Image Data Research Articles

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

Vision language models in ophthalmology.

Vision Language Models are an emerging paradigm in artificial intelligence that offers the potential to natively analyze both image and textual data simultaneously, within a single model. The fusion of these two modalities is of particular relevance to ophthalmology, which has historically involved specialized imaging techniques such as angiography, optical coherence tomography, and fundus photography, while also interfacing with electronic health records that include free text descriptions. This review then surveys the fast-evolving field of Vision Language Models as they apply to current ophthalmologic research and practice. Although models incorporating both image and text data have a long provenance in ophthalmology, effective multimodal Vision Language Models are a recent development exploiting advances in technologies such as transformer and autoencoder models. Vision Language Models offer the potential to assist and streamline the existing clinical workflow in ophthalmology, whether previsit, during, or post-visit. There are, however, also important challenges to be overcome, particularly regarding patient privacy and explainability of model recommendations.

Cardiac motion correction with a deep learning network for perfusion defect assessment in SPECT myocardial perfusion imaging

BackgroundIn myocardial perfusion imaging (MPI) with SPECT ungated studies are used for evaluation of perfusion defects despite motion blur. We investigate the potential benefit of motion correction using a deep-learning (DL) network for evaluating perfusion defects. MethodsWe employed a DL network for cardiac motion correction in ECG-gated SPECT-MPI images, wherein the image data from different cardiac phases are combined with respect to a reference gate to reduce motion blur. For training the DL network 197 cases were used. Given the variability of gated images during the cardiac cycle, we investigated the detectability of perfusion defects in two distinct reference gates. To assess perfusion defect detection, we performed receiver-operating-characteristic (ROC) analyses on the motion-corrected images using a separate test dataset of clinical 194 subjects, in which studies were created from actual patient data with inserted simulated-lesions as ground truth. The reconstructed images were assessed by the quantitative-perfusion SPECT (QPS) software. We also evaluated the performance on reduced-count studies (by two and four folds). ResultsThe quantitative results, measured by area-under-the-ROC curve (AUC), demonstrated that DL motion correction improves the detectability of perfusion defects significantly on both standard- and reduced-count studies, and that the detectability can vary with reference cardiac phases. A joint assessment from two reference-phases achieved AUC=0.841 on the quarter-count data, higher than with ungated full-count data (AUC=0.795, p-value=0.0054). ConclusionsDL motion correction can benefit assessment of perfusion defects in standard- and reduced-count SPECT-MPI studies. It can also be beneficial to evaluate perfusion images over multiple cardiac phases.

VEGAS-SSS: Tracing globular cluster populations in the interacting NGC 3640 galaxy group

Context. Globular clusters (GCs) are among the oldest stellar systems in the universe. As such, GC populations are valuable fossil tracers of galaxy formation and interaction history. This paper is part of the VEGAS-SSS series, which focuses on studying the properties of small stellar systems (SSSs) in and around bright galaxies. Aims. We used the multiband wide-field images obtained with the VST to study the properties of the GC population in an interacting pair of galaxies. Methods. We derived ugri photometry over 1.5 × 1.5 sq. degrees centered on the galaxy group composed of two elliptical galaxies: NGC 3640 and its fainter companion NGC 3641. We studied the GC system properties from both the ugri and gri matched catalogs. GC candidates were identified based on a combination of photometric properties (colors and magnitudes) and morphometric criteria (concentration index, elongation, FWHM, etc.), using sources with well-defined classifications from spectroscopic or imaging data available in the literature and numerical simulations as references. The selection criteria were also applied to empty fields to determine a statistical background correction for the number of identified GC candidates. Results. The 2D density maps of GCs appear to align with the diffuse light patches resulting from the merging events of the galaxies. The highest density peak of GCs is observed to be on NGC 3641 rather than NGC 3640, despite the latter being the more massive galaxy. The azimuthal averaged radial density profiles in both galaxies reveal that the GC population extends beyond the galaxy light profile and this indicates the likely presence of an intra-group GC component. A color bimodality in (u − r) and (g − i) is observed for NGC 3641, whereas NGC 3640 shows a broad unimodal distribution. Analysis of the GC luminosity function indicates that both galaxies are roughly located at the same distance (∼27 Mpc). We provide an estimate of the total number of GCs, and determine the specific frequency for NGC 3640, SN = 2.0 ± 0.6, which aligns with expectations, while for NGC 3641 we find a large SN = 4.5 ± 1.6.

Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study.

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time. This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations. A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection. Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304 (±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast DWI and T2WI images (p>0.05). DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the kidneys.

Intrastromal Corneal Ring Segments and Keratoconus Progression: A Case Series Study.

To assess keratoconus (KC) progression following the implant of intrastromal corneal ring segments (ICRSs) in young patients. Retrospective, longitudinal, observational, controlled nonrandomized case series study. Keratoconus patients aged 25 years or younger who underwent uneventful ICRS surgery and completed at least 3 years of follow-up were enrolled. Controls were of similar age and treatment-naive patients with KC. The following Pentacam imaging (Oculus, Wetzlar, Germany) data were analyzed: keratometric (maximum, in-flattest meridian, in-steepest meridian, and mean), aberrometric (higher-order aberrations and coma), pachymetric (thinnest corneal thickness), and elevation (maximum posterior elevation). The main outcome measure was KC progression. The study sample comprised 20 eyes of 18 cases (age 20.20 ± 3.70 years, nine right eyes, 14 male patients) and 30 eyes of 24 controls (age 20.80 ± 3.20 years, 15 right eyes, 21 male patients). The mean follow-up duration was 4.90 ± 1.70 years (range 3-8 years) for cases and 4.50 ± 1.40 years (range 3-8 years) for controls. Four cases and two controls met criteria for KC progression. Intrastromal corneal ring segments did not have significant impact on KC progression in the cohort.

Imaging and clinical manifestations of hematogenous dissemination in melioidosis

BackgroundAlthough there is a high incidence of hematogenous infections in melioidosis, a tropical infectious disease, there are few systematic analyses of hematogenous melioidosis in imaging articles. A comprehensive clinical and imaging evaluation of hematogenous melioidosis be conducted in order to achieve early diagnosis of the disease.Materials and methodsWe conducted an analysis of 111 cases of melioidosis diagnosed by bacteriological culture between August 2001 and September 2022. The analysis focused on observing the main manifestations of chest imaging and clinical data, including nodules, cavities, consolidation, ground glass opacity(GGO), pleural effusion, centrilobular nodules, and temperature, leucocyte count, diabetes, etc. Our study involved univariate and multivariate analyses to identify significant diagnostic variables and risk predictive factors.ResultsA total of 71.2% (79/111) of melioidosis cases were caused by hematogenous infection, and the most common organ involved was the lungs (88.5%, 100/113). The incidence of sepsis in patients with lung abnormalities was high (73%, 73/100), and the mortality rate of septic shock was 22% (22/100). Univariate analysis showed that the radiologic signs of blood culture-positive cases were more likely to have bilateral pulmonary and subpleural nodules (p = 0.003), bilateral GGO (p = 0.001), bilateral hydrothorax (p = 0.011). The multivariate analysis revealed a significant improvement in the area under the receiver operating characteristic curve (AUC) when comparing the model that included both clinical and radiologic variables to the model with clinical variables alone. The AUC increased from 0.818 to 0.932 (p = 0.012). The most important variables in the logistic regression with backward elimination were found to be nodule, GGO, and diabetes.ConclusionThe combination of CT features and clinical variables provided a valuable and timely warning for blood borne infectious melioidosis.

Explainable machine learning by SEE-Net: closing the gap between interpretable models and DNNs

Deep Neural Networks (DNNs) have achieved remarkable accuracy for numerous applications, yet their complexity often renders the explanation of predictions a challenging task. This complexity contrasts with easily interpretable statistical models, which, however, often suffer from lower accuracy. Our work suggests that this underperformance may stem more from inadequate training methods than from the inherent limitations of model structures. We hereby introduce the Synced Explanation-Enhanced Neural Network (SEE-Net), a novel architecture integrating a guiding DNN with a shallow neural network, functionally equivalent to a two-layer mixture of linear models. This shallow network is trained under the guidance of the DNN, effectively bridging the gap between the prediction power of deep learning and the need for explainable models. Experiments on image and tabular data demonstrate that SEE-Net can leverage the advantage of DNNs while providing an interpretable prediction framework. Critically, SEE-Net embodies a new paradigm in machine learning: it achieves high-level explainability with minimal compromise on prediction accuracy by training an almost “white-box” model under the co-supervision of a “black-box” model, which can be tailored for diverse applications.

Prognostic Implications of MRI-assessed Intratumoral Fat in Hepatocellular Carcinoma: An Asian and European Cohort Study.

Background The clinicopathologic-radiologic and prognostic characteristics of intratumoral fat in hepatocellular carcinoma (HCC) are critical for personalized treatment but remain understudied. Purpose To investigate the clinicopathologic-radiologic associations and prognostic implications of MRI-assessed intratumoral fat in HCCs. Materials and Methods This retrospective cohort study included consecutive adult patients who underwent resection for solitary HCCs and preoperative contrast-enhanced MRI from two tertiary-care hospitals in East Asia (March 2011 to December 2021) and Western Europe (September 2012 to December 2019). MRI scans were independently evaluated by three radiologists at each hospital. Based on Liver Imaging Reporting and Data System (LI-RADS) version 2018, intratumoral fat was defined as "fat in mass more than adjacent liver," and the homogeneous subtype was defined as intratumoral fat "in absence of mosaic and nodule-in-nodule architecture." Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox regression analyses were conducted to identify factors associated with RFS and OS. Results A total of 933 patients were included in the Asian (n = 736; median age, 53 years [IQR, 45-62 years]; 626 male) and European (n = 207; median age, 64 years [IQR, 55-70 years]; 161 male) cohorts. MRI-assessed intratumoral fat was detected in 30% (215 of 726) and 31% (64 of 207) of patients in the Asian and European cohorts, respectively (P = .72). In both cohorts, the steatohepatitic subtype, nonperipheral washout, enhancing capsule, and mosaic architecture were more frequent in tumors with intratumoral fat (P value range, <.001 to .04). Intratumoral fat in general was not associated with RFS or OS in either cohort (P value range, .48-.97). However, in the Asian cohort, homogeneous intratumoral fat was associated with longer RFS (hazard ratio [HR], 0.60; P = .009) and OS (HR, 0.33; P = .008) in multivariable Cox regression analyses. Conclusion MRI-assessed intratumoral fat was more frequent in steatohepatitic HCCs and associated with nonperipheral washout, enhancing capsule, and mosaic architecture. Although intratumoral fat was generally nonprognostic, homogeneous intratumoral fat was associated with longer RFS and OS in the Asian cohort. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs &gt;0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions.

Evaluation of Glymphatic System Development in Neonatal Brain via Diffusion Analysis along the Perivascular Space Index.

Glymphatic system is a recently discovered macroscopic waste clearance system associated with numerous neurological diseases. However, little is known about glymphatic system development in neonates. We sought to evaluate diffusion along the perivascular space (ALPS) index, a proxy for glymphatic system function, in neonates and investigate its potential associations with maturation, sex, and preterm birth. Diffusion magnetic resonance imaging (MRI) data in 418 neonates, including 92 preterm neonates (57 males) and 326 term neonates (175 males), from the Developing Human Connectome Project were used for evaluating ALPS index. Linear regression modeling was performed to assess group differences in the ALPS index according to preterm birth and sex. Pearson's and partial correlation analysis were performed to assess the association between the ALPS index and gestational age (GA) as well as postmenstrual age (PMA) at MRI. Moderation analysis was performed to assess the moderation effect of preterm birth on the relationship between the ALPS index and PMA. Compared to term neonates, preterm neonates exhibited lower ALPS indices (p < 0.001). The ALPS index positively correlated with PMA (p = 0.004) and GA (p < 0.001). Preterm birth (p = 0.013) had a significant moderation effect on the relationship between the ALPS index and PMA. Sex had no significant direct effect (p = 0.639) or moderation effect (p = 0.333) on ALPS index. Glymphatic system development is a dynamic process in neonates, which can be moderated by preterm birth, the ALPS index could serve as a sensitive biomarker for monitoring this process. ANN NEUROL 2024;96:970-980.

A Patient-Specific 3D Printed Lingual Nerve Protector in Third Molar Extraction.

We present a novel, patient-specific 3D-printed lingual nerve protector designed to minimize the risk of nerve injury during mandibular third molar extraction, a common cause of lingual nerve damage. Lingual nerve injuries, often resulting from trauma, dental procedures, or maxillofacial surgeries, lead to significant functional impairments. Using computed tomography (CT) data, the custom protector is fabricated through 3D printing with a self-retaining structure to shield the lingual mucosa and nerve. The device effectively separates the lingual nerve from the surgical field without requiring retraction of the lingual flap, thereby reducing the risk of nerve injury and enhancing the safety and ease of the procedure.

Automated self-adjustment of array probe with a robotic ultrasonic test system

Ultrasonic testing of objects with complex geometries often requires the use of a robotic arm to position the probe perpendicular to the local surface. Using immersion makes it possible to test these objects with standard ultrasonic linear array probes. Here, the probe positions and orientations provided by the robot are used for merging the locally acquired image data into a 3D-reconstruction. The quality of this reconstruction is highly dependent on the alignment of the position of the physical probe with the position used in the digital model. For common industrial tools, the tool center point (TCP) is usually acquired using geometric features of the tools. However, for ultrasonic arrays in immersion, there is a water standoff between the probe and the test object, therefore the TCP is in free space in front of the array and cannot be acquired with the common method. To overcome this challenge, we propose a method that allows the robotic ultrasonic system to automatically self-adjust the position and orientation of the ultrasonic probe using a test block made of steel with defined geometric features as a target for referencing. For each of the six degrees of freedom, a scan and adjustment routine are established using the data based on the actual ultrasound characteristics of the probe. Given a coarse pre-definition of the tool position and the known target test block, minimal human interaction is required to supervise the adjustment method, leading to higher quality reconstructions than with manual adjustment.

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.

YOLOv7-based insulator defect detection with progressive feature fusion and DFC attention

Abstract With the development of smart grids, drones have been widely used for inspecting power transmission lines, generating a large amount of insulator image data. Relying on manual inspection for this task results in a huge workload and the risk of missed detections due to human fatigue. Here, we propose an improved model based on YOLOv7-Tiny. First, we replace the activation function with the funnel activation function to optimize the activation domain dynamically. Second, we introduce a lightweight DFC attention mechanism. Experimental results show that the improved model achieves a detection accuracy and recall rate of 96.4% and 92.5%, respectively.

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study.

Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess stool form according to the Bristol Stool Form Scale (BSFS). Our smart toilet integrates a conventional toilet bowl with an engineered portal to image feces in a predetermined region of the plumbing post-flush. The smart toilet was installed in a workplace bathroom and used by six healthy volunteers. Images were annotated by three experts. A computer vision method based on deep learning segmentation and mathematically defined hand-crafted features was developed to quantify morphological attributes of stool from images. 474 bowel movements images were recorded in total from six subjects over a mean period of 10 months. 3% of images were rated abnormal with stool consistency BSFS 2 and 4% were BSFS 6. Our image analysis algorithm leverages interpretable morphological features and achieves classification of abnormal stool form with 94% accuracy, 81% sensitivity and 95% specificity. Our study supports the feasibility and accuracy of long-term, non-invasive automated stool form monitoring with the novel smart toilet system which can eliminate the patient burden of tracking bowel forms.

A Review on Kidney Stone Detection using ML and DL Techniques

Kidney stone detection is a critical healthcare challenge, as timely and accurate diagnosis can prevent complications. The motivation behind this review is the increasing prevalence of kidney stones and the need for more effective, non-invasive detection methods. Machine learning (ML) and deep learning (DL) techniques offer promising solutions, leveraging medical imaging data to enhance diagnostic accuracy. However, limitations such as high computational cost and reliance on large datasets hinder their full potential. The aim of this review is to analyze the latest advancements in kidney stone detection using ML and DL techniques. The objective is to compare existing methodologies, highlight their strengths and weaknesses, and suggest future research directions, particularly in integrating transfer learning and fine-tuning techniques to enhance performance.

Decoding Cortical Chronotopy - Comparing the Influence of Different Cortical Organizational Schemes

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.