Whole-brain R1 predicts manganese exposure and biological effects in welders

Manganese (Mn) is a neurotoxicant that, due to its paramagnetic property, also functions as a magnetic resonance imaging (MRI) T1 contrast agent. Previous studies in Mn toxicity have shown that Mn accumulates in the brain, which may lead to parkinsonian symptoms. In this article, we trained support vector machines (SVM) using whole-brain R1 (R1 = 1/T1) maps from 57 welders and 32 controls to classify subjects based on their air Mn concentration ([Mn]Air), Mn brain accumulation (ExMnBrain), gross motor dysfunction (UPDRS), thalamic GABA concentration (GABAThal), and total years welding. R1 was highly predictive of [Mn]Air above a threshold of 0.20 mg/m3 with an accuracy of 88.8% and recall of 88.9%. R1 was also predictive of subjects with GABAThal having less than or equal to 2.6 mM with an accuracy of 82% and recall of 78.9%. Finally, we used an SVM to predict age as a method of verifying that the results could be attributed to Mn exposure. We found that R1 was predictive of age below 48 years of age with accuracies ranging between 75 and 82% with recall between 94.7% and 76.9% but was not predictive above 48 years of age. Together, this suggests that lower levels of exposure (< 0.20 mg/m3 and < 18 years of welding on the job) do not produce discernable signatures, whereas higher air exposures and subjects with more total years welding produce signatures in the brain that are readily identifiable using SVM

In vivo identification of brain structures functionally involved in spatial learning and strategy switch

Spatial learning is a complex behavior which includes, among others, encoding of space, sensory and motivational processes, arousal and locomotor performance. Today, our view on spatial navigation is largely hippocampus-centrist. Less is known about the involvement of brain structures up- and downstream, or out of this circuit. Here, I provide the first in vivo assessment of the neural matrix underlying spatial learning, using functional manganese-enhanced MRI (MEMRI) and voxel-wise whole brain analysis. Mice underwent place-learning (PL) vs. response-learning (RL) in the water cross maze (WCM) and its readout was correlated to the Mn2+ contrasts. Thus, I identified structures involved in spatial learning largely overlooked in the past, due to methods focused on region of interest (ROI) analyses. These structures include several sensory-related structures and differ between place-learners and response-learners, with the former (PL) comprising mostly structures involved in different properties of visual processing, such as horizontal gaze (e.g. nucleus prepositus) and saccade (e.g. fastigial nucleus), or provide vision-input and eye movement information from parahippocampal (e.g. presubiculum, perirhinal, postrhinal and ectorhinal areas) and other regions (e.g. orbital area, superior colliculus and vestibular ocular-reflex from the vestibular nucleus) likely to head-direction, grid- and place-cells; and the latter (RL) presenting structures related to more basic rodent sensory computations, like odor (e.g main and accessory olfactory bulb, cortical amygdala, piriform, endopiriform and postpiriform areas) and acoustic stimuli representation (e.g. auditory area, nucleus of the lateral lemniscus and superior olivary complex), or sensory-motor properties, such as body representation (e.g. somatosensory area – upper limbs) and head-direction signal. Add-on experiments pointed to preferential Mn2+ accumulation towards projection terminals, suggesting that our mapping was mostly formed by projection sites of the originally activated structures. This is corroborated by in-depth analysis of MEMRI data after WCM learning showing mostly downstream targets of the hippocampus. These differ between fornical afferences from vCA1 and direct innervation from dCA1/iCA1 (for PL), and structures along the longitudinal association bundle originating in vCA1 (for RL). To elucidate the pattern of Mn2+ accumulation seen on the scans, I performed c-fos expression analyses following learning in the WCM. This helped me identify the structures initially activated during spatial learning and its underlying connectivity to establish the matrix. Finally, to test the causal involvement of selected structures from our previous findings I inhibited them (through DREADDs) while mice performed the WCM task. I also focused on the causal involvement of the vHPC-mPFC circuit on strategy switch during WCM learning. I believe that this study might shed light into new brain structures involved in spatial learning and strategy switch and complement the current knowledge on these circuits’ connectivity. Moreover, I elucidated some functional mechanisms of MEMRI, clarifying the interpretation of data obtained with this method and its possible future applications

In vivo identification of brain structures functionally involved in spatial learning and strategy switch

Spatial learning is a complex behavior which includes, among others, encoding of space, sensory and motivational processes, arousal and locomotor performance. Today, our view on spatial navigation is largely hippocampus-centrist. Less is known about the involvement of brain structures up- and downstream, or out of this circuit. Here, I provide the first in vivo assessment of the neural matrix underlying spatial learning, using functional manganese-enhanced MRI (MEMRI) and voxel-wise whole brain analysis. Mice underwent place-learning (PL) vs. response-learning (RL) in the water cross maze (WCM) and its readout was correlated to the Mn2+ contrasts. Thus, I identified structures involved in spatial learning largely overlooked in the past, due to methods focused on region of interest (ROI) analyses. These structures include several sensory-related structures and differ between place-learners and response-learners, with the former (PL) comprising mostly structures involved in different properties of visual processing, such as horizontal gaze (e.g. nucleus prepositus) and saccade (e.g. fastigial nucleus), or provide vision-input and eye movement information from parahippocampal (e.g. presubiculum, perirhinal, postrhinal and ectorhinal areas) and other regions (e.g. orbital area, superior colliculus and vestibular ocular-reflex from the vestibular nucleus) likely to head-direction, grid- and place-cells; and the latter (RL) presenting structures related to more basic rodent sensory computations, like odor (e.g main and accessory olfactory bulb, cortical amygdala, piriform, endopiriform and postpiriform areas) and acoustic stimuli representation (e.g. auditory area, nucleus of the lateral lemniscus and superior olivary complex), or sensory-motor properties, such as body representation (e.g. somatosensory area ? upper limbs) and head-direction signal. Add-on experiments pointed to preferential Mn2+ accumulation towards projection terminals, suggesting that our mapping was mostly formed by projection sites of the originally activated structures. This is corroborated by in-depth analysis of MEMRI data after WCM learning showing mostly downstream targets of the hippocampus. These differ between fornical afferences from vCA1 and direct innervation from dCA1/iCA1 (for PL), and structures along the longitudinal association bundle originating in vCA1 (for RL). To elucidate the pattern of Mn2+ accumulation seen on the scans, I performed c-fos expression analyses following learning in the WCM. This helped me identify the structures initially activated during spatial learning and its underlying connectivity to establish the matrix. Finally, to test the causal involvement of selected structures from our previous findings I inhibited them (through DREADDs) while mice performed the WCM task. I also focused on the causal involvement of the vHPC-mPFC circuit on strategy switch during WCM learning. I believe that this study might shed light into new brain structures involved in spatial learning and strategy switch and complement the current knowledge on these circuits? connectivity. Moreover, I elucidated some functional mechanisms of MEMRI, clarifying the interpretation of data obtained with this method and its possible future applications

Dissecting the neuronal basis of threat responding in mice

Environmental threats demand adaptive defensive responses of an organism that ensure its survival. Extreme stressors, however, can unbalance stress homeostasis and lead to long-term changes that impair appropriate defensive behaviors and emotional responses. In my thesis, I assessed (1) the interaction of two stress-related neuromodulatory systems, (2) the effects of a traumatic incident on brain volume and hyperarousal, and (3) sonic vocalization as a defensive behavior in mice, and discussed the topics in three independent studies.In the first study, I evaluated the interaction of two regulatory systems with respect to fear, anxiety, and trauma-related behaviors. Although the endocannabinoid and the corticotropin-releasing factor (CRF) systems are well described in modulating stressrelatedresponses, the direct interaction of both systems remained poorly understood. The generation of a new conditional knockout mouse line that selectively lacked the expression of the cannabinoid type 1 (CB1) receptor in CRF-positive neurons presented no differences in various tests of fear and anxiety-related behaviors under basal conditions or after a traumatic event. Also stress hormone levels were unaffected. However, male knockout animals exhibited a significantly increased acoustic startle response thus suggesting a specific involvement of CB1-CRF interactions in controlling arousal.In the second study, I assessed the consequences of a traumatic experience on behavior and grey matter volume in mice. Whole-brain deformation-based morphometry (DBM) by means of magnetic resonance imaging (MRI) after incubation of a traumatic incident showed changes in the dorsal hippocampus and the reticular nucleus. Using the severity of hyperarousal as regressor for cross-sectional volumetric differences between traumatized mice and controls revealed a negative correlation with the dorsal hippocampus. Further, longitudinal analysis including volumetric measurements before and after the traumatic incident showed that volume reductions in the globus pallidus reflect trauma-related changes in hyperarousal severity.In the third study, I characterized sonic vocalization as a defensive behavior in mice. Mice bred for high anxiety-related behavior (HAB) were found to have a high disposition to emit audible squeaks when taken by the tail which was not the case for any of the other five mouse lines tested. The calls emitted had a fundamental frequency of 3.8 kHz and were shown to be sensitive to anxiolytic but not panicolytic compounds. Manganese-enhanced MRI (MEMRI) scans pointed towards an increased tonic activity, among others, in the periaqueductal grey (PAG). Inhibition of the dorsal PAG by muscimol not only completely abolished sonic vocalization, but also reduced anxiety-like behavior. This suggests that sonic vocalization of mice is related to anxiety and controlled by the PAG. To explore the ecological relevance of defensive vocalization, I performed playback experiments with conspecifics and putative predators. Squeaks turned out to be aversive to HAB mice but became appetitive to both mice and rats when a stimulus mouse was present during playback.Collectively, the results of this thesis provide novel insights into fear and anxiety-related behaviors and shine light onto their mechanistic basis and ecological relevance