Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest

A macaque connectome for large-scale network simulations in TheVirtualBrain

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

Dynamic brain network states in human generalized spike-wave discharges

Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.

Differential coupling between subcortical calcium and BOLD signals during evoked and resting state through simultaneous calcium fiber photometry and fMRI

Task based and resting state fMRI has been widely utilized to study brain functions. As the foundation of fMRI, the underlying neural basis of the BOLD signal has been extensively studied, but the detailed mechanism remains elusive, particularly during the resting state. To examine the neurovascular coupling, it is important to simultaneously record neural and vascular signals. Here we developed a novel setup of camera based, scalable simultaneous calcium fiber photometry and fMRI in rats. Using this setup, we recorded calcium signals of superior colliculus (SC) and lateral geniculate nucleus (LGN) and fMRI simultaneously during visual stimulation and the resting state. Our results revealed robust, region-specific coupling between calcium and BOLD signals in the task state and weaker, whole brain correlation in the resting state. Interestingly, the spatial specificity of such correlation in the resting state was improved upon regression of white matter, ventricle signals and global signals in fMRI data. Overall, our results suggest differential coupling of calcium and BOLD signals for subcortical regions between evoked and resting states, and the coupling relationship in the resting state was related with resting state BOLD preprocessing strategies.

Ketamine has distinct electrophysiological and behavioral effects in depressed and healthy subjects

Ketamine's mechanism of action was assessed using gamma power from magnetoencephalography (MEG) as a proxy measure for homeostatic balance in 35 unmedicated subjects with major depressive disorder (MDD) and 25 healthy controls enrolled in a double-blind, placebo-controlled, randomized cross-over trial of 0.5 mg/kg ketamine. MDD subjects showed significant improvements in depressive symptoms, and healthy control subjects exhibited modest but significant increases in depressive symptoms for up to 1 day after ketamine administration. Both groups showed increased resting gamma power following ketamine. In MDD subjects, gamma power was not associated with the magnitude of the antidepressant effect. However, baseline gamma power was found to moderate the relationship between post-ketamine gamma power and antidepressant response; specifically, higher post-ketamine gamma power was associated with better response in MDD subjects with lower baseline gamma, with an inverted relationship in MDD subjects with higher baseline gamma. This relationship was observed in multiple regions involved in networks hypothesized to be involved in the pathophysiology of MDD. This finding suggests biological subtypes based on the direction of homeostatic dysregulation and has important implications for inferring ketamine's mechanism of action from studies of healthy controls alone.

On the Relationship between MRI and Local Field Potential Measurements of Spatial and Temporal Variations in Functional Connectivity

Correlations between fluctuations in resting state BOLD fMRI signals are interpreted as measures of functional connectivity (FC), but the neural basis of their origins and their relationships to specific features of underlying electrophysiologic activity, have not been fully established. In particular, the dependence of FC metrics on different frequency bands of local field potentials (LFPs), and the relationship of dynamic changes in BOLD FC to underlying temporal variations of LFP correlations, are not known. We compared the spatial profiles of resting state coherences of different frequency bands of LFP signals, with high resolution resting state BOLD FC measurements. We also compared the probability distributions of temporal variations of connectivity in both modalities using a Markov chain model-based approach. We analyzed data obtained from the primary somatosensory (S1) cortex of monkeys. We found that in areas 3b and 1 of S1 cortex, low frequency LFP signal fluctuations were the main contributions to resting state LFP coherence. Additionally, the dynamic changes of BOLD FC behaved most similarly to the LFP low frequency signal coherence. These results indicate that, within the S1 cortex meso-scale circuit studied, resting state FC measures from BOLD fMRI mainly reflect contributions from low frequency LFP signals and their dynamic changes.

Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder

Major depressive disorder (MDD) is highly prevalent and associated with considerable morbidity, yet its pathophysiology remains only partially understood. While numerous studies have investigated the neurobiological correlates of MDD, most have used only a single neuroimaging modality. In particular, diffusion tensor imaging (DTI) studies have failed to yield uniform results. In this context, examining key tracts and using information from multiple neuroimaging modalities may better characterize potential abnormalities in the MDD brain. This study analyzed data from 30 participants with MDD and 26 healthy participants who underwent DTI, magnetic resonance spectroscopy (MRS), resting-state functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Tracts connecting the subgenual anterior cingulate cortex (sgACC) and the left and right amygdala, as well as connections to the left and right hippocampus and thalamus, were examined as target areas. Reduced fractional anisotropy (FA) was observed in the studied tracts. Significant differences in the correlation between medial prefrontal glutamate concentrations and FA were also observed between MDD and healthy participants along tracts connecting the sgACC and right amygdala; healthy participants exhibited a strong correlation but MDD participants showed no such relationship. In the same tract, a correlation was observed between FA and subsequent antidepressant response to ketamine infusion in MDD participants. Exploratory models also suggested group differences in the relationship between DTI, fMRI, and MEG measures. This study is the first to combine MRS, DTI, fMRI, and MEG data to obtain multimodal indices of MDD and antidepressant response and may lay the foundation for similar future analyses.

Resting-State Brain Network Dysfunctions Associated With Visuomotor Impairments in Autism Spectrum Disorder

Background: Individuals with autism spectrum disorder (ASD) show elevated levels of motor variability that are associated with clinical outcomes. Cortical-cerebellar networks involved in visuomotor control have been implicated in postmortem and anatomical imaging studies of ASD. However, the extent to which these networks show intrinsic functional alterations in patients, and the relationship between intrinsic functional properties of cortical-cerebellar networks and visuomotor impairments in ASD have not yet been clarified. Methods: We examined the amplitude of low-frequency fluctuation (ALFF) of cortical and cerebellar brain regions during resting-state functional MRI (rs-fMRI) in 23 individuals with ASD and 16 typically developing (TD) controls. Regions of interest (ROIs) with ALFF values significantly associated with motor variability were identified for for patients and controls respectively, and their functional connectivity (FC) to each other and to the rest of the brain was examined. Results: For TD controls, greater ALFF in bilateral cerebellar crus I, left superior temporal gyrus, left inferior frontal gyrus, right supramarginal gyrus, and left angular gyrus each were associated with greater visuomotor variability. Greater ALFF in cerebellar lobule VIII was associated with less visuomotor variability. For individuals with ASD, greater ALFF in right calcarine cortex, right middle temporal gyrus (including MT/V5), left Heschl's gyrus, left post-central gyrus, right pre-central gyrus, and left precuneus was related to greater visuomotor variability. Greater ALFF in cerebellar vermis VI was associated with less visuomotor variability. Individuals with ASD and TD controls did not show differences in ALFF for any of these ROIs. Individuals with ASD showed greater posterior cerebellar connectivity with occipital and parietal cortices relative to TD controls, and reduced FC within cerebellum and between lateral cerebellum and pre-frontal and other regions of association cortex. Conclusion: Together, these findings suggest that increased resting oscillations within visuomotor networks in ASD are associated with more severe deficits in controlling variability during precision visuomotor behavior. Differences between individuals with ASD and TD controls in the topography of networks showing relationships to visuomotor behavior suggest atypical patterns of cerebellar-cortical specialization and connectivity in ASD that underlies previously documented visuomotor deficits.

Why is Clinical fMRI in a Resting State?

While resting state fMRI (rs-fMRI) has gained widespread application in neuroimaging clinical research, its penetration into clinical medicine has been more limited. We surveyed a neuroradiology professional group to ascertain their experience with rs-fMRI, identify perceived barriers to using rs-fMRI clinically and elicit suggestions about ways to facilitate its use in clinical practice. The electronic survey also collected information about demographics and work environment using Likert scales. We found that 90% of the respondents had adequate equipment to conduct rs-fMRI and 82% found rs-fMRI data easy to collect. Fifty-nine percent have used rs-fMRI in their past research and 72% reported plans to use rs-fMRI for research in the next year. Nevertheless, only 40% plan to use rs-fMRI in clinical practice in the next year and 82% agreed that their clinical fMRI use is largely confined to pre-surgical planning applications. To explore the reasons for the persistent low utilization of rs-fMRI in clinical applications, we identified barriers to clinical rs-fMRI use related to the availability of robust denoising procedures, single-subject analysis techniques, demonstration of functional connectivity map reliability, regulatory clearance, reimbursement, and neuroradiologist training opportunities. In conclusion, while rs-fMRI use in clinical neuroradiology practice is limited, enthusiasm appears to be quite high and there are several possible avenues in which further research and development may facilitate its penetration into clinical practice.

Abnormal intrinsic brain activity in individuals with peripheral vision loss because of retinitis pigmentosa using amplitude of low-frequency fluctuations

The study aimed to determine alterations in intrinsic brain activity in retinitis pigmentosa (RP) individuals using the amplitude of low-frequency fluctuation (ALFF)/fractional amplitude of low-frequency fluctuation (fALFF) method. Sixteen RP individuals (10 men and six women) and 14 healthy controls (HCs) (six men and eight women) closely matched in age, sex, and education were enrolled in the study. The ALFF/fALFF method was applied to compare different intrinsic brain activities between the RP group and the HC group. The relationship between the mean ALFF/fALFF signal values of different brain regions and the visual measurements in RP group was analyzed by Pearson correlation. Compared with HCs, RP individuals had significantly lower ALFF values in the bilateral lingual gyrus (LIGG)/cerebellum posterior lobe [Brodmann area (BA) 17,18], but lower fALFF values in the bilateral LIGG/cerebellum anterior lobe (BA 17,18). Meanwhile, RP individuals had significantly higher ALFF in the bilateral precuneus cortex/middle cingulate cortex (BA 7,31), as well as higher fALFF values in the left superior/middle frontal gyrus (BA 9,10) and bilateral supplementary motor area (BA 6,8) (voxel-level P<0.01, cluster-level P<0.05). Moreover, the fALFF values of the bilateral LIGG/cerebellum anterior lobe showed positive relationships with the best-corrected visual acuity (BCVA)-oculus dexter (r=0.574, P=0.020) and BCVA-oculus sinister (r=0.570, P=0.021) in RP individuals; our results provide evidence that RP individuals may have impaired intrinsic brain activity in the primary visual area and the visuomotor coordination area that correlates with BCVA. Moreover, our findings indicate that reorganization of the dorsal visual stream and the parietoprefrontal pathway occurs in RP individuals.

Structural and functional abnormalities of vision-related brain regions in cirrhotic patients: a MRI study

Purpose

Previous studies have focused on global cerebral alterations observed in cirrhosis. However, little was known about the specific abnormalities of vision-related brain regions in cirrhotic patients. In this study, we sought to explore neurological alterations of vision-related regions by measuring brain resting-state network connectivity, based on the structural investigation in cirrhotic patients without clinical sign of hepatic encephalopathy (HE).

Methods

Structural and functional magnetic resonance image (MRI) data were collected from 20 hepatitis B virus (HBV)-related cirrhotic patients without clinical sign of HE and from 20 healthy controls (HC). Voxel-based morphometric (VBM) analysis and brain functional network analysis were performed to detect abnormalities in cerebral structure and function.

Results

Cirrhotic patients showed regions with the most significant gray matter reduction primarily in vision-related brain regions, including the bilateral lingual gyri, left putamen, right fusiform gyrus, and right calcarine gyrus, and other significant gray matter reductions were distributed in bilateral hippocampus. Based on structural investigation focused on vision-related regions, brain functional network analysis revealed decreased functional connectivity between brain functional networks within vision-related regions (primary visual network (PVN), higher visual network (HVN), visuospatial network (VSN)) in the patient group compared with HC group.

Conclusion

These results indicate that structural and functional impairment were evident in the vision-related brain regions in cirrhotic patients without clinical sign of hepatic encephalopathy. The physiopathology and clinical relevance of these changes could not be ascertained from the present study, which provided a basis for further evolution of the disease.

Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension

Communication requires the abilities to generate and interpret utterances and to infer the beliefs, desires, and goals of others ("Theory of Mind"; ToM). These two abilities have been shown to dissociate: individuals with aphasia retain the ability to think about others' mental states; and individuals with autism are impaired in social reasoning, but their basic language processing is often intact. In line with this evidence from brain disorders, functional MRI (fMRI) studies have shown that linguistic and ToM abilities recruit distinct sets of brain regions. And yet, language is a social tool that allows us to share thoughts with one another. Thus, the language and ToM brain networks must share information despite being implemented in distinct neural circuits. Here, we investigated potential interactions between these networks during naturalistic cognition using functional correlations in fMRI. The networks were functionally defined in individual participants, in terms of preference for sentences over nonwords for language, and for belief inference over physical-event processing for ToM, with both a verbal and a nonverbal paradigm. Although, across experiments, interregion correlations within each network were higher than between-network correlations, we also observed above-baseline synchronization of blood oxygenation level-dependent signal fluctuations between the two networks during rest and story comprehension. This synchronization was functionally specific: neither network was synchronized with the executive control network (functionally defined in terms of preference for a harder over easier version of an executive task). Thus, coordination between the language and ToM networks appears to be an inherent and specific characteristic of their functional architecture. NEW & NOTEWORTHY Humans differ from nonhuman primates in their abilities to communicate linguistically and to infer others' mental states. Although linguistic and social abilities appear to be interlinked onto- and phylogenetically, they are dissociated in the adult human brain. Yet successful communication requires language and social reasoning to work in concert. Using functional MRI, we show that language regions are synchronized with social regions during rest and language comprehension, pointing to a possible mechanism for internetwork interaction.

Cocaine Decreases Spontaneous Neuronal Activity and Increases Low-Frequency Neuronal and Hemodynamic Cortical Oscillations

Low-frequency oscillations (LFOs) in hemodynamics assessed by fMRI reflect synchronized neuronal activities and are the basis for mapping brain function and its disruption by drugs and disease. Here we assess if cocaine disrupts coupling between neuronal and vascular LFOs by simultaneously measuring cortical field potentials (FP) and cerebral blood flow (CBF) regarding their LFOs (0-1 Hz) spectral bandwidths in the somatosensory cortex of naïve and chronic cocaine-exposed rats at baseline and during cocaine intoxication. While across all conditions the dominant oscillation frequencies for FP and CBF LFOs were ~0.1 Hz, the bandwidth of FP LFOs was about 4.8 ± 0.67 times broader than that of CBF LFOs. Acute cocaine depressed high-frequency FP events but increased the relative intensity of neuronal and hemodynamic LFOs, an effect that was markedly accentuated in magnitude and duration in chronic cocaine-exposed animals. Neuronal LFOs were correlated with CBF LFOs in control animals but not in chronically cocaine-exposed animals, which suggests neurovascular uncoupling. The marked increases in neuronal LFOs with chronic cocaine, which we interpret to reflect increases in neuronal synchronization in the LFOs, and the uncoupling of hemodynamics with resting neuronal activities could contribute to brain dysfunction in cocaine abusers and confound the interpretation of fMRI studies.

The impact of fasting on resting state brain networks in mice

Fasting is known to influence learning and memory in mice and alter the neural networks that subserve these cognitive functions. We used high-resolution functional MRI to study the impact of fasting on resting-state functional connectivity in mice following 12 h of fasting. The cortex and subcortex were parcellated into 52 subregions and functional connectivity was measured between each pair of subregions in groups of fasted and non-fasted mice. Functional connectivity was globally increased in the fasted group compared to the non-fasted group, with the most significant increases evident between the hippocampus (bilateral), retrosplenial cortex (left), visual cortex (left) and auditory cortex (left). Functional brain networks in the non-fasted group comprised five segregated modules of strongly interconnected subregions, whereas the fasted group comprised only three modules. The amplitude of low frequency fluctuations (ALFF) was decreased in the ventromedial hypothalamus in the fasted group. Correlation in gamma oscillations derived from local field potentials was increased between the left visual and retrosplenial cortices in the fasted group and the power of gamma oscillations was reduced in the ventromedial hypothalamus. These results indicate that fasting induces profound changes in functional connectivity, most likely resulting from altered coupling of neuronal gamma oscillations.

How reliable is cerebral blood flow to map changes in neuronal activity?

Neuroimaging techniques, such as functional MRI, map brain activity through hemodynamic-based signals, and are invaluable diagnostic tools in several neurological disorders such as stroke and dementia. Hemodynamic signals are normally precisely related to the underlying neuronal activity through neurovascular coupling mechanisms that ensure the supply of blood, glucose and oxygen to neurons at work. The knowledge of neurovascular coupling has greatly advanced over the last 30 years, it involves multifaceted interactions between excitatory and inhibitory neurons, astrocytes, and the microvessels. While the tight relationship between blood flow and neuronal activity forms a fundamental brain function, whether neurovascular coupling mechanisms are reliable across physiological and pathological conditions has been questioned. In this review, we interrogate the relationship between blood flow and neuronal activity during activation of different brain pathways: a sensory stimulation driven by glutamate, and stimulation of neuromodulatory pathways driven by acetylcholine or noradrenaline, and we compare the underlying neurovascular coupling mechanisms. We further question if neurovascular coupling mechanisms are affected by changing brain states, as seen in behavioral conditions of sleep, wakefulness, attention and in pathological conditions. Finally, we provide a short overview of how alterations of the brain vasculature could compromise the reliability of neurovascular coupling. Overall, while neurovascular coupling requires activation of common signalling pathways, alternate unique cascades exist depending on the activated pathways. Further studies are needed to fully elucidate the alterations in neurovascular coupling across brain states and pathological conditions.

Learning to optimize perceptual decisions through suppressive interactions in the human brain

Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known about the brain mechanisms that mediate learning-dependent suppression. Here, we employ ultra-high field magnetic resonance spectroscopy of GABA to test whether suppressive processing in decision-related and visual areas facilitates perceptual judgments during training. We demonstrate that parietal GABA relates to suppression of task-irrelevant information, while learning-dependent changes in visual GABA relate to enhanced performance in target detection and feature discrimination tasks. Combining GABA measurements with functional brain connectivity demonstrates that training on a target detection task involves local connectivity and disinhibition of visual cortex, while training on a feature discrimination task involves inter-cortical interactions that relate to suppressive visual processing. Our findings provide evidence that learning optimizes perceptual decisions through suppressive interactions in decision-related networks.

The thalamus in drug addiction: from rodents to humans

Impairments in response inhibition and salience attribution (iRISA) have been proposed to underlie the clinical symptoms of drug addiction as mediated by cortico-striatal-thalamo-cortical networks. The bulk of evidence supporting the iRISA model comes from neuroimaging research that has focused on cortical and striatal influences with less emphasis on the role of the thalamus. Here, we highlight the importance of the thalamus in drug addiction, focusing on animal literature findings on thalamic nuclei in the context of drug-seeking, structural and functional changes of the thalamus as measured by imaging studies in human drug addiction, particularly during drug cue and non-drug reward processing, and response inhibition tasks. Findings from the animal literature suggest that the paraventricular nucleus of the thalamus, the lateral habenula and the mediodorsal nucleus may be involved in the reinstatement, extinction and expression of drug-seeking behaviours. In support of the iRISA model, the human addiction imaging literature demonstrates enhanced thalamus activation when reacting to drug cues and reduced thalamus activation during response inhibition. This pattern of response was further associated with the severity of, and relapse in, drug addiction. Future animal studies could widen their field of focus by investigating the specific role(s) of different thalamic nuclei in different phases of the addiction cycle. Similarly, future human imaging studies should aim to specifically delineate the structure and function of different thalamic nuclei, for example, through the application of advanced imaging protocols at higher magnetic fields (7 Tesla).This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'.

Frequency-dependent neural activity in primary angle-closure glaucoma

OBJECTIVE

In this study, we aimed to investigate the frequency-dependent spontaneous neural activity in primary angle-closure glaucoma (PACG) using the amplitude of low-frequency fluctuations (ALFF) method.

PATIENTS AND METHODS

In total, 52 PACG individuals (24 males and 28 females) and 52 normal-sighted controls (NS; 24 males and 28 females) who were closely matched in age, sex, and education underwent resting-state magnetic resonance imaging scans. A repeated-measures ANOVA and post hoc two-sample t-tests were conducted to analyze the different ALFF values in two different frequency bands (slow-4, 0.027-0.073 Hz and slow-5, 0.010-0.027 Hz) between the two groups. Pearson's correlation analysis was conducted to reveal the relationship between the mean ALFF values and clinical variables in the PACG group.

RESULTS

Compared to the NS group, the PACG group had high ALFF values in the right inferior occipital gyrus and low ALFF values in the left middle occipital gyrus, left precentral gyrus, and left postcentral gyrus in the slow-4 band. The PACG group had high ALFF values in the right inferior occipital gyrus and low ALFF values in the left inferior parietal lobule, left postcentral gyrus, and right precentral/postcentral gyrus in the slow-5 band. Specifically, we found that the abnormal ALFF values in the bilateral posterior cingulate gyrus and bilateral precuneus were higher in the slow-4 than in the slow-5 band, whereas ALFF in the bilateral frontal lobe, right fusiform, and right cerebellum posterior lobe were higher in the slow-5 than in the slow-4 band. The greater mean ALFF values of the right inferior occipital gyrus were associated with smaller retinal nerve fiber layer thickness and greater visual fields in PACG group in the slow-4 band.

CONCLUSION

Our results highlighted that individuals in the PACG group showed abnormal spontaneous neural activities in the visual cortices, sensorimotor cortices, frontal lobe, frontoparietal network, and default mode network at two frequency bands, which might indicate impaired vision and cognition and emotion function in PACG individuals. These findings offer important insight into the understanding of the neural mechanism of PACG.

Changes in Endogenous Dopamine Induced by Methylphenidate Predict Functional Connectivity in Nonhuman Primates

Dopamine (DA) levels in the striatum are increased by many therapeutic drugs, such as methylphenidate (MPH), which also alters behavioral and cognitive functions thought to be controlled by the PFC dose-dependently. We linked DA changes and functional connectivity (FC) using simultaneous [¹⁸F]fallypride PET and resting-state fMRI in awake male rhesus monkeys after oral administration of various doses of MPH. We found a negative correlation between [¹⁸F]fallypride nondisplaceable binding potential (BP_ND) and MPH dose in the head of the caudate (hCd), demonstrating increased extracellular DA resulting from MPH administration. The decreased BP_ND was negatively correlated with FC between the hCd and the PFC. Subsequent voxelwise analyses revealed negative correlations with FC between the hCd and the dorsolateral PFC, hippocampus, and precuneus. These results, showing that MPH-induced changes in DA levels in the hCd predict resting-state FC, shed light on a mechanism by which changes in striatal DA could influence function in the PFC.SIGNIFICANCE STATEMENT Dopamine transmission is thought to play an essential role in shaping large scale-neural networks that underlie cognitive functions. It is the target of therapeutic drugs, such as methylphenidate (Ritalin), which blocks the dopamine transporter, thereby increasing extracellular dopamine levels. Methylphenidate is used extensively to treat attention deficit hyperactivity disorder, even though its effects on cognitive functions and their underlying neural mechanisms are not well understood. To date, little is known about the link between changes in dopamine levels and changes in functional brain organization. Using simultaneous PET/MR imaging, we show that methylphenidate-induced changes in endogenous dopamine levels in the head of the caudate predict changes in resting-state functional connectivity between this structure and the prefrontal cortex, precuneus, and hippocampus.

Amyloid beta-positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity

Amyloid beta (Aβ) deposition and cognitive decline are key features of Alzheimer's disease. The relationship between Aβ status and changes in neuronal function over time, however, remains unclear. We evaluated the effect of baseline Aβ status on reference region spontaneous brain activity (SBA-rr) using resting-state functional magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in patients with mild cognitive impairment. Patients (N = 62, [43 Aβ-positive]) from the Alzheimer's Disease Neuroimaging Initiative were divided into Aβ-positive and Aβ-negative groups via prespecified cerebrospinal fluid Aβ42 or 18F-florbetapir positron emission tomography standardized uptake value ratio cutoffs measured at baseline. We analyzed interaction of biomarker-confirmed Aβ status with SBA-rr change over a 2-year period using mixed-effects modeling. SBA-rr differences between Aβ-positive and Aβ-negative subjects increased significantly over time within subsystems of the default and visual networks. Changes exhibit an interaction with memory performance over time but were independent of glucose metabolism. Results reinforce the value of resting-state functional magnetic resonance imaging in evaluating Alzheimer''s disease progression and suggest spontaneous neuronal activity changes are concomitant with cognitive decline.

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.

An Open Resource for Non-human Primate Imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity

In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual's active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.

Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity

Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations.

In human neuroscience, there is growing interest in temporal fluctuations in coactivation patterns of resting-state brain activity. To elucidate generative mechanisms of these fluctuations, theoretical studies try to reproduce their empirical properties by simulations using dynamic models of large-scale spontaneous brain activity. However, evaluations of the reproducibility have not been extended so far to the fluctuations in global network topology of coactivation patterns, recently shown to be related to human cognition and behavior. Here we examine the extent to which a stationary model typically used for simulating resting-state activity can reproduce spatial and temporal patterns of the empirically observed fluctuations in global network topology. We found that such a model successfully reproduced the magnitude of empirical fluctuations as well as their temporal dynamics, whereas their spatial patterning was not fully accounted for by the simulation. Our results suggest that stationary models can explain many empirical properties in the fluctuations in global network topology, while modeling of non-stationary dynamics and/or greater estimation accuracy of anatomical connections underlying the simulation would be required for complete replication. This finding provides new insights into how fluctuations in global network topology of coactivation patterns emerge in the human brain.

Effective Connectivity Measured Using Optogenetically Evoked Hemodynamic Signals Exhibits Topography Distinct from Resting State Functional Connectivity in the Mouse

Brain connectomics has expanded from histological assessment of axonal projection connectivity (APC) to encompass resting state functional connectivity (RS-FC). RS-FC analyses are efficient for whole-brain mapping, but attempts to explain aspects of RS-FC (e.g., interhemispheric RS-FC) based on APC have been only partially successful. Neuroimaging with hemoglobin alone lacks specificity for determining how activity in a population of cells contributes to RS-FC. Wide-field mapping of optogenetically defined connectivity could provide insights into the brain's structure-function relationship. We combined optogenetics with optical intrinsic signal imaging to create an efficient, optogenetic effective connectivity (Opto-EC) mapping assay. We examined EC patterns of excitatory neurons in awake, Thy1-ChR2 transgenic mice. These Thy1-based EC (Thy1-EC) patterns were evaluated against RS-FC over the cortex. Compared to RS-FC, Thy1-EC exhibited increased spatial specificity, reduced interhemispheric connectivity in regions with strong RS-FC, and appreciable connection strength asymmetry. Comparing the topography of Thy1-EC and RS-FC patterns to maps of APC revealed that Thy1-EC more closely resembled APC than did RS-FC. The more general method of Opto-EC mapping with hemoglobin can be determined for 100 sites in single animals in under an hour, and is amenable to other neuroimaging modalities. Opto-EC mapping represents a powerful strategy for examining evolving connectivity-related circuit plasticity.

Reorganization of cerebro-cerebellar circuit in patients with left hemispheric gliomas involving language network: A combined structural and resting-state functional MRI study

The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain’s dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter “dwell time” implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a “default mode” in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

Brain alterations in low-frequency fluctuations across multiple bands in obsessive compulsive disorder

The extent of functional abnormalities in frontal-subcortical circuits in obsessive-compulsive disorder (OCD) is still unclear. Although neuroimaging studies, in general, and resting-state functional Magnetic Resonance Imaging (rs-fMRI), in particular, have provided relevant information regarding such alterations, rs-fMRI studies have been typically limited to the analysis of between-region functional connectivity alterations at low-frequency signal fluctuations (i.e., <0.08 Hz). Conversely, the local attributes of Blood Oxygen Level Dependent (BOLD) signal across different frequency bands have been seldom studied, although they may provide valuable information. Here, we evaluated local alterations in low-frequency fluctuations across different oscillation bands in OCD. Sixty-five OCD patients and 50 healthy controls underwent an rs-fMRI assessment. Alterations in the fractional amplitude of low-frequency fluctuations (fALFF) were evaluated, voxel-wise, across four different bands (from 0.01 Hz to 0.25 Hz). OCD patients showed decreased fALFF values in medial orbitofrontal regions and increased fALFF values in the dorsal-medial prefrontal cortex (DMPFC) at frequency bands <0.08 Hz. This pattern was reversed at higher frequencies, where increased fALFF values also appeared in medial temporal lobe structures and medial thalamus. Clinical variables (i.e., symptom-specific severities) were associated with fALFF values across the different frequency bands. Our findings provide novel evidence about the nature and regional distribution of functional alterations in OCD, which should contribute to refine neurobiological models of the disorder. We suggest that the evaluation of the local attributes of BOLD signal across different frequency bands may be a sensitive approach to further characterize brain functional alterations in psychiatric disorders.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions

Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.

Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.

Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective

Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.

Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks

Evidence for intrinsic functional connectivity (FC) within the human brain is largely from neuroimaging studies of hemodynamic activity. Data are lacking from anatomically precise electrophysiological recordings in the most widely studied nodes of human brain networks. Here we used a combination of fMRI and electrocorticography (ECoG) in five human neurosurgical patients with electrodes in the canonical "default" (medial prefrontal and posteromedial cortex), "dorsal attention" (frontal eye fields and superior parietal lobule), and "frontoparietal control" (inferior parietal lobule and dorsolateral prefrontal cortex) networks. In this unique cohort, simultaneous intracranial recordings within these networks were anatomically matched across different individuals. Within each network and for each individual, we found a positive, and reproducible, spatial correlation for FC measures obtained from resting-state fMRI and separately recorded ECoG in the same brains. This relationship was reliably identified for electrophysiological FC based on slow (<1 Hz) fluctuations of high-frequency broadband (70-170 Hz) power, both during wakeful rest and sleep. A similar FC organization was often recovered when using lower-frequency (1-70 Hz) power, but anatomical specificity and consistency were greatest for the high-frequency broadband range. An interfrequency comparison of fluctuations in FC revealed that high and low-frequency ranges often temporally diverged from one another, suggesting that multiple neurophysiological sources may underlie variations in FC. Together, our work offers a generalizable electrophysiological basis for intrinsic FC and its dynamics across individuals, brain networks, and behavioral states.SIGNIFICANCE STATEMENT The study of human brain networks during wakeful "rest", largely with fMRI, is now a major focus in both cognitive and clinical neuroscience. However, little is known about the neurophysiology of these networks and their dynamics. We studied neural activity during wakeful rest and sleep within neurosurgical patients with directly implanted electrodes. We found that network activity patterns showed striking similarities between fMRI and direct recordings in the same brains. With improved resolution of direct recordings, we also found that networks were best characterized with specific activity frequencies and that different frequencies show different profiles of within-network activity over time. Our work clarifies how networks spontaneously organize themselves across individuals, brain networks, and behavioral states.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Quantitative relations between BOLD responses, cortical energetics, and impulse firing

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow and oxygen usage that in turn are modulated by changes in neural activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neural activity. Here we identify the best energetic theory for the steady-state BOLD signal on the basis of correct predictions of experimental observations. This theory is then used, together with the recently determined relationship between energetics and neural activity, to predict how the BOLD signal changes with activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both sustained positive and negative BOLD signals. We also show that the increase in BOLD signal for a given increase in activity is significantly smaller the larger the baseline activity, as is experimentally observed. Furthermore, the decline of the positive BOLD signal arising from deeper cortical laminae in response to an increase in neural firing is shown to arise as a consequence of the larger baseline activity in deeper laminae. Finally, we provide quantitative relations integrating BOLD responses, energetics, and impulse firing, which among other predictions give the same results as existing theories when the baseline activity is zero. NEW & NOTEWORTHY We use a recently established relation between energetics and neural activity to give a quantitative account of BOLD dependence on neural activity. The incorporation of a nonzero baseline neural activity accounts for positive and negative BOLD signals, shows that changes in neural activity give BOLD changes that are smaller the larger the baseline, and provides a basis for the observed inverse relation between BOLD responses and the depth of cortical laminae giving rise to them.

Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals

Functional MRI has been used to map brain activity and functional connectivity based on the strength and temporal coherence of neurovascular-coupled hemodynamic signals. Here, single-vessel fMRI reveals vessel-specific correlation patterns in both rodents and humans. In anesthetized rats, fluctuations in the vessel-specific fMRI signal are correlated with the intracellular calcium signal measured in neighboring neurons. Further, the blood-oxygen-level-dependent (BOLD) signal from individual venules and the cerebral-blood-volume signal from individual arterioles show correlations at ultra-slow (<0.1 Hz), anesthetic-modulated rhythms. These data support a model that links neuronal activity to intrinsic oscillations in the cerebral vasculature, with a spatial correlation length of ∼2 mm for arterioles. In complementary data from awake human subjects, the BOLD signal is spatially correlated among sulcus veins and specified intracortical veins of the visual cortex at similar ultra-slow rhythms. These data support the use of fMRI to resolve functional connectivity at the level of single vessels.

Partial correlation analysis reveals abnormal retinotopically organized functional connectivity of visual areas in amblyopia

Amblyopia is a prevalent developmental visual disorder of childhood that typically persists in adults. Due to altered visual experience during critical periods of youth, the structure and function of adult visual cortex is abnormal. In addition to substantial deficits shown with task-based fMRI, previous studies have used resting state measures to demonstrate altered long-range connectivity in amblyopia. This is the first study in amblyopia to analyze connectivity between regions of interest that are smaller than a single cortical area and to apply partial correlation analysis to reduce network effects. We specifically assess short-range connectivity between retinotopically defined regions of interest within the occipital lobe of 8 subjects with amblyopia and 7 subjects with normal vision (aged 19–45). The representations of visual areas V1, V2, and V3 within each of the four quadrants of visual space were further subdivided into three regions based on maps of visual field eccentricity. Connectivity between pairs of all nine regions of interest in each quadrant was tested via correlation and partial correlation for both groups. Only the tests of partial correlation, i.e., correlation between time courses of two regions following the regression of time courses from all other regions, yielded significant differences between resting state functional connectivity in amblyopic and normal subjects. Subjects with amblyopia showed significantly higher partial correlation between para-foveal and more eccentric representations within V1, and this effect associated with poor acuity of the worse eye. In addition, we observed reduced correlation in amblyopic subjects between isoeccentricity regions in V1 and V2, and separately, between such regions in V2 and V3. We conclude that partial correlation-based connectivity is altered in an eccentricity-dependent pattern in visual field maps of amblyopic patients. Moreover, results are consistent with known clinical and psychophysical vision loss. More broadly, this provides evidence that abnormal cortical adaptations to disease may be better isolated with tests of partial correlation connectivity than with the regular correlation techniques that are currently widely used.

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Cortical functional connectivity abnormalities exist in amblyopia at a scale finer than previously reported.

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Connectivity changes within primary visual cortex are consistent with known loss of function.

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Connectivity changes between visual areas are consistent with concept of deafferentation.

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Partial correlation differentiates patients from controls, whereas correlation does not.

Spontaneous activity forms a foundation for odor-evoked activation maps in the rat olfactory bulb

Fluctuations in spontaneous activity have been observed by many neuroimaging techniques, but because these resting-state changes are not evoked by stimuli, it is difficult to determine how they relate to task-evoked activations. We conducted multi-modal neuroimaging scans of the rat olfactory bulb, both with and without odor, to examine interaction between spontaneous and evoked activities. Independent component analysis of spontaneous fluctuations revealed resting-state networks, and odor-evoked changes revealed activation maps. We constructed simulated activation maps using resting-state networks that were highly correlated to evoked activation maps. Simulated activation maps derived by intrinsic optical signal (IOS), which covers the dorsal portion of the glomerular sheet, significantly differentiated one odor's evoked activation map from the other two. To test the hypothesis that spontaneous activity of the entire glomerular sheet is relevant for representing odor-evoked activations, we used functional magnetic resonance imaging (fMRI) to map the entire glomerular sheet. In contrast to the IOS results, the fMRI-derived simulated activation maps significantly differentiated all three odors' evoked activation maps. Importantly, no evoked activation maps could be significantly differentiated using simulated activation maps produced using phase-randomized resting-state networks. Given that some highly organized resting-state networks did not correlate with any odors' evoked activation maps, we posit that these resting-state networks may characterize evoked activation maps associated with odors not studied. These results emphasize that fluctuations in spontaneous activity form a foundation for active processing, signifying the relevance of resting-state mapping to functional neuroimaging.

Subcortical evidence for a contribution of arousal to fMRI studies of brain activity

Cortical activity during periods of rest is punctuated by widespread, synchronous events in both electrophysiological and hemodynamic signals, but their behavioral relevance remains unclear. Here we report that these events correspond to momentary drops in cortical arousal and are associated with activity changes in the basal forebrain and thalamus. Combining fMRI and electrophysiology in macaques, we first establish that fMRI transients co-occur with spectral shifts in local field potentials (LFPs) toward low frequencies. Applying this knowledge to fMRI data from the human connectome project, we find that the fMRI transients are strongest in sensory cortices. Surprisingly, the positive cortical transients occur together with negative transients in focal subcortical areas known to be involved with arousal regulation, most notably the basal forebrain. This subcortical involvement, combined with the prototypical pattern of LFP spectral shifts, suggests that commonly observed widespread variations in fMRI cortical activity are associated with momentary drops in arousal.

Resting cortical activity fluctuates, but it is unclear what underlies these variations in activity. Here, the authors show that large-scale fluctuations in fMRI cortical activity are associated with momentary decreases in cortical arousal and opposite activity changes in the basal forebrain and thalamus.

Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder

The ability to predict relapse is a major goal of drug addiction research. Clinical and diagnostic measures are useful in this regard, but these measures do not fully and consistently identify who will relapse and who will remain abstinent. Neuroimaging approaches have the potential to complement these standard clinical measures to optimize relapse prediction. The goal of this review was to survey the existing drug addiction literature that either used a baseline functional or structural neuroimaging phenotype to longitudinally predict a clinical outcome, or that examined test-retest of a neuroimaging phenotype during a course of abstinence or treatment. Results broadly suggested that, relative to individuals who sustained abstinence, individuals who relapsed had (1) enhanced activation to drug-related cues and rewards, but reduced activation to non-drug-related cues and rewards, in multiple corticolimbic and corticostriatal brain regions; (2) weakened functional connectivity of these same corticolimbic and corticostriatal regions; and (3) reduced gray and white matter volume and connectivity in prefrontal regions. Thus, beyond these regions showing baseline group differences, reviewed evidence indicates that function and structure of these regions can prospectively predict - and normalization of these regions can longitudinally track - important clinical outcomes including relapse and adherence to treatment. Future clinical studies can leverage this information to develop novel treatment strategies, and to tailor scarce therapeutic resources toward individuals most susceptible to relapse.

Alteration of Brain Structure With Long-Term Abstinence of Methamphetamine by Voxel-Based Morphometry

Background: A large portion of previous studies that have demonstrated brain gray matter reduction in individuals who use methamphetamine (MA) have focused on short-term abstinence, but few studies have focused on the effects of long-term abstinence of methamphetamine on brain structures. Materials and Methods: Our study includes 40 healthy controls and 44 abstinent methamphetamine-dependent (AMD) subjects who have abstained for at least 14 months. For every AMD subject, the age when they first used MA, the total time of MA use, the frequency of MA use in the last month before abstinence, the duration of abstinence and the craving score were recorded. Here we used magnetic resonance imaging (MRI) to measure the gray matter volume (GMV) of each subject with voxel-based morphometry method. Two-sample t-test (AlphaSim corrected) was performed to obtain brain regions with different gray matter volume (GMV) between groups. In addition, partial correlation coefficients adjusted for age, years of education, smoking, and drinking were calculated in the AMD group to assess associations between the mean GMV values in significant clusters and variables of MA use and abstinence. Results: Compared with the healthy control group, AMD group showed increased gray matter volumes in the bilateral cerebellum and decreased volumes in the right calcarine and right cuneus. Moreover, GMV of left cerebellum are positively correlated with the duration of abstinence in the AMD group (p = 0.040, r = 0.626). Conclusions: The present study showed that the gray matter volume in some brain regions is abnormal in the AMD subjects with long-term abstinence. Changes in gray matter volume of visual and cognitive function regions suggested that these areas play important roles in the progress of MA addiction and abstinence. In addition, positive correlation between GMV of the left cerebellum crus and duration of abstinence suggested that prolonged abstinence is beneficial to cognitive function recovery.

Quenching of spontaneous fluctuations by attention in human visual cortex

In the absence of a task, the human brain enters a mode of slow spontaneous fluctuations. A fundamental, unresolved question is whether these fluctuations are ongoing and thus persist during task engagement, or alternatively, are quenched and replaced by task-related activations. Here, we examined this issue in the human visual cortex, using fMRI. Participants were asked to either perform a recognition task of randomly appearing face and non-face targets (attended condition) or watch them passively (unattended condition). Importantly, in approximately half of the trials, all sensory stimuli were absent. Our results show that even in the absence of stimuli, spontaneous fluctuations were suppressed by attention. The effect occurred in early visual cortex as well as in fronto-parietal attention network regions. During unattended trials, the activity fluctuations were negatively linked to pupil diameter, arguing against attentional fluctuations as underlying the effect. The results demonstrate that spontaneous fluctuations do not remain unchanged with task performance, but are rather modulated according to behavioral and cognitive demands.

Morphological and Functional Changes of Locus Coeruleus in Patients with Primary Open-Angle Glaucoma and Animal Models

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness. Magnetic resonance imaging (MRI) studies report an association between POAG and nonvisual pathway alterations. Locus coeruleus (LC) is a major source of norepinephrine released in the brain, and norepinephrine can reduce intraocular pressure via increasing aqueous outflow. This study aimed to investigate the relationship between glaucoma and the LC in patients with POAG and animal models. Resting-state functional MRI was performed using a 3-Tesla MR scanner with an eight-channel phased-array head coil, and MRI data were analyzed. A rat model of chronic glaucoma was generated by episcleral vein ligation and cauterization. DBA/2J mice that develop glaucoma with age were also acquired. Immunohistochemistry and immunofluorescence staining were used to investigate LC tyrosine hydroxylase (TH) and dopamine β-hydroxylase (DβH) expression, as well as cell apoptosis by terminal deoxynucleotidyl transferase dUTP Nick-End Labeling staining. Patients with POAG showed significantly increased amplitude of low-frequency fluctuation (ALFF) in the LC compared with controls. LC ALFF values showed significant correlations with cup-to-disk ratio, retinal nerve fiber layer thickness, and visual function (P < 0.05). Functional connectivity (FC) between the LC and frontal and insular lobes was reduced, but elevated between the LC and parahippocampal gyrus, compared with controls. Glaucoma animal models revealed reduced expression of TH and DβH in the LC, and increased cell apoptosis. In this study, we provide novel evidence for the relationship between the LC and glaucoma. The LC may act as a network in POAG pathogenesis and intraocular pressure regulation.

Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance

Spontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These "resting state networks" (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a nonstationary process which could be described as state switching. In this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal. We find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also report some interesting characteristics that clearly support nonstationary features in the data. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.

Macroscale variation in resting-state neuronal activity and connectivity assessed by simultaneous calcium imaging, hemodynamic imaging and electrophysiology

Functional imaging of spontaneous activity continues to play an important role in the field of connectomics. The most common imaging signal used for these experiments is the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal, but how this signal relates to spontaneous neuronal activity remains incompletely understood. Genetically encoded calcium indicators represent a promising tool to study this problem, as they can provide brain-wide measurements of neuronal activity compared to point measurements afforded by electrophysiological recordings. However, the relationship between the calcium signal and neurophysiological parameters at the mesoscopic scale requires further systematic characterization. Therefore, we collected simultaneous resting-state measurements of electrophysiology, along with calcium and hemodynamic imaging, in lightly anesthetized mice to investigate two aims. First, we examined the relationship between each imaging signal and the simultaneously recorded electrophysiological signal in a single brain region, finding that both signals are better correlated with multi-unit activity compared to local field potentials, with the calcium signal possessing greater signal-to-noise ratio and regional specificity. Second, we used the resting-state imaging data to model the relationship between the calcium and hemodynamic signals across the brain. We found that this relationship varied across brain regions in a way that is consistent across animals, with delays increasing by600 ms towards posterior cortical regions. Furthermore, while overall functional connectivity (FC) measured by the hemodynamic signal is significantly correlated with FC measured by calcium, the two estimates were found to be significantly different. We hypothesize that these differences arise at least in part from the observed regional variation in the hemodynamic response. In total, this work highlights some of the caveats needed in interpreting hemodynamic-based measurements of FC, as well as the need for improved modeling methods to reduce this potential source of bias.

[Neuronet restructuring in focal and generalized epilepsy according to resting state fMRI]

AIM

To compare neuronet restructuring in focal and generalized epilepsy.

MATERIAL AND METHODS

Seventy-seven patients, aged from 18 to 65 years, with the diagnosis of epilepsy, including 63 patients with focal epilepsy and 14 with generalized epilepsy, were examined. A control group included 23 healthy people. Neuronet restructuring was studied using fMRI.

RESULTS AND CONCLUSION

According to resting state fMRI, there were between-group differences in spatial organization (activity map) of the brain structures as well as in the results of cross-correlation analysis of interaction maps of resting-state networks. It has been concluded that functional restructuring in connectomes in focal and generalized epilepsy have the opposite patterns of disorganization (toward increase or decrease) in most structures studied though there are structures with the same direction of connectivity changes.

What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging

When we consider all of the methods we employ to detect brain function, from electrophysiology to optical techniques to functional magnetic resonance imaging (fMRI), we do not really have a 'golden technique' that meets all of the needs for studying the brain. We have methods, each of which has significant limitations but provide often complimentary information. Clearly, there are many questions that need to be answered about fMRI, which unlike other methods, allows us to study the human brain. However, there are also extraordinary accomplishments or demonstration of the feasibility of reaching new and previously unexpected scales of function in the human brain. This article reviews some of the work we have pursued, often with extensive collaborations with other co-workers, towards understanding the underlying mechanisms of the methodology, defining its limitations, and developing solutions to advance it. No doubt, our knowledge of human brain function has vastly expanded since the introduction of fMRI. However, methods and instrumentation in this dynamic field have evolved to a state that discoveries about the human brain based on fMRI principles, together with information garnered at a much finer spatial and temporal scale through other methods, are poised to significantly accelerate in the next decade.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses.

When a person is in a deep non-dreaming sleep, neurons in their brain alternate slowly between periods of silence and periods of activity. This gives rise to low-frequency brain rhythms called slow waves, which are thought to help stabilize memories. Slow wave activity can be detected on multiple scales, from the pattern of electrical impulses sent by an individual neuron to the collective activity of the brain’s entire outer layer, the cortex. But does slow wave activity in an individual group of neurons in the cortex affect the activity of the rest of the brain?

To find out, Schwalm, Schmid, Wachsmuth et al. took advantage of the fact that slow waves also occur under general anesthesia, and placed anesthetized rats inside miniature whole-brain scanners. A small region of cortex in each rat had been injected with a dye that fluoresces whenever the neurons in that region are active. An optical fiber was lowered into the rat’s brain to transmit the fluorescence signals to a computer. Monitoring these signals while the animals lay inside the scanner revealed that slow-wave activity in any one group of cortical neurons was accompanied by slow-wave activity across the cortex as a whole. This relationship was seen only for slow waves, and not for other brain rhythms.

Slow waves seem to occur in all species of animal with a backbone, and in both healthy and diseased brains. While it is not possible to inject fluorescent dyes into the human brain, it is possible to monitor neuronal activity using electrodes. Comparing local electrode recordings with measures of whole-brain activity from scanners could thus allow similar experiments to be performed in people. There is growing evidence – from animal models and from studies of patients – that slow waves may be altered in Alzheimer’s disease. Further work is required to determine whether detecting these changes could help diagnose disease at earlier stages, and whether reversing them may have therapeutic potential.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain’s inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance.

Neural and metabolic basis of dynamic resting state fMRI

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.