Modules and brain mapping

An Attempt to Explain Visual Aesthetic Appreciation

We suggest an evolutionary based explanation for why humans are preoccupied with aesthetic aspects of visual input. Briefly, humans evolved to be swayed by positive and negative feelings in the form of rewards and punishments, and to pursue situations that induce rewards, even when the feeling is not sufficiently strong to be recognized as a reward. The brain is designed to offer rewards when a person focuses on certain types of visual stimuli. For example, warm colors are typically pleasant because they are associated with edible fruits, and complex images appeal to curiosity. At some point people began exploiting these types of brain rewards by beautifying objects and creating art. The utility of objects, and the associative (or communicative) aspects of art, may dominate the design, but the artist tends to add aesthetic elements. These elements imply visual aspects that do not add to the functional value or evoke memories or associations based on easily recognized features in the picture. The adaptive rationale for the rewards offered by the aesthetic elements should help explain human aesthetic appreciation.

Amygdala-frontoparietal effective connectivity in creativity and humor processing

Although both creativity and humor elicit experiences of surprise followed by appreciation, it remains unknown whether shared or distinct patterns of effective connectivity are involved in their processing. The present fMRI study used dynamic causal modeling and parametrical empirical Bayes analysis to examine the effective connectivity between the amygdala and frontoparietal network during two-stage creativity and humor processing. We examined processing during the setup and punch line stages for creativity and humor, including typical forms (alternate uses for creativity and incongruity-resolution humor), atypical forms (aesthetic uses for creativity and nonsense humor), and baseline forms. Our focus was on the mesolimbic pathway during the punch line stage. We found that the amygdala plays a key role in expectation violation and appreciation. Broadly, amygdala-to-IFG connectivity was important for evaluating typical and atypical forms of both creativity and humor, while amygdala-to-precuneus connectivity was involved in evaluating typical forms. Amygdala-to-IFG connectivity was involved in the expectation violation to resolution stage of processing for typical and atypical forms of creativity and humor. Amygdala-to-precuneus connectivity was involved in processing the novelty and usefulness of typical forms of creativity (alternate uses) and understanding others' intentions in typical forms of humor (incongruity-resolution). Interestingly, VTA-to-amygdala connectivity was involved in processing the appreciation of both typical (incongruity-resolution humor) and atypical (nonsense humor) forms of humor while amygdala-to-VTA connectivity was involved in processing the appreciation of atypical (aesthetic uses) forms of creativity. Altogether, these findings suggest that the amygdala and frontoparietal circuitry are critical for creativity and humor processing.

Distinct spatiotemporal patterns of syntactic and semantic processing in human inferior frontal gyrus

Human languages are based on syntax, a set of rules which allow an infinite number of meaningful sentences to be constructed from a finite set of words. A theory associated with Chomsky and others holds that syntax is a mind-internal, universal structure independent of semantics. This theory, however, has been challenged by studies of the Chinese language showing that syntax is processed under the semantic umbrella, and is secondary and not independent. Here, using intracranial high-density electrocorticography, we find distinct spatiotemporal patterns of neural activity in the left inferior frontal gyrus that are specifically associated with syntactic and semantic processing of Chinese sentences. These results suggest that syntactic processing may occur before semantic processing. Our findings are consistent with the view that the human brain implements syntactic structures in a manner that is independent of semantics.

Tracking longitudinal language network reorganisation using functional MRI connectivity fingerprints

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FMRI connectivity fingerprints represent patient-unique language networks.

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Fingerprints can be statistically tested to detect reorganisation in individuals.

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Connectivity fingerprints track surgery-related adaptations in individual patients.

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Network-level changes appear related to presence of language symptoms.

Large individual differences in how brain networks respond to treatment hinder efforts to personalise treatment in neurological conditions. We used a brain network fingerprinting approach to longitudinally track re-organisation of complementary phonological and semantic language networks in 19 patients before and after brain-tumour surgery. Patient task fingerprints were individually compared to normal networks established in 17 healthy controls. Additionally, pre- and post-operative patient fingerprints were directly compared to assess longitudinal network adaptations. We found that task networks remained stable over time in healthy controls, whereas treatment induced reorganisation in 47.4% of patient fluency networks and 15.8% of semantic networks. How networks adapted after surgery was highly unique; a subset of patients (10%) showed ‘normalisation’ while others (21%) developed newly atypical networks after treatment. The strongest predictor of adaptation of the fluency network was the presence of clinically reported language symptoms. Our findings indicate a tight coupling between processes disrupting performance and neural network adaptation, the patterns of which appear to be both task- and individually-unique. We propose that connectivity fingerprinting offers potential as a clinical marker to track adaptation of specific functional networks across treatment interventions over time.

Neuro-Clinical Signatures of Language Impairments: A Theoretical Framework for Function-to-structure Mapping in Clinics

In the past decades, neuroscientists and clinicians have collected a considerable amount of data and drastically increased our knowledge about the mapping of language in the brain. The emerging picture from the accumulated knowledge is that there are complex and combinatorial relationships between language functions and anatomical brain regions. Understanding the underlying principles of this complex mapping is of paramount importance for the identification of the brain signature of language and Neuro-Clinical signatures that explain language impairments and predict language recovery after stroke. We review recent attempts to addresses this question of language-brain mapping. We introduce the different concepts of mapping (from diffeomorphic one-to-one mapping to many-to-many mapping). We build those different forms of mapping to derive a theoretical framework where the current principles of brain architectures including redundancy, degeneracy, pluri-potentiality and bow-tie network are described.

Modules or Mean-Fields?

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

Maturational Changes in Human Dorsal and Ventral Visual Networks

Developmental neuroimaging studies report the emergence of increasingly diverse cognitive functions as closely entangled with a rise-fall modulation of cortical thickness (CTh), structural cortical and white-matter connectivity, and a time-course for the experience-dependent selective elimination of the overproduced synapses. We examine which of two visual processing networks, the dorsal (DVN; prefrontal, parietal nodes) or ventral (VVN; frontal-temporal, fusiform nodes) matures first, thus leading the neuro-cognitive developmental trajectory. Three age-dependent measures are reported: (i) the CTh at network nodes; (ii) the matrix of intra-network structural connectivity (edges); and (iii) the proficiency in network-related neuropsychological tests. Typically developing children (age ~6 years), adolescents (~11 years), and adults (~21 years) were tested using multiple-acquisition structural T1-weighted magnetic resonance imaging (MRI) and neuropsychology. MRI images reconstructed into a gray/white/pial matter boundary model were used for CTh evaluation. No significant group differences in CTh and in the matrix of edges were found for DVN (except for the left prefrontal), but a significantly thicker cortex in children for VVN with reduced prefrontal ventral-fusiform connectivity and with an abundance of connections in adolescents. The higher performance in children on tests related to DVN corroborates the age-dependent MRI structural connectivity findings. The current findings are consistent with an earlier maturational course of DVN.

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.

Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

In behavioral neuroscience, the adaptability of humans facing different constraints has been addressed on one side at the brain level, where a variety of functional networks dynamically support the same performance, and on the other side at the behavioral level, where fractal properties in sensorimotor variables have been considered as a hallmark of adaptability. To bridge the gap between the two levels of observation, we have jointly investigated the changes of network connectivity in the sensorimotor cortex assessed by modularity analysis and the properties of motor variability assessed by multifractal analysis during a prolonged tapping task. Four groups of participants had to produce the same tapping performance while being deprived from 0, 1, 2, or 3 sensory feedbacks simultaneously (auditory and/or visual and/or tactile). Whereas tapping performance was not statistically different across groups, the number of brain networks involved and the degree of multifractality of the inter-tap interval series were significantly correlated, increasing as a function of feedback deprivation. Our findings provide first evidence that concomitant changes in brain modularity and multifractal properties characterize adaptations underlying unchanged performance. We discuss implications of our findings with respect to the degeneracy properties of complex systems, and the entanglement of adaptability and effective adaptation.

Neural correlates of processing emotional prosody in unipolar depression

Major depressive disorder (MDD) is characterized by a biased emotion perception. In the auditory domain, MDD patients have been shown to exhibit attenuated processing of positive emotions expressed by speech melody (prosody). So far, no neuroimaging studies examining the neural basis of altered processing of emotional prosody in MDD are available. In this study, we addressed this issue by examining the emotion bias in MDD during evaluation of happy, neutral, and angry prosodic stimuli on a five-point Likert scale during functional magnetic resonance imaging (fMRI). As expected, MDD patients rated happy prosody less intense than healthy controls (HC). At neural level, stronger activation in the middle superior temporal gyrus (STG) and the amygdala was found in all participants when processing emotional as compared to neutral prosody. MDD patients exhibited an increased activation of the amygdala during processing prosody irrespective of valence while no significant differences between groups were found for the STG, indicating that altered processing of prosodic emotions in MDD occurs rather within the amygdala than in auditory areas. Concurring with the valence-specific behavioral effect of attenuated evaluation of positive prosodic stimuli, activation within the left amygdala of MDD patients correlated with ratings of happy, but not neutral or angry prosody. Our study provides first insights in the neural basis of reduced experience of positive information and an abnormally increased amygdala activity during prosody processing.

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.

From cognitivism to autopoiesis: towards a computational framework for the embodied mind

Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications-with a firm commitment to modular, internalistic mental representation-to more moderate views emphasizing the importance of 'body-representations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal 'representations' arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.

Hallucinations as top-down effects on perception

The problem of whether and how information is integrated across hierarchical brain networks embodies a fundamental tension in contemporary cognitive neuroscience, and by extension, cognitive neuropsychiatry. Indeed, the penetrability of perceptual processes in a 'top-down' manner by higher-level cognition-a natural extension of hierarchical models of perception-may contradict a strictly modular view of mental organization. Furthermore, some in the cognitive science community have challenged cognitive penetration as an unlikely, if not impossible, process. We review the evidence for and against top-down influences in perception, informed by a predictive coding model of perception and drawing heavily upon the literature of computational neuroimaging. We extend these findings to propose a way in which these processes may be altered in mental illness. We propose that hallucinations - perceptions without stimulus - can be understood as top-down effects on perception, mediated by inappropriate perceptual priors.

Neuropsychological profile in new-onset benign epilepsy with centrotemporal spikes (BECTS): Focusing on executive functions

PURPOSE

Increased evidence of subnormal neuropsychological functioning in new-onset childhood epilepsy has been obtained, although results are still rare and controversial. With a prospective study, we aimed to define the very early neuropsychological profile of children with benign epilepsy with centrotemporal spikes (BECTS), including executive functions (EF) because of their key role in learning. Additionally, we enrolled drug-naïve children, with a NREM sleep frequency of discharges <85% and with a Performance Intelligence Quotient equal or superior to 85, in order to exclude additional effects on the neuropsychological functioning.

METHODS

Fifteen school-aged children with BECTS (mean age: 8.8years, standard deviation [SD]: 2.4years) and fifteen healthy children (mean age: 9.2years, [SD]: 2.5years) were enrolled and assessed with a comprehensive neuropsychological battery. The assessment included domain-specific standardized tests of language, EF, academic skills, visuomotor and visuospatial skills, and short-term memory. A p-value<0.05 was considered significant.

RESULTS

Significant differences between patients and controls emerged with respect to 3 domains. Language was affected in color naming (p=.026), spoonerism (p=.003), and phonemic synthesis (p=.009). Executive functions appeared inadequate in the five point test with respect to the number of correct figures (p=.003) and errors (p=.008). In the domain of academic skills, significant differences between groups emerged regarding the number of mistakes in nonword writing (p=.001), nonword reading speed (p=.027), nonword reading number of mistakes (p=.019), and word reading errors (p=.023).

DISCUSSION

Results showed that children with new-onset BECTS may demonstrate a range of neuropsychological dysfunctions, particularly affecting executive attention, despite a normal IQ, a low frequency of NREM sleep discharges, and the absence of drugs. These difficulties indicate a frontal dysfunction with cascading effects on language and academic skills. The inclusion of EF in the assessment battery and in the intervention since the very onset is warranted in order to avoid further and persistent academic difficulties.

Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender, and mental illness on the spatiotemporal organization of resting-state brain activity

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.

Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be “modules” (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioral recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging.

The connectomics of brain disorders

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Roles of human hippocampal subfields in retrieval of spatial and temporal context

While numerous studies indicate the involvement of the hippocampus in encoding and retrieval of spatial and temporal context, the neural basis of spatial and temporal processing within the hippocampal circuit remains unclear. We employed a novel paradigm in which participants encoded stores within a spatial layout by visiting them in a specific temporal order. Participants then underwent high-resolution functional magnetic resonance imaging (fMRI) targeting the hippocampus while retrieving details of the spatial or temporal context in alternating blocks. During retrieval, participants made judgments about either near or far intervals within the spatial layout or temporal sequence. Across both near and far intervals, we found that retrieving spatial layout and temporal order information resulted in comparable levels of activation in the hippocampus that was not preferentially localized to a specific subfield. Furthermore, using a multivariate approach called multivariate pattern similarity analysis (MPSA), we found that correct near judgments vs. correct far judgments differed in their patterns of activity for spatial vs. temporal order judgments. Despite these differences in MPSA patterns, we did not find any specific subfields differentially recruited for spatial vs. temporal order retrieval. We discuss our results in terms of their relation to computational models of hippocampal subfield function and suggest mechanisms by which the hippocampus could process space and temporal order without the need for specific contributions from hippocampal subfields.

What affects detectability of lesion-deficit relationships in lesion studies?

Elucidating the brain basis for psychological processes and behavior is a fundamental aim of cognitive neuroscience. The lesion method, using voxel-based statistical analysis, is an important approach to this goal, identifying neural structures that are necessary for the support of specific mental operations, and complementing the strengths of functional imaging techniques.

Lesion coverage in a population is by nature spatially heterogeneous and biased, systematically affecting the ability of lesion–deficit correlation methods to detect and localize functional associations. We have developed a simulator that allows investigators to model parameters in a lesion–deficit study and characterize the statistical bias in lesion deficit detection coverage that will result from specific assumptions. We used the simulator to assess the signal detection properties and localization accuracy of standard lesion–deficit correlation methods, under a simple truth model — that a critical region of interest (CR), when damaged, gives rise to a deficit. We considered voxel-based lesion-symptom mapping (VLSM) and proportional MAP-3 (PM3). Using regression analysis, we examined if the pattern of outcome statistics can be explained by simulation parameters, factors that are inherent to anatomic parcels, and lesion coverage of the population, which consisted of a representative sample of 351 subjects drawn from the Iowa Patient Registry. We examined the effect of using nonparametric versus parametric statistics to obtain thresholded maps and the effect of correcting for multiple comparisons using false discovery rate or cluster-based correction.

Our results, which are derived from samples of realistic lesions, indicate that even a simple truth model yields localization errors that are systematic and pervasive, averaging 2 cm in the standard anatomic space, and tending to be directed towards areas of greater anatomic coverage. This displacement positions the center of mass of the detected region in a different anatomical region 87% of the time. This basic result is not affected by the choice of PM3 vs VLSM as the fundamental approach, nor is localization error ameliorated by incorporation of lesion size as a covariate in the VLSM approach, or by data distribution-driven approaches to controlling multiple spatial comparisons (false discovery rate or cluster-based correction approaches).

Our simulations offer a quantitative basis for interpreting lesion studies in cognitive neuroscience. We suggest ways in which lesion simulation and analysis frameworks could be productively extended.

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We assessed the signal detection properties and localization accuracy of lesion–deficit correlation methods

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Localization errors are pervasive regardless of statistical method or whether controlling for multiple comparisons

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The power of lesion deficit analysis is limited by the number of subjects with a lesion and a deficit

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The detected center of mass tends to be skewed towards regions of higher coverage, and is a function of the spatial distribution of lesion coverage

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The simulator approach offers a way to evaluate modifications to the lesion method to address weaknesses of the method

A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective

While the widely studied allocentric spatial representation holds a special status in neuroscience research, its exact nature and neural underpinnings continue to be the topic of debate, particularly in humans. Here, based on a review of human behavioral research, we argue that allocentric representations do not provide the kind of map-like, metric representation one might expect based on past theoretical work. Instead, we suggest that almost all tasks used in past studies involve a combination of egocentric and allocentric representation, complicating both the investigation of the cognitive basis of an allocentric representation and the task of identifying a brain region specifically dedicated to it. Indeed, as we discuss in detail, past studies suggest numerous brain regions important to allocentric spatial memory in addition to the hippocampus, including parahippocampal, retrosplenial, and prefrontal cortices. We thus argue that although allocentric computations will often require the hippocampus, particularly those involving extracting details across temporally specific routes, the hippocampus is not necessary for all allocentric computations. We instead suggest that a non-aggregate network process involving multiple interacting brain areas, including hippocampus and extra-hippocampal areas such as parahippocampal, retrosplenial, prefrontal, and parietal cortices, better characterizes the neural basis of spatial representation during navigation. According to this model, an allocentric representation does not emerge from the computations of a single brain region (i.e., hippocampus) nor is it readily decomposable into additive computations performed by separate brain regions. Instead, an allocentric representation emerges from computations partially shared across numerous interacting brain regions. We discuss our non-aggregate network model in light of existing data and provide several key predictions for future experiments.

How cognitive neuroscience could be more biological-and what it might learn from clinical neuropsychology

Three widespread assumptions of Cognitive-affective Neuroscience are discussed: first, mental functions are assumed to be localized in circumscribed brain areas which can be exactly determined, at least in principle (localizationism). Second, this assumption is associated with the more general claim that these functions (and dysfunctions, such as in neurological or mental diseases) are somehow generated inside the brain (internalism). Third, these functions are seen to be “biological” in the sense that they can be decomposed and finally explained on the basis of elementary biological causes (i.e., genetic, molecular, neurophysiological etc.), causes that can be identified by experimental methods as the gold standard (isolationism). Clinical neuropsychology is widely assumed to support these tenets. However, by making reference to the ideas of Kurt Goldstein (1878–1965), one of its most important founders, I argue that none of these assumptions is sufficiently supported. From the perspective of a clinical-neuropsychological practitioner, assessing and treating brain damage sequelae reveals a quite different picture of the brain as well as of us “brain carriers”, making the organism (or person) in its specific environment the crucial reference point. This conclusion can be further elaborated: all experimental and clinical research on humans presupposes the notion of a situated, reflecting, and interacting subject, which precedes all kinds of scientific decomposition, however useful. These implications support the core assumptions of the embodiment approach to brain and mind, and, as I argue, Goldstein and his clinical-neuropsychological observations are part of its very origin, for both theoretical and historical reasons.

Lesions impairing regular versus irregular past tense production

We investigated selective impairments in the production of regular and irregular past tense by examining language performance and lesion sites in a sample of twelve stroke patients. A disadvantage in regular past tense production was observed in six patients when phonological complexity was greater for regular than irregular verbs, and in three patients when phonological complexity was closely matched across regularity. These deficits were not consistently related to grammatical difficulties or phonological errors but were consistently related to lesion site. All six patients with a regular past tense disadvantage had damage to the left ventral pars opercularis (in the inferior frontal cortex), an area associated with articulatory sequencing in prior functional imaging studies. In addition, those that maintained a disadvantage for regular verbs when phonological complexity was controlled had damage to the left ventral supramarginal gyrus (in the inferior parietal lobe), an area associated with phonological short-term memory. When these frontal and parietal regions were spared in patients who had damage to subcortical (n = 2) or posterior temporo-parietal regions (n = 3), past tense production was relatively unimpaired for both regular and irregular forms. The remaining (12th) patient was impaired in producing regular past tense but was significantly less accurate when producing irregular past tense. This patient had frontal, parietal, subcortical and posterior temporo-parietal damage, but was distinguished from the other patients by damage to the left anterior temporal cortex, an area associated with semantic processing. We consider how our lesion site and behavioral observations have implications for theoretical accounts of past tense production.

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Lesions were isolated in MNI space using overlap and subtraction methods

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Left vPOP lesions were associated with impaired regular past tense production

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Additional left vSMG lesions were associated with robust effects of regularity

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If these regions were spared production accuracy showed no effects of regularity

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Lesion overlap demonstrates a network underpinning past-tense production

How to discover modules in mind and brain: the curse of nonlinearity, and blessing of neuroimaging. A comment on Sternberg (2011)

Sternberg (2011) elegantly formalizes how certain sets of hypotheses, specifically modularity and pure or composite measures, imply certain patterns of behavioural and neuroimaging data. Experimentalists are often interested in the converse, however: whether certain patterns of data distinguish certain hypotheses, specifically whether more than one module is involved. In this case, there is a striking reversal of the relative value of the data patterns that Sternberg considers. Foremost, the example of additive effects of two factors on one composite measure becomes noninformative for this converse question. Indeed, as soon as one allows for nonlinear measurement functions and nonlinear module processes, even a cross-over interaction between two factors is noninformative in this respect. Rather, one requires more than one measure, from which certain data patterns do provide strong evidence for multiple modules, assuming only that the measurement functions are monotonic. If two measures are not monotonically related to each other across the levels of one or more experimental factors, then one has evidence for more than one module (i.e., more than one nonmonotonic transform). Two special cases of this are illustrated here: a “reversed association” between two measures across three levels of a single factor, and Sternberg's example of selective effects of two factors on two measures. Fortunately, functional neuroimaging methods normally do provide multiple measures over space (e.g., functional magnetic resonance imaging, fMRI) and/or time (e.g., electroencephalography, EEG). Thus to the extent that brain modules imply mind modules (i.e., separate processors imply separate processes), the performance data offered by functional neuroimaging are likely to be more powerful in revealing modules than are the single behavioural measures (such as accuracy or reaction time, RT) traditionally considered in psychology.

The specialization of function: cognitive and neural perspectives

A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are "abstracted away" from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences.