Functional ontologies for cognition: The systematic definition of structure and function

An inclusive multivariate approach to neural localization of language components

To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex.

The importance of covert memory consolidation in schizophrenia: Dysfunctional network profiles of the hippocampus and the dorsolateral prefrontal cortex

Altered brain network profiles in schizophrenia (SCZ) during memory consolidation are typically observed during task-active periods such as encoding or retrieval. However active processes are also sub served by covert periods of memory consolidation. These periods are active in that they allow memories to be recapitulated even in the absence of overt sensorimotor processing. It is plausible that regions central to memory formation like the dlPFC and the hippocampus, exert network signatures during covert periods. Are these signatures altered in patients? The question is clinically relevant because real world learning and memory is facilitated by covert processing, and may be impaired in schizophrenia. Here, we compared network signatures of the dlPFC and the hippocampus during covert periods of a learning and memory task. Because behavioral proficiency increased non-linearly, functional connectivity of the dlPFC and hippocampus [psychophysiological interaction (PPI)] was estimated for each of the Early (linear increases in performance) and Late (asymptotic performance) covert periods. During Early periods, we observed hypo-modulation by the hippocampus but hyper-modulation by dlPFC. Conversely, during Late periods, we observed hypo-modulation by both the dlPFC and the hippocampus. We stitch these results into a conceptual model of network deficits during covert periods of memory consolidation.

The effects of sleep deprivation, acute hypoxia, and exercise on cognitive performance: A multi-experiment combined stressors study

INTRODUCTION

Both sleep deprivation and hypoxia have been shown to impair executive function. Conversely, moderate intensity exercise is known to improve executive function. In a multi-experiment study, we tested the hypotheses that moderate intensity exercise would ameliorate any decline in executive function after i) three consecutive nights of partial sleep deprivation (PSD) (Experiment 1) and ii) the isolated and combined effects of a single night of total sleep deprivation (TSD) and acute hypoxia (Experiment 2).

METHODS

Using a rigorous randomised controlled crossover design, 12 healthy participants volunteered in each experiment (24 total, 5 females). In both experiments seven executive function tasks (2-choice reaction time, logical relations, manikin, mathematical processing, 1-back, 2-back, 3-back) were completed at rest and during 20 min semi-recumbent, moderate intensity cycling. Tasks were completed in the following conditions: before and after three consecutive nights of PSD and habitual sleep (Experiment 1) and in normoxia and acute hypoxia (FIO2 = 0.12) following one night of habitual sleep and one night of TSD (Experiment 2).

RESULTS

Although the effects of three nights of PSD on executive functions were inconsistent, one night of TSD (regardless of hypoxic status) reduced executive functions. Significantly, regardless of sleep or hypoxic status, executive functions are improved during an acute bout of moderate intensity exercise.

CONCLUSION

These novel data indicate that moderate intensity exercise improves executive function performance after both PSD and TSD, regardless of hypoxic status. The key determinants and/or mechanism(s) responsible for this improvement still need to be elucidated. Future work should seek to identify these mechanisms and translate these significant findings into occupational and skilled performance settings.

Fractionation of neural reward processing into independent components by novel decoding principle

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

Perception and Memory in the Ventral Visual Stream and Medial Temporal Lobe

Perception and memory are traditionally thought of as separate cognitive functions, supported by distinct brain regions. The canonical perspective is that perceptual processing of visual information is supported by the ventral visual stream, whereas long-term declarative memory is supported by the medial temporal lobe. However, this modular framework cannot account for the increasingly large body of evidence that reveals a role for early visual areas in long-term recognition memory and a role for medial temporal lobe structures in high-level perceptual processing. In this article, we review relevant research conducted in humans, nonhuman primates, and rodents. We conclude that the evidence is largely inconsistent with theoretical proposals that draw sharp functional boundaries between perceptual and memory systems in the brain. Instead, the weight of the empirical findings is best captured by a representational-hierarchical model that emphasizes differences in content, rather than in cognitive processes within the ventral visual stream and medial temporal lobe.

Common and distinct patterns of task-related neural activation abnormalities in patients with remitted and current major depressive disorder: A systematic review and coordinate-based meta-analysis

Whether remitted major depressive disorder (rMDD) and MDD present common or distinct neuropathological mechanisms remains unclear. We performed a meta-analysis of task-related whole-brain functional magnetic resonance imaging (fMRI) using anisotropic effect-size signed differential mapping software to compare brain activation between rMDD/MDD patients and healthy controls (HCs). We included 18 rMDD studies (458 patients and 476 HCs) and 120 MDD studies (3746 patients and 3863 HCs). The results showed that MDD and rMDD patients shared increased neural activation in the right temporal pole and right superior temporal gyrus. Several brain regions, including the right middle temporal gyrus, left inferior parietal, prefrontal cortex, left superior frontal gyrus and striatum, differed significantly between MDD and rMDD. Meta-regression analyses revealed that the percentage of females with MDD was positively associated with brain activity in the right lenticular nucleus/putamen. Our results provide valuable insights into the underlying neuropathology of brain dysfunction in MDD, developing more targeted and efficacious treatment and intervention strategies, and more importantly, providing potential neuroimaging targets for the early screening of MDD.

Enriched learning: behavior, brain, and computation

The presence of complementary information across multiple sensory or motor modalities during learning, referred to as multimodal enrichment, can markedly benefit learning outcomes. Why is this? Here, we integrate cognitive, neuroscientific, and computational approaches to understanding the effectiveness of enrichment and discuss recent neuroscience findings indicating that crossmodal responses in sensory and motor brain regions causally contribute to the behavioral benefits of enrichment. The findings provide novel evidence for multimodal theories of enriched learning, challenge assumptions of longstanding cognitive theories, and provide counterevidence to unimodal neurobiologically inspired theories. Enriched educational methods are likely effective not only because they may engage greater levels of attention or deeper levels of processing, but also because multimodal interactions in the brain can enhance learning and memory.

Neurobiological reduction: From cellular explanations of behavior to interventions

Scientific reductionism, the view that higher level functions can be explained by properties at some lower-level or levels, has been an assumption of nervous system analyses since the acceptance of the neuron doctrine in the late 19th century, and became a dominant experimental approach with the development of intracellular recording techniques in the mid-20th century. Subsequent refinements of electrophysiological approaches and the continual development of molecular and genetic techniques have promoted a focus on molecular and cellular mechanisms in experimental analyses and explanations of sensory, motor, and cognitive functions. Reductionist assumptions have also influenced our views of the etiology and treatment of psychopathologies, and have more recently led to claims that we can, or even should, pharmacologically enhance the normal brain. Reductionism remains an area of active debate in the philosophy of science. In neuroscience and psychology, the debate typically focuses on the mind-brain question and the mechanisms of cognition, and how or if they can be explained in neurobiological terms. However, these debates are affected by the complexity of the phenomena being considered and the difficulty of obtaining the necessary neurobiological detail. We can instead ask whether features identified in neurobiological analyses of simpler aspects in simpler nervous systems support current molecular and cellular approaches to explaining systems or behaviors. While my view is that they do not, this does not invite the opposing view prevalent in dichotomous thinking that molecular and cellular detail is irrelevant and we should focus on computations or representations. We instead need to consider how to address the long-standing dilemma of how a nervous system that ostensibly functions through discrete cell to cell communication can generate population effects across multiple spatial and temporal scales to generate behavior.

The Brain Connectome for Chinese Reading

Chinese, as a logographic language, fundamentally differs from alphabetic languages like English. Previous neuroimaging studies have mainly focused on alphabetic languages, while the exploration of Chinese reading is still an emerging and fast-growing research field. Recently, a growing number of neuroimaging studies have explored the neural circuit of Chinese reading. Here, we summarize previous research on Chinese reading from a connectomic perspective. Converging evidence indicates that the left middle frontal gyrus is a specialized hub region that connects the ventral with dorsal pathways for Chinese reading. Notably, the orthography-to-phonology and orthography-to-semantics mapping, mainly processed in the ventral pathway, are more specific during Chinese reading. Besides, in addition to the left-lateralized language-related regions, reading pathways in the right hemisphere also play an important role in Chinese reading. Throughout, we comprehensively review prior findings and emphasize several challenging issues to be explored in future work.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known.

METHODS

An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions.

RESULTS

Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex.

CONCLUSIONS

The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

Integrating Philosophy of Understanding With the Cognitive Sciences

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

Common functional brain networks between attention deficit and disruptive behaviors in youth

Attention deficits (AD) and disruptive behavior (DB) are highly comorbid youth externalizing behaviors. This study aimed to study reliable functional brain networks shared by AD and DB in youth aged from 8 to 21 years from the Philadelphia Neurodevelopmental Cohort (PNC). The PNC study assessed AD and DB behaviors via Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS). This study employed sparse canonical correlation analysis (SCCA) to examine the correlation of AD and DB behaviors with resting-state functional connectivity maps of the brain regions identified via activation likelihood estimation (ALE) meta-analyses on attention deficit/hyperactivity disorder (ADHD) and DB disorder (DBD). Our meta-analyses identified that the middle cingulate cortex, pre-supplementary motor area (pre-SMA), and striatum had a great consensus in existing ADHD studies and the amygdala and inferior parietal lobule were consistently found in existing DBD studies. Our SCCA analysis revealed that the AD and DB behavioral items relevant to inattention and delinquency were correlated with the functional connectivity of the pre-SMA with the ventral attentional and frontoparietal networks (FPN), and the striatum with the default mode (DMN) and dorsal attentional networks. The AD and DB behavioral items relevant to inattention and irritability were associated with the functional connectivity between the amygdala and the DMN and FPN. Our findings suggest that the functional organization of the ADHD- and DBD-related brain regions provides insights on the shared neural basis in AD and DB.

Dissociating the functions of three left posterior superior temporal regions that contribute to speech perception and production

Prior studies have shown that the left posterior superior temporal sulcus (pSTS) and left temporo-parietal junction (TPJ) both contribute to phonological short-term memory, speech perception and speech production. Here, by conducting a within-subjects multi-factorial fMRI study, we dissociate the response profiles of these regions and a third region – the anterior ascending terminal branch of the left superior temporal sulcus (atSTS), which lies dorsal to pSTS and ventral to TPJ. First, we show that each region was more activated by (i) 1-back matching on visually presented verbal stimuli (words or pseudowords) compared to 1-back matching on visually presented non-verbal stimuli (pictures of objects or non-objects), and (ii) overt speech production than 1-back matching, across 8 types of stimuli (visually presented words, pseudowords, objects and non-objects and aurally presented words, pseudowords, object sounds and meaningless hums). The response properties of the three regions dissociated within the auditory modality. In left TPJ, activation was higher for auditory stimuli that were non-verbal (sounds of objects or meaningless hums) compared to verbal (words and pseudowords), irrespective of task (speech production or 1-back matching). In left pSTS, activation was higher for non-semantic stimuli (pseudowords and hums) than semantic stimuli (words and object sounds) on the dorsal pSTS surface (dpSTS), irrespective of task. In left atSTS, activation was not sensitive to either semantic or verbal content. The contrasting response properties of left TPJ, dpSTS and atSTS was cross-validated in an independent sample of 59 participants, using region-by-condition interactions. We also show that each region participates in non-overlapping networks of frontal, parietal and cerebellar regions. Our results challenge previous claims about functional specialisation in the left posterior superior temporal lobe and motivate future studies to determine the timing and directionality of information flow in the brain networks involved in speech perception and production.

A data-driven framework for mapping domains of human neurobiology

Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. Interpretation of fMRI data has often occurred within knowledge frameworks crafted by experts, which have the potential to amplify biases that limit the replicability of findings. Here, we employ a computational approach to derive a data-driven framework for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. Across multiple levels of domain specificity, the structure-function links within domains better replicate in held-out articles than those mapped from dominant frameworks in neuroscience and psychiatry. We further show that the data-driven framework partitions the literature into modular subfields, for which domains serve as generalizable prototypes of structure-function patterns in single articles. The approach to computational ontology we present here is the most comprehensive characterization of human brain circuits quantifiable with fMRI and may be extended to synthesize other scientific literatures.

From mathematics to medicine: A practical primer on topological data analysis (TDA) and the development of related analytic tools for the functional discovery of latent structure in fMRI data

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. “Structure” within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA (“persistent homology”) to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.

Ecological stress, amygdala reactivity, and internalizing symptoms in preadolescence: Is parenting a buffer?

Ecological stress during adolescent development may increase the sensitivity to negative emotional processes that can contribute to the onset and progression of internalizing behaviors during preadolescence. Although a small number of studies have considered the link among the relations between ecological stress, amygdala reactivity, and internalizing symptoms in childhood and adolescence, these studies have largely been small, cross-sectional, and often do not consider unique roles of parenting or sex. In the current study, we evaluated the interrelations between ecological stress, amygdala reactivity, subsequent internalizing symptoms, and the moderating roles of parenting and sex among 9- and 10-year-old preadolescents from the Adolescent Brain Cognitive Development (ABCD) Study ®. A subset of participants who met a priori quality control criteria for bilateral amygdala activation during the EN-back faces versus places contrast (N = 7,385; Mean Age = 120 months, SD = 7.52; 49.5% Female) were included in the study. A confirmatory factor analysis was performed to create a latent variable of ecological stress, and multiple structural equation models were tested to evaluate the association among baseline ecological stress and internalizing symptoms one year later, the mediating role of amygdala reactivity, and moderating effects of parental acceptance and sex. The results revealed a significant association between ecological stress and subsequent internalizing symptoms, which was greater in males than females. There was no association between amygdala reactivity during the Faces versus Places contrast and ecological stress or subsequent internalizing symptoms, and no mediating role of amygdala or moderating effect of parental acceptance on the association between ecological stress and internalizing symptoms. An alternative mediation model was tested which revealed that there was a small mediating effect of parental acceptance on the association between ecological stress and internalizing symptoms, demonstrating lower internalizing symptoms among preadolescents one year later. Given the lack of association in brain function, ecological stress and internalizing symptoms in preadolescents in this registered report, effects from comparable small studies should be reconsidered in larger samples.

Shared Neural Phenotypes for Mood and Anxiety Disorders A Meta-Analysis of 226 Task-Related Functional Imaging Studies

(Appeared originally in JAMA Psychiatry 2020;77(2):172-179).

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

INTRODUCTION

Phenomena related to reward responsiveness have been extensively studied in their associations with substance use and socioemotional functioning. One important task in this literature is the Monetary Incentive Delay (MID) task. By cueing and delivering performance-contingent reward, the MID task has been demonstrated to elicit robust activation of neural circuits involved in different phases of reward responsiveness. However, systematic evaluations of common MID task contrasts have been limited to between-study comparisons of group-level activation maps, limiting their ability to directly evaluate how researchers' choice of contrasts impacts conclusions about individual differences in reward responsiveness or brain-behavior associations.

METHODS

In a sample of 104 participants (Age Mean = 19.3, SD = 1.3), we evaluate similarities and differences between contrasts in: group- and individual-level activation maps using Jaccard's similarity index, region of interest (ROI) mean signal intensities using Pearson's r, and associations between ROI mean signal intensity and psychological measures using Bayesian correlation.

RESULTS

Our findings demonstrate more similarities than differences between win and loss cues during the anticipation contrast, dissimilarity between some win anticipation contrasts, an apparent deactivation effect in the outcome phase, likely stemming from the blood oxygen level-dependent undershoot, and behavioral associations that are less robust than previously reported.

CONCLUSION

Consistent with recent empirical findings, this work has practical implications for helping researchers interpret prior MID studies and make more informed a priori decisions about how their contrast choices may modify results.

Multiple b-values improve discrimination of cortical gray matter regions using diffusion MRI: an experimental validation with a data-driven approach

Objective

To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach.

Methods

Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm² along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm²) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs.

Results

Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates.

Conclusion

Acquisitions with varying b-values are more suitable for discriminating cortical areas.

Shared Neural Phenotypes for Mood and Anxiety Disorders: A Meta-analysis of 226 Task-Related Functional Imaging Studies

IMPORTANCE

Major depressive disorder, bipolar disorder, posttraumatic stress disorder, and anxiety disorders are highly comorbid and have shared clinical features. It is not yet known whether their clinical overlap is reflected at the neurobiological level.

OBJECTIVE

To detect transdiagnostic convergence in abnormalities in task-related brain activation.

DATA SOURCE

Task-related functional magnetic resonance imaging articles published in PubMed, Web of Science, and Google Scholar during the last decade comparing control individuals with patients with mood, posttraumatic stress, and anxiety disorders were examined.

STUDY SELECTION

Following Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines, articles were selected if they reported stereotactic coordinates of whole-brain-based activation differences between adult patients and control individuals.

DATA EXTRACTION AND SYNTHESIS

Coordinates of case-control differences coded by diagnosis and by cognitive domain based on the research domain criteria were analyzed using activation likelihood estimation.

MAIN OUTCOMES AND MEASURES

Identification of transdiagnostic clusters of aberrant activation and quantification of the contribution of diagnosis and cognitive domain to each cluster.

RESULTS

A total of 367 experiments (major depressive disorder, 149; bipolar disorder, 103; posttraumatic stress disorder, 55; and anxiety disorders, 60) were included comprising observations from 4507 patients and 4755 control individuals. Three right-sided clusters of hypoactivation were identified centered in the inferior prefrontal cortex/insula (volume, 2120 mm3), the inferior parietal lobule (volume, 1224 mm3), and the putamen (volume, 888 mm3); diagnostic differences were noted only in the putamen (χ23 = 8.66; P = .03), where hypoactivation was more likely in bipolar disorder (percentage contribution = 72.17%). Tasks associated with cognitive systems made the largest contribution to each cluster (percentage contributions >29%). Clusters of hyperactivation could only be detected using a less stringent threshold. These were centered in the perigenual/dorsal anterior cingulate cortex (volume, 2208 mm3), the left amygdala/parahippocampal gyrus (volume, 2008 mm3), and the left thalamus (volume, 1904 mm3). No diagnostic differences were observed (χ23 < 3.06; P > .38), while tasks associated with negative valence systems made the largest contribution to each cluster (percentage contributions >49%). All findings were robust to the moderator effects of age, sex, and magnetic field strength of the scanner and medication.

CONCLUSIONS AND RELEVANCE

In mood disorders, posttraumatic stress disorder, and anxiety disorders, the most consistent transdiagnostic abnormalities in task-related brain activity converge in regions that are primarily associated with inhibitory control and salience processing. Targeting these shared neural phenotypes could potentially mitigate the risk of affective morbidity in the general population and improve outcomes in clinical populations.

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

Examining the neural and cognitive processes that underlie reading through naming speed tasks

We combined fMRI with eye tracking and speech recording to examine the neural and cognitive mechanisms that underlie reading. To simplify the study of the complex processes involved during reading, we used naming speed (NS) tasks (also known as rapid automatized naming or RAN) as a focus for this study, in which average reading right-handed adults named sets of stimuli (letters or objects) as quickly and accurately as possible. Due to the possibility of spoken output during fMRI studies creating motion artifacts, we employed both an overt session and a covert session. When comparing the two sessions, there were no significant differences in behavioral performance, sensorimotor activation (except for regions involved in the motor aspects of speech production) or activation in regions within the left-hemisphere-dominant neural reading network. This established that differences found between the tasks within the reading network were not attributed to speech production motion artifacts or sensorimotor processes. Both behavioral and neuroimaging measures showed that letter naming was a more automatic and efficient task than object naming. Furthermore, specific manipulations to the NS tasks to make the stimuli more visually and/or phonologically similar differentially activated the reading network in the left hemisphere associated with phonological, orthographic and orthographic-to-phonological processing, but not articulatory/motor processing related to speech production. These findings further our understanding of the underlying neural processes that support reading by examining how activation within the reading network differs with both task performance and task characteristics.

The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior

The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.

The hierarchically mechanistic mind: A free-energy formulation of the human psyche

This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.

Duality of Function: Activation for Meaningless Nonwords and Semantic Codes in the Same Brain Areas

Studies of the neural substrates of semantic (word meaning) processing have typically focused on semantic manipulations, with less consideration for potential differences in difficulty across conditions. While the idea that particular brain regions can support multiple functions is widely accepted, studies of specific cognitive domains rarely test for co-location with other functions. Here we start with standard univariate analyses comparing words to meaningless nonwords, replicating our recent finding that this contrast can activate task-positive regions for words, and default-mode regions in the putative semantic network for nonwords, pointing to difficulty effects. Critically, this was followed up with a multivariate analysis to test whether the same areas activated for meaningless nonwords contained semantic information sufficient to distinguish high- from low-imageability words. Indeed, this classification was performed reliably better than chance at 75% accuracy. This is compatible with two non-exclusive interpretations. Numerous areas in the default-mode network are task-negative in the sense of activating for less demanding conditions, and the same areas contain information supporting semantic cognition. Therefore, while areas of the default mode network have been hypothesized to support semantic cognition, we offer evidence that these areas can respond to both domain-general difficulty effects, and to specific aspects of semantics.

Hierarchical control systems for the regulation of physiological homeostasis and affect: Can their interactions modulate mood and anhedonia?

Predominant concepts assert that conscious willful processes do not assert a significant influence on autonomic functions associated with physiological homeostasis (e.g., thermal regulation). The singular purpose of this review is to promote a reappraisal of concepts regarding the circumscribed role of hierarchical control systems. To effect this reappraisal, we assess the interaction between top-down and bottom-up regulatory mechanisms, specifically by highlighting the intersection between the "physiological" (specifically thermoregulatory pathways) and the "psychological" (specifically mood/anhedonia related processes). This reappraisal suggests that the physiological and psychological processes can interact in unanticipated ways, and is grounded in multiple lines of recent experimental evidence. For example, behavioral techniques that through a combination of hormesis (forced breathing, cold exposure) and meditation appear to exert unusual effects on homeostatic function (cold tolerance) and suppression of aberrant auto-immune responses. The molecular correlates of these effects (the putative release of endogenous cannabinoids and endorphins) may exert salutary effects on mood/anhedonia, even more significant than those exerted by cognitive behavioral techniques or meditation alone. By focusing on this interaction, we present a putative mechanistic model linking physiology with psychology, with particular implications for disturbances of mood/anhedonia. We suggest that volitional changes in breathing patterns can activate primary control centers for descending pain/cold stimuli in periaqueductal gray, initiating a stress-induced analgesic response mediated by endocannabinoid/endorphin release. The analgesic effects, and the feelings of euphoria generated by endocannbinoid release are prolonged via a top-down "outcome expectancy" control mechanism regulated by cortical areas. By focusing on modification strategies that principally target homeostatic function (but may also exert ancillary effects on mood), we articulate a novel framework for how hierarchical control systems for the regulation of physiological homeostasis and affect interact. This interaction may allow practitioners of focused modification strategies to assert increased control over key components of the affective system, allowing for viable treatment approaches for patients with disturbances of mood/anhedonia.

Connecting levels of analysis in educational neuroscience: A review of multi-level structure of educational neuroscience with concrete examples

In its origins educational neuroscience has started as an endeavor to discuss implications of neuroscience studies for education. However, it is now on its way to become a transdisciplinary field, incorporating findings, theoretical frameworks and methodologies from education, and cognitive and brain sciences. Given the differences and diversity in the originating disciplines, it has been a challenge for educational neuroscience to integrate both theoretical and methodological perspectives in education and neuroscience in a coherent way. We present a multi-level framework for educational neuroscience, which argues for integration of multiple levels of analysis, some originating in brain and cognitive sciences, others in education, as a roadmap for the future of educational neuroscience, with concrete examples in mathematical learning and moral education.

Body representations and cognitive ontology: Drawing the boundaries of the body image

The distinction between body image and body schema has been incredibly influential in cognitive neuroscience. Recently, researchers have begun to speculate about the relationship between these representations (Gadsby, 2017, 2018; Pitron & de Vignemont, 2017; Pitron et al., 2018). Within this emerging literature, Pitron et al. (2018) proposed that the long-term body image and long-term body schema co-construct one another, through a process of reciprocal interaction. In proposing this model, they make two assumptions: that the long-term body image incorporates the spatial characteristics of tools, and that it is distorted in the case of Alice in wonderland syndrome. Here, I challenge these assumptions, with a closer examination of what the term "long-term body image" refers to. In doing so, I draw out some important taxonomic principles for research into body representation.

Annual Research Review: Educational neuroscience: progress and prospects

Educational neuroscience is an interdisciplinary research field that seeks to translate research findings on neural mechanisms of learning to educational practice and policy and to understand the effects of education on the brain. Neuroscience and education can interact directly, by virtue of considering the brain as a biological organ that needs to be in the optimal condition to learn ('brain health'); or indirectly, as neuroscience shapes psychological theory and psychology influences education. In this article, we trace the origins of educational neuroscience, its main areas of research activity and the principal challenges it faces as a translational field. We consider how a pure psychology approach that ignores neuroscience is at risk of being misleading for educators. We address the major criticisms of the field comprising, respectively, a priori arguments against the relevance of neuroscience to education, reservations with the current practical operation of the field, and doubts about the viability of neuroscience methods for diagnosing disorders or predicting individual differences. We consider future prospects of the field and ethical issues it raises. Finally, we discuss the challenge of responding to the (welcome) desire of education policymakers to include neuroscience evidence in their policymaking, while ensuring recommendations do not exceed the limitations of current basic science.

A Non-cognitive Behavioral Model for Interpreting Functional Neuroimaging Studies

The dominant model for interpreting brain imaging experiments, which we refer to as the Standard Cognitive Model (SCM), assumes that the brain is organized in support of mental processes that control behavior. However, functional neuroimaging experiments of cognitive tasks have not shown clear anatomic segregation between mental processes originally proposed by this model. This failing has been blamed on limitations in imaging technology and non-linearity in the brain's implementation of these processes. However, the validity of the underlying cognitive models used to describe the brain has rarely been questioned or directly tested against imaging results. We propose an alternative model of brain function, that we term the Non-cognitive Behavioral Model (NBM), which correlates observed human behavior directly with measured brain activity without making assumptions about intervening cognitive processes. Our model derives from behavioral psychology but is extended to include brain activity, in addition to behavior, as observables. A further extension is the role of neuroplasticity, as opposed to innate cognitive processes, in developing the brain's support of cognitive behavior. We present the theoretical basis with which the SCM maps cognitive processes onto functional magnetic resonance and positron emission tomography images and compare and contrast with the NBM. We also describe how the NBM can be used experimentally to study how the brain supports behavior. Two applications are presented that support the usefulness of the NBM. In one, the NBM use of the total functional imaging signal (not just the differences between states) provides a stronger correlation of neural activity with the behavioral state of consciousness than the SCM approach in both anesthesia and coma. The second example reviews studies of facial and object recognition that provide evidence for the NBM proposal that neuroplasticity and experience play key roles in the brain's support of recognition and other behaviors. The conclusions regarding neuroplasticity are then generalized to explain the incomplete functional segregation observed in the application of the SCM to neuroimaging.

Using resting-state DMN effective connectivity to characterize the neurofunctional architecture of empathy

Neuroimaging studies in social neuroscience have largely relied on functional connectivity (FC) methods to characterize the functional integration between different brain regions. However, these methods have limited utility in social-cognitive studies that aim to understand the directed information flow among brain areas that underlies complex psychological processes. In this study we combined functional and effective connectivity approaches to characterize the functional integration within the Default Mode Network (DMN) and its role in self-perceived empathy. Forty-two participants underwent a resting state fMRI scan and completed a questionnaire of dyadic empathy. Independent Component Analysis (ICA) showed that higher empathy scores were associated with an increased contribution of the medial prefrontal cortex (mPFC) to the DMN spatial mode. Dynamic causal modelling (DCM) combined with Canonical Variance Analysis (CVA) revealed that this association was mediated indirectly by the posterior cingulate cortex (PCC) via the right inferior parietal lobule (IPL). More specifically, in participants with higher scores in empathy, the PCC had a greater effect on bilateral IPL and the right IPL had a greater influence on mPFC. These results highlight the importance of using analytic approaches that address directed and hierarchical connectivity within networks, when studying complex psychological phenomena, such as empathy.

National Institutes of Health initiatives for advancing scientific developments in clinical neuropsychology

OBJECTIVE

The current review briefly addresses the history of neuropsychology as a context for discussion of developmental milestones that have advanced the profession, as well as areas where the progression has lagged. More recently in the digital/information age, utilization and incorporation of emerging technologies has been minimal, which has stagnated ongoing evolution of the practice of neuropsychology despite technology changing many aspects of daily living. These authors advocate for embracing National Institutes of Health (NIH) initiatives, or interchangeably referred to as transformative opportunities, for the behavioral and social sciences. These initiatives address the need for neuropsychologists to transition from fragmented and data-poor approaches to integrated and data-rich scientific approaches that ultimately improve translational applications. Specific to neuropsychology is the need for the adoption of novel means of brain-behavior characterizations.

METHOD

Narrative review Conclusions: Clinical neuropsychology has reached a developmental plateau where it is ready to embrace the measurement science and technological advances which have been readily adopted by the human neurosciences. While there are ways in which neuropsychology is making inroads into these areas, a great deal of growth is needed to maintain relevance as a scientific discipline (see Figures 1, 2, and 3) consistent with NIH initiatives to advance scientific developments. Moreover, implications of such progress require discussion and modification of training, ethical, and legal mandates of the practice of neuropsychology.

Activations in gray and white matter are modulated by uni-manual responses during within and inter-hemispheric transfer: effects of response hand and right-handedness

Because the visual cortices are contra-laterally organized, inter-hemispheric transfer tasks have been used to behaviorally probe how information briefly presented to one hemisphere of the visual cortex is integrated with responses resulting from the ipsi- or contra-lateral motor cortex. By forcing rapid information exchange across diverse regions, these tasks robustly activate not only gray matter regions, but also white matter tracts. It is likely that the response hand itself (dominant or non-dominant) modulates gray and white matter activations during within and inter-hemispheric transfer. Yet the role of uni-manual responses and/or right hand dominance in modulating brain activations during such basic tasks is unclear. Here we investigated how uni-manual responses with either hand modulated activations during a basic visuo-motor task (the established Poffenberger paradigm) alternating between inter- and within-hemispheric transfer conditions. In a large sample of strongly right-handed adults (n = 49), we used a factorial combination of transfer condition [Inter vs. Within] and response hand [Dominant(Right) vs. Non-Dominant (Left)] to discover fMRI-based activations in gray matter, and in narrowly defined white matter tracts. These tracts were identified using a priori probabilistic white matter atlases. Uni-manual responses with the right hand strongly modulated activations in gray matter, and notably in white matter. Furthermore, when responding with the left hand, activations during inter-hemispheric transfer were strongly predicted by the degree of right-hand dominance, with increased right-handedness predicting decreased fMRI activation. Finally, increasing age within the middle-aged sample was associated with a decrease in activations. These results provide novel evidence of complex relationships between uni-manual responses in right-handed subjects, and activations during within- and inter-hemispheric transfer suggest that the organization of the motor system exerts sophisticated functional effects. Moreover, our evidence of activation in white matter tracts is consistent with prior studies, confirming fMRI-detectable white matter activations which are systematically modulated by experimental condition.

Diversity in action: exchange of perspectives and reflections on taxonomies of individual differences

Throughout the last 2500 years, the classification of individual differences in healthy people and their extreme expressions in mental disorders has remained one of the most difficult challenges in science that affects our ability to explore individuals' functioning, underlying psychobiological processes and pathways of development. To facilitate analyses of the principles required for studying individual differences, this theme issue brought together prominent scholars from diverse backgrounds of which many bring unique combinations of cross-disciplinary experiences and perspectives that help establish connections and promote exchange across disciplines. This final paper presents brief commentaries of some of our authors and further scholars exchanging perspectives and reflecting on the contributions of this theme issue.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.

Atlases of cognition with large-scale human brain mapping

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

Cognitive neuroscience uses neuroimaging to identify brain systems engaged in specific cognitive tasks. However, linking unequivocally brain systems with cognitive functions is difficult: each task probes only a small number of facets of cognition, while brain systems are often engaged in many tasks. We develop a new approach to generate a functional atlas of cognition, demonstrating brain systems selectively associated with specific cognitive functions. This approach relies upon an ontology that defines specific cognitive functions and the relations between them, along with an analysis scheme tailored to this ontology. Using a database of thirty neuroimaging studies, we show that this approach provides a highly-specific atlas of mental functions, and that it can decode the mental processes engaged in new tasks.

The evolution of cognitive models: From neuropsychology to neuroimaging and back

This paper provides a historical and future perspective on how neuropsychology and neuroimaging can be used to develop cognitive models of human brain functions. Section 1 focuses on the emergence of cognitive modelling from neuropsychology, why lesion location was considered to be unimportant and the challenges faced when mapping symptoms to impaired cognitive processes. Section 2 describes how established cognitive models based on behavioural data alone cannot explain the complex patterns of distributed brain activity that are observed in functional neuroimaging studies. This has led to proposals for new cognitive processes, new cognitive strategies and new functional ontologies for cognition. Section 3 considers how the integration of data from lesion, behavioural and functional neuroimaging studies of large cohorts of brain damaged patients can be used to determine whether inter-patient variability in behaviour is due to differences in the premorbid function of each brain region, lesion site or cognitive strategy. This combination of neuroimaging and neuropsychology is providing a deeper understanding of how cognitive functions can be lost and re-learnt after brain damage - an understanding that will transform our ability to generate and validate cognitive models that are both physiologically plausible and clinically useful.

Response Hand and Motor Set Differentially Modulate the Connectivity of Brain Pathways During Simple Uni-manual Motor Behavior

We investigated the flexible modulation of undirected functional connectivity (uFC) of brain pathways during simple uni-manual responding. Two questions were central to our interests: (1) does response hand (dominant vs. non-dominant) differentially modulate connectivity and (2) are these effects related to responding under varying motor sets. fMRI data were acquired in twenty right-handed volunteers who responded with their right (dominant) or left (non-dominant) hand (blocked across acquisitions). Within acquisitions, the task oscillated between periodic responses (promoting the emergence of motor sets) or randomly induced responses (disrupting the emergence of motor sets). Conjunction analyses revealed eight shared nodes across response hand and condition, time series from which were analyzed. For right hand responses connectivity of the M1 ←→ Thalamus and SMA ←→ Parietal pathways was more significantly modulated during periodic responding. By comparison, for left hand responses, connectivity between five network pairs (including M1 and SMA, insula, basal ganglia, premotor cortex, parietal cortex, thalamus) was more significantly modulated during random responding. uFC analyses were complemented by directed FC based on multivariate autoregressive models of times series from the nodes. These results were complementary and highlighted significant modulation of dFC for SMA → Thalamus, SMA → M1, basal ganglia → Insula and basal ganglia → Thalamus. The results demonstrate complex effects of motor organization and task demand and response hand on different connectivity classes of fMRI data. The brain's sub-networks are flexibly modulated by factors related to motor organization and/or task demand, and our results have implications for assessment of medical conditions associated with motor dysfunction.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

How to Characterize the Function of a Brain Region

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.

The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis

Higher-order cognition emerges through the flexible interactions of large-scale brain networks, an aspect of temporal coordination that may be impaired in psychosis. Here, we map the dynamic functional architecture of the cerebral cortex in healthy young adults, leveraging this atlas of transient network configurations (states), to identify state- and network-specific disruptions in patients with schizophrenia and psychotic bipolar disorder. We demonstrate that dynamic connectivity profiles are reliable within participants, and can act as a fingerprint, identifying specific individuals within a larger group. Patients with psychotic illness exhibit intermittent disruptions within cortical networks previously associated with the disease, and the individual connectivity profiles within specific brain states predict the presence of active psychotic symptoms. Taken together, these results provide evidence for a reconfigurable dynamic architecture in the general population and suggest that prior reports of network disruptions in psychosis may reflect symptom-relevant transient abnormalities, rather than a time-invariant global deficit.

Temporal changes in brain dynamics are linked with cognitive abilities, but neither their stability nor relationship to psychosis is clear. Here, authors describe the dynamic neural architecture in healthy controls and patients with psychosis and find that they are stable over time and can predict psychotic symptoms.

Answering Schrödinger's question: A free-energy formulation

The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.

Cortical-hippocampal functional connectivity during covert consolidation sub-serves associative learning: Evidence for an active "rest" state

We studied modulation of undirected functional connectivity (uFC) in cortical-hippocampal sub-networks during associative learning. Nineteen healthy individuals were studied (fMRI acquired on a Siemens Verio 3T), and uFC was studied between nodes in a network of regions identified by standard activation models based on bivariate correlational analyses of time series data. The paradigm alternated between Memory Encoding, Rest and Retrieval. "Rest" intervals promoted covert consolidation. Over the task, performance was broadly separable into linear (Early) and asymptomatic (Late) regimes, with late performance reflecting successful memory consolidation. Significant modulation of uFC was observed during periods of covert consolidation. The sub-networks which were modulated constituted connections between frontal regions such as the dorsal prefrontal cortex (dPFC) and dorsal anterior cingulate cortex (dACC), the medial temporal lobe (hippocampus, HPC), the superior parietal cortex (SPC) and the fusiform gyrus (FG). uFC patterns were dynamic in that sub-networks modulated during Early learning (dACC ↔ SPC, dACC ↔ FG, dPFC ↔ HPC) were not identical to those modulated during Late learning (dACC ↔ HPC, dPFC ↔ FG, FG ↔ SPC). Covert consolidation exerts systematic effects, and these results add to emerging evidence for the constructive role of the brain's "resting state" in potentiating action.

Single-case cognitive neuropsychology in the age of big data

Historically, single-case studies of brain-damaged individuals have contributed substantially to our understanding of cognitive processes. However, the role of single-case cognitive neuropsychology has diminished with the proliferation of techniques that measure neural activity in humans. Instead, large-scale informatics approaches in which data are gathered from hundreds of neuroimaging studies have become popular. It has been claimed that utilizing these informatics approaches can address problems found in single imaging studies. We first discuss reasons for why cognitive neuropsychology is thought to be in decline. Next, we note how these informatics approaches, while having benefits, are not particularly suited for understanding functional architectures. We propose that the single-case cognitive neuropsychological approach, which is focused on developing models of cognitive processing, addresses several of the weaknesses inherent in informatics approaches. Furthermore, we discuss how using neural data from brain-damaged individuals provides data that can inform both cognitive and neural models of cognitive processing.