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Humans can recollect past events in details (recollection) and/or know that an object, person, or place has been encountered before (familiarity). During the last two decades, there has been intense debate about how recollection and familiarity are organized in the brain. Here, we propose an integrative memory model which describes the distributed and interactive neurocognitive architecture of representations and operations underlying recollection and familiarity. In this architecture, the subjective experience of recollection and familiarity arises from the interaction between core systems (storing particular kinds of representations shaped by specific computational mechanisms) and an attribution system. By integrating principles from current theoretical views about memory functioning, we provide a testable framework to refine the prediction of deficient versus preserved mechanisms in memory-impaired populations. The case of Alzheimer's disease (AD) is considered as an example because it entails progressive lesions starting with limited damage to core systems before invading step-by-step most parts of the model-related network. We suggest a chronological scheme of cognitive impairments along the course of AD, where the inaugurating deficit would relate early neurodegeneration of the perirhinal/anterolateral entorhinal cortex to impaired familiarity for items that need to be discriminated as viewpoint-invariant conjunctive entities. The integrative memory model can guide future neuropsychological and neuroimaging studies aiming to understand how such a network allows humans to remember past events, to project into the future, and possibly also to share experiences.

Background Neuroimaging studies have shown that hypothalamic/thalamic nuclei and other distant brain regions belonging to complex cerebral networks are involved in cluster headache (CH). However, the exact relationship between these areas, which may be dependent or independent, remains to be understood. We investigated differences in resting-state functional connectivity (FC) between brain networks and its relationship with the microstructure of the hypothalamus and thalamus in patients with episodic CH outside attacks and healthy controls (HCs). Methods We collected 3T MRI data from 26 patients with CH during the in-bout period outside the attacks and compared them with data from 20 HCs. From resting-state data we derived independent component (IC) networks. We calculated the fractional anisotropy (FA) and mean (MD), axial (AD), and radial (RD) diffusivity values of the hypothalamus and bilateral thalami and correlated them with resting-state IC Z-scores and CH clinical features. Results Patients with CH had less FC between the salience network (SN) and left executive control network (ECN) than HCs, but more FC between the default mode network and right ECN. Patients with CH showed lower FA and higher MD microstructural hypothalamic metrics than HCs. Patients with CH had a higher bilateral FA metric in the thalamus than HCs. The AD and RD diffusivity metrics of the hypothalamus were positively correlated with the disease history duration. We found no correlations between the hypothalamic and thalamic diffusivity metrics and the FC of the cortical networks. Conclusion Our findings presented the possibility of a correlation between the FC of the SN and the inability to switch between internalizing and externalizing brain activity during demanding cognitive tasks, such as recurring headaches. Moreover, we found differences in the thalamic and hypothalamic microstructures that may independently contribute to the pathophysiology of CH. These differences may reflect changes in directional organization, cell size, and density.

•Glutamate, a critical amino acid for the brain, can be detected by gluCEST imaging.•Whole brain gluCEST maps were recorded at high field (11.7T) MRI in a primate.•Regional differences of gluCEST contrast strongly reflect glutamate pathways.•gluCEST imaging highlights regional age-related alterations.•gluCEST imaging highlights age-related alterations in large-scale networks. Glutamate is the amino acid with the highest cerebral concentration. It plays a central role in brain metabolism. It is also the principal excitatory neurotransmitter in the brain and is involved in multiple cognitive functions. Alterations of the glutamatergic system may contribute to the pathophysiology of many neurological disorders. For example, changes of glutamate availability are reported in rodents and humans during Alzheimer's and Huntington's diseases, epilepsy as well as during aging. Most studies evaluating cerebral glutamate have used invasive or spectroscopy approaches focusing on specific brain areas. Chemical Exchange Saturation Transfer imaging of glutamate (gluCEST) is a recently developed imaging technique that can be used to study relative changes in glutamate distribution in the entire brain with higher sensitivity and at higher resolution than previous techniques. It thus has strong potential clinical applications to assess glutamate changes in the brain. High field is a key condition to perform gluCEST images with a meaningful signal to noise ratio. Thus, even if some studies started to evaluate gluCEST in humans, most studies focused on rodent models that can be imaged at high magnetic field. In particular, systematic characterization of gluCEST contrast distribution throughout the whole brain has never been performed in humans or non-human primates. Here, we characterized for the first time the distribution of the gluCEST contrast in the whole brain and in large-scale networks of mouse lemur primates at 11.7 Tesla. Because of its small size, this primate can be imaged in high magnetic field systems. It is widely studied as a model of cerebral aging or Alzheimer's disease. We observed high gluCEST contrast in cerebral regions such as the nucleus accumbens, septum, basal forebrain, cortical areas 24 and 25. Age-related alterations of this biomarker were detected in the nucleus accumbens, septum, basal forebrain, globus pallidus, hypophysis, cortical areas 24, 21, 6 and in olfactory bulbs. An age-related gluCEST contrast decrease was also detected in specific neuronal networks, such as fronto-temporal and evaluative limbic networks. These results outline regional differences of gluCEST contrast and strengthen its potential to provide new biomarkers of cerebral function in primates. [Display omitted]

Spinocerebellar ataxia type 3 (SCA3), the commonest dominantly inherited ataxia worldwide, is characterized by disruption in the cerebellar-cerebral and striatal-cortical networks. Findings on SCA3-associated cognitive impairments are mixed. The classification models, tests and scoring systems used, language, culture, ataxia severity, and depressive symptoms are all potential confounders in neuropsychological assessments and may have contributed to the heterogeneity of the neurocognitive profile of SCA3. We conducted a systematic review of studies evaluating neurocognitive function in SCA3 patients. Of 1304 articles identified, 15 articles met the eligibility criteria. All articles were of excellent quality according to the National Institutes of Health quality assessment tool for case–control studies. In line with the disrupted cerebellar-cerebral and striatal-cortical networks in SCA3, this systematic review found that the neurocognitive profile of SCA3 is characterized by a core impairment of executive function that affects processes such as nonverbal reasoning, executive aspects of language, and recall. Conversely, neurocognitive domains such as general intelligence, verbal reasoning, semantic aspect of language, attention/processing speed, recognition, and visuospatial perception and construction are relatively preserved. This review highlights the importance of evaluating neurocognitive function in SCA3 patients. Considering the negative impact of cognitive and affective impairment on quality of life, this review points to the profound impairments that existing or future treatments should prioritize.

The development of language functions is of great interest to neuroscientists, as these functions are among the fundamental capacities of human cognition. For many years, researchers aimed at identifying cerebral correlates of language abilities. More recently, the development of new data analysis tools has generated a shift toward the investigation of complex cerebral networks. In 2015, Weiss-Croft and Baldeweg published a very interesting systematic review on the development of functional language networks, explored through the use of functional magnetic resonance imaging (fMRI). Compared to fMRI and because of their excellent temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) provide different and important information on brain activity. Both therefore constitute crucial neuroimaging techniques for the investigation of the maturation of functional language brain networks. The main objective of this systematic review is to provide a state of knowledge on the investigation of language-related cerebral networks in children, through the use of EEG and MEG, as well as a detailed portrait of relevant MEG and EEG data analysis methods used in that specific research context. To do so, we have summarized the results and systematically compared the methodological approach of 24 peer-reviewed EEG or MEG scientific studies that included healthy children and children with or at high risk of language disabilities, from birth up to 18 years of age. All included studies employed functional and effective connectivity measures, such as coherence, phase locking value, and Phase Slope Index, and did so using different experimental paradigms (e.g., at rest or during language-related tasks). This review will provide more insight into the use of EEG and MEG for the study of language networks in children, contribute to the current state of knowledge on the developmental path of functional connectivity in language networks during childhood and adolescence, and finally allow future studies to choose the most appropriate type of connectivity analysis.

•Mouse lemur, one of the smallest primates, was imaged with high field 11.7T MRI.•First functional atlas of mouse lemur brain.•First characterization of its brain networks and comparison with human networks.•High-level cortical networks of lemurs are not homologous to human ones.•Hubs are grouped in lemurs while they are split into clusters in humans. Measures of resting-state functional connectivity allow the description of neuronal networks in humans and provide a window on brain function in normal and pathological conditions. Characterizing neuronal networks in animals is complementary to studies in humans to understand how evolution has modelled network architecture. The mouse lemur (Microcebus murinus) is one of the smallest and more phylogenetically distant primates as compared to humans. Characterizing the functional organization of its brain is critical for scientists studying this primate as well as to add a link for comparative animal studies. Here, we created the first functional atlas of mouse lemur brain and describe for the first time its cerebral networks. They were classified as two primary cortical networks (somato-motor and visual), two high-level cortical networks (fronto-parietal and fronto-temporal) and two limbic networks (sensory-limbic and evaluative-limbic). Comparison of mouse lemur and human networks revealed similarities between mouse lemur high-level cortical networks and human networks as the dorsal attentional (DAN), executive control (ECN), and default-mode networks (DMN). These networks were however not homologous, possibly reflecting differential organization of high-level networks. Finally, cerebral hubs were evaluated. They were grouped along an antero-posterior axis in lemurs while they were split into parietal and frontal clusters in humans. [Display omitted]

•Population-based metabolic connectome determined by PET.•Connectivity information used for statistical classification of single subjects.•Connectivity-based classification superior to univariate ML classifier.•Metabolic connectome as potential PET image biomarker. Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).

Since 1998, when Schmahmann first proposed the concept of the “cognitive affective syndrome” that linked cerebellar damage to cognitive and emotional impairments, a substantial body of literature has emerged. Anatomical, neurophysiological, and functional neuroimaging data suggest that the cerebellum contributes to cognitive functions through specific cerebral–cerebellar connections organized in a series of parallel loops. The aim of this paper is to review the current findings on the involvement of the cerebellum in selective cognitive functions, using a psychophysiological perspective with event-related potentials (ERPs), alone or in combination with non-invasive brain stimulation techniques. ERPs represent a very informative method of monitoring cognitive functioning online and have the potential to serve as valuable biomarkers of brain dysfunction that is undetected by other traditional clinical tools. This review will focus on the data on attention, executive functions, and time processing obtained in healthy subjects and patients with varying clinical conditions, thus confirming the role of ERPs in understanding the role of the cerebellum in cognition and exploring the potential diagnostic and therapeutic implications of ERP-based assessments in patients.

Interactive brain stimulation is a new generation of neurofeedback characterized by a radical change in the targets of cognitive (volitional, adaptive) influence. These targets are represented by specific cerebral structures and neural networks, the reconstruction of which leads to the brain functions’ restoration and behavioral metamorphoses. Functional magnetic resonance imaging (fMRI) in the neurofeedback contour uses a natural intravascular tracer, a blood-oxygenation-level-dependent (BOLD) signal as feedback. The subject included into the \"interactive brain contour\" learns to modulate and modify his or her own cerebral networks, creating new ones or \"awakening\" pre-existing ones, in order to improve (or restore) mental, sensory, or motor functions. In this review we focus on interactive brain stimulation based on BOLD signal and its role in the motor rehabilitation of stroke, briefly introducing the basic concepts of the so-called “network vocabulary” and general biophysical basis of the BOLD signal. We also discuss a bimodal fMRI-EEG neurofeedback platform and the prospects of fMRI technology in controlling functional connectivity, a numerical assessment of neuroplasticity.

This paper presents the methodology and computational results of simulated medical drug targeting (MDT) via induced magnetism intended for administering intravenous patient-specific doses of therapeutic agents in a Circle of Willis (CoW) model. The multi-physics computational model used in this work is from our previous works. The computational model is used to analyze pulsatile blood flow, particle motion, and particle capture efficiency in a magnetized region using the magnetic properties of magnetite (Fe3O4) and equations describing the magnetic forces acting on particles produced by an external cylindrical electromagnetic coil. A Eulerian–Lagrangian technique is implemented to resolve the hemodynamic flow and the motion of particles under the influence of a range of magnetic field strengths (Br = 2T, 4T, 6T, and 8T). Particle diameter sizes of 10 nm to 4 µm in diameter were assessed. Two dimensionless numbers are also investigated a priori in this study to characterize relative effects of Brownian motion (BM), magnetic force-induced particle motion, and convective blood flow on particle motion. Similar to our previous works, the computational simulations demonstrate that the greatest particle capture efficiency results for particle diameters within the micron range, specifically in regions where flow separation and vortices are at a minimum. Additionally, it was observed that the capture efficiency of particles decreases substantially with smaller particle diameters, especially in the superparamagnetic regime. The highest capture efficiency observed for superparamagnetic particles was 99% with an 8T magnetic field strength and 95% with a 2T magnetic field strength when analyzing 100 nm particles. For 10 nm particles and an 8T magnetic field strength, the particle capture efficiency was 48%, and for a 2T magnetic field strength the particle capture efficiency was 33%. Furthermore, it was found that larger magnetic field strengths, large particle diameter sizes (1 µm and above), and slower blood flow velocity increase the particle capture efficiency. The key finding in this work is that favorable capture efficiencies for superparamagnetic particles were observed in the CoW model for weak fields (Br < 4T) which demonstrates MDT as a possible viable treatment candidate for cardiovascular disease.