Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care. METHODS: Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70). RESULTS: The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor. CONCLUSION: The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.

PURPOSE: Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS: GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models. RESULTS: The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks. CONCLUSION: We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.

Dorsal Anterior Cingulate Cortex Encodes the Integrated Incentive Motivational Value of Cognitive Task Performance.

Humans can seamlessly combine value signals from diverse motivational incentives, yet it is not well understood how these signals are "bundled" in the brain to modulate cognitive control. The dorsal ACC (dACC) is theorized to integrate motivational value dimensions in the service of goal-directed action, although this hypothesis has yet to receive rigorous confirmation. In the present study, we examined the role of human dACC in motivational incentive integration. Healthy young adult men and women were scanned with fMRI while engaged in an experimental paradigm that quantifies the combined effects of liquid (e.g., juice, neutral, saltwater) and monetary incentives on cognitive task performance. Monetary incentives modulated trial-by-trial dACC activation, whereas block-related effects of liquid incentives on dACC activity were observed. When bundled together, incentive-related dACC modulation predicted fluctuations in both cognitive performance and self-report motivation ratings. Statistical mediation analyses suggest that dACC encoded the incentives in terms of their integrated subjective motivational value, and that this value signal was most proximally associated with task performance. Finally, we confirmed that these incentive integration effects were selectively present in dACC. Together, the results support an account in which dACC integrates motivational signals to compute the expected value of goal-directed cognitive control.SIGNIFICANCE STATEMENT How are primary and secondary incentives integrated in the brain to influence goal-directed behavior? Using an innovative experimental fMRI paradigm that combines motivational incentives that have historically been studied independently between species (e.g., monetary rewards for humans, food rewards for animals), we examine the relationship between incentive motivational value and cognitive control allocation. We find evidence that the integrated incentive motivational value of combined incentives is encoded in human dorsal ACC. Further, self-reported motivational shifts mediated the effects of incentive-modulated dorsal ACC activity on task performance, revealing convergence in how self-reported and experimentally induced motivation are encoded in the human brain. Our findings may inform future translational studies examining affective/motivational and cognitive impairments in psychopathology (e.g., anxiety, depression, addiction).

The Subjective Value of Cognitive Effort is Encoded by a Domain-General Valuation Network.

Cognitive control is necessary for goal-directed behavior, yet people treat cognitive control demand as a cost, which discounts the value of rewards in a similar manner as other costs, such as delay or risk. It is unclear, however, whether the subjective value (SV) of cognitive effort is encoded in the same putatively domain-general brain valuation network implicated in other cost domains, or instead engages a distinct frontoparietal network, as implied by recent studies. Here, we provide rigorous evidence that the valuation network, with core foci in the ventromedial prefrontal cortex and ventral striatum, also encodes SV during cognitive effort-based decision-making in healthy, male and female adult humans. We doubly dissociate this network from frontoparietal regions that are instead recruited as a function of decision difficulty. We show that the domain-general valuation network jointly and independently encodes both reward benefits and cognitive effort costs. We also demonstrate that cognitive effort SV signals predict choice and are influenced by state and trait motivation, including sensitivity to reward and anticipated task performance. These findings unify cognitive effort with other cost domains, and suggest candidate neural mechanisms underlying state and trait variation in willingness to expend cognitive effort.SIGNIFICANCE STATEMENT Subjective effort costs are increasingly understood to diminish cognitive control over task performance and can thus undermine functioning across health and disease. Yet, we are only beginning to understand how decisions about cognitive effort are made. A key question is how subjective values are computed. Recent work suggests that the value of cognitive effort might be computed by networks that are distinct from those involved in other domains like intertemporal and risky decision-making, implying distinct mechanisms. Here we demonstrate that the domain-general network also encodes effort-discounted value, linking cognitive effort closely with other domains. Our results thus elucidate key mechanisms supporting decisions about cognitive effort, and point to candidate neural targets for intervention in disorders involving impaired cognitive motivation.

Exploring brain-behavior relationships in the N-back task.

Working memory (WM) function has traditionally been investigated in terms of two dimensions: within-individual effects of WM load, and between-individual differences in task performance. In human neuroimaging studies, the N-back task has frequently been used to study both. A reliable finding is that activation in frontoparietal regions exhibits an inverted-U pattern, such that activity tends to decrease at high load levels. Yet it is not known whether such U-shaped patterns are a key individual differences factor that can predict load-related changes in task performance. The current study investigated this question by manipulating load levels across a much wider range than explored previously (N â€‹= â€‹1-6), and providing a more comprehensive examination of brain-behavior relationships. In a sample of healthy young adults (n â€‹= â€‹57), the analysis focused on a distinct region of left lateral prefrontal cortex (LPFC) identified in prior work to show a unique relationship with task performance and WM function. In this region it was the linear slope of load-related activity, rather than the U-shaped pattern, that was positively associated with individual differences in target accuracy. Comprehensive supplemental analyses revealed the brain-wide selectivity of this pattern. Target accuracy was also independently predicted by the global resting-state connectivity of this LPFC region. These effects were robust, as demonstrated by cross-validation analyses and out-of-sample prediction, and also critically, were primarily driven by the high-load conditions. Together, the results highlight the utility of high-load conditions for investigating individual differences in WM function.

Age-related changes in neural mechanisms of prospective memory.

The capability to remember and execute intentions in the future - termed prospective memory (PM) - may be of special significance for older adults to enable successful completion of important activities of daily living. Despite the importance of this cognitive function, mixed findings have been obtained regarding age-related decline in PM, and, currently, there is limited understanding of potential contributing mechanisms. In the current study, older (N=41) and younger adults (N=47) underwent task-functional MRI during performance of PM conditions that encouraged either spontaneous retrieval (Focal) or sustained attentional monitoring (Non-focal) to detect PM targets. Older adults exhibited a reduction in PM-related sustained activity within the anterior prefrontal cortex (aPFC) and associated dorsal frontoparietal cognitive control network, due to an increase in non-specific sustained activation in (no-PM) control blocks (i.e., an age-related compensatory shift). Transient PM-trial specific activity was observed in both age groups within a ventral parietal memory network that included the precuneus. However, within a left posterior inferior parietal node of this network, transient PM-related activity was selectively reduced in older adults during the non-focal condition. These age differences in sustained and transient brain activity statistically mediated age-related declines in PM performance, and were potentially linked via age-related changes in functional connectivity between the aPFC and precuneus. Together, they support an account consistent with the Dual Mechanisms of Control framework, in which age-related PM declines are due to neural mechanisms that support proactive cognitive control processes, such as sustained attentional monitoring, while leaving reactive control mechanisms relatively spared.

Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity.

Oscillatory interactions within functionally specialized but distributed brain regions are believed to be central to perceptual and cognitive functions. Here, using human scalp electroencephalography (EEG) recordings combined with source reconstruction techniques, we study how oscillatory activity functionally organizes different neocortical regions during a tactile discrimination task near the limit of spatial acuity. While undergoing EEG recordings, blindfolded participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. The average brain response differed significantly for trials with correct and incorrect perceptual responses in the timeframe approximately between 130 and 175ms. During trials with correct responses, source-level peak activity appeared in the left primary somatosensory cortex (SI) at around 45ms, in the right lateral occipital complex (LOC) at 130ms, in the right posterior intraparietal sulcus (pIPS) at 160ms, and finally in the left dorsolateral prefrontal cortex (dlPFC) at 175ms. Spectral interdependency analysis of activity in these nodes showed two distinct distributed networks, a dominantly feedforward network in the beta band (12-30Hz) that included all four nodes and a recurrent network in the gamma band (30-100Hz) that linked SI, pIPS and dlPFC. Measures of network activity in both bands were correlated with the accuracy of task performance. These findings suggest that beta and gamma band oscillatory networks coordinate activity between neocortical regions mediating sensory and cognitive processing to arrive at tactile perceptual decisions.

Dorsal striatal response to taste is modified by obesity and insulin resistance.

OBJECTIVE: In preclinical models, insulin resistance in the dorsal striatum (DS) contributes to overeating. Although human studies support the concept of central insulin resistance, they have not investigated its effect on consummatory reward-induced brain activity. METHODS: Taste-induced activation was assessed in the caudate and putamen of the DS with blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Three phenotypically distinct groups were studied: metabolically healthy lean, metabolically healthy obesity, and metabolically unhealthy obesity (MUO; presumed to have central insulin resistance). Participants with MUO also completed a weight loss intervention followed by a second functional magnetic resonance imaging session. RESULTS: The three groups were significantly different at baseline consistent with the design. The metabolically healthy lean group had a primarily positive BOLD response, the MUO group had a primarily negative BOLD response, and the metabolically healthy obesity group had a response in between the two other groups. Food craving was predicted by taste-induced activation. After weight loss in the MUO group, taste-induced activation increased in the DS. CONCLUSIONS: These data support the hypothesis that insulin resistance and obesity contribute to aberrant responses to taste in the DS, which is only partially attenuated by weight loss. Aberrant responses to food exposure may be a barrier to weight loss.

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival.

Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.

Background: Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients. Methods: We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival. Results: Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%. Conclusions: These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Delay of gratification dissociates cognitive control and valuation brain regions in healthy young adults.

Delay of gratification (DofG) refers to an inter-temporal choice phenomenon that is of great interest in many domains, including animal learning, cognitive development, economic decision-making, and executive control. Yet experimental tools for investigating DofG in human adults are almost non-existent, and as a consequence, very little is known regarding the brain basis of core DofG behaviors. Here, we utilize a novel DofG paradigm, adapted for use in neuroimaging contexts, to examine event-related changes in neural activity as healthy young adult participants made repeated choices to continue waiting for a delayed reward, rather than take an immediately available one of lesser value. On DofG trials, choose-to-wait events were associated with increased activation in fronto-parietal and cingulo-opercular regions associated with cognitive control. Activity in the right lateral prefrontal cortex (PFC) was also associated with individual variability in task performance and strategy. Fronto-parietal activity was clearly dissociable from that observed in ventromedial PFC, as this latter region exhibited a ramping-up pattern of activity during the waiting period prior to reward delivery. Ventromedial PFC ramping activity dynamics were further selective to DofG trials associated with increased future reward rate, consistent with the involvement of this region in subjective reward valuation that incorporates higher-order task structure. These results provide important initial validation of this experimental paradigm as a useful tool for investigating and isolating unique DofG neural mechanisms, which can now be utilized to study a wide-variety of populations and task factors.

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability.

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients.

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

Multi-modal biomarkers of low back pain: A machine learning approach.

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66-0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

Examining delay of gratification in healthy aging.

Delay of gratification (DofG) refers to the capacity to forego an immediate reward in order to receive a more desirable reward later. As a core executive function, it might be expected that DofG would follow the standard pattern of age-related decline observed in older adults for other executive tasks. However, there actually have been few studies of aging and DofG, and even these have shown mixed results, suggesting the need for further investigation and new approaches. The present study tested a novel reward-based decision-making paradigm enabling examination of age-related DofG effects in adult humans. Results showed that older adults earned fewer overall rewards than young adults, both before and after instruction regarding the optimal DofG strategy. Prior to instruction, learning this strategy was challenging for all participants, regardless of age. The finding of age-related impairments even after strategy instruction indicated that these impairments were not due to a failure to understand the task or follow the optimal strategy, but instead were related to self-reported difficulty in waiting for delayed rewards. These results suggest the presence of age-related changes in DofG capacity and highlight the advantages of this new experimental paradigm for use in future investigations, including both behavioral and neuroimaging studies.

Face or House Image Perception: Beta and Gamma Bands of Oscillations in Brain Networks Carry Out Decision-Making.

Previous functional magnetic resonance imaging studies have consistently shown that perception of visual objects, such as faces and houses, involves distributed brain networks that include the fusiform face area (FFA), parahippocampal place area (PPA), and dorsolateral prefrontal cortex (DLPFC). These regions are commonly observed to be coactivated in BOLD measurements during perception of visual objects. In this study, we aimed to disentangle node-level and network-level activities in millisecond timescale of perception and decision-making in attempts to answer questions about timing and frequency of brain oscillatory activities. We used clear and noisy face-house image categorization tasks and human scalp electroencephalography recordings combined with source reconstruction techniques to study when and how oscillatory activity organizes within the FFA, PPA, and DLPFC. We uncovered the dynamics of two oscillatory networks-beta (13-30 Hz) and gamma (30-100 Hz). In beta band, the node and network activities were enhanced in time frame of 125-250 msec after stimulus onset, the FFA and PPA acted as main outflow hubs and the DLPFC as a main inflow hub, and network activities negatively correlated with behavior measures of noise levels (response times). In gamma band, node and network activities were elevated in time frame of 0-125 msec after stimulus onset, the DLPFC acted as a main outflow hub, and finally network activities were positively correlated with the noise level. These findings broaden our understanding of temporal evolution of node and network features associated with visual perceptual decision-making.

Salience Network Activity in Perceptual Decisions.

The dorsal anterior cingulate cortex (dACC) and the anterior insulae (AIs) are coactivated in various perceptual decision-making (PDM) tasks and form the salience network (SN): a key network in sensory perception and the coordination of behavioral responses. However, what the functional role of SN is, how these key SN nodes interact with each other to form a network in a perceptual decision, and how the network depends on the perceptual difficulty remain largely unknown. In the present study, we measured blood oxygen level-dependent (BOLD) signals using functional magnetic resonance imaging (fMRI). During four PDM tasks (1) face-house discrimination, (2) happy-angry face discrimination, (3) audiovisual asynchrony and synchrony perception, and a (4) random dot motion direction task, we varied the task difficulty and examined the interactions between these SN nodes. In all the experiments, behavioral accuracy decreased and response time increased with task difficulty. The BOLD signal increased in SN nodes with the ambiguity in the sensory information. We also found that there were significant directed functional connections between AIs and dACC in all four tasks and that the interactions between these nodes increased with task difficulty. The observed difficulty-dependent functional architecture of SN suggests that the dACC and AIs are part of a large-scale cognitive system that facilitates sensory integration in PDM.

The activity in the anterior insulae is modulated by perceptual decision-making difficulty.

Previous neuroimaging studies provide evidence for the involvement of the anterior insulae (INSs) in perceptual decision-making processes. However, how the insular cortex is involved in integration of degraded sensory information to create a conscious percept of environment and to drive our behaviors still remains a mystery. In this study, using functional magnetic resonance imaging (fMRI) and four different perceptual categorization tasks in visual and audio-visual domains, we measured blood oxygen level dependent (BOLD) signals and examined the roles of INSs in easy and difficult perceptual decision-making. We created a varying degree of degraded stimuli by manipulating the task-specific stimuli in these four experiments to examine the effects of task difficulty on insular cortex response. We hypothesized that significantly higher BOLD response would be associated with the ambiguity of the sensory information and decision-making difficulty. In all of our experimental tasks, we found the INS activity consistently increased with task difficulty and participants' behavioral performance changed with the ambiguity of the presented sensory information. These findings support the hypothesis that the anterior insulae are involved in sensory-guided, goal-directed behaviors and their activities can predict perceptual load and task difficulty.

Perceptual decision-making difficulty modulates feedforward effective connectivity to the dorsolateral prefrontal cortex.

Diverse cortical structures are known to coordinate activity as a network in relaying and processing of visual information to discriminate visual objects. However, how this discrimination is achieved is still largely unknown. To contribute to answering this question, we used face-house categorization tasks with three levels of noise in face and house images in functional magnetic resonance imaging (fMRI) experiments involving thirty-three participants. The behavioral performance error and response time (RT) were correlated with noise in face-house images. We then built dynamical causal models (DCM) of fMRI blood-oxygenation level dependent (BOLD) signals from the face and house category-specific regions in ventral temporal (VT) cortex, the fusiform face area (FFA) and parahippocampal place area (PPA), and the dorsolateral prefrontal cortex (dlPFC). We found a strong feed-forward intrinsic connectivity pattern from FFA and PPA to dlPFC. Importantly, the feed-forward connectivity to dlPFC was significantly modulated by the perception of both faces and houses. The dlPFC-BOLD activity, the connectivity from FFA and PPA to the dlPFC all increased with noise level. These results suggest that the FFA-PPA-dlPFC network plays an important role for relaying and integrating competing sensory information to arrive at perceptual decisions.

The Salience Network and Its Functional Architecture in a Perceptual Decision: An Effective Connectivity Study.

The anterior insulae (INSs) are involved in accumulating sensory evidence in perceptual decision-making independent of the motor response, whereas the dorsal anterior cingulate cortex (dACC) is known to play a role in choosing appropriate behavioral responses. Recent evidence suggests that INSs and dACC are part of the salience network (SN), a key network known to be involved in decision-making and thought to be important for the coordination of behavioral responses. However, how these nodes in the SN contribute to the decision-making process from segregation of stimuli to the generation of an appropriate behavioral response remains unknown. In this study, the authors scanned 33 participants in functional magnetic resonance imaging and asked them to decide whether the presented pairs of audio (a beep of sound) and visual (a flash of light) stimuli were synchronous or asynchronous. Participants reported their perception with a button press. Stimuli were presented in block of eight pairs with a temporal lag (ΔT) between the first (audio) and the second (visual) stimulus in each pair. They used dynamic causal modeling (DCM) and the Bayesian model evidence technique to elucidate the functional architecture between the nodes of SN. Both the synchrony and the asynchrony perception resulted in strong activation in the SN. Most importantly, the DCM analyses demonstrated that the INSs were integrating as well as driving hubs in the SN. The INSs were found to a play an important role in the integration of sensory information; input to the SN is most likely through INSs. Furthermore, significant INSs to dACC intrinsic connectivity established by these task conditions help us conclude that INSs drive the dACC to guide the behavior of choosing the appropriate response. The authors therefore argue that the dACC and INS are part of a system involved in the decision-making process from perception to planning of a motor response, and that this observed functional mechanism might be important during the performance of cognitively demanding goal-directed tasks.