Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype.

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Transvenous biopsy of inferior vena cava leiomyosarcoma: two case reports.

Leiomyosarcomas of the inferior vena cava (IVC) are uncommon malignancies. There is limited research detailing optimal diagnostic and clinical management. Here, we present 2 unique cases of IVC leiomyosarcoma including one in which the mass was partially ruptured through the vessel at initial presentation. We detail radiologic findings, 2 different transvenous approaches for biopsy of these masses, and subsequent oncological management.

Endovascular resolution of complete common iliac vein stenosis in a case of May-Thurner syndrome with underlying malignancy.

INTRODUCTION AND IMPORTANCE: May-Thurner Syndrome (MTS) is an uncommon etiology of left common iliac vein thrombosis due to arterial compression. In this report, we describe a case of MTS with severe occlusion of the left common iliac vein in the context of a previously undiagnosed pancreatic cancer. We detail the endovascular resolution of the iliac vein compression and show long-term patency. CASE PRESENTATION: A 33-year-old woman on oral contraceptive pills presented with extensive thrombosis of the left common iliac vein extending cephalad into the lower IVC and inferiorly to the femoral vein. The thrombus was refractory to therapeutic heparin. Mechanical thrombectomy removed the occluding thrombus. Intravenous ultrasound identified severe compression of the left common iliac vein by the right common iliac artery. Angioplasty and stenting provided complete resolution of the lesion. Imaging and hematologic workup revealed a pancreatic malignancy and concomitant hypercoagulable state that likely precipitated the patient's presentation. CLINICAL DISCUSSION: Endovascular intervention provided complete resolution of severe iliac vein compression. Patency was maintained at 6-month follow-up. Research suggests that the anatomical lesion predisposing individuals to MTS is relatively common despite infrequent occurrence of the syndrome. This case highlights the importance of a high clinical suspicion for associated hypercoagulable states when MTS is discovered. CONCLUSION: There is limited research exploring the relationship between severity of iliac vein compression and endovascular treatment outcome. This case documents endovascular resolution of a severe lesion with maintained patency.

Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks.

Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brain's ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.

Perineuronal Nets in the Dorsomedial Striatum Contribute to Behavioral Dysfunction in Mouse Models of Excessive Repetitive Behavior.

Background: Excessive repetitive behavior is a debilitating symptom of several neuropsychiatric disorders. Parvalbumin-positive inhibitory interneurons in the dorsal striatum have been linked to repetitive behavior, and a sizable portion of these cells are surrounded by perineuronal nets (PNNs), specialized extracellular matrix structures. Although PNNs have been associated with plasticity and neuropsychiatric disease, no previous studies have investigated their involvement in excessive repetitive behavior. Methods: We used histochemistry and confocal imaging to investigate PNNs surrounding parvalbumin-positive cells in the dorsal striatum of 4 mouse models of excessive repetitive behavior (BTBR, Cntnap2, Shank3, prenatal valproate treatment). We then investigated one of these models, the BTBR mouse, in detail, with DiI labeling, in vivo and in vitro recordings, and behavioral analyses. We next degraded PNNs in the dorsomedial striatum (DMS) using the enzyme chondroitinase ABC and assessed dendritic spine density, electrophysiology, and repetitive behavior. Results: We found a greater percentage of parvalbumin-positive interneurons with PNNs in the DMS of all 4 mouse models of excessive repetitive behavior compared with control mice. In BTBR mice, we found fewer dendritic spines on medium spiny neurons (targets of parvalbumin-positive interneurons) and differences in neuronal oscillations as well as inhibitory postsynaptic potentials compared with control mice. Reduction of DMS PNNs in BTBR mice altered dendritic spine density and inhibitory responses and normalized repetitive behavior. Conclusions: These findings suggest that cellular abnormalities in the DMS are associated with maladaptive repetitive behaviors and that manipulating PNNs can restore normal levels of repetitive behavior while altering DMS dendritic spines and inhibitory signaling.

A Goldilocks theory of cognitive control: Balancing precision and efficiency with low-dimensional control states.

Cognitive control orchestrates interactions between brain regions, guiding the transformation of information to support contextually appropriate and goal-directed behaviors. In this review, we propose a hierarchical model of cognitive control where low-dimensional control states direct the flow of high-dimensional representations between regions. This allows cognitive control to flexibly adapt to new environments and maintain the representational capacity to capture the richness of the world.

Learning to control the brain through adaptive closed-loop patterned stimulation.

OBJECTIVE: Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. APPROACH: The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5-15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4-16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. MAIN RESULTS: In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. SIGNIFICANCE: Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.

Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity.

Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 "motifs" of neural activity. Each motif captured a unique spatiotemporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states, and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.

Use of electric field sensors for recording respiration, heart rate, and stereotyped motor behaviors in the rodent home cage.

BACKGROUND: Numerous environmental and genetic factors can contribute significantly to behavioral and cardiorespiratory variability observed experimentally. Affordable technologies that allow for noninvasive home cage capture of physio-behavioral variables should enhance understanding of inter-animal variability including after experimental interventions. NEW METHOD: We assessed whether EPIC electric field sensors (Plessey Semiconductors) embedded within or attached externally to a rodent's home cage could accurately record respiration, heart rate, and motor behaviors. COMPARISON WITH EXISTING METHODS: Current systems for quantification of behavioral variables require expensive specialty equipment, while measures of respiratory and heart rate are often provided by surgically implanted or chronically affixed devices. RESULTS: Sensors accurately encoded imposed sinusoidal changes in electric field tested at frequencies ranging from 0.5-100Hz. Mini-metronome arm movements were easily detected, but response magnitude was highly distance dependent. Sensors accurately reported respiration during whole-body plethysmography. In anesthetized rodents, PVC tube-embedded sensors provided accurate mechanical detection of both respiratory and heart rate. Comparable success was seen in naturally behaving animals at rest or sleeping when sensors were attached externally. Video-verified motor behaviors (sniffing, grooming, chewing, and rearing) were detectable and largely separable by their characteristic voltage fluctuations. Larger movement-related events had comparably larger voltage dynamics that easily allowed for a broad approximation of overall motor activity. Spectrograms were used to quickly depict characteristic frequencies in long-lasting recordings, while filtering and thresholding software allowed for detection and quantification of movement-related physio-behavioral events. CONCLUSIONS: EPIC electric field sensors provide a means for affordable non-contact home cage detection of physio-behavioral variables.