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Introduction: The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs. Methods: The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days. Results: The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided. Conclusion: IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain. [ABSTRACT FROM AUTHOR]

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans. This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-theart of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, brain functional connectivity studies are extended using the advanced statistical methods. Functional connectivity is identified by synchronous activation in a spatially distinct region of the brain in resting-state functional Magnetic Resonance Imaging (MRI) data. For this purpose there are several methods such as seed-based correlation analysis based on temporal correlation between different Regions of Interests (ROIs) or between brain's voxels of prior seed. Methods: In the current study, test-retest Resting State functional MRI (rs-fMRI) data of 21 healthy subjects were analyzed to predict second replication connectivity map using first replication data. A potential estimator is "raw estimator" that uses the first replication data from each subject to predict the second replication connectivity map of the same subject. The second estimator, "mean estimator" uses the average of all sample subjects' connectivity to estimate the correlation map. Shrinkage estimator is made by shrinking raw estimator towards the average connectivity map of all subjects' first replicate. Prediction performance of the second replication correlation map is evaluated by Mean Squared Error (MSE) criteria. Results: By the employment of seed-based correlation analysis and choosing precentral gyrus as the ROI over 21 subjects in the study, on average MSE for raw, mean and shrinkage estimator were 0.2169, 0.1118, and 0.1103, respectively. Also, percent reduction of MSE for shrinkage and mean estimator in comparison with raw estimator is 49.14 and 48.45, respectively. Conclusion: Shrinkage approach has the positive effect on the prediction of functional connectivity. When data has a large between session variability, prediction of connectivity map can be improved by shrinking towards population mean. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated the bibliometric data of the most common publication types of the neuroimaging literature. Methods: PubMed was searched to identify all published papers with "neuroimaging" as their MeSH Major Topics, and they were further searched by the following publication types: case report, clinical trial, comparative study, editorial, evaluation study, guideline, meta-analysis, multicenter study, randomized controlled trial, review, technical report, and validation study. The proportion of papers belonging to each publication type published in neuroimaging journals was calculated. Year-adjusted mean citation counts for each publication type were computed using data from Web of Science. Publication trend and its correlation with citation performance were assessed. Results: Review and comparative study were the most common publication types. Publication types with the highest proportion in neuroimaging journals were guideline, validation study, and technical reports. Since the year 2000, multicenter study, review, and meta-analysis showed the strongest linear increase in annual publication count. These publication types also had the highest year-adjusted citation counts (4.7–10.0). Publication types with the lowest year-adjusted citation counts were editorial and case report (0.5–1.0). It was estimated that 12.5% of the publications labeled as case reports were incorrectly labeled. Conclusions: Neuroimaging literature has been expanding with papers of higher levels of evidence, such as meta-analyses, multicenter studies, and randomized controlled trials. [ABSTRACT FROM AUTHOR]

Over the past three decades, insights into the role of the cerebellum in emotional processing have substantially increased. Indeed, methodological refinements in cerebellar lesion studies and major technological advancements in the field of neuroscience are in particular responsible to an exponential growth of knowledge on the topic. It is timely to review the available data and to critically evaluate the current status of the role of the cerebellum in emotion and related domains. The main aim of this article is to present an overview of current facts and ongoing debates relating to clinical, neuroimaging, and neurophysiological findings on the role of the cerebellum in key aspects of emotion. Experts in the field of cerebellar research discuss the range of cerebellar contributions to emotion in nine topics. Topics include the role of the cerebellum in perception and recognition, forwarding and encoding of emotional information, and the experience and regulation of emotional states in relation to motor, cognitive, and social behaviors. In addition, perspectives including cerebellar involvement in emotional learning, pain, emotional aspects of speech, and neuropsychiatric aspects of the cerebellum in mood disorders are briefly discussed. Results of this consensus paper illustrate how theory and empirical research have converged to produce a composite picture of brain topography, physiology, and function that establishes the role of the cerebellum in many aspects of emotional processing. [ABSTRACT FROM AUTHOR]

After scanning the patient, the data processing presented here follows. First, we must convert 'Dicom' files to 'nifti' files. Then, we need eddy correction (movement to correct distorted photos). After this we use B vectors correction and use MATLAB program, also we use MATLAB for PI, which means background noise correction. For masking we use FSL program, in order to obtain a correct size of brain and finally we use 'MainExploreDTI' to see the direction of fibers. [ABSTRACT FROM AUTHOR]

The purpose of this reply is to address the comments given by Kelly et al. on our original paper "Unique Brain Network Identification Number for Parkinson's and Healthy Individuals using Structural MRI". We agree to the inadvertent rounding pitfall in our original paper due to the non-inclusion of symbolic math toolbox (MATLAB). We now provide the actual ranges (with decimal values) of the UBNIN values of healthy individuals and those with Parkinson's disease and further observations. Upon further introspection, we propose another variant, called Modified-UBNIN (UBNIN-MT,MN) which is highly weighted on the node with the highest network degree (i.e., connections). The italicized sentences within inverted commas are statements from Kelly et al.'s comment paper. [ABSTRACT FROM AUTHOR]

Brain stimulation techniques are important in both basic and clinical studies. Majority of well-known brain stimulating techniques have low spatial resolution or entail invasive processes. Low intensity focused ultrasound (LIFU) seems to be a proper candidate for dealing with such deficiencies. This review recapitulates studies which explored the effects of LIFU on brain structures and its function, in both research and clinical areas. Although the mechanism of LIFU action is still unclear, its different effects from molecular level up to behavioral level can be explored in animal and human brain. It can also be coupled with brain imaging assessments in future research. [ABSTRACT FROM AUTHOR]

Background Although the visual cortex does not typically receive much attention in addiction literature, neuroimaging studies often report significant activity in visual areas when drug users are exposed to drug cues. The purpose of this meta-analysis was to investigate the frequency with which occipital cortex activity is observed during drug cue exposure and to determine its spatial distribution. Methods A comprehensive literature search was performed of human functional neuroimaging studies of drug cue-reactivity. Fifty-five studies were used to determine the frequency with which clusters of significant visual cortex activity during visual drug cues versus non-drug cues were reported. The spatial distribution of visual cortex activations was determined via activation likelihood estimation (ALE; FDR corrected, p < 0.01) in a subset of these studies ( n = 24). Results Eighty-six percent of studies that reported fMRI results for drug versus neutral visual cues within a substance-dependent group showed significant drug-elicited activity in the visual cortex. ALE revealed clusters in the left secondary visual cortex (BA 19) and clusters in the primary visual cortex (BA 17) that were consistently activated by drug cues. Conclusions These data demonstrate that the visual cortex, often overlooked in our discussions of the neural circuitry of addiction, consistently discriminates drug cues from neutral cues in substance dependent populations. While it remains unclear whether drug cue-elicited activation in occipital cortex is related to the rewarding properties of the drug and/or attentional mechanisms, these data support further exploration. [ABSTRACT FROM AUTHOR]

Background: From time to time, neuroimaging research findings receive press coverage and attention by the general public. Scientific articles therefore should be written in a readable manner to facilitate knowledge translation and dissemination. However, no published readability report on neuroimaging articles like those published in education, medical and marketing journals is available. As a start, this study therefore aimed to evaluate the readability of the most-cited neuroimaging articles. Methods: The 100 most-cited articles in neuroimaging identified in a recent study by Kim et al. (2016) were evaluated. Headings, mathematical equations, tables, figures, footnotes, appendices, and reference lists were trimmed from the articles. The rest was processed for number of characters, words and sentences. Five readability indices that indicate the school grade appropriate for that reading difficulty (Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Gunning Fog index and Simple Measure of Gobbledygook index) were computed. An average reading grade level (AGL) was calculated by taking the mean of these five indices. The Flesch Reading Ease (FRE) score was also computed. The readability of the trimmed abstracts and full texts was evaluated against number of authors, country of corresponding author, total citation count, normalized citation count, article type, publication year, impact factor of the year published and type of journal. Results: Mean AGL ± standard deviation (SD) of the trimmed abstracts and full texts were 17.15 ± 2.81 (college graduate level) and 14.22 ± 1.66 (college level) respectively. Mean FRE score ± SD of the abstracts and full texts were 15.70 ± 14.11 (college graduate level) and 32.11 ± 8.56 (college level) respectively. Both items indicated that the full texts were significantly more readable than the abstracts (p < 0.001). Abstract readability was not associated with any factors under investigation. ANCOVAs showed that review/meta-analysis (mean AGL ± SD: 16.0 ± 1.4) and higher impact factor significantly associated with lower readability of the trimmed full texts surveyed. Conclusion: Concerning the 100 most-cited articles in neuroimaging, the full text appears to be more readable than the abstracts. Experimental articles and methodology papers were more readable than reviews/meta-analyses. Articles published in journals with higher impact factors were less readable. [ABSTRACT FROM AUTHOR]

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value. [ABSTRACT FROM AUTHOR]

Introduction: Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies. Methods: The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles. Results: The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section "Methods" of the article. Articles returned from NeuroQuery based on the same search are also presented. Conclusion: The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in fulltext papers. Toward the overall goal of discovering "enough data of the right kind," ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility. [ABSTRACT FROM AUTHOR]

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for whitematter segmentation tasks inMICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research. [ABSTRACT FROM AUTHOR]

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real‐world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real‐time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta‐Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full‐text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross‐frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1‐weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI‐PAD combinations. These combinations enable in‐depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain–behavior models that extend beyond the scanner and into real‐world contexts. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a severe psychiatric illness known to be highly complex due to its difficulty in identifying genetic factors and its polygenic nature. While genetic studies and research into epigenetic changes have advanced our understanding of MDD, the precise role of genetics in MDD's manifestation remains elusive. This paper reviews the current state of research on MDD genetics, encompassing findings on MDD's biological blueprint and investigations into epigenetic modifications associated with MDD development. Furthermore, this paper explores the interplay between depression and the immune system and the impact of inflammation on the disorder. Neuroimaging studies have also impacted comprehension of MDD, revealing alterations in brain size and chemistry. These insights shed light on the biological underpinnings of MDD and hold promise for developing more effective treatments and therapies for patients. [ABSTRACT FROM AUTHOR]

Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. [ABSTRACT FROM AUTHOR]

The article introduces a new method called the Unique Brain Network Identification Number (UBNIN) for storing binary neuroimaging connectivity data. It compares the UBNIN method with three other methods for storing brain-state data and discusses the pros and cons of each approach. The UBNIN method has three variations with different levels of compactness and information loss. The article concludes that while the UBNIN method has advantages, it requires more storage space compared to other methods and does not provide detailed information about connections between brain areas. The article also includes two tables comparing different properties of brain-state representations. The authors of the paper were involved in various aspects of the research and declare no conflicts of interest. [Extracted from the article]

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops. [ABSTRACT FROM AUTHOR]

Purpose: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. Materials and methods: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. Results: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors. [ABSTRACT FROM AUTHOR]

Background: Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets. Methods: Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. Results: The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. Conclusion: The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research. [ABSTRACT FROM AUTHOR]

Time is an omnipresent aspect of almost everything we experience internally or in the external world. The experience of time occurs through such an extensive set of contextual factors that, after decades of research, a unified understanding of its neural substrates is still elusive. In this study, following the recent best-practice guidelines, we conducted a coordinate-based meta-analysis of 95 carefully-selected neuroimaging papers of duration processing. We categorized the included papers into 14 classes of temporal features according to six categorical dimensions. Then, using the activation likelihood estimation (ALE) technique we investigated the convergent activation patterns of each class with a cluster-level family-wise error correction at p < 0.05. The regions most consistently activated across the various timing contexts were the pre-SMA and bilateral insula, consistent with an embodied theory of timing in which abstract representations of duration are rooted in sensorimotor and interoceptive experience, respectively. Moreover, class-specific patterns of activation could be roughly divided according to whether participants were timing auditory sequential stimuli, which additionally activated the dorsal striatum and SMA-proper, or visual single interval stimuli, which additionally activated the right middle frontal and inferior parietal cortices. We conclude that temporal cognition is so entangled with our everyday experience that timing stereotypically common combinations of stimulus characteristics reactivates the sensorimotor systems with which they were first experienced. [ABSTRACT FROM AUTHOR]

Olfaction is understudied in neuroimaging research compared to other senses, but there is growing evidence of its therapeutic benefits on mood and wellbeing. Olfactory imagery can provide similar health benefits as olfactory interventions. Harnessing crossmodal visual-olfactory interactions can facilitate olfactory imagery. Understanding and employing these cross-modal interactions between visual and olfactory stimuli could aid in the research and applications of olfaction and olfactory imagery interventions for health and wellbeing. This review examines current knowledge, debates, and research on olfaction, olfactive imagery, and crossmodal visual-olfactory integration. A total of 56 papers, identified using the PRISMA method, were evaluated to identify key brain regions, research themes and methods used to determine the suitability of fNIRS as a tool for studying these topics. The review identified fNIRS-compatible protocols and brain regions within the fNIRS recording depth of approximately 1.5 cm associated with olfactory imagery and crossmodal visual-olfactory integration. Commonly cited regions include the orbitofrontal cortex, inferior frontal gyrus and dorsolateral prefrontal cortex. The findings of this review indicate that fNIRS would be a suitable tool for research into these processes. Additionally, fNIRS suitability for use in naturalistic settings may lead to the development of new research approaches with greater ecological validity compared to existing neuroimaging techniques. [ABSTRACT FROM AUTHOR]

Background: Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. Research objective: This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. Methods: This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. Results: Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. Conclusion: The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics including a comment on the growing interest of the psychiatric community in brain scan research, an examination of the effectiveness of psychiatric drugs in treating emotional and behavioral problems, and the link between violence and psychiatric drug use.

BACKGROUND CONTEXT Peripheral nerve injuries of the upper extremities in patients undergoing posterior thoracolumbar surgery is a well described, yet preventable complication. Upper extremity (UE) neuromonitoring has been utilized to alert the surgeon of the development of such an injury. PURPOSE The purpose of this study is to investigate the occurrence of upper extremity neuromonitoring changes in patients undergoing thoracolumbar surgery in prone position and to assess the significance of these changes in terms of procedure and patient characteristics, and in preventing postoperative UE neuropathy. STUDY DESIGN/SETTING Pair-matched case-control study (Level III) design, university hospital setting. PATIENT SAMPLE A total of 843 consecutive patients from single institution. OUTCOME MEASURES Presence or absence of postoperative upper extremity neuropathy. METHODS All patients who underwent posterior thoracic, lumbar, or thoracolumbar surgery at a single institution from 2014 to 2016 with neuromonitoring were included in the study. UE neuromonitoring consisted of ulnar somatosensory evoked potentials (SSEPs). Patients with intraoperative ulnar SSEP signal changes were identified and compared with a group of pair-matched patients who did not develop intraoperative ulnar SSEP signal changes. The groups were matched based on the number of vertebral levels undergoing surgery. Data regarding intraoperative attempts to resolve signal changes and outcomes were collected. The two groups were compared to identify the risk factors for the development of UE neuromonitoring changes. RESULTS Between January 2014 and December 2016, 843 patients in our institution underwent thoracic, lumbar or thoracolumbar spine surgeries in prone position with intraoperative bilateral ulnar SSEPs neuromonitoring data available. Of these, 37 patients (4.4%) had intraoperative signal changes in the UE; an equal number of patients without signal changes were also selected. In each group, six patients underwent thoracic, 20 patients underwent lumbar, and 11 patients underwent thoracolumbar procedures. In eight patients (21.6%), there was no resolution of SSEP signal changes despite intraoperative attempts. The two groups were similar with respect to age and co-morbidities including diabetes, ischemic heart disease, and peripheral arterial disease. There was also no significant difference in the mean BMI (p=0.22). The mean duration of the procedures was 324 minutes in the SSEP signal change patients and 260 minutes in the patients who did not experience SSEP signal changes (p=0.03). No patient with UE SSEP changes had a clinically detectable neurologic deficit postoperatively. CONCLUSIONS Upper extremity SSEP signal changes during multilevel posterior thoracolumbar procedures are more likely to occur as the duration of the operation increases. The presence of UE signal changes does not coincide with clinically significant peripheral neuropathies. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper,methods used to classify brain tumors, including pre-processing, screening, tissue extraction, and statistical features of the tumor using two types of T1W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic C-Means Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis. [ABSTRACT FROM AUTHOR]

The article presents abstracts on magnetic resonance imaging (MRI) topics including MRI pulse sequences optimization for detection of multiple sclerosis plaques, evaluation of multidimensional model space by dynamic causal modeling, and echo planar spectroscopic imaging of the brain.

Medical image registration is an essential task in researching and applying medical images. Doctors can observe and extract relevant pathological features to quickly analyze the disease by registered images to diagnose the infection. After more than ten years of research and development, medical image registration has achieved good research results in traditional and deep learning methods. However, most existing methods only focus on unidirectional medical image registration research and rarely consider bidirectional medical image registration research. This paper proposes a new, unsupervised bidirectional medical image registration method based on this aspect. This method guarantees the registration effect in the forward and reverses directions and adds a cascade connection-based channel attention network to the registration model to enable better automatic learning of the registration model, optimizes feature weights, and extracts essential information from images to improve registration performance. We verified the effectiveness of our method by conducting experiments on large-scale 3D brain MRI images and achieved a comparable registration speed and effect with most existing medical image registration methods. [ABSTRACT FROM AUTHOR]

Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within "normal" brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures—Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE—aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models. [ABSTRACT FROM AUTHOR]

Introduction: Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings. Methods: This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols. Results: The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments. Discussion: Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained. [ABSTRACT FROM AUTHOR]

MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to avoid miscues in results. This paper is divided into two parts: In the first half, preprocessing was accomplished using HE, CLAHE, and BPDFHE enhancement techniques. An analysis is the sequel to the above methods to check for the best method based on performance metrics, i.e., MSE, PSNR, RMSE, and AMBE. The second half deals with the segmentation process. We propose a novel ARKFCM to use for segmentation. Finally, the trends in the performance metrics (dice similarity and Jaccard similarity) as well as the segmentation results are discussed in comparison with the conventional FCM method. [ABSTRACT FROM AUTHOR]

With the application of magnetic resonance imaging (MRI) in the diagnosis of brain diseases, MRI has become a powerful tool for clinical neuroimaging analysis, and whole brain segmentation is a very important step of neuroimaging analysis. In order to promote segmentation accuracy, this paper makes full use of the spatial constraint information between adjacent slice of MRI brain image sequence, and combines three consecutive images into one image named 2.5D slice image. Through 2.5D slice image, a triple U-Net-based whole brain segmentation method is proposed, which is composed of one main U-Net and two auxiliary U-Nets. Moreover, this network introduces three auxiliary outputs to provide inter-layer constraints for the final prediction, and uses a multi-auxiliary hybrid loss function based on binary cross-entropy loss and dice loss to optimize the training model. In this paper, a comprehensive comparative experiment is carried out on LPBA40 dataset and IBSR18 dataset. The experimental results show that the dice coefficient, specificity and sensitivity of this method are 98.23%, 99.62% and 98.54%, respectively, on LPBA40 dataset, and 97.01%, 99.45% and 98%, respectively, on IBSR18 dataset. The experiments show that the accuracy of whole brain segmentation is greatly improved by the proposed triple U-Net. [ABSTRACT FROM AUTHOR]

In this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images. [ABSTRACT FROM AUTHOR]

This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke. [ABSTRACT FROM AUTHOR]

Background: A growing number of advanced neuroimaging studies have compared brain structure and function in long term meditators to non-meditators. The goal is to determine if there may be long term effects on the brain from practicing meditation. In this paper, we present new data on the long term effects of a novel meditation practice in which the focus is on clitoral stimulation. The findings from such a study have implications for potential therapeutic uses with regard to various neurological or psychiatric conditions. Methods: We evaluated the cerebral glucose metabolism in 40 subjects with an extended history (>1 year of practice, 2-3 times per week) performing the meditation practice called Orgasmic Meditation (OM) and compared their brains to a group of non-meditating healthy controls (N = 19). Both meditation and non-meditation subjects underwent brain PET after injection with 148 to 296 MBq of FDG using a standard imaging protocol. Resting FDG PET scans of theOM group were compared to the resting scans of healthy, non-meditating, controls using statistical parametric mapping. Results: The OM group showed significant differences in metabolic activity at rest compared to the controls. Specifically, there was significantly lower metabolism in select areas of the frontal, temporal, and parietal lobes, as well as the anterior cingulate, insula, and thalamus, in the OM group compared to the controls. In addition, there were notable distinctions between the males and females with the females demonstrating significantly lower metabolism in the thalamus and insula. Conclusions: Overall, these findings suggest that the long term meditation practitioners of OM have different patterns of resting brain metabolism. Since these areas of the brain in which OM practitioners differ from controls are involved in cognition, attention, and emotional regulation, such findings have implications for understanding how this meditation practice might affect practitioners over long periods of time. [ABSTRACT FROM AUTHOR]

A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their "view" of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology. [ABSTRACT FROM AUTHOR]

Background: Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. Methods: We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Results: Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. Conclusions: This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations. [ABSTRACT FROM AUTHOR]