Decision-making under risk: A graph-based network analysis using functional MRI

Mapping the Neural Basis of Neuroeconomics with Functional Magnetic Resonance Imaging: A Narrative Literature Review

Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies.

Diffusion tensor imaging brain structural clustering patterns in major depressive disorder

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .

Aberrant Cortical Connectivity During Ambiguous Object Recognition Is Associated With Schizophrenia

BACKGROUND

Dysfunctional connectivity within the perceptual hierarchy is proposed to be an integral component of psychosis. The fragmented ambiguous object task was implemented to investigate neural connectivity during object recognition in patients with schizophrenia (SCZ) and bipolar disorder and first-degree relatives of patients with SCZ (SREL).

METHODS

We analyzed 3T functional magnetic resonance imaging data collected from 27 patients with SCZ, 23 patients with bipolar disorder, 24 control subjects, and 19 SREL during the administration of the fragmented ambiguous object task. Fragmented ambiguous object task stimuli were line-segmented versions of objects and matched across a number of low-level features. Images were categorized as meaningful or meaningless based on ratings assigned by the participants.

RESULTS

An a priori region of interest was defined in the primary visual cortex (V1). In addition, the lateral occipital complex/ventral visual areas, intraparietal sulcus (IPS), and middle frontal gyrus (MFG) were identified functionally via the contrast of cortical responses to stimuli judged as meaningful or meaningless. SCZ was associated with altered neural activations at V1, IPS, and MFG. Psychophysiological interaction analyses revealed negative connectivity between V1 and MFG in patient groups and altered modulation of connectivity between conditions from right IPS to left IPS and right IPS to left MFG in patients with SCZ and SREL.

CONCLUSIONS

Results provide evidence that SCZ is associated with inefficient processing of ambiguous visual objects at V1, which is likely attributable to altered feedback from higher-level visual areas. We also observed distinct patterns of aberrant connectivity among low-level, mid-level, and high-level visual areas in patients with SCZ, patients with bipolar disorder, and SREL.

The Age-related Neural Strategy Alterations in Decision Making Under Risk

Previous studies have shown that aging is associated with changes in decision behavior. However, the neural mechanisms that underpin such age differences are inadequately understood. In this study, we aim to characterize the optimal neural model underlying a dynamic decision making task in both young and older adults, and further examine the age differences from the perspective of effective connectivity. Twenty-five young and 23 older adults performed a dynamic risk taking task, i.e., the balloon analogue risk task, in the functional magnetic resonance imaging scanner. The dynamic causal modeling analysis, with the coupling between the ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC) and anterior insula (AI) that were identified in our task-related activation and psychophysiological interaction analysis, was performed to address the best fitting neural model and characterize age differences. Although both age groups adopted the same optimal model with bidirectional connection between the VMPFC and DLPFC, older adults exhibited up-regulation in several connections and among which the increased modulatory effect of AI-to-VMPFC subserving their decision quality. Our finding suggests that older adults might utilize different neural strategy via compensation to counteract the impact of advanced age in risk taking process.

Psychopathy is associated with shifts in the organization of neural networks in a large incarcerated male sample

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Psychopathy is linked to disruptions in neural information processing.

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Graph analysis revealed that psychopathy impacts neural network organization.

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Psychopathy is linked to a hyper-efficiently organized dorsal attention network.

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Subcortical structures are less central to neural communication in psychopathy.

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No psychopathy differences were found in default or salience network graph metrics.

Psychopathy is a personality disorder defined by antisocial behavior paired with callousness, low empathy, and low interpersonal emotions. Psychopathic individuals reliably display complex atypicalities in emotion and attention processing that are evident when examining task performance, activation within specific neural regions, and connections between regions. Recent advances in neuroimaging methods, namely graph analysis, attempt to unpack this type of processing complexity by evaluating the overall organization of neural networks. Graph analysis has been used to better understand neural functioning in several clinical disorders but has not yet been used in the study of psychopathy. The present study applies a minimum spanning tree graph analysis to resting-state fMRI data collected from male inmates assessed for psychopathy with the Hare Psychopathy Checklist-Revised (n = 847). Minimum spanning tree analysis provides several metrics of neural organization optimality (i.e., the effectiveness, efficiency, and robustness of neural network organization). Results show that inmates higher in psychopathy exhibit a more efficiently organized dorsal attention network (β = =0.101, p_corrected = =0.018). Additionally, subcortical structures (e.g., amygdala, caudate, and hippocampus) act as less of a central hub in the global flow of information in inmates higher in psychopathy (β = =−0.104, p_corrected = =0.048). There were no significant effects of psychopathy on neural network organization in the default or salience networks. Together, these shifts in neural organization suggest that the brains of inmates higher in psychopathy are organized in a fundamentally different way than other individuals.

Transforming brain signals related to value evaluation and self-control into behavioral choices

The processes involved in value evaluation and self-control are critical when making behavioral choices. However, the evidence linking these two types of processes to behavioral choices in intertemporal decision-making remains elusive. As the ventromedial prefrontal cortex (vmPFC), striatum, and dorsolateral prefrontal cortex (dlPFC) have been associated with these two processes, we focused on these three regions. We employed functional magnetic resonance imaging during a delayed discounting task (DDT) using a relatively large sample size, three independent samples. We evaluated how much information about a specific choice could be decoded from local patterns in each brain area using multivoxel pattern analysis (MVPA). To investigate the relationship between the dlPFC and vmPFC/striatum regions, we performed a psychophysiological interaction (PPI) analysis. In Experiment I, we found that the vmPFC and dlPFC, but not the striatum, could determine choices in healthy participants. Furthermore, we found that the dlPFC showed significant functional connectivity with the vmPFC, but not the striatum, when making decisions. These results could be replicated in Experiment II with an independent sample of healthy participants. In Experiment III, the choice-decoding accuracy in the vmPFC and dlPFC was lower in patients with addiction (smokers and participants with Internet gaming disorder) than in healthy participants, and decoding accuracy in the dlPFC was related to impulsivity in addicts. Taken together, our findings may provide neural evidence supporting the hypothesis that value evaluation and self-control processes both guide the intertemporal choices, and might provide potential neural targets for the diagnosis and treatment of impulsivity-related brain disorders.

I lie, why don't you: Neural mechanisms of individual differences in self-serving lying

People tend to lie in varying degrees. To advance our understanding of the underlying neural mechanisms of this heterogeneity, we investigated individual differences in self-serving lying. We performed a functional magnetic resonance imaging study in 37 participants and introduced a color-reporting game where lying about the color would in general lead to higher monetary payoffs but would also be punished if get caught. At the behavioral level, individuals lied to different extents. Besides, individuals who are more dishonest showed shorter lying response time, whereas no significant correlation was found between truth-telling response time and the degree of dishonesty. At the neural level, the left caudate, ventromedial prefrontal cortex (vmPFC), right inferior frontal gyrus (IFG), and left dorsolateral prefrontal cortex (dlPFC) were key regions reflecting individual differences in making dishonest decisions. The dishonesty associated activity in these regions decreased with increased dishonesty. Subsequent generalized psychophysiological interaction analyses showed that individual differences in self-serving lying were associated with the functional connectivity among the caudate, vmPFC, IFG, and dlPFC. More importantly, regardless of the decision types, the neural patterns of the left caudate and vmPFC during the decision-making phase could be used to predict individual degrees of dishonesty. The present study demonstrated that lying decisions differ substantially from person to person in the functional connectivity and neural activation patterns which can be used to predict individual degrees of dishonesty.

Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study

Background: The functional mechanism behind autism spectrum disorder (ASD) is not clear, but it is related to a brain connectivity disorder. Previous studies have found that functional brain connectivity of ASD is linked to both increased connections and weakened connections, and the inconsistencies in functional brain connectivity may be related to age. The functional connectivity in adolescents and adults with ASD is generally less than in age-matched controls; functional connectivity in younger children with the disorder appears to be higher. As the basis of the functional network, the structural network is less studied. This study intends to further study the pathogenesis of ASD by analyzing the white matter network of ASD preschool children. Materials and Methods: In this study, Diffusion Tensor Imaging (DTI) was used to scan preschool children (aged 2-6 years, 39 children with ASD, 19 children as controls), and graph theory was used for analysis. Result: Enhanced topological network efficiency was found in the preschool children with ASD. A higher nodal efficiency was found in the left precuneus, thalamus, and bilateral superior parietal cortex, and the nodal efficiency of the left precuneus was positively associated with the severity of ASD. Conclusion: Our research shows the white matter network efficiency of preschoolers with ASD. It supports the theory of excessive early brain growth in ASD, and it shows left brain lateralization. It opens the way for new research perspectives of children with ASD.

Combining task-related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks

Task-related effects in functional magnetic resonance imaging (fMRI) data are usually analyzed with local activation approaches or integrative connectivity approaches, for example, by psychophysiological interaction (PPI) analysis. While both approaches are often applied to the same data set, a systematic combination of the results with a whole-brain (WB) perspective is rarely conducted and the relationship between task-dependent activation and connectivity effects is relatively unexplored. Here, we combined brain activation and graph theoretical analysis of WB-PPI results in an exemplary episodic memory data set of N = 136 healthy human participants and found regions with congruent as well as incongruent activation and connectivity changes between task and control conditions. A comparison with large-scale resting state networks showed that in congruent as well as incongruent regions task-positively modulated connections were mainly between-network connections, especially with the default mode network, while task-negatively modulated connections were mainly found within resting state networks. Over all regions, the strength of absolute activation effects was associated with the tendency to exhibit task-positive connectivity changes, mainly driven by a strong relationship in negatively activated regions. These results demonstrate that task demands lead to a complex modulation of brain networks and provide evidence that task-evoked activation and connectivity effects reflect separable and complementary information on the macroscale brain level assessed by fMRI. Hum Brain Mapp 38:5726-5739, 2017. © 2017 Wiley Periodicals, Inc.

A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning

Introduction

Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram.

Methods

A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions.

Results

In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation.

Conclusion

An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that belonged to the same class, thereby making it strong to separate the desired objects from the background.

Neural Mechanisms Underlying Compensatory and Noncompensatory Strategies in Risky Choice

Individuals may differ systematically in their applied decision strategies, which has critical implications for decision neuroscience but is yet scarcely studied. Our study's main focus was therefore to investigate the neural mechanisms underlying compensatory versus noncompensatory strategies in risky choice. Here, we compared people using a compensatory expected value maximization with people using a simplified noncompensatory loss-minimizing choice strategy. To this end, we used a two-choice paradigm including a set of "simple" items (e.g., simple condition), in which one option was superior on all attributes, and a set of "conflict" items, in which one option was superior on one attribute but inferior on other attributes. A binomial mixture analysis of the decisions elicited by these items differentiated between decision-makers using either a compensatory or a noncompensatory strategy. Behavioral differences were particularly pronounced in the conflict condition, and these were paralleled by neural results. That is, we expected compensatory decision-makers to use an integrated value comparison during choice in the conflict condition. Accordingly, the compensatory group tracked the difference in expected value between choice options reflected in neural activation in the parietal cortex. Furthermore, we expected noncompensatory, compared with compensatory, decision-makers to experience increased conflict when attributes provided conflicting information. Accordingly, the noncompensatory group showed greater dorsomedial PFC activation only in the conflict condition. These pronounced behavioral and neural differences indicate the need for decision neuroscience to account for individual differences in risky choice strategies and to broaden its scope to noncompensatory risky choice strategies.

Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques

Neuroimaging and neurophysiology have revealed that multiple areas in the prefrontal cortex (PFC) are activated in a specific memory task, but severity of impairment after PFC lesions is largely different depending on which activated area is damaged. The critical relationship between lesion sites and impairments has not yet been given a clear mechanistic explanation. Although recent works proposed that a whole-brain network contains hubs that play integrative roles in cortical information processing, this framework relying on an anatomy-based structural network cannot account for the vulnerable locus for a specific task, lesioning of which would bring impairment. Here, we hypothesized that (i) activated PFC areas dynamically form an ordered network centered at a task-specific “functional hub” and (ii) the lesion-effective site corresponds to the “functional hub,” but not to a task-invariant “structural hub.” To test these hypotheses, we conducted functional magnetic resonance imaging experiments in macaques performing a temporal contextual memory task. We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns. Using a novel simulated-lesion method based on support vector machine, we estimated severity of impairment after lesioning of each area, which accorded well with a known dissociation in contextual memory impairment in macaques (impairment after lesioning in area 9/46d, but not in area 8Ad). The predicted severity of impairment was proportional to the network “hubness” of the virtually lesioned area in the task-evoked directed connectivity network, rather than in the anatomical network known from tracer studies. Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs. These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

Patterns of whole-brain activity while macaques perform a memory retrieval task show that the task-specific functional hub in the dynamic cortical network predicts the task-specific consequences of brain damage better than a task-invariant structural hub does.

Patients with lesions in the front part of the brain’s frontal lobe—the prefrontal cortex—suffer from severe memory deficits. Neuroimaging and neurophysiology studies have revealed that multiple areas in the prefrontal cortex are activated during a specific memory task. However, the severity of the memory deficit after a lesion in the prefrontal cortex largely depends on which of the activated areas is damaged; lesions in only a fraction of the activated areas actually lead to memory deficits. It is currently unknown why some activated areas are “lesion effective” and others are not. Here, by using functional magnetic resonance imaging (fMRI) to measure macaque whole-brain activity during a memory task, we found that the activated areas and the task-specific functional connectivity among them formed a hierarchical network centered on a hub. The task-specific “functional hub” in this dynamic network accurately corresponds to the well-documented lesion-effective site and avoids the neighboring non-lesion-effective sites. Quantitatively, the predicted severity of memory impairment is proportional to the network “hubness” of the lesioned area in the functional network, rather than in the anatomical network, which is statically determined by axonal projection patterns. Our results suggest that the areas of the prefrontal cortex dynamically shape a hub-centric network, reallocating the lesion-effective site apart from the static anatomical hubs depending on the cognitive requirements of the specific memory task.

Neural mechanisms underlying urgent and evaluative behaviors: An fMRI study on the interaction of automatic and controlled processes

Dual-process theories have dominated the study of risk perception and risk-taking over the last two decades. However, there is a lack of objective brain-level evidence supporting the two systems of processing in every-day risky behavior. To address this issue, we propose the dissociation between evaluative and urgent behaviors as evidence of dual processing in risky driving situations. Our findings show a dissociation of evaluative and urgent behavior both at the behavioral and neural level. fMRI data showed an increase of activation in areas implicated in motor programming, emotional processing, and visuomotor integration in urgent behavior compared to evaluative behavior. These results support a more automatic processing of risk in urgent tasks, relying mainly on heuristics and experiential appraisal. The urgent task, which is characterized by strong time pressure and the possibility for negative consequences among others factors, creates a suitable context for the experiential-affective system to guide the decision-making process. Moreover, we observed greater frontal activation in the urgent task, suggesting the participation of cognitive control in safe behaviors. The findings of this research are relevant for the study of the neural mechanisms underlying dual process models in risky perception and decision-making, especially because of their proximity to everyday activities.

Anticipatory anxiety disrupts neural valuation during risky choice

Incidental negative emotions unrelated to the current task, such as background anxiety, can strongly influence decisions. This is most evident in psychiatric disorders associated with generalized emotional disturbances. However, the neural mechanisms by which incidental emotions may affect choices remain poorly understood. Here we study the effects of incidental anxiety on human risky decision making, focusing on both behavioral preferences and their underlying neural processes. Although observable choices remained stable across affective contexts with high and low incidental anxiety, we found a clear change in neural valuation signals: during high incidental anxiety, activity in ventromedial prefrontal cortex and ventral striatum showed a marked reduction in (1) neural coding of the expected subjective value (ESV) of risky options, (2) prediction of observed choices, (3) functional coupling with other areas of the valuation system, and (4) baseline activity. At the same time, activity in the anterior insula showed an increase in coding the negative ESV of risky lotteries, and this neural activity predicted whether the risky lotteries would be rejected. This pattern of results suggests that incidental anxiety can shift the focus of neural valuation from possible positive consequences to anticipated negative consequences of choice options. Moreover, our findings show that these changes in neural value coding can occur in the absence of changes in overt behavior. This suggest a possible pathway by which background anxiety may lead to the development of chronic reward desensitization and a maladaptive focus on negative cognitions, as prevalent in affective and anxiety disorders.

Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis

While fMRI activation studies contrasting task conditions regularly assess the whole brain, this is usually not true for studies analyzing task-dependent brain connectivity changes by psychophysiological interactions (PPI). Here we combine standard PPI (sPPI) and generalized PPI (gPPI) with a priori brain parcellation by spatially constrained normalized cut spectral clustering (NCUT) to analyze task-dependent connectivity changes in a whole brain manner, and compare the results to multiseed conventional PPI analyses over all activation peaks in an episodic memory recall task. We show that, depending on the chosen parcellation frame, the whole-brain PPI approach is able to detect a large amount of the information that is detected by the conventional approach. Over and above, whole-brain PPI allows identification of several additional task-modulated connections, particularly from seed regions without significant activation differences between conditions.

Age-related differences in advantageous decision making are associated with distinct differences in functional community structure

Human decision making is dependent on not only the function of several brain regions but also their synergistic interaction. The specific function of brain areas within the ventromedial prefrontal cortex has long been studied in an effort to understand choice evaluation and decision making. These data specifically focus on whole-brain functional interconnectivity using the principles of network science. The Iowa Gambling Task (IGT) was the first neuropsychological task used to model real-life decisions in a way that factors reward, punishment, and uncertainty. Clinically, it has been used to detect decision-making impairments characteristic of patients with prefrontal cortex lesions. Here, we used performance on repeated blocks of the IGT as a behavioral measure of advantageous and disadvantageous decision making in young and mature adults. Both adult groups performed poorly by predominately making disadvantageous selections in the beginning stages of the task. In later phases of the task, young adults shifted to more advantageous selections and outperformed mature adults. Modularity analysis revealed stark underlying differences in visual, sensorimotor and medial prefrontal cortex community structure. In addition, changes in orbitofrontal cortex connectivity predicted behavioral deficits in IGT performance. Contrasts were driven by a difference in age but may also prove relevant to neuropsychiatric disorders associated with poor decision making, including the vulnerability to alcohol and/or drug addiction.

Effects of transcranial direct current stimulation on risky decision making are mediated by 'hot' and 'cold' decisions, personality, and hemisphere

Previous results point towards a lateralization of dorsolateral prefrontal cortex (DLPFC) function in risky decision making. While the right hemisphere seems involved in inhibitory cognitive control of affective impulses, the left DLPFC is crucial in the deliberative processing of information relevant for the decision. However, a lack of empirical evidence precludes definitive conclusions. The aim of our study was to determine whether anodal transcranial direct current stimulation (tDCS) over the right DLPFC with cathodal tDCS over the lDLPFC (anodal right/cathodal left) or vice versa (anodal left/cathodal right) differentially modulates risk-taking in a task [the Columbia Card Task (CCT)] specifically engaging affect-charged (Hot CCT) vs. deliberative (Cold CCT) decision making. The facilitating effect of the anodal stimulation on neuronal activity was emphasized by the use of a small anode and a big cathode. To investigate the role of individual differences in risk-taking, participants were either smokers or non-smokers. Anodal left/cathodal right stimulation decreased risk-taking in the 'cold' cognition version of the task, in both groups, probably by modulating deliberative processing. In the 'hot' version, anodal right/cathodal left stimulation led to opposite effects in smokers and non-smokers, which might be explained by the engagement of the same inhibitory control mechanism: in smokers, improved controllability of risk-seeking impulsivity led to more conservative decisions, while inhibition of risk-aversion in non-smokers resulted in riskier choices. These results provide evidence for a hemispheric asymmetry and personality-dependent tDCS effects in risky decision making, and may be important for clinical research on addiction and depression.

Effective connectivity reveals strategy differences in an expert calculator

Mathematical reasoning is a core component of cognition and the study of experts defines the upper limits of human cognitive abilities, which is why we are fascinated by peak performers, such as chess masters and mental calculators. Here, we investigated the neural bases of calendrical skills, i.e. the ability to rapidly identify the weekday of a particular date, in a gifted mental calculator who does not fall in the autistic spectrum, using functional MRI. Graph-based mapping of effective connectivity, but not univariate analysis, revealed distinct anatomical location of "cortical hubs" supporting the processing of well-practiced close dates and less-practiced remote dates: the former engaged predominantly occipital and medial temporal areas, whereas the latter were associated mainly with prefrontal, orbitofrontal and anterior cingulate connectivity. These results point to the effect of extensive practice on the development of expertise and long term working memory, and demonstrate the role of frontal networks in supporting performance on less practiced calculations, which incur additional processing demands. Through the example of calendrical skills, our results demonstrate that the ability to perform complex calculations is initially supported by extensive attentional and strategic resources, which, as expertise develops, are gradually replaced by access to long term working memory for familiar material.

Detection of scale-freeness in brain connectivity by functional MRI: signal processing aspects and implementation of an open hardware co-processor

An outstanding issue in graph-theoretical studies of brain functional connectivity is the lack of formal criteria for choosing parcellation granularity and correlation threshold. Here, we propose detectability of scale-freeness as a benchmark to evaluate time-series extraction settings. Scale-freeness, i.e., power-law distribution of node connections, is a fundamental topological property that is highly conserved across biological networks, and as such needs to be manifest within plausible reconstructions of brain connectivity. We demonstrate that scale-free network topology only emerges when adequately fine cortical parcellations are adopted alongside an appropriate correlation threshold, and provide the full design of the first open-source hardware platform to accelerate the calculation of large linear regression arrays.

Graph analysis of the human connectome: promise, progress, and pitfalls

The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.

Functional brain networks before the onset of psychosis: A prospective fMRI study with graph theoretical analysis

Individuals with an at-risk mental state (ARMS) have a risk of developing a psychotic disorder significantly greater than the general population. However, it is not currently possible to predict which ARMS individuals will develop psychosis from clinical assessment alone. Comparison of ARMS subjects who do, and do not, develop psychosis can reveal which factors are critical for the onset of illness. In the present study, 37 patients with an ARMS were followed clinically at least 24 months subsequent to initial referral. Functional MRI data were collected at the beginning of the follow-up period during performance of an executive task known to recruit frontal lobe networks and to be impaired in psychosis. Graph theoretical analysis was used to compare the organization of a functional brain network in ARMS patients who developed a psychotic disorder following the scan (ARMS-T) to those who did not become ill during the same follow-up period (ARMS-NT) and aged-matched controls. The global properties of each group's representative network were studied (density, efficiency, global average path length) as well as regionally-specific contributions of network nodes to the organization of the system (degree, farness-centrality, betweenness-centrality). We focused our analysis on the dorsal anterior cingulate cortex (ACC), a region known to support executive function that is structurally and functionally impaired in ARMS patients. In the absence of between-group differences in global network organization, we report a significant reduction in the topological centrality of the ACC in the ARMS-T group relative to both ARMS-NT and controls. These results provide evidence that abnormalities in the functional organization of the brain predate the onset of psychosis, and suggest that loss of ACC topological centrality is a potential biomarker for transition to psychosis.