Task-induced subjective fatigue and resting-state striatal connectivity following traumatic brain injury

Decoding visual fatigue in a visual search task selectively manipulated via myopia-correcting lenses

Introduction

Visual fatigue resulting from sustained, high-workload visual activities can significantly impact task performance and general wellbeing. So far, however, little is known about the underlying brain networks of visual fatigue. This study aimed to identify such potential networks using a unique paradigm involving myopia-correcting lenses known to directly modulate subjectively-perceived fatigue levels.

Methods

A sample of N = 31 myopia participants [right eye-SE: -3.77D (SD: 2.46); left eye-SE: -3.75D (SD: 2.45)] performed a demanding visual search task with varying difficulty levels, both with and without the lenses, while undergoing fMRI scanning. There were a total of 20 trials, after each of which participants rated the perceived difficulty and their subjective visual fatigue level. We used representational similarity analysis to decode brain regions associated with fatigue and difficulty, analyzing their individual and joint decoding pattern.

Results and discussion

Behavioral results showed correlations between fatigue and difficulty ratings and above all a significant reduction in fatigue levels when wearing the lenses. Imaging results implicated the cuneus, lingual gyrus, middle occipital gyrus (MOG), and declive for joint fatigue and difficulty decoding. Parts of the lingual gyrus were able to selectively decode perceived difficulty. Importantly, a broader network of visual and higher-level association areas showed exclusive decodability of fatigue (culmen, middle temporal gyrus (MTG), parahippocampal gyrus, precentral gyrus, and precuneus). Our findings enhance our understanding of processing within the context of visual search, attention, and mental workload and for the first time demonstrate that it is possible to decode subjectively-perceived visual fatigue during a challenging task from imaging data. Furthermore, the study underscores the potential of myopia-correcting lenses in investigating and modulating fatigue.

Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study

BACKGROUND

Post-stroke fatigue (PSF) was a common complication after stroke. This study aimed to explore the neuroimaging mechanism of PSF, which was rarely studied.

METHODS

Patients with the first episode of ischemic stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University between March 2021 and December 2022. The fatigue severity scale (FSS) was used to assess fatigue symptoms. PSF was diagnosed by a neurologist based on the FSS score and PSF diagnostic criteria. All the patients were scanned by resting-state functional MRI (rs-fMRI). Precuneus, the posterior node of default-mode network (pDMN), was related to fatigue. Therefore, imaging data were further analyzed by the seed-based resting-state functional connectivity (FC) approach, with the left (PCUN.L) and right precuneus (PCUN.R) being the seeds.

RESULTS

A total of 70 patients with acute ischemic stroke were finally recruited, comprising 40 patients with PSF and 30 patients without PSF. Both the PCUN.L and PCUN.R seeds (pDMN) exhibited decreased FC with the prefrontal lobes located at the anterior part of DMN (aDMN), and the FC values were negatively correlated with FSS scores (both p < 0.001). These two seeds also exhibited increased FC with the right insula, and the FC values were positively correlated with FSS scores (both p < 0.05).

CONCLUSION

The abnormal FC between the aDMN and pDMN was associated with PSF. Besides, the insula, related to interoception, might also play an important role in PSF.

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79-91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings.

Advanced Neuroimaging Role in Traumatic Brain Injury: A Narrative Review

Traumatic brain injury (TBI) is a common source of morbidity and mortality among civilians and military personnel. Initial routine neuroimaging plays an essential role in rapidly assessing intracranial injury that may require intervention. However, in the context of TBI, limitations of routine neuroimaging include poor visualization of more subtle changes of brain parenchymal after injury, poor prognostic ability and inability to analyze cerebral perfusion, metabolite and mechanical properties. With the development of modern neuroimaging techniques, advanced neuroimaging techniques have greatly boosted the studies in the diagnosis, prognostication, and eventually impacting treatment of TBI. Advances in neuroimaging techniques have shown potential, including (1) Ultrasound (US) based techniques (contrast-enhanced US, intravascular US, and US elastography), (2) Magnetic resonance imaging (MRI) based techniques (diffusion tensor imaging, magnetic resonance spectroscopy, perfusion weighted imaging, magnetic resonance elastography and functional MRI), and (3) molecular imaging based techniques (positron emission tomography and single photon emission computed tomography). Therefore, in this review, we aim to summarize the role of these advanced neuroimaging techniques in the evaluation and management of TBI. This review is the first to combine the role of the US, MRI and molecular imaging based techniques in TBI. Advanced neuroimaging techniques have great potential; still, there is much to improve. With more clinical validation and larger studies, these techniques will be likely applied for routine clinical use from the initial research.