Scholarly Publications - BAM

Permanent URI for this collectionhttps://hdl.handle.net/11693/115580

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Open Access
Denoising diffusion adversarial models for unconditional medical image generation
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Dalmaz, Onat; Sağlam, Baturay; Elmas, Gökberk; Mirza, Muhammad Usama; Çukur, Tolga
Unconditional medical image synthesis is the task of generating realistic and diverse medical images from random noise without any prior information or constraints. Synthesizing realistic medical images can enrich the quality and diversity of medical imaging datasets, which in turn, enhance the performance and generalization of deep learning models for medical imaging. Prevalent approach for synthesizing medical images involves generative adversarial networks (GAN) or denoising diffusion probabilistic models (DDPM). However, GAN models that implicitly learn the image distribution are prone to limited sample fidelity and diversity. On the other hand, diffusion models suffer from slow sampling speed due to small diffusion steps. In this paper, we propose a novel diffusion-based method for unconditional medical image synthesis, Diff-Med-Synth, that generates realistic and diverse medical images from random noise. Diff-Med-Synth combines the advantages of denoising diffusion probabilistic models and GANs to achieve fast and efficient image sampling. We evaluate our method on two multi-contrast MRI datasets and show that it outperforms state-of-the-art methods in terms of quality, diversity, and fidelity of the synthesized images.
Open Access
Zebrafish optomotor response to second-order motion illustrates that age-related changes in motion detection depend on the activated motion system
(Elsevier Inc., 2023-06-10) Karaduman, Ayşenur; Karoğlu-Eravşar, Elif Tuğçe; Kaya, Utku; Aydın, Alaz; Adams, Michelle Marie; Kafalıgönül, Hulusi
Various aspects of visual functioning, including motion perception, change with age. Yet, there is a lack of comprehensive understanding of age-related alterations at different stages of motion processing and in each motion system. To understand the effects of aging on second-order motion processing, we investigated optomotor responses (OMR) in younger and older wild-type (AB-strain) and acetylcholinesterase (achesb55/+) mutant zebrafish. The mutant fish with decreased levels of acetylcholinesterase have been shown to have delayed age-related cognitive decline. Compared to previous results on first-order motion, we found distinct changes in OMR to second-order motion. The polarity of OMR was dependent on age, such that second-order stimulation led to mainly negative OMR in the younger group while older zebrafish had positive responses. Hence, these findings revealed an overall aging effect on the detection of second-order motion. Moreover, neither the genotype of zebrafish nor the spatial frequency of motion significantly changed the response magnitude. Our findings support the view that age-related changes in motion detection depend on the activated motion system. © 2023 Elsevier Inc.
Open Access
Content-based medical image retrieval with opponent class adaptive margin loss
(Elsevier Inc., 2023-04-13) Öztürk, Şaban; Çelik, Emin; Çukur, Tolga
The increasing utilization of medical imaging technology with digital storage capabilities has facilitated the compilation of large-scale data repositories. Fast access to image samples with similar appearance to suspected cases in these repositories can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on quantitative assessment of image similarity based on image features in a latent space. Since conventional methods based on hand-crafted features typically show poor generalization performance, learning-based CBIR methods have received attention recently. A common framework in this domain involves classifier-guided models that are trained to detect different image classes. Similarity assessments are then performed on the features captured by the intermediate stages of the trained models. While classifier-guided methods are powerful in inter-class discrimination, they are suboptimally sensitive to within-class differences in image features. An alternative framework instead performs task-agnostic training to learn an embedding space that enforces the representational discriminability of images. Within this representational-learning framework, a powerful method is triplet-wise learning that addresses the deficiencies of point-wise and pair-wise learning in characterizing the similarity relationships between image classes. However, the traditional triplet loss enforces separation between only a subset of image samples within the triplet via a manually-set constant margin value, so it can lead to suboptimal segregation of opponent classes and limited generalization performance. To address these limitations, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. To maintain optimally discriminative representations, OCAM considers relationships among all image pairs within the triplet and utilizes an adaptive margin value that is automatically selected per dataset and during the course of training iterations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). On average, OCAM shows an mAP performance of 86.30% in the KVASIR dataset, 70.30% in the ISIC 2019 dataset, and 85.57% in the X-RAY dataset. Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against competing triplet-wise methods at 1.52%, classifier-guided methods at 2.29%, and non-triplet representational-learning methods at 4.56%.
Open Access
Caloric restriction reinforces the stem cell pool in the aged brain without affecting overall proliferation status
(Elsevier BV, 2022-11-01) Erbaba, Begün; Macaroğlu, Duygu; Avcı, N. İlgim Ardıç; Ergül , Ayça Arslan; Adams, Michelle M.
Overfeeding (OF) and obesity increase the risk for brain aging and neurodegenerative diseases due to increased oxidative stress and neuroinflammation, which likely contribute to cellular dysfunction. In contrast, caloric restriction (CR) is an intervention known for its effects on extending both life- and health-span. In the current study, the effects on the aging brain of two short-term feeding regimens, OF and CR, were investigated. We applied these diets for 12 weeks to both young and aged zebrafish. We performed protein and mRNA level analysis to examine diet-mediated effects on any potential age-related alterations in the brain. Markers implicated in the regulation of brain aging, cell cycle, proliferation, inflammation, and cytoskeleton were analyzed. The most prominent result observed was a downregulation in the expression levels of the stem cell marker, Sox2, in CR-fed animals as compared to OF-fed fish. Furthermore, our data highlighted significant age-related downregulations in Tp53, Myca, and L-plastin levels. The multivariate analyses of all datasets suggested that as opposed to OF, the adaptive mechanisms increasing lifespan via CR are likely exerting their effects by reinforcing the stem cell pool and downregulating inflammation. The data reveal important therapeutic targets with respect to the state of nutrient uptake for the slowing down of the detrimental effects of aging, resulting in a healthy and extended lifespan, as well as lowering the risk for neurodegenerative disease.
Open Access
X-space image reconstruction for lissajous trajectory using multidimensional image tensor
(Infinite Science Publishing, 2023-03-19) Ömeroglu, Osmanalp; Erol, Hasan Sabri Melihcan; Özaslan, Ali Alper; Sarıtaş, Emine Ülkü
The tensor-based theory of multidimensional x-space MPI provides useful insight into MPI image reconstruction. Using this theory, it was shown that x-space MPI images with isotropic resolution can be achieved by scanning in two orthogonal directions separately and combining the resulting images. In this work, we propose an x-space image reconstruction that resolves the multidimensional image tensor, allowing us to reconstruct the isotropic MPI image for the Lissajous trajectory. The proposed method takes advantage of the self-crossing property of the Lissajous trajectory.
Open Access
Single-pass relaxation mapping at multiple frequencies using an arbitrary waveform MPI scanner
(Infinite Science Publishing, 2023-03-19) Alyüz, Beril; Arslan, Musa Tunç; Utkur, Mustafa; Sarıtaş, Emine Ülkü
In Magnetic Particle Imaging (MPI), relaxation behavior of magnetic nanoparticles (MNPs) has enabled the infer-ence of information about different MNP types and their local environments, such as viscosity and temperature. Previously, we have proposed and demonstrated an arbitrary waveform (AW) MPI scanner that facilitates operation in a wide range of drive field (DF) frequencies by eliminating the need for impedance matching. In this work, we propose a technique for simultaneous relaxation mapping at multiple DF frequencies in a single pass using an AWMPI scanner.
Open Access
A dictionary-based algorithm for MNP signal prediction at unmeasured drive field frequencies
(Infinite Science Publishing, 2023-03-19) Alpman, Aslı; Utkur, Mustafa; Sarıtaş, Emine Ülkü
The signal in MPI depends on magnetic nanoparticle (MNP) parameters and environmental conditions, as wellas drive field (DF) settings and measurement system response. In this study, we propose a dictionary-basedalgorithm using a coupled Brown-Néel rotation model to simultaneously estimate the MNP parameters togetherwith system transfer function. We then propose an empirical method that enables signal prediction at unmeasuredDF frequencies, where measurement data is not available.
Open Access
FD-Net: an unsupervised deep forward-distortion model forsusceptibility artifact correction in EPI
(WILEY, 2023-08-15) Zaid Alkilani, Abdallah; Çukur, Tolga; Sarıtaş, Emine Ülkü
PurposeTo introduce an unsupervised deep-learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) image pairs acquired with echo planar imaging (EPI).MethodsRecent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield a corrected image. Unsupervised learning in these unwarping-based methods is commonly attained via a similarity constraint between the unwarped images in reversed-PE directions, neglecting consistency to the acquired EPI images. This work introduces a novel unsupervised deep Forward-Distortion Network (FD-Net) that predicts both the susceptibility-induced displacement field and the underlying anatomically correct image. Unlike previous methods, FD-Net enforces the forward-distortions of the correct image in both PE directions to be consistent with the acquired reversed-PE image pair. FD-Net further leverages a multiresolution architecture to maintain high local and global performance.ResultsFD-Net performs competitively with a gold-standard reference method (TOPUP) in image quality, while enabling a leap in computational efficiency. Furthermore, FD-Net outperforms recent unwarping-based methods for unsupervised correction in terms of both image and field quality.ConclusionThe unsupervised FD-Net method introduces a deep forward-distortion approach to enable fast, high-fidelity correction of susceptibility artifacts in EPI by maintaining consistency to measured data. Therefore, it holds great promise for improving the anatomical accuracy of EPI imaging.
Open Access
Parallel-stream fusion of scan-specific and scan-general priors for learning deep MRI reconstruction in low-data regimes
(Elsevier, 2023-12) Dar, Salman Ul Hassan; Öztürk, Şaban; Özbey, Muzaffer; Oğuz, Kader Karlı; Çukur, Tolga
Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan times. Reconstruction methods can alleviate this limitation by recovering clinically usable images from accelerated acquisitions. In particular, learning-based methods promise performance leaps by employing deep neural networks as data-driven priors. A powerful approach uses scan-specific (SS) priors that leverage information regarding the underlying physical signal model for reconstruction. SS priors are learned on each individual test scan without the need for a training dataset, albeit they suffer from computationally burdening inference with nonlinear networks. An alternative approach uses scan-general (SG) priors that instead leverage information regarding the latent features of MRI images for reconstruction. SG priors are frozen at test time for efficiency, albeit they require learning from a large training dataset. Here, we introduce a novel parallel-stream fusion model (PSFNet) that synergistically fuses SS and SG priors for performant MRI reconstruction in low-data regimes, while maintaining competitive inference times to SG methods. PSFNet implements its SG prior based on a nonlinear network, yet it forms its SS prior based on a linear network to maintain efficiency. A pervasive framework for combining multiple priors in MRI reconstruction is algorithmic unrolling that uses serially alternated projections, causing error propagation under low-data regimes. To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters. Demonstrations are performed on multi-coil brain MRI for varying amounts of training data. PSFNet outperforms SG methods in low-data regimes, and surpasses SS methods with few tens of training samples. On average across tasks, PSFNet achieves 3.1 dB higher PSNR, 2.8% higher SSIM, and 0.3 × lower RMSE than baselines. Furthermore, in both supervised and unsupervised setups, PSFNet requires an order of magnitude lower samples compared to SG methods, and enables an order of magnitude faster inference compared to SS methods. Thus, the proposed model improves deep MRI reconstruction with elevated learning and computational efficiency.
Open Access
Focal modulation network for lung segmentation in chest X-ray images
(2023-08-09) Öztürk, Şaban; Çukur, Tolga
Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants fall short in addressing these challenges, primarily due to the limited ability to model long-range dependencies between image features. While vision transformers equipped with self-attention mechanisms excel at capturing long-range relationships, either a coarse-grained global self-attention or a fine-grained local self-attention is typically adopted for segmentation tasks on high-resolution images to alleviate quadratic computational cost at the expense of performance loss. This paper introduces a focal modulation UNet model (FMN-UNet) to enhance segmentation performance by effectively aggregating fine-grained local and coarse-grained global relations at a reasonable computational cost. FMN-UNet first encodes CXR images via a convolutional encoder to suppress background regions and extract latent feature maps at a relatively modest resolution. FMN-UNet then leverages global and local attention mechanisms to model contextual relationships across the images. These contextual feature maps are convolutionally decoded to produce segmentation masks. The segmentation performance of FMN-UNet is compared against state-of-the-art methods on three public CXR datasets (JSRT, Montgomery, and Shenzhen). Experiments in each dataset demonstrate the superior performance of FMN-UNet against baselines.
Open Access
Personality traits prediction model from Turkish contents with semantic structures
(Springer, 2023-04-23) Kosan, Muhammed Ali; Karacan, Hacer; Ürgen, Burcu Ayşen
Users' personality traits can provide different clues about them in the Internet environment. Some areas where these clues can be used are law enforcement, advertising agencies, recruitment processes, and e-commerce applications. In this study, it is aimed to create a dataset and a prediction model for predicting the personality traits of Internet users who produce Turkish content. The main contribution of the study is the personality traits dataset composed of the Turkish Twitter content. In addition, the preprocessing, vectorization, and deep learning model comparisons made in the proposed prediction system will contribute to both current usages and future studies in the relevant literature. It has been observed that the success of the Bidirectional Encoder Representations from Transformers vectorization method and the Stemming preprocessing step on the Turkish personality traits dataset is high. In the previous studies, the effects of these processes on English datasets were reported to have lower success rates. In addition, the results show that the Bidirectional Long Short-Term Memory deep learning method has a better level of success than other methods both for the Turkish dataset and English datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Open Access
Long-term acetylcholinesterase depletion alters the levels of key synaptic proteins while maintaining neuronal markers in the aging zebrafish (Danio rerio) Brain
(S. Karger AG, 2023-10-04) Karoğlu-Eravsar, Elif Tuğçe; Tüz-Şaşik, Melek Umay; Karaduman, Ayşenur; Keşküş, Ayse Gökçe; Arslan-Ergul, Ayça; Konu, Özlen; Kafalıgönül, Hulusi; Adams, Michelle M.
Introduction: Interventions targeting cholinergic neurotransmission like acetylcholinesterase (AChE) inhibition distinguish potential mechanisms to delay age-related impairments and attenuate deficits related to neurodegenerative diseases. However, the chronic effects of these interventions are not well described. Methods: In the current study, global levels of cholinergic, cellular, synaptic, and inflammation-mediating proteins were assessed within the context of aging and chronic reduction of AChE activity. Long-term depletion of AChE activity was induced by using a mutant zebrafish line, and they were compared with the wildtype group at young and old ages. Results: Results demonstrated that AChE activity was lower in both young and old mutants, and this decrease coincided with a reduction in ACh content. Additionally, an overall age-related reduction in AChE activity and the AChE/ACh ratio was observed, and this decline was more prominent in wildtype groups. The levels of an immature neuronal marker were upregulated in mutants, while a glial marker showed an overall reduction. Mutants had preserved levels of inhibitory and presynaptic elements with aging, whereas glutamate receptor subunit levels declined. Conclusion: Long-term AChE activity depletion induces synaptic and cellular alterations. These data provide further insights into molecular targets and adaptive responses following the long-term reduction of AChE activity that was also targeted pharmacologically to treat neurodegenerative diseases in human subjects.
Open Access
Tilt aftereffect spreads across the visual field
(Elsevier, 2023-01-09) Gürbüz, Büşra Tuğçe; Boyacı, Hüseyin
The tilt aftereffect (TAE) is observed when adaptation to a tilted contour alters the perceived tilt of a subsequently presented contour. Thus far, TAE has been treated as a local aftereffect observed only at the location of the adapter. Whether and how TAE spreads to other locations in the visual field has not been systematically studied. Here, we sought an answer to this question by measuring TAE magnitudes at locations including but not limited to the adapter location. The adapter was a tilted grating presented at the same peripheral location throughout an experimental session. In a single trial, participants indicated the perceived tilt of a test grating presented after the adapter at one of fifteen locations in the same visual hemifield as the adapter. We found non-zero TAE magnitudes in all locations tested, showing that the effect spreads across the tested visual hemifield. Next, to establish a link between neuronal activity and behavioral results and to predict the possible neuronal origins of the spread, we built a computational model based on known characteristics of the visual cortex. The simulation results showed that the model could successfully capture the pattern of the behavioral results. Furthermore, the pattern of the optimized receptive field sizes suggests that mid-level visual areas, such as V4, could be critically involved in TAE and its spread across the visual field.
Open Access
Psychosis endophenotypes: a gene-set-specific polygenic risk score analysis
(Oxford University Press, 2023-08-14) Wang, B.; Irizar, H.; Thygesen, J. H.; Zartaloudi, E.; Austin-Zimmerman, I.; Bhat, A.; Harju-Seppänen, J.; Pain, O.; Bass, N.; Gkofa, V.; Alizadeh, B. Z.; Van Amelsvoort, T.; Arranz, M. J.; Bender, S.; Cahn, W.; Stella Calafato, M.; Crespo-Facorro, B.; Di Forti, M.; Giegling, I.; De Haan, L.; Hall, J.; Hall, M.; Van Haren, N.; Iyegbe, C.; Kahn, R. S.; Kravariti, E.; Lawrie, S. M.; Lin, K.; Luykx, J. J.; Mata, I.; McDonald, C.; McIntosh, A. M.; Murray, R. M.; Picchioni, M.; Powell, J.; Prata, D. P.; Rujescu, D.; Rutten, B. P. F.; Shaikh, M.; Simons, C. J. P.; Toulopoulou, Timothea; Weisbrod, M.; Van Winkel, R.; Kuchenbaecker, K.; McQuillin, A.; Bramon, E.
Background and Hypothesis: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. Study Design: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. Study Results: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 μV; 95% CI: -1.70 to -0.59 μV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. Conclusions: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.
Open Access
Passive exposure to visual motion leads to short-term changes in the optomotor response of aging zebrafish
(Cambridge University Press, 2024-03) Karaduman, Ayşenur; Karaoğlu-Eravşar, Elif Tuğçe; Adams, Michelle M.; Kafalıgönül, Hulusi
Numerous studies have shown that prior visual experiences play an important role in sensory processing and adapting behavior in a dynamic environment. A repeated and passive presentation of visual stimulus is one of the simplest procedures to manipulate acquired experiences. Using this approach, we aimed to investigate exposure- based visual learning of aging zebrafish and how cholinergic intervention is involved in exposure-induced changes. Our measurements included younger and older wild-type zebrafish and achesb55/+mutants with decreased acetylcholinesterase activity. We examined both within-session and across-day changes in the zebrafish optomotor responses to repeated and passive exposure to visual motion. Our findings revealed short- term (within-session) changes in the magnitude of optomotor response (i.e., the amount of position shift by fish as a response to visual motion) rather than long-term and persistent effects across days. Moreover, the observed short-term changes were age- and genotype-dependent. Compared to the initial presentations of motion within a session, the magnitude of optomotor response to terminal presentations decreased in the older zebrafish. There was a similar robust decrease specific to achesb55/+mutants. Taken together, these results point to short- term (within-session) alterations in the motion detection of adult zebrafish and suggest differential effects of neural aging and cholinergic system on the observed changes. These findings further provide important insights into adult zebrafish optomotor response to visual motion and contribute to understanding this reflexive behavior in the short- and long-term stimulation profiles.
Open Access
Impacts of biophilic design on the development of gerotranscendence and the profile of mood states during the COVID-19 pandemic
(Cambridge University Press, 2023-11-16) Afacan, Yasemin
To live in a good mood is not only a key consideration for future age-friendly communities, but also a critical necessity for positive ageing. Despite growing evidence of correlations between contact with nature and stress reduction, little is known about the effect of nature integration in indoor environments. Thus, this study aimed to answer the following research questions: (a) How do biophilic characteristics of home environments correlate with older adults’ experience of the multiple levels of the theory of gerotranscendence? and (b) What is the relationship between these experiences and the mood states of these older adults? The study was based on a comparative analysis to scrutinise the impact of the COVID-19 pandemic on these questions. The data were gathered through questionnaires with 450 older adults aged between 65 and 95 years, and stratified by the biophilic characteristics of their living environments: indoor biophilic, outdoor biophilic and non-biophilic. Two sets of data were collected with the same participants, respectively, before the COVID-19 pandemic (June to October 2018) and during the COVID-19 pandemic (June to October 2020). It found that the biophilic characteristics of home environments are correlated dynamically with older adults’ ageing experience and mood states. The study indicates that outdoor biophilic features facilitate the recovery of tension mood effects of the COVID-19 pandemic, whereas indoor biophilic features facilitate recovery from depression and anger.
Open Access
Unmet expectations about material properties delay perceptual decisions
(Elsevier, 2023-05-21) Malik, Amna; Doerschner, Katja; Boyacı, Hüseyin
Based on our expectations about material properties, we can implicitly predict an object’s future states, e.g., a wine glass falling down will break when it hits the ground. How these expectations affect relatively low-level perceptual decisions, however, has not been systematically studied previously. To seek an answer to this question, we conducted a behavioral experiment using animations of various familiar objects (e.g., key, wine glass, etc.) freely falling and hitting the ground. During a training session, participants first built expectations about the dynamic properties of those objects. Half of the participants (N = 28) built expectations consistent with their daily lives (e.g., a key bounces rigidly), whereas the other half learned an atypical behavior (e.g., a key wobbles). This was followed by experimental sessions, in which expectations were unmet in 20% of the trials. In both training and experimental sessions, the participant’s task was to report whether the objects broke or not upon hitting the ground. Critically, a specific object always remained intact or broke - only the manner in which it did so differed. For example, a key could wobble or remain rigid but never break. We found that participants’ reaction times were longer when expectations were unmet, not only for typical material behavior but also when those expectations were atypical and learned during the training session. Furthermore, we found an interplay between long-term and newly learned expectations. Overall, our results show that expectations about material properties can impact relatively low-level perceptual decision-making processes.
Open Access
Neural processing of bottom-up perception of biological motion under attentional load
(Elsevier, 2023-11-04) Nizamoğlu, Hilal; Ürgen, Burcu Ayşen
Considering its importance for one’s survival and social significance, biological motion (BM) perception is assumed to occur automatically. Previous behavioral results showed that task-irrelevant BM in the periphery interfered with task performance at the fovea. Under selective attention, BM perception is supported by a network of regions including the occipito-temporal (OTC), parietal, and premotor cortices. Retinotopy studies that use BM stimulus showed distinct maps for its processing under and away from selective attention. Based on these findings, we investigated how bottom-up perception of BM would be processed in the human brain under attentional load when it was shown away from the focus of attention as a task-irrelevant stimulus. Participants (N = 31) underwent an fMRI study in which they performed an attentionally demanding visual detection task at the fovea while intact or scrambled point light displays of BM were shown at the periphery. Our results showed the main effect of attentional load in fronto-parietal regions and both univariate activity maps and multivariate pattern analysis results support the attentional load modulation on the task-irrelevant peripheral stimuli. However, this effect was not specific to intact BM stimuli and was generalized to motion stimuli as evidenced by the motion-sensitive OTC involvement during the presence of dynamic stimuli in the periphery. These results confirm and extend previous work by showing that task-irrelevant distractors can be processed by stimulus-specific regions when there are enough attentional resources available. We discussed the implications of these results for future studies.
Open Access
A large video set of natural human actions for visual and cognitive neuroscience studies and its validation with fMRI
(MDPI, 2022-12-29) Ürgen, Burcu Ayşen; Nizamoğlu, Hilal; Eroğlu, Aslı; Orban, G. A.
The investigation of the perception of others’ actions and underlying neural mechanisms has been hampered by the lack of a comprehensive stimulus set covering the human behavioral repertoire. To fill this void, we present a video set showing 100 human actions recorded in natural settings, covering the human repertoire except for emotion-driven (e.g., sexual) actions and those involving implements (e.g., tools). We validated the set using fMRI and showed that observation of the 100 actions activated the well-established action observation network. We also quantified the videos’ low-level visual features (luminance, optic flow, and edges). Thus, this comprehensive video set is a valuable resource for perceptual and neuronal studies.
Open Access
Highly potent peptide therapeutics to prevent protein aggregation in huntington’s disease
(American Chemical Society, 2023-12-14) Khan, Anooshay; Özçelik, Cemile Elif; Begli, Özge; Oğuz, Oğuzhan; Kesici, M. S.; Kasırga, Talip Serkan; Özçubukcu, S.; Yuca, E.; Şeker, Urartu Özgür Şafak
Huntington’s disease (HD) is a neurodegenerative disorder resulting from a significant amplification of CAG repeats in exon 1 of the Huntingtin (Htt) gene. More than 36 CAG repeats result in the formation of a mutant Htt (mHtt) protein. These amino-terminal mHtt fragments lead to the formation of misfolded proteins, which then form aggregates in the relevant brain regions. Therapies that can delay the progression of the disease are imperative to halting the course of the disease. Peptide-based drug therapies provide such a platform. Inhibitory peptides were screened against monomeric units of both wild type (Htt(Q25)) and mHtt fragments, Htt(Q46) and Htt(Q103). Fibril kinetics was studied by utilizing the Thioflavin T (ThT) assay. Atomic force microscopy was also used to study the influence of the peptides on fibril formation. These experiments demonstrate that the chosen peptides suppress the formation of fibrils in mHtt proteins and can provide a therapeutic lead for further optimization and development.