Brain Image Analysis Research Articles

Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets

BackgroundThe sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs principle (replacement, reduction and refinement), and other factors make it challenging to obtain NHPs individuals for research. Consequently, there is a need for methods that can comprehensively analyze small datasets using artificial intelligence (AI). New methodsWe employed AI to identify cocaine dependence patterns from collected data. First, we collected behavioral data from cocaine SA marmosets (Callithrix jacchus) to develop a dependence prediction model. SHapley Additive exPlanations (SHAP) values were used to demonstrate the importance of various variables. Additionally, we collected positron emission tomographic (PET) images showing dopamine transporter (DAT) binding potential and developed an algorithm for PET image segmentation. ResultsThe prediction model indicated that the Random Forest (RF) algorithm performed best, with an area under the curve (AUC) of 0.92. The top five variables influencing the model were identified using SHAP values. The PET image segmentation model achieved an accuracy of 0.97, a mean squared error of 0.02, an intersection over union (IoU) of 0.845, and a Dice coefficient of 0.913. Comparison with existing methods and conclusionUtilizing data from the marmoset SA experiment, we developed an ML-based dependence prediction model and analyzed variable importance rankings using SHAP. AI-based imaging segmentation methods offer a valuable tool for evaluating DAT availability in NHPs following chronic cocaine administration.

Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.

Brain mechanisms discriminating enactive mental simulations of running and plogging.

Enactive cognition emphasizes co-constructive roles of humans and their environment in shaping cognitive processes. It is specifically engaged in the mental simulation of behaviors, enhancing the connection between perception and action. Here we investigated the core network of brain regions involved in enactive cognition as applied to mental simulations of physical exercise. We used a neuroimaging paradigm in which participants (N = 103) were required to project themselves running or plogging (running while picking-up litter) along an image-guided naturalistic trail. Using both univariate and multivariate brain imaging analyses, we find that a broad spectrum of brain activation discriminates between the mental simulation of plogging versus running. Critically, we show that self-reported ratings of daily life running engagement and the quality of mental simulation (how well participants were able to imagine themselves running) modulate the brain reactivity to plogging versus running. Finally, we undertook functional connectivity analyses centered on the insular cortex, which is a key region in the dynamic interplay between neurocognitive processes. This analysis revealed increased positive and negative patterns of insular-centered functional connectivity in the plogging condition (as compared to the running condition), thereby confirming the key role of the insular cortex in action simulation involving complex sets of mental mechanisms. Taken together, the present findings provide new insights into the brain networks involved in the enactive mental simulation of physical exercise.

Bidirectional brain image translation using transfer learning from generic pre-trained models

Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. CycleGAN and other GANs are valuable tools for generating synthetic images across various fields. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models’ performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model’s exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks.

Specific brain MRI features of constitutional mismatch repair deficiency syndrome in children with high-grade gliomas.

Children with constitutional mismatch repair deficiency (CMMRD) syndrome have an increased risk of high-grade gliomas (HGG), and brain imaging abnormalities. This study analyzes brain imaging features in CMMRD syndrome children versus those with HGG without CMMRD. Retrospective comparative analysis of brain imaging in 30 CMMRD children (20 boys, median age eight years, 22 with HGG), seven with Lynch syndrome (7 HGG), 39 with type 1 neurofibromatosis (NF1) (four with HGG) and 50 with HGG without MMR or NF1 pathogenic variant ("no-predisposition" patients). HGG in CMMRD and Lynch patients were predominantly hemispheric (versus midline) compared to NF1 and no-predisposition patients (91% and 86%, vs 25% and 54%, p = 0.004). CMMRD-associated tumors often had ill-defined boundaries (p = 0.008). All CMMRD patients exhibited at least one developmental venous anomaly (DVA), versus 14%, 10%, and 6% of Lynch, NF1, and no-predisposition patients (p < 0.0001). Multiple DVAs were observed in 83% of CMMRD patients, one NF1 patient (3%), and never in other groups (p < 0.0001). Cavernomas were discovered in 21% of CMMRD patients, never in other groups (p = 0.01). NF1-like focal areas of high T2-FLAIR signal intensity (FASI) were more prevalent in CMMRD patients than in Lynch or no-predisposition patients (50%, vs 20% and 0%, respectively, p < 0.0001). Subcortical and ill-limited FASI, possibly involving the cortex, were specific to CMMRD (p < 0.0001) and did not evolve in 93% of patients (13/14). Diffuse hemispherically located HGG associated with multiple DVAs, cavernomas, and NF1-like or subcortical FASI strongly suggests CMMRD syndrome compared to children with HGG in other contexts. The radiologic suggestion of CMMRD syndrome when confronted with HGGs in children may prompt genetic testing. This can influence therapeutic plans. Therefore, imaging features could potentially be incorporated into CMMRD testing recommendations. Using imaging to detect CMMRD syndrome early may improve patient care. CMMRD features include: hemispheric HGG with multiple developmental venous anomalies and NF1-like or subcortical areas with high T2-FLAIR intensity. We propose novel imaging features to improve the identification of potential CMMRD patients.

Selective inference for fMRI cluster-wise analysis, issues, and recommendations for critical vector selection: A comment on Blain et al.

Abstract Two permutation-based methods for simultaneous inference on the proportion of active voxels in cluster-wise brain imaging analysis have recently been published: Notip and pARI. Both rely on the definition of a critical vector of ordered p-values, chosen from a family of candidate vectors, but differ in how the family is defined: computed from randomization of external data for Notip and determined a priori for pARI. These procedures were compared to other proposals in the literature, but an extensive comparison between the two methods is missing due to their parallel publication. We provide such a comparison and find that pARI outperforms Notip if both methods are applied under their recommended settings. However, each method carries different advantages and drawbacks.

Changes in neuroinflammatory biomarkers correlate with disease severity and neuroimaging alterations in patients with COVID-19 neurological complications

COVID-19 induces acute and persistent neurological symptoms in mild and severe cases. Proposed concomitant mechanisms include direct viral infection and strain, coagulopathy, hypoxia, and neuroinflammation. However, underlying molecular alterations associated with multiple neurological outcomes in both mild and severe cases are majorly unexplored. To illuminate possible mechanisms leading to COVID-19 neurological disease, we retrospectively investigated in detail a cohort of 35 COVID-19 mild and severe hospitalized patients presenting neurological alterations subject to clinically indicated cerebrospinal fluid (CSF) sampling. Clinical and neurological investigation, brain imaging, viral sequencing, and cerebrospinal CSF analyses were carried out. We found that COVID-19 patients presented heterogeneous neurological symptoms dissociated from lung burden. Nasal swab viral sequencing revealed a dominant strain at the time of the study, and we could not detect traces of SARS-CoV-2's spike protein in patients' CSF by multiple reaction monitoring analysis. Patients presented ubiquitous systemic hyper-inflammation and broad alterations in CSF proteomics related to inflammation, innate immunity, and hemostasis, irrespective of COVID-19 severity or neuroimaging alterations. Elevated CSF interleukin-6 (IL6) correlated with disease severity (sex-, age-, and comorbidity-adjusted mean Severe 24.5 pg/ml, 95% confidence interval (CI) 9.62–62.23 vs. Mild 3.91 pg/mL CI 1.5–10.3 patients, p = 0.019). CSF tumor necrosis factor-alpha (TNFα) and IL6 levels were higher in patients presenting pronounced neuroimaging alterations compared to those who did not (sex-, age-, and comorbidity-adjusted mean TNFα Pronounced 3.4, CI 2.4–4.4 vs. Non-Pronounced 2.0, CI 1.4–2.5, p = 0.022; IL6 Pronounced 33.11, CI 8.89–123.31 vs Non-Pronounced 6.22, CI 2.9–13.34, p = 0.046). Collectively, our findings put neuroinflammation as a possible driver of COVID-19 acute neurological disease in mild and severe cases.

Gradient Matching Federated Domain Adaptation for Brain Image Classification.

Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

ReMIND: The Brain Resection Multimodal Imaging Database

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon’s ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Atypical Presentations of Huntington Disease-like 2 in South African Individuals.

Huntington disease-like 2 (HDL2) is a neurodegenerative disorder, affecting only individuals of African ancestry. Full penetrance occurs in individuals with 40 repeats or more. To describe the phenotypic variability of HDL2 in a group of mixed ancestry individuals from South Africa. Eight patients were assessed with analysis of repeat size and magnetic resonance brain imaging. We applied the Unified Huntington's Disease Rating Scale (UHDRS), but in deceased patients (4), this was estimated from video material. Cognitive domains were more severely affected than motor; UHDRS motor scores were notable for bradykinesia, and to a slightly lesser extent, for rigidity and dystonia; a single patient had marked chorea. Repeat lengths ranged from 45 to 63 (median, 52). This South African group of mixed ancestry HDL2 individuals presented with severe cognitive and behavioral impairments, with lesser degrees or absence of chorea. This presentation is possibly related to large repeat sizes.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

IntroductionSpatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. MethodsWe propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). ResultsIn total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). ConclusionThe automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis.

Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.

Detection of Autism Spectrum Disorder using Machine Learning: A Review

Abstract: Social interaction and communication impairments are caused by an illness known as Autism Spectrum Disorder (ASD), which has neurological and genetic components. Statistics from the World Health Organization (WHO) show that the number of people with ASD diagnoses is progressively rising. The majority of recent research focuses on data gathering, brain image analysis, and clinical diagnosis; it does not address the diagnosis of ASD using machine learning. Currently, the only techniques available for diagnosing ASD are clinical standardized testing. This results in longer diagnostic times as well as a sharp rise in medical expenses. Machine learning approaches are being used to supplement traditional methods in order to enhance diagnosis precision and time required. The dataset is being subjected to several machine learning approaches, with the aim of constructing predictive models that are contingent on the results. The best accurate machine learning model to identify the disease in its early stages is then found by analyzing each technique according to its assessment metrics.

Enhancing Brain Tumor Diagnosis: A Synergistic Approach with Support Vector Machines and Decision Trees for Improved Detection and Segmentation

Abstract: Brain tumor identification and segmentation are critical components of modern medical imaging, facilitating timely diagnoses and effective therapy planning. This paper introduces an innovative methodology that harmoniously integrates Support Vector Machines (SVM) and Decision Tree methods. SVM adeptly classifies brain images into tumor and nonneoplastic regions, harnessing its proficiency with complex datasets. Decision Tree methods complement this process, providing transparent segmentation that accurately delineates intricate tumor boundaries. The synergy between SVM and Decision Tree methods establishes a robust framework for brain tumor identification. This novel approach holds promise for enhanced diagnosis speed, particularly valuable in time-sensitive medical scenarios. Emphasizing potential advancements in patient care outcomes, the methodology enables tailored treatment plans with greater precision. Positioned at the core of brain image analysis, this novel methodology signifies a substantial step forward in healthcare practices, fostering personalized approaches to brain tumor diagnosis and treatment.

Montreal cognitive assessment in Brazilian adults with sickle cell disease: The burdens of poor sociocultural background.

Sickle cell disease (SCD) patients are at higher risk of developing silent cerebral infarcts and overt stroke, which may reflect cognitive impairment, functional limitations, and worse quality of life. The cognitive function of Brazilian adult SCD patients (n=124; 19-70 years; 56 men; 79 SS, 28 SC, 10 S/β0, 7 S/β+) was screened through Montreal Cognitive Assessment (MoCA) and correlated the results with possible predictive factors for test performance, including sociocultural, clinical, laboratory data and brain imaging. The Median MoCA score was 23 (8-30); 70% had a 25-or-less score, suggesting some level of cognitive impairment. There were no significant associations between MoCA results and any clinical or laboratory data in SS and SC patients; however, a significant correlation (P=0.03) with stroke was found in HbS/β-thalassemic patients. Correlations were further detected according to sociodemographic conditions, such as age (r=-0.316; P<0.001), age at first job (r=0.221; P=0.018), personal (r=0.23; P=0.012) andper capitafamiliar incomes (r=0.303; P=0.001), personal (r=0.61; P=0), maternal (r=0.536; P=0), and paternal educational status (r=0.441; P=0). We further sought independent predictors of performance using multivariable regressions and increased education was an independent predictor of better scores in MoCA (0.8099, 95% confidence interval [CI]: 0.509-1.111). Brain imaging analysis showed significant and progressive atrophy in important cerebral areas related to memory, learning, and executive function. These data point to the high prevalence and impact of cognitive decline in adult SCD patients, mirrored in brain atrophic areas. It is also possible to observe the influence of sociodemographic conditions on patients' cognitive performances and the need for creating focused therapeutic plans that address these deficiencies. Moreover, the absence of a significant correlation of MoCA values with stroke in the SS and SC groups may be related to the worst sociocultural and economic conditions of the Brazilian African descent population, in which the impact of low educational stimulation on cognitive function can outweigh even the anatomical damage caused by the disease.

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification.

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.

Exploring the Complex Interplay of Procrastination between Biological, Cognitive, Developmental, Social, and Psychological Factors

Procrastination is a common behavior that affects individuals in various aspects of life, with persistent procrastination having cumulative negative impacts. Through a literature review, a sizable percentage of adults worldwide are chronic procrastinators, which increases their chance of suffering from mental conditions, including melancholy, anxiety, depression, and low self-confidence. On top of these detrimental effects on people's mental well-being, procrastination has been shown to negatively impact one's physical health, social interactions, and productivity. This paper investigates the complex interplay between biological, cognitive, developmental, social, and psychological factors in procrastination. The main findings of this review paper were: 1) Biological and cognitive factors play a significant role in procrastination behavior, 2) Procrastination affects performance in various domains, including the workplace, academic, and social spheres, and 3) Utilizing modern technologies such as brain imaging and sentiment analysis can enhance our understanding of procrastination and its effects across different demographics. Future studies in this area can gain insight into the underlying mechanisms and a more thorough understanding of procrastination's manifestations and effects across different demographics. By doing this, psychologists can create more potent interventions and ways to lessen procrastination's many detrimental effects, enhancing people's quality of life in the academic, professional, and personal domains. Understanding and addressing procrastination is crucial, given its far-reaching impacts and prevalence in modern society.

CAT: a computational anatomy toolbox for the analysis of structural MRI data.

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

BrainNPT: Pre-Training Transformer Networks for Brain Network Classification.

Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in many domains, such as natural language processing. However, this technique is under-explored in brain network analysis. In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification. First, we proposed a Transformer-based neural network, named as BrainNPT, for brain functional network classification. The proposed method leveraged <cls> token as a classification embedding vector for the Transformer model to effectively capture the representation of brain networks. Second, we proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data to learn the structure information of brain functional networks. The results of classification experiments demonstrated the BrainNPT model without pre-training achieved the best performance with the state-of-the-art models, and the BrainNPT model with pre-training strongly outperformed the state-of-the-art models. The pre-training BrainNPT model improved 8.75% of accuracy compared with the model without pre-training. We further compared the pre-training strategies and the data augmentation methods, analyzed the influence of the parameters of the model, and explained the trained model.