Connectome alterations following perinatal deafness in the cat

Following sensory deprivation, areas and networks in the brain may adapt and reorganize to compensate for the loss of input. These adaptations are manifestations of compensatory crossmodal plasticity, which has been documented in both human and animal models of deafness-including the domestic cat. Although there are abundant examples of structural plasticity in deaf felines from retrograde tracer-based studies, there is a lack of diffusion-based knowledge involving this model compared to the current breadth of human research. The purpose of this study was to explore white matter structural adaptations in the perinatally-deafened cat via tractography, increasing the methodological overlap between species. Plasticity was examined by identifying unique group connections and assessing altered connectional strength throughout the entirety of the brain. Results revealed a largely preserved connectome containing a limited number of group-specific or altered connections focused within and between sensory networks, which is generally corroborated by deaf feline anatomical tracer literature. Furthermore, five hubs of cortical plasticity and altered communication following perinatal deafness were observed. The limited differences found in the present study suggest that deafness-induced crossmodal plasticity is largely built upon intrinsic structural connections, with limited remodeling of underlying white matter.

Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy

INTRODUCTION/BACKGROUND

Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method.

METHODS

This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas.

RESULTS

In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS.

CONCLUSIONS

Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.

Optimizing automated white matter hyperintensity segmentation in individuals with stroke

White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.

Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions

The "replication crisis" in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.

Isolating the sources of pipeline-variability in group-level task-fMRI results

Task-fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first-level signal model had the most impact for the balloon analog risk task dataset, while first-level noise model and group-level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group-level results were largely unaffected by which software package was used to model the low-frequency fMRI drifts.

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Predictors of diabetes-related distress before and after FreeStyle Libre-1 use: Lessons from the Association of British Clinical Diabetologists nationwide study

AIM

To identify the baseline demographic and clinical characteristics associated with diabetes-related distress (DRD) and factors associated with improvement in DRD after initiating use of the FreeStyle Libre (FSL) in people living with type 1 diabetes (T1D).

METHODS

The study was performed using baseline and follow-up data from the Association of British Clinical Diabetologists nationwide audit of people with diabetes who initiated use of the FSL in the United Kingdom. DRD was assessed using the two-item diabetes-related distress scale (DDS; defined as the average of the two-item score ≥3). People living with T1D were categorized into two groups: those with high DRD, defined as an average DDS score ≥3 and those with lower DRD, defined as a DDS score <3. We used a gradient-boosting machine-learning (GBM) model to identify the relative influence (RI) of baseline variables on average DDS score.

RESULTS

The study population consisted of 9159 patients, 96.6% of whom had T1D. The median (interquartile range [IQR]) age was 45.1 (32-56) years, 50.1% were women, the median (IQR) baseline body mass index was 26.1 (23.2-29.6) kg/m² and the median (IQR) duration of diabetes was 20 (11-32) years. The two components of the DDS were significantly correlated (r²  = 0.73; P < 0.0001). Higher DRD was prevalent in 53% (4879/9159) of people living with T1D at baseline. In the GBM model, the top baseline variables associated with average DDS score were baseline glycated haemoglobin (HbA1c; RI = 51.1), baseline Gold score (RI = 23.3), gender (RI = 7.05) and fear of hypoglycaemia (RI = 4.96). Follow-up data were available for 3312 participants. The top factors associated with improvement in DDS score following use of the FSL were change in Gold score (RI = 28.2) and change in baseline HbA1c (RI = 19.3).

CONCLUSIONS

In this large UK cohort of people living with T1D, diabetes distress was prevalent and associated with higher HbA1c, impaired awareness of hypoglycaemia and female gender. Improvement in glycaemic control and hypoglycaemia unawareness with the use of the FSL was associated with improvement in DRD in people living with T1D.

A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison

BACKGROUND

The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.

PURPOSE

The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.

METHODS

The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.

CONCLUSION

Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Estimation of intracranial volume: A comparative study between synthetic MRI and FSL-brain extraction tool (BET)2

Brain extraction represents an important step in numerous neuroimaging analyses. The brain extraction tool (BET)2 is a widely used deformable model-based approach for extraction of intracranial volume (ICV). The aim of this study is to estimate the ICV extraction accuracy using synthetic MR(SyMRI) method and BET2 in healthy adult participants and patients with Sturge-Weber Syndrome (SWS), including infants. 'Quantification of relaxation times and proton density by multi-echo acquisition of saturation recovery with turbo-spin-echo readout' (QRAPMASTER) with a 3.0 T magnetic resonance image (MRI) system was used for data acquisition. Statistical evaluations were performed with linear regression analysis and the Jaccard similarity coefficient (J). ICV extraction accuracy with synthetic MR method is found to be higher than BET2, for both aged healthy participants and SWS.

Blocking Astrocytic GABA Restores Synaptic Plasticity in Prefrontal Cortex of Rat Model of Depression

A decrease in synaptic plasticity and/or a change in excitation/inhibition balance have been suggested as mechanisms underlying major depression disorder. However, given the crucial role of astrocytes in balancing synaptic function, particular attention should be given to the contribution of astrocytes in these mechanisms, especially since previous findings show that astrocytes are affected and exhibit reactive-like features in depression. Moreover, it has been shown that reactive astrocytes increase the synthesis and release of GABA, contributing significantly to tonic GABA inhibition. In this study we found decreased plasticity and increased tonic GABA inhibition in the prelimbic area in acute slices from the medial prefrontal cortex in the Flinders Sensitive Line (FSL) rat model of depression. The tonic inhibition can be reduced by either blocking astrocytic intracellular Ca²⁺ signaling or by reducing astrocytic GABA through inhibition of the synthesizing enzyme MAO-B with Selegiline. Blocking GABA synthesis also restores the impaired synaptic plasticity in the FSL prefrontal cortex, providing a new antidepressant mechanism of Selegiline.

A structural magnetic resonance imaging study in therapy-naïve transsexual individuals

BACKGROUND

Transsexuality is explained and defined as a gender-identity disorder, characterised by very strong conviction of belonging to the opposite sex and has been associated with a distinct neuroanatomical pattern.

MATERIALS AND METHODS

We performed a structural analysis in search of possible differences in grey matter structures based on magnetic resonance imaging scans of the brains of 26 individuals between 19 and 38 years of age. The participants were divided into two groups of 15 controls and 11 transgender individuals. The segmentation of subcortical grey matter was performed using FIRST model a model-based segmentation/registration tool, from FSL software package.

RESULTS

The results showed that the volume of the brain region called nucleus accumbens on the left side was significantly smaller in the group of transgender individuals compared to the control. It was the most important parameter which was shown to make distinction between two examined groups.

CONCLUSIONS

The results also showed decreased volumes of the left thalamus, right hippocampus and right caudate nucleus.

Effects of using different software packages for BOLD analysis in planning a neurosurgical treatment in patients with brain tumours

BACKGROUND

The authors of the present thesis carried out a comparative analysis of three different computer software packages - FSL, SPM and FuncTool - for the processing of fMRI scans.

PURPOSE

The aim of the thesis was the analysis of the volume of regions functionally active during the stimulation of the centres evaluated as well as the location of those regions in relation to the tumour boundaries, and then the comparison of the results.

MATERIAL AND METHODS

Thirty eight patients with a diagnosed tumour of the left hemisphere, qualified for a neurosurgical operation, underwent fMRI. The functions of speech, motion and sensation were evaluated. Imaging data were processed for all the acquired scans with the use of each of the three software packages assessed.

RESULTS

For the FuncTool software package the calculated differences in the distances were several times greater than those calculated using FSL and SPM. The differences in the volume of the functionally active regions derived from the calculations with the use of the FSL and SPM software packages were statistically different for four out of the six functions evaluated.

CONCLUSIONS

The conclusions of the analysis in question showed that the FSL and SPM packages could be used interchangeably in the functional mapping of the brain with the purpose of the planning of neurosurgical operations. The FuncTool software package is less precise than FSL and SPM.

An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies

Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.

Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter

BACKGROUND

Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure.

PURPOSE

To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations.

STUDY TYPE

Prospective longitudinal study.

SUBJECTS

Twenty-one healthy subjects (18-31 years old) underwent nine MRI scanning sessions each.

FIELD STRENGTH/SEQUENCE

3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence.

ASSESSMENT

Diffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA-based vs. T₁ -based templates), and the use of intermediate individual (FA-based or T₁ -based) targets. We compared the across-session test-retest reproducibility error of these normalization approaches for FA and MD in white and gray matter.

STATISTICAL TESTS

Reproducibility errors were compared using a repeated-measures analysis of variance with pipeline as the within-subject factor.

RESULTS

The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (P < 0.0001) with respect to FSL. Moreover, using the MNI152 T₁ template as the target of registration resulted in lower reproducibility errors for MD (P < 0.0001), whereas the FMRIB58 FA template performed better for FA (P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T₁ was carried out within modality (FA-FA) (P < 0.05), but not via a T₁ -based individual template.

DATA CONCLUSION

A normalization approach using ANTs to register FA images to the MNI152 T₁ template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:766-775.

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BACKGROUND

Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI - including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms - to evaluate their performance in the presence of MS-related pathologies.

METHODS

3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.

RESULTS

All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.

CONCLUSIONS

SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Effect of changing the analyzed image contrast on the accuracy of intracranial volume extraction using Brain Extraction Tool 2

The aim of this study was to evaluate the effect of changing the contrast of an analyzed image on the accuracy of intracranial volume (ICV) extraction using the Brain Extraction Tool (BET2) in healthy adults and patients with Sturge-Weber syndrome (SWS), including infants. Twelve SWS patients, including infants, and 12 healthy participants were imaged on a 3.0-T magnetic resonance imaging (MRI) machine. All individuals underwent quantification of relaxation times and proton density using multi-echo acquisition of saturation recovery with turbo-spin-echo readout (QRAPMASTER). Based on the QRAPMASTER data, we created images with seven contrasts (T1-WI, T2-WI, PD-WI, T2 short-tau inversion recovery [STIR], proton density [PD] STIR, T2STIR + PDSTIR, and T1-WI + T2-WI + PD-WI) by post-processing with SyMRI software. ICVs extracted with BET2 from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library with each of the seven image contrasts were compared with manually extracted ICVs, which is the gold standard reviewed by a board-certificated neuroradiologist. Manual extraction was performed on T1-WI and T2STIR. Statistical analyses were performed with Jaccard similarity coefficients (J). The highest J score was found in T1-WI + T2-WI + PD-WI in all participants (0.8451); T1-WI in healthy participants (0.8984); T2STIR in participants with SWS (0.8325). Our findings suggest that T1-WI and T2STIR should be used in ICV extraction performed using BET2 on healthy participants and infants, respectively. Additionally, if the analyzed individuals include both healthy participants and infants, T1-WI + T2-WI + PD-WI should be used.

Exploring the impact of analysis software on task fMRI results

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this "methodological plurality" comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. In this work, our goal is to understand how choice of software package impacts on analysis results. We use publicly shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL, and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyse, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. Qualitatively we find similarities between packages, backed up by Neurosynth association analyses that correlate similar words and phrases to all three software package's unthresholded results for each of the studies we reanalyse. However, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 to 0.684 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our efforts to make this research reproducible.

Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model

Group-level repeated measurements are common in neuroimaging, yet their analysis remains complex. Although a variety of specialized tools now exist, it is surprising that to-date there has been no clear discussion of how repeated-measurements can be analyzed appropriately using the standard general linear model approach, as implemented in software such as SPM and FSL. This is particularly surprising given that these implementations necessitate the use of multiple models, even for seemingly conventional analyses, and that without care it is very easy to specify contrasts that do not correctly test the effects of interest. Despite this, interest in fitting these types of models using conventional tools has been growing in the neuroimaging community. As such it has become even more important to elucidate the correct means of doing so. To begin, this paper will discuss the key concept of the expected mean squares (EMS) for defining suitable F-ratios for testing hypotheses. Once this is understood, the logic of specifying correct repeated measurements models in the GLM should be clear. The ancillary issue of specifying suitable contrast weights in these designs will also be discussed, providing a complimentary perspective on the EMS. A set of steps will then be given alongside an example of specifying a 3-way repeated-measures ANOVA in SPM. Equivalency of the results compared to other statistical software will be demonstrated. Additional issues, such as the inclusion of continuous covariates and the assumption of sphericity, will also be discussed. The hope is that this paper will provide some clarity on this confusing topic, giving researchers the confidence to correctly specify these forms of models within traditional neuroimaging analysis tools.

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

BACKGROUND AND PURPOSE

Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse.

METHODS

We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in-painting algorithm (Lesion Segmentation Tool-LST). Volumes of 14 DGM structures were calculated from non-in-painted and in-painted images and compared via paired t-tests, intraclass correlation coefficient, and Dice similarity coefficient.

RESULTS

There were no significant differences in DGM structural volumes between non-in-painted and in-painted images. Automatic lesion-segmentation/in-painting tool provided similar results to manual segmentation/in-painting.

CONCLUSIONS

Our results suggest that lesion in-painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/in-painting can be replaced by an automatic segmentation/in-painting process.

Volumetric Reductions of Subcortical Structures and Their Localizations in Alcohol-Dependent Patients

Changes in brain morphometry have been extensively reported in various studies examining the effects of chronic alcohol use in alcohol-dependent patients. Such studies were able to confirm the association between chronic alcohol use and volumetric reductions in subcortical structures using FSL (FMRIB software library). However, each study that utilized FSL had different sets of subcortical structures that showed significant volumetric reduction. First, we aimed to investigate the reproducibility of using FSL to assess volumetric differences of subcortical structures between alcohol-dependent patients and control subjects. Second, we aimed to use Vertex analysis, a less utilized program, to visually inspect 3D meshes of subcortical structures and observe significant shape abnormalities that occurred in each subcortical structure. Vertex analysis results from the hippocampus and thalamus were overlaid on top of their respective subregional atlases to further pinpoint the subregional locations where shape abnormalities occurred. We analyzed the volumes of 14 subcortical structures (bilateral thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens) in 21 alcohol-dependent subjects and 21 healthy controls using images acquired with 3T MRI. The images were run through various programs found in FSL, such as SIENAX, FIRST, and Vertex analysis. We found that in alcohol-dependent patients, the bilateral thalamus (left: p < 0.01, right: p = 0.01), bilateral putamen (left: p = 0.02, right: p < 0.01), right globus pallidus (p < 0.01), bilateral hippocampus (left: p = 0.05, right: p = 0.03) and bilateral nucleus accumbens (left: p = 0.05, right: p = 0.03) were significantly reduced compared to the corresponding subcortical structures of healthy controls. With vertex analysis, we observed surface reductions of the following hippocampal subfields: Presubiculum, hippocampal tail, hippocampal molecular layer, hippocampal fissure, fimbria, and CA3. We reproduced the assessment made in previous studies that reductions in subcortical volume were negatively associated with alcohol dependence by using the FMRIB Software Library. In addition, we identified the subfields of the thalamus and hippocampus that showed volumetric reduction.

Amnestic mild cognitive impairment individuals with dissimilar pathologic origins show common regional vulnerability in the default mode network

INTRODUCTION

Alzheimer's and Parkinson's disease (AD and PD) are distinct disorders but share similar biomarker profiles. The regions of the default mode network are implicated in these diseases and are associated with amnestic symptoms. The role of apolipoprotein-ε4 (APOE-ε4), which is associated with cognitive function, is unclear in PD.

METHODS

In this work, we evaluated cortical thickness of default mode network regions that are likely affected in both early AD and PD individuals, that is, with amnestic mild cognitive impairment. We identified the prevalence of APOE-ε4 and evaluated its association with cortical atrophy.

RESULTS

We observed significant parahippocampal atrophy and hippocampal atrophy rates in amnestic mild cognitive impairment subjects, regardless of disease origins (AD or PD). Similarly, mild cognitive impairment ε4 carriers showed significant precuneal atrophy compared with noncarriers.

DISCUSSION

This work supports that converging changes to default mode network regions, especially the temporal lobe and precuneus, are shared in AD and PD.

Optimized partial-coverage functional analysis pipeline (OPFAP): a semi-automated pipeline for skull stripping and co-registration of partial-coverage, ultra-high-field functional images

OBJECTIVE

Ultra-high-field functional MRI (UHF-fMRI) allows for higher spatiotemporal resolution imaging. However, higher-resolution imaging entails coverage limitations. Processing partial-coverage images using standard pipelines leads to sub-optimal results. We aimed to develop a simple, semi-automated pipeline for processing partial-coverage UHF-fMRI data using widely used image processing algorithms.

MATERIALS AND METHODS

We developed automated pipelines for optimized skull stripping and co-registration of partial-coverage UHF functional images, using built-in functions of the Centre for Functional Magnetic Resonance Imaging of the Brain's (FMRIB's) Software library (FSL) and advanced normalization tools. We incorporated the pipelines into the FSL's functional analysis pipeline and provide a semi-automated optimized partial-coverage functional analysis pipeline (OPFAP).

RESULTS

Compared to the standard pipeline, the OPFAP yielded images with 15 and 30% greater volume of non-zero voxels after skull stripping the functional and anatomical images, respectively (all p = 0.0004), which reflected the conservation of cortical voxels lost when the standard pipeline was used. The OPFAP yielded the greatest Dice and Jaccard coefficients (87 and 80%, respectively; all p < 0.0001) between the co-registered participant gyri maps and the template gyri maps, demonstrating the goodness of the co-registration results. Furthermore, the greatest volume of group-level activation in the most number of functionally relevant regions was observed when the OPFAP was used. Importantly, group-level activations were not observed when using the standard pipeline.

CONCLUSION

These results suggest that the OPFAP should be used for processing partial-coverage UHF-fMRI data for detecting high-resolution macroscopic blood oxygenation level-dependent activations.

Emotional intelligence is associated with connectivity within and between resting state networks

Emotional intelligence (EI) is defined as an individual’s capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI.

Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST

BACKGROUND AND PURPOSE

Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST.

METHODS

Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances.

RESULTS

PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease.

CONCLUSIONS

PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.

Short-term mechanisms influencing volumetric brain dynamics

With the use of magnetic resonance imaging (MRI) and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist. When we focus on short-term changes of the brain, changes may be either physiological or pathological. As such determining the cause of volumetric dynamics of the brain is essential. Additionally for an accurate interpretation of longitudinal brain volume measures by means of neurodegeneration, knowledge about the short-term changes is needed. Therefore, in this review, we discuss the possible mechanisms influencing brain volumes on a short-term basis and set-out a framework of MRI techniques to be used for volumetric changes as well as the used analysis tools. 3D T₁-weighted images are the images of choice when it comes to MRI of brain volume. These images are excellent to determine brain volume and can be used together with an analysis tool to determine the degree of volume change. Mechanisms that decrease global brain volume are: fluid restriction, evening MRI measurements, corticosteroids, antipsychotics and short-term effects of pathological processes like Alzheimer's disease, hypertension and Diabetes mellitus type II. Mechanisms increasing the brain volume include fluid intake, morning MRI measurements, surgical revascularization and probably medications like anti-inflammatory drugs and anti-hypertensive medication. Exercise was found to have no effect on brain volume on a short-term basis, which may imply that dehydration caused by exercise differs from dehydration by fluid restriction. In the upcoming years, attention should be directed towards studies investigating physiological short-term changes within the light of long-term pathological changes. Ultimately this may lead to a better understanding of the physiological short-term effects of pathological processes and may aid in early detection of these diseases.

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Fluid-restriction, evening MRI, corticosteroids, & antipsychotics decrease volume

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Fluid-intake, morning MRI, surgical revascularization & medications increase volume

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Short-term changes within the light of long-term pathological changes should be investigated

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Short-term changes may introduce bias in longitudinal data

Automated volumetry of hippocampus is useful to confirm unilateral mesial temporal sclerosis in patients with radiologically positive findings

Background and purpose We evaluated two methods to identify mesial temporal sclerosis (MTS): visual inspection by experienced epilepsy neuroradiologists based on structural magnetic resonance imaging sequences and automated hippocampal volumetry provided by a processing pipeline based on the FMRIB Software Library. Methods This retrospective study included patients from the epilepsy monitoring unit database of our institution. All patients underwent brain magnetic resonance imaging in 1.5T and 3T scanners with protocols that included thin coronal T2, T1 and fluid-attenuated inversion recovery and isometric T1 acquisitions. Two neuroradiologists with experience in epilepsy and blinded to clinical data evaluated magnetic resonance images for the diagnosis of MTS. The diagnosis of MTS based on an automated method included the calculation of a volumetric asymmetry index between the two hippocampi of each patient and a threshold value to define the presence of MTS obtained through statistical tests (receiver operating characteristics curve). Hippocampi were segmented for volumetric quantification using the FIRST tool and fslstats from the FMRIB Software Library. Results The final cohort included 19 patients with unilateral MTS (14 left side): 14 women and a mean age of 43.4 ± 10.4 years. Neuroradiologists had a sensitivity of 100% and specificity of 73.3% to detect MTS (gold standard, k = 0.755). Automated hippocampal volumetry had a sensitivity of 84.2% and specificity of 86.7% (k = 0.704). Combined, these methods had a sensitivity of 84.2% and a specificity of 100% (k = 0.825). Conclusions Automated volumetry of the hippocampus could play an important role in temporal lobe epilepsy evaluation, namely on confirmation of unilateral MTS diagnosis in patients with radiological suggestive findings.

The rabbit as a behavioral model system for magnetic resonance imaging

BACKGROUND

fMRI requires that subjects not move during image acquisition. This has been achieved by instructing people not to move, or by anesthetizing experimental animal subjects to induce immobility. We have demonstrated that a surgically implanted headbolt onto the skull of a rabbit allows their brain to be imaged comfortably while the animal is awake. This article provides a detailed method for the preparation.

NEW METHOD

We took advantage of the rabbit's tolerance for restraint to image the brain while holding the head at the standard stereotaxic angle. Visual stimulation was produced by flashing green LEDs and whisker stimulation was done by powering a small coil of wire attached to a fiber band. Blinking was recorded with an infrared emitter/detector directed at the eye with fiber-optic cabling.

RESULTS

Results indicate that a single daily session of habituation is sufficient to produce adequate immobility on subsequent days to avoid movement artifacts. Results include high resolution images in the stereotaxic plane of the rabbit.

COMPARISON WITH EXISTING METHOD(S)

We see no degradation or distortion of MR signal, and the headbolt provides a means for rapid realignment of the head in the magnet from day to day, and across subjects. The use of rabbits instead of rodents allows much shorter periods of habituation, and the rabbit allows behavior to be observed during the day while the animal is in its normal wake cycle.

CONCLUSIONS

The natural tolerance of the rabbit for restraint makes it a valuable subject for MRI studies of the brain.

Gene-environment interactions in a rat model of depression. Maternal separation affects neurotensin in selected brain regions

Although the etiology of major psychiatric disorders has not been elucidated, accumulating evidence indicates that both genetic and early environmental factors play a role. We have previously demonstrated behavioral and neurochemical changes both in non-manipulated genetic rat models of depression, such as Flinders Sensitive Line (FSL) and Fawn Hooded (FH), and in normal rats following maternal separation (MS). The aim of the present study was to extend this work by exploring whether neurotensin (NT), a peptide implicated in several psychiatric disorders, is altered in a new animal model based on gene - environment interactions. More specifically, we used the FSL rats as a genetic model of depression and the Flinders Resistant Line (FRL) as controls and subjected them to MS. Pups randomly assigned to the MS procedure were separated from the dam as a litter for 180min daily between postnatal day 2 to 14. On postnatal day 90, rats were weighed and sacrificed by a two second high energy focused microwave irradiation and several brain regions were obtained by micropuncture. Neurotensin-like immunoreactivity (NT-LI) was measured by radioimmunoassay (RIA). The results showed that the FSL rats compared to the FRL rats have higher baseline NT-LI concentrations in the temporal cortex and periaqueductal gray and a markedly different response to maternal separation. The only observed change following maternal separation in the FRL rats was an NT-LI increase in the periaqueductal gray. In contrast, in the FSL significant increases were found in the nucleus accumbens, hippocampus, and entorhinal cortex and a decrease was seen in the temporal cortex after MS. The present study revealed baseline regional differences in NT-LI concentrations between the FSL and FRL strains and demonstrated that early MD differentially affects the two strains. The relevance of these alterations for depression as well as possible mechanisms underlying this gene-environment interaction are discussed.

Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism

Previous studies applying automatic preprocessing methods on Structural Magnetic Resonance Imaging (sMRI) report inconsistent neuroanatomical abnormalities in Autism Spectrum Disorder (ASD). In this study we investigate inter-method differences as a possible cause behind these inconsistent findings. In particular, we focus on the estimation of the following brain volumes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intra cranial volume (TIV). T1-weighted sMRIs of 417 ASD subjects and 459 typically developing controls (TDC) from the ABIDE dataset were estimated using three popular preprocessing methods: SPM, FSL, and FreeSurfer (FS). Brain volumes estimated by the three methods were correlated but had significant inter-method differences; except TIVSPM vs. TIVFS, all inter-method differences were significant. ASD vs. TDC group differences in all brain volume estimates were dependent on the method used. SPM showed that TIV, GM, and CSF volumes of ASD were larger than TDC with statistical significance, whereas FS and FSL did not show significant differences in any of the volumes; in some cases, the direction of the differences were opposite to SPM. When methods were compared with each other, they showed differential biases for autism, and several biases were larger than ASD vs. TDC differences of the respective methods. After manual inspection, we found inter-method segmentation mismatches in the cerebellum, sub-cortical structures, and inter-sulcal CSF. In addition, to validate automated TIV estimates we performed manual segmentation on a subset of subjects. Results indicate that SPM estimates are closest to manual segmentation, followed by FS while FSL estimates were significantly lower. In summary, we show that ASD vs. TDC brain volume differences are method dependent and that these inter-method discrepancies can contribute to inconsistent neuroimaging findings in general. We suggest cross-validation across methods and emphasize the need to develop better methods to increase the robustness of neuroimaging findings.

Testable Hypotheses for Unbalanced Neuroimaging Data

Unbalanced group-level models are common in neuroimaging. Typically, data for these models come from factorial experiments. As such, analyses typically take the form of an analysis of variance (ANOVA) within the framework of the general linear model (GLM). Although ANOVA theory is well established for the balanced case, in unbalanced designs there are multiple ways of decomposing the sums-of-squares of the data. This leads to several methods of forming test statistics when the model contains multiple factors and interactions. Although the Type I-III sums of squares have a long history of debate in the statistical literature, there has seemingly been no consideration of this aspect of the GLM in neuroimaging. In this paper we present an exposition of these different forms of hypotheses for the neuroimaging researcher, discussing their derivation as estimable functions of ANOVA models, and discussing the relative merits of each. Finally, we demonstrate how the different hypothesis tests can be implemented using contrasts in analysis software, presenting examples in SPM and FSL.

White matter and task-switching in young adults: A Diffusion Tensor Imaging study

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DTI and performance data on three task-switching paradigms were collected on young adults.

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Frontal inter-hemispheric white matter integrity favors sustained task-switching.

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This result was observed when switching between spatial rules or color-shape ones.

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No relation between behavior and white matter was observed for verbal rule switching.

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Task-specific features determine whether white matter mediates task-switching performance.

The capacity to flexibly switch between different task rules has been previously associated with distributed fronto-parietal networks, predominantly in the left hemisphere for phasic switching sub-processes, and in the right hemisphere for more tonic aspects of task-switching, such as rule maintenance and management. It is thus likely that the white matter (WM) connectivity between these regions is critical in sustaining the flexibility required by task-switching. This study examined the relationship between WM microstructure in young adults and task-switching performance in different paradigms: classical shape-color, spatial and grammatical tasks. The main results showed an association between WM integrity in anterior portions of the corpus callosum (genu and body) and a sustained measure of task-switching performance. In particular, a higher fractional anisotropy and a lower radial diffusivity in these WM regions were associated with smaller mixing costs both in the spatial task-switching paradigm and in the shape-color one, as confirmed by a conjunction analysis. No association was found with behavioral measures obtained in the grammatical task-switching paradigm. The switch costs, a measure of phasic switching processes, were not correlated with WM microstructure in any task. This study shows that a more efficient inter-hemispheric connectivity within the frontal lobes favors sustained task-switching processes, especially with task contexts embedding non-verbal components.

Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer's dementia or mild cognitive impairment

Magnetic resonance imaging (MRI) and brain volumetry allow for the quantification of changes in brain volume using automatic algorithms which are widely used in both, clinical and scientific studies. However, studies comparing the reliability of these programmes are scarce and mainly involved MRI derived from younger healthy controls. This study evaluates the reliability of frequently used segmentation programmes (SPM, FreeSurfer, FSL) using a realistic digital brain phantom and MRI brain acquisitions from patients with manifest Alzheimer's disease (AD, n=34), mild cognitive impairment (MCI, n=60), and healthy subjects (n=32) matched for age and sex. Analysis of the brain phantom dataset demonstrated that SPM, FSL and FreeSurfer underestimate grey matter and overestimate white matter volumes with increasing noise. FreeSurfer calculated overall smaller brain volumes with increasing noise. Image inhomogeneity had only minor, non- significant effects on the results obtained with SPM and FreeSurfer 5.1, but had effects on the FSL results (increased white matter volumes with decreased grey matter volumes). The analysis of the patient data yielded decreasing volumes of grey and white matter with progression of brain atrophy independent of the method used. FreeSurfer calculated the largest grey matter and the smallest white matter volumes. FSL calculated the smallest grey matter volumes; SPM the largest white matter volumes. Best results are obtained with good image quality. With poor image quality, especially noise, SPM provides the best segmentation results. An optimised template for segmentation had no significant effect on segmentation results. While our findings underline the applicability of the programmes investigated, SPM may be the programme of choice when MRIs with limited image quality or brain images of elderly should be analysed.

Hippocampal-Dependent Antidepressant Action of the H3 Receptor Antagonist Clobenpropit in a Rat Model of Depression

BACKGROUND

Histamine is a modulatory neurotransmitter regulating neuronal activity. Antidepressant drugs target modulatory neurotransmitters, thus ultimately regulating glutamatergic transmission and plasticity. Histamine H3 receptor (H3R) antagonists have both pro-cognitive and antidepressant effects; however, the mechanism by which they modulate glutamate transmission is not clear. We measured the effects of the H3R antagonist clobenpropit in the Flinders Sensitive Line (FSL), a rat model of depression with impaired memory and altered glutamatergic transmission.

METHODS

Behavioral tests included the forced swim test, memory tasks (passive avoidance, novel object recognition tests), and anxiety-related paradigms (novelty suppressed feeding, social interaction, light/dark box tests). Hippocampal protein levels were detected by Western blot. Hippocampal plasticity was studied by in slice field recording of CA3-CA1 long-term synaptic potentiation (LTP), and glutamatergic transmission by whole-cell patch clamp recording of excitatory postsynaptic currents (EPSCs) in CA1 pyramidal neurons.

RESULTS

Clobenpropit, administered systemically or directly into the hippocampus, decreased immobility during the forced swim test; systemic injections reversed memory deficits and increased hippocampal GluN2A protein levels. FSL rats displayed anxiety-related behaviors not affected by clobenpropit treatment. Clobenpropit enhanced hippocampal plasticity, but did not affect EPSCs. H1R and H2R antagonists prevented the clobenpropit-induced increase in LTP and, injected locally into the hippocampus, blocked clobenpropit's effect in the forced swim test.

CONCLUSIONS

Clobenpropit's antidepressant effects and the enhanced synaptic plasticity require hippocampal H1R and H2R activation, suggesting that clobenpropit acts through disinhibition of histamine release. Clobenpropit reverses memory deficits and increases hippocampal GluN2A expression without modifying anxiety-related phenotypes or EPSCs in CA1 pyramidal neurons.

Reproducibility of neuroimaging analyses across operating systems

Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed.

FRIEND Engine Framework: a real time neurofeedback client-server system for neuroimaging studies

In this methods article, we present a new implementation of a recently reported FSL-integrated neurofeedback tool, the standalone version of “Functional Real-time Interactive Endogenous Neuromodulation and Decoding” (FRIEND). We will refer to this new implementation as the FRIEND Engine Framework. The framework comprises a client-server cross-platform solution for real time fMRI and fMRI/EEG neurofeedback studies, enabling flexible customization or integration of graphical interfaces, devices, and data processing. This implementation allows a fast setup of novel plug-ins and frontends, which can be shared with the user community at large. The FRIEND Engine Framework is freely distributed for non-commercial, research purposes.

The SIENA/FSL whole brain atrophy algorithm is no more reproducible at 3T than 1.5 T for Alzheimer's disease

The back-to-back (BTB) acquisition of MP-RAGE MRI scans of the Alzheimer׳s Disease Neuroimaging Initiative (ADNI1) provides an excellent data set with which to check the reproducibility of brain atrophy measures. As part of ADNI1, 131 subjects received BTB MP-RAGEs at multiple time points and two field strengths of 3T and 1.5 T. As a result, high quality data from 200 subject-visit-pairs was available to compare the reproducibility of brain atrophies measured with FSL/SIENA over 12 to 18 month intervals at both 3T and 1.5 T. Although several publications have reported on the differing performance of brain atrophy measures at 3T and 1.5 T, no formal comparison of reproducibility has been published to date. Another goal was to check whether tuning SIENA options, including -B, -S, -R and the fractional intensity threshold (f) had a significant impact on the reproducibility. The BTB reproducibility for SIENA was quantified by the 50th percentile of the absolute value of the difference in the percentage brain volume change (PBVC) for the BTB MP-RAGES. At both 3T and 1.5 T the SIENA option combination of "-B f=0.2", which is different from the default values of f=0.5, yielded the best reproducibility as measured by the 50th percentile yielding 0.28 (0.23-0.39)% and 0.26 (0.20-0.32)%. These results demonstrated that in general 3T had no advantage over 1.5 T for the whole brain atrophy measure - at least for SIENA. While 3T MRI is superior to 1.5 T for many types of measurements, and thus worth the additional cost, brain atrophy measurement does not seem to be one of them.

An automatic tool to facilitate the statistical group analysis of DTI

BACKGROUND

Users may have difficulty calculating DTI group statistics since they need to master several complex tools that require high user intervention. A tool called DTIStatistics for the automatic and easy calculation of DTI group statistics was developed to reduce analysis times and possible errors.

METHODS

The proposed software was designed by using a user-centred methodology in which we performed an iterative usability evaluation with an expert committee. Once the experts׳ requirements were fulfilled, we performed a validation of the final version of DTIStatistics with target users, comparing the execution time of this tool and the standard pipeline normally used.

RESULTS

Target users needed significantly less time to complete the tasks with DTIStatistics, reducing the analysis time from 1383.78 to 57.2s. They were able to complete all the tasks and barely made errors. Moreover, target users were not able to display the analysis results with the standard pipeline, but when using our tool they only needed 34s. Target users found DTIStatistics easy to learn, use and interact with, and they concluded that they could effectively complete the tasks with it. Additionally, we present example results in the study of depression to demonstrate the validity of DTIStatistics for clinical research.

CONCLUSIONS

DTIStatistics facilitates and significantly automates the calculation of DTI group statistics by reducing the analysis times, which implies lower costs. DTIStatistics is highly applicable in clinical research, as demonstrated by the fact that it is currently being used at the University Hospital, University of Navarra (Spain).

Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

BACKGROUND

Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is needed for the neuroimaging applications.

METHODS

In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i) default segmentation, ii) SPM8 New-segmentation and iii) modified version using hidden Markov random field as implemented in SPM8-VBM toolbox.

RESULTS

The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results.

CONCLUSION

A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

Distinct patterns of cortical atrophy in ALS patients with or without dementia: an MRI VBM study

Voxel based morphometry (VBM) allows objective and automated detection of structural changes in brains of patients with amyotrophic lateral sclerosis (ALS). We investigated whether VBM could identify cortical atrophy from T1-weighted images obtained during routine 1.5T studies of ALS patients with various clinically defined phenotypes. For this purpose T1-weighted brain MRI was obtained at 1.5T during routine clinical study in neurologic disease controls (n = 15) and ALS patients (n = 88) categorized into four subgroups based on their clinical phenotypes: predominant upper motor neuron (UMN) dysfunction with or without corticospinal tract (CST) hyperintensity (ALS-CST+/-), combined UMN and prominent lower motor neuron (LMN) dysfunction (classic ALS), and frontotemporal dementia (ALS-FTD). VBM analysis of gray matter (GM) was carried out using FSL. Results demonstrated that clinically obtained brain MRI at 1.5T revealed significantly reduced GM volume in brains of only ALS-FTD patients and not of those with predominant UMN dysfunction or classic ALS, compared to neurologic disease controls. In conclusion, GM volume loss in motor and extramotor regions of only ALS patients with FTD and not of ALS patients without FTD suggests distinct sites of predominant pathology and possibly of disease onset. Brain volumetric measures supplemented by histopathological correlations and other neuroimaging techniques, such as diffusion tensor imaging, may provide insight into ALS pathophysiology.

Accurate GM atrophy quantification in MS using lesion-filling with co-registered 2D lesion masks

Background

In multiple sclerosis (MS), brain atrophy quantification is affected by white matter lesions. LEAP and FSL-lesion_filling, replace lesion voxels with white matter intensities; however, they require precise lesion identification on 3DT1-images.

Aim

To determine whether 2DT2 lesion masks co-registered to 3DT1 images, yield grey and white matter volumes comparable to precise lesion masks.

Methods

2DT2 lesion masks were linearly co-registered to 20 3DT1-images of MS patients, with nearest-neighbor (NNI), and tri-linear interpolation. As gold-standard, lesion masks were manually outlined on 3DT1-images. LEAP and FSL-lesion_filling were applied with each lesion mask. Grey (GM) and white matter (WM) volumes were quantified with FSL-FAST, and deep gray matter (DGM) volumes using FSL-FIRST. Volumes were compared between lesion mask types using paired Wilcoxon tests.

Results

Lesion-filling with gold-standard lesion masks compared to native images reduced GM overestimation by 1.93 mL (p < .001) for LEAP, and 1.21 mL (p = .002) for FSL-lesion_filling. Similar effects were achieved with NNI lesion masks from 2DT2. Global WM underestimation was not significantly influenced. GM and WM volumes from NNI, did not differ significantly from gold-standard. GM segmentation differed between lesion masks in the lesion area, and also elsewhere. Using the gold-standard, FSL-FAST quantified as GM on average 0.4% of the lesion area with LEAP and 24.5% with FSL-lesion_filling. Lesion-filling did not influence DGM volumes from FSL-FIRST.

Discussion

These results demonstrate that for global GM volumetry, precise lesion masks on 3DT1 images can be replaced by co-registered 2DT2 lesion masks. This makes lesion-filling a feasible method for GM atrophy measurements in MS.

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In multiple sclerosis brain atrophy measurement is affected by white matter lesions.

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LEAP and FSL-lesion_filling replace lesion voxels with white matter intensities.

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The two lesion-filling methods show differences.

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2D lesion masks can be registered and used for lesion-filling on 3DT1 images.

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This makes lesion-filling a feasible method for atrophy measurements in MS.

Interdependent effects of sound duration and amplitude on neuronal onset response in mice inferior colliculus

In this study, we adopted iso-frequency pure tone bursts to investigate the interdependent effects of sound amplitude/intensity and duration on mice inferior colliculus (IC) neuronal onset responses. On the majority of the sampled neurons (n=57, 89.1%), sound amplitude and duration had effects on the neuronal response to each other by showing complex changes of the rat-intensity function/duration selectivity types and/or best amplitudes (BAs)/durations (BDs), evaluated by spike counts. These results suggested that the balance between the excitatory and inhibitory inputs set by one acoustic parameter, amplitude or duration, affected the neuronal spike counts responses to the other. Neuronal duration selectivity types were altered easily by the low-amplitude sounds while the changes of rate-intensity function types had no obvious preferred stimulus durations. However, the first spike latencies (FSLs) of the onset response neurons were relative stable to iso-amplitude sound durations and changing systematically along with the sound levels. The superimposition of FSL and duration threshold (DT) as a function of stimulus amplitude after normalization indicated that the effects of the sound levels on FSLs are considered on DT actually.

Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations

Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.

Impaired empathic abilities and reduced white matter integrity in schizophrenia

Empathic abilities are impaired in schizophrenia. Although the pathology of schizophrenia is thought to involve disrupted white matter integrity, the relationship between empathic disabilities and altered white matter in the disorder remains unclear. The present study tested associations between empathic disabilities and white matter integrity in order to investigate the neural basis of impaired empathy in schizophrenia. Sixty-nine patients with schizophrenia and 69 age-, gender-, handedness-, education- and IQ level-matched healthy controls underwent diffusion-weighted imaging. Empathic abilities were assessed using the Interpersonal Reactivity Index (IRI). Using tract-based spatial statistics (TBSS), the associations between empathic abilities and white matter fractional anisotropy (FA), a measure of white matter integrity, were examined in the patient group within brain areas that showed a significant FA reduction compared with the controls. The patients with schizophrenia reported lower perspective taking and higher personal distress according to the IRI. The patients showed a significant FA reduction in bilateral deep white matter in the frontal, temporal, parietal and occipital lobes, a large portion of the corpus callosum, and the corona radiata. In schizophrenia patients, fantasy subscales positively correlated with FA in the left inferior fronto-occipital fasciculi and anterior thalamic radiation, and personal distress subscales negatively correlated with FA in the splenium of the corpus callosum. These results suggest that disrupted white matter integrity in these regions constitutes a pathology underpinning specific components of empathic disabilities in schizophrenia, highlighting that different aspects of empathic impairments in the disorder would have, at least partially, distinct neuropathological bases.

White matter connectivity and aerobic fitness in male adolescents

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DTI was collected for 34 male adolescents, ages 15–17.

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Aerobic fitness related to white matter connectivity in frontal and motor tracts.

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HF had higher tractography streamline counts in CST and Fminor compared to LF.

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A negative relationship was seen between VO₂ peak and FA in the L CST.

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Exercise is an important environmental factor to consider during neurodevelopment.

Exercise has been shown to have positive effects on the brain and behavior throughout various stages of the lifespan. However, little is known about the impact of exercise on neurodevelopment during the adolescent years, particularly with regard to white matter microstructure, as assessed by diffusion tensor imaging (DTI). Both tract-based spatial statistics (TBSS) and tractography-based along-tract statistics were utilized to examine the relationship between white matter microstructure and aerobic exercise in adolescent males, ages 15–18. Furthermore, we examined the data by both (1) grouping individuals based on aerobic fitness self-reports (high fit (HF) vs. low fit (LF)), and (2) using VO₂ peak as a continuous variable across the entire sample. Results showed that HF youth had an overall higher number of streamline counts compared to LF peers, which was driven by group differences in corticospinal tract (CST) and anterior corpus callosum (Fminor). In addition, VO₂ peak was negatively related to FA in the left CST. Together, these results suggest that aerobic fitness relates to white matter connectivity and microstructure in tracts carrying frontal and motor fibers during adolescence. Furthermore, the current study highlights the importance of considering the environmental factor of aerobic exercise when examining adolescent brain development.

An eight month randomized controlled exercise intervention alters resting state synchrony in overweight children

Children with low aerobic fitness have altered brain function compared to higher-fit children. This study examined the effect of an 8-month exercise intervention on resting state synchrony. Twenty-two sedentary, overweight (body mass index ≥85th percentile) children 8-11 years old were randomly assigned to one of two after-school programs: aerobic exercise (n=13) or sedentary attention control (n=9). Before and after the 8-month programs, all subjects participated in resting state functional magnetic resonance imaging scans. Independent components analysis identified several networks, with four chosen for between-group analysis: salience, default mode, cognitive control, and motor networks. The default mode, cognitive control, and motor networks showed more spatial refinement over time in the exercise group compared to controls. The motor network showed increased synchrony in the exercise group with the right medial frontal gyrus compared to controls. Exercise behavior may enhance brain development in children.

Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: a BOLD and perfusion MRI study

In young individuals, caffeine-mediated blockade of adenosine receptors and vasoconstriction has direct repercussions on task-related activations, changes in functional connectivity, as well as global vascular effects. To date, no study has explored the effect of caffeine on brain activation patterns during highly demanding cognitive tasks in the elderly. This prospective, placebo-controlled crossover design comprises 24 healthy elderly individuals (mean age 68.8 ± 4.0 years, 17 females) performing a 2-back working memory (WM) task in functional magnetic resonance imaging (fMRI). Analyses include complimentary assessment of task-related activations (general linear model, GLM), functional connectivity (tensorial independent component analysis, TICA), and baseline perfusion (arterial spin labeling). Despite a reduction in whole-brain global perfusion (-22.7%), caffeine-enhanced task-related GLM activation in a local and distributed network is most pronounced in the bilateral striatum and to a lesser degree in the right middle and inferior frontal gyrus, bilateral insula, left superior and inferior parietal lobule as well as in the cerebellum bilaterally. TICA was significantly enhanced (+8.2%) in caffeine versus placebo in a distributed and task-relevant network including the pre-frontal cortex, the supplementary motor area, the ventral premotor cortex and the parietal cortex as well as the occipital cortex (visual stimuli) and basal ganglia. The inverse comparison of placebo versus caffeine had no significant difference. Activation strength of the task-relevant-network component correlated with response accuracy for caffeine yet not for placebo, indicating a selective cognitive effect of caffeine. The present findings suggest that acute caffeine intake enhances WM-related brain activation as well as functional connectivity of blood oxygen level-dependent fMRI in elderly individuals.

Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson's disease

Although Parkinson's disease is a neurodegenerative disease primarily involving basal ganglia and midbrain, the deficit of white matter is also involved during the disease progression. As the diffusion tensor imaging method is sensitive to the microstructural changes, we investigated the microstructural alterations in white matter and deep gray matter in patients with Parkinson's disease. Brain images of 64 patients and sex- and age-matched 64 healthy controls were obtained from a 3T MRI scanner. Tract-based spatial statistics were used to compare the mean diffusivity of the white matter tract between the groups. Voxel-based analysis was used to compare the mean diffusivity of the subcortical gray matter between the groups. There were white matter deficits in the corticofugal tract, cingulum, uncinate fasciculus, crus of fornix or stria terminalis, corpus callosum, external capsule, superior longitudinal fasciculus, posterior thalamic radiation including optic radiation, and the tracts adjacent to the precuneus and supramarginal gyrus, as indicated by higher mean diffusivity in Parkinson's disease patients than in controls. There were also deficits in the left putamen, pallidum, thalamus, and caudate as indicated by higher mean diffusivity in Parkinson's disease patients than in controls. Using diffusion tensor imaging and multi-methods of image analysis, we successfully characterized and visualized brain white matter and deep gray matter areas with microstructural deficits in Parkinson's disease patients.