How well do we understand the neural origins of the fMRI BOLD signal?

Neural response to monetary and social rewards in adolescent girls and their parents

Functional magnetic resonance imaging (fMRI) studies have indicated that the mesocorticolimbic dopamine system is heavily involved in all stages of reward processing. However, the majority of research has been conducted using monetary rewards and it is unclear to what extent other types of rewards, such as social rewards, evoke similar or different neural activation. There have also been few investigations into potential differences or similarities between reward processing in parents and offspring. The present study examined fMRI neural activation in response to monetary and social reward in a sample of 14-22-year-old adolescent girls (N = 145) and a biological parent (N = 124) and compared activation across adolescent-parent dyads (N = 82). Across all participants, both monetary and social reward elicited bilateral striatal activation, which did not differ between reward types or between adolescents and their parents. Neural activation in response to the different reward types were positively correlated in the striatum among adolescents and in the mPFC and OFC among parents. Overall, the present study suggests that both monetary and social reward elicit striatal activation regardless of age and provides evidence that neural mechanisms underlying reward processing may converge differentially among youth and adults.

Dysfunction of neurovascular coupling in patients with cerebral small vessel disease: A combined resting-state fMRI and arterial spin labeling study

BACKGROUND

Cerebral small vessel disease (CSVD) closely correlates to cognitive impairment, but its pathophysiology and the neurovascular mechanisms of cognitive deficits were unclear. We aimed to explore the dysfunctional patterns of neurovascular coupling (NVC) in patients with CSVD and further investigate the neurovascular mechanisms of CSVD-related cognitive impairment.

METHODS

Forty-three patients with CSVD and twenty-four healthy controls were recruited. We adopted resting-state functional magnetic resonance imaging combined with arterial spin labeling to investigate the NVC dysfunctional patterns in patients with CSVD. The Human Brain Atlas with 246 brain regions was applied to extract the NVC coefficients for each brain region. Partial correlation analysis and mediation analysis were used to explore the relationship between CSVD pathological features, NVC dysfunctional patterns, and cognitive decline.

RESULTS

8 brain regions with NVC dysfunction were found in patients with CSVD (p < 0.025, Bonferroni correction). The NVC dysfunctional patterns in regions of the default mode network and subcortical nuclei were negatively associated with lacunes, white matter hyperintensities burden, and the severity of CSVD (FDR correction, q < 0.05). The NVC decoupling in regions located in the default mode network positively correlated with delayed recall deficits (FDR correction, q < 0.05). Mediation analysis suggested that the decreased NVC pattern of the left superior frontal gyrus partially mediated the impact of white matter hyperintensities on delayed recall (Mediation effect: -0.119; 95%CI: -11.604,-0.458; p < 0.05).

CONCLUSION

The findings of this study reveal the NVC dysfunctional pattern in patients with CSVD and illustrate the neurovascular mechanism of CSVD-related cognitive impairment. The NVC function in the left superior frontal gyrus may serve as a promising biomarker and therapeutic target for memory deficits in patients with CSVD.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Does 'portion size' matter? Brain responses to food and non-food cues presented in varying amounts

Larger portions of food elicit greater intake than smaller portions of food, particularly when foods are high in energy density (kcal/g; ED). The neural mechanisms underlying this effect remain unclear. The present study used fMRI to assess brain activation to food (higher-ED, lower-ED) and non-food (office supplies) images presented in larger and smaller (i.e., age-appropriate) amounts in 61, 7-8-year-olds (29 male, 32 female) without obesity. Larger amounts of food increased activation in bilateral visual and right parahippocampal areas compared to smaller amounts; greater activation to food amount (larger > smaller) in this cluster was associated with smaller increases in food intake as portions increased. Activation to amount (larger > smaller) was stronger for food than office supplies in primary and secondary visual areas, but, for office supplies only, extended into bilateral parahippocampus, inferior parietal cortex, and additional visual areas (e.g., V7). Activation was greater for higher-vs. lower-ED food images in ventromedial prefrontal cortex for both larger and smaller amounts of food; however, this activation extended into left lateral orbital frontal cortex for smaller amounts only. Activation to food cues did not differ by familial risk for obesity. These results highlight potentially distinct neural pathways for encoding food energy content and quantity.

Repetition Suppression Reveals Cue-Specific Spatial Representations for Landmarks and Self-Motion Cues in the Human Retrosplenial Cortex

The efficient use of various spatial cues within a setting is crucial for successful navigation. Two fundamental forms of spatial navigation, landmark-based and self-motion-based, engage distinct cognitive mechanisms. The question of whether these modes invoke shared or separate spatial representations in the brain remains unresolved. While nonhuman animal studies have yielded inconsistent results, human investigation is limited. In our previous work (Chen et al., 2019), we introduced a novel spatial navigation paradigm utilizing ultra-high field fMRI to explore neural coding of positional information. We found that different entorhinal subregions in the right hemisphere encode positional information for landmarks and self-motion cues. The present study tested the generalizability of our previous finding with a modified navigation paradigm. Although we did not replicate our previous finding in the entorhinal cortex, we identified adaptation-based allocentric positional codes for both cue types in the retrosplenial cortex (RSC), which were not confounded by the path to the spatial location. Crucially, the multi-voxel patterns of these spatial codes differed between the cue types, suggesting cue-specific positional coding. The parahippocampal cortex exhibited positional coding for self-motion cues, which was not dissociable from path length. Finally, the brain regions involved in successful navigation differed from our previous study, indicating overall distinct neural mechanisms recruited in our two studies. Taken together, the current findings demonstrate cue-specific allocentric positional coding in the human RSC in the same navigation task for the first time and that spatial representations in the brain are contingent on specific experimental conditions.

First implementation of dynamic oxygen-17 (17O) magnetic resonance imaging at 7 Tesla during neuronal stimulation in the human brain

OBJECTIVE

First implementation of dynamic oxygen-17 (17O) MRI at 7 Tesla (T) during neuronal stimulation in the human brain.

METHODS

Five healthy volunteers underwent a three-phase 17O gas (17O2) inhalation experiment. Combined right-side visual stimulus and right-hand finger tapping were used to achieve neuronal stimulation in the left cerebral hemisphere. Data analysis included the evaluation of the relative partial volume (PV)-corrected time evolution of absolute 17O water (H217O) concentration and of the relative signal evolution without PV correction. Statistical analysis was performed using a one-tailed paired t test. Blood oxygen level-dependent (BOLD) experiments were performed to validate the stimulation paradigm.

RESULTS

The BOLD maps showed significant activity in the stimulated left visual and sensorimotor cortex compared to the non-stimulated right side. PV correction of 17O MR data resulted in high signal fluctuations with a noise level of 10% due to small regions of interest (ROI), impeding further quantitative analysis. Statistical evaluation of the relative H217O signal with PV correction (p = 0.168) and without (p = 0.382) did not show significant difference between the stimulated left and non-stimulated right sensorimotor ROI.

DISCUSSION

The change of cerebral oxygen metabolism induced by sensorimotor and visual stimulation is not large enough to be reliably detected with the current setup and methodology of dynamic 17O MRI at 7 T.

Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions

This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.

Cleaning up the Brickyard: How Theory and Methodology Shape Experiments in Cognitive Neuroscience of Language

The capacity for language is a defining property of our species, yet despite decades of research, evidence on its neural basis is still mixed and a generalized consensus is difficult to achieve. We suggest that this is partly caused by researchers defining "language" in different ways, with focus on a wide range of phenomena, properties, and levels of investigation. Accordingly, there is very little agreement among cognitive neuroscientists of language on the operationalization of fundamental concepts to be investigated in neuroscientific experiments. Here, we review chains of derivation in the cognitive neuroscience of language, focusing on how the hypothesis under consideration is defined by a combination of theoretical and methodological assumptions. We first attempt to disentangle the complex relationship between linguistics, psychology, and neuroscience in the field. Next, we focus on how conclusions that can be drawn from any experiment are inherently constrained by auxiliary assumptions, both theoretical and methodological, on which the validity of conclusions drawn rests. These issues are discussed in the context of classical experimental manipulations as well as study designs that employ novel approaches such as naturalistic stimuli and computational modeling. We conclude by proposing that a highly interdisciplinary field such as the cognitive neuroscience of language requires researchers to form explicit statements concerning the theoretical definitions, methodological choices, and other constraining factors involved in their work.

Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.

Assessment of brainstem function and haemodynamics by MRI: challenges and clinical prospects

MRI offers techniques for non-invasively measuring a range of aspects of brain tissue function. Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to assess neural activity, based on the brain's haemodynamic response, while arterial spin labelling (ASL) MRI is a non-invasive method of quantitatively mapping cerebral perfusion. Both techniques can be applied to measure cerebrovascular reactivity (CVR), an important marker of the health of the cerebrovascular system. BOLD, ASL and CVR have been applied to study a variety of disease processes and are already used in certain clinical circumstances. The brainstem is a critical component of the central nervous system and is implicated in a variety of disease processes. However, its function is difficult to study using MRI because of its small size and susceptibility to physiological noise. In this article, we review the physical and biological underpinnings of BOLD and ASL and their application to measure CVR, discuss the challenges associated with applying them to the brainstem and the opportunities for brainstem MRI in the research and clinical settings. With further optimisation, functional MRI techniques could feasibly be used to assess brainstem haemodynamics and neural activity in the clinical setting.

Respiration-entrained brain oscillations in healthy fMRI participants with high anxiety

Brain-body interactions can be studied by using directed coupling measurements of fMRI oscillations in the low (0.1-0.2 Hz) and high frequency bands (HF; 0.2-0.4 Hz). Recently, a preponderance of oscillations in the information flow between the brainstem and the prefrontal cortex at around 0.15/0.16 Hz was shown. The goal of this study was to investigate the information flow between BOLD-, respiratory-, and heart beat-to-beat interval (RRI) signals in the HF band in healthy subjects with high anxiety during fMRI examinations. A multivariate autoregressive model was concurrently applied to the BOLD signals from the middle frontal gyrus (MFG), precentral gyrus and the brainstem, as well as to respiratory and RRI signals. Causal coupling between all signals was determined using the Directed Transfer Function (DTF). We found a salience of fast respiratory waves with a period of 3.1 s (corresponding to ~ 0.32 Hz) and a highly significant (p < 0.001) top-down information-flow from BOLD oscillations in the MFG to the brainstem. Additionally, there was a significant (p < 0.01) information flow from RRI to respiratory oscillations. We speculate that brain oscillations around 0.32 Hz, triggered by nasal breathing, are projected downwards to the brainstem. Particularly interesting is the driving force of cardiac to respiratory waves with a ratio of 1:1 or 1:2. These results support the binary hierarchy model with preferred respiratory frequencies at 0.32 Hz and 0.16 Hz.

Variability in Resting-State Functional Magnetic Resonance Imaging: The Effect of Body Mass, Blood Pressure, Hematocrit, and Glycated Hemoglobin on Hemodynamic and Neuronal Parameters

Introduction: Replicability has become an increasing focus within the scientific communities with the ongoing "replication crisis." One area that appears to struggle with unreliable results is resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, the current study aimed at improving the knowledge of endogenous factors that contribute to inter-individual variability. Methods: Arterial blood pressure (BP), body mass, hematocrit, and glycated hemoglobin were investigated as potential sources of between-subject variability in rs-fMRI, in healthy individuals. Whether changes in resting-state networks (rs-networks) could be attributed to variability in the blood-oxygen-level-dependent (BOLD)-signal, changes in neuronal activity, or both was of special interest. Within-subject parameters were estimated by utilizing dynamic-causal modeling, as it allows to make inferences on the estimated hemodynamic (BOLD-signal dynamics) and neuronal parameters (effective connectivity) separately. Results: The results of the analyses imply that BP and body mass can cause between-subject and between-group variability in the BOLD-signal and that all the included factors can affect the underlying connectivity. Discussion: Given the results of the current and previous studies, rs-fMRI results appear to be susceptible to a range of factors, which is likely to contribute to the low degree of replicability of these studies. Interestingly, the highest degree of variability seems to appear within the much-studied default mode network and its connections to other networks. Impact statement We believe that thanks to the evidence that we have collected by analyzing the well-controlled data of the Human Connectome Project with dynamic-causal modeling (DCM) and by focusing not only on the effective connectivity, which is the typical way of using DCM, but also by analyzing the underlying hemodynamic parameters, we were able to explore the underlying vascular dependencies in a much broader perspective. Our results challenge the premise for studying changes in the default mode network as a clinical marker of disease, and we add to the growing list of factors that contribute to resting-state network variability.

Functional network antagonism and consciousness

Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the concept of segregation, namely the brain's capacity to show selectivity in the way areas will be functionally connected. We postulate that this effect is mediated by the process of neural inhibition, by regulating global and local inhibitory activity. While recognizing that this effect can also result from other mechanisms, neural inhibition helps the understanding of how network metastability is affected after disrupting local and global neural balance. In combination with relevant theories of consciousness, we suggest that anticorrelations are a physiological prior that can work as a marker of preserved consciousness. We predict that if the brain is not in a state to host anticorrelations, then most likely the individual does not entertain subjective experience. We believe that this link between anticorrelations and the underlying physiology will help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating consciousness disorders in which anticorrelations seem particularly affected.

Altered resting-state functional connectivity in hiPSCs-derived neuronal networks from schizophrenia patients

Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connectivity. In the present work, we used Human Induced Pluripotent Stem Cells (hiPSCs)-derived neuronal cultures to study neural communicational dynamics during early development in SZ. We conducted gene and protein expression profiling, calcium imaging recordings, and applied a mathematical model to quantify the dynamism of functional connectivity (FC) in hiPSCs-derived neuronal networks. Along the neurodifferentiation process, SZ networks displayed altered gene expression of the glutamate receptor-related proteins HOMER1 and GRIN1 compared to healthy control (HC) networks, suggesting a possible tendency to develop hyperexcitability. Resting-state FC in neuronal networks derived from HC and SZ patients emerged as a dynamic phenomenon exhibiting connectivity configurations reoccurring in time (hub states). Compared to HC, SZ networks were less thorough in exploring different FC configurations, changed configurations less often, presented a reduced repertoire of hub states and spent longer uninterrupted time intervals in this less diverse universe of hubs. Our results suggest that alterations in the communicational dynamics of SZ emerging neuronal networks might contribute to the previously described brain FC anomalies in SZ patients, by compromising the ability of their neuronal networks for rapid and efficient reorganization through different activity patterns.

An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders

It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.

Linking α-synuclein-induced synaptopathy and neural network dysfunction in early Parkinson’s disease

The prodromal phase of Parkinson’s disease is characterized by aggregation of the misfolded pathogenic protein α-synuclein in select neural centres, co-occurring with non-motor symptoms including sensory and cognitive loss, and emotional disturbances. It is unclear whether neuronal loss is significant during the prodrome. Underlying these symptoms are synaptic impairments and aberrant neural network activity. However, the relationships between synaptic defects and network-level perturbations are not established. In experimental models, pathological α-synuclein not only impacts neurotransmission at the synaptic level, but also leads to changes in brain network-level oscillatory dynamics—both of which likely contribute to non-motor deficits observed in Parkinson’s disease. Here we draw upon research from both human subjects and experimental models to propose a ‘synapse to network prodrome cascade’ wherein before overt cell death, pathological α-synuclein induces synaptic loss and contributes to aberrant network activity, which then gives rise to prodromal symptomology. As the disease progresses, abnormal patterns of neural activity ultimately lead to neuronal loss and clinical progression of disease. Finally, we outline goals and research needed to unravel the basis of functional impairments in Parkinson’s disease and other α-synucleinopathies.

Processing of fMRI-related anxiety and information flow between brain and body revealed a preponderance of oscillations at 0.15/0.16 Hz

Slow oscillations of different center frequencies and their coupling play an important role in brain-body interactions. The crucial question analyzed by us is, whether the low frequency (LF) band (0.05-0.15 Hz) or the intermediate frequency (IMF) band (0.1-0.2 Hz) is more eminent in respect of the information flow between body (heart rate and respiration) and BOLD signals in cortex and brainstem. A recently published study with the LF band in fMRI-naïve subjects revealed an intensive information flow from the cortex to the brainstem and a weaker flow from the brainstem to the cortex. The comparison of both bands revealed a significant information flow from the middle frontal gyrus (MFG) to the precentral gyrus (PCG) and from brainstem to PCG only in the IMF band. This pattern of directed coupling between slow oscillations in the cortex and brainstem not only supports the existence of a pacemaker-like structure in brainstem, but provides first evidence that oscillations centered at 0.15/0.16 Hz can also emerge in brain networks. BOLD oscillations in resting states are dominating at ~ 0.08 Hz and respiratory rates at ~ 0.32 Hz. Therefore, the frequency component at ~ 0.16 Hz (doubling-halving 0.08 Hz or 0.32 Hz) is of special interest, because phase coupled oscillations can reduce the energy demand.

Processing of fMRI-related anxiety and bi-directional information flow between prefrontal cortex and brain stem

Brain-heart synchronization is fundamental for emotional-well-being and brain-heart desynchronization is characteristic for anxiety disorders including specific phobias. Recording BOLD signals with functional magnetic resonance imaging (fMRI) is an important noninvasive diagnostic tool; however, 1-2% of fMRI examinations have to be aborted due to claustrophobia. In the present study, we investigated the information flow between regions of interest (ROI's) in the cortex and brain stem by using a frequency band close to 0.1 Hz. Causal coupling between signals important in brain-heart interaction (cardiac intervals, respiration, and BOLD signals) was studied by means of Directed Transfer Function based on the Granger causality principle. Compared were initial resting states with elevated anxiety and final resting states with low or no anxiety in a group of fMRI-naïve young subjects. During initial high anxiety the results showed an increased information flow from the middle frontal gyrus (MFG) to the pre-central gyrus (PCG) and to the brainstem. There also was an increased flow from the brainstem to the PCG. While the top-down flow during increased anxiety was predominant, the weaker ascending flow from brainstem structures may characterize a rhythmic pacemaker-like activity that (at least in part) drives respiration. We assume that these changes in information flow reflect successful anxiety processing.

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake nonhuman primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF models based on the fMRI blood-oxygen-level-dependent (BOLD) signal, multi-unit spiking activity (MUA), and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. fMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF size with increasing eccentricity, as well as a retinotopically specific deactivation of default mode network nodes similar to previous observations in humans.

[Activation Dynamics Analysis of Language-related Areas with High Temporal Resolution fMRI]

PURPOSE

To investigate the activation dynamics of language-related areas from multiple activation maps by performing analysis while shifting the signal change model from the actual stimulation timing along the temporal axis using high temporal resolution fMRI data.

METHODS

High temporal resolution fMRI data were obtained using 3T MRI. Ten healthy right-handed volunteers participated in the study. Task paradigm was block design to carry out two sets of the rest periods and word-generation tasks. Data analysis was performed using SPM 12 software. We created several different activation maps of different phases by shifting the signal change model along the temporal axis, and the activation dynamics of activation areas were analyzed.

RESULTS

In the activation dynamics analysis, there was a tendency for activation to become stronger in the order of bilateral superior temporal gyrus and supplementary motor area, left angular gyrus with slight delay, and then left middle and inferior frontal gyrus. This result was considered to reflect the processing process in the brain during the word-generation task.

CONCLUSIONS

It was suggested that this analysis method is useful for activation dynamics analysis.

Peter JV. Sugars and Sweet Taste: Addictive or Rewarding?

The notion of food "addiction" often focuses on the overconsumption of sweet tasting foods or so-called sugar "addiction". In the extreme, some have suggested that sugar and sweet tastes elicit neural and behavioral responses analogous to those observed with drugs of abuse. These concepts are complicated by the decades long uncertainty surrounding the validity and reproducibility of functional magnetic resonance imaging (fMRI) methodologies used to characterize neurobiological pathways related to sugar and sweet taste stimuli. There are also questions of whether sweet taste or post-ingestion metabolic consequences of sugar intake would lead to addiction or excessive caloric intake. Here, we present a focused narrative review of literature related to the reward value of sweet taste which suggests that reward value can be confounded with the construct of "addictive potential". Our review seeks to clarify some key distinctions between these constructs and questions the applicability of the addiction construct to human over-eating behaviors. To adequately frame this broad discussion requires the flexibility offered by the narrative review paradigm. We present selected literature on: techniques used to link sugar and sweet tastes to addiction neurobiology and behaviors; sugar and sweet taste "addiction"; the relationship of low calorie sweetener (LCS) intake to addictive behaviors and total calorie intake. Finally, we examined the reward value of sweet tastes and contrasted that with the literature describing addiction. The lack of reproducibility of fMRI data remains problematic for attributing a common neurobiological pathway activation of drugs and foods as conclusive evidence for sugar or sweet taste "addiction". Moreover, the complicated hedonics of sweet taste and reward value are suggested by validated population-level data which demonstrate that the consumption of sweet taste in the absence of calories does not increase total caloric intake. We believe the neurobiologies of reward value and addiction to be distinct and disagree with application of the addiction model to sweet food overconsumption. Most hypotheses of sugar "addiction" attribute the hedonics of sweet foods as the equivalent of "addiction". Further, when addictive behaviors and biology are critically examined in totality, they contrast dramatically from those associated with the desire for sweet taste. Finally, the evidence is strong that responses to the palatability of sweets rather than their metabolic consequences are the salient features for reward value. Thus, given the complexity of the controls of food intake in humans, we question the usefulness of the "addiction" model in dissecting the causes and effects of sweet food over-consumption.

Effects of an external compared to an internal focus of attention on the excitability of fast and slow(er) motor pathways

The neurophysiological mechanisms underlying the behavioural improvements usually associated with an external (EF) compared with an internal focus of attention (IF) remain poorly investigated. Surround inhibition in the primary cortex has been shown to be more pronounced with an EF, indicating a more spatial restriction of the motor command. However, the influence of different foci on the temporal aspect of the motor command, such as the modulation of fast versus slow(er) motor pathways, remains unknown and was therefore investigated in this study. Fourteen participants were asked to press on a pedal with the right foot to match its position with a target line displayed on a screen. The deviation of the pedal from the target line was used as a behavioural parameter and compared between both foci (EF vs IF). Additionally, conditioned H-reflexes were evoked during the motor task to assess the excitability of fast (direct) and slower (more indirect) motor pathways when adopting an EF or IF. With an EF compared to an IF, the motor performance was enhanced (P = .001; + 24%) and the activation of slow(er) motor pathways was reduced (P < 0.001, − 11.73%). These findings demonstrate for the first time that using different attentional strategies (EF and IF) has an influence on the excitability of slow(er) motor pathways. Together with the increased intracortical inhibition and surround inhibition known from previous studies, the diminished activation in the slow(er) motor pathways further explains why using an EF is a more economic strategy.

Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions

The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.

Frequency-Dependent Energy Demand of Dendritic Responses to Deep Brain Stimulation in Thalamic Neurons: A Model-Based Study

Thalamic deep brain stimulation (DBS) generates excitatory postsynaptic currents and action potentials (APs) by triggering large numbers of synaptic inputs to local cells, which also activates axonal spikes to antidromically invade the soma and dendrites. To maintain signaling, the evoked dendritic responses require metabolic energy to restore ion gradients in each dendrite. The objective of this study is to estimate the energy demand associated with dendritic responses to thalamic DBS. We use a morphologically realistic computational model to simulate dendritic activity in thalamocortical (TC) relay neurons with axonal intracellular stimulation or DBS-like extracellular stimulation. We determine the metabolic cost by calculating the number of adenosine triphosphate (ATP) expended to pump Na⁺ and Ca²⁺ ions out of each dendrite. The ATP demand of dendritic activity exhibits frequency dependence, which is determined by the number of spikes in the dendrites. Each backpropagating AP from the soma activates a spike in the dendrites, and the dendritic firing is dominated by antidromic activation of the soma. High stimulus frequencies decrease dendritic ATP cost by reducing the fidelity of antidromic activation. Synaptic inputs and stimulus-induced polarization govern the ATP cost of dendritic responses by facilitating/suppressing antidromic activation, which also influences the ATP cost by depolarizing/hyperpolarizing each dendrite. These findings are important for understanding the synaptic signaling energy in TC relay neurons and metabolism-dependent functional imaging data of thalamic DBS.

The causal interaction in human basal ganglia

The experimental study of the human brain has important restrictions, particularly in the case of basal ganglia, subcortical centers whose activity can be recorded with fMRI methods but cannot be directly modified. Similar restrictions occur in other complex systems such as those studied by Earth system science. The present work studied the cause/effect relationships between human basal ganglia with recently introduced methods to study climate dynamics. Data showed an exhaustive (identifying basal ganglia interactions regardless of their linear, non-linear or complex nature) and selective (avoiding spurious relationships) view of basal ganglia activity, showing a fast functional reconfiguration of their main centers during the execution of voluntary motor tasks. The methodology used here offers a novel view of the human basal ganglia which expands the perspective provided by the classical basal ganglia model and may help to understand BG activity under normal and pathological conditions.

Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information

Dorsolateral prefrontal cortex (dlPFC) is proposed to drive brain-wide focus by biasing processing in favour of task-relevant information. A longstanding debate concerns whether this is achieved through enhancing processing of relevant information and/or by inhibiting irrelevant information. To address this, we applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding. Participants attended to one feature, whilst ignoring another feature, of a visual object. If dlPFC is necessary for facilitation, disruptive TMS should decrease coding of attended features. Conversely, if dlPFC is crucial for inhibition, TMS should increase coding of ignored features. Here, we show that TMS decreases coding of relevant information across frontoparietal cortex, and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable. This provides causal evidence for a specific role of dlPFC in enhancing task-relevant representations and demonstrates the cognitive-neural insights possible with concurrent TMS-fMRI-MVPA.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

The peripartum human brain: Current understanding and future perspectives

The peripartum period offers a unique opportunity to improve our understanding of how dramatic fluctuations in endogenous ovarian hormones affect the human brain and behavior. This notwithstanding, peripartum depression remains an underdiagnosed and undertreated disorder. Here, we review recent neuroimaging findings with respect to the neuroplastic changes in the maternal brain during pregnancy and the postpartum period. We seek to provide an overview of multimodal neuroimaging designs of current peripartum depression models of hormone withdrawal, changes in monoaminergic signaling, and maladaptive neuroplasticity, which likely lead to the development of a condition that puts the lives of mother and infant at risk. We discuss the need to effectively integrate the available information on psychosocial and neurobiological risk factors contributing to individual vulnerability. Finally, we propose a systematic approach to neuroimaging the peripartum brain that acknowledges important co-morbidities and variation in disease onset.

Cerebral blood flow in 5- to 8-month-olds: Regional tissue maturity is associated with infant affect

Infancy is marked by rapid neural and emotional development. The relation between brain function and emotion in infancy, however, is not well understood. Methods for measuring brain function predominantly rely on the BOLD signal; however, interpretation of the BOLD signal in infancy is challenging because the neuronal-hemodynamic relation is immature. Regional cerebral blood flow (rCBF) provides a context for the infant BOLD signal and can yield insight into the developmental maturity of brain regions that may support affective behaviors. This study aims to elucidate the relations among rCBF, age, and emotion in infancy. One hundred and seven mothers reported their infants' (infant age M ± SD = 6.14 ± 0.51 months) temperament. A subsample of infants completed MRI scans, 38 of whom produced usable perfusion MRI during natural sleep to quantify rCBF. Mother-infant dyads completed the repeated Still-Face Paradigm, from which infant affect reactivity and recovery to stress were quantified. We tested associations of infant age at scan, temperament factor scores, and observed affect reactivity and recovery with voxel-wise rCBF. Infant age was positively associated with CBF in nearly all voxels, with peaks located in sensory cortices and the ventral prefrontal cortex, supporting the formulation that rCBF is an indicator of tissue maturity. Temperamental Negative Affect and recovery of positive affect following a stressor were positively associated with rCBF in several cortical and subcortical limbic regions, including the orbitofrontal cortex and inferior frontal gyrus. This finding yields insight into the nature of affective neurodevelopment during infancy. Specifically, infants with relatively increased prefrontal cortex maturity may evidence a disposition toward greater negative affect and negative reactivity in their daily lives yet show better recovery of positive affect following a social stressor.

Verification of a Central Pacemaker in Brain Stem by Phase-Coupling Analysis Between HR Interval- and BOLD-Oscillations in the 0.10-0.15 Hz Frequency Band

The origin of slow intrinsic oscillations in resting states of functional magnetic resonance imaging (fMRI) signals is still a matter of debate. The present study aims to test the hypothesis that slow blood oxygenation level-dependent (BOLD) oscillations with frequency components greater than 0.10 Hz result from a central neural pacemaker located in the brain stem. We predict that a central oscillator modulates cardiac beat-to-beat interval (RRI) fluctuations rapidly, with only a short neural lag around 0.3 s. Spontaneous BOLD fluctuations in the brain stem, however, are considerably delayed due to the hemodynamic response time of about ∼2–3 s. In order to test these predictions, we analyzed the time delay between slow RRI oscillations from thorax and BOLD oscillations in the brain stem by calculating the phase locking value (PLV). Our findings show a significant time delay of 2.2 ± 0.2 s between RRI and BOLD signals in 12 out of 23 (50%) participants in axial slices of the pons/brain stem. Adding the neural lag of 0.3 s to the observed lag of 2.2 s we obtain 2.5 s, which is the time between neural activity increase and BOLD increase, termed neuro-BOLD coupling. Note, this time window for neuro-BOLD coupling in awake humans is surprisingly of similar size as in awake head-fixed adult mice (Mateo et al., 2017).

Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder

Recent medical imaging technologies, specifically functional magnetic resonance imaging (fMRI), have advanced the diagnosis of neurological and neurodevelopmental disorders by allowing scientists and physicians to observe the activity within and between different regions of the brain. Deep learning methods have frequently been implemented to analyze images produced by such technologies and perform disease classification tasks; however, current state-of-the-art approaches do not take advantage of all the information offered by fMRI scans. In this paper, we propose a deep multimodal model that learns a joint representation from two types of connectomic data offered by fMRI scans. Incorporating two functional imaging modalities in an automated end-to-end autism diagnosis system will offer a more comprehensive picture of the neural activity, and thus allow for more accurate diagnoses. Our multimodal training strategy achieves a classification accuracy of 74% and a recall of 95%, as well as an F1 score of 0.805, and its overall performance is superior to using only one type of functional data.

Monitoring of Stimulus Evoked Murine Somatosensory Cortex Hemodynamic Activity With Volumetric Multi-Spectral Optoacoustic Tomography

Sensory stimulation is an attractive paradigm for studying brain activity using various optical-, ultrasound- and MRI-based functional neuroimaging methods. Optoacoustics has been recently suggested as a powerful new tool for scalable mapping of multiple hemodynamic parameters with rich contrast and previously unachievable spatio-temporal resolution. Yet, its utility for studying the processing of peripheral inputs at the whole brain level has so far not been quantified. We employed volumetric multi-spectral optoacoustic tomography (vMSOT) to non-invasively monitor the HbO, HbR, and HbT dynamics across the mouse somatosensory cortex evoked by electrical paw stimuli. We show that elevated contralateral activation is preserved in the HbO map (invisible to MRI) under isoflurane anesthesia. Brain activation is shown to be predominantly confined to the somatosensory cortex, with strongest activation in the hindpaw region of the contralateral sensorimotor cortex. Furthermore, vMSOT detected the presence of an initial dip in the contralateral hindpaw region in the delta HbO channel. Sensorimotor cortical activity was identified over all other regions in HbT and HbO but not in HbR. Pearson’s correlation mapping enabled localizing the response to the sensorimotor cortex further highlighting the ability of vMSOT to bridge over imaging performance deficiencies of other functional neuroimaging modalities.

Does the child brain rest?: An examination and interpretation of resting cognition in developmental cognitive neuroscience

In cognitive neuroscience, measurements of "resting baseline" are often considered stable across age and used as a reference point against which to judge cognitive state. The task-based approach-comparing resting baseline to task conditions-implies that resting baseline is an equalizer across participants and-in the case of studies of developmental changes in cognition-across age groups. In contrast, network neuroscience explicitly examines the development of "resting state" networks across age, at odds with the idea of a consistent resting baseline. Little attention has been paid to how cognition during rest may shift across development, particularly in children under the age of eight. Childhood is marked by striking maturation of neural systems, including a protracted developmental period for cognitive control systems. To grow and shape these cognitive systems, children have a developmental imperative to engage their neural circuitry at every possible opportunity. Thus, periods of "rest" without specific instructions may require additional control for children as they fight against developmental expectation to move, speak, or otherwise engage. We therefore theorize that the child brain does not rest in a manner consistent with the adult brain as longer rest periods may represent increased cognitive control. To shape this theory, we first review the extant literature on neurodevelopment across early childhood within the context of cognitive development. Next, we present nascent evidence for a destabilized baseline for comparisons across age. Finally, we present recommendations for designing, analyzing, and interpreting tasks conducted with young children as well as for resting state. Future work must aim to tease apart the cognitive context under which we examine functional brain development in young children and take considerations into account unique to each age.

A response-locking protocol to boost sensitivity for fMRI-based neurochronometry

The timeline of brain‐wide neural activity relative to a behavioral event is crucial when decoding the neural implementation of a cognitive process. Yet, fMRI assesses neural processes indirectly via delayed and regionally variable hemodynamics. This method‐inherent temporal distortion impacts the interpretation of behavior‐linked neural timing. Here we describe a novel behavioral protocol that aims at disentangling the BOLD dynamics of the pre‐ and post‐response periods in response time tasks. We tested this response‐locking protocol in a perceptual decision‐making (random dot) task. Increasing perceptual difficulty produced expected activity increases over a broad network involving the lateral/medial prefrontal cortex and the anterior insula. However, response‐locking revealed a previously unreported functional dissociation within this network. preSMA and anterior premotor cortex (prePMV) showed post‐response activity modulations while anterior insula and anterior cingulate cortex did not. Furthermore, post‐response BOLD activity at preSMA showed a modulation in timing but not amplitude while this pattern was reversed at prePMV. These timeline dissociations with response‐locking thus revealed three functionally distinct sub‐networks in what was seemingly one shared distributed network modulated by perceptual difficulty. These findings suggest that our novel response‐locked protocol could boost the timing‐related sensitivity of fMRI.

Methodology for tDCS integration with fMRI

Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

From Light to Sound: Photoacoustic and Ultrasound Imaging in Fundamental Research of Alzheimer's Disease

Alzheimer's disease (AD) causes severe cognitive dysfunction and has long been studied for the underlining physiological and pathological mechanisms. Several biomedical imaging modalities have been applied, including MRI, PET, and high-resolution optical microscopy, for research purposes. However, there is still a strong need for imaging tools that can provide high spatiotemporal resolutions with relatively deep penetration to enhance our understanding of AD pathology and monitor treatment progress in fundamental research. Photoacoustic (PA) imaging and ultrasound (US) imaging can potentially address these unmet needs in AD research. PA imaging provides functional information with endogenous and/or exogenous contrast, while US imaging provides structural information. Recent studies have demonstrated the ability to monitor physiological parameters in small-animal brains with PA and US imaging as well as the feasibility of using US imaging as a therapeutic tool for AD. This concise review aims to introduce recent advances in AD research using PA and US imaging, provide the fundamentals, and discuss the potentials and challenges for future advances.

Status of Brain Imaging in Gastroparesis

The pathophysiology of nausea and vomiting in gastroparesis is complicated and multifaceted involving the collaboration of both the peripheral and central nervous systems. Most treatment strategies and studies performed in gastroparesis have focused largely on the peripheral effects of this disease, while our understanding of the central nervous system mechanisms of nausea in this entity is still evolving. The ability to view the brain with different neuroimaging techniques has enabled significant advances in our understanding of the central emetic reflex response. However, not enough studies have been performed to further explore the brain–gut mechanisms involved in nausea and vomiting in patients with gastroparesis. The purpose of this review article is to assess the current status of brain imaging and summarize the theories about our present understanding on the central mechanisms involved in nausea and vomiting (N/V) in patients with gastroparesis. Gaining a better understanding of the complex brain circuits involved in the pathogenesis of gastroparesis will allow for the development of better antiemetic prophylactic and treatment strategies.

Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison - asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.

Central and Peripheral Neuromuscular Adaptations to Ageing

Ageing is accompanied by a severe muscle function decline presumably caused by structural and functional adaptations at the central and peripheral level. Although researchers have reported an extensive analysis of the alterations involving muscle intrinsic properties, only a limited number of studies have recognised the importance of the central nervous system, and its reorganisation, on neuromuscular decline. Neural changes, such as degeneration of the human cortex and function of spinal circuitry, as well as the remodelling of the neuromuscular junction and motor units, appear to play a fundamental role in muscle quality decay and culminate with considerable impairments in voluntary activation and motor performance. Modern diagnostic techniques have provided indisputable evidence of a structural and morphological rearrangement of the central nervous system during ageing. Nevertheless, there is no clear insight on how such structural reorganisation contributes to the age-related functional decline and whether it is a result of a neural malfunction or serves as a compensatory mechanism to preserve motor control and performance in the elderly population. Combining leading-edge techniques such as high-density surface electromyography (EMG) and improved diagnostic procedures such as functional magnetic resonance imaging (fMRI) or high-resolution electroencephalography (EEG) could be essential to address the unresolved controversies and achieve an extensive understanding of the relationship between neural adaptations and muscle decline.

The dynamic of basal ganglia activity with a multiple covariance method: influences of Parkinson's disease

The closed-loop cortico-subcortical pathways of basal ganglia have been extensively used to describe the physiology of these centres and to justify the functional disorders of basal ganglia diseases. This approach justifies some experimental and clinical data but not others, and furthermore, it does not include a number of subcortical circuits that may produce a more complex basal ganglia dynamic than that expected for closed-loop linear networks. This work studied the functional connectivity of the main regions of the basal ganglia motor circuit with magnetic resonance imaging and a new method (functional profile method), which can analyse the multiple covariant activity of human basal ganglia. The functional profile method identified the most frequent covariant functional status (profiles) of the basal ganglia motor circuit, ordering them according to their relative frequency and identifying the most frequent successions between profiles (profile transitions). The functional profile method classified profiles as input profiles that accept the information coming from other networks, output profiles involved in the output of processed information to other networks and highly interconnected internal profiles that accept transitions from input profiles and send transitions to output profiles. Profile transitions showed a previously unobserved functional dynamic of human basal ganglia, suggesting that the basal ganglia motor circuit may work as a dynamic multiple covariance network. The number of internal profiles and internal transitions showed a striking decrease in patients with Parkinson’s disease, a fact not observed for input and output profiles. This suggests that basal ganglia of patients with Parkinson’s disease respond to requirements coming from other neuronal networks, but because the internal processing of information is drastically weakened, its response will be insufficient and perhaps also self-defeating. These marked effects were found in patients with few motor disorders, suggesting that the functional profile method may be an early procedure to detect the first stages of the Parkinson’s disease when the motor disorders are not very evident. The multiple covariance activity found presents a complementary point of view to the cortico-subcortical closed-loop model of basal ganglia. The functional profile method may be easily applied to other brain networks, and it may provide additional explanations for the clinical manifestations of other basal ganglia disorders.

From State-to-Trait Meditation: Reconfiguration of Central Executive and Default Mode Networks

While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN).

While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN). However, the gradual change between DMN and CEN configuration from pre-meditation, during meditation, and post-meditation is unknown. Here, we investigated the change in DMN and CEN configuration by means of brain activity and functional connectivity (FC) analyses in EMs across three back-to-back functional magnetic resonance imaging (fMRI) scans: pre-meditation baseline (trait), meditation (state), and post-meditation (state-to-trait). Pre-meditation baseline group comparison was also performed between EMs and healthy controls (HCs). Meditation trait was characterized by a significant reduction in activity and FC within DMN and increased anticorrelations between DMN and CEN. Conversely, meditation state and meditation state-to-trait periods showed increased activity and FC within the DMN and between DMN and CEN. However, the latter anticorrelations were only present in EMs with limited practice. The interactions between networks during these states by means of positive diametric activity (PDA) of the fractional amplitude of low-frequency fluctuations (fALFFs) defined as CEN fALFF¯ − DMN fALFF¯ revealed no trait differences but significant increases during meditation state that persisted in meditation state-to-trait. The gradual reconfiguration in DMN and CEN suggest a neural mechanism by which the CEN negatively regulates the DMN and is probably responsible for the long-term trait changes seen in meditators and reported psychological well-being.

The Relationship Between Local Field Potentials and the Blood-Oxygenation-Level Dependent MRI Signal Can Be Non-linear

Functional magnetic resonance imaging (fMRI) is currently one of the most important neuroimaging methods in neuroscience. The image contrast in fMRI relies on the blood-oxygenation-level dependent (BOLD) signal, which indirectly reflects neural activity through neurovascular coupling. Because the mechanism that links the BOLD signal to neural activities involves multiple complicated processes, where neural activity, regional metabolism, hemodynamics, and the BOLD signal are all inter-connected, understanding the quantitative relationship between the BOLD signal and the underlying neural activities is crucial for interpreting fMRI data. Simultaneous local field potential (LFP) and fMRI recordings provide a method to study neurovascular coupling. There were a few studies that have shown non-linearities in stimulus related responses, but whether there is any non-linearity in LFP—BOLD relationship at rest has not been specifically quantified. In this study, we analyzed the simultaneous LFP and resting state-fMRI data acquired from rodents, and found that the relationship between LFP and BOLD is non-linear under isoflurane (ISO) anesthesia, but linear under dexmedetomidine (DMED) anesthesia. Subsequent analysis suggests that such non-linearity may come from the non-Gaussian distribution of LFP power and switching from LFP power to LFP amplitude can alleviate the problem to a degree. We also confirmed that, despite the non-linearity in the mean LFP—BOLD curve, the Pearson correlation between the two signals is relatively unaffected.

Serotonin 5-HT1A Receptor Biased Agonists Induce Different Cerebral Metabolic Responses: A [18F]-Fluorodesoxyglucose Positron Emission Tomography Study in Conscious and Anesthetized Rats

Serotonin 5-HT_1A receptors constitute an attractive therapeutic target for various psychiatric or neurodegenerative disorders. These receptors are expressed in multiple brain regions on different neuronal populations and can be coupled with distinct G-protein subtypes; such functional diversity complicates the use of 5-HT_1A ligands in several pathologies where it would be desirable to stimulate the receptors in a precise region. Therefore, using "biased agonists" able to target specifically certain subpopulations of 5-HT_1A receptors would enable achievement of better therapeutic benefit. Several 5-HT_1A receptor biased agonists are currently in development, including NLX-101 (aka F15599) and NLX-112 (aka F13640, befiradol), with preclinical data suggesting that they preferentially target different populations of 5-HT_1A receptors. However, most previous studies used invasive and regionally limited approaches. In this context, [¹⁸F]-fluorodesoxyglucose (FDG)-positron emission tomography (PET) imaging constitutes an interesting technique as it enables noninvasive mapping of the regional brain activity changes following a pharmacological challenge in conscious animals. We report here the evaluation of cerebral glucose metabolism following intraperitoneal injection of different doses of NLX-112 or NLX-101 in conscious or isoflurane-anesthetized rats. The biased agonists produced different metabolic "fingerprints" with distinct regional preferences, consistent with previous studies. At equal doses, the effect of NLX-101 was less marked than NLX-112 in the piriform cortex, in the striatum (in terms of inhibition), and in the pontine nuclei and the cerebellum (in terms of activation); furthermore, only NLX-112 increased the glucose metabolism in the parietal cortex, whereas only NLX-101 induced a clear activation in the colliculi and the frontal cortex, which may be related to its distinctive procognitive profile. Both agonist effects were almost completely unapparent in anesthetized animals, underlining the importance of studying serotonergic neurotransmission in the conscious state. In this regard, [¹⁸F]FDG-PET imaging seems very complementary with other functional imaging techniques such as pharmacological MRI.

Cognitive Neuroscience of Dyslexia

Purpose

This review summarizes what is known about the structural and functional brain bases of dyslexia.

Method

We review the current literature on structural and functional brain differences in dyslexia. This includes evidence about differences in gray matter anatomy, white matter connectivity, and functional activations in response to print and language. We also summarize findings concerning brain plasticity in response to interventions.

Results

We highlight evidence relating brain function and structure to instructional issues such as diagnosis and prognosis. We also highlight evidence about brain differences in early childhood, before formal reading instruction in school, which supports the importance of early identification and intervention.

Conclusion

Neuroimaging studies of dyslexia reveal how the disorder is related to differences in structure and function in multiple neural circuits.

Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging

The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.

A Systematic Review of Integrated Functional Near-Infrared Spectroscopy (fNIRS) and Transcranial Magnetic Stimulation (TMS) Studies

Background: The capacity for TMS to elicit neural activity and manipulate cortical excitability has created significant expectation regarding its use in both cognitive and clinical neuroscience. However, the absence of an ability to quantify stimulation effects, particularly outside of the motor cortex, has led clinicians and researchers to pair noninvasive brain stimulation with noninvasive neuroimaging techniques. fNIRS, as an optical and wearable neuroimaging technique, is an ideal candidate for integrated use with TMS. Together, TMS+fNIRS may offer a hybrid alternative to "blind" stimulation to assess NIBS in therapy and research. Objective: In this systematic review, the current body of research into the transient and prolonged effects of TMS on fNIRS-based cortical hemodynamic measures while at rest and during tasks are discussed. Additionally, studies investigating the relation of fNIRS to measures of cortical excitability as produced by TMS-evoked Motor-Evoked-Potential (MEP) are evaluated. The aim of this review is to outline the integrated use of TMS+fNIRS and consolidate findings related to use of fNIRS to monitor changes attributed to TMS and the relationship of fNIRS to cortical excitability itself. Methods: Key terms were searched in PubMed and Web-of-Science to identify studies investigating the use of both fNIRS and TMS. Works from Google-Scholar and referenced works in identified papers were also assessed for relevance. All published experimental studies using both fNIRS and TMS techniques in the study methodology were included. Results: A combined literature search of neuroimaging and neurostimulation studies identified 53 papers detailing the joint use of fNIRS and TMS. 22/53 investigated the immediate effects of TMS at rest in the DLPFC and M1 as measured by fNIRS. 21/22 studies reported a significant effect in [HbO] for 40/54 stimulation conditions with 14 resulting an increase and 26 in a decrease. While 15/22 studies also reported [HbR], only 5/37 conditions were significant. Task effects of fNIRS+TMS were detailed in 16 studies, including 10 with clinical populations. Most studies only reported significant changes in [HbO] related measures. Studies comparing fNIRS to changes in MEP-measured cortical excitability suggest that fNIRS measures may be spatially more diffuse but share similar traits. Conclusion: This review summarizes the progress in the development of this emerging hybrid neuroimaging & neurostimulation methodology and its applications. Despite encouraging progress and novel applications, a lack of replicated works, along with highly disparate methodological approaches, highlight the need for further controlled studies. Interpretation of current research directions, technical challenges of TMS+fNIRS, and recommendations regarding future works are discussed.