Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease

The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore, bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico-striato-pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease, particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremor-dominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia-thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity, and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.

The burden of upper motor neuron involvement is correlated with the bilateral limb involvement interval in patients with amyotrophic lateral sclerosis: a retrospective observational study

JOURNAL/nrgr/04.03/01300535-202505000-00032/figure1/v/2024-07-28T173839Z/r/image-tiff Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons. Early bilateral limb involvement significantly affects patients' daily lives and may lead them to be confined to bed. However, the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear. To address this issue, we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022. A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis. We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients. Multiple factor analyses revealed that higher upper motor neuron scores (hazard ratio [HR] = 1.05, 95% confidence interval [CI] = 1.01-1.09, P = 0.018), onset in the left limb (HR = 0.72, 95% CI = 0.58-0.89, P = 0.002), and a horizontal pattern of progression (HR = 0.46, 95% CI = 0.37-0.58, P < 0.001) were risk factors for a shorter interval until bilateral limb involvement. The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients. These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.

Resting-state brain network remodeling after different nerve reconstruction surgeries: a functional magnetic resonance imaging study in brachial plexus injury rats

JOURNAL/nrgr/04.03/01300535-202505000-00031/figure1/v/2024-07-28T173839Z/r/image-tiff Distinct brain remodeling has been found after different nerve reconstruction strategies, including motor representation of the affected limb. However, differences among reconstruction strategies at the brain network level have not been elucidated. This study aimed to explore intra-network changes related to altered peripheral neural pathways after different nerve reconstruction surgeries, including nerve repair, end-to-end nerve transfer, and end-to-side nerve transfer. Sprague-Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight: no nerve repair, grafted nerve repair, phrenic nerve end-to-end transfer, and end-to-side transfer with a graft sutured to the anterior upper trunk. Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery. The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component. Alterations in intra-network resting-state functional connectivity were compared among the groups. Target muscle reinnervation was assessed by behavioral observation (elbow flexion) and electromyography. The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway. Nerve repair was related to enhanced connectivity within the sensorimotor network, while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation. The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer. Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer. Our study revealed important brain networks related to different nerve reconstructions. These networks may be potential targets for enhancing motor recovery.

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe, relapsing, and multifactorial neurological disorder. Studies regarding the accurate diagnosis, prognosis, and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy. The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression, protein expression, ion channel activity, energy metabolites, and gut microbiota composition. Satisfactory results are lacking for conventional treatments for epilepsy. Surgical resection of lesions, drug therapy, and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy. Non-pharmacological treatments, such as a ketogenic diet, gene therapy for nerve regeneration, and neural regulation, are currently areas of research focus. This review provides a comprehensive overview of the pathogenesis, diagnostic methods, and treatments of epilepsy. It also elaborates on the theoretical basis, treatment modes, and effects of invasive nerve stimulation in neurotherapy, including percutaneous vagus nerve stimulation, deep brain electrical stimulation, repetitive nerve electrical stimulation, in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation. Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures. Additionally, many new technologies for the diagnosis and treatment of epilepsy are being explored. However, current research is mainly focused on analyzing patients' clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level, which has led to a lack of consensus regarding the mechanisms related to the disease.

Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets

Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein-protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as "causative" for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration-approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.

Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation

Alzheimer's disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis. The Alzheimer's disease brain tends to be hyperexcitable and hypersynchronized, thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life, leaving patients incapacitated. Repetitive transcranial magnetic stimulation is a cost-effective, neuro-modulatory technique used for multiple neurological conditions. Over the past two decades, it has been widely used to predict cognitive decline; identify pathophysiological markers; promote neuroplasticity; and assess brain excitability, plasticity, and connectivity. It has also been applied to patients with dementia, because it can yield facilitatory effects on cognition and promote brain recovery after a neurological insult. However, its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of studies. This study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment, evaluate its effects on synaptic plasticity, and identify the associated mechanisms. This review essentially focuses on changes in the pathology, amyloidogenesis, and clearance pathways, given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer's disease. Apoptotic mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription, which are closely related to the neural regeneration process, are also highlighted. Finally, we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation, with the aim to highlight future directions for better clinical translations.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy

The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below.•The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process.•An archive-based update strategy was introduced to maintain diversity in the training set.•Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.

Inhibition of prefrontal cortex parvalbumin interneurons mitigates behavioral and physiological sequelae of chronic stress in male mice

Chronic stress leads to hypofunction of the medial prefrontal cortex (mPFC), mechanisms of which remain to be determined. Enhanced activation of GABAergic of parvalbumin (PV) expressing interneurons (INs) is thought to play a role in stress-induced prefrontal inhibition. In this study, we tested whether chemogenetic inhibition of mPFC PV INs after chronic stress can rescue chronic stress-related behavioral and physiological phenotypes. Mice underwent 2 weeks of chronic variable stress (CVS) followed by a battery of behavioral tests known to be affected by chronic stress exposure, e.g. an open field (OF), novel object recognition (NOR), tail suspension test (TST), sucrose preference test (SPT), and light dark (LD) box. Inhibitory DREADDs were actuated by 3 mg/kg CNO administered 30 min prior to each behavioral test. CVS caused hyperactivity in the OF, reduced sucrose preference in the SPT (indicative of enhanced anhedonia), and increased anxiety-like behavior in the LD box. Inhibition of PV IN after stress mitigated these effects. In addition, CVS also resulted in reduced thymus weight and body weight loss, which were also mitigated by PV IN inhibition. Our results indicate that chronic stress leads to plastic changes in PV INs that may be mitigated by chemogenetic inhibition. Our findings implicate cortical GABAergic INs as a therapeutic target in stress-related diseases.

Iron depletion has different consequences on the growth and survival of Toxoplasma gondii strains

Toxoplasma gondii is an obligate intracellular parasite responsible for a pathology called toxoplasmosis, which primarily affects immunocompromised individuals and developing foetuses. The parasite can scavenge essential nutrients from its host to support its growth and survival. Among them, iron is one of the most important elements needed to sustain basic cellular functions as it is involved in a number of key metabolic processes, including oxygen transport, redox balance, and electron transport. We evaluated the effects of an iron chelator on the development of several parasite strains and found that they differed in their ability to tolerate iron depletion. The growth of parasites usually associated with a model of acute toxoplasmosis was strongly affected by iron depletion, whereas cystogenic strains were less sensitive as they were able to convert into persisting developmental forms that are associated with the chronic form of the disease. Ultrastructural and biochemical characterization of the impact of iron depletion on parasites also highlighted striking changes in both their metabolism and that of the host, with a marked accumulation of lipid droplets and perturbation of lipid homoeostasis. Overall, our study demonstrates that although acute iron depletion has an important effect on the growth of T. gondii, it has a more profound impact on actively dividing parasites, whereas less metabolically active parasite forms may be able to avoid some of the most detrimental consequences.

Combined cold-water immersion and breathwork may be associated with improved mental health and reduction in the duration of upper respiratory tract infection - a case-control study

A polar plunge is a term referring to an ice-cold water immersion (CWI), usually in the winter period. It is also a part of a specific training program (STP) which currently gains popularity worldwide and was proven to display paradigm-shifting characteristics. The aim of this study was to compare the indices of mental functioning (including depression, anxiety, mindfulness) and duration of upper respiratory tract infection (URTI) measured among the study participants. A set of questionnaires was distributed via the Internet. Participants declaring regular STP practice were selected (N = 77). Two groups were matched based on a case-control principle: the first one (the control group) comprised participants who did not declare nor CWI practice, nor STP practice. The second one comprised participants declaring regular CWI practice only. The CWI only group displayed better mental health indices and shorter URTIs compared to the control group. Moreover, the STP group also displayed better general mental health, less somatic complaints, and shorter URTIs compared to the CWI only group. This study suggests the existence of CWI's potential in boosting mental health and immune system functioning, however when complemented by a specific breathwork, this potential can be increased. However, further research is required.

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Alteration of fractional anisotropy in preterm-born individuals: a systematic review and meta-analysis

BACKGROUD

Neurological disorders are common in preterm (PT) born individuals. Diffusion tensor imaging (DTI) studies using tract-based spatial statistics (TBSS) effectively detect microstructural white matter (WM) abnormalities in the brain. We conducted this systematic review to integrate the findings of TBSS studies to determine the most consistent WM alterations in PT born individuals.

METHODS

PubMed, Embase, Web of Science and Science Direct were searched. DTI studies using TBSS in PT born individuals were screened up to October 2022. The systematic review included studies reporting alterations in FA values for the entire brain in a stereotactic space, with three coordinates (x, y, z), according to the seed-based d mapping method.

RESULTS

The search strategy identified seventeen studies that fulfilled our inclusion criteria, with a total of 911 PT-born individuals and 563 matched controls were analysed. Of the seventeen studies, eight were dedicated to 650 adults, five to 411 children and four to 413 infants. Ten studies recruited 812 individuals born very prematurely (GA <29 weeks), six studies recruited 386 moderately premature individuals (GA = 29-32 weeks) and one study recruited 276 individuals born late prematurely (GA >32 weeks). This meta-analysis of six studies including 388 individuals highlighted four brain regions in which fractional anisotropy (FA) was lower in PT group than in people born at term. The quantitative meta-analysis found that the most robust WM alterations were located in the corpus callosum (CC), the bilateral thalamus and the left superior longitudinal fasciculus (SLF) II. Significant changes in FA reflect WM abnormalities in PT born individuals from infant to young adulthood.

CONCLUSIONS

Significant changes in FA reflect WM abnormalities in individuals born PT from infancy to young adulthood. The abnormal development of the CC, bilateral thalamus and left SLF may play a vital role in the neurodevelopment of PT individuals.

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation

Objective

Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods

We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.

Results

Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.

Conclusion

This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

Cerebral white matter burden is linked to cognitive function in patients undergoing hemodialysis

INTRODUCTION

Chronic kidney disease is related to neurodegeneration and structural changes in the brain which might lead to cognitive decline. The Fazekas scale used for assessing white matter hyperintensities (WMHs) was associated with poor cognitive performance. Therefore, this study investigated the associations between the mini-mental status examination (MMSE), Montreal cognitive assessment (MoCA), cognitive abilities screening instrument (CASI), and Fazekas scale in patients under hemodialysis (HD).

METHODS

The periventricular (PV) WMHs and deep WMHs (DWMHs) in brain magnetic resonance images of 59 patients under dialysis were graded using the Fazekas scale. Three cognition function tests were also performed, then multivariable ordinal regression and logistic regression were used to identify the associations between cognitive performance and the Fazekas scale.

RESULTS

There were inverse associations between the three cognitive function tests across the Fazekas scale of PVWMHs (p = .037, .006, and .008 for MMSE, MoCA, and CASI, respectively), but the associations were attenuated in the DWMHs group. In CASI, significant differences were identified in short-term memory, mental manipulation, abstract thinking, language, spatial construction, and name fluency in the PVWMHs group. However, DWMHs were only significantly correlated with abstract thinking and short-term memory.

CONCLUSION

An inverse correlation existed between the Fazekas scale, predominantly in PVWMHs, and cognition in patients undergoing HD. The PVWMHs were associated with cognitive performance assessed by MMSE, MoCA, and CASI, as well as with subdomains of CASI such as memory, language and name fluency in patients undergoing HD.

Brain-Derived neurotrophic factor and inflammatory biomarkers are unaffected by acute and chronic intermittent hypoxic-hyperoxic exposure in geriatric patients: a randomized controlled trial

BACKGROUND

Animal and human studies have shown that exposure to hypoxia can increase brain-derived neurotrophic factor (BDNF) protein transcription and reduce systematic inflammatory cytokine response. Therefore, the aim of this study was to investigate the acute and chronic effects of intermittent hypoxic-hyperoxic exposure (IHHE) prior to aerobic exercise on BDNF, interleukin-6 (IL-6), and C-reactive protein (CRP) blood levels in geriatric patients.

PATIENTS AND METHODS

Twenty-five geriatric patients (83.1 ± 5.0 yrs, 71.1 ± 10.0 kg, 1.8 ± 0.9 m) participated in a placebo-controlled, single-blinded trial and were randomly assigned to either an intervention (IG) or control group (CG) performing an aerobic cycling training (17 sessions, 20 min·session-1, 3 sessions·week-1). Prior to aerobic cycling exercise, the IG was additionally exposed to IHHE for 30 min, whereas the CG received continuous normoxic air. Blood samples were taken immediately before (pre-exercise) and 10 min (post-exercise) after the first session as well as 48 h (post-training) after the last session to determine serum (BDNFS) and plasma BDNF (BDNFP), IL-6, and CRP levels. Intervention effects were analyzed using a 2 x 2 analysis of covariance with repeated measures. Results were interpreted based on effect sizes with a medium effect considered as meaningful (ηp2 ≥ 0.06, d ≥ 0.5).

RESULTS

CRP was moderately higher (d = 0.51) in the CG compared to the IG at baseline. IHHE had no acute effect on BDNFS (ηp2 = 0.01), BDNFP (ηp2 < 0.01), BDNF serum/plasma-ratio (ηp2 < 0.01), IL-6 (ηp2 < 0.01), or CRP (ηp2 = 0.04). After the 6-week intervention, an interaction was found for BDNF serum/plasma-ratio (ηp2 = 0.06) but not for BDNFS (ηp2 = 0.04), BDNFP (ηp2 < 0.01), IL-6 (ηp2 < 0.01), or CRP (ηp2 < 0.01). BDNF serum/plasma-ratio increased from pre-exercise to post-training (d = 0.67) in the CG compared to the IG (d = 0.51). A main effect of time was found for BDNFP (ηp2 = 0.09) but not for BDNFS (ηp2 = 0.02). Within-group post-hoc analyses revealed a training-related reduction in BDNFP in the IG and CG by 46.1% (d = 0.73) and 24.7% (d = 0.57), respectively.

CONCLUSION

The addition of 30 min IHHE prior to 20 min aerobic cycling seems not to be effective to increase BDNFS and BDNFP or to reduce IL-6 and CRP levels in geriatric patients after a 6-week intervention.The study was retrospectively registered at drks.de (DRKS-ID: DRKS00025130).

Eye-tracking measurement of attention bias to social threat among youth: A replication and extension study

Attentional bias to social threat cues has been linked to heightened anxiety and irritability in youth. Yet, inconsistent methodology has limited replication and led to mixed findings. The current study aims to 1) replicate and extend two previous pediatric studies demonstrating a relationship between negative affectivity and attentional bias to social threat and 2) examine the test-retest reliability of an eye-tracking paradigm among a subsample of youth. Attention allocation to negative versus non-negative emotional faces was measured using a free-viewing eye-tracking task among youth (N=185 total, 60% female, M age=13.10 years, SD age=2.77) with three face-pair conditions: happy-angry, neutral-disgust, sad-happy. Replicating procedures of two previous studies, linear mixed-effects models compared attention bias between children with anxiety disorders and healthy controls. Bifactor analysis was used to parse shared versus unique facets of general negative affectivity (i.e., anxiety, irritability), which were then examined in relation to attention bias. Test-retest reliability of the bias-index was estimated among a subsample of youth (N=36). No significant differences in attention allocation or bias emerged between anxiety and healthy control groups. While general negative affectivity across the sample was not associated with attention bias, there was a positive relationship for anxiety and irritability on duration of attention allocation toward negative faces. Test-retest reliability for attention bias was moderate (r=0.50, p<.01). While anxiety-related findings from the two previous studies were not replicated, the relationship between attention bias and facets of negative affect suggests a potential target for treatment. Evidence for test-retest reliability encourages future use of the eye-tracking task for researchers.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Associations between PTSD and temporal discounting: The role of future thinking

BACKGROUND AND OBJECTIVES

Despite documented alterations in future thinking in posttraumatic stress disorder (PTSD), our understanding of how individuals with PTSD make future-oriented decisions is limited. We tested the hypothesis that increased discounting in association with PTSD reflects failure to spontaneously envision future rewarding situations.

METHODS

Thirty-seven trauma exposed war-zone veterans completed a standard temporal discounting task as well as a temporal discounting task accompanied by episodic future thinking cues.

RESULTS

Severity of PTSD symptoms was associated with preference for sooner, smaller rewards in the standard task. Consistent with our hypothesis, when participants engaged in future thinking, greater PTSD symptom severity was no longer associated with steeper discounting. Moreover, difficulty anticipating future events, as measured contemporaneously in a separate task (Verfaellie et al., 2024), mediated the relationship between PTSD symptom severity and degree of discounting in the standard task. Among PTSD symptom clusters, the severity of avoidance and negative alterations in cognition and mood was related to steeper discounting. Measures of depression and alcohol use were not associated with discounting.

LIMITATIONS

The sample included mostly male, predominantly White veterans who experienced primarily combat-related trauma.

CONCLUSIONS

PTSD-associated alterations in temporal discounting reflect failure to spontaneously imagine future positive events. Two common correlates of PTSD, depression and alcohol use, could not account for the observed associations between PTSD and future-oriented decisions.

Uncertainties Regarding Cerebral Palsy Diagnosis: Opportunities to Clarify the Consensus Definition

Background and Objectives

We have established that physicians, including neurologists, variably diagnose cerebral palsy (CP) when using the most recent CP definition from 2006. We also know that child neurologists and neurodevelopmentalists view themselves to be optimally suited to diagnose CP based on their training backgrounds. Therefore, to reduce variability in CP diagnosis, our objective was to elucidate uncertainties child neurologists and neurodevelopmentalists may have regarding practical application of the 2006 definition.

Methods

We conducted a cross-sectional survey of child neurologists and neurodevelopmentalists built into a discussion seminar at the 2022 Child Neurology Society (CNS) Annual Meeting, the largest professional meeting of these specialists in North America. Seminar attendees were provided the 2006 definition and asked whether they had any uncertainties about the practical application of the definition across 4 hypothetical clinical vignettes. A group of national and international CP leaders then processed these data through iterative discussions to develop recommendations for clarifying the 2006 definition.

Results

The seminar was attended by 50% of all conference attendees claiming CME (202/401). Of the 164 closing survey respondents, 145 (88%) expressed uncertainty regarding the clinical application of the 2006 definition. These uncertainties focused on 1) age, both regarding the minimum and maximum ages of brain disturbance or motor symptom onset (67/164, 41%), and 2) interpretation of the term "nonprogressive" (48/164, 29%). Almost all respondents (157/164, 96%) felt that we should revise the 2006 consensus definition of CP.

Discussion

To address the most common CP diagnostic uncertainties we identified, we collectively propose 4 points of clarification to the 2006 definition: 1) motor symptoms/signs should be present by 2 years old; 2) CP can and should be diagnosed as early as possible; 3) the clinical motor disability phenotype should be nonprogressive through 5 years old; and 4) a CP diagnosis should be re-evaluated if motor disability is progressive or absent by 5 years old. We anticipate that clarifying the 2006 definition of CP in this manner could address the uncertainties we identified among child neurologists and neurodevelopmentalists and reduce the diagnostic variability that currently exists.

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network

Background

Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity.

Purpose

To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN).

Methods

Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores.

Results

The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores.

Conclusion

The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI.

Supplementary Information

The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

Oligodendrocyte-specific expression of PSG8-AS1 suggests a role in myelination with prognostic value in oligodendroglioma

The segmentally duplicated Pregnancy-specific glycoprotein (PSG) locus on chromosome 19q13 may be one of the most rapidly evolving in the human genome. It comprises ten coding genes (PSG1-9, 11) and one predominantly non-coding gene (PSG10) that are expressed in the placenta and gut, in addition to several poorly characterized long non-coding RNAs. We report that long non-coding RNA PSG8-AS1 has an oligodendrocyte-specific expression pattern and is co-expressed with genes encoding key myelin constituents. PSG8-AS1 exhibits two peaks of expression during human brain development coinciding with the most active periods of oligodendrogenesis and myelination. PSG8-AS1 orthologs were found in the genomes of several primates but significant expression was found only in the human, suggesting a recent evolutionary origin of its proposed role in myelination. Additionally, because co-deletion of chromosomes 1p/19q is a genomic marker of oligodendroglioma, expression of PSG8-AS1 was examined in these tumors. PSG8-AS1 may be a promising diagnostic biomarker for glioma, with prognostic value in oligodendroglioma.

Modern methods of neuroanatomical and neurophysiological research

Our article explores a variety of modern research techniques employed in neuroanatomy and neurophysiology. We highlight the use of computer technologies, image analysis methods, and innovative approaches that expand our understanding of anatomical structures. The techniques we discuss include fractal analysis, the Pickworth method, scanning microscopy, and advanced computer image processing systems. Fractal analysis, in particular, offers a unique perspective on brain structures and functions and is a key tool in neuroanatomical research. We also focus on its application in neuroanatomical studies, particularly in cases of Alzheimer's disease and epilepsy. These modern research methods not only enhance our knowledge but also have significant clinical potential. Their use in diagnosing neurological diseases like Alzheimer's and epilepsy promises faster and more accurate diagnoses. We emphasize the combination of multiple methods for improved quality of anatomical structure imaging.

Exercise training and chronic kidney disease: characterization and bibliometrics of citation classics of clinical intervention trials

BACKGROUND

Exercise research targeting chronic kidney disease (CKD) has been conducted for more than 30 years, and the benefits of exercise for CKD patients have been progressively demonstrated. This study analyzes citation classics on clinical intervention trials on exercise training and CKD to describe the research landscape and hotspots through bibliometric analysis.

METHODS

To identify clinical trials of exercise training interventions for CKD with more than 100 citations from the Web of Science Core Collection database. Extracted bibliometric information, participant information, and study characteristics of the included articles. The total citations, annual average citations, publication of year, author keywords, and study-related data were bibliometric analyzed and described using Excel 2019 and VOSviewer software.

RESULTS

A total of 30 citation classics were included, with a total citation frequency of 102 to 279 (mean ± standard deviation: 148.4 ± 49.4). The American Journal of Kidney Diseases (n = 7) published the most (n = 7) classic citations in the field of CKD exercise research, and the Journal of the American Society of Nephrology was the most cited. The hotspot of research around CKD and exercise training interventions focused on population (hemodialysis and end-stage renal disease), exercise type (resistance training, yoga, and leg-cycling), and outcomes (cardiovascular indices, physical performance, psychological status, kidney function, physical activity). Reported dropout rates ranged from 0.0% to 47.4%.

CONCLUSION

A bibliometric analysis of citation classics on exercise training and CKD highlights the potential benefits of exercise as a non-pharmacological therapy for patients with CKD, as well as developments and hotspots in the field.

Glymphatic system: a gateway for neuroinflammation

The glymphatic system is a relatively recently identified fluid exchange and transport system in the brain. Accumulating evidence indicates that glymphatic function is impaired not only in central nervous system disorders but also in systemic diseases. Systemic diseases can trigger the inflammatory responses in the central nervous system, occasionally leading to sustained inflammation and functional disturbance of the central nervous system. This review summarizes the current knowledge on the association between glymphatic dysfunction and central nervous system inflammation. In addition, we discuss the hypothesis that disease conditions initially associated with peripheral inflammation overwhelm the performance of the glymphatic system, thereby triggering central nervous system dysfunction, chronic neuroinflammation, and neurodegeneration. Future research investigating the role of the glymphatic system in neuroinflammation may offer innovative therapeutic approaches for central nervous system disorders.

Visual effects on tactile stimulation and its perception: A pilot study using near-infrared spectroscopy

In rubber hand illusion, visual information affects tactile information, whereas in the mirror box illusion, visual information has the opposite effect. However, its underlying mechanisms are not fully understood. As one of the reasons, non-invasive neuroimaging techniques, such as functional magnetic resonance, positron emission tomography, and electroencephalography, often fail to detect complex and fragile responses in the sensory-motor cortex. Using near-infrared spectroscopy (NIRS), we examined neural activity during tactile tracing on a sine-shaped acrylic board to investigate the effects of (1) visual information and (2) the spatial frequency of the sine shape on brain activity. We used spatial frequencies of 2-3 and 20-30 Hz as low- and high-tactile stimuli, respectively. Two types of experiments, with and without an acrylic board, were conducted. Participants performed the tracing tasks with their index finger at 1 Hz of temporal frequency of a 200 mm length of the acrylic board as main tasks and only space moving without touching as a control task. We show effect of visual information on neural activation, including not only activation intensity but also activation patterns.•Testing of mutual effects of vision and haptics.•Testing of sensory-motor paradox using NIRS.•A high NIRS sensitivity to stimulus-induced hemodynamic change.

Integrated approach to reducing polypharmacy in older people: exploring the role of oxidative stress and antioxidant potential therapy

Increased life expectancy, attributed to improved access to healthcare and drug development, has led to an increase in multimorbidity, a key contributor to polypharmacy. Polypharmacy is characterised by its association with a variety of adverse events in the older persons. The mechanisms involved in the development of age-related chronic diseases are largely unknown; however, altered redox homeostasis due to ageing is one of the main theories. In this context, the present review explores the development and interaction between different age-related diseases, mainly linked by oxidative stress. In addition, drug interactions in the treatment of various diseases are described, emphasising that the holistic management of older people and their pathologies should prevail over the individual treatment of each condition.

Contralateral renal change in a unilateral ureteral obstruction rat model using intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

Most functional magnetic resonance research has primarily examined alterations in the affected kidney, often neglecting the contralateral kidney. Our study aims to investigate whether imaging parameters accurately depict changes in both the renal cortex and medulla in a unilateral ureteral obstruction rat model, thereby showcasing the utility of intravoxel incoherent motion (IVIM) in evaluating contralateral renal changes.

METHODS

Six rats underwent MR scans and were subsequently sacrificed for baseline histological examination. Following the induction of left ureteral obstruction, 48 rats were scanned, and the histopathological examinations were conducted on days 3, 7, 10, 14, 21, 28, 35, and 42. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudodiffusion (D*), and perfusion fraction (f) values were measured using IVIM.

RESULTS

On the 10th day of obstruction, both cortical and medullary ADC values differed significantly between the UUO10 group and the sham group (p < 0.01). The cortical D values showed statistically significant differences between UUO3 group and sham group (p < 0.01) but not among UUO groups at other time point. Additionally, the cortical and medullary f values were statistically significant between the UUO21 group and the sham group (p < 0.01). Especially, the cortical f values exhibited significant differences between the UUO21 group and the UUO groups with shorter obstruction time (at time point of 3, 7, 10, 14 day) (p < 0.01).

CONCLUSIONS

Significant hemodynamic alterations were observed in the contralateral kidney following renal obstruction. IVIM accurately captures changes in the unobstructed kidney. Particularly, the cortical f value exhibits the highest potential for assessing contralateral renal modifications.

Brain plasticity and adolescent HIV: A randomised controlled trial protocol investigating behavioural and hemodynamic responses in attention cognitive rehabilitation therapy

Despite advances in antiretroviral pharmacology, neuroHIV in the central nervous system (CNS), causes neuronal dysregulation, which is associated with compromised neurocognition. Non-pharmaceutical interventions such as HIV cognitive rehabilitation training (HIV-CRT), have shown potential to partially reverse cognitive deficits, sequent HIV neuroinvasion. Nonetheless, no studies exist pairing cognitive outcomes with objective neuroimaging biomarkers in adolescent HIV-CRT. This longitudinal pre-post-quasi-experimental protocol examined cognitive outcomes, paired with optimal neuroimaging outcomes following customised attention training in adolescent HIV. Twenty-six adolescents living with HIV were randomly assigned to either the treatment group, which received attention CRT using ACTIVATE™, (n = 13), or to the treatment as usual group (n = 13). Cognitive outcomes were examined using the NEPSY-II, and BRIEF; whilst neuroimaging outcomes were determined by changes in oxygenated haemoglobin (HbO), as determined by functional near-infrared spectrometry (fNIRS). Functional connectivity fNIRS measures were evaluated using seed-based correlation analysis, located in the central executive network (CEN). This study serves to guide the development and identification of objective biomarkers for adolescent neuroHIV, sequent CRT amongst children living with HIV in Sub-Saharan Africa.

Self-control problems in Intermittent Explosive Disorder: Presentation of an explanatory approach

BACKGROUND AND OBJECTIVES

Intermittent Explosive Disorder (IED) is an impulsive aggression disorder with self-control problems. However, the mechanisms underpinning the self-control problems in IED have not been clearly investigated. Therefore, this study examined the nature of self-control problems and their types, including cognitive inhibition, behavioral inhibition, and emotional interference in IED.

METHODS

Participants included three groups: IED (n = 54), psychiatric control (n = 59), and healthy control (n = 62). They were first screened with SCL-90-R, and then they were clinically interviewed. They all did computerized neurocognitive tasks, including Color-Word Stroop Task, Emotional Stroop Task, Go-NoGo Task, and Stop-Signal Task.

RESULTS

MANOVA analyses showed that the IED group had poorer performance in cognitive inhibition, response inhibition, and increased emotional interference than the two psychiatric and healthy control groups. They performed much worse than the other two groups, particularly in action cancellation (Stop-Signal Task), and showed increasingly emotional interference.

LIMITATIONS

The brain reaction of individuals while doing the tasks was not examined, and some variables were not measured. Also, it is unclear how the emotional eruption interferes with cognitive content and behavioral inhibition.

CONCLUSIONS

These findings indicate that self-control problems in IED can be due to deficient cognitive, emotional, and behavioral inhibitions, each appearing sequentially during a step-by-step process and facilitating the onset of IED signs and symptoms. Such a distinguished understanding of the role of neurocognitive mechanisms can lead to the development of accurate explanatory approaches and increase the effectiveness of treatment.

Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness

The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.

Investigation of the structural changes in the hippocampus and prefrontal cortex using FTIR spectroscopy in sleep deprived mice

Sleep is a basic, physiological requirement for living things to survive and is a process that covers one third of our lives. Melatonin is a hormone that plays an important role in the regulation of sleep. Sleep deprivation affect brain structures and functions. Sleep deprivation causes a decrease in brain activity, with particularly negative effects on the hippocampus and prefrontal cortex. Despite the essential role of protein and lipids vibrations, polysaccharides, fatty acid side chains functional groups, and ratios between amides in brain structures and functions, the brain chemical profile exposed to gentle handling sleep deprivation model versus Melatonin exposure remains unexplored. Therefore, the present study, aims to investigate a molecular profile of these regions using FTIR spectroscopy measurement's analysis based on lipidomic approach with chemometrics and multivariate analysis to evaluate changes in lipid composition in the hippocampus, prefrontal regions of the brain. In this study, C57BL/6J mice were randomly assigned to either the control or sleep deprivation group, resulting in four experimental groups: Control (C) (n = 6), Control + Melatonin (C + M) (n = 6), Sleep Deprivation (S) (n = 6), and Sleep Deprivation + Melatonin (S + M) (n = 6). Interventions were administered each morning via intraperitoneal injections of melatonin (10 mg/kg) or vehicle solution (%1 ethanol + saline), while the S and S + M groups underwent 6 h of daily sleep deprivation from using the Gentle Handling method. All mice were individually housed in cages with ad libitum access to food and water within a 12-hour light-dark cycle. Results presented that the brain regions affected by insomnia. The structure of phospholipids, changed. Yet, not only changes in lipids but also in amides were noticed in hippocampus and prefrontal cortex tissues. Additionally, FTIR results showed that melatonin affected the lipids as well as the amides fraction in cortex and hippocampus collected from both control and sleep deprivation groups.

Enhanced and robust contrast in CEST MRI: Saturation pulse shape design via optimal control

PURPOSE

To employ optimal control for the numerical design of Chemical Exchange Saturation Transfer (CEST) saturation pulses to maximize contrast and stability againstB 0 $$ {\mathrm{B}}_0 $$ inhomogeneities.

THEORY AND METHODS

We applied an optimal control framework for the design pulse shapes for CEST saturation pulse trains. The cost functional minimized both the pulse energy and the discrepancy between the corresponding CEST spectrum and the target spectrum based on a continuous radiofrequency (RF) pulse. The optimization is subject to hardware limitations. In measurements on a 7 T preclinical scanner, the optimal control pulses were compared to continuous-wave and Gaussian saturation methods. We conducted a comparison of the optimal control pulses with Gaussian, block pulse trains, and adiabatic spin-lock pulses.

RESULTS

The optimal control pulse train demonstrated saturation levels comparable to continuous-wave saturation and surpassed Gaussian saturation by up to 50 % in phantom measurements. In phantom measurements at 3 T the optimized pulses not only showcased the highest CEST contrast, but also the highest stability against field inhomogeneities. In contrast, block pulse saturation resulted in severe artifacts. Dynamic Bloch-McConnell simulations were employed to identify the source of these artifacts, and underscore theB 0 $$ {\mathrm{B}}_0 $$ robustness of the optimized pulses.

CONCLUSION

In this work, it was shown that a substantial improvement in pulsed saturation CEST imaging can be achieved by using Optimal Control design principles. It is possible to overcome the sensitivity of saturation to B0 inhomogeneities while achieving CEST contrast close to continuous wave saturation.

Self-supervised learning for denoising of multidimensional MRI data

PURPOSE

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image.

THEORY AND METHODS

Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability.

RESULTS

The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images.

CONCLUSION

The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis

Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis

OBJECTIVES

To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT).

METHODS

21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored.

RESULTS

Compared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp.

CONCLUSIONS

Small-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.

Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring

PURPOSE

To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach.

METHODS

A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-highb $$ b $$ -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract.

RESULTS

Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach.

CONCLUSION

Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.

Simulation-based optimization and experimental comparison of intracranial T2-weighted DANTE-SPACE vessel wall imaging at 3T and 7T

PURPOSE

T2-weighted DANTE-SPACE (Delay Alternating with Nutation for Tailored Excitation - Sampling Perfection with Application optimized Contrasts using different flip angle Evolution) sequences facilitate non-invasive intracranial vessel wall imaging at 7T through simultaneous suppression of blood and CSF. However, the achieved vessel wall delineation depends closely on the selected sequence parameters, and little information is available about the performance of the sequence using more widely available 3T MRI. Therefore, in this paper a comprehensive DANTE-SPACE simulation framework is used for the optimization and quantitative comparison of T2-weighted DANTE-SPACE at both 7T and 3T.

METHODS

Simulations are used to propose optimized sequence parameters at both 3T and 7T. At 7T, an additional protocol which uses a parallel transmission (pTx) shim during the DANTE preparation for improved suppression of inflowing blood is also proposed. Data at both field strengths using optimized and literature protocols are acquired and quantitatively compared in six healthy volunteers.

RESULTS

At 7T, more vessel wall signal can be retained while still achieving sufficient CSF suppression by using fewer DANTE pulses than described in previous implementations. The use of a pTx shim during DANTE at 7T provides a modest further improvement to the inner vessel wall delineation. At 3T, aggressive DANTE preparation is required to achieve CSF suppression, resulting in reduced vessel wall signal. As a result, the achievable vessel wall definition at 3T is around half that of 7T.

CONCLUSION

Simulation-based optimization of DANTE parameters facilitates improved T2-weighted DANTE-SPACE contrasts at 7T. The improved vessel definition of T2-weighted DANTE-SPACE at 7T makes DANTE preparation more suitable for T2-weighted VWI at 7T than at 3T.

Quantitative BOLD (qBOLD) imaging of oxygen metabolism and blood oxygenation in the human body: A scoping review

PURPOSE

There are many approaches to the quantitative BOLD (qBOLD) technique described in the literature, differing in pulse sequences, MRI parameters and data processing. Thus, in this review, we summarized the acquisition methods, approaches used for oxygenation quantification and clinical populations investigated.

METHODS

Three databases were systematically searched (Medline, Embase, and Web of Science) for published research that used qBOLD methods for quantification of oxygen metabolism. Data extraction and synthesis were performed by one author and reviewed by a second author.

RESULTS

A total of 93 relevant papers were identified. Acquisition strategies were summarized, and oxygenation parameters were found to have been investigated in many pathologies such as steno-occlusive diseases, stroke, glioma, and multiple sclerosis disease.

CONCLUSION

A summary of qBOLD approaches for oxygenation measurements and applications could help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.

Acoustic noise reduction in the NexGen 7 T scanner

PURPOSE

Driven by the Lorentz force, acoustic noise may arguably be the next physiological challenge associated with ultra-high field MRI scanners and powerful gradient coils. This work consisted of isolating and mitigating the main sound pathway in the NexGen 7 T scanner equipped with the investigational Impulse head gradient coil.

METHODS

Sound pressure level (SPL) measurements were performed with and without the RF coil to assess its acoustic impact. Vibration measurements were carried out on the gradient coil, the RF coil, and on the patient table to distinguish the different vibration mechanisms and pathways. Vibrations of the RF coil were modified by either making contact with the patient bore liner with padding material or by changing directly the RF shield with phosphor bronze mesh material.

RESULTS

SPL and vibration measurements demonstrated that eddy-currents induced in the RF shield were the primary cause of acoustic noise. Replacing the conventional solid copper shield with phosphor bronze mesh material altered the vibrations of the RF shield and decreased SPL by 6 to 8 dB at the highest frequencies in EPI, depending on the gradient axis, while boosting the transmit B1 + field by 15%. Padding led to slightly less sound reduction on the X and Z gradient axes, but with minimal impact for the Y axis.

CONCLUSION

This study demonstrates the potential importance of eddy-current induced vibrations in the RF coil in terms of acoustic noise and opens new horizons for mitigation measures.

The impact of neural emotion reactivity and regulation on the association between depression and suicide ideation in high-risk adults

BACKGROUND

Depression is closely related to suicidal ideation (SI); however, it is unclear who is most vulnerable to SI within the context of depression. Research suggests that individual differences in emotion reactivity and regulation may be potential moderators of the link between depression and SI. Therefore, the current study tested this hypothesis using objective markers of emotion reactivity and volitional cognitive regulation capacity during functional magnetic resonance imaging (fMRI).

METHODS

Adults (n = 91) with active SI completed validated self-report measures of current depressive symptoms and SI severity. Participants completed an fMRI task designed to probe neural response to aversive stimuli and during cognitive reappraisal - a form of volitional emotion regulation. Activation of the amygdala during aversive emotion reactivity was measured. Activation of ventrolateral, dorsolateral, and dorsomedial prefrontal cortex (vlPFC, dlPFC, and dmPFC) during cognitive reappraisal were also measured. A series of hierarchical linear regressions testing the unique and interactive effects of depression symptoms and neural activation on severity of SI were conducted.

RESULTS

Analyses revealed a depression x amygdala activation interaction. The positive association between depression and SI severity was more robust in the context of high amygdala reactivity than low amygdala reactivity. Analyses also indicated there was no PFC activity (neural cognitive reappraisal) by depression interaction.

LIMITATIONS

Psychoactive medications were allowed and all participants endorsed suicidal intent.

CONCLUSION

Strategies aimed at targeting exaggerated emotion reactivity within the context of depression may be beneficial.

Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

PURPOSE

The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times.

METHODS

Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution.

RESULTS

SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels.

CONCLUSION

Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Testing quantitative magnetization transfer models with membrane lipids

PURPOSE

Quantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models.

METHODS

We formulated liposomes with various lipid types and water-to-lipids fractions and measured their signals with spoiled gradient-echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water-to-lipid fractions and with a simple MTnorm (= MTon/MToff) calculation.

RESULTS

We found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water-to-lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MTnorm analysis.

CONCLUSION

qMT parameters are sensitive to both the water-to-lipid fraction and to the lipid type. Estimating the water-to-lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MTnorm analysis.

Multi-instance learning attention model for amyloid quantification of brain sub regions in longitudinal cognitive decline

Amyloid PET scans help in identifying the beta-amyloid deposition in different brain regions. The purpose of this study is to develop a deep learning model that can automate the task of finding amyloid deposition in different regions of the brain only by using PET scan and without the corresponding MRI scan. 2647 18F-Florbetapir PET scans are collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) from multiple centres taken over a period. A deep learning model based on multi-instance learning and attention is proposed which is trained and validated using 80% of the scans and the remaining 20% of the scans are used for testing the model. The performance of the model is validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The proposed model is further tested upon an external dataset consisting of 1413 18F-Florbetapir PET scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study. The proposed model achieves MAE of 0.0243 and RMSE of 0.0320 for summary Standardized Uptake Value Ratio (SUVR) based on composite reference region for ADNI test set. When tested on the A4-study dataset, the proposed model achieves MAE of 0.038 and RMSE of 0.0495 for summary SUVR based on the composite region. The results show that the proposed model provides less MAE and RMSE when compared with existing models. A graphical user interface is developed based on the proposed model where the predictions are made by selecting the files of 18F-Florbetapir PET scans.

Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals

PURPOSE

This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast.

THEORY AND METHODS

PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion.

RESULTS

The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach.

CONCLUSIONS

This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.

A quantitative comparison of atlas parcellations on the human superior temporal sulcus

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

Spatio-temporal learning and explaining for dynamic functional connectivity analysis: Application to depression

BACKGROUND

Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology.

METHODS

The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified.

RESULTS

We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified.

LIMITATIONS

The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population.

CONCLUSIONS

The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression.

Role of L- and T-type voltage-dependent calcium channels in the hierarchical organization of defensive responses to electrical stimulation of the rat dorsolateral periaqueductal gray

Stimulation of the dorsal half of the rat periaqueductal gray (DPAG) with 60-Hz pulses of increasing intensity, 30-μA pulses of increasing frequency, or increasing doses of an excitatory amino acid elicits sequential defensive responses of exophthalmia, immobility, trotting, galloping, and jumping. These responses may be controlled by voltage-gated calcium channel-specific firing patterns. Indeed, a previous study showed that microinjection of the DPAG with 15 nmol of verapamil, a putative blocker of L-type calcium channels, attenuated all defensive responses to electrical stimulation at the same site as the injection. Accordingly, here we investigated the effects of microinjection of lower doses (0.7 and 7 nmol) of both verapamil and mibefradil, a preferential blocker of T-type calcium channels, on DPAG-evoked defensive behaviors of the male rat. Behaviors were recorded either 24 h before or 10 min, 24 h, and 48 h after microinjection. Effects were analyzed by both threshold logistic analysis and repeated measures analysis of variance for treatment by session interactions. Data showed that the electrodes were all located within the dorsolateral PAG. Compared to the effects of saline, verapamil significantly attenuated exophthalmia, immobility, and trotting. Mibefradil significantly attenuated exophthalmia and marginally attenuated immobility while facilitating trotting. While galloping was not attenuated by either antagonist, jumping was unexpectedly attenuated by 0.7 nmol verapamil only. These results suggest that T-type calcium channels are involved in the low-threshold freezing responses of exophthalmia and immobility, whereas L-type calcium channels are involved in the trotting response that precedes the full-fledged escape responses of galloping and jumping.

Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach

BACKGROUND

Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The incidence of SI among high (junior and senior) school students is significantly higher than that of other age groups. The aim of this study is to explore the gender differences in SI among high school students in Yunnan Province.

METHOD

A total of 6180 students in grades 7-12 in Yunnan province, China from May 2021 to May 2022 participated in this survey. Univariate analysis was employed to describe the influencing factors of male and female students' SI. Subsequently, data were stratified by gender. Adopting machine learning technique, including Least Absolute Shrinkage and Selection Operator (Lasso) and Boruta algorithm, and logistic regression model to estimate the direction and effect magnitude of the influencing factors.

RESULTS

The prevalence of SI was significantly higher for females (31.34 %) than males (16.73 %). The logistic regression model was established using the variables screened by Boruta algorithm, indicated that anxiety, depression, suffering emotion abuse or emotion neglect in childhood, non-suicidal self-injury, evening chronotype are common risk factors for SI in male and female students. Notably, female students who exhibited aggressive behavior, have experienced bullying, and were in the junior high school learning stage were more likely to report SI than their male counterparts.

CONCLUSIONS

Females showed more vulnerability to SI than males especially among females in junior high school, reporting aggressive behavior and bullying experiences. Tailored prevention strategies, informed by these gender-related distinctions, should be developed and implemented.