fMRI Methods Research Articles

A Free-breathing fMRI Method to Study Human Olfactory Function

A Free-breathing fMRI Method to Study Human Olfactory Function

A Free-breathing fMRI Method to Study Human Olfactory Function.

The study of human olfaction is a highly complex and valuable field with applications ranging from biomedical research to clinical evaluation. Currently, evaluation of the functions of the human central olfactory system with functional magnetic resonance imaging (fMRI) is still a challenge because of several technical difficulties. There are some significant variables to take into account when considering an effective method for mapping the function of the central olfactory system using fMRI, including proper odorant selection, the interaction between odor presentation and respiration, and potential anticipation of or habituation to odorants. An event-related, respiration-triggered olfactory fMRI technique can accurately administer odorants to stimulate the olfactory system while minimizing potential interference. It can effectively capture the precise onsets of fMRI signals in the primary olfactory cortex using our data post-processing method. The technique presented here provides an efficient and practical means for generating reliable olfactory fMRI results. Such a technique can ultimately be applied in the clinical realm as a diagnostic tool for diseases associated with olfactory degeneration, including Alzheimer's and Parkinson's disease, as we begin to further understand the complexities of the human olfactory system.

Characterizing cross-subject spatial interaction patterns in functional magnetic resonance imaging studies: A two-stage point-process model.

We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.

Simultaneous GCaMP6-based fiber photometry and fMRI in rats

BackgroundUnderstanding the relationship between neural and vascular signals is essential for interpretation of functional MRI (fMRI) results with respect to underlying neuronal activity. Simultaneously measuring neural activity using electrophysiology with fMRI has been highly valuable in elucidating the neural basis of the blood oxygenation-level dependent (BOLD) signal. However, this approach is also technically challenging due to the electromagnetic interference that is observed in electrophysiological recordings during MRI scanning. New methodRecording optical correlates of neural activity, such as calcium signals, avoids this issue, and has opened a new avenue to simultaneously acquire neural and BOLD signals. ResultsThe present study is the first to demonstrate the feasibility of simultaneously and repeatedly acquiring calcium and BOLD signals in animals using a genetically encoded calcium indicator, GCaMP6. This approach was validated with a visual stimulation experiment, during which robust increases of both calcium and BOLD signals in the superior colliculus were observed. In addition, repeated measurement in the same animal demonstrated reproducible calcium and BOLD responses to the same stimuli. Comparison with existing method(s)Taken together, simultaneous GCaMP6-based fiber photometry and fMRI recording presents a novel, artifact-free approach to simultaneously measuring neural and fMRI signals. Furthermore, given the cell-type specificity of GCaMP6, this approach has the potential to mechanistically dissect the contributions of individual neuron populations to BOLD signal, and ultimately reveal its underlying neural mechanisms. ConclusionsThe current study established the method for simultaneous GCaMP6-based fiber photometry and fMRI in rats.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla.

PurposeConcurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality.Materials and methodsThe study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18–70 years) and 13 patients with epilepsy (8 males, age range 21–67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients).ResultsRF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality.ConclusionThis study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI.

A new denoising method for fMRI based on weighted three-dimensional wavelet transform

This study presents a new three-dimensional discrete wavelet transform (3D-DWT)-based denoising method for functional magnetic resonance images (fMRI). This method is called weighted three-dimensional discrete wavelet transform (w-3D-DWT), and it is based on the principle of weighting the volume subbands which are obtained by 3D-DWT. Briefly, classical DWT denoising consists of wavelet decomposition, thresholding, and image reconstruction steps. In the thresholding algorithm, the thresholding value for each image cannot be chosen exclusively. Namely, a specific thresholding value is chosen and it is used for all images. The proposed algorithm in this study can be considered as a data-driven denoising model for fMRI. It consists of three-dimensional wavelet decomposition, subband weighting, and image reconstruction. The purposes of subband weighting algorithm are to increase the effect of the subband which represents the image better and to decrease the effect of the subband which represents the image in the worst way and thus to reduce the noises of the image adaptively. fMRI is one of the popular methods used to understand brain functions which are often corrupted by noises from various sources. The traditional denoising method used in fMRI is smoothing images with a Gaussian kernel. This study suggests an adaptive approach for fMRI filtering different from Gaussian smoothing and 3D-DWT thresholding. In this study, w-3D-DWT denoising results were evaluated with mean-square error (MSE), peak signal/noise ratio (PSNR), and structural similarity (SSIM) metrics, and the results were compared with Gaussian smoothing and 3D-DWT thresholding methods. According to this comparison, w-3D-DWT gave low-MSE and high-PSNR results for fMRI data.

Pharmacological Functional MRI in Rodent Model

The pharmacological effects on the brain will occur widely in whole brain. Therefore, pharmacological functional MRI (fMRI) is a promising non-invasive tool to investigate the whole brain activation following pharmacological stimulation. The multimodal fMRI techniques, which reflect the vascular response (blood oxygenation level dependent fMRI), neuron/astrocyte activity (diffusion fMRI) and Ca2+ influx in excitatory neurons (manganese enhanced MRI), enable to observe the neuronal activation in rodent model. In the present study, the multimodal pharmacological fMRI method in rodents will be introduced. Keywords: Functional MRI, rodent, anesthesia, BOLD, DfMRI, MEMRI, pharmacological fMRI.

Physiological substrates of executive functioning: a systematic review of the literature.

Executive function (EF) is a multifaceted construct that has been defined as a set of higher-order cognitive processes that allow for flexibility, self-regulation, strategic planning, and goal-directed behaviors. EFs have been studied in numerous clinical disorders using a variety of neuropsychological tasks and, more recently, neuroimaging techniques. The underlying physiological substrates of EF were historically attributed to the frontal lobes; however, recent studies suggest more widespread involvement of additional brain regions. The purpose of the present study was to conduct a systematic review (using PRISMA 2009 guidelines) of neuroimaging studies employing functional magnetic resonance imaging and diffusion tensor imaging methods investigating the physiological substrates of EFs in attention-deficit/hyperactivity disorder compared to other clinical groups and non-clinical participants. Research articles were retrieved using PsycINFO, PsycARTICLES, MEDLINE, and ScienceDirect, beginning February 2015 through May 2016. A total of 42 studies met eligibility. Of those 42 studies, 22 studies included clinical participants and 20 studies included non-clinical participants. Results revealed increased activation of the frontal brain region in the majority of non-clinical studies and approximately 50% of the clinical studies, albeit with some inconsistencies across subregions, tasks, and age groups. Implications, methodological limitations, and suggestions for future research are discussed.

P217 Disrupting cognitive control in dual-task situations by transcranial magnetic stimulation of the lateral prefrontal cortex

Introduction Performance in dual-task situations is usually impaired compared to performance in single-task situations, which is reflected in prolonged reaction times as well as increased error rates. These dual-task costs can be explained by the requirement to recruit additional task-order control processes that are crucial for scheduling, monitoring and regulating the processing order of two temporally overlapping tasks. Recent neuroimaging studies with functional magnetic resonance imagery (fMRI) could show that the lateral prefrontal cortex (lPFC) may play an essential role for implementing these control processes. However, the association between prefrontal activity and task-order control remains only correlational due to the nature of the fMRI method. Objectives The aim of this study was to provide evidence for the causal involvement of prefrontal activity in dual-task processing by applying transcranial magnetic stimulation (TMS). Methods 16 Participants performed a dual-task consisting of two simple choice reaction tasks. Demands on task-order control processes were manipulated by introducing dual-task blocks with either fixed or random order of both tasks. We compared the effects of TMS over the lPFC to two control conditions. Results As a result, we could show that in the stimulation condition dual-task performance decreased compared to control conditions only if demands on task-order control processes where high. Conclusion This pattern of results supports the conclusion that the lPFC plays a causal role for the implementation of task-order control processes in situations with temporally overlapping tasks. More specifically, we argue that the lPFC is recruited for activating an internal order representation into working memory.

Structural imaging of mild traumatic brain injury may not be enough: overview of functional and metabolic imaging of mild traumatic brain injury

A majority of patients with traumatic brain injury (TBI) present as mild injury with no findings on conventional clinical imaging methods. Due to this difficulty of imaging assessment on mild TBI patients, there has been much emphasis on the development of diffusion imaging modalities such as diffusion tensor imaging (DTI). However, basic science research in TBI shows that many of the functional and metabolic abnormalities in TBI may be present even in the absence of structural damage. Moreover, structural damage may be present at a microscopic and molecular level that is not detectable by structural imaging modality. The use of functional and metabolic imaging modalities can provide information on pathological changes in mild TBI patients that may not be detected by structural imaging. Although there are various differences in protocols of positron emission tomography (PET), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) methods, these may be important modalities to be used in conjunction with structural imaging in the future in order to detect and understand the pathophysiology of mild TBI. In this review, studies of mild TBI patients using these modalities that detect functional and metabolic state of the brain are discussed. Each modality's advantages and disadvantages are compared, and potential future applications of using combined modalities are explored.

Dopamine Modulates the Functional Organization of the Orbitofrontal Cortex.

Neuromodulators such as dopamine can alter the intrinsic firing properties of neurons and may thereby change the configuration of larger functional circuits. The primate orbitofrontal cortex (OFC) receives dopaminergic input from midbrain nuclei, but the role of dopamine in the OFC is still unclear. Here we tested the idea that dopaminergic activity changes the pattern of connectivity between the OFC and the rest of the brain and thereby reconfigures functional networks in the OFC. To this end, we combined double-blind, placebo-controlled pharmacology [D2 receptor (D2R) antagonist amisulpride] in humans with resting-state functional magnetic resonance imaging and clustering methods. In the placebo group, we replicated previously observed parcellations of the OFC into two and six subregions based on connectivity patterns with the rest of the brain. Most importantly, while the twofold clustering did not differ significantly between groups, blocking D2Rs significantly changed the composition of the sixfold parcellation, suggesting a dopamine-dependent reconfiguration of functional OFC subregions. Moreover, multivariate decoding analyses revealed that amisulpride changed the whole-brain connectivity patterns of individual OFC subregions. In particular, D2R blockade shifted the balance of OFC connectivity from associative areas in the temporal and parietal lobe toward functional connectivity with the frontal cortex. In summary, our results suggest that dopamine alters the composition of functional OFC circuits, possibly indicating a broader role for neuromodulators in the dynamic reconfiguration of functional brain networks.SIGNIFICANCE STATEMENT A key role of any neuromodulator may be the reconfiguration of functional brain circuits. Here we test this idea with regard to dopamine and the organization of functional networks in the orbitofrontal cortex (OFC). We show that blockade of dopamine D2 receptors has profound effects on the functional connectivity patterns of the OFC, yielding altered connectivity-based subdivisions of this region. Our results suggest that dopamine changes the connectional configuration of the OFC, possibly leading to transitions between different operating modes that favor either sensory input or recurrent processing in the prefrontal cortex. More generally, our findings support a broader role for neuromodulators in the dynamic reconfiguration of functional brain networks and may have clinical implications for understanding the actions of antipsychotic agents.

Within and between subject variability of effective connectivity in (small) resting state networks: A spectral dynamic causal modeling study

Within and between subject variability of effective connectivity in (small) resting state networks: A spectral dynamic causal modeling study

A three-dimensional single-scan approach for the measurement of changes in cerebral blood volume, blood flow, and blood oxygenation-weighted signals during functional stimulation

The blood-oxygenation-level-dependent (BOLD) effect reflects ensemble changes in several physiological parameters such as cerebral blood volume (CBV), blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO2). Quantitative BOLD approaches have been developed to estimate CMRO2 dynamics from BOLD, CBF and CBV responses, generally using separate scans. The ability to detect changes in these parameters in a single scan would shorten the total scan time and reduce temporal variations in physiology or neuronal responses. Here, an acquisition strategy, named 3D TRiple-acquisition after Inversion Preparation (3D-TRIP), is demonstrated for 3D acquisition of CBV, CBF, and BOLD signal changes in a single scan by incorporating VASO, FAIR-ASL and T2-prepared BOLD fMRI methods. Using a visual stimulation paradigm, we demonstrate that the activation patterns, relative signal changes, temporal signal-to-noise ratio (tSNR), contrast-to-noise ratio (CNR), and estimated CMRO2 changes during visual stimulation are all comparable between the concurrent imaging proposed here and the separate scans conventionally applied. This approach is expected to provide a useful alternative for quantitative BOLD fMRI studies where information about oxygen metabolism alterations can be extracted from changes in hemodynamic signals associated with CBV, CBF, and blood oxygenation.

Changed Hub and Corresponding Functional Connectivity of Subgenual Anterior Cingulate Cortex in Major Depressive Disorder.

Major depressive disorder (MDD) is one of the most prevalent mental disorders. In the brain, the hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of MDD remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in MDD. First, we constructed the whole-brain voxel-wise functional networks and calculated a functional connectivity strength (FCS) map in each subject in 34 MDD patients and 34 gender-, age- and education level-matched healthy controls (HCs). Next, the two-sample t-test was applied to compare the FCS maps between HC and MDD patients and identified significant decrease of FCS in subgenual anterior cingulate cortex (sgACC) in MDD patients. Subsequent functional connectivity analyses of sgACC showed disruptions in functional connectivity with posterior insula, middle and inferior temporal gyrus, lingual gyrus and cerebellum in MDD patients. Furthermore, the changed FCS of sgACC and functional connections to sgACC were significantly correlated with the Hamilton Depression Rating Scale (HDRS) scores in MDD patients. The results of the present study revealed the abnormal hub of sgACC and its corresponding disrupted frontal-limbic-visual cognitive-cerebellum functional networks in MDD. These findings may provide a new insight for the diagnosis and treatment of MDD.

Imaging serotonin reuptake in the living brain.

A new molecular fMRI method allows for investigation of serotonin dynamics in the living brain.

An fMRI study of training voluntary smooth circular eye movements.

Despite a large number of recent studies, the promise of fMRI methods to produce valuable insights into motor skill learning has been restricted to sequence learning paradigms, or manual training paradigms where a relatively advanced capacity for sensory-motor integration and effector coordination already exists. We therefore obtained fMRIs from 16 healthy adults trained in a new paradigm that demanded voluntary smooth circular eye movements without a moving target. This aimed to monitor neural activation during two possible motor learning processes: (a) the smooth pursuit control system develops a new perceptual-motor relationship and successfully becomes involved in voluntary action in which it is not normally involved or (b) the saccadic system normally used for voluntary eye movement and which only exhibits linear action skill develops new dynamic coordinative control capable of smooth circular movement. Participants were able to improve within half an hour, typically demonstrating saccadic movement with progressively reduced amplitudes, which better approximated smooth circular movement. Activity in the inferior premotor cortex was significantly modulated and decreased during the progress of learning. In contrast, activations in dorsal premotor and parietal cortex along the intraparietal sulcus, the supplementary eye field and the anterior cerebellum did not change during training. Thus, the decrease of activity in inferior premotor cortex was critically related to the learning progress in visuospatial eye movement control.

A CCA and ICA-Based Mixture Model for Identifying Major Depression Disorder.

The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures.

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of “synchronicity” of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

Journal Club: Change in brain network connectivity during PACAP38-induced migraine attacks.

There is evidence to suggest that migraine attacks involve changes in complex neuronal networks that can be measured using functional MRI (fMRI) methods including resting state functional connectivity (RSFC). Networks that have previously been shown to be involved in the sensory and affective aspects of pain perception include the default mode network (DMN), salience network (SN), and sensorimotor network (SMN) (figure).1 In a recent study, Amin et al.2 examined the RSFC of these 3 networks at baseline and in the early phases of experimentally induced migraine in patients with migraine without aura. In order to capture the early phases of migraine, corresponding to the time closest to migraine attack onset, migraine attacks were induced using the potent vasodilator neuropeptide pituitary adenylate cyclase-activating polypeptide-38 (PACAP38).3 Since vasodilators can alter hemodynamics and potentially confound the fMRI signal, another vasodilator, vasoactive intestinal peptide (VIP), was used as a control since it also acts on extracranial arteries, but is much less likely (<20%) to provoke migraine-like attacks.3

Behavioral and Neural Sustained Attention Deficits in Bipolar Disorder and Familial Risk of Bipolar Disorder

BackgroundFew neuroimaging studies compare individuals affected with bipolar disorder (BP), at high familial risk of BP, and at low risk to identify endophenotypes for BP. None have examined variability in attention, despite promising behavioral work in this area. We used functional magnetic resonance imaging (fMRI) methods uniquely powered to compare the neural correlates of attention variability in these three groups. MethodsThe present study examined 8- to 25-year-old individuals (n = 106) who completed an fMRI attention task: 24 with BP, 29 at risk based on a first-degree relative with BP, and 53 healthy, low-risk individuals. Group differences in intrasubject variability in reaction time were examined, and a sophisticated fMRI analytic approach was used to quantify precisely trialwise associations between reaction time and brain activity. The latter has not been examined previously in BP or risk of BP. ResultsRelative to healthy individuals, those with BP or at risk for BP exhibited increased reaction time variability (F2,102 = 4.26, p = .02, ηp2 = .08). Importantly, we identified blunted relationships between trialwise variation in reaction time and brain activity in the inferior and middle frontal gyri, precuneus, cingulate cortex, caudate, and postcentral gyrus (all regions: p < .001, ηp2 > .06) in both at-risk and BP individuals compared with healthy, low-risk individuals. This blunting partially mediated group differences in reaction time variability (β = .010, 95% confidence interval 0.002 to 0.020, Sobel Z = 2.08, p = .038). ConclusionsBlunting in key frontal, cingulate, and striatal areas was evident in unaffected, at-risk individuals and in euthymic BP patients. Elucidating such novel neural endophenotypes can facilitate new approaches to BP prediction, diagnosis, and prevention.