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.remotexs.ntu.edu.sg/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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6.4K 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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

신경 영상 데이터를 사용하여 질병과 관련된 공간 공분산 패턴의 식별

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

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의학

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

병변 탐색기를 정확하고 신뢰할 수있는 MRI 유도 알츠하이머 병의 볼륨 측정 및 일반 노인을위한 영상 유도, 표준화 된 프로토콜

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (&#62;3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice&#39;s kappa metric. Mean Dice&#39;s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

높은 해상도<em&gt; 생체</em&gt; 3T 자기 공명 영상을 이용하여 인간의 해마 서브 필드에 대한 수동 분할 프로토콜

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

신경과학

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of structural MRI in vivo is ultimately limited by physiological noise, including pulsation, respiration and head movement. Although imaging hardware continues to improve, it is still difficult to resolve structures on the millimeter scale. For example, the primary visual sensory pathways synapse at the lateral geniculate nucleus (LGN), a visual relay and control nucleus in the thalamus that normally is organized into six interleaved monocular layers. Neuroimaging studies have not been able to reliably distinguish these layers due their small size that are less than 1 mm thick.
The resolving limit of structural MRI, in a postmortem brain was tested using multiple images averaged over a long duration (~24 h). The purpose was to test whether it was possible to resolve the individual layers of the LGN in the absence of physiological noise. A proton density (PD)1 weighted pulse sequence was used with varying resolution and other parameters to determine the minimum number of images necessary to be registered and averaged to reliably distinguish the LGN and other subcortical regions. The results were also compared to images acquired in living human brains. In vivo subjects were scanned in order to determine the additional effects of physiological noise on the minimum number of PD scans needed to differentiate subcortical structures, useful in clinical applications.

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Here we present a protocol to determine the minimum number images that needed to be registered and averaged to resolve subcortical structures and test whether the individual layers of the LGN could be resolved in the absence of physiological noise.

인간 Subcortex의 고해상도 구조 자기 공명 영상<I&gt; 생체</I&gt; 및 사후

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of ...

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

T1-가중치 MRI 이미지에서 외피 회색 물질의 자동된 분할

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

전체 두뇌 세분화 및 해 부 뇌 MRI의 변화 포인트 분석-Premanifest Huntington의 질병에 응용

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e., voxels can only be &ldquo;statistically significant&rdquo; or &ldquo;non-statistically significant&rdquo;). Here, we detail how to conduct a meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), a novel method that uses a standard permutation test to assess whether effects are not null. We also show how to grade the strength of the evidence according to a set of criteria that considers a range of statistical significance levels (from more liberal to more conservative), the amount of data or the detection of potential biases (e.g., small-study effect and excess of significance). To exemplify the procedure, we detail the conduction of a meta-analysis of voxel-based morphometry studies in obsessive-compulsive disorder, and we provide all the data already extracted from the manuscripts to allow the reader to replicate the meta-analysis easily. SDM-PSI can also be used for meta-analyses of functional magnetic resonance imaging, diffusion tensor imaging, position emission tomography and surface-based morphometry studies.

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).

피사체 이미지 순열을 이용한 종자 기반 d 매핑을 사용한 복셀 기반 신경 이미징 연구의 메타 분석(SDM-PSI)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a ...

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

The dopaminergic system plays a crucial role in healthy cognition (e.g., reward learning and uncertainty) and neuropsychiatric disorders (e.g., Parkinson's disease and schizophrenia). Neuromelanin is a byproduct of dopamine synthesis that accumulates in dopaminergic neurons of the substantia nigra. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a noninvasive method for measuring neuromelanin in those dopaminergic neurons, providing a direct measure of dopaminergic cell loss in the substantia nigra and a proxy measure of dopamine function. Although NM-MRI has been shown to be useful for studying various neuropsychiatric disorders, it is challenged by a limited field-of-view in the inferior-superior direction resulting in the potential loss of data from the accidental exclusion of part of the substantia nigra. In addition, the field is lacking a standardized protocol for the acquisition of NM-MRI data, a critical step in facilitating large-scale multisite studies and translation into the clinic. This protocol describes a step-by-step NM-MRI volume placement procedure and online quality control checks to ensure the acquisition of good-quality data covering the entire substantia nigra.

This protocol shows how to acquire neuromelanin-sensitive magnetic resonance imaging data of the substantia nigra.

흑질의 뉴로멜라닌 민감성 자기공명영상을 위한 표준화된 데이터 수집

The dopaminergic system plays a crucial role in healthy cognition (e.g., reward learning and uncertainty) and neuropsychiatric disorders (e.g., ...

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond its historical association with motor control. Structural and functional neuroimaging studies have further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions, highlighting the need for the examination of individual cerebellar subunits in healthy variability and neurological diseases. This paper presents a standardized pipeline for examining cerebellum grey matter morphometry that combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation (Automatic Cerebellum Anatomical Parcellation using U-Net Locally Constrained Optimization; ACAPULCO) and voxel-based registration of the cerebellum (Spatially Unbiased Infra-tentorial Template; SUIT) for volumetric quantification. 
The pipeline has broad applicability to a range of neurological diseases and is fully automated, with manual intervention only required for quality control of the outputs. The pipeline is freely available, with substantial accompanying documentation, and can be run on Mac, Windows, and Linux operating systems. The pipeline is applied in a cohort of individuals with Friedreich ataxia (FRDA), and representative results, as well as recommendations on group-level inferential statistical analyses, are provided. This pipeline could facilitate reliability and reproducibility across the field, ultimately providing a powerful methodological approach for characterizing and tracking cerebellar structural changes in neurological diseases.

A standardized pipeline is presented for examining cerebellum grey matter morphometry. The pipeline combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation and voxel-based registration of the cerebellum for volumetric quantification.

구조적 자기 공명 영상을 사용하여 인간의 소뇌 회색 물질 Morphometry를 검사하기위한 표준화 된 파이프 라인

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond ...

뇌 질환의 신경영상 분석 강화

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

저자 스포트라이트: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies (해부학의 격차 해소 및 알고리즘 연구의 기초 구축) 

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

뇌 MRI를 사용한 인간 맥락총의 수동 분할

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

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

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