A picture is worth a thousand words: Framing of food choice options affects decision conflict and mid-fontal theta in food choice task

In food choices, conflict arises when choosing between a healthy, but less tasty food item and a tasty, but less healthy food item. The underlying assumption is that people trade-off the health and taste properties of food items to reach a decision. To probe this assumption, we presented food items either as colored images (image condition, e.g. photograph of a granola bar) or as pre-matched percentages of taste and health values (text condition, e.g., 20% healthy and 80% tasty). We recorded choices, response times and electroencephalography activity to calculate mid-frontal theta power as a marker of conflict. At the behavioral level, we found higher response times for healthy compared to unhealthy choices, and for difficult compared to easy decisions in both conditions, indicating the experience of a decision conflict. At the neural level, mid-frontal theta power was higher for healthy choices than unhealthy choices and difficult choices compared to easy choices, but only in the image condition. Those results suggest that either conflict type and/or decision strategies differ between the image and text conditions. The present results can be helpful in understanding how dietary decisions can be influenced towards healthier food choices.

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding

Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.

Electrophysiological markers of vestibular-mediated self-motion perception - A pilot study

Peripheral vestibular activation results in multi-level responses, from brainstem-mediated reflexes (e.g. vestibular ocular reflex - VOR) to perception of self-motion. While VOR responses indicate preserved vestibular peripheral and brainstem functioning, there are no automated measures of vestibular perception of self-motion - important since some patients with brain disconnection syndromes manifest a vestibular agnosia (intact VOR but impaired self-motion perception). Electroencephalography ('EEG') - may provide a surrogate marker of vestibular perception of self-motion. A related objective is obtaining an EEG marker of vestibular sensory signal processing, distinct from vestibular-motion perception. We performed a pilot study comparing EEG responses in the dark when healthy participants sat in a vibrationless computer-controlled motorised rotating chair moving at near threshold of self-motion perception, versus a second situation in which subjects sat in the chair at rest in the dark who could be induced (or not) into falsely perceiving self-motion. In both conditions subjects could perceive self-motion perception, but in the second there was no bottom-up reflex-brainstem activation. Time-frequency analyses showed: (i) alpha frequency band activity is linked to vestibular sensory-signal activation; and (ii) theta band activity is a marker of vestibular-mediated self-motion perception. Consistent with emerging animal data, our findings support the role of theta activity in the processing of self-motion perception.

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments

INTRODUCTION AND PURPOSE

We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security.

METHODS AND RESULTS

The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing.

DISCUSSION AND CONCLUSIONS

The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Cerebral microhemorrhages (CMHs, also known as cerebral microbleeds) are a critical but frequently underestimated aspect of cerebral small vessel disease (CSVD), bearing substantial clinical consequences. Detectable through sensitive neuroimaging techniques, CMHs reveal an extensive pathological landscape. They are prevalent in the aging population, with multiple CMHs often being observed in a given individual. CMHs are closely associated with accelerated cognitive decline and are increasingly recognized as key contributors to the pathogenesis of vascular cognitive impairment and dementia (VCID) and Alzheimer's disease (AD). This review paper delves into the hypothesis that atherosclerosis, a prevalent age-related large vessel disease, extends its pathological influence into the cerebral microcirculation, thereby contributing to the development and progression of CSVD, with a specific focus on CMHs. We explore the concept of vascular aging as a continuum, bridging macrovascular pathologies like atherosclerosis with microvascular abnormalities characteristic of CSVD. We posit that the same risk factors precipitating accelerated aging in large vessels (i.e., atherogenesis), primarily through oxidative stress and inflammatory pathways, similarly instigate accelerated microvascular aging. Accelerated microvascular aging leads to increased microvascular fragility, which in turn predisposes to the formation of CMHs. The presence of hypertension and amyloid pathology further intensifies this process. We comprehensively overview the current body of evidence supporting this interconnected vascular hypothesis. Our review includes an examination of epidemiological data, which provides insights into the prevalence and impact of CMHs in the context of atherosclerosis and CSVD. Furthermore, we explore the shared mechanisms between large vessel aging, atherogenesis, microvascular aging, and CSVD, particularly focusing on how these intertwined processes contribute to the genesis of CMHs. By highlighting the role of vascular aging in the pathophysiology of CMHs, this review seeks to enhance the understanding of CSVD and its links to systemic vascular disorders. Our aim is to provide insights that could inform future therapeutic approaches and research directions in the realm of neurovascular health.

Hippocampal infarction and generalized seizures predict early mortality after endovascular middle cerebral artery occlusion in mice

Endovascular middle cerebral artery occlusion (MCAO) is a widely used experimental ischemic stroke model. However, the model carries high early mortality. Our aim was to investigate the factors that influence early mortality within 48 h of reperfusion after transient MCAO. Using C57BL/6 mice, we induced 1-hour endovascular filament MCAO. To introduce heterogeneity of infarct volumes, a subset of animals had additional tandem common carotid artery occlusion (MCAO+CCAO). Continuous video monitoring was used to gain insight into the cause of death. Mortality within 48 h was 25% in the pooled cohort. All animals with early mortality suffered from infarcts in the hippocampus, sometimes accompanied by infarcts in the thalamus and midbrain, which occurred exclusively in the MCAO+CCAO group. All animals with early mortality developed convulsive seizures captured on video monitoring. None of the animals that did not develop convulsive seizures died. Among the three regions, hippocampal infarction appeared necessary for convulsive seizures and early mortality. Our data highlight seizures as the primary cause of mortality within the first 48 h after endovascular filament MCAO, linked to hippocampal infarction. Since hippocampal blood supply is mainly from the posterior cerebral artery (PCA), avoiding concurrent PCA ischemia can decrease mortality in proximal MCAO models.

ADSC-derived exosomes provide neuroprotection in sepsis-associated encephalopathy by regulating hippocampal pyroptosis

AIMS

Adipose-derived stem cell (ADSC)-derived exosomes have been recognized for their neuroprotective effects in various neurological diseases. This study investigates the potential neuroprotective effects of ADSC-derived exosomes in sepsis-associated encephalopathy (SAE).

METHODS

Behavioral cognitive functions were evaluated using the open field test, Y-maze test, and novel object recognition test. Brain activity was assessed through functional magnetic resonance imaging (fMRI). Pyroptosis was measured using immunofluorescence staining and western blotting.

RESULTS

Our findings indicate that ADSC-derived exosomes mitigate cognitive impairment, improve survival rates, and prevent weight loss in SAE mice. Additionally, exosomes protect hippocampal function in SAE mice, as demonstrated by fMRI evaluations. Furthermore, SAE mice exhibit neuronal damage and infiltration of inflammatory cells in the hippocampus, conditions which are reversed by exosome treatment. Moreover, our study highlights the downstream regulatory role of the NLRP3/caspase-1/GSDMD signaling pathway as a crucial mechanism in alleviating hippocampal inflammation.

CONCLUSION

ADSC-derived exosomes confer neuroprotection in SAE models by mediating the NLRP3/caspase-1/GSDMD pathway, thereby ameliorating cognitive impairment.

Cerebral hypoperfusion exacerbates vascular dysfunction after traumatic brain injury

Traumatic brain injuries are extremely common, and although most patients recover from their injuries many TBI patients suffer prolonged symptoms and remain at a higher risk for developing cardiovascular disease and neurodegeneration. Moreover, it remains challenging to identify predictors of poor long-term outcomes. Here, we tested the hypothesis that preexisting cerebrovascular impairment exacerbates metabolic and vascular dysfunction and leads to worse outcomes after TBI. Male mice underwent a mild surgical reduction in cerebral blood flow using a model of bilateral carotid artery stenosis (BCAS) wherein steel microcoils were implanted around the carotid arteries. Then, 30 days post coil implantation, mice underwent TBI or sham surgery. Gene expression profiles, cerebral blood flow, metabolic function, oxidative damage, vascular health and angiogenesis were assessed. Single nuclei RNA sequencing of endothelial cells isolated from mice after TBI showed differential gene expression profiles after TBI and BCAS, that were further altered when mice underwent both challenges. TBI but not BCAS increased mitochondrial oxidative metabolism. Both BCAS and TBI decreased cerebrovascular responses to repeated whisker stimulation. BCAS induced oxidative damage and inflammation in the vasculature as well as loss of vascular density, and reduced the numbers of angiogenic tip cells. Finally, intravascular protein accumulation was increased among mice that experienced both BCAS and TBI. Overall, our findings reveal that a prior vascular impairment significantly alters the profile of vascular health and function of the cerebrovasculature, and when combined with TBI may result in worsened outcomes.

Relationship of the accessory maxillary ostium to mucosal thickening and maxillary sinus cysts: a 3D segmentation study

OBJECTIVES

We aimed to investigate the relationship between the presence of the accessory maxillary ostium (AMO) with mucosal thickening (MT) and maxillary sinus cysts (MSCs).

STUDY DESIGN

This single-center retrospective study included patients who underwent paranasal sinus computed tomography (CT) between November 2022 and February 2023. We excluded patients with natural maxillary ostium (NMO) occlusion. Two radiologists evaluated the presence of AMOs, MT, and MSCs in CT images and measured their dimensions using a three-dimensional volumetric analysis program.

RESULTS

We included 125 patients (mean age: 36.47 ± 16.0 years; male/female: 67/58). In total, 65 patients (52%) had single or multiple AMOs. No significant relationship was found between the presence of an AMO and the presence of MT on either side (P ≥ .306). There was a statistically significant difference between the presence of an AMO and MSC on the right side (P = .002), but not on the left side (P = .477). The diameter of the AMO and the distance from the AMO to the maxillary sinus floor were not correlated with mucosal thickness or MSC volume, but the distance from the AMO to the sinus floor had a small positive correlation with total maxillary sinus volume.

CONCLUSIONS

The presence of an AMO and its increased diameter may not contribute to ipsilateral maxillary sinusitis in patients whose unilateral or bilateral NMOs are not occluded.

Diffusion tensor transformation for personalizing target volumes in radiation therapy

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

A library for innovative category exemplars (ALICE) database: Streamlining research with printable 3D novel objects

This paper introduces A Library for Innovative Category Exemplars (ALICE) database, a resource that enhances research efficiency in cognitive and developmental studies by providing printable 3D objects representing 30 novel categories. Our research consists of three experiments to validate the novelty and complexity of the objects in ALICE. Experiment 1 assessed the novelty of objects through adult participants' subjective familiarity ratings and agreement on object naming and descriptions. The results confirm the general novelty of the objects. Experiment 2 employed multidimensional scaling (MDS) to analyze perceived similarities between objects, revealing a three-dimensional structure based solely on shape, indicative of their complexity. Experiment 3 used two clustering techniques to categorize objects: k-means clustering for creating nonoverlapping global categories, and hierarchical clustering for allowing global categories that overlap and have a hierarchical structure. Through stability tests, we verified the robustness of each clustering method and observed a moderate to good consensus between them, affirming the strength of our dual approach in effectively and accurately delineating meaningful object categories. By offering easy access to customizable novel stimuli, ALICE provides a practical solution to the challenges of creating novel physical objects for experimental purposes.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index

PURPOSE

For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design.

METHODS

By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects.

RESULTS

The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences.

CONCLUSION

We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Serotonergic neuromodulation of synaptic plasticity

Synaptic plasticity constitutes a fundamental process in the reorganization of neural networks that underlie memory, cognition, emotional responses, and behavioral planning. At the core of this phenomenon lie Hebbian mechanisms, wherein frequent synaptic stimulation induces long-term potentiation (LTP), while less activation leads to long-term depression (LTD). The synaptic reorganization of neuronal networks is regulated by serotonin (5-HT), a neuromodulator capable of modify synaptic plasticity to appropriately respond to mental and behavioral states, such as alertness, attention, concentration, motivation, and mood. Lately, understanding the serotonergic Neuromodulation of synaptic plasticity has become imperative for unraveling its impact on cognitive, emotional, and behavioral functions. Through a comparative analysis across three main forebrain structures-the hippocampus, amygdala, and prefrontal cortex, this review discusses the actions of 5-HT on synaptic plasticity, offering insights into its role as a neuromodulator involved in emotional and cognitive functions. By distinguishing between plastic and metaplastic effects, we provide a comprehensive overview about the mechanisms of 5-HT neuromodulation of synaptic plasticity and associated functions across different brain regions.

Relationships between the gut microbiome and brain functional alterations in first-episode, drug-naïve patients with major depressive disorder

OBJECTIVE

Increasing evidence has shown that the microbiota-gut-brain axis (MGB) is involved in the mechanism of major depressive disorder (MDD). However, the relationship between the gut microbiome and brain function in MDD patients has not been determined. Here, we intend to identify specific changes in the gut microbiome and brain function in first-episode, drug-naïve MDD patients and then explore the associations between the two omics to elucidate how the MGB axis plays a role in MDD development.

METHODS

We recruited 38 first-episode, drug-naïve MDD patients and 37 healthy controls (HC). The composition of the fecal microbiome and neural spontaneous activity alterations were examined using 16S rRNA gene amplicon sequencing analysis and regional homogeneity (ReHo). Spearman correlation analyses were conducted to assess the associations between the gut microbiome and brain function.

RESULTS

Compared with HC, MDD patients exhibited distinct alterations in the gut microbiota and elevated ReHo in the frontal regions. In the MDD group, a positive relationship was noted between the relative abundance of Blautia and the HAMD-17 and HAMA scores, as well as between the relative abundance of Oxalobacteraceae and the HAMD-17 score. The relative abundances of Porphyromonadaceae and Parabacteroides were negatively correlated with the ReHo values of frontal regions.

LIMITATIONS

Our study utilized a cross-sectional design, and the number of subjects was relatively small.

CONCLUSION

We found that some specific gut microbiomes were associated with frontal function, and others were associated with clinical symptoms in MDD patients, which may support the role of the MGB axis underlying MDD.

Lumbosacral plexus and pudendal nerve magnetic resonance tractography: A systematic review of the clinical applications for pudendal neuralgia

Diffusion tensor imaging (DTI) is commonly used to establish three-dimensional mapping of white-matter bundles in the supraspinal central nervous system. DTI has also been the subject of many studies on cranial and peripheral nerves. This non-invasive imaging technique enables virtual dissection of nerves in vivo and provides specific measurements of microstructural integrity. Adverse effects on the lumbosacral plexus may be traumatic, compressive, tumoral, or malformative and thus require dedicated treatment. DTI could lead to new perspectives in pudendal neuralgia diagnosis and management. We performed a systematic review of all articles or posters reporting results and protocols for lumbosacral plexus mapping using the DTI technique between January 2011 and December 2023. Twenty-nine articles published were included. Ten studies with a total of 351 participants were able to track the lumbosacral plexus in a physiological context and 19 studies with a total of 402 subjects tracked lumbosacral plexus in a pathological context. Tractography was performed on a 1.5T or 3T MRI system. DTI applied to the lumbosacral plexus and pudendal nerve is feasible but no microstructural normative value has been proposed for the pudendal nerve. The most frequently tracking parameters used in our review are: 3T MRI, b-value of 800 s/mm2, 33 directions, 3 × 3 × 3 mm3, AF threshold of 0.1, minimum fiber length of 10 mm, bending angle of 30°, and 3DT2 TSE anatomical resolution. Increased use of DTI could lead to new perspectives in the management of pudendal neuralgia due to entrapment syndrome, whether at the diagnostic, prognostic, or preoperative planning level. Prospective studies of healthy subjects and patients with the optimal acquisition parameters described above are needed to establish the accuracy of MR tractography for diagnosing pudendal neuralgia and other intrapelvic nerve entrapments.

Homogeneous B0 coil design method for open-access ultra-low field magnetic resonance imaging: A simulation study

A multimodal brain function measurement system integrating functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) is expected to be a tool that will provide new insights into neuroscience. To integrate fMRI and MEG, an ultra-low-field MRI (ULF-MRI) scanner that can generate a static magnetic field (B0) with an electromagnetic coil and turn off the B0 during MEG measurements is desirable. While electromagnetic B0 coil has the above advantages, it also has a trade-off between size and the broadness of the magnetic field homogeneity. In this study, we proposed a method for designing a B0 multi-stage circular coil arrangement that determines the number of coils required to maximize magnetic field homogeneity and minimize the total wiring length of the coils. The optimized multi-stage coil arrangement had an external shape of 600 mm in diameter and a maximum height of 600 mm, with an aperture of 600 mm in diameter and 300 mm in height. The magnetic field homogeneity was <100 ppm over a 210 mm diameter spherical volume (DSV). Compared to a previous two coil pairs arrangement with the same magnetic field homogeneity, the diameter was 1/1.9 times smaller, indicating that the newly designed B0 coil arrangement realized a smaller size and wider magnetic field homogeneity.

Effects of focal cortical cooling on somatosensory evoked potentials in rats

Although the focal brain cooling technique is widely used to examine brain function, the effects of cortical temperature at various levels on sensory information processing and neural mechanisms remain underexplored. To elucidate the mechanisms of temperature modulation in somatosensory processing, this study aimed to examine how P1 and N1 deflections of somatosensory evoked potentials (SEPs) depend on cortical temperature and how excitatory and inhibitory inputs contribute to this temperature dependency. SEPs were generated through electrical stimulation of the contralateral forepaw in anesthetized rats. The SEPs were recorded while cortical temperatures were altered between 17-38 °C either without any antagonists, with a gamma-aminobutyric acid type A (GABAA) receptor antagonist (gabazine), with an aminomethylphosphonic acid (AMPA) receptor antagonist (NBQX), or with an N-Methyl-D-aspartic acid (NMDA) receptor antagonist ([R]-CPP). The effects of different gabazine concentrations (0, 1, and 10 µM) were examined in the 35-38 °C range. The P1/N1 amplitudes and their peak-to-peak differences plotted against cortical temperature showed an inverted U relationship with a maximum at approximately 27.5 °C when no antagonists were administered. The negative correlation between these amplitudes and temperatures of ≥ 27.5 °C plateaued after gabazine administration, which occurred progressively as the gabazine concentration increased. In contrast, the correlation remained negative after the administration of NBQX and (R)-CPP. These results suggest that GABAergic inhibitory inputs contribute to the negative correlation between SEP amplitude and cortical temperature around the physiological cortical temperature.

The effects of experimental pain on episodic memory and its top-down modulation: a preregistered pooled analysis

Introduction

Pain can automatically interfere with ongoing cognitive processes such as attention and memory. The extent of pain's negative effects on cognitive functioning seems to depend on a balance between top-down and bottom-up factors.

Objectives

In this large, preregistered, pooled reanalysis of 8 studies, we investigated the robustness of the detrimental effect of acute pain on recognition memory and whether top-down mechanisms such as pain-related expectations or cognitions (pain-related fear, pain catastrophizing) modulate this effect.

Methods

Two hundred forty-seven healthy participants underwent similar experimental paradigms, including a visual categorization task with images randomly paired with (or without) concomitant painful stimulation and a subsequent unannounced recognition task. Recognition memory (ie, d', recollection, and familiarity) and categorization performance (ie, reaction time, accuracy) served as proxies for the effect of pain on cognitive performance.

Results

Acute painful stimulation significantly impaired recognition performance (d', familiarity). However, recognition performance was not significantly modulated by participants' expectations regarding the effect of pain on task performance or pain-related cognitions in this sample of healthy participants.

Conclusion

Our results corroborate the negative effects of pain on (visual) memory encoding reported in previous studies and reports of "memory problems" from patients with chronic pain. To characterize the role of bottom-up and top-down factors for the detrimental effects of pain, large-scale studies with more nuanced study designs are necessary. Future studies in patient cohorts must unravel the interaction of maladaptive pain-related cognitions and the often-reported impaired cognitive performance in chronic pain patients.

Improving mentalizing deficits in older age with region-specific transcranial direct current stimulation

Older adults have difficulties to detect the intentions, thoughts, and feelings of others, indicating an age-associated decline of socio-cognitive abilities that are known as "mentalizing". These deficits in mental state recognition are driven by neurofunctional alterations in brain regions that are implicated in mentalizing, such as the right temporo-parietal junction (rTPJ) and the dorso-medial prefrontal cortex (dmPFC). We tested whether focal transcranial current stimulation (tDCS) of the rTPJ and dmPFC has the potential to eliminate mentalizing deficits in older adults. Mentalizing deficits were assessed with a novel mindreading task that required the recognition of mental states in child faces. Older adults (n = 60) performed worse than younger adults (n = 30) on the mindreading task, indicating age-dependent deficits in mental state recognition. These mentalizing deficits were ameliorated in older adults who received sham-controlled andodal tDCS over the rTPJ (n = 30) but remained unchanged in older adults who received sham-controlled andodal tDCS over the dmPFC (n = 30). We, thus, showed for the first time that anodal tDCS over the rTPJ has the potential to remediate age-dependent mentalizing deficits in a region-specific way. This provides a rationale for exploring stimulation-based interventions targeting mentalizing deficits in older age.

Lactoferrin, chitosan double-coated oleosomes loaded with clobetasol propionate for remyelination in multiple sclerosis: Physicochemical characterization and in-vivo assessment in a cuprizone-induced demyelination model

Multiple sclerosis is a chronic inflammatory demyelinating disorder of the CNS characterized by continuous myelin damage accompanied by deterioration in functions. Clobetasol propionate (CP) is the most potent topical corticosteroid with serious side effects related to systemic absorption. Previous studies introduced CP for remyelination without considering systemic toxicity. This work aimed at fabrication and optimization of double coated nano-oleosomes loaded with CP to achieve brain targeting through intranasal administration. The optimized formulation was coated with lactoferrin and chitosan for the first time. The obtained double-coated oleosomes had particle size (220.07 ± 0.77 nm), zeta potential (+30.23 ± 0.41 mV) along with antioxidant capacity 9.8 μM ascorbic acid equivalents. Double coating was well visualized by TEM and significantly decreased drug release. Three different doses of CP were assessed in-vivo using cuprizone-induced demyelination in C57Bl/6 mice. Neurobehavioral tests revealed improvement in motor and cognitive functions of mice in a dose-dependent manner. Histopathological examination of the brain showed about 2.3 folds increase in corpus callosum thickness in 0.3 mg/kg CP dose. Moreover, the measured biomarkers highlighted the significant antioxidant and anti-inflammatory capacity of the formulation. In conclusion, the elaborated biopolymer-integrating nanocarrier succeeded in remyelination with 6.6 folds reduction in CP dose compared to previous studies.

Efficacy, safety, and response predictors of Astragalus in patients with mild to moderate Alzheimer's disease: A study protocol of an assessor-blind, statistician-blind open-label randomized controlled trial

Background

This pragmatic clinical trial aims to determine the efficacy and safety of add-on Astragalus membranaceus (AM) for cognition and non-cognition in patients with of mild to moderate Alzheimer's disease complicated with orthostatic hypotension in orthostatic hypotension, elucidate the underlying mechanisms, identify related response predictors, and explore effective drug components.

Methods

This is an add-on, assessor-blinded, parallel, pragmatic, randomized controlled trial. At least 66 adults with mild to moderate Alzheimer's disease (AD) and OH aged 50-85 years will be recruited. Participants will be randomized in a 1:1:1 ratio to receive 24 weeks of routine care or add-on low dose AM or add-on high dose AM group. The primary efficacy outcome will be measured by the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Chinese version. Secondary efficacy outcome assessment will include neuropsychological tests, blood pressure, plasma biomarkers, multimodal electroencephalograms, and neuroimaging. Safety outcome measures will include physical examinations, vital signs, electrocardiography, laboratory tests (such as hematologic and blood chemical tests), and adverse event records.

Ethics and dissemination

This trial was approved and supervised by Fujian Medical University Union Hospital (2021KJCX040). Independent results, findings will be published in peer-reviewed journals and presented at national and international conferences.

Trial registration number

NCT05647473; ClinicalTrials.gov Identifier.

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention

Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.

Physical Exercise Counteracts Aging-Associated White Matter Demyelination Causing Cognitive Decline

In the central nervous system, oligodendrocytes wrap around neuronal axons to form myelin, an insulating layer or sheath that allows for the efficient conductance of action potentials. In addition to structural insulation, myelin provides encased axons with nutrient, metabolic and defensive support. Demyelination, or myelin loss, can therefore cause axonal dysfunction, leading to neurological impairment and disease. In Alzheimer's disease (AD), progressive white matter demyelination is acknowledged as one of the earliest pathologies preceding symptom onset. Unfortunately, current pharmacotherapy for slowing demyelination or promoting remyelination in AD is nonexistent. Exercise is recognized for its wide-ranging benefits to human health, including improved mental health and the prevention of lifestyle-related diseases. Mounting evidence suggests the contribution of physical activity in delaying the progression of dementia in elderly populations. Recent mechanistic studies have shown that exercise facilitates myelination in the brain through the vitalization of intrinsic pro-myelination cues, such as increased neurotrophic factors and electrical activity. In this review, we summarize and discuss the potential of physical exercise on counteracting aging-associated white matter demyelination, which causes cognitive decline in AD. We highlight the need of further basic and clinical research investigations on this topic to establish novel approaches for healthy and improved brain aging.

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.

Medical image registration via neural fields

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Longitudinal Morphometric Changes in the Corticospinal Tract Shape After Hemorrhagic Stroke

Deep intracerebral hemorrhage (ICH) exerts a direct force on corticospinal tracts (CST) causing shape deformation. Using serial MRI, Generalized Procrustes Analysis (GPA), and Principal Components Analysis (PCA), we temporally evaluated the change in CST shape. Thirty-five deep ICH patients with ipsilesional-CST deformation were serially imaged on a 3T-MRI with a median imaging time of day-2 and 84 of onset. Anatomical and diffusion tensor images (DTI) were acquired. Using DTI color-coded maps, 15 landmarks were drawn on each CST and the centroids were computed in 3 dimensions. The contralesional-CST landmarks were used as a reference. The GPA outlined the shape coordinates and we superimposed the ipsilesional-CST shape at the two-time points. A multivariate PCA was applied to identify eigenvectors associated with the highest percentile of change. The first three principal components representing CST deformation along the left-right (PC1), anterior-posterior (PC2), and superior-inferior (PC3) respectively were responsible for 57.9% of shape variance. The PC1 (36.1%, p < 0.0001) and PC3 (9.58%, p < 0.01) showed a significant deformation between the two-time points. Compared to the contralesional-CST, the ipsilesional PC scores were significantly (p < 0.0001) different only at the first-timepoint. A significant positive association between the ipsilesional-CST deformation and hematoma volume was observed. We present a novel method to quantify CST deformation caused by ICH. Deformation most often occurs in left-right axis (PC1) and superior-inferior (PC3) directions. As compared to the reference, the significant temporal difference at the first time point suggests CST restoration over time.

Leveraging segmentation-guided spatial feature embedding for overall survival prediction in glioblastoma with multimodal magnetic resonance imaging

BACKGROUND AND OBJECTIVE

Patients with glioblastoma have a five-year relative survival rate of less than 5 %. Thus, accurately predicting the overall survival (OS) of patients with glioblastoma is crucial for effective treatment planning.

METHODS

To fully leverage the imaging characteristics of glioblastomas, we propose a segmentation-guided regression method for predicting OS of patients with brain tumors using multimodal magnetic resonance imaging. Specifically, a brain tumor segmentation network was first pre-trained without leveraging survival information. Subsequently, the survival regression network was jointly trained with the guidance of brain tumor segmentation, focusing on tumor voxels and suppressing irrelevant backgrounds.

RESULTS

Our proposed framework, based on the well-known backbone of UNETR++, achieved a Dice score of 0.7910, Spearman correlation of 0.4112, and Harrell's concordance index of 0.6488. The model consistently showed promising results compared with baseline methods on two different datasets (BraTS and UCSF-PDGM). Furthermore, ablation studies on our training configurations demonstrated that both the pre-training segmentation network and contrastive loss significantly improved all metrics for OS prediction.

CONCLUSIONS

In this study, we propose a joint learning framework based on a pre-trained segmentation backbone for OS prediction by leveraging a brain tumor segmentation map. By utilizing a spatial feature map, our model can operate using a sliding-window approach, which can be adopted by varying the matrix sizes and resolutions of the input images.

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods

PURPOSE

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods.

METHODS

A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of theB 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters.

RESULTS

The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy ofv p $$ {v}_p $$ andPS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction forB 1 + $$ {\mathrm{B}}_1^{+} $$ field.

CONCLUSION

We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI

PURPOSE

To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks.

THEORY AND METHODS

The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo.

RESULTS

The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from8 × 8 × 15 $$ 8\times 8\times 15 $$ mm3 to8 × 8 × 8 $$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from2 × 31 $$ 2\times 31 $$ min to2 × 23 $$ 2\times 23 $$ min.

CONCLUSION

The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

Modeling and measurement of lead tip heating and resonant length for implanted, insulated wires

PURPOSE

To study implant lead tip heating because of the RF power deposition by developing mathematical models and comparing them with measurements acquired at 1.5 T and 3 T, especially to predict resonant length.

THEORY AND METHODS

A simple exponential model and an adapted transmission line model for the electric field transfer function were developed. A set of wavenumbers, including that calculated from insulated antenna theory (King wavenumber) and that of the embedding medium were considered. Experiments on insulated, capped wires of varying lengths were performed to determine maximum temperature rise under RF exposure. The results are compared with model predictions from analytical expressions derived under the assumption of a constant electric field, and with those numerically calculated from spatially varying, simulated electric fields from body coil transmission. Simple expressions for the resonant length bounded between one-quarter and one-half wavelength are developed based on the roots of transcendental equations.

RESULTS

The King wavenumber for both models more closely matched the experimental data with a maximum root mean square error of 9.81°C at 1.5 T and 5.71°C at 3 T compared to other wavenumbers with a maximum root mean square error of 27.52°C at 1.5 T and 22.01°C for 3 T. Resonant length was more accurately predicted compared to values solely based on the embedding medium.

CONCLUSION

Analytical expressions were developed for implanted lead heating and resonant lengths under specific assumptions. The value of the wavenumber has a strong effect on the model predictions. Our work could be used to better manage implanted device lead tip heating.

Decoupling of cortical activity from behavioral state following administration of the classic psychedelic DOI

Administration or consumption of classic psychedelics (CPs) leads to profound changes in experience which are often described as highly novel and meaningful. They have shown substantial promise in treating depressive symptoms and may be therapeutic in other situations. Although research suggests that the therapeutic response is correlated with the intensity of the experience, the neural circuit basis for the alterations in experience caused by CPs requires further study. The medial prefrontal cortex (mPFC), where CPs have been shown to induce rapid, 5-HT2A receptor-dependent structural and neurophysiological changes, is believed to be a key site of action. To investigate the acute neural circuit changes induced by CPs, we recorded single neurons and local field potentials in the mPFC of freely behaving male mice after administration of the 5-HT2A/2C receptor-selective CP, 2,5-Dimethoxy-4-iodoamphetamine (DOI). We segregated recordings into active and rest periods in order to examine cortical activity during desynchronized (active) and synchronized (rest) states. We found that DOI induced a robust decrease in low frequency power when animals were at rest, attenuating the usual synchronization that occurs during less active behavioral states. DOI also increased broadband gamma power and suppressed activity in fast-spiking neurons in both active and rest periods. Together, these results suggest that the CP DOI induces persistent desynchronization in mPFC, including during rest when mPFC typically exhibits more synchronized activity. This shift in cortical dynamics may in part underlie the longer-lasting effects of CPs on plasticity, and may be critical to their therapeutic properties.

Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells

Myelin Basic Protein (MBP) is essential for both elaboration and maintenance of CNS myelin, and its reduced accumulation results in hypomyelination. How different Mbp mRNA levels affect myelin dimensions across the lifespan and how resident glial cells may respond to such changes are unknown. Here, to investigate these questions, we used enhancer-edited mouse lines that accumulate Mbp mRNA levels ranging from 8% to 160% of wild type. In young mice, reduced Mbp mRNA levels resulted in corresponding decreases in Mbp protein accumulation and myelin sheath thickness, confirming the previously demonstrated rate-limiting role of Mbp transcription in the control of initial myelin synthesis. However, despite maintaining lower line specific Mbp mRNA levels into old age, both MBP protein levels and myelin thickness improved or fully normalized at rates defined by the relative Mbp mRNA level. Sheath length, in contrast, was affected only when mRNA levels were very low, demonstrating that sheath thickness and length are not equally coupled to Mbp mRNA level. Striking abnormalities in sheath structure also emerged with reduced mRNA levels. Unexpectedly, an increase in the density of all glial cell types arose in response to reduced Mbp mRNA levels. This investigation extends understanding of the role MBP plays in myelin sheath elaboration, architecture, and plasticity across the mouse lifespan and illuminates a novel axis of glial cell crosstalk.

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism

BACKGROUND

Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood.

METHODS

Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors.

RESULTS

Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels.

CONCLUSIONS

This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

Unraveling the causal relationships between depression and brain structural imaging phenotypes: A bidirectional Mendelian Randomization study

BACKGROUND

Previous studies have revealed structural brain abnormalities in individuals with depression, but the causal relationship between depression and brain structure remains unclear.

METHODS

A genetic correlation analysis was conducted using summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 1,265 brain structural imaging-derived phenotypes (IDPs, N = 33,224). Subsequently, a bidirectional two-sample Mendelian Randomization (MR) approach was employed to explore the causal relationships between depression and the IDPs that showed genetic correlations with depression. The main MR results were obtained using the inverse variance weighted (IVW) method, and other MR methods were further employed to ensure the reliability of the findings.

RESULTS

Ninety structural IDPs were identified as being genetically correlated with depression and were included in the MR analyses. The IVW MR results indicated that reductions in the volume of several brain regions, including the bilateral subcallosal cortex, right medial orbitofrontal cortex, and right middle-posterior part of the cingulate cortex, were causally linked to an increased risk of depression. Additionally, decreases in surface area of the right middle temporal visual area, right middle temporal cortex, right inferior temporal cortex, and right middle-posterior part of the cingulate cortex were causally associated with a heightened risk of depression. Validation and sensitivity analyses supported the robustness of these findings. However, no evidence was found for a causal effect of depression on structural IDPs.

CONCLUSIONS

Our findings reveal the causal influence of specific brain structures on depression, providing evidence to consider brain structural changes in the etiology and treatment of depression.

3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease

Alzheimer's is a progressive neurodegenerative disorder that leads to cognitive impairment and ultimately death. To select the most effective treatment options, it is crucial to diagnose and classify the disease early, as current treatments can only delay its progression. However, previous research on Alzheimer's disease (AD) has had limitations, such as inaccuracies and reliance on a small, unbalanced binary dataset. In this study, we aimed to evaluate the early stages of AD using three multiclass datasets: OASIS, EEG, and ADNI MRI. The research consisted of three phases: pre-processing, feature extraction, and classification using hybrid learning techniques. For the OASIS and ADNI MRI datasets, we computed the mean RGB value and used an averaging filter to enhance the images. We balanced and augmented the dataset to increase its size. In the case of the EEG dataset, we applied a band-pass filter for digital filtering to reduce noise and also balanced the dataset using random oversampling. To extract and classify features, we utilized a hybrid technique consisting of four algorithms: AlexNet-MLP, AlexNet-ETC, AlexNet-AdaBoost, and AlexNet-NB. The results showed that the AlexNet-ETC hybrid algorithm achieved the highest accuracy rate of 95.32% for the OASIS dataset. In the case of the EEG dataset, the AlexNet-MLP hybrid algorithm outperformed other approaches with the highest accuracy of 97.71%. For the ADNI MRI dataset, the AlexNet-MLP hybrid algorithm achieved an accuracy rate of 92.59%. Comparing these results with the current state of the art demonstrates the effectiveness of our findings.

Baseline circulating biomarkers, their changes, and subsequent suicidal ideation and depression severity at 6 months: A prospective analysis in patients with mood disorders

BACKGROUND

Identifying circulating biomarkers associated with prospective suicidal ideation (SI) and depression could help better understand the dynamics of these phenomena and identify people in need of intense care. In this study, we investigated the associations between baseline peripheral biomarkers implicated in neuroplasticity, vascular homeostasis and inflammation, and prospective SI and depression severity during 6 months of follow-up in patients with mood disorders.

METHODS

149 patients underwent a psychiatric evaluation and gave blood to measure 32 plasma soluble proteins. At follow-up, SI incidence over six months was measured with the Columbia Suicide Severity Rating Scale, and depressive symptoms were assessed with the Inventory for Depressive Symptomatology. Ninety-six patients provided repeated blood samples. Statistical analyses included Spearman partial correlation and Elastic Net regression, followed by the covariate-adjusted regression models.

RESULTS

51.4 % (N = 71) of patients reported SI during follow-up. After adjustment for covariates, higher baseline levels of interferon-γ were associated with SI occurrence during follow-up. Higher baseline interferon-γ and lower orexin-A were associated with increased depression severity, and atypical and anxious, but not melancholic, symptoms. There was also a tendency for associations of elevated baseline levels of interferon-γ, interleukin-1β, and lower plasma serotonin levels with SI at the six-month follow-up time point. Meanwhile, reduction in transforming growth factor- β1 (TGF-β1) plasma concentration correlated with atypical symptoms reduction.

CONCLUSION

We identified interferon-γ and orexin-A as potential predictive biomarkers of SI and depression, whereas TGF-β1 was identified as a possible target of atypical symptoms.

Anatomically plausible segmentations: Explicitly preserving topology through prior deformations

Since the rise of deep learning, new medical segmentation methods have rapidly been proposed with extremely promising results, often reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection errors are often revealed, such as topological mistakes (e.g. holes or folds), that are not detected using traditional evaluation metrics. Incorrect topology can often lead to errors in clinically required downstream image processing tasks. Therefore, there is a need for new methods to focus on ensuring segmentations are topologically correct. In this work, we present TEDS-Net: a segmentation network that preserves anatomical topology whilst maintaining segmentation performance that is competitive with SOTA baselines. Further, we show how current SOTA segmentation methods can introduce problematic topological errors. TEDS-Net achieves anatomically plausible segmentation by using learnt topology-preserving fields to deform a prior. Traditionally, topology-preserving fields are described in the continuous domain and begin to break down when working in the discrete domain. Here, we introduce additional modifications that more strictly enforce topology preservation. We illustrate our method on an open-source medical heart dataset, performing both single and multi-structure segmentation, and show that the generated fields contain no folding voxels, which corresponds to full topology preservation on individual structures whilst vastly outperforming the other baselines on overall scene topology. The code is available at: https://github.com/mwyburd/TEDS-Net.

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Spectral changes in electroencephalography linked to neuroactive medications: A computational pipeline for data mining and analysis

BACKGROUND AND OBJECTIVES

The increasing amount of open-access medical data provides new opportunities to gain clinically relevant information without recruiting new patients. We developed an open-source computational pipeline, that utilizes the publicly available electroencephalographic (EEG) data of the Temple University Hospital to identify EEG profiles associated with the usage of neuroactive medications. It facilitates access to the data and ensures consistency in data processing and analysis, thus reducing the risk of errors and creating comparable and reproducible results. Using this pipeline, we analyze the influence of common neuroactive medications on brain activity.

METHODS

The pipeline is constructed using easily controlled modules. The user defines the medications of interest and comparison groups. The data is downloaded and preprocessed, spectral features are extracted, and statistical group comparison with visualization through a topographic EEG map is performed. The pipeline is adjustable to answer a variety of research questions. Here, the effects of carbamazepine and risperidone were statistically compared with control data and with other medications from the same classes (anticonvulsants and antipsychotics).

RESULTS

The comparison between carbamazepine and the control group showed an increase in absolute and relative power for delta and theta, and a decrease in relative power for alpha, beta, and gamma. Compared to antiseizure medications, carbamazepine showed an increase in alpha and theta for absolute powers, and for relative powers an increase in alpha and theta, and a decrease in gamma and delta. Risperidone compared with the control group showed a decrease in absolute and relative power for alpha and beta and an increase in theta for relative power. Compared to antipsychotic medications, risperidone showed a decrease in delta for absolute powers. These results show good agreement with state-of-the-art research. The database allows to create large groups for many different medications. Additionally, it provides a collection of records labeled as "normal" after expert assessment, which is convenient for the creation of control groups.

CONCLUSIONS

The pipeline allows fast testing of different hypotheses regarding links between medications and EEG spectrum through ecological usage of readily available data. It can be utilized to make informed decisions about the design of new clinical studies.

Diffusion tensor brain imaging at 0.55T: A feasibility study

PURPOSE

To investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T.

METHODS

Diffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal-to-noise ratio (SNR) of the 0.55T data was improved using a previous SNR-enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k-space using shared-edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test-retest assessment of repeatability at each field strength.

RESULTS

After applying SNR-enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated (R 2 ≥ 0 . 70 $$ {R}^2\ge 0.70 $$ ) with those obtained from 3T data. Test-retest analysis showed that SNR-enhancing reconstruction improved the repeatability of the 0.55T diffusion tensor parameters.

CONCLUSION

High-resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise-mitigation strategies are applied.

Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting

PURPOSE

A method is proposed to quantify cerebral blood volume (v b $$ {v}_b $$ ) and intravascular water residence time (τ b $$ {\tau}_b $$ ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent.

METHODS

An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement tov b $$ {v}_b $$ andτ b $$ {\tau}_b $$ changes. Its accuracy in the presence of variations inT 1 , t $$ {\mathrm{T}}_{1,t} $$ ,T 1 , b $$ {\mathrm{T}}_{1,b} $$ , andB 1 $$ {\mathrm{B}}_1 $$ was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24± $$ \pm $$ 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability.

RESULTS

Simulations show that assuming literature values forT 1 , b $$ {\mathrm{T}}_{1,b} $$ andT 1 , t $$ {\mathrm{T}}_{1,t} $$ , no variation inB 1 $$ {\mathrm{B}}_1 $$ , while fitting onlyv b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , leads to large errors in quantification ofv b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , regardless of noise levels. However, simulations also show that matchingT 1 , t $$ {\mathrm{T}}_{1,t} $$ ,T 1 , b $$ {\mathrm{T}}_{1,b} $$ ,B 1 + $$ {\mathrm{B}}_1^{+} $$ ,v b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matterT 1 , t $$ {\mathrm{T}}_{1,t} $$ (p < 0.0001) andv b $$ {v}_b $$ (p < 0.0001) are observed,T 1 , b $$ {\mathrm{T}}_{1,b} $$ andτ b $$ {\tau}_b $$ show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient ofT 1 , t : 0 . 91 $$ {\mathrm{T}}_{1,t}:0.91 $$ ,T 1 , b : 0 . 58 $$ {\mathrm{T}}_{1,b}:0.58 $$ ,v b : 0 . 90 $$ {v}_b:0.90 $$ , andτ b : 0 . 96 $$ {\tau}_b:0.96 $$ .

CONCLUSION

We demonstrate that regional simultaneous quantification ofv b $$ {v}_b $$ ,τ b $$ {\tau}_b $$ ,T 1 , b , T 1 , t $$ {\mathrm{T}}_{1,b},{T}_{1,t} $$ , andB 1 + $$ {\mathrm{B}}_1^{+} $$ using MRF is feasible in vivo.

Comparisons of the amplitude of low-frequency fluctuation and functional connectivity in major depressive disorder and social anxiety disorder: A resting-state fMRI study

BACKGROUND

Studies comparing the brain functions of major depressive disorder (MDD) and social anxiety disorder (SAD) at the regional and network levels remain scarce. This study aimed to elucidate their pathogenesis using neuroimaging techniques and explore biomarkers that can differentiate these disorders.

METHODS

Resting-state fMRI data were collected from 48 patients with MDD, 41 patients with SAD, and 82 healthy controls. Differences in the amplitude of low-frequency fluctuations (ALFF) among the three groups were examined to identify regions showing abnormal regional spontaneous activity. A seed-based functional connectivity (FC) analysis was conducted using ALFF results as seeds and different connections were identified between regions showing abnormal local spontaneous activity and other regions. The correlation between abnormal brain function and clinical symptoms was analyzed.

RESULTS

Patients with MDD and SAD exhibited similar abnormal ALFF and FC in several brain regions; notably, FC between the right superior frontal gyrus (SFG) and the right posterior supramarginal gyrus (pSMG) in patients with SAD was negatively correlated with depressive symptoms. Furthermore, patients with MDD showed higher ALFF in the right SFG than HCs and those with SAD.

LIMITATION

Potential effects of medications, comorbidities, and data type could not be ignored.

CONCLUSION

MDD and SAD showed common and distinct aberrant brain function patterns at the regional and network levels. At the regional level, we found that the ALFF in the right SFG was different between patients with MDD and those with SAD. At the network level, we did not find any differences between these disorders.

Oxytocin enhances group-based guilt in high moral disengagement individuals through increased moral responsibility

Group-based guilt (collective guilt) refers to the negative emotions experienced when group members violate moral standards and can motivate prosocial behavior. Individuals exhibiting high levels of moral disengagement are prone to engaging in unethical conduct without experience of guilt, thereby prolonging or exacerbating conflicts and hindering conflict resolution. Oxytocin is believed to play key role in shaping social cognition and behaviors associated with morality and prosociality. So, this study (N = 79) explores oxytocin's potential to enhance group-based guilt and compensation for victims among individuals with high moral disengagement. Employing a randomized placebo-controlled design, participants received either oxytocin or placebo before undertaking a task designed to induce group-based guilt, during which they made decisions regarding the allocation of money to victims. Results revealed that participants with high moral disengagement who received oxytocin perceived higher levels of moral responsibility, experienced increased group-based guilt, and allocated significantly more money to victims compared to those who received the placebo. These findings suggested that oxytocin holds promise as an intervention to mitigate moral disengagement and foster moral behavior in individuals predisposed to avoiding responsibility and guilt feelings.

Three-dimensional simultaneous T1 and T2* relaxation times and quantitative susceptibility mapping at 3 T: A multicenter validation study

We aimed to determine the intra-site repeatability and cross-site reproducibility of T1 and T2* relaxation times and quantitative susceptibility (χ) values obtained through quantitative parameter mapping (QPM) at 3 T. This prospective study included three 3-T scanners with the same hardware and software platform at three sites. The brains of twelve healthy volunteers were scanned three times using QPM at three sites. Intra-site repeatability and cross-site reproducibility were evaluated based on voxel-wise and region-of-interest analyses. The within-subject coefficient of variation (wCV), within-subject standard deviation (wSD), linear regression, Bland-Altman plot, and intraclass correlation coefficient (ICC) were used for evaluation. The intra-site repeatability wCV was 11.9 ± 6.86% for T1 and 3.15 ± 0.03% for T2*, and wSD of χ at 3.35 ± 0.10 parts per billion (ppb). Intra-site ICC(1,k) values for T1, T2*, and χ were 0.878-0.904, 0.972-0.976, and 0.966-0.972, respectively, indicating high consistency within the same scanner. Linear regression analysis revealed a strong agreement between measurements from each site and the site-average measurement, with R-squared values ranging from 0.79 to 0.83 for T1, 0.94-0.95 for T2*, and 0.95-0.96 for χ. The cross-site wCV was 13.4 ± 5.47% for T1 and 3.69 ± 2.25% for T2*, and cross-site wSD of χ at 4.08 ± 3.22 ppb. The cross-site ICC(2,1) was 0.707, 0.913, and 0.902 for T1, T2*, and χ, respectively. QPM provides T1, T2*, and χ values with an intra-site repeatability of <12% and cross-site reproducibility of <14%. These findings may contribute to the development of multisite studies.

The time course of Cantonese and Hong Kong Sign Language phonological activation: An ERP study of deaf bimodal bilingual readers of Chinese

This study investigated Cantonese and Hong Kong Sign Language (HKSL) phonological activation patterns in Hong Kong deaf readers using the ERP technique. Two experiments employing the error disruption paradigm were conducted while recording participants' EEGs. Experiment 1 focused on orthographic and speech-based phonological processing, while Experiment 2 examined sign-phonological processing. ERP analyses focused on the P200 (180-220 ms) and N400 (300-500 ms) components. The results of Experiment 1 showed that hearing readers exhibited both orthographic and phonological effects in the P200 and N400 windows, consistent with previous studies on Chinese reading. In deaf readers, significant speech-based phonological effects were observed in the P200 window, and orthographic effects spanned both the P200 and N400 windows. Comparative analysis between the two groups revealed distinct spatial distributions for orthographic and speech-based phonological ERP effects, which may indicate the engagement of different neural networks during early processing stages. Experiment 2 found evidence of sign-phonological activation in both the P200 and N400 windows among deaf readers, which may reflect the involvement of sign-phonological representations in early lexical access and later semantic integration. Furthermore, exploratory analysis revealed that higher reading fluency in deaf readers correlated with stronger orthographic effects in the P200 window and diminished effects in the N400 window, indicating that efficient orthographic processing during early lexical access is a distinguishing feature of proficient deaf readers.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Applying the area fraction fractionator (AFF) probe for total volume estimations of somatic, dendritic and axonal domains of the nigrostriatal dopaminergic system in a murine model

BACKGROUND

The Cavalieri estimator is used for volume measurement of brain and brain regions. Derived from this estimator is the Area Fraction Fractionator (AFF), used for efficient area and number estimations of small 2D elements, such as axons in cross-sectioned nerves. However, to our knowledge, the AFF has not been combined with serial sectioning analysis to measure the volume of small-size nervous structures.

NEW METHOD

Using the nigrostriatal dopaminergic system as an illustrative case, we describe a protocol based on Cavalieri's principle and AFF to estimate the volume of its somatic, nuclear, dendritic, axonal and axon terminal cellular compartments in the adult mouse. The protocol consists of (1) systematic random sampling of sites within and across sections in regions of interest (substantia nigra, the nigrostriatal tract, caudate-putamen), (2) confocal image acquisition of sites, (3) marking of cellular domains using Cavalieri's 2D point-counting grids, and 4) determination of compartments' total volume using the estimated area of each compartment, and between-sections distance.

RESULTS

The volume of the nigrostriatal system per hemisphere is ∼0.38 mm3, with ∼5 % corresponding to perikarya and cell nuclei, ∼10 % to neuropil/dendrites, and ∼85 % to axons and varicosities.

COMPARISON WITH EXISTING METHODS

In contrast to other methods to measure volume of discrete objects, such as the optical nucleator or 3D reconstructions, it stands out for its versatility and ease of use.

CONCLUSIONS

The use of a simple quantitative, unbiased approach to assess the global state of a system may allow quantification of compartment-specific changes that may accompany neurodegenerative processes.

Facilitation of working memory capacity by transcranial direct current stimulation: a secondary analysis from the augmenting cognitive training in older adults (ACT) study

Aging is a public health concern with an ever-increasing magnitude worldwide. An array of neuroscience-based approaches like transcranial direct current stimulation (tDCS) and cognitive training have garnered attention in the last decades to ameliorate the effects of cognitive aging in older adults. This study evaluated the effects of 3 months of bilateral tDCS over the frontal cortices with multimodal cognitive training on working memory capacity. Two hundred ninety-two older adults without dementia were allocated to active or sham tDCS paired with cognitive training. These participants received repeated sessions of bilateral tDCS over the bilateral frontal cortices, combined with multimodal cognitive training. Working memory capacity was assessed with the digit span forward, backward, and sequencing tests. No baseline differences between active and sham groups were observed. Multiple linear regressions indicated more improvement of the longest digit span backward from baseline to post-intervention (p = 0.021) and a trend towards greater improvement (p = 0.056) of the longest digit span backward from baseline to 1 year in the active tDCS group. No significant between-group changes were observed for digit span forward or digit span sequencing. The present results provide evidence for the potential for tDCS paired with cognitive training to remediate age-related declines in working memory capacity. These findings are sourced from secondary outcomes in a large randomized clinical trial and thus deserve future targeted investigation in older adult populations.

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation

Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-width, which plays a crucial role in the fast-growing AI industry. Despite model quantization's success in producing well-performing low-bit models, the quantization process itself can still be expensive, which may involve a long fine-tuning stage on a large, well-annotated training set. To make the quantization process more efficient in terms of both time and data requirements, this paper proposes a fast and accurate post-training quantization method, namely EfficientQ. We develop this new method with a layer-wise optimization strategy and leverage the powerful alternating direction method of multipliers (ADMM) algorithm to ensure fast convergence. Furthermore, a weight regularization scheme is incorporated to provide more guidance for the optimization of the discrete weights, and a self-adaptive attention mechanism is proposed to combat the class imbalance problem. Extensive comparison and ablation experiments are conducted on two publicly available medical image segmentation datasets, i.e., LiTS and BraTS2020, and the results demonstrate the superiority of the proposed method over various existing post-training quantization methods in terms of both accuracy and optimization speed. Remarkably, with EfficientQ, the quantization of a practical 3D UNet only requires less than 5 min on a single GPU and one data sample. The source code is available at https://github.com/rongzhao-zhang/EfficientQ.