Search bioRxiv⌕ Search

Biology subjects

Hechler, A.

Publications and source records attributed to Hechler, A..

4 recordsLinked to original sources

Subjective confidence modulates individual BOLD patterns of predictive processing

Humans are adept at extracting and learning sequential patterns from sensory input. This ability enables predictions about future states, resulting in anticipation both on a behavioral and neural level. Stimuli deviating from predictions usually evoke higher neural and hemodynamic activity than predicted stimuli. This difference indicates increased surprise, or prediction error signaling in the context of predictive coding. However, interindividual differences in learning performance and uncertainty have rarely been taken into account. Under Bayesian formulations of cortical function, surprise should be strongest if a subject makes incorrect predictions with high confidence. In the present study, we studied the impact of subjective confidence on imaging markers of predictive processing. Participants viewed visual object sequences of varying predictability over multiple days. After each day, we instructed them to complete partially presented sequences and to rate their confidence in the decision. During fMRI scanning, participants saw sequences that either confirmed predictions, deviated from them, or were random. We replicated findings of increased BOLD responses to surprising input in the ventral visual stream. In line with our hypothesis, response magnitude increased with the level of confidence after the training phase. Interestingly, the activity difference between predictable and random input also scaled with confidence: In the anterior cingulate, we found tentative evidence that predictable sequences elicited higher activity for low levels of confidence, but lower activity for high levels of confidence. In summary, we showed that confidence is a crucial moderator of the link between predictive processing and BOLD activity.

neuroscience↗

The energy metabolic footprint of predictive processing in the human brain

Our expectations about the world influence how we interpret visual information, improving the speed and accuracy of perception. However, the underlying neural activity requires energy which is strictly limited in the brain. While predictive processing is a prevalent framework to explain perception, it remains unclear whether it also serves energy-efficient processing. Here, we employed metabolic brain imaging to quantify oxygen consumption during visual perception under varying levels of input predictability and subjective uncertainty. For three days, we presented participants with object sequences that were either predictable or unpredictable, and assessed their performance and confidence in predicting follow-up objects from partial sequences. On the fourth day, we first tested for behavioral consequences of predictability. We found that subjects detected predictable objects quicker than unpredictable ones. We then quantified cortical oxygen consumption during passive viewing of predictable, unpredictable or surprising sequences. Despite highly similar sensory load, predictable visual input elicited reduced oxygen metabolism when subjects were confident, across both sensory and higher cognitive areas. Crucially, this summed up to cortical energy savings of up to 12%, or 118 mol oxygen per minute, given average brain size. In contrast, cost increases due to surprising input were restricted to a network of fronto-parietal areas. In summary, we found that predictive processing enhances behavioral performance and notably reduces signaling costs, moderated by subjective confidence. This suggests that examining energy efficiency alongside behavioral performance may uncover novel computational strategies of human cognition and behavior.

neuroscience↗

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis -Insights from structural connectome graph theoretical network analysis

IntroductionAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. ObjectiveTo investigate changes in structural connectivity and network efficiency in NMDAR encephalitis by use of probabilistic whole-brain tractography and graph theoretical analysis of structural brain networks. MethodsStructural networks from sixty-one NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and sixty-one age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically-constrained tractography and spherical deconvolution-informed filtering of tractograms. We calculated global, modular, and nodal graph measures indicative of structural connectivity and network reorganization with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were included in multiple regression analyses to investigate their potential association with clinical course, disease severity, and cognitive outcome. ResultsPatients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p=0.049; right: p=0.013) and increased node strength of right precuneus (p=0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p=0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p=0.021) ConclusionFocal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

neuroscience↗

An energy costly architecture of neuromodulators for human brain evolution and cognition

Humans spend more energy on the brain than any other species. However, the high energy demand cannot be fully explained by brain size scaling alone. We hypothesized that energy-demanding signaling strategies may have contributed to human cognitive development. We measured the energy distribution along signaling pathways using multimodal brain imaging and found that evolutionarily novel connections have up to 67% higher energetic costs of signaling than sensory-motor pathways. Additionally, histology, transcriptomic data, and molecular imaging independently reveal an upregulation of signaling at G-protein coupled receptors in energy-demanding regions. We found that neuromodulators are predominantly involved in complex cognition such as reading or memory processing. Our study suggests that the upregulation of neuromodulator activity, alongside increased brain size, is a crucial aspect of human brain evolution.

neuroscience↗