Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex

Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention.

A neuropsychosocial signature predicts longitudinal symptom changes in women with irritable bowel syndrome

Irritable bowel syndrome (IBS) is a common disorder of brain-gut interactions characterized by chronic abdominal pain, altered bowel movements, often accompanied by somatic and psychiatric comorbidities. We aimed to test the hypothesis that a baseline phenotype composed of multi-modal neuroimaging and clinical features predicts clinical improvement on the IBS Symptom Severity Scale (IBS-SSS) at 3 and 12 months without any targeted intervention. Female participants (N = 60) were identified as “improvers” (50-point decrease on IBS-SSS from baseline) or “non-improvers.” Data integration analysis using latent components (DIABLO) was applied to a training and test dataset to determine whether a limited number of sets of multiple correlated baseline’omics data types, including brain morphometry, anatomical connectivity, resting-state functional connectivity, and clinical features could accurately predict improver status. The derived predictive models predicted improvement status at 3-months and 12-months with 91% and 83% accuracy, respectively. Across both time points, non-improvers were classified as having greater correlated morphometry, anatomical connectivity and resting-state functional connectivity characteristics within salience and sensorimotor networks associated with greater pain unpleasantness, but lower default mode network integrity and connectivity. This suggests that non-improvers have a greater engagement of attentional systems to perseverate on painful visceral stimuli, predicting IBS exacerbation. The ability of baseline multimodal brain-clinical signatures to predict symptom trajectories may have implications in guiding integrative treatment in the age of precision medicine, such as treatments targeted at changing attentional systems such as mindfulness or cognitive behavioral therapy.

Anatomical connectivity along the anterior-posterior axis of the human hippocampus: new insights using quantitative fibre-tracking.

The hippocampus supports multiple cognitive functions including episodic memory. Recent work has highlighted functional differences along the anterior-posterior axis of the human hippocampus but the neuroanatomical underpinnings of these differences remain unclear. We leveraged track-density imaging to systematically examine anatomical connectivity between the cortical mantle and the anterior-posterior axis of the in-vivo human hippocampus. We first identified the most highly connected cortical areas and detailed the degree to which they preferentially connect along the anterior-posterior axis of the hippocampus. Then, using a tractography pipeline specifically tailored to measure the location and density of streamline endpoints within the hippocampus, we characterised where, within the hippocampus, these cortical areas preferentially connect. Our results were striking in showing that different parts of the hippocampus preferentially connect with distinct cortical areas. Furthermore, we provide evidence that both gradients and circumscribed areas of dense extrinsic anatomical connectivity exist within the human hippocampus. These findings inform conceptual debates in the field by unveiling how specific regions along the anterior-posterior axis of the hippocampus are associated with different cortical inputs/outputs. Overall, our results represent a major advance in our ability to map the anatomical connectivity of the human hippocampus in-vivo and inform our understanding of the neural architecture of hippocampal dependent memory systems in the human brain. This detailed characterization of how specific portions of the hippocampus anatomically connect with cortical brain regions may promote a better understanding of its role in cognition and we emphasize the importance of considering the hippocampus as a heterogeneous structure.

Mechanisms of Network Changes in Cognitive Impairment in Multiple Sclerosis

Background and Objectives:Cognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers there remains to be a full understanding of why they are affected in MS. In this cross-sectional study we tested the hypothesis that functional network regions may be susceptible to disease-related ‘wear-and-tear’ and that this can be observable on co-occuring abnormalities on other MR metrics. We tested whether functional connectivity abnormalities in cognitively impaired MS patients co-occur with either 1) overlapping, 2) local, or 3) distal changes in anatomical connectivity and cerebral blood flow abnormalities.Methods:Multimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests was performed in 102 relapsing-remitting MS patients and 27 healthy controls. MS patients were classified as cognitively impaired if they scored ≥1.5 standard deviations below the control mean on ≥2 tests (n=55), or else cognitively preserved (n=47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated and anatomical connectivity was assessed with anatomical connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomical connectivity were assessed within resting state networks that showed functional connectivity abnormalities in cognitively impaired MS patients.Results:Functional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved MS patients (TFCE-corrected at p≤0.05, two-sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomical connectivity locally and distally but not in overlapping locations.Discussion:We provide the first evidence that FC abnormalities are accompanied with local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathological mechanism for altered functional connectivity in brain networks in MS.

Aging-Related Differences in Structural and Functional Interhemispheric Connectivity

There is substantial evidence of age-related declines in anatomical connectivity during adulthood, with associated alterations in functional connectivity. But the relation of those functional alterations to the structural reductions is unclear. The complexities of both the structural and the functional connectomes make it difficult to determine such relationships. We pursue this question with methods, based on animal research, that specifically target the interhemispheric connections between the visual cortices. We collect t1- and diffusion-weighted imaging data from which we assess the integrity of the white matter interconnecting the bilateral visual cortices. Functional connectivity between the visual cortices is measured with electroencephalography during the presentation of drifting sinusoidal gratings that agree or conflict across hemifields. Our results show age-related reductions in the integrity of the white matter interconnecting the visual cortices, and age-related increases in the difference in functional interhemispheric lagged coherence between agreeing versus disagreeing visual stimuli. We show that integrity of the white matter in the splenium of the corpus callosum predicts the differences in lagged coherence for the agreeing versus disagreeing stimuli; and that this relationship is mediated by age. These results give new insight into the causal relationship between age and functional connectivity.

Thalamic bursts modulate cortical synchrony locally to switch between states of global functional connectivity in a cognitive task

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states.

Meditation-Induced Effects on Whole-Brain Structural and Effective Connectivity

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

The macaque ventral intraparietal area has expanded into three homologue human parietal areas

Macaque ventral intraparietal area (VIP) in the fundus of the intraparietal sulcus has been implicated in a diverse range of sensorimotor and cognitive functions such as motion processing, multisensory integration, processing of head peripersonal space, defensive behavior, and numerosity coding. Here, we exhaustively review macaque VIP function, cytoarchitectonics, and anatomical connectivity and integrate it with human studies that have attempted to identify a potential human VIP homologue. We show that human VIP research has consistently identified three, rather than one, bilateral parietal areas that each appear to subsume some, but not all, of the macaque area’s functionality. Available evidence suggests that this human “VIP complex” has evolved as an expansion of the macaque area, but that some precursory specialization within macaque VIP has been previously overlooked. The three human areas are dominated, roughly, by coding the head or self in the environment, visual heading direction, and the peripersonal environment around the head, respectively. A unifying functional principle may be best described as prediction in space and time, linking VIP to state estimation as a key parietal sensorimotor function. VIP’s expansive differentiation of head and self-related processing may have been key in the emergence of human bodily self-consciousness.

Meditation-induced effects on whole-brain structural and effective connectivity

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.