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Background: Alzheimer’s disease neuropathologic change (ADNC) may contribute to dementia in patients with Lewy body disease (LBD) pathology. Objective: To examine how co-occurring ADNC impacts domain specific cognitive impairments at each pathologic stage (brainstem, limbic, cerebral cortical) of LBD. Methods: 2,433 participants with antemortem longitudinal neuropsychological assessment and postmortem neuropathological assessment from the National Alzheimer’s Coordinating Center’s Uniform Data Set were characterized based on the evaluation of ADNC and LBD. Longitudinal mixed-models were used to derive measures of cumulative cognitive deficit for each cognitive domain at each pathologic stage of LBD (brainstem, limbic, and cerebral cortical). Results: 111 participants with a pathologic diagnosis of LBD, 741 participants with combined LBD and ADNC, 1,357 participants with ADNC only, and 224 with no pathology (healthy controls) were included in the analyses. In the executive/visuospatial domain, combined LBD and ADNC showed worse deficits than LBD only when Lewy bodies were confined to the brainstem, but no difference when Lewy bodies extended to the limbic or cerebral cortical regions. The cerebral cortical LBD only group exhibited greater executive/visuospatial deficits than the ADNC only group. By contrast, the ADNC only group and the combined pathology group both demonstrated significantly greater cumulative memory deficits relative to Lewy body disease only, regardless of stage. Conclusion: The impact of co-occurring ADNC on antemortem cumulative cognitive deficits varies not only by domain but also on the pathological stage of Lewy bodies. Our findings stress the cognitive impact of different patterns of neuropathological progression in Lewy body diseases.

Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://neurodata.io/mri/.

Identifying biomarkers that can assess the risk of developing Alzheimer’s Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.

Background Disrupted proactive cognitive control, a form of early selection and active goal maintenance, is hypothesized to underlie the broad cognitive deficits observed in patients with schizophrenia (SPs). Current research suggests that the disrupted activation within and connectivity between regions of the cognitive control network contribute to disrupted proactive cognitive control; however, no study has examined these mechanisms using an AX Continuous Performance Test task in schizophrenia. Methods Twenty-six SPs (17 male subjects; mean age 34.46 ± 8.77 years) and 28 healthy control participants (HCs; 16 male subjects; mean age 31.43 ± 7.23 years) underwent an electroencephalogram while performing the AX Continuous Performance Test. To examine the extent of activation and level of connectivity within the cognitive control network, power, intertrial phase clustering, and intersite phase clustering metrics were calculated and analyzed. Results SPs exhibited expected general decrements in behavioral performance relative to HCs and a more selective deficit in conditions requiring proactive cognitive control. Additionally, SPs exhibited deficits in midline theta power and connectivity during proactive cognitive control trials. Specifically, HCs exhibited significantly greater theta power for B cues relative to A cues, whereas SPs exhibited no significant differences between A- and B-cue theta power. Additionally, differential theta connectivity patterns were observed in SPs and HCs. Behavioral measures of proactive cognitive control predicted functional outcomes in SPs. Conclusions This study suggests that low-frequency midline theta activity is selectively disrupted during proactive cognitive control in SPs. The disrupted midline theta activity may reflect a failure of SPs to proactively recruit cognitive control processes.

Patients with psychotic spectrum disorders (PSD) exhibit similar patterns of atrophy and microstructural changes that may be associated with common symptomatology (e.g., symptom burden and/or cognitive impairment). Gray matter concentration values (proxy for atrophy), fractional anisotropy (FA), mean diffusivity (MD), intracellular neurite density (V(ic)) and isotropic diffusion volume (V(iso)) measures were therefore compared in 150 PSD (schizophrenia, schizoaffective disorder, and bipolar disorder Type I) and 63 healthy controls (HC). Additional analyses evaluated whether regions showing atrophy and/or microstructure abnormalities were better explained by DSM diagnoses, symptom burden or cognitive dysfunction. PSD exhibited increased atrophy within bilateral medial temporal lobes and subcortical structures. Gray matter along the left lateral sulcus showed evidence of increased atrophy and MD. Increased MD was also observed in homotopic fronto-temporal regions, suggesting it may serve as a precursor to atrophic changes. Global cognitive dysfunction, rather than DSM diagnoses or psychotic symptom burden, was the best predictor of increased gray matter MD. Regions of decreased FA (i.e., left frontal gray and white matter) and V(ic) (i.e., frontal and temporal regions and along central sulcus) were also observed for PSD, but were neither spatially concurrent with atrophic regions nor associated with clinical symptoms. Evidence of expanding microstructural spaces in gray matter demonstrated the greatest spatial overlap with current and potentially future regions of atrophy, and was associated with cognitive deficits. These results suggest that this particular structural abnormality could potentially underlie global cognitive impairment that spans traditional diagnostic categories.

Background Functional underpinnings of cognitive control deficits in unbiased samples (i.e., all comers) of patients with psychotic spectrum disorders (PSD) remain actively debated. While many studies suggest hypofrontality in the lateral prefrontal cortex (PFC) and greater deficits during proactive relative to reactive control, few have examined the full hemodynamic response. Methods Patients with PSD (n = 154) and healthy controls (n = 65) performed the AX continuous performance task (AX-CPT) during rapid (460 ms) functional neuroimaging and underwent full clinical characterization. Results Behavioural results indicated generalized cognitive deficits (slower and less accurate) across proactive and reactive control conditions in patients with PSD relative to healthy controls. We observed a delayed/prolonged neural response in the left dorsolateral PFC, the sensorimotor cortex and the superior parietal lobe during proactive control for patients with PSD. These proactive hemodynamic abnormalities were better explained by negative rather than by positive symptoms or by traditional diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, Text Revision (DSM-IV-TR), with subsequent simulations unequivocally demonstrating how these abnormalities could be erroneously interpreted as hypoactivation. Conversely, true hypoactivity, unassociated with clinical symptoms or DSM-IV-TR diagnoses, was observed within the ventrolateral PFC during reactive control. Limitations In spite of guidance for AX-CPT use in neuroimaging studies, one-third of patients with PSD could not perform the task above chance and were more clinically impaired. Conclusion Current findings question the utility of the AX-CPT for neuroimaging-based appraisal of cognitive control across the full spectrum of patients with PSD. Previously reported lateral PFC "hypoactivity" during proactive control may be more indicative of a delayed/prolonged neural response, important for rehabilitative purposes. Negative symptoms may better explain certain behavioural and hemodynamic abnormalities in patients with PSD relative to DSM-IV-TR diagnoses.

Sensorimotor synchronization (SMS) is frequently dependent on coordination of excitatory and inhibitory activity across hemispheres, as well as the cognitive control over environmental distractors. However, the timing (motor planning versus execution) and cortical regions involved in these processes remain actively debated. Functional magnetic resonance imaging data were therefore analyzed from 34 strongly right-handed healthy adults performing a cued (to initiate motor planning) SMS task with either their right or left hand (motor execution phase) based on spatially congruent or incongruent visual stimuli. Behavioral effects of incongruent stimuli were limited to the first stimulus. Functionally, greater activation was observed in left sensorimotor cortex (SMC) and right cerebellar Lobule V for congruent versus incongruent stimuli. A negative blood-oxygen level dependent response, a putative marker of neural inhibition, was present in bilateral SMC, right supplemental motor area (SMA) and bilateral cerebellar Lobule V during the motor planning, but not execution phase. The magnitude of the inhibitory response was greater in right cortical regions and cerebellar Lobule V. Homologue connectivity was associated with inhibitory activity in the right SMA, suggesting that individual differences in intrinsic connectivity may mediate transcallosal inhibition. In summary, results suggest increased inhibition (i.e., greater negative BOLD response) within the right relative to left hemisphere, which was released once motor programs were executed. Both task and intrinsic functional connectivity results highlight a critical role of the left SMA in interhemispheric inhibition and motor planning.

Cognitive impairment in older adults is a rapidly growing public health concern as the elderly population dramatically grows worldwide. While it is generally assumed that cognitive deficits in older adults are associated with reduced brain flexibility, quantitative evidence has been lacking. Here, we investigate brain flexibility in healthy older adults (ages 60–85) using a novel Bayesian switching dynamical system algorithm and ultrafast temporal resolution (TR = 490 ms) whole-brain fMRI data during performance of a Sternberg working memory task. We identify latent brain states and characterize their dynamic temporal properties, including state transitions, associated with encoding, maintenance, and retrieval. Crucially, we demonstrate that brain inflexibility is associated with slower and more fragmented transitions between latent brain states, and that brain inflexibility mediates the relation between age and cognitive inflexibility. Our study provides a novel neurocomputational framework for investigating latent dynamic circuit processes underlying brain flexibility and cognition in the context of aging.

An important dimension of individual differences, independent of general cognitive ability (GCA), is specialization for verbal or spatial ability. In this study we investigated neuroanatomic, network, and personality features associated with verbal vs. spatial ability. Healthy young adults (N = 244) were evaluated with (1) a cognitive battery yielding measures of verbal and spatial ability independent of GCA, (2) structural MRI scans providing measures of surface area, cortical thickness, and DTI scans allowing calculation of diverse network metrics, and (3) Big-5 personality measures. Sex differences were found for cognitive, personality, anatomic, and network measures. In men only, cortical surface was significantly, differentially related to the cognitive variables, predicting spatial but not verbal ability. Similarly, in men only, neuroticism and quirkiness (the overall deviation from the mean across scales) were significantly, differentially related to the two cognitive variables. Different graph metrics predicted spatial ability in men (overall connectivity) and women (clustering). Verbal-spatial specialization was related to sex, cortical surface area, network organization, personality, and vocational interests. Most of the identified correlates of this cognitive specialization were found only in men, and mostly for spatial ability. Taken together, these results identify a suite of neurobehavioral features whose covariance is partially sex-specific.

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

The role of ventral versus dorsolateral prefrontal regions in instantiating proactive and reactive cognitive control remains actively debated, with few studies parsing cue versus probe-related activity. Rapid sampling (460 ms), long cue-probe delays, and advanced analytic techniques (deconvolution) were therefore used to quantify the magnitude and variability of neural responses during the AX Continuous Performance Test (AX-CPT; N = 46) in humans. Behavioral results indicated slower reaction times during reactive cognitive control (AY trials) in conjunction with decreased accuracy and increased variability for proactive cognitive control (BX trials). The anterior insula/ventrolateral prefrontal cortex (aI/VLPFC) was commonly activated across comparisons of both proactive and reactive cognitive control. In contrast, activity within the dorsomedial and dorsolateral prefrontal cortex was limited to reactive cognitive control. The instantiation of proactive cognitive control during the probe period was also associated with sparse neural activation relative to baseline, potentially as a result of the high degree of neural and behavioral variability observed across individuals. Specifically, the variability of the hemodynamic response function (HRF) within motor circuitry increased after the presentation of B relative to A cues (i.e., late in HRF) and persisted throughout the B probe period. Finally, increased activation of right aI/VLPFC during the cue period was associated with decreased motor circuit activity during BX probes, suggesting a possible role for the aI/VLPFC in proactive suppression of neural responses. Considered collectively, current results highlight the flexible role of the VLPFC in implementing cognitive control during the AX-CPT task but suggest large individual differences in proactive cognitive control strategies.

While there are minimal sex differences in overall intelligence, males, on average, have larger total brain volume and corresponding regional brain volumes compared to females, measures that are consistently related to intelligence. Limited research has examined which other brain characteristics may differentially contribute to intelligence in females to facilitate equal performance on intelligence measures. Recent reports of sex differences in the neural characteristics of the brain further highlight the need to differentiate how the structural neural characteristics relate to intellectual ability in males and females. The current study utilized a graph network approach in conjunction with structural equation modeling to examine potential sex differences in the relationship between white matter efficiency, fronto-parietal gray matter volume, and general cognitive ability (GCA). Participants were healthy adults (n = 244) who completed a battery of cognitive testing and underwent structural neuroimaging. Results indicated that in males, a latent factor of fronto-parietal gray matter was significantly related to GCA when controlling for total gray matter volume. In females, white matter efficiency and total gray matter volume were significantly related to GCA, with no specificity of the fronto-parietal gray matter factor over and above total gray matter volume. This work highlights that different neural characteristics across males and females may contribute to performance on intelligence measures. Hum Brain Mapp 37:4006-4016, 2016. © 2016 Wiley Periodicals, Inc.

The developmental roots of individual variation in general cognitive ability (GCA) are the subject of intense scientific interest. One unique perspective is offered by developmental instability theory, which suggests that variation in GCA in part reflects the ability to buffer brain development from key environmental and genetic perturbations. Support for this approach comes mainly from assessment of fluctuating asymmetry, or deviations from symmetry of body features that are symmetric at the population level. In this study of healthy young adults (N = 244) we assessed fluctuating asymmetry (FA) of total cortical surface area (CSFA) from 33 regions using automated analysis. Overall CSFA was negatively related to GCA, consistent with meta-analytic results from body FA studies. A correlated vectors analysis indicated that the CSFA-cognition relationship varied systematically with a test's g loading. Further, FA of frontoparietal regions was a significant predictor of GCA, but FA non-frontoparietal regions was not, consistent with the Parieto-Frontal Integration Theory (P-FIT) model of intelligence. Frontoparietal FA was less than non-frontoparietal FA, i.e., consistent with the hypothesis that regions linked with GCA are better buffered from perturbation, perhaps a reflection of the importance of GCA for important life outcomes.

Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249)

Background The developmental period of adolescence marks the initiation of new socioemotional and physical behaviors, including sexual intercourse. However, little is known about neurodevelopmental influences on adolescent sexual decision-making. Purpose We sought to determine how subcortical brain volume correlated with condom use, and whether those associations differed by gender and pubertal development. Methods We used FreeSurfer to extract subcortical volume among N = 169 sexually experienced youth (mean age 16.07 years; 31.95% female). We conducted multiple linear regressions to examine the relationship between frequency of condom use and subcortical volume, and whether these associations would be moderated by gender and pubertal development. Results We found that the relationship between brain volume and condom use was better accounted for by pubertal development than by gender, and moderated the association between limbic brain volume and condom use. No significant relationships were observed in reward areas (e.g., nucleus accumbens) or prefrontal cortical control areas. Conclusions These data highlight the potential relevance of subcortical socioemotional processing structures in adolescents' sexual decision-making.

Neurosensory abnormalities are frequently observed following pediatric mild traumatic brain injury (pmTBI) and may underlie the expression of several common concussion symptoms and delay recovery. Importantly, active evaluation of neurosensory functioning more closely approximates real-world (e.g., physical and academic) environments that provoke symptom worsening. The current study determined whether symptom provocation (i.e., during neurosensory examination) improved classification accuracy relative to pre-examination symptom levels and whether symptoms varied as a function of point of care. Eighty-one pmTBI were recruited from the pediatric emergency department (PED; n = 40) or outpatient concussion clinic (n = 41), along with matched (age, sex, and education) healthy controls (HC; n = 40). All participants completed a brief (∼ 12 min) standardized neurosensory examination and clinical questionnaires. The magnitude of symptom provocation upon neurosensory examination was significantly higher for concussion clinic than for PED patients. Symptom provocation significantly improved diagnostic classification accuracy relative to pre-examination symptom levels, although the magnitude of improvement was modest, and was greater in the concussion clinic. In contrast, PED patients exhibited worse performance on measures of balance, vision, and oculomotor functioning than the concussion clinic patients, with no differences observed between both samples and HC. Despite modest sample sizes, current findings suggest that point of care represents a critical but highly under-studied variable that may influence outcomes following pmTBI. Studies that rely on recruitment from a single point of care may not generalize to the entire pmTBI population in terms of how neurosensory deficits affect recovery.

This study examined the association between size of the caudate nuclei and intelligence. Based on the central role of the caudate in learning, as well as neuroimaging studies linking greater caudate volume to better attentional function, verbal ability, and dopamine receptor availability, we hypothesized the existence of a positive association between intelligence and caudate volume in three large independent samples of healthy adults (total N = 517). Regression of IQ onto bilateral caudate volume controlling for age, sex, and total brain volume indicated a significant positive correlation between caudate volume and intelligence, with a comparable magnitude of effect across each of the three samples. No other subcortical structures were independently associated with IQ, suggesting a specific biological link between caudate morphology and intelligence. Hum Brain Mapp 36:1407–1416, 2015. © 2014 Wiley Periodicals, Inc.

The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence.

Background Previous studies of response inhibition in patients with schizophrenia have focused on reactive inhibition tasks (e.g., stop-signal, go/no-go), primarily observing lateral prefrontal cortex abnormalities. However, recent studies suggest that purposeful and sustained (i.e., proactive) inhibition may also be affected in these patients. Methods Patients with chronic schizophrenia and healthy controls underwent fMRI while inhibiting motor responses during multisensory (audiovisual) stimulation. Resting state data were also collected. Results We included 37 patients with schizophrenia and 37 healthy controls in our study. Both controls and patients with schizophrenia successfully inhibited the majority of overt motor responses. Functional results indicated basic inhibitory failure in the lateral premotor and sensorimotor cortex, with opposing patterns of positive (schizophrenia) versus negative (control) activation. Abnormal activity was associated with independently assessed signs of psychomotor retardation. Patients with schizophrenia also exhibited unique activation of the pre-supplementary motor area (pre-SMA)/SMA and precuneus relative to baseline as well as a failure to deactivate anterior nodes of the default mode network. Independent resting-state connectivity analysis indicated reduced connectivity between anterior (task results) and posterior regions of the sensorimotor cortex for patients as well as abnormal connectivity between other regions (cerebellum, thalamus, posterior cingulate gyrus and visual cortex). Limitations Aside from rates of false-positive responses, true proactive response inhibition tasks do not provide behavioural metrics that can be independently used to quantify task performance. Conclusion Our results suggest that basic cortico-cortico and intracortical connections between the sensorimotor cortex and adjoining regions are impaired in patients with schizophrenia and that these impaired connections contribute to inhibitory failures (i.e., a positive rather than negative hemodynamic response).

Human adolescents engage in very high rates of unprotected sex. This behavior has a high potential for unintended, serious, and sustained health consequences including HIV/AIDS. Despite these serious health consequences, we know little about the neural and cognitive factors that influence adolescents' decision-making around sex, and their potential overlap with behaviorally co-occurring risk behaviors, including alcohol use. Thus, in this review, we evaluate the developmental neuroscience of sexual risk and alcohol use for human adolescents with an eye to relevant prevention and intervention implications.

The creativity research community is in search of a viable cognitive measure providing support for behavioral observations that higher ideational output is often associated with higher creativity (known as the equal-odds rule). One such measure has included divergent thinking: the production of many examples or uses for a common or single object or image. We sought to test the equal-odds rule using a measure of divergent thinking, and applied the consensual assessment technique to determine creative responses as opposed to merely original responses. We also sought to determine structural brain correlates of both ideational fluency and ideational creativity. Two-hundred forty-six subjects were subjected to a broad battery of behavioral measures, including a core measure of divergent thinking (Foresight), and measures of intelligence, creative achievement, and personality (i.e., Openness to Experience). Cortical thickness and subcortical volumes (e.g., thalamus) were measured using automated techniques (FreeSurfer). We found that higher number of responses on the divergent thinking task was significantly associated with higher creativity (r = 0.73) as independently assessed by three judges. Moreover, we found that creativity was predicted by cortical thickness in regions including the left frontal pole and left parahippocampal gyrus. These results support the equal-odds rule, and provide neuronal evidence implicating brain regions involved with "thinking about the future" and "extracting future prospects."

Creative cognition emerges from a complex network of interacting brain regions. This study investigated the relationship between the structural organization of the human brain and aspects of creative cognition tapped by divergent thinking tasks. Diffusion weighted imaging (DWI) was used to obtain fiber tracts from 83 segmented cortical regions. This information was represented as a network and metrics of connectivity organization, including connectivity strength, clustering and communication efficiency were computed, and their relationship to individual levels of creativity was examined. Permutation testing identified significant sex differences in the relationship between global connectivity and creativity as measured by divergent thinking tests. Females demonstrated significant inverse relationships between global connectivity and creative cognition, whereas there were no significant relationships observed in males. Node specific analyses revealed inverse relationships across measures of connectivity, efficiency, clustering and creative cognition in widespread regions in females. Our findings suggest that females involve more regions of the brain in processing to produce novel ideas to solutions, perhaps at the expense of efficiency (greater path lengths). Males, in contrast, exhibited few, relatively weak positive relationships across these measures. Extending recent observations of sex differences in connectome structure, our findings of sexually dimorphic relationships suggest a unique topological organization of connectivity underlying the generation of novel ideas in males and females.

Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art., Published as part of 2013 IEEE GlobalSIP conference

This study examined the association between size of the caudate nuclei and intelligence. Based on the central role of the caudate in learning, as well as neuroimaging studies linking greater caudate volume to better attentional function, verbal ability, and dopamine receptor availability, we hypothesized the existence of a positive association between intelligence and caudate volume in three large independent samples of healthy adults (total N = 517). Regression of IQ onto bilateral caudate volume controlling for age, sex, and total brain volume indicated a significant positive correlation between caudate volume and intelligence, with a comparable magnitude of effect across each of the three samples. No other subcortical structures were independently associated with IQ, suggesting a specific biological link between caudate morphology and intelligence. Hum Brain Mapp 36:1407–1416, 2015. © 2014 Wiley Periodicals, Inc.

The study of individual differences encompasses broad constructs including intelligence, creativity, and personality. However, substantially less research is devoted to the study of specific aptitudes in spite of their importance to educational, occupational, and avocational success. We sought to determine subcortical brain structural correlates of several broad aptitudes including Math, Vocabulary, Foresight, Paper Folding, and Inductive Reasoning in a large (N = 107), healthy, young (age range = 16–29) cohort. Subcortical volumes were measured using an automated technique (FreeSurfer) across structures including bilateral caudate, putamen, globus pallidus, thalamus, nucleus accumbens, hippocampus, amygdala, and five equal regions of the corpus callosum. We found that performance on measures of each aptitude was predicted by different subcortical structures: Math – higher right nucleus accumbens volume; Vocabulary – higher left hippocampus volume; Paper Folding – higher right thalamus volume; Foresight – lower right thalamus and higher mid anterior corpus callosum volume; Inductive Reasoning – higher mid anterior corpus callosum volume. Our results support general findings, within the cognitive neurosciences, showing lateralization of structure-function relationships, as well as more specific relationships between individual structures (e.g., left hippocampus) and functions relevant to particular aptitudes (e.g., Vocabulary).

The human brain and the neuronal networks comprising it are of immense interest to the scientific community. In this work, we focus on the structural connectivity of human brains, investigating sex differences across male and female connectomes (brain-graphs) for the knowledge discovery problem "Which brain regions exert differences in connectivity across the two sexes?". One of our main findings discloses the statistical difference at the pars orbitalis of the connectome between sexes, which has been shown to function in language production. Moreover, we use these discriminative regions for the related learning problem "Can we classify a given human connectome to belong to one of the sexes just by analyzing its connectivity structure?" . We show that we can learn decision tree as well as support vector machine classification models for this task. We show that our models achieve up to 79% prediction accuracy with only a handful of brain regions as discriminating factors. Importantly, our results are consistent across two data sets, collected at two different centers, with two different scanning sequences, and two different age groups (children and elderly). This is highly suggestive that we have discovered scientifically meaningful sex differences.

Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called "connectomics". Connectomics studies the brain as a graph; vertices correspond to neurons (or collections thereof) and edges correspond to structural or functional connections between them. To explore the variability of connectomes---to address both basic science questions regarding the structure of the brain, and medical health questions about psychiatry and neurology---one can study the topological properties of these brain-graphs. We define multivariate glocal graph invariants: these are features of the graph that capture various local and global topological properties of the graphs. We show that the collection of features can collectively be computed via a combination of daisy-chaining, sparse matrix representation and computations, and efficient approximations. Our custom open-source Python package serves as a back-end to a Web-service that we have created to enable researchers to upload graphs, and download the corresponding invariants in a number of different formats. Moreover, we built this package to support distributed processing on multicore machines. This is therefore an enabling technology for network science, lowering the barrier of entry by providing tools to biologists and analysts who otherwise lack these capabilities. As a demonstration, we run our code on 120 brain-graphs, each with approximately 16M vertices and up to 90M edges., Comment: Published as part of 2013 IEEE GlobalSIP conference

Parsing multisensory information from a complex external environment is a fundamental skill for all organisms. However, different organizational schemes currently exist for how multisensory information is processed in human (supramodal; organized by cognitive demands) versus primate (organized by modality/cognitive demands) lateral prefrontal cortex (LPFC). Functional magnetic resonance imaging results from a large cohort of healthy controls (N = 64; Experiment 1) revealed a rostral-caudal stratification of LPFC for auditory versus visual attention during an audio-visual Stroop task. The stratification existed in spite of behavioral and functional evidence of increased interference from visual distractors. Increased functional connectivity was also observed between rostral LPFC and auditory cortex across independent samples (Experiments 2 and 3) and multiple methodologies. In contrast, the caudal LPFC was preferentially activated during visual attention but functioned in a supramodal capacity for resolving multisensory conflict. The caudal LPFC also did not exhibit increased connectivity with visual cortices. Collectively, these findings closely mirror previous nonhuman primate studies suggesting that visual attention relies on flexible use of a supramodal cognitive control network in caudal LPFC whereas rostral LPFC is specialized for directing attention to auditory inputs (i.e., human auditory fields).

To investigate the biochemical correlates of normal personality we utilized proton magnetic resonance spectroscopy ((1)H-MRS). Our sample consisted of 60 subjects ranging in age from 18 to 32 (27 females). Personality was assessed with the NEO Five-Factor Inventory (NEO-FFI). We measured brain biochemistry within the precuneus, the cingulate cortex, and underlying white matter. We hypothesized that brain biochemistry within these regions would predict individual differences across major domains of personality functioning. Biochemical models were fit for all personality domains including Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Our findings involved differing concentrations of Choline (Cho), Creatine (Cre), and N-acetylaspartate (NAA) in regions both within (i.e., posterior cingulate cortex) and white matter underlying (i.e., precuneus) the Default Mode Network (DMN). These results add to an emerging literature regarding personality neuroscience, and implicate biochemical integrity within the default mode network as constraining major personality domains within normal human subjects.