Disordered directional brain network interactions during learning dynamics in schizophrenia revealed by multivariate autoregressive models.

Directional network interactions underpin normative brain function in key domains including associative learning. Schizophrenia (SCZ) is characterized by altered learning dynamics, yet dysfunctional directional functional connectivity (dFC) evoked during learning is rarely assessed. Here, nonlinear learning dynamics were induced using a paradigm alternating between conditions (Encoding and Retrieval). Evoked fMRI time series data were modeled using multivariate autoregressive (MVAR) models, to discover dysfunctional direction interactions between brain network constituents during learning stages (Early vs. Late), and conditions. A functionally derived subnetwork of coactivated (healthy controls [HC] ∩ SCZ] nodes was identified. MVAR models quantified directional interactions between pairs of nodes, and coefficients were evaluated for intergroup differences (HC ≠ SCZ). In exploratory analyses, we quantified statistical effects of neuroleptic dosage on performance and MVAR measures. During Early Encoding, SCZ showed reduced dFC within a frontal-hippocampal-fusiform network, though during Late Encoding reduced dFC was associated with pathways toward the dorsolateral prefrontal cortex (dlPFC). During Early Retrieval, SCZ showed increased dFC in pathways to and from the dorsal anterior cingulate cortex, though during Late Retrieval, patients showed increased dFC in pathways toward the dlPFC, but decreased dFC in pathways from the dlPFC. These discoveries constitute novel extensions of our understanding of task-evoked dysconnection in schizophrenia and motivate understanding of the directional aspect of the dysconnection in schizophrenia. Disordered directionality should be investigated using computational psychiatric approaches that complement the MVAR method used in our work.

Directional Interactions Between Constituents of the Human Large-Scale Thermoregulatory Network.

In humans, dynamic thermoregulation is (presumably) underpinned by a complex hierarchy of functional interactions between constituents of the human thermoregulatory large-scale network. However, these interactions have not been quantified from in vivo fMRI signals acquired during the experimental delivery of whole-body thermal stress. Here, we used directed functional connectivity (dFC) analysis (based on multi-variate autoregressive models) to recover directed interactions within a single thermoregulatory network during an experimental paradigm that involved controlled exposure to whole-body cooling and warming. MRI studies were performed in 30 young adults (15 M/15F, mean age 25.1 ± 3.4 years). Gradient echo EPI fMRI data were acquired on a 3 T Siemens Verio system. The thermoregulatory challenge was applied using a specialized whole-body garment covering the entire body. Tubes lining the innards of the suit were infused with cold (2-4 °C) or neutral (31-34 °C) water to induce whole-body Cooling or Warming while fMRI data were contemporaneously acquired. dFC was estimated within and between the hierarchically organized homeostatic (midbrain, pons), interoceptive (insula) and executive (anterior cingulate, orbitofrontal and superior parietal cortices) sub-networks using multi-variate autoregressive models applied to the fMRI time series data. Estimates of directed interactions (akin to Granger Causality) between nodes were analyzed to recover ascending (homeostatic sub-network "upward"), descending (executive sub-network "downward"), and lateral (within sub-network) directional ("causal") effects. Both Cooling and Warming induced complex hierarchical interactions in the thermoregulatory large-scale network. Cooling induced ascending interactions from the homeostatic (midbrain) to both the executive (OFC) and interoceptive (insula) sub-networks, particularly to the superior parietal, ACC and the anterior and posterior insulae. In comparison, descending interactions were induced from the posterior insula. Warming induced ascending interactions from the homeostatic sub-network to notably the OFC (executive) and the insulae (interoceptive). Descending interactions were induced from the ACC and the OFC. Sparser effects appear from the executive to the interoceptive sub-network during warming. Our study demonstrates a hierarchical organization of thermoregulatory function between homeostatic, interoceptive and executive sub-networks. The observed information flow between/within these is consistent with a reentrant property of the hierarchical regulatory structure, characterized by the ongoing bi-directional exchange of signals along reciprocal axonal fibers linking the various nodes.

Learning without contingencies: A loss of synergy between memory and reward circuits in schizophrenia.

Motivational deficits in schizophrenia may interact with foundational cognitive processes including learning and memory to induce impaired cognitive proficiency. If such a loss of synergy exists, it is likely to be underpinned by a loss of synchrony between the brains learning and reward sub-networks. Moreover, this loss should be observed even during tasks devoid of explicit reward contingencies given that such tasks are better models of real world performance than those with artificial contingencies. Here we applied undirected functional connectivity (uFC) analyses to fMRI data acquired while participants engaged in an associative learning task without contingencies or feedback. uFC was estimated and inter-group differences (between schizophrenia patients and controls, n = 54 total, n = 28 patients) were assessed within and between reward (VTA and NAcc) and learning/memory (Basal Ganglia, DPFC, Hippocampus, Parahippocampus, Occipital Lobe) sub-networks. The task paradigm itself alternated between Encoding, Consolidation, and Retrieval conditions, and uFC differences were quantified for each of the conditions. Significantly reduced uFC dominated the connectivity profiles of patients across all conditions. More pertinent to our motivations, these reductions were observed within and across classes of sub-networks (reward-related and learning/memory related). We suggest that disrupted functional connectivity between reward and learning sub-networks may drive many of the performance deficits that characterize schizophrenia. Thus, cognitive deficits in schizophrenia may in fact be underpinned by a loss of synergy between reward-sensitivity and cognitive processes.

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia.

There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

Effective connectivity of brain networks controlling human thermoregulation.

Homeostatic centers in the mammalian brainstem are critical in responding to thermal challenges. These centers play a prominent role in human thermoregulation, but humans also respond to thermal challenges through behavior modification. Behavioral modifications are presumably sub served by interactions between the brainstem and interoceptive, cognitive and affective elements in human brain networks. Prior evidence suggests that interoceptive regions such as the insula, and cognitive/affective regions such as the orbitofrontal cortex and anterior cingulate cortex are crucial. Here we used dynamic causal modeling (DCM) to discover likely generative network architectures and estimate changes in the effective connectivity between nodes in a hierarchically organized thermoregulatory network (homeostatic-interoceptive-cognitive/affective). fMRI data were acquired while participants (N = 20) were subjected to a controlled whole body thermal challenge that alternatingly evoked sympathetic and parasympathetic responses. Using a competitive modeling framework (ten competing modeling architectures), we demonstrated that sympathetic responses (evoked by whole-body cooling) resulted in more complex network interactions along two ascending pathways: (i) homeostatic interoceptive and (ii) homeostatic cognitive/affective. Analyses of estimated connectivity coefficients demonstrated that sympathetic responses evoked greater network connectivity in key pathways compared to parasympathetic responses. These results reveal putative mechanisms by which human thermoregulatory networks evince a high degree of contextual sensitivity to thermoregulatory challenges. The patterns of the discovered interactions also reveal how information propagation from homeostatic regions to both interoceptive and cognitive/affective regions sub serves the behavioral repertoire that is an important aspect of thermoregulatory defense in humans.

Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.

In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. METHODS: We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. RESULTS: The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. DISCUSSION: Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.