Attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex. We addressed attentional modulation of effective connectivity using variable parameter regression and functional magnetic resonance imaging. We present data from a single subject scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Variable parameter regression of the influence of V5 on PP revealed increased effective connectivity during attention to visual motion. With this dynamic measure of effective connectivity we were able to make inferences about the source of modulation by looking for regions that predicted the observed changes in connectivity. Using an ordinary regression analysis, we showed that activity in the prefrontal cortex could explain these changes and was sufficient to account for these modulatory influences on connections in the dorsal visual pathway. Hum. Brain Mapping 6:403–408, 1998. © 1998 Wiley-Liss, Inc.
Introduction Schizophrenia (SZ) and bipolar disorder (BD) are severe mental illnesses with large overlapping heritability. Both disorders are associated with altered neurophysiological responses, as measured with magnetoencephalography (MEG) or electroencephalography (EEG), particularly reduced mismatch negativity (MMN) and 40 Hz auditory steady-state responses (ASSR). These deficits could potentially both serve as early markers of illness and provide insight into the underlying pathophysiology as endophenotypes. First-degree relatives to patients with SZ and BD also show some neurophysiological deficits, however whether these deficits can be detected in adolescent offspring of patients is undetermined. Objectives We aim to investigate whether adolescents at familial high risk of schizophrenia and bipolar disorder show aberrant MMN and ASSR compared to population-based controls. Methods We will investigate MMN and 40 Hz ASSR in 15 year old adolescents (n ≈ 175) born to parents diagnosed with either SZ (FHR-SZ), BD (FHR-BD), or neither SZ or BD (population-based controls, PBC) using MEG. We will first perform sensor-level analyses and subsequently apply dynamic causal modeling (DCM) to investigate effective connectivity and make inferences about the underlying neurobiological mechanisms. We will investigate whether current psychopathology, cognitive status, and genetic risk for SZ and BD predict neurophysiological responses in the adolescents. Investigations are part of The Danish High Risk and Resilience Study - VIA (VIA15), a population-based longitudinal cohort study integrating social, psychological and biological risk factors and outcomes for SZ and BD. Results Final results are expected in 2024 Conclusions The VIA15 study will allow for unprecedented insight into the neurobiological development of schizophrenia and bipolar disorder. Disclosure No significant relationships.
The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.
This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology.
Bettinger, J. S., K. J. Friston. Conceptual Foundations of Physiological Regulation incorporating the Free Energy Principle & Self-Organized Criticality. NEUROSCI BIOBEHAV REV 23(x) 144-XXX, 2022. Since the late nineteen-nineties, the concept of homeostasis has been contextualized within a broader class of "allostatic" dynamics characterized by a wider-berth of causal factors including social, psychological and environmental entailments; the fundamental nature of integrated brain-body dynamics; plus the role of anticipatory, top-down constraints supplied by intrinsic regulatory models. Many of these evidentiary factors are integral in original descriptions of homeostasis; subsequently integrated; and/or cite more-general operating principles of self-organization. As a result, the concept of allostasis may be generalized to a larger category of variational systems in biology, engineering and physics in terms of advances in complex systems, statistical mechanics and dynamics involving heterogenous (hierarchical/heterarchical, modular) systems like brain-networks and the internal milieu. This paper offers a three-part treatment. 1) interpret "allostasis" to emphasize a variational and relational foundation of physiological stability; 2) adapt the role of allostasis as "stability through change" to include a "return to stability" and 3) reframe the model of homeostasis with a conceptual model of criticality that licenses the upgrade to variational dynamics.
Functional magnetic resonance imaging (fMRI) provides an indirect measure of neural activity changes in the brain. fMRI designs use a sequence of events (such as the presentation of a picture to the subject). A common goal of fMRI data analysis is to estimate the amplitudes of local brain responses to these experimental events. Designs involving a random component in the temporal structure of the event sequence are called stochastic. Stochastic designs are favored in situations where predictability of the event sequence is a potential confound. In this paper, discrete-time models of the efficiency of response amplitude estimation (using generalized least-squares) in single event-type, stationary stochastic fMRI designs (Friston et al., 1999) are extended to continuous-time. This allows one to determine certain results concerning efficiency as the average spacing between experimental events gets very small. Furthermore, we derive expressions for efficiency in the frequency domain. This provides a direct connection between efficiency and formulations of designs and noise in the frequency domain. This is useful from a signal processing and filtering perspective. These results can be used, given other constraints, to optimize fMRI stochastic design parameters.
In this paper, we compare mean-field and neural-mass models of electrophysiological responses using Bayesian model comparison. In previous work, we presented a mean-field model of neuronal dynamics as observed with magnetoencephalography and electroencephalography. Unlike neural-mass models, which consider only the mean activity of neuronal populations, mean-field models track the distribution (e.g., mean and dispersion) of population activity. This can be important if the mean affects the dispersion or vice versa. Here, we introduce a dynamical causal model based on mean-field (i.e., population density) models of neuronal activity, and use it to assess the evidence for a coupling between the mean and dispersion of hidden neuronal states using observed electromagnetic responses. We used Bayesian model comparison to compare homologous mean-field and neural-mass models, asking whether empirical responses support a role for population variance in shaping neuronal dynamics. We used the mismatch negativity (MMN) and somatosensory evoked potentials (SEP) as representative neuronal responses in physiological and non-physiological paradigms respectively. Our main conclusion was that although neural-mass models may be sufficient for cognitive paradigms, there is clear evidence for an effect of dispersion at the high levels of depolarization evoked in SEP paradigms. This suggests that (i) the dispersion of neuronal states within populations generating evoked brain signals can be manifest in observed brain signals and that (ii) the evidence for their effects can be accessed with dynamic causal model comparison.
Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.
Synopsis We have previously reported focal abnormalities of regional cerebral blood flow (rCBF) in a group of 33 patients with major depression. This report, on an extended sample of 40 patients who demonstrated identical regional deficits to those previously described, examines the relationships between depressive symptoms and patterns of rCBF. Patients' symptom ratings were subjected to factor analysis, producing a three-factor solution. The scores for these three factors, which corresponded to recognizable dimensions of depressive illness, were then correlated with rCBF. The first factor had high loadings for anxiety and correlated positively with rCBF in the posterior cingulate cortex and inferior parietal lobule bilaterally. The second factor had high loadings for psychomotor retardation and depressed mood and correlated negatively with rCBF in the left dorsolateral prefrontal cortex and left angular gyrus. The third factor had a high loading for cognitive performance and correlated positively with rCBF in the left medial prefrontal cortex. These data indicate that symptomatic specificity may be ascribed to regional functional deficits in major depressive illness.
