BOLD fMRI Data Research Articles

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters. In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method. Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions. This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.

Identification of functional white matter networks in BOLD fMRI.

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

The cortical representation of language timescales is shared between reading and listening

Language comprehension involves integrating low-level sensory inputs into a hierarchy of increasingly high-level features. Prior work studied brain representations of different levels of the language hierarchy, but has not determined whether these brain representations are shared between written and spoken language. To address this issue, we analyze fMRI BOLD data that were recorded while participants read and listened to the same narratives in each modality. Levels of the language hierarchy are operationalized as timescales, where each timescale refers to a set of spectral components of a language stimulus. Voxelwise encoding models are used to determine where different timescales are represented across the cerebral cortex, for each modality separately. These models reveal that between the two modalities timescale representations are organized similarly across the cortical surface. Our results suggest that, after low-level sensory processing, language integration proceeds similarly regardless of stimulus modality.

Generation of a whole-brain hemodynamic response function and sex-specific differences in cerebral processing of mechano-sensation in mice detected by BOLD fMRI.

BOLD fMRI has become a prevalent method to study cerebral sensory processing in rodent disease models, including pain and mechanical hypersensitivity. fMRI data analysis is frequently combined with a general-linear-model (GLM) -based analysis, which uses the convolution of a hemodynamic response function (HRF) with the stimulus paradigm. However, several studies indicated that the HRF differs across species, sexes, brain structures, and experimental factors, including stimulation modalities or anesthesia, and hence might strongly affect the outcome of BOLD analyzes. While considerable work has been done in humans and rats to understand the HRF, much less is known in mice. As a prerequisite to investigate mechano-sensory processing and BOLD fMRI data in male and female mice, we (1) designed a rotating stimulator that allows application of two different mechanical modalities, including innocuous von Frey and noxious pinprick stimuli and (2) determined and statistically compared HRFs across 30 brain structures and experimental conditions, including sex and, stimulus modalities. We found that mechanical stimulation lead to brain-wide BOLD signal changes thereby allowing extraction of HRFs from multiple brain structures. However, we did not find differences in HRFs across all brain structures and experimental conditions. Hence, we computed a whole-brain mouse HRF, which is based on 88 functional scans from 30 mice. A comparison of this mouse-specific HRF with our previously reported rat-derived HRF showed significantly slower kinetics in mice. Finally, we detected pronounced differences in cerebral BOLD activation between male and female mice with mechanical stimulation, thereby exposing divergent processing of noxious and innocuous stimuli in both sexes.

Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities.

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

Heavy and Chronic Cannabis Use Impact on Human Emotions: BOLD-fMRI Study

IntroductionLong term cannabis use has been expanding drastically over the last two decades and has become a major health issue worldwide. Recent studies demonstrate that brain complications in adults with cannabis use are associated with cognitive and emotional impairments, but little is known about the relationship between structural alterations and behavioral manifestations. Therefore, studying the relationship between alterations of emotional system, in parallel with structural degenerative phenomena is very critical.ObjectivesHence, the aim of this study is to demonstrate such alterations by making use of appropriate paradigms during BOLD-fMRI scans. Positive, negative and neutral emotions were examined, in relations with DTI and functional connectivity.Methods11 cannabis addicted patients volunteered for the study. Volunteers were fully healthy. However, any additional comorbidity was a strict criterion of exclusion, and a healthy general state was an indispensable criterion of inclusion. Additionally, it was excluded any patient that have any additional substance use such as tobacco, alcohol, cocaine, etc. And strict use of cannabis was a must.All patients underwent blood and urine assessments to ensure the selection criteria.All patients underwent BOLD-fMRI and anatomical MRI using both motor and emotional paradigm.The motor task consisted of rest alternating with finger tapping. The emotional task included 3 conditions. Positive, neutral and negative were each alternating with silent mental counting. The fMRI data was processed using SPM12 package. A sample of 12 age-matched controls was also included.ResultsThe present results are based on analysis of behavioral and BOLD-fMRI data of 11patients and similar age-matched controls. Analysis of behavioral data showed an alteration of emotional abilities in cannabis addicted patients compared to controls. Analysis of fMRI data revealed significant changes of activation within a large cortical network including prefrontal cortex and parietal cortex, and that emotional responses and BOLD signal were inversely correlated.ConclusionsThese findings demonstrate that the brain of cannabis addicted patients undergoes and emotional alterations that parallel silent structural degenerative phenomena. Although the causal mechanisms are still to be investigated, the fact that functional impairments can be detected in emotional, cognitive and motor domains calls for the development of preventive measures using neurobehavioral tools for this population of patient, and even in at risk users.Disclosure of InterestNone Declared

Poorer sleep health is associated with altered brain activation during cognitive control processing in healthy adults.

This study investigated how proactive and reactive cognitive control processing in the brain was associated with habitual sleep health. BOLD fMRI data were acquired from 81 healthy adults with normal sleep (41 females, age 20.96-39.58years) during a test of cognitive control (Not-X-CPT). Sleep health was assessed in the week before MRI scanning, using both objective (actigraphy) and self-report measures. Multiple measures indicating poorer sleep health-including later/more variable sleep timing, later chronotype preference, more insomnia symptoms, and lower sleep efficiency-were associated with stronger and more widespread BOLD activations in fronto-parietal and subcortical brain regions during cognitive control processing (adjusted for age, sex, education, and fMRI task performance). Most associations were found for reactive cognitive control activation, indicating that poorer sleep health is linked to a "hyper-reactive" brain state. Analysis of time-on-task effects showed that, with longer time on task, poorer sleep health was predominantly associated with increased proactive cognitive control activation, indicating recruitment of additional neural resources over time. Finally, shorter objective sleep duration was associated with lower BOLD activation with time on task and poorer task performance. In conclusion, even in "normal sleepers," relatively poorer sleep health is associated with altered cognitive control processing, possibly reflecting compensatory mechanisms and/or inefficient neural processing.

Contrastive Study of Brain Activation Regions during Verb Production in Uyghur and Chinese Broca Aphasia Patients after Stroke

<bold>Objective</bold> Blood oxygenation level dependent-functional magnetic resonance image (Bold-fMRI) was used to compare the brain activation of Uyghur and Chinese broca aphasia patients during verb production task, exploring the neural mechanism of verb production, in order to provide basis for the evaluation and treatment of Uyghur and Chinese broca aphasia patients. <bold>Methods</bold> A total of thirty-one patients with broca aphasia who met the inclusion and exclusion criteria were selected, including 15 patients in the Uyghur patient group and 16 patients among the Chinese patient group. Thirty-six healthy volunteers were recruited and matched with the patients in the ethnicity, sex, age and years of education, with 18 in each group. Language function was evaluated by Uyghur and Chinese standard aphasia test. Using Uyghur and Chinese nouns as basic experimental materials, Bold-fMRI data were collected when subjects performed the verb production task. SPM 8.0 software was used for statistical analysis, language and aphasia factors were used for double factor variance analysis to compare the interaction at the whole brain level of the four groups. Simple effect analysis was performed in the brain regions with significant interaction, and the differences in brain activation were compared between the two groups. <bold>Results</bold> In terms of language functions, there were no significant differences in repetition, naming, listening and understanding, reading, writing scores and BDAE grading between the Uyghur and Chinese patient groups ( <italic>P</italic>&gt;0.05). The four groups showed significant interaction in the activation of bilateral lateral occipital lobe, middle frontal gyrus, precentral gyrus, superior temporal gyrus and fronto-orbital gyrus; there were statistically significant differences in activation intensity of bilateral middle frontal gyrus between the Uyghur healthy group and patient group ( <italic>P</italic>&lt;0.05), but there were no statistically significant differences in activation intensity of precentral gyrus and left lateral occipital lobe ( <italic>P</italic>&gt;0.05). There were statistically significant differences in activation intensity of precentral gyrus and left lateral occipital lobe between the Chinese healthy group and patient group ( <italic>P</italic>&lt;0.05), while there were no statistically significant differences in activation intensity of bilateral middle frontal gyrus ( <italic>P</italic>&gt;0.05). There were no statistically significant differences in simple effect comparison between the Uyghur healthy group and Chinese healthy group, or the Uyghur patient group and Chinese patient group ( <italic>P</italic>&gt;0.05). <bold>Conclusion</bold> Language type and aphasia both affect neural mechanism of brain verb production; the left middle frontal gyrus may play an important role in morphological processing of Uyghur verbs; the precentral gyrus may be involved in phonetic coding output of Chinese.

P565. Exploring Cortical and Thalamic Disfunction in Schizophrenia Using the Hilbert Huang Transform

Most approaches to the analysis of resting-state BOLD-fMRI data assume that BOLD signal reflects spontaneous neural oscillations, the frequency-domain properties of which are poorly understood. The frequency of neural signaling from these sources varies rapidly with time. Techniques which capture these nonstationary dynamics may therefore reflect clinically significant differences in neural activity. One such technique is the Hilbert-Huang transform, which empirically decomposes a signal into different rhythms of variable frequency, termed Intrinsic Mode Functions (IMFs).

Enhancing cognitive restructuring with concurrent fMRI-guided neurostimulation for emotional dysregulation–A randomized controlled trial

BackgroundTransdiagnostic clinical emotional dysregulation is a key component of many mental health disorders and offers an avenue to address multiple disorders with one transdiagnostic treatment. In the current study, we pilot an intervention that combines a one-time teaching and practice of cognitive restructuring (CR) with repetitive transcranial magnetic stimulation (rTMS), targeted based on functional magnetic resonance imaging (fMRI). MethodsThirty-seven clinical adults who self-reported high emotional dysregulation were enrolled in this randomized, double-blind, placebo-controlled trial. fMRI was collected as participants were reminded of lifetime stressors and asked to downregulate their distress using CR tactics. fMRI BOLD data were analyzed to identify the cluster of voxels within the left dorsolateral prefrontal cortex (dlPFC) with the highest activation when participants attempted to downregulate, versus passively remember, distressing memories. Participants underwent active or sham rTMS (10 Hz) over the left dlPFC target while practicing CR following emotional induction using recent autobiographical stressors. ResultsReceiving active versus sham rTMS led to significantly higher high frequency heart rate variability during regulation, lower regulation duration during the intervention, and higher likelihood to use CR during the week following the intervention. There were no differences between conditions when administering neurostimulation alone without the CR skill and compared to sham. Participants in the sham versus active condition experienced less distress the week after the intervention. There were no differences between conditions at the one-month follow up. ConclusionThis study demonstrated that combining active rTMS with emotion regulation training for one session significantly enhances emotion regulation and augments the impact of training for as long as a week. These findings are a promising step towards a combined intervention for transdiagnostic emotion dysregulation.

Intelligibility of audiovisual sentences drives multivoxel response patterns in human superior temporal cortex

Regions of the human posterior superior temporal gyrus and sulcus (pSTG/S) respond to the visual mouth movements that constitute visual speech and the auditory vocalizations that constitute auditory speech, and neural responses in pSTG/S may underlie the perceptual benefit of visual speech for the comprehension of noisy auditory speech. We examined this possibility through the lens of multivoxel pattern responses in pSTG/S. BOLD fMRI data was collected from 22 participants presented with speech consisting of English sentences presented in five different formats: visual-only; auditory with and without added auditory noise; and audiovisual with and without auditory noise. Participants reported the intelligibility of each sentence with a button press and trials were sorted post-hoc into those that were more or less intelligible. Response patterns were measured in regions of the pSTG/S identified with an independent localizer. Noisy audiovisual sentences with very similar physical properties evoked very different response patterns depending on their intelligibility. When a noisy audiovisual sentence was reported as intelligible, the pattern was nearly identical to that elicited by clear audiovisual sentences. In contrast, an unintelligible noisy audiovisual sentence evoked a pattern like that of visual-only sentences. This effect was less pronounced for noisy auditory-only sentences, which evoked similar response patterns regardless of intelligibility. The successful integration of visual and auditory speech produces a characteristic neural signature in pSTG/S, highlighting the importance of this region in generating the perceptual benefit of visual speech.

Altered functional brain dynamics in chromosome 22q11.2 deletion syndrome during facial affect processing

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a multisystem disorder associated with multiple congenital anomalies, variable medical features, and neurodevelopmental differences resulting in diverse psychiatric phenotypes, including marked deficits in facial memory and social cognition. Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks. However, time-varying interactions between brain areas during facial affect processing have not yet been studied with BOLD fMRI in 22q11.2DS. We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with 22q11.2DS and 58 age-, race-, and sex-matched healthy controls. Delayed frontal-motor feedback signals were diminished in individuals with 22q11.2DS, as were delayed emotional memory signals engaging amygdala, hippocampus, and entorhinal cortex. Early task-related engagement of motor and visual cortices and salience-related insular activation were relatively preserved in 22q11.2DS. Insular activation was associated with task performance within the 22q11.2DS sample. Differences in cortical surface area, but not cortical thickness, showed spatial alignment with an activation pattern associated with face processing. These findings suggest that relative to matched controls, primary visual processing and insular function are relatively intact in individuals with 22q11.22DS, while motor feedback, face processing, and emotional memory processes are more affected. Such insights may help inform potential interventional targets and enhance the specificity of neuroimaging indices of cognitive dysfunction in 22q11.2DS.

Pictorial essay: How co-registered BOLD fMRI and DTI data can improve diffusion tensor tractography

Diffusion tensor tractography (DTT) and blood oxygen level dependent fMRI are tools frequently used in the preoperative evaluation of patients with brain tumors. A major limitation of DTT is difficulty in obtaining a relatively complete, useful representation of a white matter tract of interest due to challenges with artificial truncation of tracts. This pictorial essay will illustrate how coregistration of fMRI and DTI can be performed in clinical practice to obtain better, more complete tractograms of the pyramidal tract and arcuate fasciculus in patients with brain tumors.

Neuroinflammatory and functional connectivity signatures in radicular and axial chronic low back pain

We recently showed elevated levels of the 18kDa translocator protein (TSPO), a marker of neuroinflammation, in chronic low back pain patients (cLBP) compared to healthy controls(Loggia et al., 2015). Here, we test whether TSPO signal 1) can further subtype cLBP patients based on their clinical presentation, and 2) is associated with functional connectivity measures (because neuroinflammation may affect neuronal communication(Clark et al., 2015)). Patients with axial (cLBPAX; n=26; 43.7±16.6 y.o.) or radicular cLBP (cLBPRAD; n=28; 48.3±13.2 y.o.) received an integrated PET/MRI scan with the TSPO ligand [11C]PBR28. TSPO signal was quantified using standardized uptake values normalized by whole-brain signal (SUVR). Functional connectivity from primary somatosensory cortex (S1) (i.e., a region significant in the PET group comparison) was calculated from BOLD resting-state fMRI data. Connectivity and PET measures were compared across groups, correlated with and against each other and against the Fibromyalgia Screening Questionnaire (FSQ) scores, measure of pain “centralization”. Because two different scanners were used (BrainPET: 10 cLBPAX, 15 cLBPRAD; Biograph mMR: 16 cLBPAX, 13 cLBPRAD), scanner was included as a covariate in all analyses. Moreover, PET analyses also included Ala147Thr TSPO genotype, which predicts binding affinity to [11C]PBR28. In S1, TSPO signal and functional connectivity to the thalamus were: 1) higher in cLBPRAD compared to cLBPAX; 2) positively correlated with each other and 3) positively correlated with FSQ scores. Our data support the existence of different “neuroinflammatory signatures” in patients with different clinical presentation and that S1 neuroinflammatory signal is more pronounced in patients with higher pain “centralization”. Further, because S1 TSPO signal was correlated to S1-thalamus connectivity, our data support an association between neuroinflammatory changes and changes in neuronal communication, possibly indicating that the observed alterations reflect “neurogenic neuroinflammation”. Future studies are needed to determine whether neuroinflammation and related connectivity changes are possible therapeutic targets for chronic pain. 1R21NS087472-01A1; 1R01NS095937-01A; 1R01NS094306-01A1; W81XWH-14-1-0543.

S1 functional connectivity during rest and electro-acupuncture tracks median nerve and patient function improvement following acupuncture for carpal tunnel syndrome

Carpal Tunnel Syndrome (CTS) is an entrapment neuropathy of the median nerve that alters the organization of the brain's primary somatosensory cortex (S1), both structurally and functionally. Our prior research also demonstrated that electro-acupuncture (EA), both local and remote/distal to the wrist, but not sham acupuncture, improved S1 somatotopy and peripheral (median) nerve conduction. However, the mechanisms linking EA-evoked brain response during therapy with improved clinical outcomes are unknown. We investigated functional connectivity during both rest and sustained electro-acupuncture at baseline in healthy controls and CTS patients and after 8 weeks of acupuncture therapy (local, distal, or sham EA) in CTS patients. Six minutes of 3T BOLD fMRI data were collected at rest and during EA. Data were processed with FMRIPREP and FSL to generate maps of connectivity between the S1 hand region and the rest of the brain (z > 2.3, pFWE

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Evaluation of brain function during different types of breathing using FDG-PET compared with using BOLD-fMRI

Mouth breathing can occur due to obstructive, habitual, or anatomical factors, and it causes various side effects such as decreased blood oxygen saturation, respiratory diseases, skeletal disorders in the facial area, decreased concentration, and sleep disorders. However, previous studies on these side effects of mouth breathing have mainly focused on structural changes. Because oxygen and glucose are essential for brain metabolism, studies on their changes due to mouth breathing have been required. Therefore, this study was to investigate the activation of brain regions during different types of breathing (nasal and mouth breathing) using fluorodeoxyglucose (FDG) positron emission tomography (PET) and blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). FDG-PET and BOLD-fMRI data were obtained from ten healthy subjects. Image pre-processing and group analysis were conducted using statistical parametric mapping (SPM12). FDG-PET showed that cerebral areas, such as middle frontal, superior and inferior parietal and inferior frontal gyrus, were highly active during nasal breathing and that the cerebellar areas were highly active during mouth breathing. BOLD-fMRI showed that the inferior occipital and the superior frontal gyrus, and the gyrus rectus were highly active during nasal breathing. This study confirmed that mouth breathing interferes with the normal functioning of the cerebrum, such as its metabolism, thereby reducing the activations of the olfactory, autonomic nervous system, as well as the default mode network. These, along with structural changes, may cause deteriorations in brain functions.

Neural bases of risky decisions involving nicotine vapor versus monetary reward

Substantial effort has gone into neuroimaging studies of neural mechanisms underlying addiction. Human studies of smoking typically either give monetary reward during an fMRI task or else allow subjects to smoke outside the scanner, after the session. This raises a fundamental issue of construct validity, as it is unclear whether the same neural mechanisms process decisions about nicotine that process decisions about money. To address this, we developed a novel MR-compatible nicotine vaping device, such that access to nicotine vapor could be controlled and monitored. We recruited heavy smokers (Money: 45 subjects, 13 females and 32 males; Nicotine: 21 subjects, 4 females and 17 males) to perform a gambling task with nicotine and monetary reward on separate days. We collected BOLD fMRI data while they performed the task inside the scanner and analyzed it using general linear modeling, with inference based on cluster-size correction. This allowed a direct comparison between the neural mechanisms of choosing and receiving immediate drug vs. monetary reward. We found substantial differences in the neural mechanisms that underlie risky choices about money vs. drug reward, including a reversal of the well-known error effects in the medial prefrontal cortex.

Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold.

The acquisition of functional magnetic resonance imaging (fMRI) images of blood oxygen level-dependent (BOLD) effect and the signals to be analyzed is based on weak changes in the magnetic field caused by small changes in blood oxygen physiological levels, which are weak signals and complex in noise. In order to model and analyze the pathological and hemodynamic parameters of BOLD-fMRI images effectively, it is urgent to use effective signal analysis techniques to reduce the interference of noise and artifacts. In this paper, the noise characteristics of functional magnetic resonance imaging and the traditional signal denoising methods are analyzed. The Bayesian decision criterion takes into account the probability of the total occurrence of all kinds of references and the loss caused by misjudgment and has strong discriminability. So, an improved adaptive wavelet threshold denoising method based on Bayesian estimation is proposed. By using the correlation characteristics of multiscale wavelet coefficients, the corresponding wavelet components of useful signals and noises are processed differently; while retaining useful frequency information, the noise is weakened to the greatest extent. The new adaptive threshold wavelet denoising method based on Bayesian estimation is applied to the actual experiment, and the results of OEF (oxygen extraction fraction) are optimized. A series of simulation experiments are carried out to verify the effectiveness of the proposed method.