Cognitive Neuroscience最新文献

Neuroscience is uncovering sex influences at all levels of mammalian brain function at an accelerating rate. Unfortunately, persistent biases against the topic remain among some investigators. One is that sex influences are small and unreliable, despite the existence of no evidence supporting this general assertion. In this volume, Spets and Slotnick provide clear evidence for a consistent sex influence on one aspect of human cognition, retrieval from long-term memory.

Spets and Slotnick (in Press) applied activation likelihood estimation meta-analysis to examine sex differences in regional brain activation during long-term memory retrieval. Based on their analyses, each of the eight studies they analyzed showed greater activity for males than females during long-term memory retrieval. These results suggest that analyses of magnetic resonance imaging data should not be collapsed by sex and expand this discussion to the challenge of meta-analysis on studies with small to modest sample sizes in favor of larger imaging studies with more rigorous thresholds.

Spets and Slotnick present a meta-analysis on long-term memory and sex differences. Overall findings indicate greater brain activation in men than women during memory performance. Merits of the activation likelihood estimation meta-analysis include considering study parameters and equating performance to enhance interpretability of activation differences. Variables and concepts relevant to memory and sex differences research also are discussed. As memory is essential for survival, characterizing neurobiological profiles, parsing sex and gender, will help broaden the field of long-term memory and sex differences research.

Sex differences in the brain are widely studied, but results are often inconsistent and it is assumed that many negative findings are not even being reported. The lack of consistent findings might be based on the highly questionable assumption of a clear-cut sexual dimorphism in brain structure and function, that underlies commonly used group comparisons between males and females. Without having to rely on this assumption, state of the art statistical learning methods based on large neuroimaging data sets might offer the tools necessary to disentangle the complex pattern of sex-related variations in brain structure and organization.

The degree to which sex differences exist in the brain is a current topic of debate. In the present discussion paper, we reviewed eight functional magnetic resonance imaging (fMRI) papers to determine whether there are sex differences in brain activity during long-term memory retrieval. The objectives were: 1) to compare the experimental parameters in studies reporting significant versus null long-term memory sex differences, and 2) to identify whether specific brain regions were associated with sex differences during long-term memory. The following experimental parameters were extracted from each paper: the number of participants, the average age of participants, stimulus type(s), whether or not performance was matched, whether or not sex differences were reported, the type of between-subject statistical test used, and the contrast(s) employed. The particular experimental parameters employed in each study did not appear to determine whether sex differences were observed, as there were sex differences in all eight studies. An activation likelihood estimation (ALE) meta-analysis was conducted to identify brain regions activated to a greater degree by females than males or males than females. This ALE meta-analysis revealed sex differences (male > female) in the lateral prefrontal cortex, visual processing regions, parahippocampal cortex, and the cerebellum. This constitutes compelling evidence that there are substantial sex differences in brain activity during long-term memory retrieval. More broadly, the present findings question the widespread practice of collapsing across sex in the field of cognitive neuroscience.

Many functional resonance imaging (fMRI) studies have reported sex differences during long-term memory. The present fMRI investigation aimed to identify whether sex differences exist during high- versus low-confidence accurate spatial memories. During the study phase, abstract shapes were presented to the left or right of fixation. During the test phase, each shape was presented at fixation and participants made an old-"left" or old-"right" judgment followed by an "unsure" or "sure" response. The conjunction of female high- versus low-confidence spatial memory and male high- versus low-confidence spatial memory identified common activity in visual processing regions and parietal cortex, which suggests amplification of activity in some of the regions commonly associated with long-term memory yields high confidence. The contrast of female high- versus low-confidence spatial memory and male high- versus low-confidence spatial memory did not produce any significant activity. However, the reverse contrast produced greater male than female activity in the lateral prefrontal cortex, parietal cortex, sensorimotor cortex, and visual processing regions. An independent region-of-interest (ROI) analysis (ROIs were identified by contrasting hits versus misses) produced complementary results in the lateral prefrontal cortex. Greater lateral prefrontal cortex activity suggests a higher degree of subjective confidence in males than females, greater parietal cortex and visual processing activity suggests more vivid visualization in males than females, and greater activity in sensorimotor cortex indicates that males have a more reactive processing style than females. More broadly, the present and previous functional sex differences argue against the practice of collapsing across sex in cognitive neuroscience studies.

Whether in neurotransmitters or large-scale circuits, sex differences have long been of interest in neuroscience. Spets and Slotnick conducted a meta-analysis of fMRI studies of long-term memory to identify sex differences in brain-behavior associations, demonstrating that sex differences are pervasive across many sub-types of long-term memory. Meta-analyses are a workhorse toward aggregating larger sample sizes to arrive at a more comprehensive understanding of such topics. However, more research is crucial to elucidate complex relationships in how fMRI signals translate to behavioral outcomes. We propose big data and open-science as a solution toward finding robust sex differences in brain-behavior associations.

Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate 'Brain Sex' using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during adolescence. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age. That is, the very large effect size of d = 1.2 which was already evident during childhood increased even further from age 11 onward, and eventually reached an effect size of d = 1.6 at age 17. Altogether these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during adolescence.

In their article, Doerig et al suggest a set of criteria for evaluating theories of consciousness. Naturally, their criteria are situated in their own particular perspective on consciousness science, which we have critiqued in the past. Their first criterion is likely to be the one that is most productive and least contentious: if the field can agree to a family of paradigm cases for consciousness, this would be an important endeavor for the field.

In the attention schema theory (AST), having an automatically constructed self-model that depicts you as containing consciousness makes you intuitively believe that you have consciousness. The reason why such a self-model evolved in the brains of complex animals is that it serves the useful role of modeling, and thus helping to control, the powerful and subtle process of attention, by which the brain seizes on and deeply processes information.