10 CIP patients and 12 NPCU were recruited for this study. All patients were recruited at the psychiatric inward of the University Policlinico Hospital of Milan, Italy, whereas the cannabis users were enrolled in the Milan catchment area. All patients were in stable pharmacological treatment. Either left- or right- handed participants were included. All participants had a habitual cannabis consumption and the type of drug, the frequency and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. The study was approved by the local ethical committee.
1. Participants
<ol>
	<li>Use the following inclusion criteria: For patients: age 18-45 years old, DSM-IV diagnosis of Cannabis-induced Psychotic Disorder, heavy cannabis consumption at the time of the study and in the previous 6 months. For NPCU: age 18-45 years old, no DSM-IV diagnosis, heavy cannabis consumption at the time of the study and in the previous 6 months.</li>
	<li>Use the following exclusion criteria: a diagnosis of mental retardation, any current major medical or neurological illness, a history of traumatic head injury with loss of consciousness, and any other Axis I, including alcohol abuse, or Axis II disorders and pregnancy. Verify that psychotic symptoms do not precede the onset of the cannabis use and do not persist for a substantial period of time after the cessation of acute withdrawal or severe intoxication. Verify that there is no history of recurrent nonsubstance-related episodes.</li>
	<li>To obtain informed consent read the consent form to the participants. Have both the participant and the investigator sign the consent form in duplicate. Store the consent form for records.</li>
	<li>To evaluate the diagnosis of CIP patients, use the Structured Clinical Interview for Diagnosis (SCID-I) of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR)27.</li>
	<li>To establish the frequency and the duration of dependency, use the manual for the semistructured clinical interview for children and adolescents SCICA28.</li>
</ol>
2. Clinical and psychosocial evaluation
NOTE: Several clinical and psychosocial scales were administered to all the participants.
<ol>
	<li>To evaluate psychiatric symptoms, use the Brief Psychiatric Rating Scale (BPRS)29, the Young Mania Rating Scale (YMRS)30, the Montgomery-&#197;sberg Depression Rating Scale (MADRS)31, the Hamilton Depression Rating Scale (HAM-D)32 and the Hamilton Anxiety Rating Scale (HAM-A)33.</li>
	<li>To explore the presence of trauma or infection during or immediately after the partum, use the Murray-Lewis Obstetric Complications Scale (MLOCS)34.</li>
	<li>To assess experiences of neglect or abuse, use the Childhood Experience of Care and Abuse Questionnaire (CECA-Q)35.</li>
	<li>To estimate the Socio-economic status (SES), use the Socio Economic Status Scale of MacArthur36.</li>
	<li>Use the Neighbourhood Scale (NS)37 to assess the specific characteristics of the neighbourhood, in terms of neighbourhood satisfaction (NS-A), sense of security (NS-B), level of degradation (NS-C), willingness on the part of fellow citizens to intervene in adverse situations (NS-D), and degree of acceptance of substances (NS-E).</li>
	<li>Employ the Temperament and Character Inventory (TCI-125) for exploring personality traits38,39.</li>
	<li>To assess the quality of life and the global functioning use the Manchester Short Assessment of Quality of Life (MANSA)40 and the Quality of Life Index (QL-index)41 and the Global Assessment of Functioning (GAF)27 scales, respectively. 
	NOTE: All socio-demographic and clinical data are summarized in Table 1.</li>
</ol>
3. Magnetic resonance imaging
<ol>
	<li>Insert the participant in a supine position on the bed of the 3 Tesla MRI scanner.</li>
	<li>Place a radio frequency (RF) coil over the participant&#8217;s head.</li>
	<li>Provide earplugs and headphones to block background noise.</li>
	<li>Attach foam pads to immobilize the head.</li>
	<li>Instruct the subject to remain still.</li>
	<li>Run MRI session from the workstation in the control room.
	<ol>
		<li>Run a 3-plane gradient echo scan for alignment and localization and perform a shim procedure to generate a homogeneous, constant magnetic field.</li>
		<li>Start an echo-planar-imaging protocol for MRI. The acquisition parameters for the acquisition of high-resolution T1-weighted three-dimensional brain scan are already set in the imaging program and should not be changed. The parameters are: repetition time [TR] = 9.8, echo time [TE] = 4.6 ms, in plane voxel size= 0.9375 &#215; 0.9375, matrix= 256 &#215; 256, flip angle = 8&#176;.</li>
	</ol>
	</li>
	<li>Remove the participant from the MR scanner room. Transfer the MR data to disk and close the session. 
	NOTE: A total of 185 contiguous 1 mm sagittal slices extending superiorly from the inferior aspect of the cerebellum to encompass most of the brain were selected from a sagittal localizer scan.</li>
</ol>
4. Pre-processing steps
NOTE: A voxel-based morphometry analysis should be performed using Statistical Parametric Mapping (SPM12) implemented in MATLAB.
<ol>
	<li>Perform the following pre-processing steps, shown in the Script_pre-processing script file, before carrying out group analyses.
	<ol>
		<li>Segmentation: Process the structural image to distinguish and separate the white matter tissues, the grey matter tissues and the cerebrospinal fluid into different images. This separation is obtained thanks to the combination of probability maps, elaborated from the general knowledge of tissue distribution combined with model cluster analyses that identifies voxel distributions of specific tissues in the original image. Run the segment.mat batch file.</li>
		<li>DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) tools: determine the nonlinear deformations for registering the GM and white matter images of all participants. Run the create_template.mat batch file.</li>
		<li>Normalization: during the spatial normalization phase, adapt MRI images to an anatomical standard template. This is because every subject has little differences in the form and organization of the brain such as the size and morphologic differences in structures. Run the normalize_to_MNI.mat batch file.</li>
		<li>Spatial Smoothing: after motion correction, perform an isotropic Gaussian kernel of 6 mm full width at half maximum Gaussian kernel to increase the signal-to-noise ratio and to account for subtle variations in anatomic structures. Run the normalize_to_MNI.mat batch file.</li>
		<li>Extract the total intracranial volume (ICV) using SPM12: it can be obtained by adding up the density values in GM, white matter, and CSF class images and multiplying by the voxel volumes. 
		NOTE: Once the pre-processing is completed, it is possible to elaborate the data. 
		NOTE: Please refer to the SPM manual (https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf) that provides a detailed description of the pre-processing steps employed in this study and the SPM commands to use. Please also refer to the script and Matlab batches included in the supplementary materials with the exact pre-processing steps used for this study.</li>
	</ol>
	</li>
</ol>
5. Statistical analyses
<ol>
	<li>Perform chi-square tests (categorical variables) and two sample t tests (quantitative variables) for exploring differences between the two groups on demographic, clinical and psychosocial scale.</li>
	<li>Perform a one-way Analysis of Variance (ANOVA), in the context of a General Linear Model (GLM) design to compare GM volumes between CIP patients and NPCU. Gender and age were used as controlling variables in all the analyses. Run the one-way ANOVA batch file.</li>
	<li>Carry out whole-brain regression analyses, only for the CIP group, to explore whether the scores in all the clinical and psychosocial scales employed in this study were significantly correlated with GM volumes changes. Do not use any brain mask but consider all voxels. Run the Regression analysis batch file with the clinical scale of interest.</li>
	<li>Convert stereotactic coordinates of the peak maxima of the suprathreshold clusters from the MNI spatial array (www.mni.mcgill.ca) to that of Talairach and Tournoux42. 
	NOTE: In all the neuroanatomical analyses, the volumetric differences among subjects were considered by proportional scaling for the total intracranial volume (ICV).
	<ol>
		<li>For the ANOVA, set the significance threshold to p &#60; 0.001 uncorrected, with a minimum cluster size of k=30, whereas for the multiple regression analyses, a p &#60; 0.05 peak Family-Wise Error (pFWE) corrected was considered significant and a minimum cluster size of k=10 was employed. The former threshold was considered due to the small sample size employed in this study and therefore the results emerged from this analysis must be considered as preliminary. The latter threshold is more stringent since the p-value is corrected for multiple comparisons. 
		NOTE: Please refer to the VBM8 Manual for more details about post-processing steps (http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf). Please also refer to the Matlab batches named &#8220;one-way ANOVA&#8221; and &#8220;Regression analysis&#8221; included in the supplementary materials with the exact model used for this study. Due to the exploratory nature of this study, a formal sample size calculation would have been of little value and therefore it was not performed.</li>
	</ol>
	</li>
</ol>

In the present study, we observed that only the presence of psychotic symptoms discriminated the detection of brain morphological alterations. Indeed, chronic cannabis users with CIP showed decreased GM volumes mainly in the prefronto-temporo-limbic network compared to non-psychotic cannabis users (NPCU). Moreover, regarding the psychometric questionnaires, correlations between the domain BPRS-Activity and selective GM volumes have been highlighted. Specifically, we observed a negative correlation between such BPRS scale and left superior temporal cortex and left cerebellum together with a positive correlation with the cuneus bilaterally, the left inferior occipital gyrus, the right inferior parietal lobule and the right superior prefrontal cortex. However, we should mention that the lack of a control group of healthy subjects with no cannabis dependency prevented us from exploring if cannabis use caused brain alterations or not.
In general, the results are not surprising since previous MRI studies showed that psychotic disorders, such as schizophrenia, shared similar GM abnormalities, especially in frontal and temporo-limbic regions29,30. However, it is still unclear why some chronic cannabis users developed psychotic symptoms while others remained healthy. Indeed, in the sample, we only detected small clinical differences between the two groups and therefore the extensive GM abnormalities observed in the CIP group may not be associated with their specific clinical profile. Specifically, 9 of 10 CIP patients reported a daily cannabis use compared to 7 of 12 in the NPCU group. Moreover, no differences in terms of age, gender, age of onset of cannabis use and educational level were found between the two groups. However, we should consider that this lack of differences could be due to the small sample size that also limited the possibility to statistically analyse and interpret these factors. One hypothesis is that the psychotic process itself is responsible for the decrease in brain volume, regardless cannabis use. Indeed, previous studies showed no GM differences between psychotic patients with and without cannabis consumption, thus finding no clear evidence for cannabis use to be related to GM alterations in first episode psychotic patients45. However, cannabis use may have contributed to brain alterations and subsequently induced psychosis only in a subgroup of susceptible cannabis users.
The first hypothesis is in line with those studies showing brain abnormalities in psychotic disorders. Specifically, the results showed that CIP patients compared to NPCU had extensive GM volume decreases in some brain areas known to be involved in emotional regulation, such as frontotemporal cortices, insula, hippocampus, and fusiform gyrus46. Interestingly, disruptions in these structures, especially in prefrontal regions, might explain the mood instability and greater emotional reactivity in adolescents and young adults, as well as impulsive behaviors and substance-seeking33,34. Indeed, it has been consistently reported that emotion regulation/processing are associated with recruitment of a set of prefrontal brain regions involved in cognitive control over emotional limbic structures. For example, greater difficulties in emotional regulation among tobacco smokers have been associated to a weaker connectivity between inferior frontal gyrus and amygdala compared to non-smokers49. Therefore, it might be plausible that among CIP patients the development of psychotic symptoms was associated with interfered balance between these structures.
Additionally, we observed that the group of CIP patients showed disruptions in the dorsolateral prefrontal cortex (DLPFC), a key region involved in major cognitive functions, including working memory, executive functions50 and emotional regulation51. Indeed, this finding is not surprising since the DLPFC works together with risk-monitoring regions, such as the insula (a key structure of the salience network recently found to be involved in addiction52), which has been also found altered in the group of CIP patients, and anterior cingulate cortex, ultimately suggesting that impairments in distinguishing risky from safe choices may result from a disruption between DLPFC and such risk-monitoring regions53.
Moreover, CIP patients showed a GM volume decrease in the superior temporal cortex. Interestingly, this result is in line with the evidence reported by a previous multimodal neuroimaging study54, which employed a larger sample of CIP patients (N=16), the majority of whom overlap with the sample employed in this study that found extensive GM alteration in temporal cortices in CIP patients. Overall such evidence further confirms the key role of the superior temporal cortex in psychosis, since the involvement of this structure has been consistently reported in abilities often found disrupted in psychotic patients, including language processing and theory of mind abilities39,40. Also, the results aligned with previous evidence reporting the association between volume reductions of this region and auditory hallucination or thought disorders41,42 as well as with a previous MRI study suggesting the disruption of this area in substance dependent individuals compared to healthy controls59.
Finally, a significant GM volume decrease in hippocampus in CIP patients emerged from the results. Such finding is in line with previous evidence showing structural and functional changes in this structure in early psychoses and in at-risk mental state/first-episode psychosis, in comparison to healthy controls60,61,62,63. Normal hippocampal function is required for a number of mental functions including memory and emotional behaviour48,49 and it has been proposed that reduced volume in this structure may represent a marker of a negative clinical outcome in patients with a first-episode psychosis66. However, in contrast to the results, hippocampal deficits have been also reported in young and adult cannabis users, who have been found to have thinner cortices and reduced volumes in this region67,68,69,70. Therefore, a clear picture on the role of the hippocampus in substance abuse has still not be attained. Nonetheless, the results point towards the hypothesis that the cortico-limbic system is compromised in the group of CIP patients, as also suggested by a previous MRI study54 and might explain the emotional elaboration deficits, which has been proposed to be a critical precursor of future psychotic development55,56, often observed in these patients.
Therefore, it seems reasonable to hypothesize that cannabis-induced psychosis is associated with brain alterations in regions within the prefronto-temporo-limbic network, which may therefore represent a common neurodevelopmental substrate of multiple forms of psychosis. Interestingly, longitudinal studies proposed that some brain disruptions, including smaller orbitofrontal cortex volumes73, increased fronto-parietal and decreased visual association regions activation, as well as cognitive deficits, such as poorer executive functions74, may be present even before the initiation of cannabis dependency. Therefore, it might be that these individuals with underlying brain alterations are more likely to develop psychotic symptoms after cannabis use initiation. In addition, there is evidence reporting that possessing risk alleles in AKT1 and DRD2 genes, which are involved in dopamine signalling, is associated with increased risk of developing psychosis after cannabis use3. Therefore, the detection of morphological decreases in CIP patients could reflect an augmented genetic susceptibility to the neurotoxic effect of chronic cannabis use in this group of subjects.
Finally, in the CIP group, the results also showed a negative correlation between a sub-domain of the BPRS, the BPRS-Activity, and selective GM volumes within left superior temporal cortex and left cerebellum. Also, this subscale was positively correlated with cuneus bilaterally, left inferior occipital gyrus, right inferior parietal lobule, and right superior prefrontal cortex. In general, although correlations between clinical symptomatology and GM structures have been extensively reported, especially in schizophrenia75, the results are still heterogeneous, with a mixed picture of inverse76, positive77 or no78 correlations between selective GM volumes and clinical scales. Notably, the negative correlation observed between BPRS-Activity and superior temporal cortex seems to be in line with previous MRI evidence showing inverse correlations between this structure and positive symptom severity76, ultimately further suggesting the key role of this structure in the production of psychotic symptoms. Similarly, the positive correlation found between BPRS-Activity and superior prefrontal cortex seems to agree with other MRI studies reporting a similar correlation between negative symptoms and GM volumes with the prefrontal cortex79.
As a whole, the results of the current study provide preliminary evidence on the presence of significant association between brain alteration and severity of psychopathology.
The current study suffers from some limitations. First, all psychotic patients were taking pharmacological treatments that could have influenced the results. Second, the lack of a control group formed by healthy subjects not exposed to cannabis does not allow a further comparison with the two groups of cannabis users (psychotic and not). In addition, although the two groups were very similar in terms of number of subjects (10 CIP patients vs 12 NPCU), the small sample size limits the significance of the results achieved and therefore must be considered as preliminary. Further limitations are strictly connected to the nature of the population investigated. Indeed, some patients with CIP (6/10) and a rather small proportion of NPCU (3/12) had a lifetime history of other substance consumption (i.e., cocaine, LSD and heroin/methadone). Moreover, we did not examine the genetic alleles linked to addiction, which could have helped to discriminate the two groups. Nonetheless, the cannabis consumption, although assessed in terms of frequency and volume and duration with a specific tool20, was not uniform across the two groups. Finally, in this study we did not explore brain activation and we did not assess the neurocognitive state of the sample. Therefore, the lack of these information could have affected the results since previous studies demonstrated the presence of selective brain dysfunctions in patients with schizophrenia with substance abuse in the medial prefrontal cortex, the orbitofrontal cortex and the amygdala as well as a better premorbid neurocognitive profile with a greater long-term decline compared to the same patients without substance abuse80. Therefore, further functional MRI studies exploring brain activity coupled with neuropsychological assessments on larger samples and with homogenous consumption habits are needed to confirm our results.
According to our results, cannabis-induced psychosis may be characterized by GM volume decreases in selective brain structures. Therefore, in view of the crucial and comprehensive role of the endocannabinoid system in the brain, the increasing prevalence of cannabis use, its chronic use during neurodevelopment, as well as the progressively higher THC concentration in the current market, it seems mandatory to clarify which aspects of cannabis exposure (e.g., age at initiation, quantity, frequency, and duration) determine the greatest risk for the progression towards psychotic-related disorders. However, whether reductions in prefronto-temporo-limbic regions represent a substrate of the psychotic process itself or a direct consequence of cannabis exposure among susceptible subjects remains a complex issue. In this context, the methods employed in the study could be useful to better characterize the neurobiological and clinical features of cannabis-induced psychosis. Finally, longitudinal neuroimaging studies taking into account also potential confounding factors, such as cannabis dose, potency, THC/Cannabidiol ratio, frequency of use, age of onset, familiar history of psychosis, and genetic polymorphisms may provide a potential ground for identifying putative biomarkers which may ultimately help clinicians to detect those cannabis users that are more likely to develop psychosis.

According to the European Monitoring Center for Drugs and Drug Addiction, around 96 million (or 29%) of adults (aged 15-64) in the European Union are estimated to have tried illicit drugs, especially cannabis, during their life. When considering the youngest and most vulnerable part of the general population, an estimated 16% of young adults (aged 15-34) used cannabis in the last year, with a male to female ratio of about 2:11. Importantly, cannabis use seems to lead to the development of psychiatric symptoms in healthy subjects, such as mood alterations, increased anxiety, racing thoughts, distorted perceptions, difficulty in thinking and problem solving, ongoing problems with learning and memory, slow reaction time, and loss of control2. Such signs and symptoms, though, are normally transient and do not outline a psychiatric condition per se or the need of a treatment. However, cannabis, through its principal psychoactive constituent, named tetrahydrocannabinol (THC), can also induce positive psychotic symptoms including suspiciousness, paranoid delusions, disorders of thought processes, and perceptual alterations3, as well as negative symptoms similar to those observed in schizophrenia, such as blunted affect, apathy, avolition, lack of spontaneity, lack of interest, passivity, and cognitive deficits (e.g., memory, executive function, abstract ability, decision making, and attention)3. Therefore, at the present time, there is evidence that cannabis consumption can both induce transient psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk3. However, whether this relationship is causal, or purely correlational, is still controversial and debated4. Indeed, despite epidemiological studies suggesting a relationship between heavy cannabis consumption and risk of psychosis5, the worldwide increased incidence of cannabis use is not accompanied by an augmented incidence of psychosis4. This paradox could be explained by the presence of specific confounding differences between cannabis abusers, with early onset of use, daily assumption of high-potency cannabis, and consumption of synthetic cannabinoids carrying the greatest psychotic risk3. Moreover, some genetic factors, such as the presence of specific catechol-O-methyltransferase (COMT) polymorphisms, may also confer an augmented vulnerability to develop psychotic symptoms after cannabis exposure in a small proportion of users6.
In this regard, human neuroimaging studies attempted to investigate the potential neural mechanisms through which cannabis may lead to psychotic symptoms7, since preclinical studies previously showed that THC is active within brain areas rich in cannabinoid type 1 receptors (CB1R), including hippocampus, amygdala, striatum, and prefrontal cortex (PFC)8. Indeed, experimental THC administration to healthy cannabis users have been shown to attenuate ventrostriatal activation during a learning task and concurrently induce psychotic symptoms9 as well as altered prefrontal-striatal activation during attentional salience processing10. With regard to structural magnetic resonance imaging (MRI) studies, some authors detected significant grey matter (GM) volume reductions in the prefrontal cortex11,12,13, the hippocampus14,15, the amygdala16 and the putamen17 in regular cannabis users compared to nonusers while others did not report any significant brain differences between these two groups18,19,20,21 or reported increased GM volumes within the medial temporal, the amygdala, the hippocampus, the posterior cingulate and the cerebellum among adolescents with low cannabis use22.
Furthermore, few studies explored whether there are any specific brain differences between cannabis users with psychotic symptoms and cannabis users without any psychiatric conditions. One functional MRI study compared healthy subjects who did and did not experience psychotic symptoms after THC consumption and it reported increased activity during a go/no-go task in the right middle temporal gyrus and decreased activity in both parahippocampal and fusiform gyri, which was also associated with greater inhibition errors only in the psychotic group23. In contrast, Epstein and Kumra found that both psychotic and nonpsychotic adolescents with cannabis use disorder shared similar brain alterations; specifically, they detected attenuated cortical thinning in the left superior frontal gyrus, the right pars triangularis, the left pars opercularis, the left and right supramarginal gyri, the left and right inferior parietal cortices and the left superior temporal gyrus in both groups24. In a previous study, the same authors compared adolescents with early onset schizophrenia (EOS) with (EOS+) and without (EOS-) cannabis use disorder (CUD), adolescents with CUD only and healthy controls25 . Interestingly, they detected smaller grey matter volumes in the left superior parietal region in both EOS- and CUD groups compared to healthy controls. However, they did not find additive volumetric alterations in adolescents with EOS+ compared to other groups. Finally, a more recent and larger study found a significant total effect from lifetime cannabis consumption to psychotic-life experiences in a sample of adolescents. Interestingly, the authors found an association between psychotic-life experiences and reduced expansion within the uncus of the right hippocampus/parahippocampus26.
Therefore, these studies, although not all concordant, suggest that cannabis-induced psychosis may be characterized by neurobiological deficits, similar to those detected in pure psychotic disorders. However, whether brain alterations determined by cannabis dependency and highlighted by neuroimaging investigations will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life still remains unclear. In this regard, the investigation of brain morphology among psychotic cannabis users in comparison with cannabis users without any psychiatric symptoms could be of paramount importance in order to understand the neurobiological underpinnings of cannabis-induced psychosis. However, to the best of our knowledge, so far no studies have compared cannabis-induced psychotic subjects with healthy cannabis users in terms of brain structural morphology and clinical parameters, such as psychopathology, frequency and duration of dependency, quality of life, personality traits, childbirth complication and childhood abuse. In this context, the aim of this study is to investigate morphological brain differences between chronic cannabis users with substance-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) and to correlate brain deficits with selective socio-demographic, clinical and psychosocial variables. We hypothesized that CIP patients will show significant reductions in GM volumes compared to NPCU as well as possible correlations between GM volumes and socio-demographic, clinical and psychosocial scales.

<table>
	<tbody>
		<tr>
			<td>Not applicable</td>
			<td>Not applicable</td>
			<td>Not applicable</td>
			<td>Not applicable</td>
		</tr>
	</tbody>
</table>

brain morphology of cannabis users with or without psychosis: a pilot mri study

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk. Previous studies suggest that chronic and long-term cannabis exposure may exert significant negative effects in brain areas enriched with cannabinoid receptors. However, whether brain alterations determined by cannabis dependency will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life remains unclear. The aim of this study was to investigate morphological brain differences between chronic cannabis users with cannabis-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) without any psychiatric conditions and correlate brain deficits with selective socio-demographic, clinical and psychosocial variables.
3T magnetic resonance imaging (MRI) scans of 10 CIP patients and 12 NPCU were acquired. The type of drug, the frequency, and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. CIP patients had extensive grey matter (GM) decreases in right superior frontal gyrus, right precentral, right superior temporal gyrus, insula bilaterally, right precuneus, right medial occipital gyrus, right fusiform gyrus, and left hippocampus in comparison to chronic cannabis users without psychosis. Finally, in CIP patients, the results showed a negative correlation between a domain of the Brief Psychiatric Rating Scale (BPRS), BPRS-Activity, and selective GM volumes. Overall, the results suggest that cannabis-induced psychosis is characterized by selective brain reductions that are not present in NPCU. Therefore, neuroimaging studies may provide a potential ground for identifying putative biomarkers associated with the risk of developing psychosis in cannabis users.

Socio-demographic, clinical and psychosocial results 
There were no differences in terms of gender (&#967;2 =0.6, p=0.4), age (t=-0.21; p=0.83), age of onset of dependency (t=-0.79; p=0.44) and educational level (t=1.21; p=0.24) between CIP patients and NPCU. However, some differences between the two groups were observed in one temperament dimension (Harm Avoidance, t=3.71; p=0.001) and one-character dimension (Self-Transcendence, t=2.94; p=0.008) of the TCI where CIP patients showed higher scores compared to NPCU. Finally, NPCU also showed higher scores compared to CIP patients in one sub-dimension of the Neighborhood Scale (NS-E) (t=-3.55; p=0.002), in the SES total (t=-2.13; p=0.046), in the Quality of Life-Index (t=-8.1; p=0.0001), in the GAF (t=-4.71; p=0.0001) and in one character dimension of the TCI (Self Directedness, t=-3.97; p=0.001).
Specifically, for CIP, the frequency of cannabis dependency was daily for 9 subjects (90%) and several times a week for 1 subject (10%). Instead, the frequency of cannabis dependency in the NPCU group was daily for 7 subjects (60%), several times a week for 4 subjects (30%), and multiple times a month for 1 subject (10%). The mean age of onset of dependency was at 18 years old for CIP patients and at 16 years old for the NPCU group. Although all participants were taking cannabis, some CIP patients (N=6) and NPCU (N=3) also reported previous use of other drugs, including cocaine, LSD and heroin/methadone, but with lower frequency than cannabis. The frequency of cannabis use did not differ between the two groups (&#967;2=1.69, p=0.42). Moreover, no statistical difference in type and frequency of cocaine, heroin/methadone and LSD use was observed between the two groups (cocaine: &#967;2=0.06, p=0.79 and &#967;2=4.1, p=0.39; heroin/methadone: &#967;2=1.2, p=0.26 and &#967;2=1.2, p=0.26; LSD: &#967;2=0.01, p=0.89 and &#967;2=2.0, p=0.36). Although we are aware that the presence of poly-consumption in the sample might have negatively affected the generalizability of the findings, it is important to highlight that the use of other drugs was very limited compared to cannabis use. Indeed, in contrast to cannabis use, the consumption of other drugs was lifetime and not occurring during the time of the study. Nonetheless, our results should be taken cautiously and need to be replicated in a more homogeneous sample.
VBM results 
VBM analysis showed that CIP patients had extensive GM decreases compared to NPCU in right superior frontal gyrus ((Brodmann area [BA] 10), right precentral (BA 4) , right superior temporal gyrus (BA 22), insula bilaterally (BA13), right precuneus (BA7), right medial occipital gyrus (BA 19), right fusiform gyrus (BA 37) and left hippocampus (p &#60; 0.001 uncorrected; Table 2 and Figure 1). No GM differences were observed in NPCU compared to CIP patients.
Correlations between GM regions and clinical scales 
In CIP patients, the results showed a negative correlation between a domain of the BPRS, BPRS-Activity, and selective GM volumes within left superior temporal cortex (BA 38, x=-40 y=17 z=-35, z=5.9, cluster size=19) and left cerebellum (x=-12 y=-36 z=-20, z=6.1, cluster size=18). Moreover, the same scale was positively correlated with cuneus bilaterally (BA 18; left: x=-9 y=-90 z=9, z=7.0, cluster size=24; right: x=15 y=-85 z=24, z=7.3, cluster size=13), left inferior occipital gyrus (BA 17; x=-9 y=-88 z=-6, z=7.4, cluster size=34), right inferior parietal lobule (BA 40; x=58 y=-35 z=22, z=6.7, cluster size=33), right superior prefrontal cortex (BA 9; x=3 y=51 z=29, z=6.2, cluster size=23) (all p&#60;0.05 pFWE corrected). No significant correlations in any of the other clinical scales were observed in CIP patients.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60881/60881fig01.jpg" /> 
Figure 1: Regions with significant GM difference between Substance-induced psychosis (CIP) patients and non-psychotic cannabis users (p&#60;0.001, uncorrected, k=30). <a href="https://www.jove.com/files/ftp_upload/60881/60881fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>CIP patients</td>
			<td>Non-psychotic cannabis users</td>
			<td rowspan="2">Statistics</td>
			<td rowspan="2">p-value</td>
		</tr>
		<tr>
			<td>n=10</td>
			<td>n=12</td>
		</tr>
		<tr>
			<td>Age, mean (SD)</td>
			<td>27 (9.21)</td>
			<td>26 (0.89)</td>
			<td>t= -0.213</td>
			<td>p=0.833</td>
		</tr>
		<tr>
			<td>Sex, male/female</td>
			<td>8/2</td>
			<td>11/1</td>
			<td>&#967;2=0.630</td>
			<td>p=0.427</td>
		</tr>
		<tr>
			<td>Age of onset of cannabis use, mean (SD)</td>
			<td>18 (9.69)</td>
			<td>16 (1.83)</td>
			<td>t= -0.786</td>
			<td>p=0.441</td>
		</tr>
		<tr>
			<td rowspan="7">Type (N); frequency of other drug use</td>
			<td>Cannabis (N=10); Daily (N=9), multiple times a week (N=1).</td>
			<td>Cannabis (N=12); Daily (N=7), multiple times a week (N=4), multiple times a month (N=1).</td>
			<td>Frequency: &#967;2=1.69,</td>
			<td>p=0.42</td>
		</tr>
		<tr>
			<td rowspan="2">Cocaine (N=4); multiple times a week (N=2), multiple times a month (N=2).</td>
			<td rowspan="2">Cocaine (N=3); multiple times a week (N=1), multiple times a month (N=1), less than one a month (N=1).</td>
			<td>Type: &#967;2=0.06</td>
			<td>Type: p=0.79</td>
		</tr>
		<tr>
			<td>Frequency: &#967;2=4.1</td>
			<td>Frequency: p=0.39</td>
		</tr>
		<tr>
			<td rowspan="2">Heroin/Methadone (N=1); multiple times a week.</td>
			<td rowspan="2">No Heroin/Methadone users.</td>
			<td>Type: &#967;2=1.2</td>
			<td>Type: p=0.26</td>
		</tr>
		<tr>
			<td>Frequency: &#967;2=1.2</td>
			<td>Frequency: p=0.26</td>
		</tr>
		<tr>
			<td rowspan="2">LSD (N=1); less than one a month.</td>
			<td rowspan="2">LSD (N=1); multiple times a month.</td>
			<td>Type: &#967;2=0.01</td>
			<td>Type: p=0.89</td>
		</tr>
		<tr>
			<td>Frequency: &#967;2=2.0</td>
			<td>Frequency: p=0.36</td>
		</tr>
		<tr>
			<td>Age of onset, mean (SD)</td>
			<td>25 (8.46)</td>
			<td>-</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>BPRS TOT, mean (SD)</td>
			<td>43 (9)</td>
			<td>20 (3)</td>
			<td>t=8.860</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Anxiety-Depression</td>
			<td>10 (5)</td>
			<td>6 (2)</td>
			<td>t=2.629</td>
			<td>p=0.016</td>
		</tr>
		<tr>
			<td>Anergia</td>
			<td>8 (3)</td>
			<td>4 (1)</td>
			<td>t=3.284</td>
			<td>p=0.004</td>
		</tr>
		<tr>
			<td>Thought Disorders</td>
			<td>12 (3)</td>
			<td>4 (0)</td>
			<td>t=9.754</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td>Activity</td>
			<td>6 (2)</td>
			<td>3 (0)</td>
			<td>t=4.557</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td>Hostility- Suspiciousness</td>
			<td>8 (4)</td>
			<td>3 (0)</td>
			<td>t=4.053</td>
			<td>p=0.001</td>
		</tr>
		<tr>
			<td>HAM-D, mean (SD)</td>
			<td>11 (6.42)</td>
			<td>4 (4.96)</td>
			<td>t=3.258</td>
			<td>p=0.004</td>
		</tr>
		<tr>
			<td>HAM-A, mean (SD)</td>
			<td>11 (6.62)</td>
			<td>3 (3.93)</td>
			<td>t=3.487</td>
			<td>p=0.002</td>
		</tr>
		<tr>
			<td>MADRS, mean (SD)</td>
			<td>14 (7.76)</td>
			<td>6 (6.35)</td>
			<td>t=2.635</td>
			<td>p=0.016</td>
		</tr>
		<tr>
			<td>YMRS, mean (SD)</td>
			<td>13 (7.92)</td>
			<td>0 (1.44)</td>
			<td>t=5.378</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td>CECA-Q, mean (SD)</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CECA-QMA</td>
			<td>13 (5.20)</td>
			<td>13 (3.89)</td>
			<td>t=-0.069</td>
			<td>p=0.946</td>
		</tr>
		<tr>
			<td>CECA-QMN</td>
			<td>19 (5.83)</td>
			<td>19 (4.64)</td>
			<td>t=-0.284</td>
			<td>p=0.779</td>
		</tr>
		<tr>
			<td>CECA-QPA</td>
			<td>14 (6.44)</td>
			<td>14 (5.56)</td>
			<td>t=-0.130</td>
			<td>p=0.990</td>
		</tr>
		<tr>
			<td>CECA-QPN</td>
			<td>24 (11.69)</td>
			<td>24 (7.12)</td>
			<td>t=0.070</td>
			<td>p=0.945</td>
		</tr>
		<tr>
			<td>Neighbourhood scale*, mean (SD)</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>NS- A</td>
			<td>9 (1.78)</td>
			<td>8 (2.23)</td>
			<td>t=0.782</td>
			<td>p=0.443</td>
		</tr>
		<tr>
			<td>NS- B</td>
			<td>6 (2.50)</td>
			<td>7 (1.56)</td>
			<td>t=-1.070</td>
			<td>p=0.298</td>
		</tr>
		<tr>
			<td>NS- C</td>
			<td>9 (5.87)</td>
			<td>10 (7.66)</td>
			<td>t=-0.265</td>
			<td>p=0.794</td>
		</tr>
		<tr>
			<td>NS-D</td>
			<td>6 (2.31)</td>
			<td>5 (1.53)</td>
			<td>t=1.378</td>
			<td>p=0.183</td>
		</tr>
		<tr>
			<td>NS-E</td>
			<td>3 (1.35)</td>
			<td>4 (0.29)</td>
			<td>t=-3.546</td>
			<td>p=0.002</td>
		</tr>
		<tr>
			<td>SES** total, mean (SD)</td>
			<td>33.6 (12.60)</td>
			<td>45.3 (13.05)</td>
			<td>t=-2.132</td>
			<td>p=0.046</td>
		</tr>
		<tr>
			<td>Study</td>
			<td>11.3 (4.22)</td>
			<td>15.3 (5.93)</td>
			<td>t=-1.800</td>
			<td>p=0.087</td>
		</tr>
		<tr>
			<td>Occupation</td>
			<td>22.3 (10.39)</td>
			<td>30.0 (8.79)</td>
			<td>t=-1.885</td>
			<td>p=0.074</td>
		</tr>
		<tr>
			<td>QL - Index, mean (SD)</td>
			<td>6 (1.65)</td>
			<td>10 (0.62)</td>
			<td>t=-8.098</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td>GAF, mean (SD)</td>
			<td>58 (15.21)</td>
			<td>83 (9.68)</td>
			<td>t=-4.715</td>
			<td>p=0.0001</td>
		</tr>
		<tr>
			<td>MANSA, mean (SD)</td>
			<td>54 (14.16)</td>
			<td>61 (6.01)</td>
			<td>t=-1.250</td>
			<td>p=0.226</td>
		</tr>
		<tr>
			<td>TCI, mean (SD)</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>TCI Ns</td>
			<td>59.92 (10.75)</td>
			<td>55.95 (12.86)</td>
			<td>t=0.173</td>
			<td>p=0.864</td>
		</tr>
		<tr>
			<td>TCI Ha</td>
			<td>55.67 (7.71)</td>
			<td>45.61 (5.68)</td>
			<td>t=3.708</td>
			<td>p=0.001</td>
		</tr>
		<tr>
			<td>TCI Rd</td>
			<td>48.67 (10.41)</td>
			<td>50.49 (9.02)</td>
			<td>t=-0.668</td>
			<td>p=0.512</td>
		</tr>
		<tr>
			<td>TCI P</td>
			<td>49.82 (11.49)</td>
			<td>39.32 (8.83)</td>
			<td>t=2.033</td>
			<td>p=0.056</td>
		</tr>
		<tr>
			<td>TCI Sd</td>
			<td>28.64 (11.85)</td>
			<td>49.89 (7.42)</td>
			<td>t=-3.969</td>
			<td>p=0.001</td>
		</tr>
		<tr>
			<td>TCI Co</td>
			<td>42.15 (12.21)</td>
			<td>49.07 (5.60)</td>
			<td>t=-1.430</td>
			<td>p=0.168</td>
		</tr>
		<tr>
			<td>TCI St</td>
			<td>65.56 (12.34)</td>
			<td>50.82 (8.16)</td>
			<td>t=2.940</td>
			<td>p=0.008</td>
		</tr>
	</tbody>
</table>
Table 1: Socio-demographic, clinical and psychosocial variables of the whole sample. BPRS (Brief Psychiatric Rating Scale); CECA-Q (Childhood Experience of Care and Abuse Questionnaire); CIP (Cannabis-Induced Psychosis); GAF (Global Assessment of Functioning); HAM-A (Hamilton Anxiety Rating Scale); MADRS (Montgomery-Asberg Depression Rating Scale); HAM-D (Hamilton Depression Rating Scale); MANSA (Manchester Short Assessment of Quality of Life); NS-A (Neighbourhood satisfaction); NS-B (Feelings of safety); NS-C (Neighbourhood incivilities); NS-D (Collective efficacy); NS-E (Cannabis acceptance); SD (Standard Deviation); SES (Socio Economic Status); QL-Index (Quality of Life-Index); ); TCI (Temperament and Character Inventory); TCI Ns (Novelty Seeking); TCI Ha (Harm Avoidance); TCI Rd (Reward Dependence); TCI P (Persistence); TCI Sd (Self Directedness); TCI Co (Cooperativeness); TCI St (Self Transcendence); YMRS (Young Mania Rating Scale). * NS-A ranges from 0 to 16, where 16 represented extreme satisfaction with the area of residence; NS-B ranges from 0 to 8, where 8 represented a strong feeling of safety; NS-C ranges from 0 to 32, where 32 indicated a high level of incivilities; NS-D ranges from 0 to 12, where 8 represented a high level of collective efficacy amongst neighbours; NS-E ranges from &#8216;agree strongly&#8217; (score of 4) to &#8216;disagree strongly&#8217; (score of 0). ** Lower levels of schooling are associated to lower scores while higher levels of schooling are associated to higher scores (ie. Less than 7th grade = 3; Graduate degree= 21). Similarly, Occupations with lower cognitive engagement are associated to lower scores, while occupations requiring more cognitive resources are associated to higher scores (Farm worker= 5; Physician= 45).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Gyrus</td>
			<td rowspan="2">BA</td>
			<td rowspan="2">Laterality</td>
			<td colspan="3">MNI coordinates</td>
			<td rowspan="2">Cluster size</td>
			<td rowspan="2">z-values</td>
			<td rowspan="2">Cohen&#8217;s d effect size</td>
		</tr>
		<tr>
			<td colspan="3">x y z</td>
		</tr>
		<tr>
			<td colspan="8">CIP patients &#60; non-psychotic cannabis users</td>
			<td></td>
		</tr>
		<tr>
			<td>Superior Frontal</td>
			<td>10</td>
			<td>Right</td>
			<td>13</td>
			<td>65</td>
			<td>22</td>
			<td>38</td>
			<td>3.4</td>
			<td>-1,26</td>
		</tr>
		<tr>
			<td>Precentral</td>
			<td>4</td>
			<td>Right</td>
			<td>59</td>
			<td>-5</td>
			<td>26</td>
			<td>61</td>
			<td>3.8</td>
			<td>-0,83</td>
		</tr>
		<tr>
			<td>Superior Temporal</td>
			<td>22</td>
			<td>Right</td>
			<td>62</td>
			<td>-7</td>
			<td>3</td>
			<td>146</td>
			<td>4.2</td>
			<td>-0,60</td>
		</tr>
		<tr>
			<td>Insula</td>
			<td>13</td>
			<td>Right</td>
			<td>36</td>
			<td>-21</td>
			<td>13</td>
			<td>142</td>
			<td>4.1</td>
			<td>-0,43</td>
		</tr>
		<tr>
			<td>Insula</td>
			<td>13</td>
			<td>Left</td>
			<td>-33</td>
			<td>-23</td>
			<td>14</td>
			<td>32</td>
			<td>3.8</td>
			<td>-0,46</td>
		</tr>
		<tr>
			<td>Precuneus</td>
			<td>7</td>
			<td>Right</td>
			<td>6</td>
			<td>-66</td>
			<td>50</td>
			<td>41</td>
			<td>3.7</td>
			<td>-0,51</td>
		</tr>
		<tr>
			<td>Medial Occipital</td>
			<td>19</td>
			<td>Right</td>
			<td>33</td>
			<td>-86</td>
			<td>21</td>
			<td>80</td>
			<td>4</td>
			<td>-0,84</td>
		</tr>
		<tr>
			<td>Fusiform</td>
			<td>37</td>
			<td>Left</td>
			<td>-25</td>
			<td>-47</td>
			<td>-8</td>
			<td>32</td>
			<td>3.7</td>
			<td>-0,29</td>
		</tr>
		<tr>
			<td>Hippocampus</td>
			<td>-</td>
			<td>Left</td>
			<td>-33</td>
			<td>-22</td>
			<td>-5</td>
			<td>36</td>
			<td>3.8</td>
			<td>-0,68</td>
		</tr>
		<tr>
			<td colspan="8">Non-psychotic cannabis users &#60; CIP patients</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="8">No suprathreshold clusters</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2: VBM results. Brain regions showing significant reduced grey matter volumes between the CIP patients and non-psychotic cannabis users (P&#60; 0.001 uncorrected). BA (Brodmann area); CIP (Cannabis-Induced Psychosis); MNI (Montreal Neurological Institute)

Watch this Scientific Journal Video about Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study at JoVE.com

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and ...

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6.3K 조회수.  University of Milan. This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

비디오: Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Targeting of Deep Brain Structures with Microinjections for Delivery of Drugs, Viral Vectors, or Cell Transplants

Microinjections into the brain parenchyma are important procedures to deliver drugs, viral vectors or cell transplants.  The brain lesion that an injecting needle produces during its trajectory is a major concern especially in the mouse brain for not only the brain is small but also sometimes multiple injections are needed.  We show here a method to produce glass capillary needles with a 50-&mu;m lumen which significantly reduces the brain damage and allows a precise targeting into the rodent brain. This method allows a delivery of small volumes (from 20 to 100 nl), reduces bleeding risks, and minimizes passive diffusion of drugs into the brain parenchyma. By using different size of capillary glass tubes, or changing the needle lumen, several types of substances and cells can be injected. Microinjections with a glass capillary tube represent a significant improvement in injection techniques and deep brain targeting with minimal collateral damage in small rodents.

In this article, we show a method to make glass capillary needles with a 50-&mu;m lumen. This technique significantly reduces the brain damage, minimizes passive diffusion of drugs and allows a precise targeting into the rodent brain.

마약, 바이러스성 벡터, 또는 세포 이식 전달 Microinjections 깊은 뇌 구조의 타겟팅

Microinjections into the brain parenchyma are important procedures to deliver drugs, viral vectors or cell transplants.  The brain lesion that an ...

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Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

초음파 혈액 - 뇌 장벽 파괴 및 망간 향상된 MRI를 사용하여 기능성 Neuroimaging

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow for an in-depth characterization to identify pathways involved in these events. Cerebellar granule neurons (CGNs) that are derived from the developing cerebellum are an ideal model system that allows for morphological analyses. Here, we describe a method of how to genetically manipulate CGNs and how to study axono- and dendritogenesis of individual neurons. With this method the effects of RNA interference, overexpression or small molecules can be compared to control neurons. In addition, the rodent cerebellar cortex is an easily accessible in vivo system owing to its predominant postnatal development. We also present an in vivo electroporation technique to genetically manipulate the developing cerebella and describe subsequent cerebellar analyses to assess neuronal morphology and migration.

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Neuronal morphogenesis and migration are crucial events underlying proper brain development. Here, we describe methods to genetically manipulate cultured cerebellar granule neurons and the developing cerebellum for the assessment of morphology and migratory characteristics of neurons.

소뇌 과립 신경 세포의 유전자 조작<em&gt; 체외</em&gt;와<em&gt; 생체</em&gt; 신경 세포의 형태와 마이그레이션을 연구하는

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow ...

The Olfactory System as a Model to Study Axonal Growth Patterns and Morphology In Vivo

The olfactory system has the unusual capacity to generate new neurons throughout the lifetime of an organism. Olfactory stem cells in the basal portion of the olfactory epithelium continuously give rise to new sensory neurons that extend their axons into the olfactory bulb, where they face the challenge to integrate into existing circuitry. Because of this particular feature, the olfactory system represents a unique opportunity to monitor axonal wiring and guidance, and to investigate synapse formation. Here we describe a procedure for in vivo labeling of sensory neurons and subsequent visualization of axons in the olfactory system of larvae of the amphibian Xenopus laevis. To stain sensory neurons in the olfactory organ we adopt the electroporation technique. In vivo electroporation is an established technique for delivering fluorophore-coupled dextrans or other macromolecules into living cells. Stained sensory neurons and their axonal processes can then be monitored in the living animal either using confocal laser-scanning or multiphoton microscopy. By reducing the number of labeled cells to few or single cells per animal, single axons can be tracked into the olfactory bulb and their morphological changes can be monitored over weeks by conducting series of in vivo time lapse imaging experiments. While the described protocol exemplifies the labeling and monitoring of olfactory sensory neurons, it can also be adopted to other cell types within the olfactory and other systems.

The Olfactory System as a Model to Study Axonal Growth Patterns and Morphology In Vivo

We describe a protocol for in vivo labeling of olfactory sensory neurons by electroporation and subsequent confocal laser-scanning or multiphoton microscopy to visualize neuronal morphology and its development over time.

모델로 후각 시스템은 축삭 성장 패턴 및 형태를 연구하는<em&gt; 생체</em

The olfactory system has the unusual capacity to generate new neurons throughout the lifetime of an organism. Olfactory stem cells in the basal portion of ...

Stripe Assay to Study the Attractive or Repulsive Activity of a Protein Substrate Using Dissociated Hippocampal Neurons

Growing axons develop a highly motile structure at their tip, termed the growth cone. The growth cone contacts extracellular environmental cues to navigate axonal growth. Netrin, slit, semaphorin, and ephrins are known guidance molecules that can attract or repel axons upon binding to receptors and co-receptors on the axon. The activated receptors initiate various signaling molecules in the growth cone that alter the structure and movement of the neuron. Here, we describe the detailed protocol for a stripe assay to assess the ability of a guidance molecule to attract or repel neurons. In this method, dissociated hippocampal neurons from E15.5 mice are cultured on laminin-coated dishes processed with alternating stripes of ectodomain of fibronectin and leucine-rich transmembrane protein-2 (FLRT2) and control immunoglobulin G (IgG) fragment crystallizable region (Fc) protein. Both axons and cell bodies were strongly repelled from the FLRT2-coated stripe regions after 24 h of culture. Immunostaining with tau1 showed that ~90% of the neurons were distributed on the Fc-coated stripes compared to the FLRT2-Fc-coated stripes (~10%). This result indicates that FLRT2 has a strong repulsive effect on these neurons. This powerful method is applicable not only for primary cultured neurons but also for a variety of other cells, such as neuroblasts.

Axon guidance molecules regulate neuronal migration and targeted growth-cone navigation. We present a powerful method, the stripe assay, to assess the ability of guidance molecules to attract or repulse neurons. In this protocol, we demonstrate the stripe assay by showing FLRT2's ability to repel cultured hippocampal neurons.

스트라이프 분석은 해리 해마 신경 세포를 사용하여 단백질 기판의 매력적인 또는 혐오감 활동을 연구하는

Growing axons develop a highly motile structure at their tip, termed the growth cone. The growth cone contacts extracellular environmental cues to ...

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.

The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.

3D 인쇄 기술을 활용하여 조직학과 MRI를 병합하기 : 두뇌를위한 프로토콜 단면

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume ...

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

Whole-cell patch clamp is the gold-standard method to measure the electrical properties of single cells. However, the in vitro patch clamp remains a challenging and low-throughput technique due to its complexity and high reliance on user operation and control. This manuscript demonstrates an image-guided automatic patch clamp system for in vitro whole-cell patch clamp experiments in acute brain slices. Our system implements a computer vision-based algorithm to detect fluorescently labeled cells and to target them for fully automatic patching using a micromanipulator and internal pipette pressure control. The entire process is highly automated, with minimal requirements for human intervention. Real-time experimental information, including electrical resistance and internal pipette pressure, are documented electronically for future analysis and for optimization to different cell types. Although our system is described in the context of acute brain slice recordings, it can also be applied to the automated image-guided patch clamp of dissociated neurons, organotypic slice cultures, and other non-neuronal cell types.

This protocol describes how to conduct automatic image-guided patch-clamp experiments using a system recently developed for standard in vitro electrophysiology equipment.

뇌 조각에서 뉴런 연구를위한 자동 이미지 유도 패치 클램프의 적용

Whole-cell patch clamp is the gold-standard method to measure the electrical properties of single cells. However, the in vitro patch clamp remains a ...

A Human Blood-Brain Interface Model to Study Barrier Crossings by Pathogens or Medicines and Their Interactions with the Brain

The early screening of nervous system medicines on a pertinent and reliable in cellulo BBB model for their penetration and their interaction with the barrier and the brain parenchyma is still an unmet need. To fill this gap, we designed a 2D in cellulo model, the BBB-Minibrain, by combining a polyester porous membrane culture insert human BBB model with a Minibrain formed by a tri-culture of human brain cells (neurons, astrocytes and microglial cells). The BBB-Minibrain allowed us to test the transport of a neuroprotective drug candidate (e.g., Neurovita), through the BBB, to determine the specific targeting of this molecule to neurons and to show that the neuroprotective property of the drug was preserved after the drug had crossed the BBB. We have also demonstrated that BBB-Minibrain constitutes an interesting model to detect the passage of virus particles across the endothelial cells barrier and to monitor the infection of the Minibrain by neuroinvasive virus particles. The BBB-Minibrain is a reliable system, easy to handle for researcher trained in cell culture technology and predictive of the brain cells phenotypes after treatment or insult. The interest of such in cellulo testing would be twofold: introducing derisking steps early in the drug development on the one hand and reducing the use of animal testing on the other hand.

Here we present a protocol describing the setting of an in cellulo BBB (Blood brain barrier)-Minibrain polyester porous membrane culture insert system in order to evaluate the transport of biomolecules or infectious agents across a human BBB and their physiological impact on the neighboring brain cells.

병원 체 또는 의약품 및 두뇌와 그들의 상호 작용에 의해 장벽 횡단 연구를 인간의 혈액-뇌 인터페이스 모델

The early screening of nervous system medicines on a pertinent and reliable in cellulo BBB model for their penetration and their interaction with the ...

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous deoxyhemoglobin as an endogenous contrast agent to detect changes in blood-level-dependent oxygenation (BOLD effect). We combined fMRI with a novel robotic device (MR-compatible hand-induced robotic device [MR_CHIROD]) so that a person in the scanner can execute a controlled motor task, hand-squeezing, which is a very important hand movement to study in neurological motor disease. We employed parallel imaging (generalized auto-calibrating partially parallel acquisitions [GRAPPA]), which allowed higher spatial resolution resulting in increased sensitivity to BOLD. The combination of fMRI with the hand-induced robotic device allowed precise control and monitoring of the task that was executed while a participant was in the scanner; this may prove to be of utility in rehabilitation of hand motor function in patients recovering from neurological deficits (e.g., stroke). Here we outline the protocol for using the current prototype of the MR_CHIROD during an fMRI scan.

We performed functional MRI using a novel MRI-compatible hand-induced robotic device to evaluate its utility for monitoring hand motor function in individuals recovering from neurological deficits.

손 그립 적자에서 회복하는 개인의 재활을 평가하기 위해 새로운 MRI 호환 손 유도 로봇 장치와 함께 기능MRI

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous ...

Combining Multiplex Fluorescence In Situ Hybridization with Fluorescent Immunohistochemistry on Fresh Frozen or Fixed Mouse Brain Sections

Fluorescent in situ hybridization (FISH) is a molecular technique that identifies the presence and spatial distribution of specific RNA transcripts within cells. Neurochemical phenotyping of functionally identified neurons usually requires concurrent labelling with multiple antibodies (targeting protein) using immunohistochemistry (IHC) and optimization of in situ hybridization (targeting RNA), in tandem. A &#34;neurochemical signature&#34; to characterize particular neurons may be achieved however complicating factors include the need to verify FISH and IHC targets before combining the methods, and the limited number of RNAs and proteins that may be targeted simultaneously within the same tissue section.
Here we describe a protocol, using both fresh frozen and fixed mouse brain preparations, which detects multiple mRNAs and proteins in the same brain section using RNAscope FISH followed by fluorescence immunostaining, respectively. We use the combined method to describe the expression pattern of low abundance mRNAs (e.g., galanin receptor 1) and high abundance mRNAs (e.g., glycine transporter 2), in immunohistochemically identified brainstem nuclei.
Key considerations for protein labelling downstream of the FISH assay extend beyond tissue preparation and optimization of FISH probe labelling. For example, we found that antibody binding and labelling specificity can be detrimentally affected by the protease step within the FISH probe assay. Proteases catalyze hydrolytic cleavage of peptide bonds, facilitating FISH probe entry into cells, however they may also digest the protein targeted by the subsequent IHC assay, producing off target binding. The subcellular location of the targeted protein is another factor contributing to IHC success following FISH probe assay. We observed IHC specificity to be retained when the targeted protein is membrane bound, whereas IHC targeting cytoplasmic protein required extensive troubleshooting. Finally, we found handling of slide-mounted fixed frozen tissue more challenging than fresh frozen tissue, however IHC quality was overall better with fixed frozen tissue, when combined with RNAscope.

Combining Multiplex Fluorescence In Situ Hybridization with Fluorescent Immunohistochemistry on Fresh Frozen or Fixed Mouse Brain Sections

This protocol describes a method for combining fluorescence in situ hybridization (FISH) and fluorescence immunohistochemistry (IHC) in both fresh frozen and fixed mouse brain sections, with the goal of achieving multilabel FISH and fluorescence IHC signal. IHC targeted cytoplasmic and membrane attached proteins.

Multiplex Fluorescence In Situ Hybridization with Fluorescent Immunohistochemistry on Fresh Frozen or Fixed Mouse Brain Section(신선 냉동 또는 고정 마우스 뇌 절편에서 Multiplex Fluorescence In Situ Hybridization과 형광 면역조직화학의 결합)

Fluorescent in situ hybridization (FISH) is a molecular technique that identifies the presence and spatial distribution of specific RNA transcripts within ...