Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions

State-space multivariate dynamical systems (MDS) (Ryali et al., 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods is poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli

The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies

Aberrant dynamics of cognitive control and motor circuits predict distinct restricted and repetitive behaviors in children with autism

Restricted and repetitive behaviors (RRBs) are a core clinical feature of autism, yet the brain basis of RRBs is unknown. Here, the authors demonstrate that aberrant cognitive control and motor circuit dynamics differentially predict three distinct symptom clusters that define RRBs

Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development

Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood

Resting state functional hyperconnectivity within a triple network model in paranoid schizophrenia

Three large-scale intrinsic connectivity brain networks, the salience network (SN), the central executive network (CEN), and the default mode network (DMN), have fundamental roles in higher cognitive processes. In particular, SN activity is crucial in the process of salience mapping—ie, allocating attentional resources to external and internal stimuli and attributing salience to the stimuli. Recent functional neuroimaging studies suggest that the core psychopathological changes in schizophrenia could arise from aberrant connectivity (dysconnectivity) within large scale networks in the brain. However, studies have yielded inconsistent results and have largely been unsuccessful at mapping these abnormalities to core psychopathology. The triple network model of aberrant saliency mapping suggests that the dysconnectivity underlying the psychopathology could lie within these three core brain networks. We hypothesised that intrinsic connectivity within these brain networks is altered in schizophrenia when compared with healthy adults. We assessed 37 medicated patients with paranoid schizophrenia (Diagnostic and Statistical Manual of Mental Disorders IV-295.3) and 37 age-matched and sex-matched controls from the Center for Biomedical Research Excellence dataset. We did a group-level independent component analysis on resting-state functional MRI to extract 30 group-level spatial intrinsic connectivity (IC) maps (FSL, FMRIB Software Library). From the group-level IC maps, we identified maps corresponding to the SN, DMN, and CEN. Then, we estimated a version of the group-level spatial map for each individual using the dual regression method and established between-group differences using permutation tests. Using a sparse logistic regression method, we also assessed whether IC maps could predict group membership (patient vs healthy controls) and, with a similar method, whether the IC maps could predict psychotic symptomatology (the positive and negative syndrome scale [PANSS]). The schizophrenia group showed stronger functional connectivity within the left CEN, SN, and DMN than did controls. When we used leave-one-out cross-validation, the SN map best discriminated patients from controls with 82·4% accuracy, 94·6 % sensitivity, and 70% specificity. In addition, the SN maps predicted the severity of positive symptoms score on the PANSS (r2=0·95, p<0·0001), particularly delusions (r2=0·38, p=0·003). DMN hyperconnectivity predicted negative symptoms score (r2=0·98, p=0·0005). The SN, CEN, and DMN in paranoid schizophrenia are characterised by significant hyperconnectivity. SN hyperconnectivity predicted positive symptoms, especially delusions. This hyperconnectivity seems to be a prominent feature of paranoid schizophrenia and could represent the pathophysiological change underlying the psychopathology. The cross-sectional nature of our data means that causal associations are speculative. Nonetheless, our results suggest that hyperconnectivity within the SN could be a potential biomarker of schizophrenia. Jim Gatherall Travelling Fellowship awarded to RK to attend Stanford Cognitive and Systems Neuroscience laboratory

Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network

<div><p>One of the most fundamental features of the human brain is its ability to detect and attend to salient goal-relevant events in a flexible manner. The salience network (SN), anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this process through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. Here, we leverage the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and apply novel graph-theoretical techniques to investigate the dynamic spatiotemporal organization of the SN. We show that the large-scale brain dynamics of the SN are characterized by several distinctive and robust properties. First, the SN demonstrated the highest levels of flexibility in time-varying connectivity with other brain networks, including the frontoparietal network (FPN), the cingulate–opercular network (CON), and the ventral and dorsal attention networks (VAN and DAN). Second, dynamic functional interactions of the SN were among the most spatially varied in the brain. Third, SN nodes maintained a consistently high level of network centrality over time, indicating that this network is a hub for facilitating flexible cross-network interactions. Fourth, time-varying connectivity profiles of the SN were distinct from all other prefrontal control systems. Fifth, temporal flexibility of the SN uniquely predicted individual differences in cognitive flexibility. Importantly, each of these results was also observed in a second retest dataset, demonstrating the robustness of our findings. Our study provides fundamental new insights into the distinct dynamic functional architecture of the SN and demonstrates how this network is uniquely positioned to facilitate interactions with multiple functional systems and thereby support a wide range of cognitive processes in the human brain.</p></div

Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns

Music and speech are complex sound streams with hierarchical rules of temporal organization that become elaborated over time. Here, we use functional magnetic resonance imaging to measure brain activity patterns in 20 right-handed nonmusicians as they listened to natural and temporally reordered musical and speech stimuli matched for familiarity, emotion, and valence. Heart rate variability and mean respiration rates were simultaneously measured and were found not to differ between musical and speech stimuli [...

SN temporal flexibility predicts cognitive flexibility behavioral measures.

<p>Panel <b><i>(A)</i></b> shows results from a CCA using cross-validation on Session 1 data. Scatter plot shows predictions of individual cognitive flexibility based on SN temporal flexibility. Panel <b><i>(B)</i></b> shows results from Session 2 data. (‘***’: <i>p</i> < 0.001, ‘*’: <i>p</i> < 0.05).</p

Brain regions with high temporal flexibility.

<p>Panels <b><i>A–C</i></b> depict results from Session 1 data. <b>(<i>A</i>)</b> Joint profile of temporal flexibility and spatiotemporal diversity identifies a cluster of brain nodes with distinctly high temporal flexibility. <b>(<i>B</i>)</b> Detailed profile of brain nodes within the cluster with high temporal flexibility (inset from panel <b>A</b>). <b>(<i>C</i>)</b> Brain nodes with high temporal flexibility are primarily from the SN, subcortical regions, and FPN, with the highest percentage belonging to the SN. Panels <b><i>D–F</i></b> depict corresponding results from Session 2 data.</p