Neural Basis of Social and Perceptual Decision-making in Humans

We make decisions in every moment of our lives. How the brain forms those decisions has been an active topic of inquiry in the field of brain science in recent years. In this dissertation, I discuss our recent neuroimaging studies in trying to uncover the functional architecture of the human brain during social and perceptual decision-making processes. Our decisions in social context vary tremendously with many factors including emotion, reward, social norms, treatments from others, cooperation, and dependence to others. We studied the neural basis of social decision-making processes with a functional magnetic resonance imaging (fMRI) experiment using three economic exchange games with undercompensating, nearly equal, and overcompensating offers. Refusals of undercompensating offers recruited the right dorsolateral prefrontal cortex (dlPFC). Accepting of overcompensating offers recruited the brain reward pathway consisting of the caudate, the cingulate cortex, and the thalamus. Protesting of decisions activated the network consisting of the right dlPFC, the left ventrolateral prefrontal cortex, and midbrain in the substantia nigra. These findings suggested that social decisions are the results of coordination between evaluated fairness norms, self-interest, and reward. In the topic of perceptual decision-making, we contributed to answering how diverse cortical structures are involved in relaying and processing of sensory information to make a sense of environment around us. We conducted two fMRI experiments. In the first experiment, we used an audio-visual (AV) synchrony and asynchrony perceptual categorization task. In the second experiment, we used a face-house categorization task. Stimuli in the second experiment included three levels of noise in face and house images. In AV, we investigated the effective connectivity within the salience network consisting of the anterior insulae and anterior cingulate cortex. In face-house, we discovered that the BOLD activity in the dlPFC, the bidirectional connectivity between the fusiform face area (FFA) and the parahippocampal place area (PPA), and the feedforward connectivity from these regions to the dlPFC increased with the noise level – thus with difficulty of decision-making. These results support that the FFA-PPA-dlPFC network plays an important role for relaying and integrating competing sensory information to arrive at perceptual decisions of face and house

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 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

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

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%

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

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

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

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, 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\u27 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

The subjective value of cognitive effort is encoded by a domain-general valuation network

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