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Importance: Anti-β-amyloid immunotherapy using lecanemab is becoming increasingly available to patients with Alzheimer disease (AD). Individuals with Down syndrome (DS) develop AD neuropathology by age 40 years, representing a significant cohort of genetically determined AD., Objective: To investigate the binding properties of lecanemab in the brains of people with DS, in anticipation of their inclusion in clinical trials or access to antiamyloid immunotherapies., Design, Setting, Participants: The study included cases of postmortem brain tissue analysis from 15 individuals with DS aged 43 to 68 years that were acquired from Alzheimer Disease research centers at the University of California, Irvine and the University of Kentucky from 2008 to 2021. Data were analyzed from August 2023 through May 2024., Exposure: The binding properties of lecanemab were assessed in brain tissue., Main Outcome: The primary outcome was the extent of lecanemab binding to amyloid plaques and brain blood vessels., Results: Tissue from 15 people (8 were female [53%]) with DS ranging in age from 43 to 68 (mean, 56.6) years were included in the study. Lecanemab-labeled amyloid plaques appeared in all 15 DS cases studied, indicating potential target engagement. However, extensive binding of lecanemab to brain blood vessels in DS was observed, raising significant safety concerns. These findings underscore the necessity for clinical trials of lecanemab in people with DS to evaluate both safety and efficacy, particularly in individuals older than 43 years., Conclusions and Relevance: These findings suggest significant binding of lecanemab to cerebral amyloid angiopathy in DS. Lecanemab should be rigorously tested in clinical trials for AD in the DS population to determine its safety and efficacy, especially in those older than 43 years.

Characterizing somatic mutations in the brain is important for disentangling the complex mechanisms of aging, yet little is known about mutational patterns in different brain cell types. Here, we performed whole-genome sequencing (WGS) of 86 single oligodendrocytes, 20 mixed glia, and 56 single neurons from neurotypical individuals spanning 0.4-104 years of age and identified >92,000 somatic single-nucleotide variants (sSNVs) and small insertions/deletions (indels). Although both cell types accumulate somatic mutations linearly with age, oligodendrocytes accumulated sSNVs 81% faster than neurons and indels 28% slower than neurons. Correlation of mutations with single-nucleus RNA profiles and chromatin accessibility from the same brains revealed that oligodendrocyte mutations are enriched in inactive genomic regions and are distributed across the genome similarly to mutations in brain cancers. In contrast, neuronal mutations are enriched in open, transcriptionally active chromatin. These stark differences suggest an assortment of active mutagenic processes in oligodendrocytes and neurons., Competing Interests: Declaration of interests J.G. is now a Merck Research Laboratories (MRL) employee, and no work related to this manuscript was performed at MRL. P.J.P. is a member of the scientific advisory board for Bioskryb Genomics, Inc., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone's impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone's widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded-and perhaps driven-by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain's functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cognitive control is a framework for understanding the neuropsychological processes that underlie the successful completion of everyday tasks. Only recently has research in this area investigated motivational contributions to control allocation. An important gap in our understanding is the way in which intrinsic rewards associated with a task motivate the sustained allocation of control. To address this issue, we draw on flow theory, which predicts that a balance between task difficulty and individual ability results in the highest levels of intrinsic reward. In three behavioral and one functional magnetic resonance imaging studies, we used a naturalistic and open-source video game stimulus to show that changes in the balance between task difficulty and an individual's ability to perform the task resulted in different levels of intrinsic reward, which is associated with different brain states. Specifically, psychophysiological interaction analyses show that high levels of intrinsic reward associated with a balance between task difficulty and individual ability are associated with increased functional connectivity between key structures within cognitive control and reward networks. By comparison, a mismatch between task difficulty and individual ability is associated with lower levels of intrinsic reward and corresponds to increased activity within the default mode network. These results suggest that intrinsic reward motivates cognitive control allocation.

As humans age, cognition and behavior change significantly, along with associated brain function and organization. Aging has been shown to decrease variability in functional magnetic resonance imaging (fMRI) signals, and to affect the modular organization of human brain function. In this work, we use complex network analysis to investigate the dynamic community structure of large-scale brain function, asking how evolving communities interact with known brain systems, and how the dynamics of communities and brain systems are affected by age. We analyze dynamic networks derived from fMRI scans of 104 human subjects performing a word memory task, and determine the time-evolving modular structure of these networks by maximizing the multislice modularity, thereby identifying distinct communities, or sets of brain regions with strong intra-set functional coherence. To understand how community structure changes over time, we examine the number of communities as well as the flexibility, or the likelihood that brain regions will switch between communities. We find a significant positive correlation between age and both these measures: younger subjects tend to have less fragmented and more coherent communities, and their brain regions tend to change communities less often during the memory task. We characterize the relationship of community structure to known brain systems by the recruitment coefficient, or the probability of a brain region being grouped in the same community as other regions in the same system. We find that regions associated with cingulo-opercular, somatosensory, ventral attention, and subcortical circuits have a significantly higher recruitment coefficient in younger subjects. This indicates that the within-system functional coherence of these specific systems during the memory task declines with age. Such a correspondence does not exist for other systems (e.g. visual and default mode), whose recruitment coefficients remain relatively uniform across ages. These results confirm that the dynamics of functional community structure vary with age, and demonstrate methods for investigating how aging differentially impacts the functional organization of different brain systems., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain., Competing Interests: The authors have declared that no competing interests exist.

Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Objective: Psychopathy is a personality disorder with symptoms that include lack of empathy or remorse, antisocial behavior, and excessive self-focus. Previous neuroimaging studies have linked psychopathy to dysfunction in the default mode network (DMN), a brain network that deactivates during externally focused tasks and is more engaged during self-referential processing. Specifically, the DMN has been found to remain relatively active in individuals with psychopathic tendencies during externally focused tasks, suggesting a failure to properly deactivate. However, the exact extent and nature of task-induced DMN dysfunction is poorly understood, including (a) the degree to which specific DMN subregions are affected in criminal psychopaths, and (b) how activity in these subregions relates to affective/interpersonal and antisocial/lifestyle traits of psychopathy., Method: We performed a group independent component analysis to assess DMN activation during a Go/NoGo task in a group of 22 high-psychopathy and 22 low-psychopathy prisoners. The identified group-level DMN was parcellated into 6 subregions, and group differences in task-induced activity were examined., Results: In general, DMN subregions failed to deactivate beneath baseline in the high-psychopathy group. A group comparison with the low-psychopathy group localized this attenuated task-induced deactivation to the posteromedial cortical (mPC) region of the DMN. Moreover, multiple regression analyses revealed that activity in the mPC was associated with affective/interpersonal traits of psychopathy., Conclusion: These findings suggest that attenuated deactivation of the mPC subregion of the DMN is intrinsic to psychopathy, and is a pattern that may be more associated with affective psychopathic traits, including lack of concern for others., ((c) 2015 APA, all rights reserved).)

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

Decision criterion is an important factor in recognition memory, determining the amount of evidence required to judge an item as previously encountered. For a typical recognition memory test involving the prior study of a set of items, a conservative criterion establishes a higher standard of evidence for recognition and designates fewer items as previously studied. In contrast, a liberal criterion establishes a lower standard of evidence and designates more items as previously studied. Therefore, the hit rate and the correct rejection rate on a recognition memory test can be affected by both the memory strength of the studied items and the criterion used to make that judgment. Yet most neuroimaging studies of the successful retrieval effect (a contrast between hits and correct rejections) fail to measure or consider decision criterion. The goal of the current fMRI study with ninety-five participants was to directly manipulate decision criteria on two tests of recognition memory by varying the likelihood of an item's prior occurrence. Our results indicate that regions of the lateral prefrontal and parietal cortex associated with successful retrieval are significantly more active when using conservative criteria than liberal criteria. Furthermore, our results reveal that activity in these regions associated with successful retrieval can be accounted for by individual differences in the conservativeness of the decision criterion above and beyond any differences in memory strength. These results expound on the role of cognitive control in recognition memory and the neural mechanisms that mediate this processing., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

P3 amplitude is a candidate endophenotype for disinhibitory psychopathology, psychosis, and other disorders. The present study is a comprehensive analysis of the behavioral- and molecular-genetic basis of P3 amplitude and a P3 genetic factor score in a large community sample (N = 4,211) of adolescent twins and their parents, genotyped for 527,829 single nucleotide polymorphisms (SNPs). Biometric models indicated that as much as 65% of the variance in each measure was due to additive genes. All SNPs in aggregate accounted for approximately 40% to 50% of the heritable variance. However, analyses of individual SNPs did not yield any significant associations. Analyses of individual genes did not confirm previous associations between P3 amplitude and candidate genes but did yield a novel association with myelin expression factor 2 (MYEF2). Main effects of individual variants may be too small to be detected by GWAS without larger samples., (Copyright © 2014 Society for Psychophysiological Research.)

Several EEG parameters are potential endophenotypes for different psychiatric disorders. The present study consists of a comprehensive behavioral- and molecular-genetic analysis of such parameters in a large community sample (N = 4,026) of adolescent twins and their parents, genotyped for 527,829 single nucleotide polymorphisms (SNPs). Biometric heritability estimates ranged from .49 to .85, with a median of .78. The additive effect of all SNPs (SNP heritability) varied across electrodes. Although individual SNPs were not significantly associated with EEG parameters, several genes were associated with delta power. We also obtained an association between the GABRA2 gene and beta power (p < .014), consistent with findings reported by others, although this did not survive Bonferroni correction. If EEG parameters conform to a largely polygenic model of inheritance, larger sample sizes will be required to detect individual variants reliably., (Copyright © 2014 Society for Psychophysiological Research.)

The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD) signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

As scientists, it is imperative that we understand not only the power of our research tools to yield results, but also their ability to obtain similar results over time. This study is an investigation into how common decisions made during the design and analysis of a functional magnetic resonance imaging (fMRI) study can influence the reliability of the statistical results. To that end, we gathered back-to-back test-retest fMRI data during an experiment involving multiple cognitive tasks (episodic recognition and two-back working memory) and multiple fMRI experimental designs (block, event-related genetic sequence, and event-related m-sequence). Using these data, we were able to investigate the relative influences of task, design, statistical contrast (task vs. rest, target vs. nontarget), and statistical thresholding (unthresholded, thresholded) on fMRI reliability, as measured by the intraclass correlation (ICC) coefficient. We also utilized data from a second study to investigate test-retest reliability after an extended, six-month interval. We found that all of the factors above were statistically significant, but that they had varying levels of influence on the observed ICC values. We also found that these factors could interact, increasing or decreasing the relative reliability of certain Task × Design combinations. The results suggest that fMRI reliability is a complex construct whose value may be increased or decreased by specific combinations of factors.

In the mammalian brain, the majority of excitatory synapses are housed in micron-sized dendritic protrusions called spines, which can undergo rapid changes in shape and number in response to increased or decreased synaptic activity. These dynamic alterations in dendritic spines require precise control of the actin cytoskeleton. Within spines, multidomain Rho guanine nucleotide exchange factors (Rho GEFs) coordinate activation of their target Rho GTPases by a variety of pathways. In this review, we focus on the handful of disease-related Rho GEFs (Kalirin; Trio; Tiam1; P-Rex1,2; RasGRF1,2; Collybistin) localized at synapses and known to affect electrophysiology, spine morphology, and animal behavior. The goal is to integrate structure/function studies with measurements of synaptic function and behavioral phenotypes in animal models.

Magnetic resonance imaging enables the noninvasive mapping of both anatomical white matter connectivity and dynamic patterns of neural activity in the human brain. We examine the relationship between the structural properties of white matter streamlines (structural connectivity) and the functional properties of correlations in neural activity (functional connectivity) within 84 healthy human subjects both at rest and during the performance of attention- and memory-demanding tasks. We show that structural properties, including the length, number, and spatial location of white matter streamlines, are indicative of and can be inferred from the strength of resting-state and task-based functional correlations between brain regions. These results, which are both representative of the entire set of subjects and consistently observed within individual subjects, uncover robust links between structural and functional connectivity in the human brain.

Prion propagation involves a templating reaction in which the infectious form of the prion protein (PrP(Sc)) binds to the cellular form (PrP(C)), generating additional molecules of PrP(Sc). While several regions of the PrP(C) molecule have been suggested to play a role in PrP(Sc) formation based on in vitro studies, the contribution of these regions in vivo is unclear. Here, we report that mice expressing PrP deleted for a short, polybasic region at the N terminus (residues 23-31) display a dramatically reduced susceptibility to prion infection and accumulate greatly reduced levels of PrP(Sc). These results, in combination with biochemical data, demonstrate that residues 23-31 represent a critical site on PrP(C) that binds to PrP(Sc) and is essential for efficient prion propagation. It may be possible to specifically target this region for treatment of prion diseases as well as other neurodegenerative disorders due to β-sheet-rich oligomers that bind to PrP(C).

Neuroimaging is being used increasingly to make inferences about an individual. Yet, those inferences are often confounded by the fact that topographical patterns of task-related brain activity can vary greatly from person to person. This study examined two factors that may contribute to the variability across individuals in a memory retrieval task: individual differences in cognitive style and individual differences in encoding strategy. Cognitive style was probed using a battery of assessments focused on the individual's tendency to visualize or verbalize written material. Encoding strategy was probed using a series of questions designed to assess typical strategies that an individual might utilize when trying to remember a list of words. Similarity in brain activity was assessed by cross-correlating individual t-statistic maps contrasting the BOLD response during retrieval to the BOLD response during fixation. Individual differences in cognitive style and encoding strategy accounted for a significant portion of the variance in similarity. This was true above and beyond individual differences in anatomy and memory performance. These results demonstrate the need for a multidimensional approach in the use of fMRI to make inferences about an individual., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Previous studies identified two mammalian prion protein (PrP) polybasic domains that bind the disease-associated conformer PrP(Sc), suggesting that these domains of cellular prion protein (PrP(C)) serve as docking sites for PrP(Sc) during prion propagation. To examine the role of polybasic domains in the context of full-length PrP(C), we used prion proteins lacking one or both polybasic domains expressed from Chinese hamster ovary (CHO) cells as substrates in serial protein misfolding cyclic amplification (sPMCA) reactions. After ∼5 rounds of sPMCA, PrP(Sc) molecules lacking the central polybasic domain (ΔC) were formed. Surprisingly, in contrast to wild-type prions, ΔC-PrP(Sc) prions could bind to and induce quantitative conversion of all the polybasic domain mutant substrates into PrP(Sc) molecules. Remarkably, ΔC-PrP(Sc) and other polybasic domain PrP(Sc) molecules displayed diminished or absent biological infectivity relative to wild-type PrP(Sc), despite their ability to seed sPMCA reactions of normal mouse brain homogenate. Thus, ΔC-PrP(Sc) prions interact with PrP(C) molecules through a novel interaction mechanism, yielding an expanded substrate range and highly efficient PrP(Sc) propagation. Furthermore, polybasic domain deficient PrP(Sc) molecules provide the first example of dissociation between normal brain homogenate sPMCA seeding ability from biological prion infectivity. These results suggest that the propagation of PrP(Sc) molecules may not depend on a single stereotypic mechanism, but that normal PrP(C)/PrP(Sc) interaction through polybasic domains may be required to generate prion infectivity.

An incredible amount of data is generated in the course of a functional neuroimaging experiment. The quantity of data gives us improved temporal and spatial resolution with which to evaluate our results. It also creates a staggering multiple testing problem. A number of methods have been created that address the multiple testing problem in neuroimaging in a principled fashion. These methods place limits on either the familywise error rate (FWER) or the false discovery rate (FDR) of the results. These principled approaches are well established in the literature and are known to properly limit the amount of false positives across the whole brain. However, a minority of papers are still published every month using methods that are improperly corrected for the number of tests conducted. These latter methods place limits on the voxelwise probability of a false positive and yield no information on the global rate of false positives in the results. In this commentary, we argue in favor of a principled approach to the multiple testing problem--one that places appropriate limits on the rate of false positives across the whole brain gives readers the information they need to properly evaluate the results.

Fourteen subjects were scanned in two fMRI sessions separated by several months. During each session, subjects performed an episodic retrieval task, a semantic retrieval task, and a working memory task. We found that 1) despite extensive intersubject variability in the pattern of activity across the whole brain, individual activity patterns were stable over time, 2) activity patterns of the same individual performing different tasks were more similar than activity patterns of different individuals performing the same task, and 3) that individual differences in decision criterion on a recognition test predicted the degree of similarity between any two individuals' patterns of brain activity, but individual differences in memory accuracy or similarity in structural anatomy did not. These results imply that the exclusive use of group maps may be ineffective in profiling the pattern of activations for a given task. This may be particularly true for a task like episodic retrieval, which is relatively strategic and can involve widely distributed specialized processes that are peripheral to the actual retrieval of stored information. Further, these processes may be differentially engaged depending on individual differences in cognitive processing and/or physiology.

The localization of brain functions using neuroimaging techniques is commonly dependent on statistical analyses of groups of subjects in order to identify sites of activation, particularly in studies of episodic memory. Exclusive reliance on group analysis may be to the detriment of understanding the true underlying cognitive nature of brain activations. In this overview, we found that the patterns of brain activity associated with episodic retrieval are very distinct for individual subjects from the patterns of brain activity at the group level. These differences appear to go beyond the relatively small variations due to cyctoarchitectonic differences or spatial normalization. We review evidence that individual patterns of brain activity vary widely across subjects and are reliable over time despite extensive variability. We suggest that varied but reliable individual patterns of significant brain activity may be indicative of different cognitive strategies used to produce a recognition response. We argue that individual analyses in conjunction with group analyses are likely to be critical in fully understanding the relationship between retrieval processes and underlying neural systems.

Baron-Cohen (1995) proposed that the theory of mind (ToM) inference system evolved to promote strategic social interaction. Social exchange--a form of co-operation for mutual benefit--involves strategic social interaction and requires ToM inferences about the contents of other individuals' mental states, especially their desires, goals, and intentions. There are behavioral and neuropsychological dissociations between reasoning about social exchange and reasoning about equivalent problems tapping other, more general content domains. It has therefore been proposed that social exchange behavior is regulated by social contract algorithms: a domain-specific inference system that is functionally specialized for reasoning about social exchange. We report an fMRI study using the Wason selection task that provides further support for this hypothesis. Precautionary rules share so many properties with social exchange rules--they are conditional, deontic, and involve subjective utilities--that most reasoning theories claim they are processed by the same neurocomputational machinery. Nevertheless, neuroimaging shows that reasoning about social exchange activates brain areas not activated by reasoning about precautionary rules, and vice versa. As predicted, neural correlates of ToM (anterior and posterior temporal cortex) were activated when subjects interpreted social exchange rules, but not precautionary rules (where ToM inferences are unnecessary). We argue that the interaction between ToM and social contract algorithms can be reciprocal: social contract algorithms requires ToM inferences, but their functional logic also allows ToM inferences to be made. By considering interactions between ToM in the narrower sense (belief-desire reasoning) and all the social inference systems that create the logic of human social interaction--ones that enable as well as use inferences about the content of mental states--a broader conception of ToM may emerge: a computational model embodying a Theory of Human Nature (ToHN).

Previously, in a simple probability-matching experiment with two split-brain patients that involved having the participant predict which of two events will happen on the next trial, we found that the left hemisphere tended to look for patterns and match the frequency of previous occurrences but not the right hemisphere [Wolford, G., Miller, M. B., & Gazzaniga, M. S. (2000). The left hemisphere's role in hypothesis formation. Journal of Neuroscience, 20(RC64), 1-4]. In this study, we examined those findings in normal subjects using fMRI. Subjects alternated between blocks of trials in which they predicted the location of a stimulus and those in which they detected the location of a stimulus. Previous investigators using similar paradigms reported mostly right hemisphere activations, including activations in the right dorsolateral and ventrolateral prefrontal cortex, the medial prefrontal cortex, and the right lateral parietal lobe. We also found mostly right hemisphere activations, but we found that some of the activations in the dorsolateral prefrontal and parietal cortices were sensitive to individual differences in the tendency to look for patterns in random sequences. Further, we found that, by controlling for the working memory component of the predicting task, all brain activations in the normal brain associated with looking for patterns were related to the task demands of working memory processes underlying probability matching and predicting.

Typically developing children begin to produce and understand pretend play between 18 and 24 months of age, and early pretense has been argued to be a candidate ''core'' capacity central to the deployment of representations of other peoples' mental states-''theory of mind.'' In a functional magnetic resonance imaging study, 16 healthy adult volunteers were imaged while watching short (5 sec) clips of actors who either performed simple everyday actions or pretended to perform a similar set of actions, under covert conditions (e. g., participants were not directed to attend to actors' mental states). There was increased activity in the medial prefrontal areas (Brodmann's areas [BA] 9/6/32, 9, and 10), inferior frontal gyrus bilaterally (BA 44, 47), temporo-parietal regions (BA 21 and 22), and parahippocampal areas, including the amygdala, when subjects viewed pretend actions as compared with real actions. This result suggests that at least some areas previously implicated in making explicit mental state judgments are also strongly activated in response to actions that call for mental state interpretation (e. g., pretense) even when there is no explicit instruction for ''mind reading.'' This outcome is discussed in terms of accounts that propose ''theory of mind'' to be underwritten by automatic specialized mechanisms for the interpretation of the behavior of social agents.

The localization of brain functions using neuroimaging techniques is commonly dependent on statistical analyses of groups of subjects in order to identify sites of activation, particularly in studies of episodic memory. Exclusive reliance on group analysis may be to the detriment of understanding the true underlying cognitive nature of brain activations. In the present study, we found that the patterns of brain activity associated with episodic retrieval are very distinct for individual subjects from the patterns of brain activity at the group level. These differences go beyond the relatively small variations due to cyctoarchitectonic differences or spatial normalization. We quantify this individual variability by cross-correlating volumes of brain images. We demonstrate that individual patterns of brain activity are reliable over time despite their extensive variability. We suggest that varied but reliable individual patterns of significant brain activity may be indicative of different cognitive strategies used to produce a recognition response. We believe that individual analysis in conjunction with group analysis may be critical to fully understanding the relationship between retrieval processes and underlying brain regions.

Sensitivity and bias can be manipulated independently on a recognition test. The goal of this fMRI study was to determine whether neural activations associated with manipulations of a decision criterion would be anatomically distinct from neural activations associated with manipulations of memory strength and episodic retrieval. The results indicated that activations associated with shifting criteria (a manipulation of bias) were located in bilateral regions of the lateral cerebellum, lateral parietal lobe, and the dorsolateral prefrontal cortex extending from the supplementary motor area. These regions were anatomically distinct from activations in the prefrontal cortex produced during memory-based retrieval processes (manipulations of sensitivity), which tended to be more medial and anterior. These later activations are consistent with previous studies of episodic retrieval. Determining patterns of neural activations associated with decision-making processes relative to memory processes has important implications for Cognitive Neuroscience, including the use of these patterns to compare memory models in different paradigms.