Identify Brain Activity Research Articles

Vowel speech recognition from rat electroencephalography using long short-term memory neural network.

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen’s κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

Patch tensor decomposition and non-local means filter-based hybrid ASL image denoising

BackgroundArterial spin labeling magnetic resonance imaging (ASL MRI) is a noninvasive technique to measure cerebral blood flow (CBF). It is widely used in the study of neurodegenerative diseases. Image denoising is an important step in ASL image processing because the signal-to-noise ratio (SNR) of an ASL CBF perfusion image is very small. New methodWe propose a new ASL image denoising method that exploits patch-based low-rank and sparse tensor decomposition and a non-local means filter. Comparison with existing methodsThe proposed method was compared with two existing ASL denoising methods: component-based noise correction method (CompCor) and low-rank and sparse matrix decomposition-based ASL image denoising method (LS-ASLd). ResultsVarious image quality measures, namely SNR, tSNR and ASL CBF variance, show that the proposed method is more effective than existing ASL denoising methods. The proposed method was used to denoise images from a resting state ASL dataset to compute brain functional connectivity (FC) and images from a task-related ASL dataset to identify brain activation. The results show that the proposed denoising method is more effective to enhance the sensitivity of ASL CBF series when undertaking CBF time series-based FC analysis and task activation detection. ConclusionsAssessment of the performance of the proposed hybrid ASL CBF image denoising method confirms that it is especially well-suited to FC analysis and sensorimotor task analysis.

Modeling and simulation of a network of neurons regarding Glucose Transporter Deficiency induced epileptic seizures

<abstract><p>Epilepsy is a complex phenomena of a system of highly intensive and synchronized neurons simultaneously firing which can be traced to spatial and temporal patterns. Seizures are a well known physical feature for all types of epileptic disorders. The rhythms, patterns, and oscillatory dynamics explain the mechanistic nature of neurons especially in absence seizures. Previous models such as Wilson-Cowan (1973), introduced brain models showing the dynamics of a network of neurons consisting of excitatory and inhibitory neurons. Taylor et al. (2014) then adapted the Wilson-Cowan model to epileptic seizures using a thalamo-cortical based theory. Fan et al. (2018) projects that thalamic reticulus nuclei control spike wave discharges specifically in absence seizures. We identify brain activity patterns specific to Glucose (G1D) Transport Deficiency Epilepsy in a network model based on electroencephalogram device (EEG) data. Additionally, we study the EEG patterns to identify the plausible mechanism that causes G1D epileptic behavior. Our coupled thalamo-cortical model goes beyond a connection in a logical unidirectional pattern shown by Fan or in a bidirectional small world pattern. Our model is a network based on paired correlation of EEG signals more analogous to realistic seizure activity. Using our model, we are able to study stability analysis for equilibrium and periodic behavior. We also identify parameter values which cause synchronized activity or more stable activity. Lastly, we identify a synchronization index and sensitivity analysis regarding parameters that directly affect Spike Wave Discharges and other spiking behavior. We will show how our 32-unit data-driven network model reflects G1D seizure dynamics and discuss the limitations of the model.</p></abstract>

Feasibility Study to Identify Brain Activity and Eye-Tracking Features for Assessing Hazard Recognition Using Consumer-Grade Wearables in an Immersive Virtual Environment

Hazard recognition is vital to achieving effective safety management. Unmanaged or unrecognized hazards on construction sites can lead to unexpected accidents. Recent research has identified cognitive failures among workers as being a principal factor associated with poor hazard recognition levels. Therefore, understanding cognitive correlates of when individuals recognize hazards versus when they fail to recognize hazards will be useful in combating poor hazard recognition. Such efforts are now possible with recent advances in electroencephalograph (EEG) and eye-tracking technologies. This paper presents a feasibility study that combines EEG and eye tracking in an immersive virtual environment (IVE) to predict when safety hazards will be successfully recognized during hazard recognition efforts using machine learning techniques. Workers wear a virtual reality (VR) head-mounted device (HMD) that is equipped with an eye-tracking sensor. Together with an EEG sensor, brain activities and eye movements are recorded as the workers navigate a simulated virtual construction site and recognize safety hazards. Through an experiment and a feature extraction and selection process, 13 best features out of 306 features from EEG and eye tracking were selected to train a machine learning model. The results show that EEG and eye tracking together can be leveraged to predict when individuals will recognize safety hazards. The developed IVE can be potentially used to first identify hazard types that are correlated with higher arousal and valence. Also, the developed IVE can be potentially used to evaluate the correlation among arousal, valence, and hazard recognition.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

Social exclusion reliably engages the default network: A meta-analysis of Cyberball

Social exclusion refers to the experience of being disregarded or rejected by others and has wide-ranging negative consequences for well-being and cognition. Cyberball, a game where a ball is virtually tossed between players, then leads to the exclusion of the research participant, is a common method used to examine the experience of social exclusion. The neural correlates of social exclusion remain a topic of debate, particularly with regards to the role of the dorsal anterior cingulate cortex (dACC) and the concept of social pain. Here we conducted a quantitative meta-analysis using activation likelihood estimation (ALE) to identify brain activity reliably engaged by social exclusion during Cyberball task performance (Studies = 53; total N = 1,817 participants). Results revealed consistent recruitment in ventral anterior cingulate and posterior cingulate cortex, inferior and superior frontal gyri, posterior insula, and occipital pole. No reliable activity was observed in dACC. Using a probabilistic atlas to define dACC, fewer than 15% of studies reported peak coordinates in dACC. Meta-analytic connectivity mapping suggests patterns of co-activation are consistent with the topography of the default network. Reverse inference for cognition associated with reliable Cyberball activity computed in Neurosynth revealed social exclusion to be associated with cognitive terms Social, Autobiographical, Mental States, and Theory of Mind. Taken together, these findings highlight the role of the default network in social exclusion and warns against interpretations of the dACC as a key region involved in the experience of social exclusion in humans.

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

Predicting memory from study-related brain activity

To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly contrast brain activity during study of later-remembered versus later-forgotten items. This "subsequent memory effect" method has been described as identifying brain activity "predictive" of memory outcome. However, the modern field of machine learning distinguishes between descriptive analysis, subject to overfitting, and true prediction, that can classify untrained data. First, we tested whether classic event-related potential signals were, in fact, predictive of later old/new recognition memory (N = 62, 225 items/participant); this produced significant but small predictive success. Next, pattern classification of the multivariate spatiotemporal features of the single-trial EEG waveform also succeeded in predicting memory. However, the prediction was still small in magnitude. In addition, topographic maps suggested individual differences in sources of predictive activity. These findings suggest that, on average, brain activity, measured by EEG, during the study phase is only marginally predictive of subsequent memory. It is possible that this predictive approach will succeed better when other experimental factors known to influence memory outcome are also integrated into the models.NEW & NOTEWORTHY For both basic and applied reasons, an important goal is to identify brain activity present while people study materials that enable us to predict whether they will remember those materials. We show that this is possible with the conventional event-related potential "subsequent-memory-effect" signals as well as with machine learning classifiers, but only to a small degree. This is in line with behavioral research, which supports many determinants of memory apart from the cognitive processes during study.

Model-Based and Model-Free Analyses of the Neural Correlates of Tongue Movements.

The tongue performs movements in all directions to subserve its diverse functions in chewing, swallowing, and speech production. Using task-based functional MRI in a group of 17 healthy young participants, we studied (1) potential differences in the cerebral control of frontal (protrusion), horizontal (side to side), and vertical (elevation) tongue movements and (2) inter-individual differences in tongue motor control. To investigate differences between different tongue movements, we performed voxel-wise multiple linear regressions. To investigate inter-individual differences, we applied a novel approach, spatio-temporal filtering of independent components. For this approach, individual functional data were decomposed into spatially independent components and corresponding time courses using independent component analysis. A temporal filter (correlation with the expected brain response) was used to identify independent components time-locked to the tongue motor tasks. A spatial filter (cross-correlation with established neurofunctional systems) was used to identify brain activity not time-locked to the tasks. Our results confirm the importance of an extended bilateral cortical and subcortical network for the control of tongue movements. Frontal (protrusion) tongue movements, highly overlearned movements related to speech production, showed less activity in the frontal and parietal lobes compared to horizontal (side to side) and vertical (elevation) movements and greater activity in the left frontal and temporal lobes compared to vertical movements (cluster-forming threshold of Z > 3.1, cluster significance threshold of p < 0.01, corrected for multiple comparisons). The investigation of inter-individual differences revealed a component representing the tongue primary sensorimotor cortex time-locked to the task in all participants. Using the spatial filter, we found the default mode network in 16 of 17 participants, the left fronto-parietal network in 16, the right fronto-parietal network in 8, and the executive control network in four participants (Pearson's r > 0.4 between neurofunctional systems and individual components). These results demonstrate that spatio-temporal filtering of independent components allows to identify individual brain activity related to a specific task and also structured spatiotemporal processes representing known neurofunctional systems on an individual basis. This novel approach may be useful for the assessment of individual patients and results may be related to individual clinical, behavioral, and genetic information.

Increased Amygdala Activations during the Emotional Experience of Death-Related Pictures in Complicated Grief: An fMRI Study.

Complicated grief (CG) is associated with alterations in various components of emotional processing. The main aim of this study was to identify brain activations in individuals diagnosed with CG while they were observing positive, negative, and death-related pictures. The participants included 19 individuals with CG and 19 healthy non-bereaved (NB) individuals. Functional magnetic resonance imaging (fMRI) scans were obtained during an emotional experience task. The perception of death-related pictures differed between the CG group and the NB group, with a greater activation in the former of the amygdala, putamen, hypothalamus, middle frontal gyrus, and anterior cingulate cortex. Amygdala and putamen activations were significantly correlated with Texas Revised Inventory of Grief scores in the CG group, suggesting that the higher level of grief in this group was associated with a greater activation in both brain areas while watching death-related pictures. A significant interaction between image type and group was observed in the amygdala, midbrain, periaqueductal gray, cerebellum, and hippocampus, largely driven by the greater activation of these areas in the CG group when watching death-related pictures and the lower activation when watching positive-valence pictures. In this study, individuals with CG showed significantly distinct brain activations in response to different emotional images.

Brain responses to human-voice processing predict child development and intelligence.

Children make rapid transitions in their neural and intellectual development. Compared to other brain regions, the auditory cortex slowly matures, and children show immature auditory brain activity. This auditory neural plasticity largely occurs as a response to human‐voice stimuli, which are presented more often than other stimuli, and can even be observed in the brainstem. Early psychologists have proposed that sensory processing and intelligence are closely related to each other. In the present study, we identified brain activity related to human‐voice processing and investigated a crucial neural correlate of child development and intelligence. We also examined the neurophysiological activity patterns during human‐voice processing in young children aged 3 to 8 years. We investigated auditory evoked fields (AEFs) and oscillatory changes using child‐customized magnetoencephalography within a short recording time (<6 min). We examined the P1m component of AEFs, which is a predominant component observed in young children. The amplitude of the left P1m was highly correlated with age, and the amplitude of the right P1m was highly correlated with the intelligence quotient. For auditory‐related oscillatory changes, we found a positive correlation between the intelligence quotient and percent change of gamma increase relative to baseline in the right auditory cortex. We replicated the finding of age‐related changes in auditory brain activity in young children, which is related to the slow maturation of the auditory cortex. In addition, these results suggest a close link between intelligence and auditory sensory processing, especially in the right hemisphere.

Why do students fall into webtoon viewing while they give up mathematics - An fMRI study

The purpose of this study was to identify brain activation regions related to flow and motivation during mathematics problem solving and webtoon viewing based on brain imaging technology. Researchers measured brain activation using functional magnetic resonance imaging (fMRI) and investigated flow and motivation through questionnaires. Significant differences between mathematics problem solving and webtoon viewing were found in high school students' brain activation regions. Based on the functions of these brain activation regions, result-oriented reward pursuit could play an important role in students' motivation-inducing during mathematics problem solving. However, investigation of flow and motivation from webtoon viewing were ascertained to be caused from pleasure from new information presented in the story and picture. Results of this study could help devise a specific strategy for mathematics problem solving instruction.

Resting-state amplitude of low-frequency fluctuation is associated with suicidal ideation.

Identifying brain activity patterns that are associated with suicidal ideation (SI) may help to elucidate its pathogenesis and etiology. Suicide poses a significant public health problem, and SI is a risk factor for suicidal behavior. Forty-one unmedicated adult participants in a major depressive episode (MDE), 26 with SI on the Beck Scale for Suicidal Ideation and 15 without SI, underwent resting-state functional magnetic resonance imaging scanning. Twenty-one healthy volunteers (HVs) were scanned for secondary analyses. Whole brain analysis of both amplitude of low-frequency fluctuations (ALFFs) and fractional ALFF was performed in MDE subjects to identify regions where activity was associated with SI. Subjects with SI had greater ALFF than those without SI in two clusters: one in the right hippocampus and one in the thalamus and caudate, bilaterally. Multi-voxel pattern analysis distinguished between those with and without SI. Post hoc analysis of the mean ALFF in the hippocampus cluster found it to be associated with a delayed recall on the Buschke memory task. Mean ALFF from the significant clusters was not associated with depression severity and did not differ between MDE and HV groups. These results indicate that SI is associated with altered resting-state brain activity. The pattern of elevated activity in the hippocampus may be related to how memories are processed.

Different memory patterns of digits: a functional MRI study

BackgroundPsychological investigations and functional imaging technology have been used to describe neural correlations of different types of memory with various stimuli. Memory with limited storage capacity and a short retention time can be classified as short-term memory (STM) while long-term memory (LTM) can be life-long without defined capacity.MethodsTo identify brain activation pattern associated with different modes of memory for numerical figures, we detected brain activities from twenty-two healthy subjects when performing three types of memory tasks for numbers, namely STM, LTM and working memory (WM), by using functional magnetic resonance imaging (fMRI) technique.ResultsThe result revealed variable patterns of activation in different brain regions responding to different types of memory tasks. The activation regions with primary processing and transient maintenance of STM for numerical figures are located in the visual cortex and mainly encoded by visual representations, while LTM was encoded by semantics and mainly recruiting left frontal cortex. We also found that subcortical structures, such as the caudate nucleus and the marginal division of the striatum, plays important roles in working memory.ConclusionsActivation of different brain regions in these three kinds of memories, indicating that different kinds of memories rely on different neural correlates and mental processes.

Removal of Artifacts from EEG Signals: A Review.

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

Simultaneous Monitoring of Hemodynamic Response in the Pre-Frontal Cortex and Genital Organ During Sexual Arousal Using Near-Infrared Spectroscopy

BackgroundThe monitoring of brain activity along with genital organ response to sexual stimulation can play an important role in understanding the under-lying mechanisms of sexual arousal as well as diagnosing erectile dysfunction. Several studies have observed brain activity corresponding to sexual stimuli, but only a few studies have shown a simultaneous measurement of brain activation and penile response.AimTo introduce near-infrared spectroscopy (NIRS) as a portable, easily implemented, and low-cost technique to simultaneously record brain activity and hemodynamics in the genital organ during sexual arousal.MethodsHemodynamic measurements of 15 healthy men were obtained using a home-built NIRS system. In the initial experiment, hemodynamics in the pre-frontal cortex (N = 10) were measured during visual sexual stimulation (VSS) and neutral visual stimulation (NVS) to identify brain activity related to sexual arousal. In the subsequent experiment, cerebral and penile hemodynamics were simultaneously measured (N = 5) using NIRS during VSS and NVS.ResultsThe pre-frontal cortex showed activity related to VSS but not to NVS. Simultaneous measurements showed a corresponding increase of penile oxygenated and deoxygenated hemoglobin concentration indicating an increase of blood volume associated with sexual arousal in healthy men. An average response delay of 4 seconds was observed in the hemodynamic changes between the brain and genital organ.ConclusionIn this preliminary study, we presented a NIRS system capable not only of detecting cerebral hemodynamic changes related to sexual arousal but also the simultaneous measurement of penile hemodynamics. We believe the NIRS system can be a potential technique to supplement the field of sexual medicine and can be expanded further to diagnose erectile dysfunction.Kim E, Kim S, Zephaniah PV, et al. Simultaneous Monitoring of Hemodynamic Response in the Pre-Frontal Cortex and Genital Organ During Sexual Arousal Using Near-Infrared Spectroscopy. Sex Med 2018;6:234–238.

Using Expectancy Theory to quantitatively dissociate the neural representation of motivation from its influential factors in the human brain: An fMRI study

Researchers have yet to apply a formal operationalized theory of motivation to neurobiology that would more accurately and precisely define neural activity underlying motivation. We overcome this challenge with the novel application of the Expectancy Theory of Motivation to human fMRI to identify brain activity that explicitly reflects motivation. Expectancy Theory quantitatively describes how individual constructs determine motivation by defining motivation force as the product of three variables: expectancy – belief that effort will better performance; instrumentality – belief that successful performance leads to particular outcome, and valence – outcome desirability. Here, we manipulated information conveyed by reward-predicting cues such that relative cue-evoked activity patterns could be statistically mapped to individual Expectancy Theory variables. The variable associated with activity in any voxel is only reported if it replicated between two groups of healthy participants. We found signals in midbrain, ventral striatum, sensorimotor cortex, and visual cortex that specifically map to motivation itself, rather than other factors. This is important because, for the first time, it empirically clarifies approach motivation neural signals during reward anticipation. It also highlights the effectiveness of the application of Expectancy Theory to neurobiology to more precisely and accurately probe motivation neural correlates than has been achievable previously.