How neuroscience can inform the study of individual differences in cognitive abilities

Deficits in force production during multifinger tasks demarcate cognitive dysfunction

BACKGROUND

The multifinger force deficit (MFFD) is the decline in force generated by each finger as the number of fingers contributing to an action is increased. It has been shown to associate with cognitive status.

AIMS

The aim was to establish whether a particularly challenging form of multifinger grip dynamometry, that provides minimal tactile feedback via cutaneous receptors and requires active compensation for reaction forces, will yield an MFFD that is more sensitive to cognitive status.

METHODS

Associations between measures of motor function, and cognitive status (Montreal Cognitive Assessment [MoCA]) and latent components of cognitive function (derived from 11 tests using principal component analysis), were estimated cross-sectionally using generalized partial rank correlations. The participants (n  =  62) were community dwelling, aged 65-87.

RESULTS

Approximately half the participants were unable to complete the dynamometry task successfully. Cognitive status demarcated individuals who could perform the task from those who could not. Among those who complied with the task requirements, the MFFD was negatively correlated with MoCA scores-those with the highest MoCA scores tended to exhibit the smallest deficits, and vice versa. There were corresponding associations with latent components of cognitive function.

DISCUSSION

The results support the view that neurodegenerative processes that are a feature of normal and pathological aging exert corresponding effects on expressions of motor coordination-in multifinger tasks, and cognitive sufficiency, due to their dependence on shared neural systems.

CONCLUSIONS

The outcomes add weight to the assertion that deficits in force production during multifinger tasks are sensitive to cognitive dysfunction.

Educational fMRI: From the Lab to the Classroom

Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.

The neural code of intelligence: From correlation to causation

Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological concept of g. Few attempts have been made to explain intelligence exclusively based on brain characteristics - the brain perspective. From a methodological viewpoint the brain intelligence relation has been studied by means of correlational and interventional studies. The later providing a causal elucidation of the brain - intelligence relation. The best neuro-anatomical predictor of intelligence is brain volume showing a modest positive correlation with g, explaining between 9 to 16% of variance. The most likely explanation was that larger brains, containing more neurons, have a greater computational power and in that way allow more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape showed less consistent patterns. The development of diffusion tensor imaging has allowed researchers to look also into the microstructure of brain tissue. Consistently observed was a positively correlation between white matter integrity and intelligence, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain intelligence relationship three theories have been put forward: the neural efficiency, the P-FIT and the multi demand (MD) system theory. On the other hand, The Network Neuroscience Theory of g, based on methods from mathematics, physics, and computer science, is an example for the brain perspective on neurobiological underpinning of intelligence. In this framework network flexibility and dynamics provide the foundation for general intelligence. With respect to intervention studies the most promising results have been achieved with noninvasive brain stimulation and behavioral training providing tentative support for findings put forward by the correlational approach. To date the best consensus based on the diversity of results reported would be that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas. The capacity of flexible transitions between these network states represents the essence of intelligence - g.

Preclinical modeling of exposure to a global marine bio-contaminant: Effects of in utero Domoic acid exposure on neonatal behavior and infant memory

Domoic Acid (DA) is a naturally-occurring marine neurotoxin that is increasingly recognized as an important public health issue. Prenatal DA exposure occurs through the maternal consumption of contaminated shellfish/finfish. To better understand the fetal risks associated with DA, we initiated a longitudinal, preclinical study focused on the reproductive and developmental effects of chronic, low-dose oral DA exposure. To this end, 32 adult female Macaca fascicularis monkeys were orally dosed with 0, 0.075 or 0.15 mg/kg/day DA on a daily basis prior to breeding and throughout breeding and pregnancy. The doses included the proposed human Tolerable Daily Intake (TDI) (0.075 mg/kg/day) for DA. Adult females were bred to nonexposed males. To evaluate development during early infancy, offspring were administered a Neonatal Assessment modeled after the human Neonatal Behavior Assessment Scale and a series of Visual Recognition Memory problems using the novelty paradigm. Results indicated that prenatal DA exposure did not impact early survival reflexes or responsivity to the environment. Findings from the recognition memory assessment, given between 1 and 2 months of age, showed that exposed and control infants demonstrated robust novelty scores when test problems were relatively easy to solve. Performance was not diminished by the introduction of delay periods. However, when more difficult recognition problems were introduced, the looking behavior of the 0.15 mg/kg DA group was random and infants failed to show differential visual attention to novel test stimuli. This finding suggests subtle but significant impairment in recognition memory and demonstrates that chronic fetal exposure to DA may impact developing cognitive processes.

Factor-Analytic Evidence for the Complexity of the Delis-Kaplan Executive Function System (D-KEFS)

This study examined the complexity of the Delis-Kaplan Executive Function System both in terms of the overall number of factors needed to model the entire battery and the complexity of the number of factors needed to model individual tests. The correlations between tests from the Delis-Kaplan Executive Function System standardization sample were modeled with varying numbers of factors. Simulations of test scores were also done that varied the number of factors used to generate the data and considered criteria for factor retention based on eigenvalues, fit indices, and generalization to new data. The results show seven-factor solutions based on generalization of fixed weights to validation samples provided best estimates. Modification indices produced three seven-factor solutions with comparable model fit but markedly different factor loadings. These results are consistent with process overlap theory, which suggest that complex tests are determined my many distinct executive processes. Given uncertainty of factor-analytic solutions, interpretation of the meaning of test scores should be based on evidence that is external to the test items, such as their sensitivity to clinical conditions. Overly simplistic interpretations of executive test performance based on factor-analytic results are not warranted. This is consistent with the cognitive processing approach of the test authors.

Enhancing Intelligence: From the Group to the Individual

Research aimed at testing whether short-term training programs can enhance intelligence is mainly concentrated on behavior. Expected positive effects are found sometimes, but the evidence is far from conclusive. It is assumed that training must evoke changes in the brain for observing genuine improvements in behavior. However, behavioral and brain data are seldom combined in the same study. Here we present one example of this latter type of research summarizing, discussing, and integrating already published results. The training program was based on the adaptive dual n-back task, and participants completed a comprehensive battery measuring fluid and crystallized ability, along with working memory and attention control, before and after training. They were also submitted to MRI scanning at baseline and post-training. Behavioral results revealed positive effects for visuospatial processing across cognitive domains. Brain imaging data were analyzed by longitudinal voxel-based morphometry, tensor-based morphometry, surface-based morphometry, and structural connectivity. The integration of these multimodal brain results provides clues about those observed in behavior. Our findings, along with previous research and current technological advances, are considered from the perspective that we now live in ideal times for (a) moving from the group to the individual and (b) developing personalized training programs.

Evaluation of multidimensional models of WAIS-IV subtest performance

OBJECTIVE

The present study examined the extent to which the covariance structure of the WAIS-IV is best accounted for by models that assume that test performance is the result of group-level factors and multiple independent general factors.

METHOD

Structural models with one to four general factors were evaluated with either four or five group-level factors. Simulations based on four general factors were run to clarify the adequacy of the estimates of the allocation of covariance by the models.

RESULTS

Four independent general factors provided better fit than a single general factor for either model with four or five group-level factors. While one of the general factors had much larger loadings than all other factors, simulation results suggested that this might be an artifact of the statistical procedure rather than a reflection of the nature of individual differences in cognitive abilities.

CONCLUSIONS

These results argue against the contention that clinical interpretation of cognitive test batteries should primarily be at the level of general intelligence. It is a fallacy to assume that factor analysis can reveal the structure of human abilities. Test validity should not be based solely on the results of modeling the covariance of test batteries.