampal formation is a crucial brain structure for memory and cognitive function that is closely related to other subcortical and cortical brain regions. Recent neuroimaging studies have revealed differences along the hippocampal longitudinal axis in terms of structure, connectivity, and function, stressing the importance of improving the reliability of the available segmentation methods that are typically used to divide the hippocampus into its anterior and posterior parts. However, current segmentation conventions present two main sources of variability related to manual operations intended to correct in‐scanner head position across subjects and the selection of dividing planes along the longitudinal axis. Here, our aim was twofold: (1) to characterize inter‐ and intra‐rater variability associated with these manual operations and compare manual (landmark based) and automatic (percentage based) hippocampal anterior–posterior segmentation procedures; and (2) to propose and test automated rotation methods based on approximating the hippocampal longitudinal axis to a straight line (estimated with principal component analysis, PCA) or a quadratic Bézier curve (fitted with numerical methods); as well as an automated anterior–posterior hippocampal segmentation procedure based on the percentage‐based method. Our results reveal that automated rotation and segmentation procedures, used in combination or independently, minimize inconsistencies generated by the accumulation of manual operations while providing higher statistical power to detect well‐known effects. A Matlab‐based implementation of these procedures is made publicly available to the research community. Hum Brain Mapp 37:3353–3367, 2016. © 2016 Wiley Periodicals, Inc.     

Cannabinoid modulation of alpha2 adrenergic receptor function in rodent medial prefrontal cortex

Endocannabinoids acting at the cannabinoid type 1 receptor (CB1R) are known to regulate attention, cognition and mood. Previous studies have shown that, in the rat medial prefrontal cortex (mPFC), CB1R agonists increase norepinephrine release, an effect that may be attributed, in part, to CB1Rs localised to noradrenergic axon terminals. The present study was aimed at further characterising functional interactions between CB1R and adrenergic receptor (AR) systems in the mPFC using in vitro intracellular electrophysiology and high‐resolution neuroanatomical techniques. Whole‐cell patch‐clamp recordings of layer V/VI cortical pyramidal neurons in rats revealed that both acute and chronic treatment with the synthetic CB1R agonist WIN 55,212‐2 blocked elevations in cortical pyramidal cell excitability and increases in input resistance evoked by the α2‐adrenergic receptor (α2‐AR) agonist clonidine, suggesting a desensitisation of α2‐ARs. These CB1R–α2‐AR interactions were further shown to be both action potential‐ and gamma‐aminobutyric acid‐independent. To better define sites of cannabinoid–AR interactions, we localised α2A‐adrenergic receptors (α2A‐ARs) in a genetically modified mouse that expressed a hemoagglutinin (HA) tag downstream of the α2A‐AR promoter. Light and electron microscopy indicated that HA‐α2A‐AR was distributed in axon terminals and somatodendritic processes especially in layer V of the mPFC. Triple‐labeling immunocytochemistry revealed that α2A‐AR and CB1R were localised to processes that contained dopamine‐β‐hydroxylase, a marker of norepinephrine. Furthermore, HA‐α2A‐AR was localised to processes that were directly apposed to CB1R. These findings suggest multiple sites of interaction between cortical cannabinoid–adrenergic systems that may contribute to understanding the effect of cannabinoids on executive functions and mood.     

Asymmetries in long‐term and short‐term plasticity at thalamic and cortical inputs to the amygdala in vivo

Converging lines of evidence suggest that synaptic plasticity at auditory inputs to the lateral amygdala (LA) is critical for the formation and storage of auditory fear memories. Auditory information reaches the LA from both thalamic and cortical areas, raising the question of whether they make distinct contributions to fear memory storage. Here we address this by comparing the induction of long‐term potentation (LTP) at the two inputs in vivo in anesthetized rats. We first show, using field potential measurements, that different patterns and frequencies of high‐frequency stimulation (HFS) consistently elicit stronger LTP at cortical inputs than at thalamic inputs. Field potential responses elicited during HFS of thalamic inputs were also smaller than responses during HFS of cortical inputs, suggesting less effective postsynaptic depolarization. Pronounced differences in the short‐term plasticity profiles of the two inputs were also observed: whereas cortical inputs displayed paired‐pulse facilitation, thalamic inputs displayed paired‐pulse depression. These differences in short‐ and long‐term plasticity were not due to stronger inhibition at thalamic inputs: although removal of inhibition enhanced responses to HFS, it did not enhance thalamic LTP and left paired‐pulse depression unaffected. These results highlight the divergent nature of short‐ and long‐term plasticity at thalamic and cortical sensory inputs to the LA, pointing to their different roles in the fear learning system.