Biology subjects

Homann, J.

Publications and source records attributed to Homann, J..

4 recordsLinked to original sources

bioRxiv2025

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

neuroscience↗

bioRxiv2021

Passive Exposure Sparsifies Neural Activity in the Primary Visual Cortex

A notable feature of neural activity is sparseness - namely, that only a small fraction of neurons in a local circuit have high activity at any moment. Not only is sparse neural activity observed experimentally in most areas of the brain, but sparseness has been proposed as an optimization or design principle for neural circuits. Sparseness can increase the energy efficiency of the neural code as well as allow for beneficial computations to be carried out. But how does the brain achieve sparse-ness? Here, we found that when neurons in the primary visual cortex were passively exposed to a set of images over several days, neural responses became more sparse. Sparsification was driven by a decrease in the response of neurons with low or moderate activity, while highly active neurons retained similar responses. We also observed a net decorrelation of neural activity. These changes sculpt neural activity for greater coding efficiency.

neuroscience↗

bioRxiv2019

A Long Timescale Stimulus History Effect in the Primary Visual Cortex

Responses of neurons in the primary visual cortex (V1) are often understood as encoding the current visual stimulus. Yet, some studies indicate that temporal contingency effects exist in the responses of neurons in early sensory areas. We explored if the recent stimulus history would alter the response of V1 layer 2/3 pyramidal cells in head-fixed awake mice during presentation of sequences of complex images. The activity of individual neurons was sparse, such that either one or none of the images in the sequence typically yielded a strong response. We then substituted an image preceding this primary image in order to determine if responses to the primary image were affected. We found that the amplitude of the neurons response could be significantly altered by substitutions up to five images back from the primary image, even when the substituted image elicited virtually no response by itself. This stimulus history effect was heterogeneous across the population, with some cells showing facilitation and others suppression. For individual cells, the history effect was robust and reproducible across days. Our data show that responses of V1 neurons not only reflect the current stimulus but also encode, through their response amplitude, information about multiple images previously presented as far as 1000 msec in the past. This might enable V1 to retain information about the extended trajectory of past stimuli and perform complex temporal computations that are as of yet not appreciated.

neuroscience↗

bioRxiv2017

Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex

To explore theories of predictive coding, we presented mice with repeated sequences of images with novel images sparsely substituted. Under these conditions, mice could be rapidly trained to lick in response to a novel image, demonstrating a high level of performance on the first day of testing. Using 2-photon calcium imaging to record from layer 2/3 neurons in the primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. When a new stimulus sequence was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which then decayed to almost zero activity. The decay time of these transient responses was not fixed, but instead scaled with the length of the stimulus sequence. However, at the same time, we also found a small fraction of the neurons within the population ([~]2%) that continued to respond strongly and periodically to the repeated stimulus. Decoding analysis demonstrated that both the transient and sustained responses encoded information about stimulus identity. We conclude that the layer 2/3 population uses a two-channel predictive code: a dense transient code for novel stimuli and a sparse sustained code for familiar stimuli. These results extend and unify existing theories about the nature of predictive neural codes.

neuroscience↗