Biology subjects

Gale, J. T.

Publications and source records attributed to Gale, J. T..

3 recordsLinked to original sources

bioRxiv2024

Environment and diet shape the geography-specific Drosophila melanogaster microbiota composition

Geographic and environmental variation in the animal microbiota can be directly linked to the evolution and wild fitness of their hosts but often appears to be disordered. Here, we sought to better understand patterns that underlie wild variation in the microbiota composition of Drosophila melanogaster. First, environmental temperature predicted geographic variation in fly microbial communities better than latitude did. The microbiota also differed between wild flies and their diets, supporting previous conclusions that the fly microbiota is not merely a reflection of diet. Flies feeding on different diets varied significantly in their microbiota composition, and flies sampled from individual apples were exceptionally depauperate for the Lactic Acid Bacteria (LAB), a major bacterial group in wild and laboratory flies. However, flies bore significantly more LAB when sampled from other fruits or compost piles. Follow-up analyses revealed that LAB abundance in the flies uniquely responds to fruit decomposition, whereas other microbiota members better indicate temporal seasonal progression. Finally, we show that diet-dependent variation in the fly microbiota is associated with phenotypic differentiation of fly lines collected in a single orchard. These last findings link covariation between the flies dietary history, microbiota composition, and genetic variation across relatively small (single-orchard) landscapes, reinforcing the critical role that environment-dependent variation in microbiota composition can play in local adaptation and genomic differentiation of a model animal host. SIGNIFICANCE STATEMENTThe microbial communities of animals influence their hosts evolution and wild fitness, but it is hard to predict and explain how the microbiota varies in wild animals. Here, we describe that the microbiota composition of wild Drosophila melanogaster can be ordered by temperature, humidity, geographic distance, diet decomposition, and diet type. We show how these determinants of microbiota variation can help explain lactic acid bacteria (LAB) abundance in the flies, including the rarity of LAB in some previous studies. Finally, we show that wild fly phenotypes segregate with the flies diet and microbiota composition, illuminating links between the microbiota and host evolution. Together, these findings help explain how variation in microbiota compositions can shape an animals life history.

microbiology↗

bioRxiv2022

Internal states as a source of subject-dependent movement variability and their representation by large-scale networks

A humans ability to adapt and learn relies on reflecting on past performance. Such reflections form latent factors called internal states that induce variability of movement and behavior to improve performance. Internal states are critical for survival, yet their temporal dynamics and neural substrates are less understood. Here, we link internal states with motor performance and neural activity using state-space models and local field potentials captured from depth electrodes in over 100 brain regions. Ten human subjects performed a goal-directed center-out reaching task with perturbations applied to random trials, causing subjects to fail goals and reflect on their performance. Using computational methods, we identified two internal states, indicating that subjects kept track of past errors and perturbations, that predicted variability in reaction times and speed errors. These states granted access to latent information indicative of how subjects strategize learning from trial history, impacting their overall performance. We further found that large-scale brain networks differentially encoded these internal states. The dorsal attention network encoded past errors in frequencies above 100 Hz, suggesting a role in modulating attention based on tracking recent performance in working memory. The default network encoded past perturbations in frequencies below 15 Hz, suggesting a role in achieving robust performance in an uncertain environment. Moreover, these networks more strongly encoded internal states and were more functionally connected in higher performing subjects, whose learning strategy was to respond by countering with behavior that opposed accumulating error. Taken together, our findings suggest large-scale brain networks as a neural basis of strategy. These networks regulate movement variability, through internal states, to improve motor performance. Key pointsO_LIMovement variability is a purposeful process conjured up by the brain to enable adaptation and learning, both of which are necessary for survival. C_LIO_LIThe culmination of recent experiences--collectively referred to as internal states--have been implicated in variability during motor and behavioral tasks. C_LIO_LITo investigate the utility and neural basis of internal states during motor control, we estimated two latent internal states using state-space representation that modeled motor behavior during a goal-directed center-out reaching task in humans with simultaneous whole-brain recordings from intracranial depth electrodes. C_LIO_LIWe show that including these states--based on error and environment uncertainty--improves the predictability of subject-specific variable motor behavior and reveals latent information related to task performance and learning strategies where top performers counter error scaled by trial history while bottom performers maintain error tendencies. C_LIO_LIWe further show that these states are encoded by the large-scale brain networks known as the dorsal attention network and default network in frequencies above 100 Hz and below 15 Hz but found neural differences between subjects where network activity closely modulates with states and exhibits stronger functional connectivity for top performers. C_LIO_LIOur findings suggest the involvement in large-scale brain networks as a neural basis of motor strategy that orchestrates movement variability to improve motor performance. C_LI

neuroscience↗

bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗