Overview of functional magnetic resonance imaging

A Novel Approach to Evaluate Brain Activation for Lower Extremity Motor Control

BACKGROUND AND PURPOSE

The purpose of this study was to assess the consistency of a novel MR safe lower extremity motor control neuroimaging paradigm to elicit reliable sensorimotor region brain activity.

METHODS

Participants completed multiple sets of unilateral leg presses combining ankle, knee, and hip extension and flexion movements against resistance at a pace of 1.2 Hz while lying supine in a 3T MRI scanner. Regions of Interest (ROI) consisted of regions primarily involved in lower extremity motor control (right and left primary motor cortex, primary somatosensory cortex, premotor cortex, secondary somatosensory cortex, basal ganglia, and the cerebellum).

RESULTS

The group analysis based on mixed effects paired samples t-test revealed no differences for brain activity between sessions (P > .05). Intraclass correlation coefficients in the sensorimotor regions were good to excellent for average percent signal change (.621 to .918) and Z-score (.697 to .883), with the exception of the left secondary somatosensory cortex percent signal change (.165).

CONCLUSIONS

These results indicate that a loaded lower extremity force production and attenuation task that simulates the range of motion of squatting, stepping, and landing from a jump is reliable for longitudinal neuroimaging applications and support the use of this paradigm in further studies examining therapeutic interventions and changes in dynamic lower extremity motor function.

Evaluation of Task-Related Brain Activity: Is There a Role for 18F FDG-PET Imaging?

Positron emission tomography (PET) with 2-[18F]-fluorodeoxyglucose (FDG) has been widely used for the evaluation of cortical glucose metabolism in several neurodegenerative disorders while its potential role in the evaluation of cortical and subcortical activity during a task in the healthy and pathological brain still remains to be a matter of debate. Few studies have been carried out in order to investigate the potential role of this radiotracer for the evaluation of brain glucose consumption during dynamic brain activation. The aim of this review is to provide a general overview of the applications of FDG-PET in the evaluation of cortical activation at rest and during tasks, describing first the physiological basis of FDG distribution in brain and its kinetic in vivo. An overview of the imaging protocols and image interpretation will be provided as well. As a last aspect, the results of the main studies in this field will be summarized and the results of PET findings performed in healthy subjects and patients suffering from various diseases will be reported.

Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music

The ability of music to evoke activity changes in the core brain structures that underlie the experience of emotion suggests that it has the potential to be used in therapies for emotion disorders. A large volume of research has identified a network of sub-cortical brain regions underlying music-induced emotions. Additionally, separate evidence from electroencephalography (EEG) studies suggests that prefrontal asymmetry in the EEG reflects the approach-withdrawal response to music-induced emotion. However, fMRI and EEG measure quite different brain processes and we do not have a detailed understanding of the functional relationships between them in relation to music-induced emotion. We employ a joint EEG – fMRI paradigm to explore how EEG-based neural correlates of the approach-withdrawal response to music reflect activity changes in the sub-cortical emotional response network. The neural correlates examined are asymmetry in the prefrontal EEG, and the degree of disorder in that asymmetry over time, as measured by entropy. Participants’ EEG and fMRI were recorded simultaneously while the participants listened to music that had been specifically generated to target the elicitation of a wide range of affective states. While listening to this music, participants also continuously reported their felt affective states. Here we report on co-variations in the dynamics of these self-reports, the EEG, and the sub-cortical brain activity. We find that a set of sub-cortical brain regions in the emotional response network exhibits activity that significantly relates to prefrontal EEG asymmetry. Specifically, EEG in the pre-frontal cortex reflects not only cortical activity, but also changes in activity in the amygdala, posterior temporal cortex, and cerebellum. We also find that, while the magnitude of the asymmetry reflects activity in parts of the limbic and paralimbic systems, the entropy of that asymmetry reflects activity in parts of the autonomic response network such as the auditory cortex. This suggests that asymmetry magnitude reflects affective responses to music, while asymmetry entropy reflects autonomic responses to music. Thus, we demonstrate that it is possible to infer activity in the limbic and paralimbic systems from pre-frontal EEG asymmetry. These results show how EEG can be used to measure and monitor changes in the limbic and paralimbic systems. Specifically, they suggest that EEG asymmetry acts as an indicator of sub-cortical changes in activity induced by music. This shows that EEG may be used as a measure of the effectiveness of music therapy to evoke changes in activity in the sub-cortical emotion response network. This is also the first time that the activity of sub-cortical regions, normally considered “invisible” to EEG, has been shown to be characterisable directly from EEG dynamics measured during music listening.

Modeling of the BOLD signal using event-related simultaneous EEG-fMRI and convolutional sparse coding analysis

In this work, we employ simultaneous EEG-fMRI data acquired during a visually-guided attention task along with convolutional sparse coding (CSC) analysis to extract transient events from the EEG. Subsequently, we use these events in a standard voxel-wise fMRI analysis and compare the resultant activation maps with maps obtained using the subjects' response time (RT) in detection of visual target stimuli. We also employ FIR models to obtain HRF estimates using the detected CSC events. Our results show concordance between the resultant activation maps and consistent HRF shapes for most of the subjects, suggesting that CSC can be used as a tool for the detection of reliable events in the EEG.

Data-based intervention approach for Complexity-Causality measure

Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of 'cause' and 'effect' between well separated samples. In real-world processes, often 'cause' and 'effect' are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to the presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.

A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

The neural markers of MRI to differentiate depression and panic disorder

Depression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.

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

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

Neurophysiological markers of network dysfunction in neurodegenerative diseases

There is strong clinical, imaging and pathological evidence that neurodegeneration is associated with altered brain connectivity. While functional imaging (fMRI) can detect resting and activated states of metabolic activity, its use is limited by poor temporal resolution, cost and confounding vascular parameters. By contrast, electrophysiological (e.g. EEG/MEG) recordings provide direct measures of neural activity with excellent temporal resolution, and source localization methodologies can address problems of spatial resolution, permitting measurement of functional activity of brain networks with a spatial resolution similar to that of fMRI. This opens an exciting therapeutic approach focussed on pharmacological and physiological modulation of brain network activity. This review describes current neurophysiological approaches towards evaluating cortical network dysfunction in common neurodegenerative disorders. It explores how modern neurophysiologic tools can provide markers for diagnosis, prognosis, subcategorization and clinical trial outcome measures, and how modulation of brain networks can contribute to new therapeutic approaches.

GEOFFREY HARRIS PRIZE LECTURE 2018: Novel pathways regulating neuroendocrine function, energy homeostasis and metabolism in humans

The discovery of leptin, an adipocyte-secreted hormone, set the stage for unraveling the mechanisms dictating energy homeostasis, revealing adipose tissue as an endocrine system that regulates appetite and body weight. Fluctuating leptin levels provide molecular signals to the brain regarding available energy reserves modulating energy homeostasis and neuroendocrine response in states of leptin deficiency and to a lesser extent in hyperleptinemic states. While leptin replacement therapy fails to provide substantial benefit in common obesity, it is an effective treatment for congenital leptin deficiency and states of acquired leptin deficiency such as lipodystrophy. Current evidence suggests that regulation of eating behavior in humans is not limited to homeostatic mechanisms and that the reward, attention, memory and emotion systems are involved, participating in a complex central nervous system network. It is critical to study these systems for the treatment of typical obesity. Although progress has been made, further studies are required to unravel the physiology, pathophysiology and neurobehavioral mechanisms underlying potential treatments for weight-related problems in humans.

Diagnostic and prognostic biomarkers for HAND

In 2007, the nosology for HIV-1-associated neurocognitive disorders (HAND) was updated to a primarily neurocognitive disorder. However, currently available diagnostic tools lack the sensitivity and specificity needed for an accurate diagnosis for HAND. Scientists and clinicians, therefore, have been on a quest for an innovative biomarker to diagnose (i.e., diagnostic biomarker) and/or predict (i.e., prognostic biomarker) the progression of HAND in the post-combination antiretroviral therapy (cART) era. The present review examined the utility and challenges of four proposed biomarkers, including neurofilament light (NFL) chain concentration, amyloid (i.e., sAPPα, sAPPβ, amyloid β) and tau proteins (i.e., total tau, phosphorylated tau), resting-state functional magnetic resonance imaging (fMRI), and prepulse inhibition (PPI). Although significant genotypic differences have been observed in NFL chain concentration, sAPPα, sAPPβ, amyloid β, total tau, phosphorylated tau, and resting-state fMRI, inconsistencies and/or assessment limitations (e.g., invasive procedures, lack of disease specificity, cost) challenge their utility as a diagnostic and/or prognostic biomarker for milder forms of neurocognitive impairment (NCI) in the post-cART era. However, critical evaluation of the literature supports the utility of PPI as a powerful diagnostic biomarker with high accuracy (i.e., 86.7-97.1%), sensitivity (i.e., 89.3-100%), and specificity (i.e., 79.5-94.1%). Additionally, the inclusion of multiple CSF and/or plasma markers, rather than a single protein, may provide a more sensitive diagnostic biomarker for HAND; however, a pressing need for additional research remains. Most notably, PPI may serve as a prognostic biomarker for milder forms of NCI, evidenced by its ability to predict later NCI in higher-order cognitive domains with regression coefficients (i.e., r) greater than 0.8. Thus, PPI heralds an opportunity for the development of a brief, noninvasive diagnostic and promising prognostic biomarker for milder forms of NCI in the post-cART era.

Working with Hypoxia

A hypoxic environment can be defined as a region of the body or the whole body that is deprived of oxygen. Hypoxia is a feature of many diseases, such as cardiovascular disease, tissue trauma, stroke, and solid cancers. A loss of oxygen supply usually results in cell death; however, when cells gradually become hypoxic, they may survive and continue to thrive as described for conditions that promote metastatic growth. The role of hypoxia in these pathogenic pathways is therefore of great interest, and understanding the effect of hypoxia in regulating these mechanisms is fundamentally important. This chapter gives an extensive overview of these mechanisms. Moreover, given the challenges posed by tumor hypoxia we describe the current methods to simulate and detect hypoxic conditions followed by a discussion on current and experimental therapies that target hypoxic cells.

Functional Near-Infrared Spectroscopy as a Measure of Listening Effort in Older Adults Who Use Hearing Aids

Listening effort may be reduced when hearing aids improve access to the acoustic signal. However, this possibility is difficult to evaluate because many neuroimaging methods used to measure listening effort are incompatible with hearing aid use. Functional near-infrared spectroscopy (fNIRS), which can be used to measure the concentration of oxygen in the prefrontal cortex (PFC), appears to be well-suited to this application. The first aim of this study was to establish whether fNIRS could measure cognitive effort during listening in older adults who use hearing aids. The second aim was to use fNIRS to determine if listening effort, a form of cognitive effort, differed depending on whether or not hearing aids were used when listening to sound presented at 35 dB SL (flat gain). Sixteen older adults who were experienced hearing aid users completed an auditory n-back task and a visual n-back task; both tasks were completed with and without hearing aids. We found that PFC oxygenation increased with n-back working memory demand in both modalities, supporting the use of fNIRS to measure cognitive effort during listening in this population. PFC oxygenation was weakly and nonsignificantly correlated with self-reported listening effort and reaction time, respectively, suggesting that PFC oxygenation assesses a dimension of listening effort that differs from these other measures. Furthermore, the extent to which hearing aids reduced PFC oxygenation in the left lateral PFC was positively correlated with age and pure-tone average thresholds. The implications of these findings as well as future directions are discussed.

Neural pathways of maternal responding: systematic review and meta-analysis

Functional magnetic resonance imaging (fMRI) has increasingly been employed to establish whether there is a specific brain neural network dedicated to maternal responsiveness. We undertook systematic review and meta-analysis of all studies in which healthy new mothers were exposed to visual stimuli of own versus other infants to determine the quality of evidence for a dedicated maternal neural network. Systematic literature review revealed a pattern of specific neural responses commonly induced by visual infant paradigms. Brain areas consistently reported as activated in mothers in response to own versus unknown infant included the left thalamus, bilateral pre-central gyrus, left limbic lobe, uncus, amygdala and left caudate. These regions are implicated in reward, attention, emotion processing and other core social cognitive skills. Meta-analysis, however, revealed a more limited subset of brain areas activated in mothers specifically in response to their own versus unknown infant and suggested considerable inter-study variability. Further work is needed if functional imaging is to become an objective tool for the assessment of neural pathways associated with distinct patterns of maternal care behaviour. Such a tool would be invaluable in developing biomarkers of neural activity associated with healthy maternal care and for monitoring treatment/intervention effects of costly parenting interventions.

Infra-slow brain dynamics as a marker for cognitive function and decline

Functional magnetic resonance imaging (fMRI) enables measuring human brain activity, in vivo. Yet, the fMRI hemodynamic response unfolds over very slow timescales (<0.1-1 Hz), orders of magnitude slower than millisecond timescales of neural spiking. It is unclear, therefore, if slow dynamics as measured with fMRI are relevant for cognitive function. We investigated this question with a novel application of Gaussian Process Factor Analysis (GPFA) and machine learning to fMRI data. We analyzed slowly sampled (1.4 Hz) fMRI data from 1000 healthy human participants (Human Connectome Project database), and applied GPFA to reduce dimensionality and extract smooth latent dynamics. GPFA dimensions with slow (<1 Hz) characteristic timescales identified, with high accuracy (>95%), the specific task that each subject was performing inside the fMRI scanner. Moreover, functional connectivity between slow GPFA latents accurately predicted inter-individual differences in behavioral scores across a range of cognitive tasks. Finally, infra-slow (<0.1 Hz) latent dynamics predicted CDR (Clinical Dementia Rating) scores of individual patients, and identified patients with mild cognitive impairment (MCI) who would progress to develop Alzheimer's dementia (AD). Slow and infra-slow brain dynamics may be relevant for understanding the neural basis of cognitive function, in health and disease.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.

Mapping stress networks using functional magnetic resonance imaging in awake animals

The neurobiology of stress is studied through behavioral neuroscience, endocrinology, neuronal morphology and neurophysiology. There is a shift in focus toward progressive changes throughout stress paradigms and individual susceptibility to stress that requires methods that allow for longitudinal study design and study of individual differences in stress response. Functional magnetic resonance imaging (fMRI), with the advantages of noninvasiveness and a large field of view, can be used for functionally mapping brain-wide regions and circuits critical to the stress response, making it suitable for longitudinal studies and understanding individual variability of short-term and long-term consequences of stress exposure. In addition, fMRI can be applied to both animals and humans, which is highly valuable in translating findings across species and examining whether the physiology and neural circuits involved in the stress response are conserved in mammals. However, compared to human fMRI studies, there are a number of factors that are essential for the success of fMRI studies in animals. This review discussed the use of fMRI in animal studies of stress. It reviewed advantages, challenges and technical considerations of the animal fMRI methodology as well as recent literature of stress studies using fMRI in animals. It also highlighted the development of combining fMRI with other methods and the future potential of fMRI in animal studies of stress. We conclude that animal fMRI studies, with their flexibility, low cost and short time frame compared to human studies, are crucial to advancing our understanding of the neurobiology of stress.

Neurovascular dysfunction in vascular dementia, Alzheimer's and atherosclerosis

Efficient blood supply to the brain is of paramount importance to its normal functioning and improper blood flow can result in potentially devastating neurological consequences. Cerebral blood flow in response to neural activity is intrinsically regulated by a complex interplay between various cell types within the brain in a relationship termed neurovascular coupling. The breakdown of neurovascular coupling is evident across a wide variety of both neurological and psychiatric disorders including Alzheimer’s disease. Atherosclerosis is a chronic syndrome affecting the integrity and function of major blood vessels including those that supply the brain, and it is therefore hypothesised that atherosclerosis impairs cerebral blood flow and neurovascular coupling leading to cerebrovascular dysfunction. This review will discuss the mechanisms of neurovascular coupling in health and disease and how atherosclerosis can potentially cause cerebrovascular dysfunction that may lead to cognitive decline as well as stroke. Understanding the mechanisms of neurovascular coupling in health and disease may enable us to develop potential therapies to prevent the breakdown of neurovascular coupling in the treatment of vascular brain diseases including vascular dementia, Alzheimer’s disease and stroke.

A systematic review of the psychological factors that influence neurofeedback learning outcomes

Real-time functional magnetic resonance imaging (fMRI)-based neurofeedback represents the latest applied behavioural neuroscience methodology developed to train participants in the self-regulation of brain regions or networks. However, as with previous biofeedback approaches which rely on electroencephalography (EEG) or related approaches such as brain-machine interface technology (BCI), individual success rates vary significantly, and some participants never learn to control their brain responses at all. Given that these approaches are often being developed for eventual use in a clinical setting (albeit there is also significant interest in using NF for neuro-enhancement in typical populations), this represents a significant hurdle which requires more research. Here we present the findings of a systematic review which focused on how psychological variables contribute to learning outcomes in fMRI-based neurofeedback. However, as this is a relatively new methodology, we also considered findings from EEG-based neurofeedback and BCI. 271 papers were found and screened through PsycINFO, psycARTICLES, Psychological and Behavioural Sciences Collection, ISI Web of Science and Medline and 21 were found to contribute towards the aim of this survey. Several main categories emerged: Attentional variables appear to be of importance to both performance and learning, motivational factors and mood have been implicated as moderate predictors of success, while personality factors have mixed findings. We conclude that future research will need to systematically manipulate psychological variables such as motivation or mood, and to define clear thresholds for a successful neurofeedback effect. Non-responders need to be targeted for interventions and tested with different neurofeedback setups to understand whether their non-response is specific or general. Also, there is a need for qualitative evidence to understand how psychological variables influence participants throughout their training. This will help us to understand the subtleties of psychological effects over time. This research will allow interventions to be developed for non-responders and better selection procedures in future to improve the efficacy of neurofeedback.

Elastic Registration of Single Subject Task Based fMRI Signals

Single subject task-based fMRI analyses generally suffer from low detection sensitivity with parameter estimates from the general linear model (GLM) lying below the significance threshold especially for similar contrasts or conditions. In this paper, we present a shape-based approach for alignment of condition-specific time course activity for single subject task-based fMRI. Our approach extracts signals for each condition from the entire time course, constructs an unbiased average of those signals, and warps each signal to the mean. As the warping is diffeomorphic, non-linear and allows large deformations of time series if required, we term this approach as elastic functional registration. On a single subject level, our method significantly detects more clusters and more activated voxels in relevant subcortical regions in healthy controls.

The effect of nicotine patches on craving in the brain: A functional MRI study on heavy smokers

BACKGROUND

Smoking is a common phenomenon and kills over 6 million people every year. Many smokers try to quit smoking by using nicotine replacement therapy (NRT). Most of the time, relapse occurs in less than six months after finishing the program of NRT. We performed a single blinded study in which our aim was to figure out what the effect of the nicotine patch is on craving in the brain of smokers deprived from smoking.

METHODS

Five heavy smokers (Fagerström Test for Nicotine Dependence ≥4) underwent a functional magnetic resonance imaging (fMRI) in 4 random conditions: smoking (S); smoking deprivation (SD); SD combined with a NP (SD + NP); SD combined with a placebo patch (SD + PP). Visual stimulation provoked craving in block design by randomly displaying images of smoking related scenes. After image preprocessing, a fixed-effect analysis was performed to compare average group activations. The Questionnaire for Smoking Urges (QSU) was obtained before and after each scan.

RESULTS

The fMRI results showed higher activation in areas involved in craving in S compared with SD + NP, SD + PP, and SD. In the SD + NP, limbic circuit and attention area were higher activated compared with SD and SD + PP. The SD + PP and SD showed higher activation in the frontal cortex and limbic system compared with S and SD + NP. Nonsmokers showed higher limbic activation compared with SD.The QSU increased significantly after the fMRI experiment in S (P = .036).The SD had higher QSU scores compared with the S before (P = .002), and also after (P = .022) the fMRI experiment. The NP showed lower scores than the SD before the experiment (P = .046).

CONCLUSION

The fMRI experiment revealed lower activity in areas associated with attention when subjects were nicotine deprived (SD + PP and SD). Areas involved with craving showed less activity when nicotine is present (S and SD + NP). The QSU showed a significant difference between SD and when nicotine is present (S and SD + NP).

Voxel-wise detection of functional networks in white matter

Functional magnetic resonance imaging (fMRI) depicts neural activity in the brain indirectly by measuring blood oxygenation level dependent (BOLD) signals. The majority of fMRI studies have focused on detecting cortical activity in gray matter (GM), but whether functional BOLD signal changes also arise in white matter (WM), and whether neural activities trigger hemodynamic changes in WM similarly to GM, remain controversial, particularly in light of the much lower vascular density in WM. However, BOLD effects in WM are readily detected under hypercapnic challenges, and the number of reports supporting reliable detections of stimulus-induced activations in WM continues to grow. Rather than assume a particular hemodynamic response function, we used a voxel-by-voxel analysis of frequency spectra in WM to detect WM activations under visual stimulation, whose locations were validated with fiber tractography using diffusion tensor imaging (DTI). We demonstrate that specific WM regions are robustly activated in response to visual stimulation, and that regional distributions of WM activation are consistent with fiber pathways reconstructed using DTI. We further examined the variation in the concordance between WM activation and fiber density in groups of different sample sizes, and compared the signal profiles of BOLD time series between resting state and visual stimulation conditions in activated GM as well as activated and non-activated WM regions. Our findings confirm that BOLD signal variations in WM are modulated by neural activity and are detectable with conventional fMRI using appropriate methods, thus offering the potential of expanding functional connectivity measurements throughout the brain.

Frontal Alpha Asymmetry and Theta Oscillations Associated With Information Sharing Intention

Social media has gained increasing importance in many aspects of everyday life, from building relationships to establishing collaborative networks between individuals worldwide. Sharing behavior is an essential part of maintaining these dynamic networks. However, the precise neural factors that could be related to sharing behavior in online communities remain unclear. In this study, we recorded electroencephalographic (EEG) oscillations of human subjects while they were watching short videos. The subjects were later asked to evaluate the videos based on how much they liked them and whether they would share them. We found that, at the population level, subjects watching videos that would not be shared had higher power spectral density (PSD) amplitudes in the theta band (4-8 Hz), primarily over the frontal and parietal sites of the right hemisphere, than subjects watching videos that would be shared. Previous studies have associated task disengagement with an increase in scalp-wide theta activation, which can be interpreted as a mind-wandering effect. This might suggest that the decision to not share the video may lead to a more automatic/effortless neural pattern. We also found that watching videos that would be shared was associated with lower PSD amplitudes in the alpha band (8-12 Hz) over the central and right frontal sites, and with more negative scores of frontal alpha asymmetry (FAA) index scores. These results may be related to previous work linking right-sided frontal EEG asymmetry to the pursuit of social conformity and avoidance of negative outcomes, such as social isolation. Finally, using support vector machine (SVM) algorithms, we show that these EEG parameters and preference rating scores can be used to improve the predictability of sharing information behavior. The information sharing-related EEG pattern described here could therefore improve our understanding of the neural markers associated with sharing behavior and contribute to studies about stimuli propagation.

System neuroscience: Past, present, and future

In this review, we discuss first the anatomical and lesion studies that allowed the localization of fundamental functions in the cerebral cortex of primates including humans. Subsequently, we argue that the years from the end of the Second World War until the end of the last century represented the "golden age" of system neuroscience. In this period, the mechanisms-not only the localization-underlying sensory, and in particular visual functions were described, followed by those underlying cognitive functions and housed in temporal, parietal, and premotor areas. At the end of the last century, brain imaging techniques were developed that allowed the assessment of the functions of different cortical areas in a more precise and sophisticated way. However, brain imaging tells little about the neural mechanisms underlying functions. Furthermore, the brain imaging suffers from 3 major problems: time is absent, the data are merely correlative and the testing is often not ecological. We conclude our review discussing the possibility that these pitfalls might be overcome by using intracortical recordings (eg stereo-EEG), which have millisecond time resolution, allow direct electrical stimulation of specific sites, and finally enable to study patients while freely moving.

Biophysics of BOLD fMRI investigated with animal models

The widely-used BOLD fMRI signal depends on various anatomical, physiological, and imaging parameters. Thus, it is important to examine its biophysical and physiological source in order to optimize, model and accurately interpret fMRI. Animal models have been used to investigate these issues to take systematic measurements and combine with conventional invasive approaches. Here, we reviewed and discussed multiple issues, including the echo time-dependent intravascular contribution and extravascular contributions, gradient-echo vs. spin-echo fMRI, the physiological source of BOLD fMRI, arterial vs. venous cerebral blood volume change, cerebral oxygen consumption change, and arterial oxygen saturation change. We then discuss future directions of animal fMRI and translation to human fMRI. Systematic biophysical BOLD fMRI studies provide insight into the modeling and interpretation of BOLD fMRI in animals and humans.

Electrophysiological correlates of incidentally learned expectations in human vision

The human visual system is remarkably sensitive to environmental regularities, which can facilitate behavioral performance when sensory events conform to past experience. The point at which prior knowledge is integrated during visual perception is unclear, particularly for incidentally learned associations. One possibility is that expectation shapes neural activity prospectively, in an anticipatory fashion, allowing prior knowledge to affect the earliest stages of sensory processing. Alternatively, cognitive processes underlying object recognition and conflict detection may be necessary precursors, constraining effects to later stages of processing. Here we used electroencephalography (EEG) to uncover neural activity that distinguishes between visual stimuli that match prior exposure and those that deviate from it. Participants identified visual targets that were associated with possible target locations; each location was associated with a high-probability target and a low-probability target. Alongside a behavioral cost for stimuli that had occurred infrequently at a cued location compared with those that had occurred frequently, we observed a focal modulation of the evoked EEG response at 250 ms after target onset. Relative to likely targets, unlikely targets evoked an enhanced negativity at midline frontal electrodes, and individual differences in the magnitude of this effect were correlated with the response time difference between likely and unlikely targets. In contrast, the evoked response at the latency of the P1, a correlate of early sensory processing, was indistinguishable for likely and unlikely targets. Together, these results point to postperceptual processes as a key stage at which experience modulates visual processing.

NEW & NOTEWORTHY We combined electroencephalography with an incidental learning paradigm to investigate whether prior knowledge of environmental regularities modulates visual processing at early or late stages of sensory analysis. Our results reveal that modulations of neural activity arising at midlevel processing stages predict behavioral costs for unexpected stimuli rather than effects at early stages of sensory encoding.

Adolescent Brain Development: Implications for Understanding Risk and Resilience Processes Through Neuroimaging Research

This special section focuses on research that utilizes neuroimaging methods to examine the impact of social relationships and socioemotional development on adolescent brain function. Studies include novel neuroimaging methods that further our understanding of adolescent brain development. This special section has a particular focus on how study findings add to our understanding of risk and resilience. In this introduction to the special section, we discuss the role of neuroimaging in developmental science and provide a brief review of neuroimaging methods. We present key themes that are covered in the special section articles including: (1) emerging methods in developmental neuroscience, (2) emotion-cognition interaction, and (3) the role of social relationships in brain function. We conclude our introduction with future directions for integrating developmental neuroscience into the study of adolescence, and highlight key points from the special section's commentaries which include information on the landmark Adolescent Brain Cognitive Development (ABCD) study.

Detection of time-, frequency- and direction-resolved communication within brain networks

Electroencephalography (EEG) records fast-changing neuronal signalling and communication and thus can offer a deep understanding of cognitive processes. However, traditional data analyses which employ the Fast-Fourier Transform (FFT) have been of limited use as they do not allow time- and frequency-resolved tracking of brain activity and detection of directional connectivity. Here, we applied advanced qEEG tools using autoregressive (AR) modelling, alongside traditional approaches, to murine data sets from common research scenarios: (a) the effect of age on resting EEG; (b) drug actions on non-rapid eye movement (NREM) sleep EEG (pharmaco-EEG); and (c) dynamic EEG profiles during correct vs incorrect spontaneous alternation responses in the Y-maze. AR analyses of short data strips reliably detected age- and drug-induced spectral EEG changes, while renormalized partial directed coherence (rPDC) reported direction- and time-resolved connectivity dynamics in mice. Our approach allows for the first time inference of behaviour- and stage-dependent data in a time- and frequency-resolved manner, and offers insights into brain networks that underlie working memory processing beyond what can be achieved with traditional methods.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.

Both activated and less-activated regions identified by functional MRI reconfigure to support task executions

INTRODUCTION

Functional magnetic resonance imaging (fMRI) has become very important for noninvasively characterizing BOLD signal fluctuations, which reflect the changes in neuronal firings in the brain. Unlike the activation detection strategy utilized with fMRI, which only emphasizes the synchronicity between the functional nodes (activated regions) and the task design, brain connectivity and network theory are able to decipher the interactive structure across the entire brain. However, little is known about whether and how the activated/less-activated interactions are associated with the functional changes that occur when the brain changes from the resting state to a task state. What are the key networks that play important roles in the brain state changes?

METHODS

We used the fMRI data from the Human Connectome Project S500 release to examine the changes of network efficiency, interaction strength, and fractional modularity contributions of both the local and global networks, when the subjects change from the resting state to seven different task states.

RESULTS

We found that, from the resting state to each of seven task states, both the activated and less-activated regions had significantly changed to be in line with, and comparably contributed to, a global network reconfiguration. We also found that three networks, the default mode network, frontoparietal network, and salience network, dominated the flexible reconfiguration of the brain.

CONCLUSIONS

This study shows quantitatively that contributions from both activated and less-activated regions enable the global functional network to respond when the brain switches from the resting state to a task state and suggests the necessity of considering large-scale networks (rather than only activated regions) when investigating brain functions in imaging cognitive neuroscience.

Simultaneous multi-slice inverse imaging of the human brain

Ultrafast functional magnetic resonance imaging (fMRI) can measure blood oxygen level dependent (BOLD) signals with high sensitivity and specificity. Here we propose a novel method: simultaneous multi-slice inverse imaging (SMS-InI) — a combination of simultaneous multi-slice excitation, simultaneous echo refocusing (SER), blipped controlled aliasing in parallel imaging echo-planar imaging (EPI), and regularized image reconstruction. Using a 32-channel head coil array on a 3 T scanner, SMS-InI achieves nominal isotropic 5-mm spatial resolution and 10 Hz sampling rate at the whole-brain level. Compared with traditional inverse imaging, we found that SMS-InI has higher spatial resolution with lower signal leakage and higher time-domain signal-to-noise ratio with the optimized regularization parameter in the reconstruction. SMS-InI achieved higher effective resolution and higher detection power in detecting visual cortex activity than InI. SMS-InI also detected subcortical fMRI signals with the similar sensitivity and localization accuracy like EPI. The spatiotemporal resolution of SMS-InI was used to reveal that presenting visual stimuli with 0.2 s latency between left and right visual hemifield led to 0.2 s relative hemodynamic response latency between the left and right visual cortices. Together, these results indicate that SMS-InI is a useful tool in measuring cortical and subcortical hemodynamic responses with high spatiotemporal resolution.

Contributions to Understanding the Neuropsychology of Alcoholism: An INS Legacy

Alcohol use disorder (AUD) has been a major cause of family, social, and personal strife for centuries, with current prevalence estimates of 14% for 12-month and 29% lifetime AUD. Neuropsychological testing of selective cognitive, sensory, and motor functions complemented with in vivo brain imaging has enabled tracking the consequences of AUD, which follows a dynamic course of development, maintenance, and recovery or relapse. Controlled studies of alcoholism reviewed herein provide evidence for disruption of selective functions involving executive, visuospatial, mnemonic, emotional, and attentional processes, response inhibition, prosody, and postural stability and brain systems supporting these functions. On a hopeful front, longitudinal study provides convincing evidence for improvement in brain structure and function following sustained sobriety. These discoveries have a strong legacy in the International Neuropsychological Society (INS), starting from its early days when assumptions regarding which brain regions were disrupted relied solely on patterns of functional sparing and impairment deduced from testing. This review is based on the symposium presentation delivered at the 2017 annual North American meeting of the INS in celebration of the 50th anniversary since its institution in 1967. In the spirit of the meeting's theme, "Binding the Past and Present," the lecture and this review recognized the past by focusing on early, rigorous neuropsychological studies of alcoholism and their influence on research currently conducted using imaging methods enabling hypothesis testing of brain substrates of observed functional deficits. (JINS, 2017, 23, 843-859).

Understanding Peripartum Depression Through Neuroimaging: a Review of Structural and Functional Connectivity and Molecular Imaging Research

PURPOSE OF REVIEW

Imaging research has sought to uncover brain structure, function, and metabolism in women with postpartum depression (PPD) as little is known about its underlying pathophysiology. This review discusses the imaging modalities used to date to evaluate postpartum depression and highlights recent findings.

RECENT FINDINGS

Altered functional connectivity and activity changes in brain areas implicated in executive functioning and emotion and reward processing have been identified in PPD. Metabolism changes involving monoamine oxidase A, gamma-aminobutyric acid, glutamate, serotonin, and dopamine have additionally been reported. To date, no studies have evaluated gray matter morphometry, voxel-based morphometry, surface area, cortical thickness, or white matter tract integrity in PPD. Recent imaging studies report changes in functional connectivity and metabolism in women with PPD vs. healthy comparison women. Future research is needed to extend these findings as they have important implications for the prevention and treatment of postpartum mood disorders.

Decreased prefrontal functional brain response during memory testing in women with Cushing's syndrome in remission

Neurocognitive dysfunction is an important feature of Cushing's syndrome (CS). Our hypothesis was that patients with CS in remission have decreased functional brain responses in the prefrontal cortex and hippocampus during memory testing. In this cross-sectional study we included 19 women previously treated for CS and 19 controls matched for age, gender, and education. The median remission time was 7 (IQR 6-10) years. Brain activity was studied with functional magnetic resonance imaging during episodic- and working-memory tasks. The primary regions of interest were the prefrontal cortex and the hippocampus. A voxel-wise comparison of functional brain responses in patients and controls was performed. During episodic-memory encoding, patients displayed lower functional brain responses in the left and right prefrontal gyrus (p<0.001) and in the right inferior occipital gyrus (p<0.001) compared with controls. There was a trend towards lower functional brain responses in the left posterior hippocampus in patients (p=0.05). During episodic-memory retrieval, the patients displayed lower functional brain responses in several brain areas with the most predominant difference in the right prefrontal cortex (p<0.001). During the working memory task, patients had lower response in the prefrontal cortices bilaterally (p<0.005). Patients, but not controls, had lower functional brain response during a more complex working memory task compared with a simpler one. In conclusion, women with CS in long-term remission have reduced functional brain responses during episodic and working memory testing. This observation extends previous findings showing long-term adverse effects of severe hypercortisolaemia on brain function.

Motion invariant contrast enhancement of optical imaging data in the gradient domain

Functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes (VSD)) forms a group of neuroimaging techniques that possess up to date highest temporal and spatial resolution on a meso-to macroscopic scale. An inherent problem of OI is a very low signal to noise ratio (SNR), which restricts the recordings to be completely motionless and requires detailed knowledge of the properties of the different noise sources. In our experiments we performed a durectomy and did not use an imaging chamber to allow us future joint electroencephalography-optical imaging (EEG-OI) measures, which resulted in movement artifacts. With the goal of motion compensation in OI recordings and magnification of signal changes, we present a novel processing pipeline, which is based on optic flow guided denoising and gradient domain tone mapping for spatiotemporal contrast enhancement.

Radiological studies of fetal alcohol spectrum disorders in humans and animal models: An updated comprehensive review

Fetal Alcohol Spectrum Disorders encompass a wide range of birth defects in children born to mothers who consumed alcohol during pregnancy. Typical mental impairments in FASD include difficulties in life adaptation and learning and memory, deficits in attention, visuospatial skills, language and speech disabilities, mood disorders and motor disabilities. Multimodal imaging methods have enabled in vivo studies of the teratogenic effects of alcohol on the central nervous system, giving more insight into the FASD phenotype. This paper offers an up-to-date comprehensive review of radiological findings in the central nervous system in studies of prenatal alcohol exposure in both humans and translational animal models, including Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, Single Photon Emission Tomography and Ultrasonography.

Spontaneous brain network activity: Analysis of its temporal complexity

The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain's complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the "cliquiness" of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.

Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI

Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.

Intra-operative neurophysiological mapping and monitoring during brain tumour surgery in children: an update

INTRODUCTION

Over the past decade, the reluctance to operate in eloquent brain areas has been reconsidered in the light of the advent of new peri-operative functional neuroimaging techniques and new evidence from neuro-oncology. To maximise tumour resection while minimising morbidity should be the goal of brain surgery in children as much as it is in adults, and preservation of brain functions is critical in the light of the increased survival and the expectations in terms of quality of life.

DISCUSSION

Intra-operative neurophysiology is the gold standard to localise and preserve brain functions during surgery and is increasingly used in paediatric neurosurgery. Yet, the developing nervous system has peculiar characteristics in terms of anatomical and physiological maturation, and some technical aspects need to be tailored for its use in children, especially in infants. This paper will review the most recent advances in the field of intra-operative neurophysiology (ION) techniques during brain surgery, focussing on those aspects that are relevant to the paediatric neurosurgery practice.

Molecular fMRI

Comprehensive analysis of brain function depends on understanding the dynamics of diverse neural signaling processes over large tissue volumes in intact animals and humans. Most existing approaches to measuring brain signaling suffer from limited tissue penetration, poor resolution, or lack of specificity for well-defined neural events. Here we discuss a new brain activity mapping method that overcomes some of these problems by combining MRI with contrast agents sensitive to neural signaling. The goal of this "molecular fMRI" approach is to permit noninvasive whole-brain neuroimaging with specificity and resolution approaching current optical neuroimaging methods. In this article, we describe the context and need for molecular fMRI as well as the state of the technology today. We explain how major types of MRI probes work and how they can be sensitized to neurobiological processes, such as neurotransmitter release, calcium signaling, and gene expression changes. We comment both on past work in the field and on challenges and promising avenues for future development.

SIGNIFICANCE STATEMENT

Brain researchers currently have a choice between measuring neural activity using cellular-level recording techniques, such as electrophysiology and optical imaging, or whole-brain imaging methods, such as fMRI. Cellular level methods are precise but only address a small portion of mammalian brains; on the other hand, whole-brain neuroimaging techniques provide very little specificity for neural pathways or signaling components of interest. The molecular fMRI techniques we discuss have particular potential to combine the specificity of cellular-level measurements with the noninvasive whole-brain coverage of fMRI. On the other hand, molecular fMRI is only just getting off the ground. This article aims to offer a snapshot of the status and future prospects for development of molecular fMRI techniques.

Evaluation of tumour vascular distribution and function using immunohistochemistry and BOLD fMRI with carbogen inhalation

AIM

To evaluate oxygenation changes in rat subcutaneous C6 gliomas using blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) combined with non-haemodynamic response function (non-HRF) analysis.

MATERIALS AND METHODS

BOLD fMRI were performed during carbogen inhalation in 20 Wistar rats bearing gliomas. Statistical maps of spatial oxygenation changes were computed by a dedicated non-HRF analysis algorithm. Three types of regions of interest (ROIs) were defined: (1) maximum re-oxygenation zone (ROI_max), (2) re-oxygenation zones that were less than the maximum re-oxygenation (ROI_non-max), and (3) zones without significant re-oxygenation (ROI_none). The values of percent BOLD signal change (PSC), percent enhancement (ΔSI), and significant re-oxygenation (T) were extracted from each ROI. Tumours were sectioned for histology using the fMRI scan orientation and were stained with haematoxylin and eosin and CD105. The number of microvessels (MVN) in each ROI was counted. Differences and correlations among the values for T, PSC, ΔSI, and MVN were determined.

RESULTS

After carbogen inhalation, the PSC significantly increased in the ROI_max areas (p<0.01) located in the tumour parenchyma. No changes occurred in any of the ROI_none areas (20/20). Some changes occurred in a minority of the ROI_non-max areas (3/60) corresponding to tumour necrosis. MVN and PSC (R=0.59, p=0.01) were significantly correlated in the ROI_max areas. In the ROI_non-max areas, MVN was significantly correlated with PSC (R=0.55, p=0.00) and ΔSI (R=0.37, p=0.00).

CONCLUSIONS

Statistical maps obtained via BOLD fMRI with non-HRF analysis can assess the re-oxygenation of gliomas.