Functional imaging and related techniques: an introduction for rehabilitation researchers

Differences in Electroencephalography Power Levels between Poor and Good Performance in Attentional Tasks

Decreased attentional function causes problems in daily life. However, a quick and easy evaluation method of attentional function has not yet been developed. Therefore, we are searching for a method to evaluate attentional function easily and quickly. This study aimed to collect basic data on the features of electroencephalography (EEG) during attention tasks to develop a new method for evaluating attentional function using EEG. Twenty healthy young adults participated; we examined cerebral activity during a Clinical Assessment for Attention using portable EEG devices. The Mann-Whitney U test was performed to assess differences in power levels of EEG during tasks between the low- and high-attention groups. The findings revealed that the high-attention group showed significantly higher EEG power levels in the δ wave of L-temporal and bilateral parietal lobes, as well as in the β and γ waves of the R-occipital lobe, than did the low-attention group during digit-forward, whereas the high-attention group showed significantly higher EEG power levels in the θ wave of R-frontal and the α wave of bilateral frontal lobes during digit-backward. Notably, lower θ, α, and β bands of the right hemisphere found in the low-attention group may be key elements to detect attentional deficit.

Uncovering sympathetic nervous system dysfunction in disorders of consciousness via heart rate variability during head-up tilt test

Few standardized tools are available for evaluation of disorders of consciousness (DOC). The potential of heart rate variability (HRV) during head-up tilt (HUT) test was investigated as a complementary evaluation tool. Twenty-one DOC patients and 21 healthy participants were enrolled in this study comparing clinical characteristics and HRV time- and frequency-domain outcomes and temporal changes during HUT test. During the 1st-5th min of the HUT, DOC group showed a significant increase and decrease in log low frequency (LF) (p = 0.045) and log normalized high frequency (nHF) (p = 0.02), respectively, compared to the supine position and had lower log normalized LF (nLF) (p = 0.004) and log ratio of low-to-high frequency (LF/HF) (p = 0.001) compared to healthy controls. As the HUT continued from the 6th to the 20th min, DOC group exhibited a significant increase in log LF/HF (16th-20th min) (p < 0.05), along with a decrease in log nHF (6th-10th and 16th-20th min) (p < 0.05) and maintained lower log LF, log nLF, and log LF/HF than controls (p < 0.05). 1st-10th min after returning to the supine position, DOC group demonstrated a significant decrease in log nHF (p < 0.01) and increases in log LF/HF (p < 0.01) and had lower log LF (p < 0.01) and log nLF (p < 0.05) compared to controls. In contrast, the control group exhibited a significant decrease in log nHF (p < 0.05) and increase in log LF/HF (p < 0.05) throughout the entire HUT test. Notably, no significant differences were observed when comparing time-domain outcomes reflecting parasympathetic nervous system between the two groups. HRV during HUT test indicated a delayed and attenuated autonomic response, particularly in the sympathetic nervous system, in DOC patients compared with healthy individuals.

Congenital Anomalies of the Female Genital Tract: A Comprehensive Review

This comprehensive review provides an in-depth examination of congenital anomalies of the female genital tract, explicitly focusing on the American Society for Reproductive Medicine (ASRM) Müllerian Anomalies Classification. The classification system is crucial for standardizing communication and guiding accurate diagnoses in clinical practice. The review explores the diverse clinical presentations, etiological factors, and diagnostic modalities associated with these anomalies. Management strategies, ranging from conservative approaches to advanced reproductive technologies, are discussed in the context of individualized treatment plans based on the ASRM classification. The psychosocial impact of female genital tract anomalies is thoroughly examined, emphasizing the importance of holistic care and patient-centered approaches. Looking toward the future, the review outlines emerging research areas, including advances in diagnosis techniques, innovative treatment modalities, and genetic studies. It ultimately underscores the need for a comprehensive understanding of physical and psychosocial dimensions, offering insights for healthcare professionals to navigate this complex landscape and improve the lives of affected individuals.

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

The relationship between APOE genotype, CSF Tau and cognition across the Alzheimer's disease spectrum, moderation and mediation role of insula network connectivity

AIMS

To investigate whether insula network connectivity modulates the relationship between apolipoprotein E (APOE) ε4 genotype, cerebrospinal fluid (CSF) biomarkers (Aβ, Tau, and pTau) and cognition across Alzheimer's disease (AD) spectrum.

METHODS

Forty-six cognitive normal (CN), 35 subjective memory complaint (SMC), 41 mild cognitive impairment (MCI), and 32 AD subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were obtained. Multivariable linear regression analyses were conducted to investigate the main effects and interaction of the APOE genotype and disease status on the insula functional connectivity (IFC) network. Mediation and moderation analysis were performed to investigate whether IFC strengths regulate the association between APOE genotype, CSF biomarkers and cognition. Additionally, the support vector machine (SVM) model integrating APOE genotype, CSF biomarkers, and neuroimaging biomarkers (insula volumes and altered regional IFCs) was used to classify the AD spectrum.

RESULTS

The interactive effect of the APOE genotype and disease on the insula network was found in the left medial superior frontal gyrus (SFGmed.L), right anterior medial prefrontal cortex (aMPFC.R), and bilateral thalamus (THA.B). The functional connectivities (FCs) in the left insula (LIns) connecting with the left posterior middle temporal gyrus (pMTG.L), SFGmed.L, and right lingual gyrus (LING.R) were correlated with cognition. LIns-SFGmed.L and LIns-pMTG.L FCs could moderate the effects of Tau on cognition. Furthermore, LIns-SFGmed.L FC may suppress the association between APOE genotype and cognition. More importantly, the integrated biomarkers from the SVM model yielded strong powers for classifying the AD spectrum.

CONCLUSIONS

Insula functional connectivity regulated the association between APOE genotype, CSF Tau and cognition and provided stage-dependent biomarkers for early differentiation of the AD spectrum. The present study used a cross-sectional design. Follow-up studies are needed to validate the relationship.

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Introduction

Political neuromarketing is an emerging interdisciplinary field integrating marketing, neuroscience, and psychology to decipher voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding.

Methods

This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders.

Results

This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former.

Discussion

The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science, and machine learning, in turn enabling predictive insights into voter preferences and behavior.

Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies

OBJECTIVE

Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD.

METHODS

We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023.

RESULTS

Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25).

CONCLUSIONS

Alterations of alpha synchrony are present even at MCI stage.

SIGNIFICANCE

Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.

Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse

Introduction

The study of the brain continues to generate substantial volumes of data, commonly referred to as "big brain data," which serves various purposes such as the treatment of brain-related diseases, the development of neurotechnological devices, and the training of algorithms. This big brain data, generated in different jurisdictions, is subject to distinct ethical and legal principles, giving rise to various ethical and legal concerns during collaborative efforts. Understanding these ethical and legal principles and concerns is crucial, as it catalyzes the development of a global governance framework, currently lacking in this field. While prior research has advocated for a contextual examination of brain data governance, such studies have been limited. Additionally, numerous challenges, issues, and concerns surround the development of a contextually informed brain data governance framework. Therefore, this study aims to bridge these gaps by exploring the ethical foundations that underlie contextual stakeholder discussions on brain data governance.

Method

In this study we conducted a secondary analysis of interviews with 21 neuroscientists drafted from the International Brain Initiative (IBI), LATBrain Initiative and the Society of Neuroscientists of Africa (SONA) who are involved in various brain projects globally and employing ethical theories. Ethical theories provide the philosophical frameworks and principles that inform the development and implementation of data governance policies and practices.

Results

The results of the study revealed various contextual ethical positions that underscore the ethical perspectives of neuroscientists engaged in brain data research globally.

Discussion

This research highlights the multitude of challenges and deliberations inherent in the pursuit of a globally informed framework for governing brain data. Furthermore, it sheds light on several critical considerations that require thorough examination in advancing global brain data governance.

fNIRS Studies of Individuals with Speech and Language Impairment Underreport Sociodemographics: A Systematic Review

Functional near-infrared spectroscopy (fNIRS) is a promising tool for scientific discovery and clinical application. However, its utility depends upon replicable reporting. We evaluate reporting of sociodemographics in fNIRS studies of speech and language impairment and asked the following: (1) Do refereed fNIRS publications report participant sociodemographics? (2) For what reasons are participants excluded from analysis? This systematic review was preregistered with PROSPERO (CRD42022342959) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Searches in August 2022 included the terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Searches yielded 38 qualifying studies from 1997 to present. Eight studies (5%) reported at least partial information on race or ethnicity. Few studies reported SES (26%) or language background (47%). Most studies reported geographic location (100%) and gender/sex (89%). Underreporting of sociodemographics in fNIRS studies of speech and language impairment hinders the generalizability of findings. Replicable reporting is imperative for advancing the utility of fNIRS.

Cross-Subject Emotion Recognition Brain-Computer Interface Based on fNIRS and DBJNet

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.

Demographic reporting and phenotypic exclusion in fNIRS

Functional near-infrared spectroscopy (fNIRS) promises to be a leading non-invasive neuroimaging method due to its portability and low cost. However, concerns are rising over its inclusivity of all skin tones and hair types (Parker and Ricard, 2022, Webb et al., 2022). Functional NIRS relies on direct contact of light-emitting optodes to the scalp, which can be blocked more by longer, darker, and especially curlier hair. Additionally, NIR light can be attenuated by melanin, which is accounted for in neither fNIRS hardware nor analysis methods. Recent work has shown that overlooking these considerations in other modalities like EEG leads to the disproportionate exclusion of individuals with these phenotypes-especially Black people-in both clinical and research literature (Choy, 2020; Bradford et al., 2022; Louis et al., 2023). In this article, we sought to determine if (Jöbsis, 1977) biomedical optics developers and researchers report fNIRS performance variability between skin tones and hair textures, (2a) fNIRS neuroscience practitioners report phenotypic and demographic details in their articles, and thus, (2b) is a similar pattern of participant exclusion found in EEG also present in the fNIRS literature. We present a literature review of top Biomedical Optics and Human Neuroscience journals, showing that demographic and phenotypic reporting is unpopular in both fNIRS development and neuroscience applications. We conclude with a list of recommendations to the fNIRS community including examples of Black researchers addressing these issues head-on, inclusive best practices for fNIRS researchers, and recommendations to funding and regulatory bodies to achieve an inclusive neuroscience enterprise in fNIRS and beyond.

The Effect of Cognitive Training with Neurofeedback on Cognitive Function in Healthy Adults: A Systematic Review and Meta-Analysis

Background: Cognitive training aims to improve cognitive function through cognitive tasks or training games. Neurofeedback is a technique to monitor brain signals with either visual or auditory feedback. Previous studies suggest that a combination of cognitive training and neurofeedback has a superior effect on cognitive functions compared with cognitive training alone. However, no systematic reviews and meta-analyses of the benefits of cognitive training with neurofeedback (CTNF) exist. The purpose of this study was to examine the beneficial effects of CTNF in healthy adults using a systematic review and multilevel meta-analysis. Methods: PubMed, Scopus, PsychoINFO, and MEDLINE were searched for research papers reporting the results of interventions using CTNF. Results: After an initial screening of 234 records, three studies using near-infrared spectroscopy (NIRS) and one study using electroencephalography were extracted from the database. We performed a multi-level meta-analysis with three NIRS studies including 166 participants (mean ages ranged from 21.43 to 65.96 years). A multi-level meta-analysis revealed that CTNF has a beneficial effect on the episodic, long-term, and working memory domains. Conclusions: Although three studies were included in the systematic review and meta-analysis, our results indicate that CTNF using NIRS would lead to improvements in memory functioning.

What do functional neuroimaging studies tell us about the association between falls and cognition in older adults? A systematic review

Impaired cognition is a known risk factor for falls in older adults. To enhance prevention strategies and treatment of falls among an aging global population, an understanding of the neural processes and networks involved is required. We present a systematic review investigating how functional neuroimaging techniques have been used to examine the association between falls and cognition in seniors. Peer-reviewed articles were identified through searching five electronic databases: 1) Medline, 2) PsycINFO, 3) CINAHL, 4) EMBASE, and 5) Pubmed. Key author, key paper, and reference searching was also conducted. Nine studies were included in this review. A questionnaire composed of seven questions was used to assess the quality of each study. EEG, fMRI, and PET were utilized across studies to examine brain function in older adults. Consistent evidence demonstrates that cognition is associated with measures of falls/falls risk, specifically visual attention and executive function. Our results show that falls/falls risk may be implicated with specific brain regions and networks. Future studies should be prospective and long-term in nature, with standardized outcome measures. Mobile neuroimaging techniques may also provide insight into brain activity as it pertains to cognition and falls in older adults in real-world settings.

Biomarkers: Role and Scope in Neurological Disorders

Neurological disorders pose a great threat to social health and are a major cause for mortality and morbidity. Effective drug development complemented with the improved drug therapy has made considerable progress towards easing symptoms associated with neurological illnesses, yet poor diagnosis and imprecise understanding of these disorders has led to imperfect treatment options. The scenario is complicated by the inability to extrapolate results of cell culture studies and transgenic models to clinical applications which has stagnated the process of improving drug therapy. In this context, the development of biomarkers has been viewed as beneficial to easing various pathological complications. A biomarker is measured and evaluated in order to gauge the physiological process or a pathological progression of a disease and such a marker can also indicate the clinical or pharmacological response to a therapeutic intervention. The development and identification of biomarkers for neurological disorders involves several issues including the complexity of the brain, unresolved discrepant data from experimental and clinical studies, poor clinical diagnostics, lack of functional endpoints, and high cost and complexity of techniques yet research in the area of biomarkers is highly desired. The present work describes existing biomarkers for various neurological disorders, provides support for the idea that biomarker development may ease our understanding underlying pathophysiology of these disorders and help to design and explore therapeutic targets for effective intervention.

Neural mechanisms underlying peripheral facial nerve palsy: A protocol for systematic review and meta-analysis of neuroimaging studies

BACKGROUND

Peripheral facial nerve palsy is a general term used to describe diseases that cause lower motor neuron lesions in the facial nerve. The most common cause of peripheral facial nerve palsy is idiopathic Bell's palsy (BP), accounting for 60% to 70% of all peripheral facial nerve palsy cases. Idiopathic BP is found in various medical conditions, such as ischemia, viral infection, and neuroinflammation; however, the specific pathophysiology remains unclear. Neuroimaging techniques help understand the neural mechanisms underlying various neurological disorders. In recent years, several studies have applied functional neuroimaging to investigate alterations of brain function in BP patients. However, the results have been inconsistent due to differences in methodology and quality. Moreover, systematic reviews of studies on functional neuroimaging for BP are insufficient. Therefore, the present study aims to verify the neural mechanisms of brain alterations and draw conclusions about the functional reorganization in patients with BP.

METHODS

This review includes all suitable studies published on or before October 31, 2022. A thorough search will be conducted using the following databases: MEDLINE, Cochrane Library, Excerpta Medica Database (EMBASE), China Knowledge Resource Integrated Database (CNKI), Korean Medical database (KMBASE), Korean Studies Information Service System (KISS). Clinical studies of BP using functional neuroimaging will be selected. We will apply a coordinate-based meta-analysis because most individual neuroimaging studies provide their results as coordinates in the standard space. The primary outcomes will include the types of functional neuroimaging methods and alterations of brain function in BP patients. The secondary outcomes will include the information about clinical measurement of the disease. Study selection, data extraction, and risk of bias assessment will be conducted. If possible, heterogeneity tests, data synthesis, and subgroup analyses will be conducted.

RESULTS

The study will analyze the alterations in brain activity and worsening of clinical symptoms caused by idiopathic BP.

CONCLUSION

The aim of this study is to investigate functional reorganization of brain alterations in patients with BP. This review will improve the understanding of the neural mechanisms of BP based on the most recent publications through extensive data retrieval. If sufficient data are collected, a sensitivity analysis is performed to verify the robustness of the conclusions.

Classifying ASD based on time-series fMRI using spatial-temporal transformer

As the prevalence of autism spectrum disorder (ASD) increases globally, more and more patients need to receive timely diagnosis and treatment to alleviate their suffering. However, the current diagnosis method of ASD still adopts the subjective symptom-based criteria through clinical observation, which is time-consuming and costly. In recent years, functional magnetic resonance imaging (fMRI) neuroimaging techniques have emerged to facilitate the identification of potential biomarkers for diagnosing ASD. In this study, we developed a deep learning framework named spatial-temporal Transformer (ST-Transformer) to distinguish ASD subjects from typical controls based on fMRI data. Specifically, a linear spatial-temporal multi-headed attention unit is proposed to obtain the spatial and temporal representation of fMRI data. Moreover, a Gaussian GAN-based data balancing method is introduced to solve the data unbalance problem in real-world ASD datasets for subtype ASD diagnosis. Our proposed ST-Transformer is evaluated on a large cohort of subjects from two independent datasets (ABIDE I and ABIDE II) and achieves robust accuracies of 71.0% and 70.6%, respectively. Compared with state-of-the-art methods, our results demonstrate competitive performance in ASD diagnosis.

Functional connectivity impairment of thalamus-cerebellum-scratching neural circuits in pruritus of chronic spontaneous urticaria

Pruritus of chronic spontaneous urticaria (CSU) is one of the most common and irritating sensations that severely affects the quality of life. However, the changes in the functional connectivity (FC) between thalamic subregions and other brain regions have not been fully elucidated. This study aimed to explore the potential changes in brain neural circuits by focusing on various subregions of the thalamus in patients with CSU pruritus to contribute to the understanding of chronic pruritus from the perspective of central mechanisms. A total of 56 patients with CSU and 30 healthy controls (HCs) completed the data analysis. Urticaria Activity Score 7 (UAS7), pruritus visual analog score (VAS-P), Dermatological Life Quality Index (DLQI), and immunoglobulin E (IgE) values were collected to assess clinical symptoms. Seed-based resting-state functional connectivity (rs-FC) analysis was used to assess relevant changes in the neural circuits of the brain. Compared to HCs, seeds within the caudal temporal thalamus (cTtha) on the right side of patients with CSU showed increased rs-FC with the cerebellum anterior lobe (CAL). Seeds within the lateral prefrontal thalamus (lPFtha) on the right side showed increased rs-FC with both CAL and pons, while those within the medial prefrontal thalamus (mPFtha) on the right side showed increased rs-FC with both CAL and the dorsal lateral prefrontal cortex (dlPFC) on the right side. Seeds within the posterior parietal thalamus (PPtha) on the right side showed increased rs-FC with the cerebellum posterior lobe (CPL) on the left side. The UAS7 values and IgE levels were positively correlated with the rs-FC of the right dlPFC. Our results suggest that patients with CSU may exhibit stronger rs-FC alterations between certain thalamic subregions and other brain regions. These changes affect areas of the brain involved in sensorimotor and scratching.

Trial registration number

[http://www.chictr.org.cn], identifier [ChiCTR1900022994].

Cognitive deficits in primary hyperparathyroidism - what we know and what we do not know: A narrative review

Classic symptoms of primary hyperparathyroidism (PHPT) are seen in approximately 20% of patients. While features such as kidney stones and skeletal disease are often highlighted as directly related to the disease, others can be even more prevalent. For example, cognitive dysfunction and reduced quality of life are common complaints in many patients, even among those who are classified as being asymptomatic. The pathophysiology of PHPT involves the impact of excess parathyroid hormone (PTH) on calcium metabolism. Referencing putative neurocognitive issues, many animal studies have illustrated the potential roles of PTH and PTH receptors in the brain. Functional imaging and pre-and post-parathyroidectomy studies have suggested a link between the neuronal impact of elevated PTH levels on specific functional aspects of the central nervous system, such as cognition. Confounding a direct role for PTH are hypercalcemia and vitamin D deficiency, both of which could conceivably alter CNS function in PHPT. The lack of strong evidence that parathyroidectomy improves cognition in patients with PHPT raises the question as to whether parathyroid surgery should be recommended on this basis alone. This narrative review summarizes the available literature on neurocognitive function in PHPT.

Incorporating Dis/ability Studies and Critical Race Theory to combat systematic exclusion of Black, Indigenous, and People of Color in clinical neuroscience

This article reviews some of the ideological forces contributing to the systematic exclusion of Black, Indigenous, and People of Color (BIPOC) in clinical neuroscience. Limitations of functional near-infrared spectroscopy (fNIRS) and other methods systematically exclude individuals with coarse or curly hair and darker skin. Despite these well-known limitations, clinical neuroscience manuscripts frequently fail to report participant race or ethnicity or reasons for excluding participants. Grounding the discussion in Dis/ability Studies and Critical Race Theory (DisCrit), we review factors that exacerbate exclusion and contribute to the multiple marginalization of BIPOC, including (a) general methodological issues, (b) perceptions about race and disability, and (c) underreporting of methods. We also present solutions. Just as scientific practices changed in response to the replication crisis, we advocate for greater attention to the crisis of underrepresentation in clinical neuroscience and provide strategies that serve to make the field more inclusive.

Characteristics of Ultrasound and Magnetic Resonance Imaging of Normal Testes and Epididymis Besides Angiography of Testicular Artery in Dromedary Camel

Decreasing male fertility encouraged the investigators to innovate accurate diagnostic non-invasive methods for detection of changes in the testicular parenchyma. Ultrasonography (US) has the potential to be used in this manner for decades, but magnetic resonance imaging (MRI) is still of limited application in animals for this purpose. The current study was designed to describe appearances and quantitative MRI attributes of the normal testes, epididymis besides angiography of testicular artery in camels. About 30 apparently healthy male dromedary camels aged 8–14 years were slaughtered during the rutting season. Immediately after slaughtering, the male gonads (n = 30 pairs of testicles and epididymis) were subjected to morphometric evaluation using a Vernier caliper and ultrasound scanning. Epididymial sperms were evaluated for motility, vitality and abnormality. MRI was performed for testes (n=16) by using a 1.5T Excite-II MRI apparatus of Sigma. Radiography and angioarchitecture of testicular artery (n=24) were done. Camel testicular length, width, and depth showed non-significant differences between a Vernier caliper or sonar. The MRI results revealed that both the testis and epididymis have homogenously intermediate signal (T1) and testes have hyperintense signal, with slightly lower signal in the epididymis (T2). In conclusion, both the ultrasonography and MRI techniques, with each respective computer-assisted imaging, could be used to detect the histomorphological changes of the camels' testicles. However, US imaging remains the first diagnostic technique for evaluating the reproductive health in men for its lower cost and accuracy. MRI is beneficial when the sonograms are inconclusive and/or equivocal. It shows the examined tissues in greater anatomical details compared to ultrasonography. Further studies are needed to compare between characteristics of US and MRI of normal testes and epididymis with testicular artery angiography in living camel during rut season and non-rut season and between normal healthy and affected diseased genitalia.

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

Heart Rate Variability Features as Predictors of Intermittent Theta-Burst Stimulation Response in Posttraumatic Stress Disorder

BACKGROUND

Posttraumatic stress disorder (PTSD) is associated with autonomic dysfunction as indicated by deficits in the sympathetic and parasympathetic nervous systems. These abnormalities are expressed as elevated heart rate and reduced heart rate variability (HRV), respectively. Intermittent theta-burst stimulation (iTBS), a form of transcranial magnetic stimulation, has demonstrated effectiveness in PTSD. Nevertheless, it remains unclear whether HRV may be an iTBS biomarker for PTSD and whether iTBS impacts autonomic activity.

MATERIALS AND METHODS

Fifty veterans with PTSD participated in a randomized controlled trial, receiving ten daily sessions of sham-controlled iTBS (right dorsolateral prefrontal cortex, 1800 pulses/day, 80% active motor threshold, 9.5 min). With a usable dataset (N = 47), HRV parameters were assessed as predictors of clinical response immediately after stimulation. iTBS effects on autonomic response (mean RR interval, root mean square of successive differences [RMSSD], total power [TP], and low-frequency/high-frequency [LF/HF] ratio) were evaluated using an ultra-short approach.

RESULTS

TP and RMSSD were significant predictors of acute clinical response to iTBS. Individuals with higher TP had better response to iTBS with improved symptoms on the Clinician-Administered PTSD Scale (rs = -0.58, p = 0.004), and higher functionality on the Social and Occupational Function Scale (rs = 0.43, p = 0.04). Similarly, higher RMSSD was associated with superior outcomes (rs = -0.44, p = 0.04). No other significant changes in HRV metrics were observed (p ≥ 0.05).

CONCLUSIONS

Our findings indicate that autonomic activity is a potential low-cost and technically simple predictive biomarker of iTBS response in PTSD. Less autonomic dysfunction was associated with superior clinical improvements with iTBS. Future studies might consider HRV acquisition during iTBS, as well as prospective testing of these findings in patients with elevated hyperarousal.

Corticospinal and peripheral responses to heat-induced hypo-hydration: potential physiological mechanisms and implications for neuromuscular function

Heat-induced hypo-hydration (hyperosmotic hypovolemia) can reduce prolonged skeletal muscle performance; however, the mechanisms are less well understood and the reported effects on all aspects of neuromuscular function and brief maximal contractions are inconsistent. Historically, a 4–6% reduction of body mass has not been considered to impair muscle function in humans, as determined by muscle torque, membrane excitability and peak power production. With the development of magnetic resonance imaging and neurophysiological techniques, such as electromyography, peripheral nerve, and transcranial magnetic stimulation (TMS), the integrity of the brain-to-muscle pathway can be further investigated. The findings of this review demonstrate that heat-induced hypo-hydration impairs neuromuscular function, particularly during repeated and sustained contractions. Additionally, the mechanisms are separate to those of hyperthermia-induced fatigue and are likely a result of modulations to corticospinal inhibition, increased fibre conduction velocity, pain perception and impaired contractile function. This review also sheds light on the view that hypo-hydration has ‘no effect’ on neuromuscular function during brief maximal voluntary contractions. It is hypothesised that irrespective of unchanged force, compensatory reductions in cortical inhibition are likely to occur, in the attempt of achieving adequate force production. Studies using single-pulse TMS have shown that hypo-hydration can reduce maximal isometric and eccentric force, despite a reduction in cortical inhibition, but the cause of this is currently unclear. Future work should investigate the intracortical inhibitory and excitatory pathways within the brain, to elucidate the role of the central nervous system in force output, following heat-induced hypo-hydration.

Observation and motor imagery balance tasks evaluation: An fNIRS feasibility study

In this study, we aimed at exploring the feasibility of functional near-infrared spectroscopy (fNIRS) for studying the observation and/or motor imagination of various postural tasks. Thirteen healthy adult subjects followed five trials of static and dynamic standing balance tasks, throughout three different experimental setups of action observation (AO), a combination of action observation and motor imagery (AO+MI), and motor imagery (MI). During static and dynamic standing tasks, both the AO+MI and MI experiments revealed that many channels in prefrontal or motor regions are significantly activated while the AO experiment showed almost no significant increase in activations in most of the channels. The contrast between static and dynamic standing tasks showed that with more demanding balance tasks, relative higher activation patterns were observed, particularly during AO and in AO+MI experiments in the frontopolar area. Moreover, the AO+MI experiment revealed a significant difference in premotor and supplementary motor cortices that are related to balance control. Furthermore, it has been observed that the AO+MI experiment induced relatively higher activation patterns in comparison to AO or MI alone. Remarkably, the results of this work match its counterpart from previous functional magnetic resonance imaging studies. Therefore, they may pave the way for using the fNIRS as a diagnostic tool for evaluating the performance of the non-physical balance training during the rehabilitation period of temporally immobilized patients.

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.

Cerebral haemodynamic response to somatosensory stimulation in preterm lambs and 7-10-day old lambs born at term: Direct synchrotron microangiography assessment

Neurovascular coupling has been well-defined in the adult brain, but variable and inconsistent responses have been observed in the neonatal brain. The mechanisms that underlie functional haemodynamic responses in the developing brain are unknown. Synchrotron radiation (SR) microangiography enables in vivo high-resolution imaging of the cerebral vasculature. We exploited SR microangiography to investigate the microvascular changes underlying the cerebral haemodynamic response in preterm (n = 7) and 7-10-day old term lambs (n = 4), following median nerve stimulation of 1.8, 4.8 and 7.8 sec durations.Increasing durations of somatosensory stimulation significantly increased the number of cortical microvessels of ≤200 µm diameter in 7-10-day old term lambs (p < 0.05) but not preterm lambs where, in contrast, stimulation increased the diameter of cerebral microvessels with a baseline diameter of ≤200 µm. Preterm lambs demonstrated positive functional responses with increased oxyhaemoglobin measured by near infrared spectroscopy, while 7-10-day old term lambs demonstrated both positive and negative responses. Our findings suggest the vascular mechanisms underlying the functional haemodynamic response differ between the preterm and 7-10-day old term brain. The preterm brain depends on vasodilatation of microvessels without recruitment of additional vessels, suggesting a limited capacity to mount higher cerebral haemodynamic responses when faced with prolonged or stronger neural stimulation.

Transcranial Color Coded Duplex Sonography Findings in Stroke Patients Undergoing Rehabilitation: An Observational Study

Background  Transcranial color-coded duplex sonography (TCCD) provides information on intracranial blood flow status in stroke patients and can predict rehabilitation outcomes.

Objective  This study aimed to assess middle cerebral artery (MCA) parameters using TCCD in MCA territory stroke patients admitted for rehabilitation and correlate with clinical outcome measures.

Materials and Methods  Patients aged 18 to 65 years with a first MCA territory stroke, within 6 months of onset were recruited. The clinical outcome scales and TCCD parameters were assessed at both admission and discharge. The scales used were the Scandinavian stroke scale (SSS), Barthel Index (BI), modified Rankin Scale (mRS), Fugl–Meyer upper extremity scale (FMA-UE), modified motor assessment scale (mMAS) scores. TCCD parameters measured were MCA peak systolic, end diastolic, mean flow velocities (MFV), and index of symmetry (SI) and were correlated with clinical scores.

Results  Fourteen patients were recruited with median age of 56.5 years, median duration of stroke was 42.5 days. Mean flow velocities of affected and unaffected MCA were 46.2 and 50.7 cm/s, respectively. Flow velocities and SI did not change between the two assessments. There was significant improvement in clinical outcome scores at discharge. Significant correlation was observed for patient group with SI > 0.9 at admission with FMA-UE, SSS, and BI scores at discharge ( p  < 0.05).

Conclusion  Flow velocity parameters did not change during in-patient rehabilitation. Patients with symmetric flow at admission had improved clinical outcomes measure scores at discharge. Thus SI can predict rehabilitation outcomes in stroke survivors.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l ₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

The cognitive dynamics of small-sooner over large-later preferences during temporal discounting task through event-related oscillations (EROs)

Event-related oscillations (ERO) may provide a useful tool for the identification of cognitive processes during economic decisions. In the present study, we investigate peak-to-peak amplitude of task event-related oscillations of healthy subjects during delay discounting task. The study included forty-seven consecutive volunteers with mean 22 age- and matched education and socioeconomic condition. We used two temporal discounting (TD) tasks: the first was used to find individual indifference points for a set of delays and in the second, we recorded EEG as the participants made now vs delay decisions for the indifferent options. The EEG activity were recorded from 24 electrodes placed on the head surface according to the international 10-20 system. EEG activity for each choice (now and future) was averaged separately. The ERO responses were calculated for delta, theta, alpha and beta bands by the peak-to-peak measures. After Bonferroni correction, we found a significant effect of the decision process on the left frontal theta, left centroparietal delta, and frontoparietal beta oscillations. These were significantly greater during future decisions compared to now condition. These results indicate that a widespread frontoparietal network is implicated during delay discounting.

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

Functional Neurophysiological Biomarkers of Early-Stage Alzheimer's Disease: A Perspective of Network Hyperexcitability in Disease Progression

Network hyperexcitability (NH) has recently been suggested as a potential neurophysiological indicator of Alzheimer’s disease (AD), as new, more accurate biomarkers of AD are sought. NH has generated interest as a potential indicator of certain stages in the disease trajectory and even as a disease mechanism by which network dysfunction could be modulated. NH has been demonstrated in several animal models of AD pathology and multiple lines of evidence point to the existence of NH in patients with AD, strongly supporting the physiological and clinical relevance of this readout. Several hypotheses have been put forward to explain the prevalence of NH in animal models through neurophysiological, biochemical, and imaging techniques. However, some of these hypotheses have been built on animal models with limitations and caveats that may have derived NH through other mechanisms or mechanisms without translational validity to sporadic AD patients, potentially leading to an erroneous conclusion of the underlying cause of NH occurring in patients with AD. In this review, we discuss the substantiation for NH in animal models of AD pathology and in human patients, as well as some of the hypotheses considering recently developed animal models that challenge existing hypotheses and mechanisms of NH. In addition, we provide a preclinical perspective on how the development of animal models incorporating AD-specific NH could provide physiologically relevant translational experimental data that may potentially aid the discovery and development of novel therapies for AD.

Barrel cortex plasticity after photothrombotic stroke involves potentiating responses of pre-existing circuits but not functional remapping to new circuits

Recovery after stroke is thought to be mediated by adaptive circuit plasticity, whereby surviving neurons assume the roles of those that died. However, definitive longitudinal evidence of neurons changing their response selectivity after stroke is lacking. We sought to directly test whether such functional “remapping” occurs within mouse primary somatosensory cortex after a stroke that destroys the C1 barrel. Using in vivo calcium imaging to longitudinally record sensory-evoked activity under light anesthesia, we did not find any increase in the number of C1 whisker-responsive neurons in the adjacent, spared D3 barrel after stroke. To promote plasticity after stroke, we also plucked all whiskers except C1 (forced use therapy). This led to an increase in the reliability of sensory-evoked responses in C1 whisker-responsive neurons but did not increase the number of C1 whisker-responsive neurons in spared surround barrels over baseline levels. Our results argue against remapping of functionality after barrel cortex stroke, but support a circuit-based mechanism for how rehabilitation may improve recovery.

Definitive evidence for functional remapping after stroke remains lacking. Here, the authors performed in vivo intrinsic signal imaging and two-photon calcium imaging of sensory-evoked responses before and after photothrombotic stroke and found no evidence of remapping of lost functionalities to new circuits in peri-infarct cortex.

An overview of assessment tools for determination of biological Magnesium implant degradation

Medical implants made of biodegradable materials are advantageous for short-term applications as fracture fixation and mechanical support during bone healing. After completing the healing process, the implant biodegrades without any long-term side effects nor any need for surgical removal. In particular, Magnesium (Mg) implants, while degrading, can cause physiological changes in the tissues surrounding the implant. The evaluation of structural remodeling is relevant, however, the functional assessment is crucial to provide information about physiological changes in tissues, which can be applied as an early marker during the healing process. Hence, non-invasive monitoring of structural and functional changes in the surrounding tissue during the healing process is essential, and the need for new assessing methods is emerging. This paper provides an assessment of Mg based implants, and an extensive review of the literature is presented with the focus on the imaging techniques for investigation of the Mg implants' biodegradation. The potential of a hybrid analysis, including Near-Infrared Spectroscopy (NIRS) and photoacoustic imaging (PAI) technology, is further discussed. A hybrid solution may play a significant role in monitoring implants and have several advantages for monitoring tissue oxygenation in addition to tissue's acidity, which is directly connected to the Mg implants degradation process. Such a hybrid assessment system can be a simple, ambulant, and less costly technology with the potential for clinically monitoring of Mg implants at site.

The utility of deep brain stimulation surgery for treating eating disorders: A systematic review

Background:

Deep brain stimulation (DBS) has demonstrated preliminary success as a treatment for neuropsychological disorders including obsessive-compulsive disorder and substance use disorder. This systematic review aims to assess the use of DBS in treating eating disorders (EDs) to determine its utility and the extent of adverse effects.

Methods:

A PubMed search following PRISMA guidelines was executed to find studies encompassing DBS as a treatment of ED. Outcomes were extracted from the literature and summarized while a review of quality was also performed.

Results:

From a search yielding 299 publications, 11 studies published between 2010 and 2020 were found to fit the inclusion criteria. Out of 53 patients who began with an abnormal BMI before treatment, 22 patients (41.5%) achieved normal BMI on follow-up. Significant neuropsychological improvement was seen in most patients as measured by neuropsychiatric testing and questionnaires.

Conclusion:

DBS as a treatment for ED may result in significant objective and psychological benefits. Further studies should aim to increase the sample size, standardize follow-up protocol, and standardize the neuropsychiatric tests used to determine psychological and physiological benefits.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

In vitro and in vivo sigma 1 receptor imaging studies in different disease states

The sigma receptor system has been classified into two distinct subtypes, sigma 1 (σ1R) and sigma 2 (σ2R). Sigma 1 receptors (σ1Rs) are involved in many neurodegenerative diseases and different central nervous system disorders such as Alzheimer's disease, Parkinson's disease, schizophrenia, and drug addiction, and pain. This makes them attractive targets for developing radioligands as tools to gain a better understanding of disease pathophysiology and clinical diagnosis. Over the years, several σ1R radioligands have been developed to image the changes in σ1R distribution and density providing insights into their role in disease development. Moreover, the involvement of both σ1Rs and σ2Rs with cancer make these ligands, especially those that are σ2R selective, great tools for imaging different types of tumors. This review will discuss the principles of molecular imaging using PET and SPECT, known σ1R radioligands and their applications for labelling σ1Rs under different disease conditions. Furthermore, this review will highlight σ1R radioligands that have demonstrated considerable potential as biomarkers, and an opportunity to fulfill the ultimate goal of better healthcare outcomes and improving human health.

Objective Assessments in Diagnoses and Treatment: A Proposed Change in Paradigm

For patients with psychiatric disorders, current diagnostic and treatment approaches are far from optimal. The clinical interview drives the standard approach-matching symptoms to diagnostic criteria-and results in standardized pharmacological and behavioral treatments, often, with inadequate outcome; but now, recent imaging advances can correlate behavioral assessments with brain function and measure them against normative databases to provide data critical for the reevaluation of patient diagnosis and treatment. This article addresses the data that support a redefinition of our current paradigm. We believe a neurobehavioral approach provides for more personalized treatment approaches unbound from classically defined diagnostic biases.

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning

Functional near-infrared spectroscopy (fNIRS) is a promising technique for non-invasively assessing cortical brain activity during learning. This technique is safe, portable, and, compared to other imaging techniques, relatively robust to head motion, ocular and muscular artifacts and environmental noise. Moreover, the spatial resolution of fNIRS is superior to electroencephalography (EEG), a more commonly applied technique for measuring brain activity non-invasively during learning. Outcomes from fNIRS measures during learning might therefore be both sensitive to learning and to feedback on learning, in a different way than EEG. However, few studies have examined fNIRS outcomes in learning and no study to date additionally examined the effects of feedback. To address this apparent gap in the literature, the current study examined prefrontal cortex activity measured through fNIRS during visuomotor learning and how this measure is affected by task feedback. Activity in the prefrontal cortex decreased over the course of learning while being unaffected by task feedback. The findings demonstrate that fNIRS in the prefrontal cortex is valuable for assessing visuomotor learning and that this measure is robust to task feedback. The current study highlights the potential of fNIRS in assessing learning even under different task feedback conditions.

Functional connectivity of the default mode, dorsal attention and fronto-parietal executive control networks in glial tumor patients

PURPOSE

Resting state functional magnetic resonance imaging (rsfMRI) is an emerging tool to explore the functional connectivity of different brain regions. We aimed to assess the disruption of functional connectivity of the Default Mode Network (DMN), Dorsal Attention Network(DAN) and Fronto-Parietal Network (FPN) in patients with glial tumors.

METHODS

rsfMRI data acquired on 3T-MR of treatment-naive glioma patients prospectively recruited (2015-2019) and matched controls from the 1000 functional-connectomes-project were analyzed using the CONN functional toolbox. Seed-Based Connectivity Analysis (SBCA) and Independent Component Analysis (ICA, with 10 to 100 components) were performed to study reliably the three networks of interest.

RESULTS

35 patients with gliomas (17 WHO grade I-II, 18 grade III-IV) and 70 controls were included. Global increased DMN connectivity was consistently found with SBCA and ICA in patients compared to controls (Cluster1: Precuneus, height: p < 10^-6; Cluster2: subcallosum; height: p < 10^-5). However, an area of decreased connectivity was found in the posterior corpus callosum, particularly in high-grade gliomas (height: p < 10^-5). The DAN demonstrated small areas of increased connectivity in frontal and occipital regions (height: p < 10^-6). For the FPN, increased connectivity was noted in the precuneus, posterior cingulate gyrus, and frontal cortex. No difference in the connectivity of the networks of interest was demonstrated between low- and high-grade gliomas, as well as when stratified by their IDH1-R132H (isocitrate dehydrogenase) mutation status.

CONCLUSION

Altered functional connectivity is reliably found with SBCA and ICA in the DMN, DAN, and FPN in glioma patients, possibly explained by decreased connectivity between the cerebral hemispheres across the corpus callosum due to disruption of the connections.

Task- and Rest-based Functional Brain Connectivity in Food-related Reward Processes among Healthy Adolescents

It is known that the nucleus accumbens, orbitofrontal cortex and insula play a role in food-related reward processes. Although their interconnectedness would be an ideal topic for understanding food intake mechanisms, it nevertheless remains unclear especially in adolescent. Therefore, this study aims to investigate the effect of hunger on functional connectivity in healthy adolescents using task- and rest-based imaging. Fifteen participants underwent two MRI sessions, pre-lunch (hunger) and post-lunch (satiety), including food cue task and resting-state. During task- and rest-based imaging, functional connectivity was greater when hungry as opposed to satiated between the right posterior insula/nucleus accumbens, suggesting involvement of salient interoceptive stimuli signals. During task-based imaging, an increase was observed in functional connectivity when hungry as opposed to satiated between the medial and lateral orbitofrontal cortex which contributes to the perception of food deprivation as a frustration. A decrease was identified when hungry as opposed to satiated in functional connectivity in the right anterior orbitofrontal/accumbens and posterior insula/medial orbitofrontal cortices reflecting suppression of the affective and sensorial information. Conversely, functional connectivity was increased during aversive stimuli between the right medial orbitofrontal cortex and right posterior insula when hungry as opposed to satiated. This suggests that the value of valence could occur in the shift in connectivity between these two regions. In addition, during rest-based imaging, a left-sided lateralization was reported (accumbens/lateral orbitofrontal and accumbens/posterior insula) when hungry as opposed to satiated which may represent changes in internal state due to focus on the benefit of an upcoming meal.

Default Mode Network Connectivity Moderates the Relationship Between the APOE Genotype and Cognition and Individualizes Identification Across the Alzheimer's Disease Spectrum

Although previous studies have investigated the effects of the apolipoprotein E (APOE) ɛ4 genotype on the default mode network (DMN) in the Alzheimer's disease (AD) spectrum, it is still unclear how the APOE genotype regulates the DMN and subsequently affects cognitive decline in the AD spectrum. One hundred sixty-nine subjects with resting-state functional magnetic resonance imaging data and neuropsychological test scores were selected from the Alzheimer's Disease Neuroimaging Initiative. The main effects and interaction of the APOE genotype and disease status on the DMN were explored. A moderation analysis was performed to investigate the relationship among the APOE genotype, DMN connectivity, and cognition. Additionally, the pair-wised DMN connectivity was used to classify AD spectrum, and the classification accuracy was validated. Compared to APOEɛ4 non-carriers, APOEɛ4 carriers showed the opposite trajectory of DMN connectivity across the AD spectrum. Specifically, the strengths in the posterior cingulate cortex (PCC) connecting with the right precuneus, insular, and fusiform area (FFA) were positively correlated with Mini-Mental State Examination (MMSE) scores in APOEɛ4 non-carriers but not in APOEɛ4 carriers. Furthermore, PCC-right FFA connectivity could moderate the effects of the APOE genotype on MMSE scores across the disease groups. More importantly, using a receiver operating characteristic analysis, these altered connectivities yielded strong classification powers in a pathological stage-dependent manner in the AD spectrum. These findings first identified the intrinsic DMN connectivity moderating the effect of the APOE genotype on cognition and provided a pathological stage-dependent neuroimaging biomarker for early differentiation of the AD spectrum.

[Surgery 4.0-are we ready?]

The increasing networking of data systems in medicine is not only leading to modern interdisciplinarity in the sense of cooperation between different medical departments, but also poses new challenges regarding the building and room infrastructure. The surgical operating room of the future expands or augments its reality, away from the pure building characteristics, towards an intelligent and communicative space platform. The building infrastructure (operating theatre) serves as sensor and actuator. Thus, it is possible to inform about missing diagnostics as well as to register them directly in the contextualization of the planned surgical intervention or to integrate them into the processes. Integrated operating theatres represent a comprehensive computer platform based on a corresponding system architecture with software-based protocols. An underlying modular system consisting of various modules for image acquisition and analysis, interaction and visualization supports the integration and merging of heterogeneous data that are generated in a hospital operation. Integral building data (e.g., air conditioning, lighting control, device registration) are merged with patient-related data (age, type of illness, concomitant diseases, existing diagnostic CT and MRI images). New systems coming onto the market, as well as already existing systems will have to be measured by the extent to which they will be able to guarantee this integration of information-similar to the development from mobile phone to smartphone. Cost reduction should not be the only legitimizing argument for the market launch, but the vision of a new quality of surgical perception and action.