Mind the Gap: A Psychometric Approach to the Reduction Problem

Six elements test vs D-KEFS: what does "Ecological Validity" tell us?

OBJECTIVE

Extensive research shows that tests of executive functioning (EF) predict instrumental activities of daily living (IADLs) but are nevertheless often criticized for having poor ecological validity. The Modified Six Elements Test (MSET) is a pencil-and-paper test that was developed to mimic the demands of daily life, with the assumption that this would result in a more ecologically valid test. Although the MSET has been extensively validated in its ability to capture cognitive deficits in various populations, support for its ability to predict functioning in daily life is mixed. This study aimed to examine the MSET's ability to predict IADLs assessed via three different modalities relative to traditional EF measures.

METHOD

Participants (93 adults aged 60 - 85) completed the MSET, traditional measures of EF (Delis-Kaplan Executive Function System; D-KEFS), and self-reported and performance-based IADLs in the lab. Participants then completed three weeks of IADL tasks at home, using the Daily Assessment of Independent Living and Executive Skills (DAILIES) protocol.

RESULTS

The MSET predicted only IADLs completed at home, while the D-KEFS predicted IADLs across all three modalities. Further, the D-KEFS predicted home-based IADLs beyond the MSET when pitted against each other, whereas the MSET did not contribute beyond the D-KEFS.

CONCLUSIONS

Traditional EF tests (D-KEFS) appear to be superior to the MSET in predicting IADLs in community-dwelling older adults. The present results argue against replacing traditional measures with the MSET when addressing functional independence of generally high-functioning and cognitive healthy older adult patients.

Test of Practical Judgment (TOP-J): Construct, Criterion, and Incremental Validity in a Community Sample of Older Adults

OBJECTIVE

The Test of Practical Judgment (TOP-J) is a stand-alone judgment measure that is considered to tap into aspects of executive functioning (EF) and inform clinical predictions of daily functioning in older adults. Past validation research is variable and has some limitations. The present study sought to examine the reliability and construct, criterion, and incremental validities of scores on TOP-J 9-item version (TOP-J/9).

METHOD

Participants were 95 community-dwelling older adults aged 60 to 85. Participants completed TOP-J/9, measures of EF and global cognition, and three different modalities of instrumental activities of daily living (IADLs) (self-report, performance-based tasks conducted in the laboratory, and performance-based tasks completed at home over 3 weeks).

RESULTS

TOP-J/9 scores showed adequate internal consistency (α = 0.73) after correcting for the low number of items. TOP-J/9 was correlated with global cognition and EF, although EF did not survive correction for lower-order processes. Finally, although TOP-J/9 scores were associated with home-based IADL tasks (but not with self-report and laboratory-based IADLs), providing some evidence of criterion validity, they did not incrementally contribute to home-based IADL performance beyond other cognitive measures. However, when two items pertaining to social/ethical judgment were removed, this modified version of TOP-J did relate to EF beyond lower-order processes and contributed uniquely to prediction of home-based IADLs beyond other measures.

CONCLUSION

Results suggest that TOP-J/9 taps into global cognitive status (but not necessarily EF) and predicts "real-world" functioning (but not above and beyond other cognitive measures). TOP-J psychometrics may be improved by removing two social/ethical items.

Allostatic Load in Childhood, Adolescence, and Young Adulthood: Are Assumptions of Measurement Invariance Warranted?

OVERVIEW

Allostatic load represents the cumulative toll of chronic mobilization of the body's stress response systems, as indexed by biomarkers. Higher levels of stress and disadvantage predict higher levels of allostatic load, which, in turn, predict poorer physical and mental health outcomes. To maximize the efficacy of prevention efforts, screening for stress- and disadvantage-associated health conditions must occur before middle age-that is, during childhood, adolescence, and young adulthood. However, this requires that models of allostatic load display properties of measurement invariance across age groups. Because most research on allostatic load has featured older adults, it is unclear if these requirements can be met.

METHODS

To address this question, we fit a series of exploratory and confirmatory analytic models to data on eight biomarkers using a nationally representative sample of N = 4260 children, adolescents, and young adults drawn from the National Health and Nutrition Examination Survey dataset.

RESULTS

Exploratory and confirmatory models indicated that, consistent with allostatic load theory, a unidimensional model was a good fit to the data. However, this model did not display properties of measurement invariance; post-hoc analyses suggested that the biomarkers included in the final confirmatory model were most strongly intercorrelated among young adults and most weakly intercorrelated among adolescents.

CONCLUSIONS

These results underscore the importance of testing assumptions about measurement invariance in allostatic load before drawing substantive conclusions about stress, disadvantage, and health by directly comparing levels of allostatic load across different stages of development, while underscoring the need to expand investigations of measurement invariance to samples of longitudinal data.

Of differing methods, disputed estimates and discordant interpretations: the meta-analytical multiverse of brain volume and IQ associations

Brain size and IQ are positively correlated. However, multiple meta-analyses have led to considerable differences in summary effect estimations, thus failing to provide a plausible effect estimate. Here we aim at resolving this issue by providing the largest meta-analysis and systematic review so far of the brain volume and IQ association (86 studies; 454 effect sizes from k = 194 independent samples; N = 26 000+) in three cognitive ability domains (full-scale, verbal, performance IQ). By means of competing meta-analytical approaches as well as combinatorial and specification curve analyses, we show that most reasonable estimates for the brain size and IQ link yield r-values in the mid-0.20s, with the most extreme specifications yielding rs of 0.10 and 0.37. Summary effects appeared to be somewhat inflated due to selective reporting, and cross-temporally decreasing effect sizes indicated a confounding decline effect, with three quarters of the summary effect estimations according to any reasonable specification not exceeding r = 0.26, thus contrasting effect sizes were observed in some prior related, but individual, meta-analytical specifications. Brain size and IQ associations yielded r = 0.24, with the strongest effects observed for more g-loaded tests and in healthy samples that generalize across participant sex and age bands.

Capital as an Integrative Conceptualisation of Human Characteristics, Behaviour, and Outcomes Predicting Reproductive Success and Evolutionary Fitness

According to evolutionary theory, human cognition and behaviour are based on adaptations selected for their contribution to reproduction in the past, which in the present may result in differential reproductive success and inclusive fitness. Because this depiction is broad and human behaviour often separated from this ultimate outcome (e.g., increasing childlessness), evolutionary theory can only incompletely account for human everyday behaviour. Moreover, effects of most studied traits and characteristics on mating and reproductive success turned out not to be robust. In this article, an abstract descriptive level for evaluating human characteristics, behaviour, and outcomes is proposed, as a predictor of long-term reproductive success and fitness. Characteristics, behaviour, and outcomes are assessed in terms of attained and maintained capital, defined by more concrete (e.g., mating success, personality traits) and abstract (e.g., influence, received attention) facets, thus extending constructs like embodied capital and social capital theory, which focuses on resources embedded in social relationships. Situations are framed as opportunities to gain capital, and situational factors function as elicitors for gaining and evaluating capital. Combined capital facets should more robustly predict reproductive success and (theoretically) fitness than individual fitness predictors. Different ways of defining and testing these associations are outlined, including a method for empirically examining the psychometric utility of introducing a capital concept. Further theorising and empirical research should more precisely define capital and its facets, and test associations with (correlates of) reproductive success and fitness.

Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is "a better fit" or more "valid" than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Quantitative or qualitative development in decision making?

A key question in the developmental sciences is whether developmental differences are quantitative or qualitative. For example, does age increase the speed in processing a task (quantitative differences) or does age affect the way a task is processed (qualitative differences)? Until now, findings in the domain of decision making have been based on the assumption that developmental differences are either quantitative or qualitative. In the current study, we took a different approach in which we tested whether development is best described as being quantitative or qualitative. We administered a judgment version and a choice version of a decision-making task to a developmental sample (n_judgment = 109 and n_choice = 137; M_age = 12.5 years, age range = 9-18). The task, the so-called Gambling Machine Task, required decisions between two options characterized by constant gains and probabilistic losses; these characteristics were known beforehand and thus did not need to be learned from experience. Data were analyzed by comparing the fit of quantitative and qualitative latent variable models, so-called multiple indicator multiple cause (MIMIC) models. Results indicated that individual differences in both judgment and choice tasks were quantitative and pertained to individual differences in "consideration of gains," that is, to what extent decisions were guided by gains. These differences were affected by age in the judgment version, but not in the choice version, of the task. We discuss implications for theories of decision making and discuss potential limitations and extensions. We also argue that the MIMIC approach is useful in other domains, for example, to test quantitative versus qualitative development of categorization, reasoning, math, and memory.

Personality, Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health

Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast–evolving psychopathology network (e.g. a depressive episode) can influence the slow–evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

Transdiagnostic Brain Mapping in Developmental Disorders

Childhood learning difficulties and developmental disorders are common, but progress toward understanding their underlying brain mechanisms has been slow. Structural neuroimaging, cognitive, and learning data were collected from 479 children (299 boys, ranging in age from 62 to 223 months), 337 of whom had been referred to the study on the basis of learning-related cognitive problems. Machine learning identified different cognitive profiles within the sample, and hold-out cross-validation showed that these profiles were significantly associated with children's learning ability. The same machine learning approach was applied to cortical morphology data to identify different brain profiles. Hold-out cross-validation demonstrated that these were significantly associated with children's cognitive profiles. Crucially, these mappings were not one-to-one. The same neural profile could be associated with different cognitive impairments across different children. One possibility is that the organization of some children's brains is less susceptible to local deficits. This was tested by using diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected hubs. Children with these networks had only selective cognitive impairments or no cognitive impairments at all. By contrast, the same attacks had a significantly different impact on some children's networks, because their brain efficiency was less critically dependent on hubs. These children had the most widespread and severe cognitive impairments. On this basis, we propose a new framework in which the nature and mechanisms of brain-to-cognition relationships are moderated by the organizational context of the overall network.

A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.

Reductionism in retreat

We address the commentaries on our target article in terms of four major themes. First, we note that virtually all commentators agree that mental disorders are not brain disorders in the common interpretation of these terms, and establish the consensus that explanatory reductionism is not a viable thesis. Second, we address criticisms to the effect that our article was misdirected or aimed at a straw man; we argue that this is unlikely, given the widespread communication of reductionist slogans in psychopathology research and society. Third, we tackle the question of whether intentionality, extended systems, and multiple realizability are as problematic as claimed in the target article, and we present a number of nuances and extensions with respect to our article. Fourth, we discuss the question of how the network approach should incorporate biological factors, given that wholesale reductionism is an unlikely option.

Developmental cognitive neuroscience using latent change score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N = 204 (N = 32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N = 176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and Ωnyx).

Brain disorders? Not really: Why network structures block reductionism in psychopathology research

In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion ofreal patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.

First episode psychosis moderates the effect of gray matter volume on cognition

Patients with first episode psychosis (FEP) present with cognitive deficits and volume differences in certain brain areas. Brain volumetric information further correlates with cognitive testing, and multiple brain areas shows different strengths of correlation with the cognitive functions being tested. Traditionally, these cognitive functions are independently related to volumetric differences, but these functions share variance. Failing to account for this aspect of cognition hinders the proper representation of cognition in neuroimaging studies. We used modeling methods which account for this shared variance to investigate the differences of correlations between cognitive abilities and brain areas. A multiple-groups structured equation model (SEM) approach was used to design and test a model representing the relation between gray matter volumetric data and neuropsychological test scores in a sample of 100 Brazilian FEP patients and 94 controls. Models with a latent variable formed by neurological measures and reflecting cognitive measures performed better on fit tests. FEP moderated the relation between gray matter volumes and cognition by altering the profile of correlations between groups. This moderation had a large effect size. SEM provides a fine grained picture of the interdependence of structural brain changes and cognition.

Philosophical Foundations of Neuroscience in Organizational Research: Functional and Nonfunctional Approaches

Neuroscience offers a unique opportunity to elucidate the role of mental phenomena, including consciousness. However the place of such phenomena in explanations of human behavior is controversial. For example, consciousness has been construed in varied and conflicting forms, making it difficult to represent it in meaningful ways without committing researchers to one species of consciousness or another, with vastly different implications for hypothesis development, methods of study, and interpretation of findings. We explore the conceptual foundations of different explications of consciousness and consider alternative ways for studying its role in research. In the end, although no approach is flawless or dominates all others in every way, we are convinced that any viable approach must take into account, if not privilege, the self in the sense of representing the subjective, first-person process of self as observer and knower of one’s own actions and history, and the feelings and meanings attached to these. The most promising frameworks in this regard are likely to be some variant of nonreductive monism, or perhaps a kind of naturalistic dualism that remains yet to be developed coherently.

RDoC and shift of reference

OBJECTIVE

This paper aims to analyse in a philosophically informed way the recent National Institute of Mental Health proposal for the Research Domain Criteria (RDoC) framework.

CONCLUSION

Current classification systems have helped unify psychiatry and the conditions that it is most concerned with. However, by relying too much on syndromes and symptoms, they too often do not define stable constructs. As a result, inclusions and removals from the manuals are not always backed by sound reasons. The RDoC framework is an important move towards ameliorating matters. This paper argues that it improves the current situation by re-referencing constructs to physical properties (biomarkers for disorders, for example), by allowing theoretical levels within the framework, and by treating psychiatry as a special case of the cognitive sciences.

A watershed model of individual differences in fluid intelligence

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

Multiple determinants of lifespan memory differences

Memory problems are among the most common complaints as people grow older. Using structural equation modeling of commensurate scores of anterograde memory from a large (N = 315), population-derived sample (www.cam-can.org), we provide evidence for three memory factors that are supported by distinct brain regions and show differential sensitivity to age. Associative memory and item memory are dramatically affected by age, even after adjusting for education level and fluid intelligence, whereas visual priming is not. Associative memory and item memory are differentially affected by emotional valence, and the age-related decline in associative memory is faster for negative than for positive or neutral stimuli. Gray-matter volume in the hippocampus, parahippocampus and fusiform cortex, and a white-matter index for the fornix, uncinate fasciculus and inferior longitudinal fasciculus, show differential contributions to the three memory factors. Together, these data demonstrate the extent to which differential ageing of the brain leads to differential patterns of memory loss.

Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?

Positive associations between human intelligence and brain size have been suspected for more than 150 years. Nowadays, modern non-invasive measures of in vivo brain volume (Magnetic Resonance Imaging) make it possible to reliably assess associations with IQ. By means of a systematic review of published studies and unpublished results obtained by personal communications with researchers, we identified 88 studies examining effect sizes of 148 healthy and clinical mixed-sex samples (>8000 individuals). Our results showed significant positive associations of brain volume and IQ (r=.24, R(2)=.06) that generalize over age (children vs. adults), IQ domain (full-scale, performance, and verbal IQ), and sex. Application of a number of methods for detection of publication bias indicates that strong and positive correlation coefficients have been reported frequently in the literature whilst small and non-significant associations appear to have been often omitted from reports. We show that the strength of the positive association of brain volume and IQ has been overestimated in the literature, but remains robust even when accounting for different types of dissemination bias, although reported effects have been declining over time. While it is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences.

Incorporating measurement error in n = 1 psychological autoregressive modeling

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking

Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N=567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate fluid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and fluid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing.

Representational geometry: integrating cognition, computation, and the brain

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Representational geometry is a framework that enables us to relate brain, computation, and cognition.

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Representations in brains and models can be characterized by representational distance matrices.

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Distance matrices can be readily compared to test computational models.

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We review recent insights into perception, cognition, memory, and action and discuss current challenges.

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure.

A behavioral neuroenergetics theory of ADHD

Energetic insufficiency in neurons due to inadequate lactate supply is implicated in several neuropathologies, including attention-deficit/hyperactivity disorder (ADHD). By formalizing the mechanism and implications of such constraints on function, the behavioral Neuroenergetics Theory (NeT) predicts the results of many neuropsychological tasks involving individuals with ADHD and kindred dysfunctions, and entails many novel predictions. The associated diffusion model predicts that response times will follow a mixture of Wald distributions from the attentive state, and ex-Wald distributions after attentional lapses. It is inferred from the model that ADHD participants can bring only 75-85% of the neurocognitive energy to bear on tasks, and allocate only about 85% of the cognitive resources of comparison groups. Parameters derived from the model in specific tasks predict performance in other tasks, and in clinical conditions often associated with ADHD. The primary action of therapeutic stimulants is to increase norepinephrine in active regions of the brain. This activates glial adrenoceptors, increasing the release of lactate from astrocytes to fuel depleted neurons. The theory is aligned with other approaches and integrated with more general theories of ADHD. Therapeutic implications are explored.

Intelligence and the brain: A model-based approach

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

Evolution of neuroarchitecture, multi-level analyses and calibrative reductionism

Evolution has sculpted the incredibly complex human nervous system, among the most complex functions of which extend beyond the individual to an intricate social structure. Although these functions are deterministic, those determinants are legion, heavily interacting and dependent on a specific evolutionary trajectory. That trajectory was directed by the adaptive significance of quasi-random genetic variations, but was also influenced by chance and caprice. With a different evolutionary pathway, the same neural elements could subserve functions distinctly different from what they do in extant human brains. Consequently, the properties of higher level neural networks cannot be derived readily from the properties of the lower level constituent elements, without studying these elements in the aggregate. Thus, a multi-level approach to integrative neuroscience may offer an optimal strategy. Moreover, the process of calibrative reductionism, by which concepts and understandings from one level of organization or analysis can mutually inform and 'calibrate' those from other levels (both higher and lower), may represent a viable approach to the application of reductionism in science. This is especially relevant in social neuroscience, where the basic subject matter of interest is defined by interacting organisms across diverse environments.

Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links

Here we review findings from studies investigating functional and structural brain connectivity in high functioning individuals with autism spectrum disorders (ASDs). The dominant theory regarding brain connectivity in people with ASD is that there is long distance under-connectivity and local over-connectivity of the frontal cortex. Consistent with this theory, long-range cortico-cortical functional and structural connectivity appears to be weaker in people with ASD than in controls. However, in contrast to the theory, there is less evidence for local over-connectivity of the frontal cortex. Moreover, some patterns of abnormal functional connectivity in ASD are not captured by current theoretical models. Taken together, empirical findings measuring different forms of connectivity demonstrate complex patterns of abnormal connectivity in people with ASD. The frequently suggested pattern of long-range under-connectivity and local over-connectivity is in need of refinement.