Sex-specific resting state brain network dynamics in patients with major depressive disorder

Sex-specific neurobiological changes have been implicated in Major Depressive Disorder (MDD). Dysfunctions of the default mode network (DMN), salience network (SN) and frontoparietal network (FPN) are critical neural characteristics of MDD, however, the potential moderating role of sex on resting-state network dynamics in MDD has not been sufficiently evaluated. Thus, resting-state functional magnetic resonance imaging (fMRI) data were collected from 138 unmedicated patients with first-episode MDD (55 males) and 243 healthy controls (HCs; 106 males). Recurring functional network co-activation patterns (CAPs) were extracted, and time spent in each CAP (the total amount of volumes associated to a CAP), persistence (the average number of consecutive volumes linked to a CAP), and transitions across CAPs involving the SN, DMN and FPN were quantified. Relative to HCs, MDD patients exhibited greater persistence in a CAP involving activation of the DMN and deactivation of the FPN (DMN + FPN-). In addition, relative to the sex-matched HCs, the male MDD group spent more time in two CAPs involving the SN and DMN (i.e., DMN + SN- and DMN-SN + ) and transitioned more frequently from the DMN + FPN- CAP to the DMN + SN- CAP relative to the male HC group. Conversely, the female MDD group showed less persistence in the DMN + SN- CAP relative to the female HC group. Our findings highlight that the imbalance between SN and DMN could be a neurobiological marker supporting sex differences in MDD. Moreover, the dominance of the DMN accompanied by the deactivation of the FPN could be a sex-independent neurobiological correlate related to depression.

Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs)

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns (μCAPs) and employed this method on the human genetic model of schizophrenia 22q11.2 deletion syndrome (22q11.2DS). Participants underwent resting-state functional MRI and a data-driven iterative process resulting in the identification of six whole-brain μCAPs with specific activity patterns within the thalamus. Unlike conventional methods, μCAPs extract dynamic spatial patterns that reveal partially overlapping and non-mutually exclusive functional subparts. Thus, the μCAPs method detects finer foci of activity within the initial seed region, retaining valuable and clinically relevant temporal and spatial information. We found that a μCAP showing co-activation of the mediodorsal thalamus with brain-wide cortical regions was expressed significantly less frequently in patients with 22q11.2DS, and its occurrence negatively correlated with the severity of positive psychotic symptoms. Additionally, activity within the auditory-visual cortex and their respective geniculate nuclei was expressed in two different μCAPs. One of these auditory-visual μCAPs co-activated with salience areas, while the other co-activated with the default mode network (DMN). A significant shift of occurrence from the salience+visuo-auditory-thalamus to the DMN + visuo-auditory-thalamus μCAP was observed in patients with 22q11.2DS. Thus, our findings support existing research on the gatekeeping role of the thalamus for sensory information in the pathophysiology of psychosis and revisit the evidence of geniculate nuclei hyperconnectivity with the audio-visual cortex in 22q11.2DS in the context of dynamic functional connectivity, seen here as the specific hyper-occurrence of these circuits with the task-negative brain networks.

Transient resting-state salience-limbic co-activation patterns in functional neurological disorders

BACKGROUND

Functional neurological disorders were historically regarded as the manifestation of a dynamic brain lesion which might be linked to trauma or stress, although this association has not yet been directly tested yet. Analysing large-scale brain network dynamics at rest in relation to stress biomarkers assessed by salivary cortisol and amylase could provide new insights into the pathophysiology of functional neurological symptoms.

METHODS

Case-control resting-state functional magnetic resonance imaging study of 79 patients with mixed functional neurological disorders (i.e., functional movement disorders, functional seizures, persistent perceptual-postural dizziness) and 74 age- and sex-matched healthy controls. Using a two-step hierarchical data-driven neuroimaging approach, static functional connectivity was first computed between 17 resting-state networks. Second, dynamic alterations in these networks were examined using co-activation pattern analysis. Using a partial least squares correlation analysis, the multivariate pattern of correlation between altered temporal characteristics and stress biomarkers as well as clinical scores were evaluated.

RESULTS

Compared to healthy controls, patients presented with functional aberrancies of the salience-limbic network connectivity. Thus, the insula and amygdala were selected as seed-regions for the subsequent analyses. Insular co-(de)activation patterns related to the salience network, the somatomotor network and the default mode network were detected, which patients entered more frequently than controls. Moreover, an insular co-(de)activation pattern with subcortical regions together with a wide-spread co-(de)activation with diverse cortical networks was detected, which patients entered less frequently than controls. In patients, dynamic alterations conjointly correlated with amylase measures and duration of symptoms.

CONCLUSION

The relationship between alterations in insular co-activation patterns, stress biomarkers and clinical data proposes inter-related mechanisms involved in stress regulation and functional (network) integration. In summary, altered functional brain network dynamics were identified in patients with functional neurological disorder supporting previously raised concepts of impaired attentional and interoceptive processing.

Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry

Patients with drug-resistant essential tremor (ET) may undergo Gamma Knife stereotactic radiosurgical thalamotomy (SRS-T), where the ventro-intermediate nucleus of the thalamus (Vim) is lesioned by focused beams of gamma radiations to induce clinical improvement. Here, we studied SRS-T impacts on left Vim dynamic functional connectivity (dFC, n = 23 ET patients scanned before and 1 year after intervention), and on surface-based morphometric brain features (n = 34 patients, including those from dFC analysis). In matched healthy controls (HCs), three dFC states were extracted from resting-state functional MRI data. In ET patients, state 1 spatial stability increased upon SRS-T (F1,22 = 19.13, p = 0.004). More frequent expression of state 3 over state 1 before SRS-T correlated with greater clinical recovery in a way that depended on the MR signature volume (t6 = 4.6, p = 0.004). Lower pre-intervention spatial variability in state 3 expression also did (t6 = - 4.24, p = 0.005) and interacted with the presence of familial ET so that these patients improved less (t6 = 4.14, p = 0.006). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T (z ≤ - 3.66, p ≤ 0.022), and a joint analysis revealed that before thalamotomy, morphometric similarity and states 2/3 mean spatial similarity to HCs were anticorrelated, a relationship that disappeared upon SRS-T (z ≥ 4.39, p < 0.001). Our results show that left Vim functional dynamics directly relates to upper limb tremor lowering upon intervention, while morphometry instead has a supporting role in reshaping such dynamics.

The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.

Exploring the latent structure of behavior using the Human Connectome Project's data

How behavior arises from brain physiology has been one central topic of investigation in neuroscience. Considering the recent interest in predicting behavior from brain imaging using open datasets, there is the need for a principled approach to the categorization of behavioral variables. However, this is not trivial, as the definitions of psychological constructs and their relationships-their ontology-are not always clear. Here, we propose to use exploratory factor analysis (EFA) as a data-driven approach to find robust and interpretable domains of behavior in the Human Connectome Project (HCP) dataset. Additionally, we explore the clustering of behavioral variables using consensus clustering. We find that four and five factors offer the best description of the data, a result corroborated by the consensus clustering. In the four-factor solution, factors for Mental Health, Cognition, Processing Speed, and Substance Use arise. With five factors, Mental Health splits into Well-Being and Internalizing. Clustering results show a similar pattern, with clusters for Cognition, Processing Speed, Positive Affect, Negative Affect, and Substance Use. The factor structure is replicated in an independent dataset using confirmatory factor analysis (CFA). We discuss how the content of the factors fits with previous conceptualizations of general behavioral domains.

Exploring the heterogeneous morphometric data in essential tremor with probabilistic modelling

Essential tremor (ET) is a prevalent movement disorder characterized by marked clinical heterogeneity. Here, we explored the morphometric underpinnings of this cross-subject variability on a cohort of 34 patients with right-dominant drug-resistant ET and 29 matched healthy controls (HCs). For each brain region, group-wise morphometric data was modelled by a multivariate Gaussian to account for morphometric features' (co)variance. No group differences were found in terms of mean values, highlighting the limits of more basic group comparison approaches. Variance in surface area was higher in ET in the left lingual and caudal anterior cingulate cortices, while variance in mean curvature was lower in the right superior temporal cortex and pars triangularis, left supramarginal gyrus and bilateral paracentral gyrus. Heterogeneity further extended to the right putamen, for which a mixture of two Gaussians fitted the ET data better than a single one. Partial Least Squares analysis revealed the rich clinical relevance of the ET population's heterogeneity: first, increased head tremor and longer symptoms' duration were accompanied by broadly lower cortical gyrification. Second, more severe upper limb tremor and impairments in daily life activities characterized the patients whose morphometric profiles were more atypical compared to the average ET population, irrespective of the exact nature of the alterations. Our results provide candidate morphometric substrates for two different types of clinical variability in ET. They also demonstrate the importance of relying on analytical approaches that can efficiently handle multivariate data and enable to test more sophisticated hypotheses regarding its organization.

Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder

Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy

Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy.

Graph Theoretical Analysis of Structural Covariance Reveals the Relevance of Visuospatial and Attentional Areas in Essential Tremor Recovery After Stereotactic Radiosurgical Thalamotomy

Essential tremor (ET) is the most common movement disorder. Its pathophysiology is only partially understood. Here, we leveraged graph theoretical analysis on structural covariance patterns quantified from morphometric estimates for cortical thickness, surface area, and mean curvature in patients with ET before and one year after (to account for delayed clinical effect) ventro-intermediate nucleus (Vim) stereotactic radiosurgical thalamotomy. We further contrasted the observed patterns with those from matched healthy controls (HCs). Significant group differences at the level of individual morphometric properties were specific to mean curvature and the post-/pre-thalamotomy contrast, evidencing brain plasticity at the level of the targeted left thalamus, and of low-level visual, high-level visuospatial and attentional areas implicated in the dorsal visual stream. The introduction of cross-correlational analysis across pairs of morphometric properties strengthened the presence of dorsal visual stream readjustments following thalamotomy, as cortical thickness in the right lingual gyrus, bilateral rostral middle frontal gyrus, and left pre-central gyrus was interrelated with mean curvature in the rest of the brain. Overall, our results position mean curvature as the most relevant morphometric feature to understand brain plasticity in drug-resistant ET patients following Vim thalamotomy. They also highlight the importance of examining not only individual features, but also their interactions, to gain insight into the routes of recovery following intervention.

A triple-network organization for the mouse brain

The triple-network model of psychopathology is a framework to explain the functional and structural neuroimaging phenotypes of psychiatric and neurological disorders. It describes the interactions within and between three distributed networks: the salience, default-mode, and central executive networks. These have been associated with brain disorder traits in patients. Homologous networks have been proposed in animal models, but their integration into a triple-network organization has not yet been determined. Using resting-state datasets, we demonstrate conserved spatio-temporal properties between triple-network elements in human, macaque, and mouse. The model predictions were also shown to apply in a mouse model for depression. To validate spatial homologies, we developed a data-driven approach to convert mouse brain maps into human standard coordinates. Finally, using high-resolution viral tracers in the mouse, we refined an anatomical model for these networks and validated this using optogenetics in mice and tractography in humans. Unexpectedly, we find serotonin involvement within the salience rather than the default-mode network. Our results support the existence of a triple-network system in the mouse that shares properties with that of humans along several dimensions, including a disease condition. Finally, we demonstrate a method to humanize mouse brain networks that opens doors to fully data-driven trans-species comparisons.

First-person body view modulates the neural substrates of episodic memory and autonoetic consciousness: A functional connectivity study

Episodic memory (EM) is classically conceived as a memory for events, localized in space and time, and characterized by autonoetic consciousness (ANC) allowing to mentally travel back in time and subjectively relive an event. Building on recent evidence that the first-person visual co-perception of one's own body during encoding impacts EM, we used a scene recognition task in immersive virtual reality (VR) and measured how first-person body view would modulate peri-encoding resting-state fMRI, EM performance, and ANC. Specifically, we investigated the impact of body view on post-encoding functional connectivity in an a priori network of regions related either to EM or multisensory bodily processing and used these regions in a seed-to-whole brain analysis. Post-encoding connectivity between right hippocampus (rHC) and right parahippocampus (rPHC) was enhanced when participants encoded scenes while seeing their body. Moreover, the strength of connectivity between the rHC, rPHC and the neocortex displayed two main patterns with respect to body view. The connectivity with a sensorimotor fronto-parietal network, comprising primary somatosensory and primary motor cortices, correlated with ANC after - but not before - encoding, depending on body view. The opposite change of connectivity was found between rHC, rPHC and the medial parietal cortex (from being correlated with ANC before encoding to an absence of correlation after encoding), but irrespective of body view. Linking immersive VR and fMRI for the study of EM and ANC, these findings suggest that seeing one's own body during encoding impacts the brain activity related to EM formation by modulating the connectivity between the right hippocampal formation and the neocortical regions involved in the processing of multisensory bodily signals and self-consciousness.

Time-resolved effective connectivity in task fMRI: Psychophysiological interactions of Co-Activation patterns

Investigating context-dependent modulations of Functional Connectivity (FC) with functional magnetic resonance imaging is crucial to reveal the neurological underpinnings of cognitive processing. Most current analysis methods hypothesise sustained FC within the duration of a task, but this assumption has been shown too limiting by recent imaging studies. While several methods have been proposed to study functional dynamics during rest, task-based studies are yet to fully disentangle network modulations. Here, we propose a seed-based method to probe task-dependent modulations of brain activity by revealing Psychophysiological Interactions of Co-activation Patterns (PPI-CAPs). This point process-based approach temporally decomposes task-modulated connectivity into dynamic building blocks which cannot be captured by current methods, such as PPI or Dynamic Causal Modelling. Additionally, it identifies the occurrence of co-activation patterns at single frame resolution as opposed to window-based methods. In a naturalistic setting where participants watched a TV program, we retrieved several patterns of co-activation with a posterior cingulate cortex seed whose occurrence rates and polarity varied depending on the context; on the seed activity; or on an interaction between the two. Moreover, our method exposed the consistency in effective connectivity patterns across subjects and time, allowing us to uncover links between PPI-CAPs and specific stimuli contained in the video. Our study reveals that explicitly tracking connectivity pattern transients is paramount to advance our understanding of how different brain areas dynamically communicate when presented with a set of cues.

Triple Network Model Dynamically Revisited: Lower Salience Network State Switching in Pre-psychosis

Emerging evidence has attributed altered network coordination between the default mode, central executive, and salience networks (DMN/CEN/SAL) to disturbances seen in schizophrenia, but little is known for at-risk psychosis stages. Moreover, pinpointing impairments in specific network-to-network interactions, although essential to resolve possibly distinct harbingers of conversion to clinically diagnosed schizophrenia, remains particularly challenging. We addressed this by a dynamic approach to functional connectivity, where right anterior insula brain interactions were examined through co-activation pattern (CAP) analysis. We utilized resting-state fMRI in 19 subjects suffering from subthreshold delusions and hallucinations (UHR), 28 at-risk for psychosis with basic symptoms describing only self-experienced subclinical disturbances (BS), and 29 healthy controls (CTR) matched for age, gender, handedness, and intelligence. We extracted the most recurring CAPs, compared their relative occurrence and average dwell time to probe their temporal expression, and quantified occurrence balance to assess the putative loss of competing relationships. Our findings substantiate the pivotal role of the right anterior insula in governing CEN-to-DMN transitions, which appear dysfunctional prior to the onset of psychosis, especially when first attenuated psychotic symptoms occur. In UHR subjects, it is longer active in concert with the DMN and there is a loss of competition between a SAL/DMN state, and a state with insula/CEN activation paralleled by DMN deactivation. These features suggest that abnormal network switching disrupts one's capacity to distinguish between the internal world and external environment, which is accompanied by inflexibility and an excessive awareness to internal processes reflected by prolonged expression of the right anterior insula-default mode co-activation pattern.

Neural responses in autism during movie watching: Inter-individual response variability co-varies with symptomatology

Naturalistic movie paradigms are exquisitely dynamic by nature, yet dedicated analytical methods typically remain static. Here, we deployed a dynamic inter-subject functional correlation (ISFC) analysis to study movie-driven functional brain changes in a population of male young adults diagnosed with autism spectrum disorder (ASD). We took inspiration from the resting-state research field in generating a set of whole-brain ISFC states expressed by the analysed ASD and typically developing (TD) subjects along time. Change points of state expression often involved transitions between different scenes of the movie, resulting in the reorganisation of whole-brain ISFC patterns to recruit different functional networks. Both subject populations showed idiosyncratic state expression at dedicated time points, but only TD subjects were also characterised by episodes of homogeneous recruitment. The temporal fluctuations in both quantities, as well as in cross-population dissimilarity, were tied to contextual movie cues. The prominent idiosyncrasy seen in ASD subjects was linked to individual symptomatology by partial least squares analysis, as different temporal sequences of ISFC states were expressed by subjects suffering from social and verbal communication impairments, as opposed to nonverbal communication deficits and stereotypic behaviours. Furthermore, the temporal expression of several of these states was correlated with the movie context, the presence of faces on screen, or overall luminosity. Overall, our results support the use of dynamic analytical frameworks to fully exploit the information obtained by naturalistic stimulation paradigms. They also show that autism should be understood as a multi-faceted disorder, in which the functional brain alterations seen in a given subject will vary as a function of the extent and balance of expressed symptoms.

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.

Guided graph spectral embedding: Application to the C. elegans connectome

Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions—for example, based on wavelets and Slepians—that can be applied to filter signals defined on the graph. In this work, we take inspiration from these constructions to define a new guided spectral embedding that combines maximizing energy concentration with minimizing modified embedded distance for a given importance weighting of the nodes. We show that these optimization goals are intrinsically opposite, leading to a well-defined and stable spectral decomposition. The importance weighting allows us to put the focus on particular nodes and tune the trade-off between global and local effects. Following the derivation of our new optimization criterion, we exemplify the methodology on the C. elegans structural connectome. The results of our analyses confirm known observations on the nematode’s neural network in terms of functionality and importance of cells. Compared with Laplacian embedding, the guided approach, focused on a certain class of cells (sensory neurons, interneurons, or motoneurons), provides more biological insights, such as the distinction between somatic positions of cells, and their involvement in low- or high-order processing functions.

Normalization of aberrant pretherapeutic dynamic functional connectivity of extrastriate visual system in patients who underwent thalamotomy with stereotactic radiosurgery for essential tremor: a resting-state functional MRI study

OBJECTIVE

The tremor circuitry has commonly been hypothesized to be driven by one or multiple pacemakers within the cerebello-thalamo-cortical pathway, including the cerebellum, contralateral motor thalamus, and primary motor cortex. However, previous studies, using multiple methodologies, have advocated that tremor could be influenced by changes within the right extrastriate cortex, at both the structural and functional level. The purpose of this work was to evaluate the role of the extrastriate cortex in tremor generation and further arrest after left unilateral stereotactic radiosurgery thalamotomy (SRS-T).

METHODS

The authors considered 12 healthy controls (HCs, group 1); 15 patients with essential tremor (ET, right-sided, drug-resistant; group 2) before left unilateral SRS-T; and the same 15 patients (group 3) 1 year after the intervention, to account for delayed effects. Blood oxygenation level-dependent functional MRI during resting state was used to characterize the dynamic interactions of the right extrastriate cortex, comparing HC subjects against patients with ET before and 1 year after SRS-T. In particular, the authors applied coactivation pattern analysis to extract recurring whole-brain spatial patterns of brain activity over time.

RESULTS

The authors found 3 different sets of coactivating regions within the right extrastriate cortex in HCs and patients with pretherapeutic ET, reminiscent of the "cerebello-visuo-motor," "thalamo-visuo-motor" (including the targeted thalamus), and "basal ganglia and extrastriate" networks. The occurrence of the first pattern was decreased in pretherapeutic ET compared to HCs, whereas the other two patterns showed increased occurrences. This suggests a misbalance between the more prominent cerebellar circuitry and the thalamo-visuo-motor and basal ganglia networks. Multiple regression analysis showed that pretherapeutic standard tremor scores negatively correlated with the increased occurrence of the thalamo-visuo-motor network, suggesting a compensatory pathophysiological trait. Clinical improvement after SRS-T was related to changes in occurrences of the basal ganglia and extrastriate cortex circuitry, which returned to HC values after the intervention, suggesting that the dynamics of the extrastriate cortex had a role in tremor generation and further arrest after the intervention.

CONCLUSIONS

The data in this study point to a broader implication of the visual system in tremor generation, and not only through visual feedback, given its connections to the dorsal visual stream pathway and the cerebello-thalamo-cortical circuitry, with which its dynamic balance seems to be a crucial feature for reduced tremor. Furthermore, SRS-T seems to bring abnormal pretherapeutic connectivity of the extrastriate cortex to levels comparable to those of HC subjects.

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this is often achieved without considering the dynamic facet of functional processing, and analytical outputs typically account for merged influences of task-driven effects and underlying spontaneous fluctuations of brain activity. Here, we introduce a novel methodological pipeline that can go beyond these limitations: the use of a sliding-window analytical scheme permits tracking of functional changes over time, and through cross-subject correlational measurements, the approach can isolate purely stimulus-related effects. Thanks to a rigorous thresholding process, significant changes in inter-subject functional correlation can be extracted and analyzed. On a set of healthy subjects who underwent naturalistic audio-visual stimulation, we demonstrate the usefulness of the approach by tying the unraveled functional reconfigurations to particular cues of the movie. We show how, through our method, one can capture either a temporal profile of brain activity (the evolution of a given connection), or focus on a spatial snapshot at a key time point. We provide a publicly available version of the whole pipeline, and describe its use and the influence of its key parameters step by step.

Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression

Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with a significant transient activity to subsequently retrieve spatially and temporally overlapping large-scale brain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals. In this crucial step, spatial dependences can hinder the recovery of temporal overlapping activity. To overcome this effect, we propose a novel back-projection method that optimally fits activity-inducing signals given a set of transient timepoints and spatial maps of iCAPs, thus taking into account both spatial and temporal constraints. Validation on simulated data shows that transient-based constraints improve the quality of fitted time courses. Further evaluation on experimental data demonstrates that overfitting and underfitting are prevented by the use of optimized spatio-temporal constraints. Spatial and temporal properties of resulting iCAPs support that brain activity is characterized by the recurrent co-activation and co-deactivation of spatially overlapping large-scale brain networks. This new approach opens new avenues to explore the brain's dynamic core.

Brain dynamics in ASD during movie-watching show idiosyncratic functional integration and segregation

To refine our understanding of autism spectrum disorders (ASD), studies of the brain in dynamic, multimodal and ecological experimental settings are required. One way to achieve this is to compare the neural responses of ASD and typically developing (TD) individuals when viewing a naturalistic movie, but the temporal complexity of the stimulus hampers this task, and the presence of intrinsic functional connectivity (FC) may overshadow movie‐driven fluctuations. Here, we detected inter‐subject functional correlation (ISFC) transients to disentangle movie‐induced functional changes from underlying resting‐state activity while probing FC dynamically. When considering the number of significant ISFC excursions triggered by the movie across the brain, connections between remote functional modules were more heterogeneously engaged in the ASD population. Dynamically tracking the temporal profiles of those ISFC changes and tying them to specific movie subparts, this idiosyncrasy in ASD responses was then shown to involve functional integration and segregation mechanisms such as response inhibition, background suppression, or multisensory integration, while low‐level visual processing was spared. Through the application of a new framework for the study of dynamic experimental paradigms, our results reveal a temporally localized idiosyncrasy in ASD responses, specific to short‐lived episodes of long‐range functional interplays.

Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs

Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.