Computational and dynamic models in neuroimaging

Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex

Psychological research increasingly indicates that emotional processes interact with other aspects of cognition. Studies have demonstrated both the ability of emotional stimuli to influence a broad range of cognitive operations, and the ability of humans to use top-down cognitive control mechanisms to regulate emotional responses. Portions of the prefrontal cortex appear to play a significant role in these interactions. However, the manner in which these interactions are implemented remains only partially elucidated. In the present review we describe the anatomical connections between ventral and dorsal prefrontal areas as well as their connections with limbic regions. Only a subset of prefrontal areas are likely to directly influence amygdalar processing, and as such models of prefrontal control of emotions and models of emotional regulation should be constrained to plausible pathways of influence. We also focus on how the specific pattern of feedforward and feedback connections between these regions may dictate the nature of information flow between ventral and dorsal prefrontal areas and the amygdala. These patterns of connections are inconsistent with several commonly expressed assumptions about the nature of communications between emotion and cognition.

Computational psychiatry

Computational ideas pervade many areas of science and have an integrative explanatory role in neuroscience and cognitive science. However, computational depictions of cognitive function have had surprisingly little impact on the way we assess mental illness because diseases of the mind have not been systematically conceptualized in computational terms. Here, we outline goals and nascent efforts in the new field of computational psychiatry, which seeks to characterize mental dysfunction in terms of aberrant computations over multiple scales. We highlight early efforts in this area that employ reinforcement learning and game theoretic frameworks to elucidate decision-making in health and disease. Looking forwards, we emphasize a need for theory development and large-scale computational phenotyping in human subjects.

Free energy and dendritic self-organization

In this paper, we pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying a recent free-energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimize a variational free-energy bound on the self-information or surprise of presynaptic inputs that are sampled. We model this as a selective pruning of dendritic spines that are expressed on a dendritic branch. This pruning occurs when postsynaptic gain falls below a threshold. Crucially, postsynaptic gain is itself optimized with respect to free energy. Pruning suppresses free energy as the dendrite selects presynaptic signals that conform to its expectations, specified by a generative model implicit in its intracellular kinetics. Not only does this provide a principled account of how neurons organize and selectively sample the myriad of potential presynaptic inputs they are exposed to, but it also connects the optimization of elemental neuronal (dendritic) processing to generic (surprise or evidence-based) schemes in statistics and machine learning, such as Bayesian model selection and automatic relevance determination.

Network centrality in the human functional connectome

The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.

Neurological imaging: statistics behind the pictures

Neurological imaging represents a powerful paradigm for investigation of brain structure, physiology and function across different scales. The diverse phenotypes and significant normal and pathological brain variability demand reliable and efficient statistical methodologies to model, analyze and interpret raw neurological images and derived geometric information from these images. The validity, reproducibility and power of any statistical brain map require appropriate inference on large cohorts, significant community validation, and multidisciplinary collaborations between physicians, engineers and statisticians.

Upstream dysfunction of somatomotor functional connectivity after corticospinal damage in stroke

BACKGROUND

Recent studies have shown that focal injuries can have remote effects on network function that affect behavior, but these network-wide repercussions are poorly understood.

OBJECTIVE

This study tested the hypothesis that lesions specifically to the outflow tract of a distributed network can result in upstream dysfunction in structurally intact portions of the network. In the somatomotor system, this upstream dysfunction hypothesis predicted that lesions of the corticospinal tract might be associated with functional disruption within the system. Motor impairment might then reflect the dual contribution of corticospinal damage and altered network functional connectivity.

METHODS

A total of 23 subacute stroke patients and 13 healthy controls participated in the study. Corticospinal tract damage was quantified using a template of the tract generated from diffusion tensor imaging in healthy controls. Somatomotor network functional integrity was determined by resting state functional connectivity magnetic resonance imaging.

RESULTS

The extent of corticospinal damage was negatively correlated with interhemispheric resting functional connectivity, in particular with connectivity between the left and right central sulcus. Although corticospinal damage accounted for much of the variance in motor performance, the behavioral impact of resting connectivity was greater in subjects with mild or moderate corticospinal damage and less in those with severe corticospinal damage.

CONCLUSIONS

Our results demonstrated that dysfunction of cortical functional connectivity can occur after interruption of corticospinal outflow tracts and can contribute to impaired motor performance. Recognition of these secondary effects from a focal lesion is essential for understanding brain-behavior relationships after injury, and they may have important implications for neurorehabilitation.

Biophysical mechanisms of multistability in resting-state cortical rhythms

The human alpha (8-12 Hz) rhythm is one of the most prominent, robust, and widely studied attributes of ongoing cortical activity. Contrary to the prevalent notion that it simply "waxes and wanes," spontaneous alpha activity bursts erratically between two distinct modes of activity. We now establish a mechanism for this multistable phenomenon in resting-state cortical recordings by characterizing the complex dynamics of a biophysical model of macroscopic corticothalamic activity. This is achieved by studying the predicted activity of cortical and thalamic neuronal populations in this model as a function of its dynamic stability and the role of nonspecific synaptic noise. We hence find that fluctuating noisy inputs into thalamic neurons elicit spontaneous bursts between low- and high-amplitude alpha oscillations when the system is near a particular type of dynamical instability, namely a subcritical Hopf bifurcation. When the postsynaptic potentials associated with these noisy inputs are modulated by cortical feedback, the SD of power within each of these modes scale in proportion to their mean, showing remarkable concordance with empirical data. Our state-dependent corticothalamic model hence exhibits multistability and scale-invariant fluctuations-key features of resting-state cortical activity and indeed, of human perception, cognition, and behavior-thus providing a unified account of these apparently divergent phenomena.

Time scales of representation in the human brain: weighing past information to predict future events

The estimates that humans make of statistical dependencies in the environment and therefore their representation of uncertainty crucially depend on the integration of data over time. As such, the extent to which past events are used to represent uncertainty has been postulated to vary over the cortex. For example, primary visual cortex responds to rapid perturbations in the environment, while frontal cortices involved in executive control encode the longer term contexts within which these perturbations occur. Here we tested whether primary and executive regions can be distinguished by the number of past observations they represent. This was based on a decay-dependent model that weights past observations from a Markov process and Bayesian Model Selection to test the prediction that neuronal responses are characterized by different decay half-lives depending on location in the brain. We show distributions of brain responses for short and long term decay functions in primary and secondary visual and frontal cortices, respectively. We found that visual and parietal responses are released from the burden of the past, enabling an agile response to fluctuations in events as they unfold. In contrast, frontal regions are more concerned with average trends over longer time scales within which local variations are embedded. Specifically, we provide evidence for a temporal gradient for representing context within the prefrontal cortex and possibly beyond to include primary sensory and association areas.

Can neuroimaging studies identify pain endophenotypes in humans?

Pain is a complex, multidimensional experience that has defied our understanding for centuries. Through the advent of noninvasive neuroimaging techniques, we have been able to examine the human brain and its response to nociceptive inputs. As a result, our knowledge of which brain regions are critical for generating an acute pain experience has grown, as has our understanding of how cognitive, emotional, contextual and various physiological factors influence the pain experience. Furthermore, we have been able to identify key processes within the brain that underpin the transition to and maintenance of chronic pain states, as well as highlight the dramatic consequences of chronic pain on the brain's structure and neurochemistry. Building upon this knowledge, we are now in a position to consider whether any of these brain imaging 'phenotypes' of acute or chronic pain should be considered as useful endophenotypes; thereby enabling us to relate the complex genetics that underpin everyday pain sensitivity or chronic pain states to intermediate biomarkers. This endophenotypic approach-the focus of this Review-simplifies the connection between genes and behavior and is needed for complex disorders like chronic pain.

Generative models of cortical oscillations: neurobiological implications of the kuramoto model

Understanding the fundamental mechanisms governing fluctuating oscillations in large-scale cortical circuits is a crucial prelude to a proper knowledge of their role in both adaptive and pathological cortical processes. Neuroscience research in this area has much to gain from understanding the Kuramoto model, a mathematical model that speaks to the very nature of coupled oscillating processes, and which has elucidated the core mechanisms of a range of biological and physical phenomena. In this paper, we provide a brief introduction to the Kuramoto model in its original, rather abstract, form and then focus on modifications that increase its neurobiological plausibility by incorporating topological properties of local cortical connectivity. The extended model elicits elaborate spatial patterns of synchronous oscillations that exhibit persistent dynamical instabilities reminiscent of cortical activity. We review how the Kuramoto model may be recast from an ordinary differential equation to a population level description using the nonlinear Fokker-Planck equation. We argue that such formulations are able to provide a mechanistic and unifying explanation of oscillatory phenomena in the human cortex, such as fluctuating beta oscillations, and their relationship to basic computational processes including multistability, criticality, and information capacity.

Identifying abnormal connectivity in patients using dynamic causal modeling of FMRI responses

Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensorimotor and cognitive tasks can be carried out when parts of the neural system that support normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these inter-regional interactions deviate from normal. Dynamic causal modeling (DCM) offers the opportunity to assess task-dependent interactions within a set of regions. Here we review its use in patients when the question of interest concerns the characterization of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic non-linear models with two-state equations. We also highlight the importance of the new Bayesian model selection and averaging tools that allow different plausible models to be compared at the single subject and group level. These procedures allow inferences to be made at different levels of model selection, from features (model families) to connectivity parameters. Following a critical review of previous DCM studies that investigated abnormal connectivity we propose a systematic procedure that will ensure more flexibility and efficiency when using DCM in patients. Finally, some practical and methodological issues crucial for interpreting or generalizing DCM findings in patients are discussed.

On consciousness, resting state fMRI, and neurodynamics

BACKGROUND

During the last years, functional magnetic resonance imaging (fMRI) of the brain has been introduced as a new tool to measure consciousness, both in a clinical setting and in a basic neurocognitive research. Moreover, advanced mathematical methods and theories have arrived the field of fMRI (e.g. computational neuroimaging), and functional and structural brain connectivity can now be assessed non-invasively.

RESULTS

The present work deals with a pluralistic approach to "consciousness'', where we connect theory and tools from three quite different disciplines: (1) philosophy of mind (emergentism and global workspace theory), (2) functional neuroimaging acquisitions, and (3) theory of deterministic and statistical neurodynamics - in particular the Wilson-Cowan model and stochastic resonance.

CONCLUSIONS

Based on recent experimental and theoretical work, we believe that the study of large-scale neuronal processes (activity fluctuations, state transitions) that goes on in the living human brain while examined with functional MRI during "resting state", can deepen our understanding of graded consciousness in a clinical setting, and clarify the concept of "consiousness" in neurocognitive and neurophilosophy research.