Biology subjects

Akcakaya, M.

Publications and source records attributed to Akcakaya, M..

7 recordsLinked to original sources

bioRxiv2026

A rapid, machine-learning readout of cochlear neural degeneration that transfers from animals to humans

Standard hearing tests miss cochlear neural degeneration (CND), the loss of synapses between inner hair cells and auditory nerve fibers that may underlie unexplained hearing difficulty in roughly one in ten adults. Envelope following responses (EFRs) can index this neural loss noninvasively, but conventional protocols are too slow for clinical use. We tested a dynamic amplitude-modulated (dAM) EFR that sweeps the full modulation spectrum in a single brief stimulus. In Mongolian gerbils with histologically verified CND, dAM EFRs revealed selective deficits at fast modulation rates without threshold elevation. A machine-learning classifier distinguished young from middle-aged animals with high accuracy; additionally, its most informative feature, power near 400-500 Hz, tracked synapse counts, indicating a readout of cochlear neural health rather than age. Applied without retraining to 56 human listeners, the gerbil-trained classifier separated age groups above chance. dAM EFRs thus offer a rapid, objective and clinically viable marker of CND.

neuroscience↗

bioRxiv2026

Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-Tasking

High performance intracortical brain-computer interface (iBCI) control has been demonstrated in research settings, but performance can still vary within and between sessions. One potential source of this variability is the change in attentional load that comes from processing naturally occurring distractors such as thoughts, sounds, fatigue, or pain. To improve the consistency of iBCI performance in real-world environments where this sort of multi-tasking is inevitable, we must understand how shifts in attention can impact performance. Here we examined the effect of attentional load on iBCI performance and movement-related neural activity using a 2D cursor translation + click iBCI task paired with an N-Back working memory task to increase attentional load during dual-task performance. Two participants (P2 and P4) with tetraplegia completed the study while enrolled in a long-term clinical trial of an iBCI device (NCT1894802). Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). While the EEG recordings and difficulty ratings suggested increased attentional load during dual tasking, iBCI performance was quite robust across the various dual tasking conditions. One participant, P2, experienced a small but significant increase in trial completion time and normalized path length during the mild attentional load condition. Signal quality differences between the two participants may have impacted the results, as P2 had lower signal quality and was therefore likely more vulnerable to attentional load. P4s higher signal quality likely allowed him to accommodate increased attentional load without a drop in performance. Overall, iBCI performance appears to be robust to attentional load, but the complex trends observed here reflect a need for continued investigation of BCI use under different cognitive states to elucidate potential challenges and compensatory mechanisms across participants.

bioengineering↗

bioRxiv2024

Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces

Brain-computer interfaces (BCIs) exploit brain activity to bypass neuromuscular control with the aim of providing alternative means of communication with the surrounding environment. Such systems can significantly improve the quality of life for patients suffering from severe motor or speech impairment. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to improve the performance of multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD). The BCIs included in the study utilized two different paradigms to infer user intent including motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. Filter Bank Common Spatial Pattern (FBCSP) algorithm was used to extract features from the EEG data. Several time series features were extracted from the envelope of the fTCD signals. Wilcoxon rank sum test and linear kernel Support vector machines (SVM) were used for feature selection and classification respectively. Additionally, a probabilistic Bayesian fusion approach was used to fuse the information from EEG and fTCD modalities. Average accuracies of 94.53%, 94.9% and 96.29% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Whereas average accuracies of 95.27%, 85.93% and 96.97% were achieved for MR versus baseline, WG versus baseline, and MR versus WG respectively. Our results show that EEG- fTCD BCIs with the proposed analysis techniques outperformed the multimodal EEG-fNRIS BCIs in comparison.

bioengineering↗

bioRxiv2023

Locally low-rank denoising in transform domains

PurposeTo develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and MethodsLLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. ResultsFor both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. ConclusionA transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.

scientific communication and education↗

bioRxiv2023

High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI

Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard ECG-gated breath-hold segmented cine MRI for evaluation of heart function. Real-time cine MRI data acquisition during free breathing snapshot imaging enables imaging of patient cohorts that cannot be imaged with segmented or breath-hold acquisitions, but requires rapid imaging to achieve sufficient spatial-temporal resolutions. However, at high acceleration rates, conventional reconstruction techniques suffer from residual aliasing and temporal blurring, including advanced methods such as compressed sensing with radial trajectories. Recently, deep learning (DL) reconstruction has emerged as a powerful tool in MRI. However, its utility for free-breathing real-time cine MRI has been limited, as database-learning of spatio-temporal correlations with varying breathing and cardiac motion patterns across subjects has been challenging. Zero-shot self-supervised physics-guided deep learning (PG-DL) reconstruction has been proposed to overcome such challenges of database training by enabling subject-specific training. In this work, we adapt zeroshot PG-DL for real-time cine MRI with a spatio-temporal regularization. We compare our method to TGRAPPA, locally low-rank (LLR) regularized reconstruction and database-trained PG-DL reconstruction, both for retrospectively and prospectively accelerated datasets. Results on highly accelerated real-time Cartesian cine MRI show that the proposed method outperforms other reconstruction methods, both visibly in terms of noise and aliasing, and quantitatively.

bioengineering↗

bioRxiv2020

A Paradigm Change in Functional Brain Mapping: Suppressing the Thermal Noise in fMRI

Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. However, virtually all fMRI studies have relatively poor signal-to-noise ratio (SNR). Here we introduce a novel fMRI denoising technique, which suppresses noise that is indistinguishable from zero-mean, Gaussian-distributed noise. Thermal noise, falling in this category, is a major source of noise in fMRI, particularly, but not exclusively, at high spatial and/or temporal resolutions. Using 7-Tesla high-resolution data, we demonstrate improvements in temporal-SNR, the detection of stimulus-induced signal changes, and functional maps, while leaving stimulus-induced signal change amplitudes, image spatial precision, and functional point-spread-function unaltered. We also show that the method is equally applicable when using supra-millimeter resolution 3- and 7-Tesla fMRI data, different cortical regions, stimulation/task paradigms, and acquisition strategies. This denoising approach improves key metrics of functional activation detection while preserving spatial precision.

neuroscience↗

bioRxiv2020

NOise Reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing

Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with Distribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method. HighlightsO_LIWe propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statistics C_LIO_LIThe effectiveness of the proposed denoising method is distinguished by the ability to remove only signal which cannot be distinguished from thermal noise C_LIO_LIThe proposed method outperforms a state-of-art method for denoising dMRI in terms of fiber orientation dispersion C_LIO_LIQuantitative evaluation of NORDIC across different resolutions and SNR using human connectome type acquisitions and analysis shows up to 6 fold improvement in apparent SNR for 0.9mm whole brain dMRI at 3T. C_LI

neuroscience↗