Declarative memory formation critically relies on the synchronization of brain oscillations in the theta (4–8 Hz) frequency band within specific brain networks. The development of this capacity is closely linked to the functional organization of these networks already at rest. However, the relationship between theta-band resting-state functional connectivity and declarative memory abilities remains unexplored in children. Here, using magnetoencephalography, we examined the association between declarative memory performance and pre-learning resting-state functional connectivity across frequency bands in 32 school-aged children. Declarative memory was assessed as the percentage of correct retrieval of 50 new associations between non-objects and magical functions, while resting-state functional connectivity was measured through power envelope correlation of the theta, alpha, low and high beta frequency bands. We found that stronger theta-band resting-state functional connectivity within occipito-temporo-frontal networks correlated with better declarative memory retrieval, while no correlation was observed in the alpha and beta frequency bands. These findings suggest that the functional brain architecture at rest, specifically involving theta-band oscillations, supports declarative memory in children. This mechanism may facilitate the subsequent rapid transformation of sensory input into visuo-semantic representations, highlighting the critical role of theta-band connectivity in early cognitive development.

Cerebrovascular reactivity (CVR) imaging is used to assess the vasodilatory capacity of cerebral blood vessels. While blood flow ( CVR CBF ), blood velocity ( CVR v ), and preferably blood volume changes ( CVR CBV ) are used to represent physiological CVR, quantifying these measures is fraught with acquisition challenges in humans. Consequently, blood oxygenation level-dependent (BOLD)-MRI CVR ( CVR BOLD ) is the most widely used MRI-based CVR method, even though it arguably provides the most indirect estimation of CVR. In this paper, we sought to holistically address the quantitative capacity and shortcomings of CVR BOLD . To do so, we developed a CVR BOLD simulation framework and, together with data from the CVR BOLD literature, addressed whether and to what extent CVR BOLD accurately reflects CVR, and with which parameters CVR BOLD varies most. In short, we show the following: CVR BOLD does not necessarily correspond to physiological measures of CVR and depends on physiological (e.g., hematocrit) and acquisition (e.g., field strength) parameters; CVR BOLD is dependent on the stimulus protocol (e.g., breath-holding vs controlled hypercapnia) chosen to elicit a vasoactive response; resting-state CVR BOLD does not necessarily reflect breath-hold CVR BOLD , likely due to confounding neuronal activity; in stenotic disease and steal physiology, CVR BOLD results from a combination of factors which do not necessarily reflect the underlying CVR. We are confident that this work will provide researchers and clinicians with invaluable insights and advance the field of cerebrovascular imaging by enabling more accurate quantification of CVR in both health and disease.

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-called molecular connectivity approach that captures relationships of molecular imaging measures in different brain regions. Targeting these inter-regional interactions within a neuroreceptor system may allow to better understand complex brain functions. In this article, we provide a comprehensive review of molecular connectivity studies in the field of neurotransmission. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. A systematic search in three bibliographic databases MEDLINE, EMBASE, and Scopus on July 14, 2023 was conducted. A second search was rerun on April 4, 2024. Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. Thirty-nine studies were included in the scoping review. Studies were categorised based on the primary neurotransmitter system being targeted: dopamine, serotonin, opioid, muscarinic, glutamate, and synaptic density. The most investigated system was the dopaminergic and the most investigated disease was Parkinson’s disease (PD). This review highlighted the diverse applications and methodologies in molecular connectivity research, particularly for neurodegenerative diseases and psychiatric disorders. Molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings.

Prior neuroimaging studies of music-evoked emotions have shown that music listening involves the activation of both cortical and subcortical regions. However, these regions could be differentially activated by music stimuli with varying affective valence and arousal. To better understand the neural correlates involved in the processing of pleasant and unpleasant emotions induced by music, while also considering the effect of arousal, we conducted a quantitative activation likelihood estimate (ALE) meta-analysis. We performed separate ALE analyses for the overall brain activation evoked by listening to emotional music (40 studies), for the brain activation during listening to unpleasant music (15 studies), for the brain activation while listening to pleasant music (17 studies), and for the brain activation while listening to emotional contrasted with neutral music (8 studies). Our results revealed the activation of a range of cortical and subcortical regions, including the amygdala, insula, striatum, thalamus, hippocampus, anterior cingulate gyrus, and superior temporal gyrus. Moreover, our findings indicated that certain regions were specifically activated based on the hedonic valence and arousal of the stimuli. Particularly, whereas the anterior cingulate cortex (ACC), dorsal striatum, and thalamus were dependent on arousal effects, amygdala activation was dependent on hedonic valence. The identification of brain networks preferentially activated during listening to pleasant and unpleasant music provides valuable clinical insights for the development of therapies targeting psychological disorders associated with emotion reactivity problems.

Neurofeedback concurrent with mindfulness meditation may reveal meditation effects on the brain and facilitate improved mental health outcomes. Here, we systematically reviewed electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies of mindfulness meditation with neurofeedback (mbNF) and followed PRISMA guidelines. We identified 9 fMRI reports, consisting of 177 unique participants, and 9 EEG reports, consisting of 242 participants. Studies of fMRI focused primarily on downregulating the default-mode network (DMN). Although studies found decreases in DMN activations during neurofeedback, there is a lack of evidence for transfer effects, and the majority of studies did not employ adequate controls, for example, sham neurofeedback. Accordingly, DMN decreases may have been confounded by general task-related deactivation. EEG studies typically examined alpha, gamma, and theta frequency bands, with the most robust evidence supporting the modulation of theta band activity. Both EEG and fMRI mbNF have been implemented with high fidelity in clinical populations. However, the mental health benefits of mbNF have not been established. In general, mbNF studies would benefit from sham-controlled RCTs, as well as clear reporting (e.g., CRED-NF).

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high intersession and interscanner variability in the data, as well as intersubject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

The present meta-analysis investigated the impact of non-invasive stimulation, using transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) targeting the posterior cerebellum, on social and emotional mentalizing about others. Prior research has convincingly shown that the posterior cerebellum supports social and emotional cognition. We identified 14 studies targeting the cerebellum with appropriate control conditions (i.e., sham, control site), which exclude general learning effects of the task or placebo effects. The studies included 29 task conditions where stimulation before or during a social or emotional task was applied on healthy samples. The results showed significant evidence that sustained anodal tDCS and TMS generally improved social and emotional performance after stimulation, in comparison with sham or control conditions, with a small effect size. In contrast, cathodal stimulation showed mixed facilitatory and inhibitory results. In addition, short TMS pulses, administered with the aim of interfering with ongoing social or emotional processes, induced a small but consistent inhibitory effect. Control tasks without social or emotional components also showed significant improvement after sustained anodal tDCS and TMS, suggesting that transcranial stimulation of the cerebellum may also improve other functions. This was not the case for short TMS pulses, which did not modulate non-social and non-emotional control tasks. Taken together, this meta-analysis shows that cerebellar neurostimulation confirms a causal role of the cerebellum in socio-emotional cognition, has a small but significant effect on improving socio-emotional skills, and may, therefore, have important clinical applications in pathologies where social and emotional cognition is impaired.