Dysregulation of specialized delay/interference-dependent working memory following loss of dysbindin-1A in schizophrenia-related phenotypes

Dysbindin-1, a protein that regulates aspects of early and late brain development, has been implicated in the pathobiology of schizophrenia. As the functional roles of the three major isoforms of dysbindin-1, (A, B, and C) remain unknown, we generated a novel mutant mouse, dys-1A -/-, with selective loss of dysbindin-1A and investigated schizophrenia-related phenotypes in both males and females. Loss of dysbindin-1A resulted in heightened initial exploration and disruption in subsequent habituation to a novel environment, together with heightened anxiety-related behavior in a stressful environment. Loss of dysbindin-1A was not associated with disruption of either long-term (olfactory) memory or spontaneous alternation behavior. However, dys-1A -/-showed enhancement in delay-dependent working memory under high levels of interference relative to controls, ie, impairment in sensitivity to the disruptive effect of such interference. These findings in dys-1A -/-provide the first evidence for differential functional roles for dysbindin-1A vs dysbindin-1C isoforms among phenotypes relevant to the pathobiology of schizophrenia. Future studies should investigate putative sex differences in these phenotypic effects

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