Matthew Cieslak

Controllability of structural brain networks

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Stimulation-Based Control of Dynamic Brain Networks

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.

Optimal trajectories of brain state transitions

Abstract The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering‐based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury. HighlightsWe use network control theory to model mechanisms of brain state transitions.Attention and executive areas are poised to affect an array of state transitions.Patients with mild traumatic injury display less specificity in control processes.

Local termination pattern analysis: a tool for comparing white matter morphology

Disconnections between structures in the brain have long been hypothesized to be the mechanism behind numerous disease states and pathological behavioral phenotypes. Advances in diffusion weighted imaging (DWI) provide an opportunity to study white matter, and therefore brain connectivity, in great detail. DWI-based research assesses white matter at two different scales: voxelwise indexes of anisotropy such as fractional anisotropy (FA) are used to compare small units of tissue and network-based methods compare tractography-based models of whole-brain connectivity. We propose a method called local termination pattern analysis (LTPA) that considers information about both local and global brain connectivity simultaneously. LTPA itemizes the subset of streamlines that pass through a small set of white matter voxels. The “local termination pattern” is a vector defined by counts of these streamlines terminating in pairs of cortical regions. To assess the reliability of our method we applied LTPA exhaustively over white matter voxels to produce complete maps of local termination pattern similarity, based on diffusion spectrum imaging (DSI) data from 11 individuals in triplicate. Here we show that local termination patterns from an individual are highly reproducible across the entire brain. We discuss how LTPA can be deployed into a clinical database and used to characterize white matter morphology differences due to disease, developmental or genetic factors.

Effector selection precedes reach planning in the dorsal parietofrontal cortex

Experimental evidence and computational modeling suggest that target selection for reaching is associated with the parallel encoding of multiple movement plans in the dorsomedial posterior parietal cortex (dmPPC) and the caudal part of the dorsal premotor cortex (PMdc). We tested the hypothesis that a similar mechanism also accounts for arm selection for unimanual reaching, with simultaneous and separate motor goal representations for the left and right arms existing in the right and left parietofrontal cortex, respectively. We recorded simultaneous electroencephalograms and functional MRI and studied a condition in which subjects had to select the appropriate arm for reaching based on the color of an appearing visuospatial target, contrasting it to a condition in which they had full knowledge of the arm to be used before target onset. We showed that irrespective of whether subjects had to select the arm or not, activity in dmPPC and PMdc was only observed contralateral to the reaching arm after target onset. Furthermore, the latency of activation in these regions was significantly delayed when arm selection had to be achieved during movement planning. Together, these results demonstrate that effector selection is not achieved through the simultaneous specification of motor goals tied to the two arms in bilateral parietofrontal cortex, but suggest that a motor goal is formed in these regions only after an arm is selected for action.

Anomalous White Matter Morphology in Adults Who Stutter

AIMS Developmental stuttering is now generally considered to arise from genetic determinants interacting with neurologic function. Changes within speech-motor white matter (WM) connections may also be implicated. These connections can now be studied in great detail by high-angular-resolution diffusion magnetic resonance imaging. Therefore, diffusion spectrum imaging was used to reconstruct streamlines to examine white matter connections in people who stutter (PWS) and in people who do not stutter (PWNS). METHOD WM morphology of the entire brain was assayed in 8 right-handed male PWS and 8 similarly aged right-handed male PWNS. WM was exhaustively searched using a deterministic algorithm that identifies missing or largely misshapen tracts. To be abnormal, a tract (defined as all streamlines connecting a pair of gray matter regions) was required to be at least one 3rd missing, in 7 out of 8 subjects in one group and not in the other group. RESULTS Large portions of bilateral arcuate fasciculi, a heavily researched speech pathway, were abnormal in PWS. Conversely, all PWS had a prominent connection in the left temporo-striatal tract connecting frontal and temporal cortex that was not observed in PWNS. CONCLUSION These previously unseen structural differences of WM morphology in classical speech-language circuits may underlie developmental stuttering.

Feature interactions enable decoding of sensorimotor transformations for goal-directed movement.

Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations.

Regional white matter variation associated with domain-specific metacognitive accuracy

The neural mechanisms that mediate metacognitive ability (the capacity to accurately reflect on ones own cognition and experience) remain poorly understood. An important question is whether metacognitive capacity is a domain-general skill supported by a core neuroanatomical substrate or whether regionally specific neural structures underlie accurate reflection in different cognitive domains. Providing preliminary support for the latter possibility, recent findings have shown that individual differences in metacognitive ability in the domains of memory and perception are related to variation in distinct gray matter volume and resting-state functional connectivity. The current investigation sought to build on these findings by evaluating how metacognitive ability in these domains is related to variation in white matter microstructure. We quantified metacognitive ability across memory and perception domains and used diffusion spectrum imaging to examine the relation between high-resolution measurements of white matter microstructure and individual differences in metacognitive accuracy in each domain. We found that metacognitive accuracy for perceptual decisions and memory were uncorrelated across individuals and that metacognitive accuracy in each domain was related to variation in white matter microstructure in distinct brain areas. Metacognitive accuracy for perceptual decisions was associated with increased diffusion anisotropy in white matter underlying the ACC, whereas metacognitive accuracy for memory retrieval was associated with increased diffusion anisotropy in the white matter extending into the inferior parietal lobule. Together, these results extend previous findings linking metacognitive ability in the domains of perception and memory to variation in distinct brain structures and connections.

Simultaneous acquisition of functional magnetic resonance images and impedance cardiography.

While simultaneous acquisition of electrocardiography (ECG) data during MRI is a widely used clinical technique, the effects of the MRI environment on impedance cardiography (ICG) data have not been characterized. We collected echo planar MRI scans while simultaneously recording ECG and thoracic impedance using carbon fiber electrodes and customized amplifiers. Here, we show that the key changes in impedance (dZ/dt) and features of the ECG waveforms are not obstructed during MRI. We present a method for ensemble averaging ICG/ECG signals collected during MRI and show that it performs comparably with signals collected outside the MRI environment. These results indicate that ICG can be used during MRI to measure stroke volume, cardiac output, preejection period, and left ventricular ejection time.

The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure

A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of the brain. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states – characterized by minimal energy – display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiological dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within- and between-system energies cleanly separate cognitive systems into distinct categories, optimized for differential contributions to integrated versus segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, offering insights into the roles that cognitive systems play in driving whole-brain activation patterns.