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.

Cortical and subcortical morphometric changes in patients with frontal focal cortical dysplasia type II.

PURPOSE: This study investigates the morphometric changes in the brains of patients with frontal focal cortical dysplasia (FCD) Type II, distinguishing between right and left FCD, using voxel-based morphometry (VBM), surface-based morphometry (SBM), and subcortical shape analysis. METHODS: The study included 53 patients with frontal lobe FCD Type II (28 left-sided, 25 right-sided) and 66 age- and gender-matched healthy controls. VBM and SBM analyses were conducted using Computational Anatomy Toolbox 12.8 (CAT12.8) and Statistical Parametric Mapping 12 (SPM12). Subcortical structures were segmented using FSL-FIRST. Statistical analyses were performed using non-parametric tests, with a significance threshold of p < 0.05. RESULTS: VBM revealed increased gray matter volume in the bilateral ventral diencephalon, left putamen, and left thalamus in the left FCD group. SBM indicated reduced sulcal depth in the right precentral, postcentral, and caudal middle frontal gyrus in the right FCD group. Subcortical shape analysis showed internal deformation in the left hippocampus and external deformation in bilateral putamen in the left FCD group, and external deformation in the left caudate nucleus, left putamen, and right amygdala in the right FCD group. CONCLUSION: Morphometric changes in frontal FCD Type II patients vary depending on the hemisphere. Right FCD Type II is associated with sulcal shallowing and external deformation in contralateral subcortical structures, while left FCD Type II shows internal and external deformations in the hippocampus and putamen, respectively, along with increased gray matter volume in the basal ganglia. These findings highlight the need for hemisphere-specific analyses in epilepsy research.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

Development of a novel modified vaccine (TheraVacM) for curative treatment of mouse solid tumors.

Monotherapy with immune checkpoint blockade (ICB) antibodies (anti-CTLA4 and anti-PD1/PDL-1) is only effective for 20% to 30% of patients with certain cancers. Patients with cancers harboring few effector T cells (Teffs) are insensitive to ICB therapy. The lack of tumor-specific Teffs is predominantly caused by the paralysis of tumor-infiltrating dendritic cells (TiDCs) resulting from immunosuppression in the tumor microenvironment. We have identified a potent combination of high mobility group nucleosome binding domain 1 (HMGN1, N1) and fibroblast stimulating lipopeptide-1 (FSL-1) that can synergistically trigger maturation of both mouse and human DCs. Accordingly, we designed a combinational anti-cancer immunotherapy with two arms: an immune-activating arm consisting of N1 and FSL-1 to stimulate the generation of Teffs by triggering full maturation of TiDCs, and an ICB arm using anti-PDL-1 or anti-CTLA4 to prevent Teffs from being silenced in the tumor tissue. This combinational immunotherapeutic vaccination regimen dubbed modified TheraVac (TheraVacM) has proved particularly effective as it cured 100% of mice bearing established ectopic CT26 colon and RENCA kidney tumors. The resultant tumor-free mice were resistant to subsequent re-challenge with the same tumors, indicating the generation of long-term tumor specific protective immunity. Since the immune-activating arm also induces full maturation of human DCs, and anti-PDL-1 or anti-CTLA4 have been FDA-approved, this combinational immunotherapy has the potential to be an effective clinical therapy for patients with solid tumors.

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.

Toll-like receptor 2 and 6 agonist fibroblast-stimulating lipopeptide increases expression and secretion of CXCL1 and CXCL2 by uveal melanocytes.

Fibroblast-stimulating lipopeptide (FSL-1) can activate Toll-like receptor 2 and 6 (TLR2/6), which recognize relevant molecules from gram-positive pathogens, fungus, and mycoplasma, and elevates the expression of CXCL1 and CXCL2, neutrophil chemoattractants, in certain types of cells. This effect has not previously been reported in the uveal melanocytes (UM). This study was designed to test the hypothesis that FSL-1 can induce the expression and secretion of CXCL1 and CXCL2 via activation of TLR2/6 in cultured human UM and producing an acute non-infectious uveitis reaction in the mouse. Flow cytometry and fluorescent immunostaining were used to measure the effect of FSL-1 on the expression of TLR2/6 in UM. Real time PCR and ELISA analysis were used to assess the ability of FSL-1 to elevate CXCL1/CXCL2 levels in cell lysates and conditioned media of UM, respectively. Flow cytometry measured phosphorylated MAPK and activated NF-κB signals in UM, with and without FSL-1 treatment. ELISA analysis tested the impact of various signal inhibitors (NF-κB, p38 MAPK, JNK1/2 and ERK1/2) and TLR2/6 antagonists on FSL-1-induced CXCL1/CXCL2 levels in cultured UM. The effects of neutralizing antibodies to TLR2 on FSL-1-induced mouse uveitis were tested in an experimental animal model. FSL-1 induced the expression of TLR2/6 proteins in cultured UM. FSL-1 significantly elevated the CXCL1 and CXCL2 proteins and mRNA levels in cultured UM time- and dose-dependently. FSL-1 mainly activated NF-κB, JNK, and expression of TLR2. FSL-1-induced expression of CXCL1 and CXCL2 was blocked by NF-κB, JNK, ERK inhibitors and TLR2 antagonists. Intravitreal injection of FSL-1 induced acute non-infectious mouse uveitis, which was significantly reduced in severity by a TLR2 antagonist. These results suggest that UM may play a role in the immune reaction, which targets invading pathogens, especially gram-positive bacteria. On the other hand, an excessive reaction to molecules from gram-positive bacteria may promote an inflammatory state of non-infectious uveitis.

Few-Shot Fine-Grained Image Classification via GNN.

Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem, few-shot learning (FSL) is used. Currently, typical FSL methods work well on coarse-grained image data, but not as well on fine-grained image classification work, as they cannot properly assess the in-class similarity and inter-class difference of fine-grained images. In this work, an FSL framework based on graph neural network (GNN) is proposed for fine-grained image classification. Particularly, we use the information transmission of GNN to represent subtle differences between different images. Moreover, feature extraction is optimized by the method of meta-learning to improve the classification. The experiments on three datasets (CIFAR-100, CUB, and DOGS) have shown that the proposed method yields better performances. This indicates that the proposed method is a feasible solution for fine-grained image classification with FSL.

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.

Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies.

Human recognition technology is a task that determines the people existing in images with the purpose of identifying them. However, automatic human recognition at night is still a challenge because of its need to align requirements with a high accuracy rate and speed. This article aims to design a novel approach that applies integrated face and gait analyses to enhance the performance of real-time human recognition in TIR images at night under various walking conditions. Therefore, a new network is proposed to improve the YOLOv3 model by fusing face and gait classifiers to identify individuals automatically. This network optimizes the TIR images, provides more accurate features (face, gait, and body segment) of the person, and possesses it through the PDM-Net to detect the person class; then, PRM-Net classifies the images for human recognition. The proposed methodology uses accurate features to form the face and gait signatures by applying the YOLO-face algorithm and YOLO algorithm. This approach was pre-trained on three night (DHU Night, FLIR, and KAIST) databases to simulate realistic conditions during the surveillance-protecting areas. The experimental results determined that the proposed method is superior to other results-related methods in the same night databases in accuracy and detection time.

Transcriptome sequencing analysis of porcine MDM response to FSL-1 stimulation.

Mycoplasma infection can cause many diseases in pigs, resulting in great economic losses in pork production. Innate immune responses are thought to play critical roles in the pathogenesis of mycoplasma disease. However, the molecular events involved in immune responses remain to be determined. Hence, the object of this study was to use RNA-Seq to investigate the gene expression profiles of the innate immune response mediated by FSL-1 in pig monocyte-derived macrophages (MDMs). The results revealed that 1442 genes were differentially expressed in the FSL-1 group compared with the control groups, of which 777 genes were upregulated and 665 genes were downregulated. KEGG pathway analysis showed that the upregulated genes were mainly involved in innate immune-related pathways including the TNF signaling pathway, cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, Jak-STAT signaling pathway, chemokine signaling pathway, NOD-like receptor signaling pathway and NF-kappa B signaling pathway. The downregulated genes were only involved in the cGMP-PKG signaling pathway and glycerophospholipid metabolism. Our results showed that FSL-1 stimulation activated the TLR2 signaling pathway and resulted in diverse inflammatory responses. FSL-1 induced the transcription of numerous protein-coding genes involved in a complex network of innate immune-related pathways. We speculate that TNF, IL1B, IL6, NFKB1, NFKBIA, CXCL2, CXCL8, CXCL10, CCL2, CCL4 and CCL5 were the most likely hub genes that play important roles in the above pathways. This study identified the differentially expressed genes and their related signaling pathways, contributing to the comprehensive understanding of the mechanisms underlying host-pathogen interactions during mycoplasma infection and providing a reference model for further 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.

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.

Impaired neural habituation to neutral faces in families genetically enriched for social anxiety disorder.

BACKGROUND: Social anxiety disorder (SAD) is an incapacitating disorder running in families. Previous work associated social fearfulness with a failure to habituate, but the habituation response to neutral faces has, as of yet, not been investigated in patients with SAD and their family members concurrently. Here, we examined whether impaired habituation to neutral faces is a putative neurobiological endophenotype of SAD by using data from the multiplex and multigenerational Leiden Family Lab study on SAD. METHODS: Participants (n = 110; age, 9.2 - 61.5 years) performed a habituation paradigm involving neutral faces, as these are strong social stimuli with an ambiguous meaning. We used functional magnetic resonance imaging data to investigate whether brain activation related to habituation was associated with the level of social anxiety within the families. Furthermore, the heritability of the neural habituation response was estimated. RESULTS: Our data revealed a relationship between impaired habituation to neutral faces and social anxiety in the right hippocampus and right amygdala. In addition, our data indicated that this habituation response displayed moderate - to-moderately high heritability in the right hippocampus. CONCLUSION: The present results provide support for altered habituation as a candidate SAD endophenotype; impaired neural habitation cosegregrated with the disorder within families and was heritable. These findings shed light on the genetic susceptibility to SAD.

Biophysical characterization and stabilization of detergent-solubilized lipoprotein N-acyl transferase from P. aeruginosa and E. coli.

Lipoproteins are important for bacterial growth and virulence and interest in them as targets for antibiotic development is growing. Lipoprotein N-acyl transferase (Lnt) catalyzes the final step in the lipoprotein posttranslational processing pathway. The mature lipoprotein can remain in the inner membrane or be trafficked to the outer membrane in the case of diderm prokaryotes. With a view to obtaining high-resolution crystal structures of membrane integral Lnt for use in drug discovery a program was undertaken to generate milligram quantities of stable, homogenous and functional protein. This involved screening across bacterial species for suitable orthologues and optimization at the level of protein expression, solubilization and stability. Combining biophysical and functional characterization, orthologous Lnt from Escherichia coli and the opportunistic human pathogen Pseudomonas aeruginosa was identified as suitable for the proposed structure determination campaign that ultimately yielded crystal structures. The rational approaches taken that eventually provided structure-quality protein are presented in this report.

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.

Intra- and inter-network functional alterations in Parkinson's disease with mild cognitive impairment.

Mild cognitive impairment (MCI) is prevalent in 15%-40% of Parkinson's disease (PD) patients at diagnosis. In this investigation, we study brain intra- and inter-network alterations in resting state functional magnetic resonance imaging (rs-fMRI) in recently diagnosed PD patients and characterise them as either cognitive normal (PD-NC) or with MCI (PD-MCI). Patients were divided into two groups, PD-NC (N = 62) and PD-MCI (N = 37) and for comparison, healthy controls (HC, N = 30) were also included. Intra- and inter-network connectivity were investigated from participants' rs-fMRIs in 26 resting state networks (RSNs). Intra-network differences were found between both patient groups and HCs for networks associated with motor control (motor cortex), spatial attention and visual perception. When comparing both PD-NC and PD-MCI, intra-network alterations were found in RSNs related to attention, executive function and motor control (cerebellum). The inter-network analysis revealed a hyper-synchronisation between the basal ganglia network and the motor cortex in PD-NC compared with HCs. When both patient groups were compared, intra-network alterations in RSNs related to attention, motor control, visual perception and executive function were found. We also detected disease-driven negative synchronisations and synchronisation shifts from positive to negative and vice versa in both patient groups compared with HCs. The hyper-synchronisation between basal ganglia and motor cortical RSNs in PD and its synchronisation shift from negative to positive compared with HCs, suggest a compensatory response to basal dysfunction and altered basal-cortical motor control in the resting state brain of PD patients. Hum Brain Mapp 38:1702-1715, 2017. © 2016 Wiley Periodicals, Inc.

Decreased Fronto-Parietal and Increased Default Mode Network Activation is Associated with Subtle Cognitive Deficits in Elderly Controls.

BACKGROUND: Cognitive functions progressively deteriorate during aging and neurodegenerative diseases. The present study aims at investigating differences in working memory performance as well as functional brain changes during the earliest stages of cognitive decline in health elderly individuals. METHODS: 62 elderly individuals (41 females), including 41 controls (35 females) and 21 middle cognitive impairment subjects (6 females), underwent neuropsychological assessment at baseline and an fMRI examination in a N-back paradigm contrasting 2-back vs. 0-back condition. Upon a 18 months follow-up, we identified stable controls (sCON) with preserved cognition and deteriorating controls (dCON) with -1SD decrease of performances in at least two neuropsychological tests. Data analyses included accuracy and reaction time (RT) for the 2-back condition and general linear model (GLM) for the fMRI sequence. RESULTS: At the behavioral level, sCON and dCON performed better than MCI in terms of accuracy and reaction time. At the brain level, functional differences in regions of the fronto-parietal network (FPN) and of the Default Mode Network (DFM) were observed. Significantly lower neural activations in the bilateral inferior and middle frontal gyri were found in MCI versus both dCON / sCON and for dCON versus sCON. Significantly increased activations in the anterior cingulate cortex and posterior cingulate cortex and bilateral insula were found in MCI versus both dCON / sCON and in dCON versus sCON. CONCLUSION: The present study suggests that brain functional changes in FPN and DMN anticipate differences in cognitive performance in healthy elderly individuals with subsequent subtle cognitive decline.

Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation.

The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the "gold standard" technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11 years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between- and within-method correlations. Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the overestimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r=0.69, p<0.001; left r=0.77, p<0.001) and amygdala (right r=0.61, p<0.001; left r=0.67, p<0.001) volumes. Correlation coefficients between manual segmentation and FSL-FIRST were statistically significant (right hippocampus r=0.59, p<0.001; left hippocampus r=0.51, p<0.001; right amygdala r=0.35, p<0.001; left amygdala r=0.31, p<0.001) but were significantly weaker, for all investigated structures. When computing intraclass correlation coefficients between manual tracing and automatic segmentation, all comparisons, except for left hippocampus volume estimated with FreeSurfer, failed to reach 0.70. When looking at each method separately, correlations between left and right hemispheric volumes showed strong associations between bilateral hippocampus and bilateral amygdala volumes when assessed using manual segmentation or FreeSurfer. These correlations were significantly weaker when volumes were assessed with FSL-FIRST. Finally, Bland-Altman plots suggest that the difference between manual and automatic segmentation might be influenced by the volume of the structure, because smaller volumes were associated with larger volume differences between techniques. These results demonstrate that, at least in a pediatric population, the agreement between amygdala and hippocampus volumes obtained with automated FSL-FIRST and FreeSurfer protocols and those obtained with manual segmentation is not strong. Visual inspection by an informed individual and, if necessary, manual correction of automated segmentation outputs are important to ensure validity of volumetric results and interpretation of related findings.

Postmortem validation of MRI cortical volume measurements in MS.

Grey matter (GM) atrophy is a prominent aspect of multiple sclerosis pathology and an important outcome in studies. GM atrophy measurement requires accurate GM segmentation. Several methods are used in vivo for measuring GM volumes in MS, but assessing their validity in vivo remains challenging. In this postmortem study, we evaluated the correlation between postmortem MRI cortical volume or thickness and the cortical thickness measured on histological sections. Sixteen MS brains were scanned in situ using 3DT1-weighted MRI and these images were used to measure regional cortical volume using FSL-SIENAX, FreeSurfer, and SPM, and regional cortical thickness using FreeSurfer. Subsequently, cortical thickness was measured histologically in 5 systematically sampled cortical areas. Linear regression analyses were used to evaluate the relation between MRI regional cortical volume or thickness and histological cortical thickness to determine which postprocessing technique was most valid. After correction for multiple comparisons, we observed a significant correlation with the histological cortical thickness for FSL-SIENAX cortical volume with manual editing (std. ß = 0.345, adjusted R(2) = 0.105, P = 0.005), and FreeSurfer cortical volume with manual editing (std. ß = 0.379, adjusted R(2) = 0.129, P = 0.003). In addition, there was a significant correlation between FreeSurfer cortical thickness with manual editing and histological cortical thickness (std. ß = 0.381, adjusted R(2) = 0.130, P = 0.003). The results support the use of FSL-SIENAX and FreeSurfer in cases of severe MS pathology. Interestingly none of the methods were significant in automated mode, which supports the use of manual editing to improve the automated segmentation. Hum Brain Mapp 37:2223-2233, 2016. © 2016 Wiley Periodicals, Inc.

Retrospective head motion correction approaches for diffusion tensor imaging: Effects of preprocessing choices on biases and reproducibility of scalar diffusion metrics.

PURPOSE: To evaluate how retrospective head motion correction strategies affect the estimation of scalar metrics commonly used in clinical diffusion tensor imaging (DTI) studies along with their across-session reproducibility errors. MATERIALS AND METHODS: Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and their respective across-session reproducibility errors were measured on a 4T test-retest dataset of healthy participants using five processing pipelines. These differed in: 1) the number of b0 volumes used for motion correction reference (one or five); 2) the estimations of the gradient matrix rotation (based on 6 or 12 degrees of freedom derived from coregistration); and 3) the software packages used (FSL or DTIPrep). Biases and reproducibility were evaluated in three regions of interest (ROIs) (bilateral arcuate fasciculi, cingula, and the corpus callosum) and also at the full brain level with tract based skeleton images. RESULTS: Preprocessing choices affected DTI measures and their reproducibility. The DTIPrep pipeline exhibited higher DTI metrics: FA/MD and AD (P < 0.05) relative to FSL pipelines both at the ROI and full brain level, and lower RD estimates (P < 0.05) at the ROI level. Within FSL pipelines no such effects were found (P-values ranging between 0.25 and 0.97). The DTIPrep pipeline showed the highest number of white matter skeleton voxels, with significantly higher reproducibility (P < 0.001) relative to the other pipelines (tested on P < 0.01 uncorrected maps). CONCLUSION: The use of an iteratively averaged b0 image as motion correction reference (as performed by DTIPrep) affects both scalar values and improves test-retest reliability relative to the other tested pipelines. These considerations are potentially relevant for data analysis in longitudinal DTI studies.