Neurovascular Uncoupling: Much Ado about Nothing

Identification of functional white matter networks in BOLD fMRI

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well-defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

Identification of functional white matter networks in BOLD fMRI

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

Functional MRI applications for intra-axial brain tumours: uses and nuances in surgical practise

PURPOSE

Functional MRI (fMRI) has well-established uses to inform risks and plan maximally safe approaches in neurosurgery. In the field of brain tumour surgery, however, fMRI is currently in a state of clinical equipoise due to debate around both its sensitivity and specificity.

MATERIALS AND METHODS

In this review, we summarise the role and our experience of fMRI in neurosurgery for gliomas and metastases. We discuss nuances in the conduct and interpretation of fMRI that, based on our practise, most directly impact fMRI's usefulness in the neurosurgical setting.

RESULTS

Illustrated examples in which fMRI in our hands directly influences the neurosurgical treatment of brain tumours include evaluating the probability and nature of functional risks, especially for language functions. These presurgical risk assessments, in turn, help to predict the resectability of tumours, select or deselect patients for awake surgery, indicate the need for neurophysiological monitoring and guide the optimal use of intra-operative stimulation mapping. A further emerging application of fMRI is in measuring functional adaptation of functional networks after (partial) surgery, of potential use in the timing of further surgery.

CONCLUSIONS

In appropriately selected patients with a clearly defined surgical question, fMRI offers a valuable complementary tool in the pre-surgical evaluation of brain tumours. However, there is a great need for standards in the administration and analysis of fMRI as much as in the techniques that it is commonly evaluated against. Surprisingly little data exists that evaluates the accuracy of fMRI not just against complementary methods, but in terms of its ultimate clinical aim of minimising post-surgical morbidity.

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas

Functional magnetic resonance imaging (fMRI) with blood oxygen level-dependent (BOLD) technique is useful for preoperative mapping of brain functional networks in tumor patients, providing reliable in vivo detection of eloquent cortex to help reduce the risk of postsurgical morbidity. BOLD task-based fMRI (tb-fMRI) is the most often used noninvasive method that can reliably map cortical networks, including those associated with sensorimotor, language, and visual functions. BOLD resting-state fMRI (rs-fMRI) is emerging as a promising ancillary tool for visualization of diverse functional networks. Although fMRI is a powerful tool that can be used as an adjunct for brain tumor surgery planning, it has some constraints that should be taken into consideration for proper clinical interpretation. BOLD fMRI interpretation may be limited by neurovascular uncoupling (NVU) induced by brain tumors. Cerebrovascular reactivity (CVR) mapping obtained using breath-hold methods is an effective method for evaluating NVU potential.

Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI

The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.

Visualizing advances in the future of primate neuroscience research

Future neuroscience and biomedical projects involving non-human primates (NHPs) remain essential in our endeavors to understand the complexities and functioning of the mammalian central nervous system. In so doing, the NHP neuroscience researcher must be allowed to incorporate state-of-the-art technologies, including the use of novel viral vectors, gene therapy and transgenic approaches to answer continuing and emerging research questions that can only be addressed in NHP research models. This perspective piece captures these emerging technologies and some specific research questions they can address. At the same time, we highlight some current caveats to global NHP research and collaborations including the lack of common ethical and regulatory frameworks for NHP research, the limitations involving animal transportation and exports, and the ongoing influence of activist groups opposed to NHP research.

Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.

Size of the spatial correlation between ECoG and fMRI activity

Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode. Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter. We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB. In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.

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.

[123I]-IMP single-photon emission computed tomography imaging in visual snow syndrome: A case series

BACKGROUND

Visual snow syndrome (VSS) is a neurological condition characterized by persistent flickering dots in the visual fields, palinopsia, enhanced entoptic phenomenon, photophobia, and nyctalopia. Neuroimaging evidence supports the role of the visual association cortex in visual snow syndrome.Case series: We provided clinical care to three patients with visual snow syndrome, in whom [¹²³I]-IMP single-photon emission computed tomography (SPECT) imaging was performed. Case 1 was a 21-year-old male with a past history of migraine with aura who exhibited visual snow and entoptic phenomenon. In this patient, [¹²³I]-IMP SPECT imaging revealed right occipital and temporal hypoperfusion with a distribution matching the ventral visual stream. [¹²³I]-IMP SPECT imaging detected only mild bilateral frontal hypoperfusion in Case 2 and no overt abnormalities in Case 3.

CONCLUSION

Although visual snow syndrome seems to be a heterogenous condition, our observations indicate that abnormal visual processing within the ventral visual stream may play a role in the pathogenesis of this condition.

Disrupted Regional Cerebral Blood Flow, Functional Activity and Connectivity in Alzheimer's Disease: A Combined ASL Perfusion and Resting State fMRI Study

Recent studies have demonstrated a close relationship between regional cerebral blood flow (rCBF) and resting state functional connectivity changes in normal healthy people. However, little is known about the parameter changes in the most vulnerable regions in Alzheimer's disease (AD). Forty AD patients and 30 healthy controls participated in this study. The data of resting-state perfusion and functional magnetic resonance imaging (fMRI) was collected. By using voxel-wise arterial spin labeling (ASL) perfusion, we identified several regions of altered rCBF in AD patients. Then, by using resting state fMRI analysis, including amplitude low frequency fluctuation (ALFF) and seed-based functional connectivity, we investigated the changes of functional activity and connectivity among the identified rCBF regions. We extracted cognition-related parameters and searched for a sensitive biomarker to differentiate the AD patients from the normal controls (NC). Compared with controls, AD patients showed special disruptions in rCBF, which were mainly located in the left posterior cingulate cortex (PCC), the left and right dorsolateral prefrontal cortex (DLPFC), the left inferior parietal lobule (IPL), the right middle temporal gyrus (MTG), the left middle occipital gyrus (MOG), and the left precuneus (PCu). ALFF was performed based on the seven regions identified by the ASL method, and AD patients presented significantly decreased ALFF in the left PCC, left IPL, right MTG, left MOG, and left PCu and increased ALFF in the bilateral DLPFC. We constituted the network based on the seven regions and found that there was decreased connectivity among the identified regions in the AD patients, which predicted a disruption in the default mode network (DMN), executive control network (ECN) and visual network (VN). Furthermore, these abnormal parameters are closely associated with cognitive performances in AD patients. We combined the rCBF and ALFF value of PCC/PCu as a biomarker to differentiate the two groups and reached a sensitivity of 85.3% and a specificity of 88.5%. Our findings suggested that there was disrupted rCBF, functional activity and connectivity in specific cognition-related regions in Alzheimer's disease, which can be used as a valuable imaging biomarker for the diagnosis of AD.

BOLD hemodynamic response function changes significantly with healthy aging

Functional magnetic resonance imaging (fMRI) has been used to infer age-differences in neural activity from the hemodynamic response function (HRF) that characterizes the blood-oxygen-level-dependent (BOLD) signal over time. BOLD literature in healthy aging lacks consensus in age-related HRF changes, the nature of those changes, and their implications for measurement of age differences in brain function. Between-study discrepancies could be due to small sample sizes, analysis techniques, and/or physiologic mechanisms. We hypothesize that, with large sample sizes and minimal analysis assumptions, age-related changes in HRF parameters could reflect alterations in one or more components of the neural-vascular coupling system. To assess HRF changes in healthy aging, we analyzed the large population-derived dataset from the Cambridge Center for Aging and Neuroscience (CamCAN) study (Shafto et al., 2014). During scanning, 74 younger (18-30 years of age) and 173 older participants (54-74 years of age) viewed two checkerboards to the left and right of a central fixation point, simultaneously heard a binaural tone, and responded via right index finger button-press. To assess differences in the shape of the HRF between younger and older groups, HRFs were estimated using FMRIB's Linear Optimal Basis Sets (FLOBS) to minimize a priori shape assumptions. Group mean HRFs were different between younger and older groups in auditory, visual, and motor cortices. Specifically, we observed increased time-to-peak and decreased peak amplitude in older compared to younger adults in auditory, visual, and motor cortices. Changes in the shape and timing of the HRF in healthy aging, in the absence of performance differences, support our hypothesis of age-related changes in the neural-vascular coupling system beyond neural activity alone. More precise interpretations of HRF age-differences can be formulated once these physiologic factors are disentangled and measured separately.

Long-term optical imaging of neurovascular coupling in mouse cortex using GCaMP6f and intrinsic hemodynamic signals

Cerebral hemodynamics are modulated in response to changes in neuronal activity, a process termed neurovascular coupling (NVC), which can be disrupted by neuropsychiatric diseases (e.g., stroke, Alzheimer's disease). Thus, there is growing interest to image long-term NVC dynamics with high spatiotemporal resolutions. Here, by combining the use of a genetically-encoded calcium indicator with optical techniques, we develop a longitudinal multimodal optical imaging platform (MIP) that enabled time-lapse tracking of NVC over a relatively large field of view in the mouse somatosensory cortex at single cell and single vessel resolutions. Specifically, GCaMP6f was used as marker of neuronal activity, which along with MIP allowed us to simultaneously measure the changes in neuronal [Ca²⁺]_i fluorescence, cerebral blood flow velocity (CBFv) and hemodynamics longitudinally for more than eight weeks. We show that [Ca²⁺]_i fluorescence was detectable one week post viral injection and the damage to local microvasculature and perfusion recovered two weeks after injection. By three weeks post viral injection, maximal neuronal and CBFv responses to hindpaw stimulations were observed. Moreover, single neuronal activation in response to hindpaw stimulation was consistently recorded, followed by ∼2 s delayed dilation of contiguous microvessels. Additionally, resting-state spontaneous neuronal and hemodynamic oscillations were detectable throughout the eight weeks of study. Our results demonstrate the capability of MIP for longitudinal investigation of the organization and plasticity of the neurovascular network during resting state and during stimulation-evoked neuronal activation at high spatiotemporal resolutions.

Simultaneously estimating the task-related and stimulus-evoked components of hemodynamic imaging measurements

Task-related hemodynamic responses contribute prominently to functional magnetic resonance imaging (fMRI) recordings. They reflect behaviorally important brain states, such as arousal and attention, and can dominate stimulus-evoked responses, yet they remain poorly understood. To help characterize these responses, we present a method for parametrically estimating both stimulus-evoked and task-related components of hemodynamic responses from subjects engaged in temporally predictable tasks. The stimulus-evoked component is modeled by convolving a hemodynamic response function (HRF) kernel with spiking. The task-related component is modeled by convolving a Fourier-series task-related function (TRF) kernel with task timing. We fit this model with simultaneous electrode recordings and intrinsic-signal optical imaging from the primary visual cortex of alert, task-engaged monkeys. With high [Formula: see text], the model returns HRFs that are consistent across experiments and recording sites for a given animal and TRFs that entrain to task timing independent of stimulation or local spiking. When the task schedule conflicts with that of stimulation, the TRF remains locked to the task emphasizing its behavioral origins. The current approach is strikingly more robust to fluctuations than earlier ones and gives consistently, if modestly, better fits. This approach could help parse the distinct components of fMRI recordings made in the context of a task.

Brain imaging of neurovascular dysfunction in Alzheimer's disease

Neurovascular dysfunction, including blood-brain barrier (BBB) breakdown and cerebral blood flow (CBF) dysregulation and reduction, are increasingly recognized to contribute to Alzheimer's disease (AD). The spatial and temporal relationships between different pathophysiological events during preclinical stages of AD, including cerebrovascular dysfunction and pathology, amyloid and tau pathology, and brain structural and functional changes remain, however, still unclear. Recent advances in neuroimaging techniques, i.e., magnetic resonance imaging (MRI) and positron emission tomography (PET), offer new possibilities to understand how the human brain works in health and disease. This includes methods to detect subtle regional changes in the cerebrovascular system integrity. Here, we focus on the neurovascular imaging techniques to evaluate regional BBB permeability (dynamic contrast-enhanced MRI), regional CBF changes (arterial spin labeling- and functional-MRI), vascular pathology (structural MRI), and cerebral metabolism (PET) in the living human brain, and examine how they can inform about neurovascular dysfunction and vascular pathophysiology in dementia and AD. Altogether, these neuroimaging approaches will continue to elucidate the spatio-temporal progression of vascular and neurodegenerative processes in dementia and AD and how they relate to each other.

Coupling mechanism and significance of the BOLD signal: a status report

Functional magnetic resonance imaging (fMRI) provides a unique view of the working human mind. The blood-oxygen-level-dependent (BOLD) signal, detected in fMRI, reflects changes in deoxyhemoglobin driven by localized changes in brain blood flow and blood oxygenation, which are coupled to underlying neuronal activity by a process termed neurovascular coupling. Over the past 10 years, a range of cellular mechanisms, including astrocytes, pericytes, and interneurons, have been proposed to play a role in functional neurovascular coupling. However, the field remains conflicted over the relative importance of each process, while key spatiotemporal features of BOLD response remain unexplained. Here, we review current candidate neurovascular coupling mechanisms and propose that previously overlooked involvement of the vascular endothelium may provide a more complete picture of how blood flow is controlled in the brain. We also explore the possibility and consequences of conditions in which neurovascular coupling may be altered, including during postnatal development, pathological states, and aging, noting relevance to both stimulus-evoked and resting-state fMRI studies.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG-fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG-fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG-fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations.

Coupling between pupil fluctuations and resting-state fMRI uncovers a slow build-up of antagonistic responses in the human cortex

Even in absence of overt tasks, the human cortex manifests rich patterns of spontaneous "resting state" BOLD-fMRI fluctuations. However, the link of these spontaneous fluctuations to behavior is presently unclear. Attempts to directly investigate this link invariably lead to disruptions of the resting state. Here we took advantage of the well-established association between pupil diameter and attentional gain to address this issue by examining the correlation between the resting state BOLD and pupil fluctuations. Our results uncover a spontaneously emerging spatiotemporal pupil-BOLD correlation whereby a slow buildup of activity in default mode areas preceded both pupil dilation and wide-spread BOLD suppression in sensorimotor cortex. Control experiments excluded a role for luminance fluctuations or fixation. Comparing the pupil-correlated patterns to activation maps during visual imagery revealed a substantial overlap. Our results indicate a link between behavior, as indexed by pupil diameter, and resting state BOLD fluctuations. These pupil dilations, assumed to be related to attentional gain, were associated with spontaneously emerging antagonism between fundamental cortical networks.

General overview on the merits of multimodal neuroimaging data fusion

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.

A universal role of the ventral striatum in reward-based learning: evidence from human studies

Reinforcement learning enables organisms to adjust their behavior in order to maximize rewards. Electrophysiological recordings of dopaminergic midbrain neurons have shown that they code the difference between actual and predicted rewards, i.e., the reward prediction error, in many species. This error signal is conveyed to both the striatum and cortical areas and is thought to play a central role in learning to optimize behavior. However, in human daily life rewards are diverse and often only indirect feedback is available. Here we explore the range of rewards that are processed by the dopaminergic system in human participants, and examine whether it is also involved in learning in the absence of explicit rewards. While results from electrophysiological recordings in humans are sparse, evidence linking dopaminergic activity to the metabolic signal recorded from the midbrain and striatum with functional magnetic resonance imaging (fMRI) is available. Results from fMRI studies suggest that the human ventral striatum (VS) receives valuation information for a diverse set of rewarding stimuli. These range from simple primary reinforcers such as juice rewards over abstract social rewards to internally generated signals on perceived correctness, suggesting that the VS is involved in learning from trial-and-error irrespective of the specific nature of provided rewards. In addition, we summarize evidence that the VS can also be implicated when learning from observing others, and in tasks that go beyond simple stimulus-action-outcome learning, indicating that the reward system is also recruited in more complex learning tasks.

Negative cerebral blood volume fMRI response coupled with Ca²⁺-dependent brain activity in a dopaminergic road map of nociception

Decreased cerebral blood volume/flow (CBV/CBF) contributes to negative blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signals. But it is still strongly debated whether these negative BOLD or CBV/CBF signals are indicative of decreased or increased neuronal activity. The fidelity of Ca(2+) signals in reflecting neuronal excitation is well documented. However, the roles of Ca(2+) signals and Ca(2+)-dependent activity in negative fMRI signals have never been explored; an understanding of this is essential to unraveling the underlying mechanisms and correctly interpreting the hemodynamic response of interest. The present study utilized a nociception-induced negative CBV fMRI response as a model. Ca(2+) signals were investigated in vivo using Mn(2+)-enhanced MRI (MEMRI), and the downstream Ca(2+)-dependent signaling was investigated using phosphorylated cAMP response-element-binding (pCREB) immunohistology. The results showed that nociceptive stimulation led to (1) striatal CBV decreases, (2) Ca(2+) increases via the nigrostriatal pathway, and (3) substantial expression of pCREB in substantia nigra dopaminergic neurons and striatal neurons. Interestingly, the striatal negative fMRI response was abolished by blocking substantia nigra activity but was not affected by blocking the striatal activity. This suggests the importance of input activity other than output in triggering the negative CBV signals. These findings indicate that the striatal negative CBV fMRI signals are associated with Ca(2+) increases and Ca(2+)-dependent signaling along the nigrostriatal pathway. The obtained data reveal a new brain road map in response to nociceptive stimulation of hemodynamic changes in association with Ca(2+) signals within the dopaminergic system.

Optical imaging of visually guided reaching in macaque posterior parietal cortex

Sensorimotor transformation for reaching movements in primates requires a large network of visual, parietal, and frontal cortical areas. We performed intrinsic optical imaging over posterior parietal cortex including areas 7a and the dorsal perilunate in macaque monkeys during visually guided hand movements. Reaching was performed while foveating one of nine static reach targets; thus eye-position-varied concurrently with reach position. The hemodynamic reflectance signal was analyzed during specific phases of the task including pre-reach, reach, and touch epochs. The eye position maps changed substantially as the task progressed: First, direction of spatial tuning shifted from a weak preference close to the center to the lower eye positions in both cortical areas. Overall tuning strength was greater in area 7a. Second, strength of spatial tuning increased from the early pre-reach to the later touch epoch. These consistent temporal changes suggest that dynamic properties of the reflectance signal were modulated by task parameters. The peak amplitude and peak delay of the reflectance signal showed considerable differences between eye position but were similar between areas. Compared with a detection task using a lever response, the reach task yielded higher amplitudes and longer delays. These findings demonstrate a spatially tuned topographical representation for reaching in both areas and suggest a strong synergistic combination of various feedback signals that result in a spatially tuned amplification of the hemodynamic response in posterior parietal cortex.

Intracortical recordings and fMRI: an attempt to study operational modules and networks simultaneously

The brain can be envisaged as a complex adaptive system. It is characterized by a very high structural complexity and by massive connectivity, both of which change and evolve in response to experience. Information related to sensors and effectors is processed in both a parallel and a hierarchical fashion; the connectivity between different hierarchical levels is bidirectional, and its effectiveness is continuously controlled by specific associational and neuromodulatory centers. When questions are addressed at the level of a distributed, large-scale whole system such as that underlying perception and cognition, it is not clear what should be considered as an elementary operational unit because the behavior of integral, aggregate systems is always emergent and most often remains unpredicted by the behaviors of single cells. To localize and comprehend the neural mechanisms underlying our perceptual or cognitive capacities, concurrent studies of microcircuits, of local and long-range interconnectivity between small assemblies, and of the synergistic activity of larger neuronal populations are called for. In other words, multimodal methodologies that include invasive neuroscientific methods as well as global neuroimaging techniques are required, such as the various functional aspects of magnetic resonance imaging. These facts were the driving force behind the decision to begin animal-MRI in my lab. The wonderful idea of the editors of NeuroImage to publish a Special Issue commemorating 20years of functional fMRI provides me with the opportunity of sharing not only our first moments of frustration with the readers, but also our successful results.

Neural and BOLD responses across the brain

Functional Magnetic Resonance Imaging (fMRI) has quickly grown into one of the most important tools for studying brain function, especially in humans. Despite its prevalence, we still do not have a clear picture of what exactly the blood oxygenation level dependent (BOLD) signal represents or how it compares to the signals obtained with other methods (e.g., electrophysiology). We particularly refer to single neuron recordings and electroencephalography when we mention 'electrophysiological methods', given that these methods have been used for more than 50 years, and have formed the basis of much of our current understanding of brain function. Brain function involves the coordinated activity of many different areas and many different cell types that can participate in an enormous variety of processes (neural firing, inhibitory and excitatory synaptic activity, neuromodulation, oscillatory activity, etc.). Of these cells and processes, only a subset is sampled with electrophysiological techniques, and their contribution to the recorded signals is not exactly known. Functional imaging signals are driven by the metabolic needs of the active cells, and are most likely also biased toward certain cell types and certain neural processes, although we know even less about which processes actually drive the hemodynamic response. This article discusses the current status on the interpretation of the BOLD signal and how it relates to neural activity measured with electrophysiological techniques. WIREs Cogn Sci 2012, 3:75-86. doi: 10.1002/wcs.153 For further resources related to this article, please visit the WIREs website.