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FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on “20 years of fMRI” we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.

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. •Cluster-extent based thresholding is popular because of its high sensitivity.•However, cluster-extent based thresholding has several important problems.•One pitfall is low spatial specificity when significant clusters are large.•Another pitfall is increased false positives when a liberal primary threshold is used.•We recommend using stringent primary thresholds and augmented reporting procedures.

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.

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.

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 11years. 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. •In a pediatric sample, we compare hippocampus and amygdala volumes from FSL-FIRST and FreeSurfer to manual segmentation•We examine discrepancies, associations, and biases between automatic and manual segmentation volumes•In the studied pediatric population, the agreement between manual segmentation, FreeSurfer and FSL is questionable•Associations between manual segmentation and FreeSurfer were stronger than with FSL-FIRST•Associations between manual segmentation and automatic techniques were stronger for hippocampus than amygdala volumes

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random noise, and multiple samples are generated using the trained decoder. Our generator-based approach outperforms previous state-of-the-art SSL and FSL approaches. In addition, we released the Urdu digits dataset consisting of 10,000 images, including 8000 training and 2000 test images collected through three different methods for purposes of diversity. Furthermore, we explored the effectiveness of our proposed method on the Urdu digits dataset by using both SSL and FSL, which demonstrated improvement of 3.04% and 1.50% in terms of average accuracy, respectively, illustrating the superiority of the proposed method compared to the current state-of-the-art models.

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.

•First to examine DTI-based connectivity in the perinatally-deafened cat.•Most connections within the feline connectome were conserved following deafness.•Finds minimal group-specific and altered connections that were mainly sensory.•Tractography-based alterations align with retrograde tracer findings.•Suggests crossmodal plasticity is built on innate connections with limited rewiring. 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.

In recent years, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have achieved significant progress in Hyperspectral Image (HSI) classification. However, in practical applications, the high cost of sample annotation and the limited availability of training samples lead to overfitting in CNNs and ViTs under few-shot learning scenarios. Siamese networks, as an effective metric learning method, show promising performance in few-shot learning due to their low dependency on sample information. However, traditional siamese networks rely on static parameter-sharing mechanisms, lack feature interaction between the two subnetworks, and struggle to effectively capture the spatial-spectral heterogeneity in hyperspectral data. Additionally, they are prone to noise interference, resulting in insufficient discriminative power of key features. To address these challenges, this paper proposes a Contextual Interaction Siamese Network for Few-Shot Hyperspectral Image Classification (CISNet). First, an Interactive Feature Fusion Module (IFFM) is introduced to capture the similarities and differences between features from the two subnetworks, thereby enhancing the discriminative power of key features. Second, an Enhanced Token Generation Module (ETGM) is designed to generate correlated class tokens for the two subnetworks. Finally, this paper innovatively proposes a Context Interaction Transformer Block (CITB) and a Guided Attention (GA) mechanism to strengthen global context interaction between the two subnetworks. Extensive experiments demonstrate that CISNet achieves superior performance under few-shot conditions and outperforms other state-of-the-art methods in classification accuracy.

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.