Biology subjects

Boes, A.

Publications and source records attributed to Boes, A..

10 recordsLinked to original sources

bioRxiv2026

Lesion-Derived Estimated Functional and Structural Connectomes Predict Post-Stroke Recovery Outcomes

Accurate prognosis after stroke remains challenging, limiting the ability to predict individual recovery. While advanced connectome MRI can provide further prognostically-useful information regarding affected brain networks, its impractical nature has given rise instead to lesion-based estimates of network disruptions. Here, we present a novel pipeline that leverages our recently developed connectome analysis tools to estimate structural disconnection (eSDC) and functional connectivity (eFC) directly from N=96 patient lesion masks for use in predicting motor, language, attention and memory outcomes at multiple timepoints after stroke. Our approach jointly leverages our Network Modification (NeMo) Tool, a database of white matter tractography results that estimates structural disconnectivity (eSDC), and the Krakencoder, a joint autoencoder that estimates functional connectomes (eFC) from the NeMo Tool's output. Models based on eSDC and eFC predicted almost all outcomes, particularly motor outcomes, significantly better than models that used patients' fMRI-based observed FC (oFC) and models based on only lesion information and covariates. Estimated connectome measures also i) retained meaningful inter-individual variability when compared to observed functional connectomes, ii) reflected population-level, stroke-related changes in functional connectomes, and iii) resulted in outcome prediction models with feature importances that aligned with expected networks of relevance to the given outcome. Feature importance analyses also identified distinct predictive signatures across both modality and outcome domain, indicating that eSDC and eFC capture non-redundant, biologically meaningful information about post-stroke network disruption. Together, these findings establish our lesion-based estimates of post-stroke structural and functional connectomes as a practical, information-rich alternative to advanced imaging in patients. This approach allows for probing an individual's affected neural circuitry underlying stroke impairment and recovery, paving the way toward more efficient, accessible, and personalized approaches to post-stroke prognosis and intervention.

neuroscience↗

bioRxiv2025

Cognition Disconnected: The Influence of Domain-General Cognition (g) in Lesion-Deficit Mapping

Lesion-deficit mapping is a premier technique for dissociating neural correlates of cognitive functions. However, the contribution of domain-general cognition (g) to the performance of virtually all complex behavioral tasks may encumber such efforts. This possibility has been largely unexplored. Here, we examined the influence of g in lesion-deficit mapping using a combination of structural equation modeling, lesion-behavior mapping (LBM), and structural (sLNM) and functional (fLNM) lesion network mapping. We modeled latent variables for visuospatial ability, processing speed, language, and anterograde memory among 473 patients with focal brain damage (age range 20-93, 257 men, 216 women), both with and without g-associated variance embedded within each factor. Lesion-deficit maps for domain-specific cognitive abilities were statistically significantly less spatially inter-correlated after partitioning out g (p = 0.010 for LBM; p = 0.003 for sLNM; p = 0.003 for fLNM). Lesion-deficit maps also implicated the existence of distinct neuroanatomy for specific cognitive domains that was, in many cases, only apparent and clearly distinct from lesion-deficit maps of g after partitioning out the influence of g. Overall, our findings suggest that, if left unaccounted for, domain-general cognitive variance could tangibly affect conclusions about the extent to which different cognitive functions rely on distinct neuroanatomy.

neuroscience↗

bioRxiv2023

A neural network for religious fundamentalism derived from patients with brain lesions

Religious fundamentalism, characterized by rigid adherence to a set of beliefs putatively revealing inerrant truths, is ubiquitous across cultures and has a global impact on society. Understanding the psychological and neurobiological processes producing religious fundamentalism may inform a variety of scientific, sociological, and cultural questions. Research indicates that brain damage can alter religious fundamentalism. However, the precise brain regions involved with these changes remain unknown. Here, we analyzed brain lesions associated with varying levels of religious fundamentalism in two large datasets from independent laboratories. Lesions associated with greater fundamentalism were connected to a specific brain network with nodes in the right orbitofrontal, dorsolateral prefrontal, and inferior parietal lobes. This fundamentalism network was strongly right hemisphere lateralized and highly reproducible across the independent datasets (r = 0.82) with cross-validations between datasets. To explore the relationship of this network to lesions previously studied by our group, we tested for similarities to twenty-one lesion-induced conditions. Lesions associated with confabulation and criminal behavior showed a similar connectivity pattern as lesions associated with greater fundamentalism. Moreover, lesions associated with poststroke pain showed a similar connectivity pattern as lesions associated with lower fundamentalism. These findings are consistent with hemispheric specializations in reasoning and lend insight into previously observed epidemiological associations with fundamentalism, such as cognitive rigidity and outgroup hostility.

neuroscience↗

bioRxiv2023

Unsupervised mapping of causal relations between brain and behavior

Human lesion studies offer one of the most direct routes to investigating the relations between brain regions and behavioral outcomes in circumstances where experimental interventions are highly restricted. However, these studies face a major challenge in identifying the right level of granularity at which brain regions and behavioral outcomes should be analyzed to identify the relation between lesions to specific brain regions and specific behavioral outcomes. Here we showcase a novel data-driven approach, Causal Feature Learning (CFL), that learns the appropriate level of analysis and the relation between lesion and cognitive impairment at the same time. The method avoids specifying brain regions and specific outcome measures a priori, allowing for the discovery of new cross-cutting lesion-behavior maps. We show that CFL robustly recovers lesion behavior maps in a simulated dataset where Canonical Correlation Analysis fails to provide interpretable results. We then show that CFL recovers known lesion-behavior maps for language deficits and visuospatial processing using a large dataset of lesion subjects, and we illustrate how CFL can be used to identify new groupings of outcomes when mapping lesions to depression symptoms.

neuroscience↗

bioRxiv2022

Increased prevalence of a frontoparietal brain state at rest is associated with better motor recovery in individuals with pontine stroke affecting dominant-hand corticospinal tract

Strokes cause lesions that damage brain tissue, disrupt normal brain activity patterns and can lead to impairments in motor function. Although modulation of cortical activity is central to stimulation-based rehabilitative therapies, aberrant and adaptive patterns of brain activity after stroke have not yet been fully characterized. Here, we apply a brain dynamics analysis approach to study longitudinal brain activity patterns in individuals with ischemic pontine stroke. We first found 4 commonly occurring brain states largely characterized by high amplitude activations in the visual, frontoparietal, default mode, and motor networks. Stroke subjects spent less time in the frontoparietal state compared to controls. For individuals with dominant-hand CST damage, more time spent in the frontoparietal state from 1 week to 3-6 months post-stroke was associated with better motor recovery over the same time period, an association which was independent of baseline impairment. Furthermore, the amount of time spent in brain states was linked empirically to functional connectivity. This work suggests that when the dominant-hand CST is compromised in stroke, resting state configurations may include increased activation of the frontoparietal network, which may facilitate compensatory neural pathways that support recovery of motor function when traditional motor circuits of the dominant-hemisphere are compromised.

neuroscience↗

bioRxiv2022

Effects of transcranial magnetic stimulation on the human brain recorded with intracranial electrocorticography: First-in-human study

Transcranial magnetic stimulation (TMS) is increasingly used as a noninvasive technique for neuromodulation in research and clinical applications, yet its mechanisms are not well understood. Here, we present the first in-human study evaluating the effects of TMS using intracranial electrocorticography (iEEG) in neurosurgical patients. We first evaluated safety in a gel-based phantom. We then performed TMS-iEEG in 20 neurosurgical participants with no adverse events. Next, we evaluated brain-wide intracranial responses to single pulses of TMS to the dorsolateral prefrontal cortex (dlPFC) (N=10, 1414 electrodes). We demonstrate that TMS preferentially induces neuronal responses locally within the dlPFC at sites with higher electric field strength. Evoked responses were also noted downstream in the anterior cingulate and anterior insular cortex, regions functionally connected to the dlPFC. These findings support the safety and promise of TMS-iEEG in humans to examine local and network-level effects of TMS with higher spatiotemporal resolution than currently available methods.

neuroscience↗

bioRxiv2021

Functional connectome reorganization relates to post-stroke motor recovery and structural disconnection

Motor recovery following ischemic stroke is contingent on the ability of surviving brain networks to compensate for damaged tissue. In rodent models, sensory and motor cortical representations have been shown to remap onto intact tissue around the lesion site, but remapping to more distal sites (e.g. in the contralesional hemisphere) has also been observed. Resting state functional connectivity (FC) analysis has been employed to study compensatory network adaptations in humans, but mechanisms and time course of motor recovery are not well understood. Here, we examine longitudinal FC in 23 first-episode ischemic pontine stroke patients (34-74 years old; 8 female, 15 male) and utilize a graph matching approach to identify patterns of regional functional connectivity reorganization during recovery. We quantified functional reorganization between several intervals ranging from 1 week to 6 months following stroke, and demonstrated that the areas that undergo functional reorganization most frequently are in cerebellar/subcortical networks. Brain regions with more structural connectome disruption due to the stroke also had more functional remapping over time. Finally, we show that the amount of functional reorganization between time points is correlated with the extent of motor recovery observed between those time points in the early to late subacute phases, and, furthermore, individuals with greater baseline motor impairment demonstrate more extensive early subacute functional reorganization (from one to two weeks post-stroke) and this reorganization correlates with better motor recovery at 6 months. Taken together, these results suggest that our graph matching approach can quantify recovery-relevant, whole-brain functional connectivity network reorganization after stroke.

neuroscience↗

bioRxiv2021

Neuropsychological evidence of multi-domain network hubs in the human thalamus

Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.

neuroscience↗

bioRxiv2020

Biochemical and structural insights into the activation of PBP1b by the essential domain of FtsN

Peptidoglycan (PG) is an essential constituent of the bacterial cell wall. During cell division PG synthesis localizes at mid-cell under the control of a multiprotein complex, the divisome. In Escherichia coli, septal PG synthesis and cell constriction rely on the accumulation of FtsN at the division site. The region L75 to Q93 of FtsN (EFtsN) was shown to be essential and sufficient for its functioning in vivo but the specific target and the molecular mechanism remained unknown. Here, we show that EFtsN binds specifically to the major PG synthase PBP1b and is sufficient to stimulate its GTase activity. We also report the crystal structure of PBP1b in complex with EFtsN which provides structural insights into the mode of binding of EFtsN at the junction between the GTase and UB2H domains of PBP1b. Interestingly, the mutations R141A/R397A of PBP1b, within the EFtsN binding pocket, reduce the activation of PBP1b by FtsN. This mutant was unable to rescue {Delta}ponB-ponAts strain at nonpermissive temperature and induced a mild cell chaining phenotype and cell lysis. Altogether, the results show that PBP1b is a target of EFtsN and suggest that binding of FtsN to PBP1b contributes to trigger septal PG synthesis and cell constriction.

microbiology↗

bioRxiv2018

Predicting response to motor therapy in chronic stroke patients using Machine Learning

Accurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individuals motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61{+/-}11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment ([Formula]). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patients individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

neuroscience↗