Biology subjects

Haller, M.

Publications and source records attributed to Haller, M..

5 recordsLinked to original sources

bioRxiv2026

Convergence and function of distinct corticocortical and transthalamic synapses across sensory processing hierarchies

Communication among cortical regions underlies fundamental functions of the brain. Thus, it is essential to understand the synaptic mechanisms by which distinct cortical regions become functionally connected. While direct corticocortical synaptic connections between cortical regions have traditionally been considered the primary mode of cortex- wide interaction, these regions can also interact indirectly through transthalamic pathways that engage higher-order regions of the thalamus. Yet, the mechanisms by which these two distinct inter-cortical synaptic pathways exert their influence on their target cortical neurons are largely unknown. A major obstacle to the targeted synaptic analysis of these pathways has been the lack of an appropriate method to genetically label them, especially in the same preparation. Here, using a combination of viral vector methods for anterograde transsynaptic and retrograde labeling along with optogenetic activation of long-range synaptic terminals in ex vivo slices of mouse visual cortical areas, we uncover a number of previously unknown features of corticocortical pathways and their transthalamic counterparts. First, corticocortical and transthalamic pathways originating from the same cortical source are largely monosynaptically integrated by target cortical neurons, though with an integration frequency that depends on the hierarchical level of the sensory processing stream. Second, feedforward transthalamic and corticocortical synapses are dynamically distinct and activate responses with distinct postsynaptic receptor composition, whereas these same pathways in the feedback direction elicit synaptic responses that are largely comparable. Overall, these results provide a novel synaptic-level framework for understanding how cortical and thalamic axon pathways shape the flow of information across interareal cortical networks.

neuroscience↗

bioRxiv2023

Drosophila models of PIGA-CDG mirror patient phenotypes

Mutations in the phosphatidylinositol glycan biosynthesis class A (PIGA) gene cause a rare, X-linked recessive congenital disorder of glycosylation (CDG). PIGA-CDG is characterized by seizures, intellectual and developmental delay, and congenital malformations. The PIGA gene encodes an enzyme involved in the first step of GPI anchor biosynthesis. There are over 100 GPI anchored proteins that attach to the cell surface and are involved in cell signaling, immunity, and adhesion. Little is known about the pathophysiology of PIGA-CDG. Here we describe the first Drosophila model of PIGA-CDG and demonstrate that loss of PIG-A function in Drosophila accurately models the human disease. As expected, complete loss of PIG-A function is larval lethal. Heterozygous null animals appear healthy, but when challenged, have a seizure phenotype similar to what is observed in patients. To identify the cell-type specific contributions to disease, we generated neuron- and glia-specific knockdown of PIG-A. Neuron-specific knockdown resulted in reduced lifespan and a number of neurological phenotypes, but no seizure phenotype. Glia-knockdown also reduced lifespan and, notably, resulted in a very strong seizure phenotype. RNAseq analyses demonstrated that there are fundamentally different molecular processes that are disrupted when PIG-A function is eliminated in different cell types. In particular, loss of PIG-A in neurons resulted in upregulation of glycolysis, but loss of PIG-A in glia resulted in upregulation of protein translation machinery. Here we demonstrate that Drosophila is a good model of PIGA-CDG and provide new data resources for future study of PIGA-CDG and other GPI anchor disorders. Article SummaryPIGA-CDG is a rare genetic disorder. In order to study this rare disease, we generated and characterized several Drosophila models of PIGA-CDG. These models faithfully recapitulate different patient phenotypes, including movement disorder and seizures. Drosophila is a good model for PIGA-CDG and other GPI anchor disorders.

genetics↗

bioRxiv2023

Admixture as a source for HLA variation in Neolithic European farming communities

The northern European Neolithic is characterized by two major demographic events: immigration of early farmers (EF) from Anatolia (5500 BCE) and their admixture (from [~]4200 BCE) with western hunter-gatherers (WHG) forming late farmers (LF). The influence of this admixture event on variation in the immune-relevant human leukocyte antigen (HLA) region is understudied. Here, we conducted population and immunogenetic analyses on 83 individuals from six EF and LF sites located in present-day Germany. We observed significant shifts in HLA allele frequencies from EF to LF. The HLA diversity increased from EF to LF, likely due to admixture with WHG. However, it was considerably lower than in modern populations. Both EF and LF exhibited a relatively narrow HLA allele spectrum compared to today. This coincides with sparse traces of pathogen DNA, potentially indicating a lower pathogen pressure at the time. We additionally noted that LF resulted from sex-biased admixture from male WHG. TEASERMore restricted HLA allele spectrum and lower diversity in Neolithic farmers than in modern populations

genetics↗

bioRxiv2020

Analysis of ongoing neuronal activity evoked by continuous speech with computational corpus linguistics methods

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational linguistics analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

neuroscience↗

bioRxiv2018

Parameterizing neural power spectra

Electrophysiological signals across species and recording scales exhibit both periodic and aperiodic features. Periodic oscillations have been widely studied and linked to numerous physiological, cognitive, behavioral, and disease states, while the aperiodic \"background\" 1/f component of neural power spectra has received far less attention. Most analyses of oscillations are conducted on a priori, canonically-defined frequency bands without consideration of the underlying aperiodic structure, or verification that a periodic signal even exists in addition to the aperiodic signal. This is problematic, as recent evidence shows that the aperiodic signal is dynamic, changing with age, task demands, and cognitive state. It has also been linked to the relative excitation/inhibition of the underlying neuronal population. This means that standard analytic approaches easily conflate changes in the periodic and aperiodic signals with one another because the aperiodic parameters--along with oscillation center frequency, power, and bandwidth--are all dynamic in physiologically meaningful, but likely different, ways. In order to overcome the limitations of traditional narrowband analyses and to reduce the potentially deleterious effects of conflating these features, we introduce a novel algorithm for automatic parameterization of neural power spectral densities (PSDs) as a combination of the aperiodic signal and putative periodic oscillations. Notably, this algorithm requires no a priori specification of band limits and accounts for potentially-overlapping oscillations while minimizing the degree to which they are confounded with one another. This algorithm is amenable to large-scale data exploration and analysis, providing researchers with a tool to quickly and accurately parameterize neural power spectra.

neuroscience↗