Activity

spared reticulospinal axons. We demonstrate the causative relevance of locally rewired as well as compensatory reticulospinal plasticity for the recovery of locomotor functions following spinal hemisection, using chemogenetic tools to selectively silence newly formed connections in behaviorally recovered animals. Moving from a correlative to a causative understanding of the role of neuroanatomical plasticity for functional recovery is fundamental for successful translation of treatment approaches from experimental studies to the clinics.The interplay between hippocampus and medial entorhinal cortex (mEC) is of key importance for forming spatial representations. Within the hippocampal-entorhinal loop, the hippocampus receives context-specific signals from layers II/III of the mEC and feeds memory-associated activity back into layer V (LV). The processing of this output signal within the mEC, however, is largely unknown. We characterized the activation of the receiving mEC network by evoked and naturally occurring output patterns in mouse hippocampal-entorhinal cortex slices. eIF inhibitor Both types of glutamatergic neurons (mEC LVa and LVb) as well as fast-spiking inhibitory interneurons receive direct excitatory input from the intermediate/ventral hippocampus. Connections between the two types of excitatory neurons are sparse, and local processing of hippocampal output signals within mEC LV is asymmetric, favoring excitation of far projecting LVa neurons over locally projecting LVb neurons. These findings suggest a new role for mEC LV as a bifurcation gate for feedforward (telencephalic) and feedback (entorhinal-hippocampal) signal propagation.SIGNIFICANCE STATEMENT Patterned network activity in hippocampal networks plays a key role in the formation and consolidation of spatial memories. It is, however, largely unclear how information is transferred to the neocortex for long-term engrams. Here, we elucidate the propagation of network activity from the hippocampus to the medial entorhinal cortex. We show that patterned output from the hippocampus reaches both major cell types of deep entorhinal layers. These cells are, however, only weakly connected, giving rise to two parallel streams of activity for local and remote signal propagation, respectively. The relative weight of both pathways is regulated by local inhibitory interneurons. Our data reveal important insights into the hippocampal-neocortical dialogue, which is of key importance for memory consolidation in the mammalian brain.Early-life stress (ELS) is associated with increased vulnerability to mental disorders. The basolateral amygdala (BLA) plays a critical role in fear conditioning and is extremely sensitive to ELS. Using a naturalistic rodent model of ELS, the limited bedding paradigm (LB) between postnatal days 1-10, we previously documented that LB male, but not female preweaning rat pups display increased BLA neuron spine density paralleled with enhanced evoked synaptic responses and altered BLA functional connectivity. Since ELS effects are often sexually dimorphic and amygdala processes exhibit hemispheric asymmetry, we investigated changes in synaptic plasticity and neuronal excitability of BLA neurons in vitro in the left and right amygdala of postnatal days 22-28 male and female offspring from normal bedding or LB mothers. We report that LB conditions enhanced synaptic plasticity in the right, but not the left BLA of males exclusively. LB males also showed increased perineuronal net density, particularly around parvalby dimorphic, and have been reproduced using the rodent limited bedding paradigm of early adversity. The present study examines sex differences in synaptic plasticity and cellular activation occurring in the developing left and right amygdala after limited bedding exposure, a phenomenon that could shape long-term emotional behavioral outcomes. Studying how ELS selectively produces effects in one amygdala hemisphere during a critical period of brain development could guide further investigation into sex-dependent mechanisms and allow for more targeted and improved treatment of stress-and emotionality-related disorders.This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algorithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.An increasing number of automated and artificial intelligence (AI) systems make medical treatment recommendations, including personalized recommendations, which can deviate from standard care. Legal scholars argue that following such nonstandard treatment recommendations will increase liability in medical malpractice, undermining the use of potentially beneficial medical AI.  However, such liability depends in part on lay judgments by jurors when physicians use AI systems, in which circumstances would jurors hold physicians liable? Methods To determine potential jurors’ judgments of liability, we conducted an online experimental study of a nationally representative sample of 2,000 U.S. adults. Each participant read 1 of 4 scenarios in which an AI system provides a treatment recommendation to a physician. The scenarios varied the AI recommendation (standard or nonstandard care) and the physician’s decision (to accept or reject that recommendation). Subsequently, the physician’s decision caused harm. Participants then assessed the physician’s liability. Results Our results indicate that physicians who receive advice from an AI system to provide standard care can reduce the risk of liability by accepting, rather than rejecting, that advice, all else being equal. However, when an AI system recommends nonstandard care, there is no similar shielding effect of rejecting that advice and so providing standard care. Conclusion The tort law system is unlikely to undermine the use of AI precision medicine tools and may even encourage the use of these tools.