fMRI Task Research Articles

Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification

Deep neural networks (DNNs) have been adopted widely as classifiers for functional magnetic resonance imaging (fMRI) data, advancing beyond traditional machine learning models. Consequently, transfer learning of the pre-trained DNN becomes crucial to enhance DNN classification performance, specifically by alleviating an overfitting issue that occurs when a substantial number of DNN parameters are fitted to a relatively small number of fMRI samples. In this study, we first systematically compared the two most popularly used, unsupervised pretraining models for resting-state fMRI (rfMRI) volume data to pre-train the DNNs, namely autoencoder (AE) and restricted Boltzmann machine (RBM). The group in-brain mask used when training AE and RBM displayed a sizable overlap ratio with Yeo's seven functional brain networks (FNs). The parcellated FNs obtained from the RBM were fine-grained compared to those from the AE. The pre-trained AE and RBM served as the weight parameters of the first of the two hidden DNN layers, and the DNN fulfilled the task classifier role for fMRI (tfMRI) data in the Human Connectome Project (HCP). We tested two transfer learning schemes: (1) fixing and (2) fine-tuning the DNN's pre-trained AE or RBM weights. The DNN with transfer learning was compared to a baseline DNN, trained using random initial weights. Overall, DNN classification performance from the transfer learning proved superior when the pre-trained RBM weights were fixed and when the pre-trained AE weights were fine-tuned (average error rates: 14.8% for fixed RBM, 15.1% fine-tuned AE, and 15.5% for the baseline model) compared to the alternative scenarios of DNN transfer learning schemes. Moreover, the optimal transfer learning scheme between the fixed RBM and fine-tuned AE varied according to seven task conditions in the HCP. Nonetheless, the computational load reduced substantially for the fixed-weight-based transfer learning compared to the fine-tuning-based transfer learning (e.g., the number of weight parameters for the fixed-weight-based DNN model reduced to 1.9% compared with a baseline/fine-tuned DNN model). Our findings suggest that weight initialization at the DNN's first layer using RBM-based pre-trained weights provides the most promising approach when the whole-brain fMRI volume supports associated task classification. We believe that our proposed scheme could be applied to a variety of task conditions to improve their classification performance and to utilize computational resources efficiently using our AE/RBM-based pre-trained weights compared to random initial weights for DNN training.

An information-theoretic analysis of resting-state versus task fMRI.

Resting-state fMRI is an increasingly popular alternative to task-based fMRI. However, a formal quantification of the amount of information provided by resting-state fMRI as opposed to active task conditions about neural responses is lacking. We conducted a systematic comparison of the quality of inferences derived from a resting-state and a task fMRI paradigm by means of Bayesian Data Comparison. In this framework, data quality is formally quantified in information-theoretic terms as the precision and amount of information provided by the data on the parameters of interest. Parameters of effective connectivity, estimated from the cross-spectral densities of resting-state- and task time series by means of dynamic causal modelling (DCM), were subjected to the analysis. Data from 50 individuals undergoing resting-state and a Theory-of-Mind task were compared, both datasets provided by the Human Connectome Project. A threshold of very strong evidence was reached in favour of the Theory-of-Mind task (>10 bits or natural units) regarding information gain, which could be attributed to the active task condition eliciting stronger effective connectivity. Extending these analyses to other tasks and cognitive systems will reveal whether the superior informative value of task-based fMRI observed here is case specific or a more general trend.

One Year of Outpatient Dialectical Behavioral Therapy and Its Impact on Neuronal Correlates of Attachment Representation in Patients with Borderline Personality Disorder Using a Personalized fMRI Task.

(1) Background: BPD is characterized by affect dysregulation, interpersonal problems, and disturbances in attachment, but neuroimaging studies investigating attachment representations in BPD are rare. No study has examined longitudinal neural changes associated with interventions targeting these impairments. (2) Methods: We aimed to address this gap by performing a longitudinal neuroimaging study on n = 26 patients with BPD treated with Dialectic Behavioral Therapy (DBT) and n = 26 matched healthy controls (HCs; post intervention point: n = 18 BPD and n = 23 HCs). For functional imaging, we applied an attachment paradigm presenting attachment related scenes represented in drawings paired with related neutral or personalized sentences from one's own attachment narratives. In a prior cross-sectional investigation, we identified increased fMRI-activation in the human attachment network, in areas related to fear response and the conflict monitoring network in BPD patients. These were especially evident for scenes from the context of loneliness (monadic pictures paired with individual narrative sentences). Here, we tested whether these correlates of attachment representation show a near-to-normal development over one year of DBT intervention. In addition, we were interested in possible associations between fMRI-activation in these regions-of-interest (ROI) and clinical scores. (3) Results: Patients improved clinically, showing decreased symptoms of borderline personality organization (BPI) and increased self-directedness (Temperament and Character Inventory, TCI) over treatment. fMRI-activation was increased in the anterior medial cingulate cortex (aMCC) and left amygdala in BPD patients at baseline which was absent after intervention. When investigating associations between scores (BPI, TCI) and functional activation, we found significant effects in the bilateral amygdala. In contrast, aMCC activation at baseline was negatively associated with treatment outcome, indicating less effective treatment effects for those with higher aMCC activation at baseline. (4) Conclusions: Monadic attachment scenes with personalized sentences presented in an fMRI setup are capable of identifying increased activation magnitude in BPD. After successful DBT treatment, these increased activations tend to normalize which could be interpreted as signs of a better capability to regulate intensive emotions in the context of "social pain" towards a more organized/secure attachment representation. Amygdala activation, however, indicates high correlations with pre-treatment scores; activation in the aMCC is predictive for treatment gain. Functional activation of the amygdala and the aMCC as a response to attachment scenes representing loneness at baseline might be relevant influencing factors for DBT-intervention outcomes.

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.

Decoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data. In this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair. We pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model's performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks. Our results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks.

Neural evidence for advantaged representation of first items in memory

Visual areas activated during perception can retain specific information held in memory without the presence of physical stimuli via distributed activity patterns. Neuroimaging studies have shown that the delay-period representation of information in visual areas is modulated by factors such as memory load and task demands, raising the possibility of serial position as another potential modulator. Specifically, enhanced representation of first items during the post-encoding delay period may serve as a mechanism underlying the well-established but not well-understood primacy effect – the mnemonic advantage of first items. To test this hypothesis, 13 males and 16 females performed a human fMRI task, wherein each trial consisted of the sequential encoding of two stimuli (a famous face and landscape, order counterbalanced), followed by a distracting task, a delay period, and then a cued recall of one of the items. Participants exhibited the expected behavioral primacy effect, manifested as faster recall of the first items. In order to elucidate the still debated neural underpinnings of this effect, using multivariate decoding, a classifier was trained on data collected during encoding to differentiate stimulus categories (i.e., faces vs. landscapes) and tested on data collected during the post-encoding period. Greater reactivation of first versus second items was observed in the ventral occipito-temporal cortex during the entire post-encoding period but not during encoding. Moreover, trial-level analyses revealed that the degree of first-item neural advantage during the post-encoding delay predicted the behavioral primacy effect. These findings highlight the role of item reinstatement in ventral occipito-temporal cortex in the primacy effect and are discussed in the context of the uniqueness of the very first item and event boundaries, illuminating putative neural mechanisms underlying the effect.

Neuroimaging of social motivation during winning and losing: Associations with social anhedonia across the psychosis spectrum

BackgroundIndividuals with psychosis spectrum disorders (PSD) have difficulty developing social relationships. This difficulty may reflect reduced response to social feedback involving functional alterations in brain regions that support the social motivation system: ventral striatum, orbital frontal cortex, insula, dorsal anterior cingulate cortex, and amygdala. Whether these alterations span PSD is unknown. Methods71 individuals with PSD, 27 unaffected siblings, and 37 control participants completed a team-based fMRI task. After each trial, participants received performance feedback paired with the expressive face of a teammate or opponent. A 2 × 2 (win versus loss outcome x teammate versus opponent) repeated measures ANOVA by group was performed on activation in the five key regions of interest during receipt of feedback. ResultsAcross groups, three social motivation regions, ventral striatum, orbital frontal cortex, and amygdala, showed sensitivity to feedback (significant main effect of outcome), with greater activation during win versus loss trials, regardless of whether the feedback was from a teammate or opponent. In PSD, ventral striatum and orbital frontal cortex activation to win feedback was negatively correlated with social anhedonia scores. ConclusionsPatterns of neural activation during social feedback were similar in PSD, their unaffected siblings, and healthy controls. Across the psychosis spectrum, activity in key social motivation regions during social feedback was associated with individual differences in social anhedonia.

IMG-01. LONGITUDINAL FMRI OF VISUAL, AUDITORY AND MOTOR FUNCTION IN PEDIATRIC CRANIOPHARYNGIOMA AFTER TREATMENT WITH PROTON RADIOTHERAPY

Abstract INTRODUCTION Craniopharyngioma accounts for approximately 4% of childhood central nervous system tumors and has a 5-year overall survival rate exceeding 90%. Survivors of childhood craniopharyngioma may experience neurologic sequalae, including visual and fine motor deficits, and subclinical hearing loss associated with tumor and treatment. Contemporary treatment includes radical surgery or more limited surgery and proton radiotherapy (PRT). Here we report on a longitudinal fMRI study probing visual, auditory, and motor function in pediatric craniopharyngioma patients. METHOD: The study (NCT01419067) was IRB approved and informed consent was obtained for each patient. Functional MR images were acquired at 4 time points: after surgery and before PRT (TP1), and 18-months (TP2), 36-months (TP3), and 60-months (TP4) after PRT. Data from a sensory survey fMRI task (visual: flashing checkerboard; auditory: dissonant ascending/descending pure tones; and sensory/motor: button pushing when stimuli were on) for 67 patients were analyzed at the 4 time points. The median age was 11.7 years (range 8.3-20.2 years) and the female/male ratio was 37/30. RESULTS Significant brain activation was seen in visual cortices, auditory cortices, and supplementary motor area (SMA) and motor cortices for all 4 time points. Over the time, visual and motor cortices showed minimum changes in activation; however, there was a small increase in SMA. For the auditory cortices, the activation volume in Heschl’s gyri increased bilaterally, and the lateralization index decreased (TP1: 70%; TP2: 46%; TP3: 39%; and TP4: 26%) due to larger increases in the right Heschl’s gyrus. CONCLUSION Increasing activation in Heschl’s gyri, especially on the right side, after PRT suggests decreasing efficiency in distinguishing tones in these patients. These findings may be associated with late effects involving the specific gyri or neuropathways affected by surgery or irradiation.

Deep Learning Modeling for Prediction of Cognitive Task Related Features from Resting-state fMRI Data

In recent years, several neural network methods, such as brain functional networks, have been proposed to efficiently learn non-Euclidean graph structures. In this study, we modified the connectivity-based graph convolutional network (cGCN) developed by Wang et al. (2021) for autism spectrum disorder (ASD) classification into a regression model and used resting-state functional magnetic resonance imaging (rs-fMRI) data to predict the scores on the offline Kohs block-design test for a total of 615 subjects aged 33–89 years. To convert from discrimination to regression, we employed a technique that introduces a fully connected layer in the cGCN and connects the long short-term memory (LSTM) in the last output phase instead of the Softmax layer. The results showed that our cGCN–LSTM was more accurate than the baseline LASSO regression model, and that the predictions correlated significantly with the measured scores of the cognitive function test. Moreover, we used the leave-one-out and leave-two-out occlusion methods to extract important regions of interest (ROIs), as well as networks from the model. It was acknowledged that the Kohs block-design test scores were negatively correlated with age, but the results suggested the possibility of age-related cognitive decline that could not be captured by age prediction models alone. We found that only the nodes of the default mode network and cerebellum contain some significant within-networks; however, overall, between-network connectivity overwhelmingly contributed to the prediction regardless of the weight of the role in the age projection. This model and the leave-two-out occlusion method allowed us to identify the regions and networks involved in further task-based fMRI experiments in advance. Our methodology has the potential to make the design of task fMRI experiments more rational and accurate before planning and conducting actual scans.

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.

Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.

Effects of schema on the relationship between post-encoding brain connectivity and subsequent durable memory

Schemas can facilitate memory consolidation. Studies have suggested that interactions between the hippocampus and the ventromedial prefrontal cortex (vmPFC) are important for schema-related memory consolidation. However, in humans, how schema accelerates the consolidation of new information and relates to durable memory remains unclear. To address these knowledge gaps, we used a human analogue of the rodent spatial schema task and resting-state fMRI to investigate how post-encoding brain networks can predict long-term memory performance in different schema conditions. After participants were trained to obtain schema-consistent or schema-inconsistent object-location associations, they learned new object-location associations. The new associations were tested after the post-encoding rest in the scanner and 24 h later outside the scanner. The Bayesian multilevel modelling was applied to analyse the post-encoding brain networks. The results showed that during the post-encoding, stronger vmPFC- anterior hippocampal connectivity was associated with durable memory in the schema-consistent condition, whereas stronger object-selective lateral occipital cortex (LOC)-ventromedial prefrontal connectivity and weaker connectivity inside the default mode network were associated with durable memory in the schema inconsistent condition. In addition, stronger LOC-anterior hippocampal connectivity was associated with memory in both schema conditions. These results shed light on how schemas reconfigure early brain networks, especially the prefrontal-hippocampal and stimuli-relevant cortical networks and influence long-term memory performance.

Parietal/premotor lesions effects on visuomotor cognition in neuro-oncology patients: A multimodal study

BackgroundAssessing prior to surgery the functionality of brain areas exposed near the tumor requires a multimodal approach that combines the use of neuropsychological testing and fMRI tasks. Paradigms based on motor imagery, which corresponds to the ability to mentally evoke a movement, in the absence of actual action execution, can be used to test sensorimotor areas and the functionality of mental motor representations. MethodsThe most commonly used paradigm is the Limb Laterality Recognition Task (LLRT), requiring judgments about whether a limb belongs to the left or right side of the body. The group studied included 38 patients with high-grade (N = 21), low-grade (N = 11) gliomas and meningiomas (N = 6) in areas anterior (N = 21) and posterior (N = 17) to the central sulcus. Patients before surgery underwent neuropsychological assessment and fMRI. They performed the LLRT as an fMRI task. Accuracy, and neuroimaging data were collected and combined in a multimodal study. Structural MRI data analyses were performed by subtracting the overlap of volumes of interest (VOIs) plotted on lesions from the impaired patient group vs the overlap of VOIs from the spared group. The fMRI analyses were performed comparing the impaired patients and spared group. ResultsIn general, patients were within normal limits on many neuropsychological screening tests. Compared with the control group, 17/38 patients had significantly different performance. The subtraction between the VOIs overlay of the impaired patients' group vs. the VOIs overlay of the spared group revealed that the areas maximally involved by lesions in the impaired patients' group were the right postcentral gyrus, right inferior parietal lobe, right supramarginal gyrus, right precentral gyrus, paracentral lobule, left postcentral gyrus, right superior parietal lobe, left inferior parietal lobe, and left superior and middle frontal gyrus. Analysis of the fMRI data showed which of these areas contributes to a correct LLRT performance. The task (vs. rest) in the group comparison (spared vs. impaired patients) activated a cluster in the left inferior parietal lobe. ConclusionUnderlying the altered performance at LLRT in patients with lesions to the parietal and premotor areas of the right and left hemispheres is a difference in activation of the left inferior parietal lobe. This region is involved in visuomotor processes and those related to motor attention, movement selection, and motor planning.

Statistical Inferences for Complex Dependence of Multimodal Imaging Data

Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this article, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence among imaging modalities over brain regions, (b) testing independence between brain regions within imaging modalities, and (c) testing independence between brain regions across different modalities. Considering a general form for all the three tests, we develop a global testing procedure and a multiple testing procedure controlling the false discovery rate. We study theoretical properties of the proposed tests and develop a computationally efficient distributed algorithm. The proposed methods and theory are general and relevant for many statistical problems of testing independence structure among the components of high-dimensional random vectors with arbitrary dependence structures. We also illustrate our proposed methods via extensive simulations and analysis of five task fMRI contrast maps in the HCP study. Supplementary materials for this article are available online.

Heightened adolescent emotional reactivity in the brain is associated with lower future distress tolerance and higher depressive symptoms

Distress tolerance, the ability to persist while experiencing negative psychological states, is essential for regulating emotions and is a transdiagnostic risk/resiliency trait for multiple psychopathologies. Studying distress tolerance during adolescence, a period when emotion regulation is still developing, may help identify early risk and/or protective factors. This study included 40 participants (mean scan age = 17.5 years) and using an emotional Go-NoGo functional magnetic resonance imaging task and voxel-wise regression analysis, examined the association between brain response during emotional face processing and future distress tolerance (two ± 0.5 years), controlling for sex assigned at birth, age, and time between visits. Post-hoc analyses tested the mediating role of distress tolerance on the emotional reactivity and depressive symptom relationship. Whole-brain analysis showed greater inferior occipital gyrus activation was associated with less distress tolerance at follow-up. The mediating role of distress tolerance demonstrated a trend-level indirect effect. Findings suggest that individuals who allocate greater visual resources to emotionally salient information tend to exhibit greater challenges in tolerating distress. Distress tolerance may help to link emotional reactivity neurobiology to future depressive symptoms. Building distress tolerance through emotion regulation strategies may be an appropriate strategy for decreasing depressive symptoms.

Preferential signal pathways during the perception and imagery of familiar scenes: An effective connectivity study.

The perception and imagery of landmarks activate similar content-dependent brain areas, including occipital and temporo-medial brain regions. However, how these areas interact during visual perception and imagery of scenes, especially when recollecting their spatial location, remains unknown. Here, we combined functional magnetic resonance imaging (fMRI), resting-state functional connectivity (rs-fc), and effective connectivity to assess spontaneous fluctuations and task-induced modulation of signals among regions entailing scene-processing, the primary visual area and the hippocampus (HC), responsible for the retrieval of stored information. First, we functionally defined the scene-selective regions, that is, the occipital place area (OPA), the retrosplenial complex (RSC) and the parahippocampal place area (PPA), by using the face/scene localizer, observing that two portions of the PPA-anterior and posterior PPA-were consistently activated in all subjects. Second, the rs-fc analysis (n = 77) revealed a connectivity pathway similar to the one described in macaques, showing separate connectivity routes linking the anterior PPA with RSC and HC, and the posterior PPA with OPA. Third, we used dynamic causal modelling to evaluate whether the dynamic couplings among these regions differ between perception and imagery of familiar landmarks during a fMRI task (n = 16). We found a positive effect of HC on RSC during the retrieval of imagined places and an effect of occipital regions on both RSC and pPPA during the perception of scenes. Overall, we propose that under similar functional architecture at rest, different neural interactions take place between regions in the occipito-temporal higher-level visual cortex and the HC, subserving scene perception and imagery.

Depressive and anxiety symptoms, and neural correlates of reward and punishment anticipation in female athletes with amenorrhea.

Studies in estrogen deficiency states such as primary ovarian insufficiency and Turner syndrome suggest that estrogen status may be an important modulator of mood and emotions. In this study we compared depressive and anxiety symptoms between adolescent and young adult female oligo-amenorrheic athletes (AA) and eumenorrheic females (EM), and explored structural, and functional changes in related brain areas during reward processing, a behavioral construct that is altered in depression and anxiety. We included (i) 24 AA participating in ≥4 hours/week of aerobic exercise or running ≥20 miles/week for ≥6 months in the preceding year, with lack of menstrual cycles for ≥3 months within at least 6 preceding months of oligo-amenorrhea, OR in premenarchal girls, absence of menses at >15 years), and (ii) 27 EM aged 14-25 years. Participants completed the Beck Depression Inventory-II (BDI-II), State and Trait Anxiety Inventory (STAI), and Mood and Anxiety Symptoms Questionnaire (MASQ). Structural MRI and brain activation during a functional MRI (fMRI) task that probes reward and punishment processing was examined in a subset of 10 AA and 23 EM. Median (IQR) age and BMI of AA and EM groups were 20.6 (19.0-22.6) vs. 20.6 (19.2-23.7) years, p=0.6 and v 20.3 (18.8-21.5) vs. 21.9 (19.6-23.5) kg/m2, p=0.005, respectively. While groups did not differ for BDI-II scores, AA had higher anhedonic depression MASQ scores (p=0.04), and STAI (p=0.03) scores vs. EM. In the fMRI subset, AA had higher caudate volumes vs. EM [F(1, 29)=9.930, p=0.004]. Lower activation observed in the right caudate during reward anticipation in AA compared with EM (p=0.036) suggests blunted reward processing in the striatum in estrogen deficient states. Athletes with amenorrhea had higher depressive and anxiety symptomatology compared to eumenorrheic young women. Exploratory analyses demonstrated increased caudate volumes and decreased caudate activation during reward processing in athletes with amenorrhea suggesting that estrogen may play a role in reward processing.

Correspondence between BOLD fMRI task response and cerebrovascular reactivity across the cerebral cortex.

BOLD sensitivity to baseline perfusion and blood volume is a well-acknowledged fMRI confound. Vascular correction techniques based on cerebrovascular reactivity (CVR) might reduce variance due to baseline cerebral blood volume, however this is predicated on an invariant linear relationship between CVR and BOLD signal magnitude. Cognitive paradigms have relatively low signal, high variance and involve spatially heterogenous cortical regions; it is therefore unclear whether the BOLD response magnitude to complex paradigms can be predicted by CVR. The feasibility of predicting BOLD signal magnitude from CVR was explored in the present work across two experiments using different CVR approaches. The first utilized a large database containing breath-hold BOLD responses and 3 different cognitive tasks. The second experiment, in an independent sample, calculated CVR using the delivery of a fixed concentration of carbon dioxide and a different cognitive task. An atlas-based regression approach was implemented for both experiments to evaluate the shared variance between task-invoked BOLD responses and CVR across the cerebral cortex. Both experiments found significant relationships between CVR and task-based BOLD magnitude, with activation in the right cuneus (R 2 = 0.64) and paracentral gyrus (R 2 = 0.71), and the left pars opercularis (R 2 = 0.67), superior frontal gyrus (R 2 = 0.62) and inferior parietal cortex (R 2 = 0.63) strongly predicted by CVR. The parietal regions bilaterally were highly consistent, with linear regressions significant in these regions for all four tasks. Group analyses showed that CVR correction increased BOLD sensitivity. Overall, this work suggests that BOLD signal response magnitudes to cognitive tasks are predicted by CVR across different regions of the cerebral cortex, providing support for the use of correction based on baseline vascular physiology.

Variational relevance evaluation of individual fMRI data enables deconstruction of task-dependent neural dynamics

In neuroimaging research, univariate analysis has always been used to localize “representations” at the microscale, whereas network approaches have been applied to characterize transregional “operations”. How are representations and operations linked through dynamic interactions? We developed the variational relevance evaluation (VRE) method to analyze individual task fMRI data, which selects informative voxels during model training to localize the “representation”, and quantifies the dynamic contributions of single voxels across the whole-brain to different cognitive functions to characterize the “operation”. Using 15 individual fMRI data files for higher visual area localizers, we evaluated the characterization of selected voxel positions of VRE and revealed different object-selective regions functioning in similar dynamics. Using another 15 individual fMRI data files for memory retrieval after offline learning, we found similar task-related regions working in different neural dynamics for tasks with diverse familiarities. VRE demonstrates a promising horizon in individual fMRI research.

A somato-cognitive action network alternates with effector regions in motor cortex

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate–isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

Exposure to Multicultural Context Affects Neural Response to Out-Group Faces: A Functional Magnetic Resonance Imaging Study.

Recent migration and globalization trends have led to the emergence of ethnically, religiously, and linguistically diverse countries. Understanding the unfolding of social dynamics in multicultural contexts becomes a matter of common interest to promote national harmony and social cohesion among groups. The current functional magnetic resonance imaging (fMRI) study aimed to (i) explore the neural signature of the in-group bias in the multicultural context; and (ii) assess the relationship between the brain activity and people's system-justifying ideologies. A sample of 43 (22 females) Chinese Singaporeans (M = 23.36; SD = 1.41) was recruited. All participants completed the Right Wing Authoritarianism Scale and Social Dominance Orientation Scale to assess their system-justifying ideologies. Subsequently, four types of visual stimuli were presented in an fMRI task: Chinese (in-group), Indian (typical out-group), Arabic (non-typical out-group), and Caucasian (non-typical out-group) faces. The right middle occipital gyrus and the right postcentral gyrus showed enhanced activity when participants were exposed to in-group (Chinese) rather than out-group (Arabic, Indian, and Caucasian) faces. Regions having a role in mentalization, empathetic resonance, and social cognition showed enhanced activity to Chinese (in-group) rather than Indian (typical out-group) faces. Similarly, regions typically involved in socioemotional and reward-related processing showed increased activation when participants were shown Chinese (in-group) rather than Arabic (non-typical out-group) faces. The neural activations in the right postcentral gyrus for in-group rather than out-group faces and in the right caudate in response to Chinese rather than Arabic faces were in a significant positive correlation with participants' Right Wing Authoritarianism scores (p < 0.05). Furthermore, the activity in the right middle occipital gyrus for Chinese rather than out-group faces was in a significant negative correlation with participants' Social Dominance Orientation scores (p < 0.05). Results are discussed by considering the typical role played by the activated brain regions in socioemotional processes as well as the role of familiarity to out-group faces.

Influence of Strenuous Physical Activity and Cardiorespiratory Fitness on Age-Related Differences in Brain Activations During Varieties of Cognitive Control

While there is extensive literature on the beneficial effects of physical activity on age differences in cognitive control, limited research exists on comparing the contributions of strenuous physical activity (sPA) and cardiorespiratory fitness (CRF) to fluctuations in the blood oxygen level-dependent (BOLD) signals during varieties of cognitive control. The current study addresses this gap in knowledge by investigating BOLD signal differences between high-fit and low-fit older adults, determined by their sPA or CRF, during a novel fMRI task with a hybrid block and event-related design that included transient activations (during switching, updating and their combination trials) and sustained activations (during proactive and reactive control blocks). fBOLD signals from older (n = 25) were compared to more functionally efficient younger (n = 15) adults. High-sPA old showed higher task accuracy than Low-sPA old and similar accuracy as young. Whole-brain fMRI analyses identified higher BOLD activations (esp. dlPFC/MFG) in high-fit old during updating and combination trials that were similar to young, suggesting maintenance of BOLD signals in higher fit older adults during working memory updating. Additionally, both High-sPA and High-CRF related compensatory overactivation were observed in left parietal and occipital areas during sustained activations, which were positively correlated with older adults' accuracy. These results suggest that physical fitness is a modifier of age-related changes in BOLD signal modulation elicited in response to increasing cognitive control demands, with higher fitness in old contributing to both compensatory overactivations and maintenance of task-related brain activations during cognitive control, whereas lower fitness contributed to maladaptive overactivations during lower cognitive demands.