How Should Harm Reduction Strategies Differ for Adolescents and Adults?

Overall rates of opioid use are low in adolescents; however, recent increases in mortality from overdose in adolescents have outpaced increases in the general population. This article highlights the importance of expanding evidence-based treatment for adolescent opioid use, especially medication, while also addressing key ethical considerations of harm reduction practices and how application of such practices with adolescents may differ from adults. Concepts related to adolescent populations are discussed, including autonomy, confidentiality, and brain development. Application of harm reduction practices should be age appropriate, express respect for patients' autonomy, include social support, and be accompanied by broader aims to minimize adolescent initiation, escalation, and overall harm caused by opioid use.

[Clinical, imaging, and pathological characteristics of 35 cases of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes syndrome].

Objective: To summarize the clinical, imaging, and pathological characteristics of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes syndrome (MELAS) to improve the diagnosis of this rare disease. Methods: A retrospective case series was conducted to collect the clinical data and results of genetic testing, muscle biopsy, and imaging studies including computed tomography (CT), magnetic resonance imaging (MRI), and magnetic resonance spectroscopy (MRS) of 35 patients with MELAS admitted to the Nanjing Drum Tower Hospital from 2012 to 2021. Descriptive statistical analysis including mean, standard deviation, and frequency percentage were carried out. Results: The average age of onset of the patients was 30.2±2.3 years; the prevalence of family history was 20%. The two main initial symptoms were limb weakness and convulsions. The clinical manifestations of the neuromuscular system were proximal muscle weakness and exercise intolerance. The endocrine system is the most affected outside the neuromuscular system, with diabetes being the most common condition. Among the five patients who underwent brain CT, four showed hypodense lesions and two had calcified lesions. Brain MRI in 26 patients showed that the lesions more often affected the parietal lobe, basal ganglia, temporal lobe, occipital lobe, and frontal lobe than the infratentorial areas. Twelve of these individuals exhibited different levels of brain atrophy. Among the 10 patients who underwent 1H-MRS, nine showed a decrease in N-acetylaspartate (NAA) levels, eight exhibited abnormal lactate elevation (Lac peaks), whereas six had both reduced NAA levels and the presence of Lac peaks. Thirty-one patients underwent genetic testing; among them, 25 were found to have the mt.3243A>G mutation, while the remaining six exhibited rare gene alterations. Muscle biopsies were performed in 21 patients, and 15 showed abnormal mitochondrial proliferation manifested by ragged red fibers and defective oxidative phosphorylation manifested by cytochrome C oxidase (COX) enzyme-deficient muscle fibers. Conclusion: The clinical manifestations of MELAS syndrome are variable and complex, and early atypical symptoms could be missed or misdiagnosed. A detailed clinical history, imaging MRS analysis, muscle biopsy, and genetic testing are necessary to confirm the accurate diagnosis of MELAS.

[Thinking on the classification of non-invasive brain stimulation as neuromodulation technique].

Neuromodulation techniques have gradually evolved from electrical or chemical methods to various physical stimulation techniques including electricity, magnetism, sound, light, heat, and more. However, the clinical efficacy and mechanisms of each stimulation technique or paradigm vary greatly. To facilitate the understanding of the therapeutic effects and mechanisms of different neuromodulation techniques, combined with current clinical practice, the author takes the classification of non-invasive transcranial electrical stimulation as an example and proposes the idea of using energy magnitude as the primary classification and different media/stimulation routes as the secondary classification. This classification emphasizes the energy essence of various physical stimuli, followed by the transmission carriers of physical stimuli. This classification method helps to guide and design neuromodulation paradigms for different target symptoms in various brain disorders, which is beneficial for better serving clinical diagnosis and treatment. The Expert Forum also discusses the advantages and disadvantages of various neuromodulation technologies and their clinical applications.

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

[Preliminary observation on the development and dynamic changes of chronic toxoplasmosis in mice].

OBJECTIVE: To investigate the development and dynamic changes of cysts in the brain of mice following infection with different forms of Toxoplasma gondii, so as to provide insights into for toxoplasmosis prevention and control. METHODS: ICR mice at ages of 6 to 8 weeks, each weighing 20 to 25 g, were intraperitoneally injected with tachyzoites of the T. gondii PRU strain at a dose of 1 × 105 tachyzoites per mouse, orally administered with cysts at a dose of 20 oocysts per mouse or oocysts at a dose of 200 oocysts per mouse for modeling chronic T. gondii infection in mice, and the clinical symptoms and survival of mice were observed post-infection. Mice were orally infected with T. gondii cysts at doses of 10 (low-dose group), 20 (medium-dose group), 40 cysts per mouse (high-dose group), and the effect of different doses of T. gondii infections on the number of cysts was examined in the mouse brain. Mice were orally administered with T. gondii cysts at a dose of 20 cysts per mouse, and grouped according to gender (female and male) and time points of infections (20, 30, 60, 90, 120, 150, 180 days post-infection), and the effects of gender and time points of infections on the number of cysts was examined in the mouse brain. In addition, mice were divided into the tachyzoite group (Group T), the first-generation cyst group (Group C1), the second-generation cyst group (Group C2), the third-generation cyst (Group C3) and the fourth-generation cyst group (Group C4). Mice in the Group T were intraperitoneally injected with T. gondii tachyzoites at a dose of 1 × 105 tachyzoites per mouse, and the cysts were collected from the mouse brain tissues 30 days post-infection, while mice in the Group C1 were orally infected with the collected cysts at a dose of 30 cysts per mouse. Continuous passage was performed by oral administration with cysts produced by the previous generation in mice, and the effect of continuous passage on the number of cysts was examined in the mouse brain. RESULTS: Following infection with T. gondii tachyzoites, cysts and oocysts in mice, obvious clinical symptoms were observed on days 6 to 13 and mice frequently died on days 7 to 12. The survival rates of mice were 67.0%, 87.0% and 53.0%, and the mean numbers of cysts were (516.0 ± 257.2), (1 203.0 ± 502.0) and (581.0 ± 183.1) in the mouse brain (F = 11.94, P < 0.01) on day 30 post-infection with T. gondii tachyzoites, cysts and oocysts, respectively, and the numbers of cysts in the brain tissues were significantly lower in mice infected with T. gondii tachyzoites and oocysts than in those infected with cysts (all P values < 0.01). The survival rates of mice were 87.0%, 87.0% and 60.0%, and the mean numbers of cysts were (953.0 ± 355.5), (1 084.0 ± 474.3) and (1 113.0 ± 546.0) in the mouse brain in the low-, medium- and high-dose groups on day 30 post-infection, respectively (F = 0.42, P > 0.05). The survival rates of male and female mice were 73.0% and 80.0%, and the mean numbers of cysts were (946.4 ± 411.4) and (932.1 ± 322.4) in the brain tissues of male and female mice, respectively (F = 1.63, P > 0.05). Following continuous passage, the mean numbers of cysts were (516.0 ± 257.2), (1 203.0 ± 502.0), (896.8 ± 332.3), (782.5 ± 423.9) and (829.2 ± 306.0) in the brain tissues of mice in the T, C1, C2, C3 and C4 groups, respectively (F = 4.82, P < 0.01), and the number of cysts was higher in the mouse brain in Group 1 than in Group T (P < 0.01). Following oral administration of 20 T. gondii cysts in mice, cysts were found in the moues brain for the first time on day 20 post-infection, and the number of cysts gradually increased over time, peaked on days 30 and 90 post-infection and then gradually decreased; however, the cysts were still found in the mouse brain on day 180 post-infection. CONCLUSIONS: There is a higher possibility of developing chronic T. gondii infection in mice following infection with cysts than with oocysts or tachyzoites and the most severe chronic infection is seen following infection with cysts. The number of cysts does not correlate with the severity of chronic T. gondii infection, and the number of cysts peaks in the mouse brain on days 30 and 90 post-infection.

[Research Advances in the Association Between Alzheimer's Disease and Double-Stranded RNA-Dependent Protein Kinase].

Alzheimer's disease (AD) is a severe threat to human health and one of the three major causes of human death.Double-stranded RNA-dependent protein kinase (PKR) is an interferon-induced protein kinase involved in innate immunity.In the occurrence and development of AD,PKR is upregulated and continuously activated.On the one hand,the activation of PKR triggers an integrated stress response in brain cells.On the other hand,it indirectly upregulates the expression of ß-site amyloid precursor protein cleaving enzyme 1 and facilitates the accumulation of amyloid-ß protein (Aß),which could activate PKR activator to further activate PKR,thus forming a sustained accumulation cycle of Aß.In addition,PKR can promote Tau phosphorylation,thereby reducing microtubule stability in nerve cells.Inflammation in brain tissue,neurotoxicity resulted from Aß accumulation,and disruption of microtubule stability led to the progression of AD and the declines of memory and cognitive function.Therefore,PKR is a key molecule in the development and progression of AD.Effective PKR detection can aid in the diagnosis and prediction of AD progression and provide opportunities for clinical treatment.The inhibitors targeting PKR are expected to control the activity of PKR,thereby controlling the progression of AD.Therefore,PKR could be a target for the development of therapeutic drugs for AD.

The clinical and predictive value of 18F-FDG PET/CT metabolic patterns in a clinical Chinese cohort with autoimmune encephalitis.

AIMS: To investigate the diagnostic and predictive role of 18F-FDG PET/CT in patients with autoimmune encephalitis (AE) as a whole group. METHODS: Thrty-five patients (20 females and 15 males) with AE were recruited. A voxel-to-voxel semi-quantitative analysis based on SPM12 was used to analyze 18F-FDG PET/CT imaging data compared to healthy controls. Further comparison was made in different prognostic groups categorized by modified Rankin Scale (mRS). RESULTS: In total, 24 patients (68.6%) were tested positive neuronal antibodies in serum and/or CSF. Psychiatric symptoms and seizure attacks were major clinical symptoms. In the acute stage, 13 patients (37.1%) demonstrated abnormal brain MRI results, while 33 (94.3%) presented abnormal metabolism patterns. 18F-FDG PET/CT was more sensitive than MRI (p < 0.05). Patients with AE mainly presented mixed metabolism patterns compared to the matched controls, demonstrating hypermetabolism mainly in the cerebellum, BG, MTL, brainstem, insula, middle frontal gyrus, and relatively hypometabolism in the frontal cortex, occipital cortex, temporal gyrus, right parietal gyrus, left cingulate gyrus (p < 0.05, FWE corrected). After a median follow-up of 26 months, the multivariable analysis identified a decreased level of consciousness as an independent risk factor associated with poor outcome of AE (HR = 3.591, p = 0.016). Meanwhile, decreased metabolism of right superior frontal gyrus along with increased metabolism of the middle and upper brainstem was more evident in patients with poor outcome (p < 0.001, uncorrected). CONCLUSION: 18F-FDG PET/CT was more sensitive than MRI to detect neuroimaging abnormalities of AE. A mixed metabolic pattern, characterized by large areas of cortical hypometabolism with focal hypermetabolism was a general metabolic pattern. Decreased metabolism of right superior frontal gyrus with increased metabolism of the middle and upper brainstem may predict poor long-term prognosis of AE.

Altered executive control network and default model network topology are linked to acute electronic cigarette use: A resting-state fNIRS study.

In recent years, electronic cigarettes (e-cigs) have gained popularity as stylish, safe, and effective smoking cessation aids, leading to widespread consumer acceptance. Although previous research has explored the acute effects of combustible cigarettes or nicotine replacement therapy on brain functional activities, studies on e-cigs have been limited. Using fNIRS, we conducted graph theory analysis on the resting-state functional connectivity of 61 male abstinent smokers both before and after vaping e-cigs. And we performed Pearson correlation analysis to investigate the relationship between alterations in network metrics and changes in craving. E-cig use resulted in increased degree centrality, nodal efficiency, and local efficiency within the executive control network (ECN), while causing a decrease in these properties within the default model network (DMN). These alterations were found to be correlated with reductions in craving, indicating a relationship between differing network topologies in the ECN and DMN and decreased craving. These findings suggest that the impact of e-cig usage on network topologies observed in male smokers resembles the effects observed with traditional cigarettes and other forms of nicotine delivery, providing valuable insights into their addictive potential and effectiveness as aids for smoking cessation.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Effects of endogenous testosterone on oscillatory activity during verbal working memory in youth.

Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6-14 years-old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole-brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone-related effects in theta (4-7 Hz) and alpha (8-14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left-lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex-specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age.

Effect of glucocorticoid blockade on inflammatory responses to acute sleep fragmentation in male mice.

The association between sleep and the immune-endocrine system is well recognized, but the nature of that relationship is not well understood. Sleep fragmentation induces a pro-inflammatory response in peripheral tissues and brain, but it also activates the hypothalamic-pituitary-adrenal (HPA) axis, releasing glucocorticoids (GCs) (cortisol in humans and corticosterone in mice). It is unclear whether this rapid release of glucocorticoids acts to potentiate or dampen the inflammatory response in the short term. The purpose of this study was to determine whether blocking or suppressing glucocorticoid activity will affect the inflammatory response from acute sleep fragmentation (ASF). Male C57BL/6J mice were injected i.p. with either 0.9% NaCl (vehicle 1), metyrapone (a glucocorticoid synthesis inhibitor, dissolved in vehicle 1), 2% ethanol in polyethylene glycol (vehicle 2), or mifepristone (a glucocorticoid receptor antagonist, dissolved in vehicle 2) 10 min before the start of ASF or no sleep fragmentation (NSF). After 24 h, samples were collected from brain (prefrontal cortex, hypothalamus, hippocampus) and periphery (liver, spleen, heart, and epididymal white adipose tissue (EWAT)). Proinflammatory gene expression (TNF-α and IL-1ß) was measured, followed by gene expression analysis. Metyrapone treatment affected pro-inflammatory cytokine gene expression during ASF in some peripheral tissues, but not in the brain. More specifically, metyrapone treatment suppressed IL-1ß expression in EWAT during ASF, which implies a pro-inflammatory effect of GCs. However, in cardiac tissue, metyrapone treatment increased TNF-α expression in ASF mice, suggesting an anti-inflammatory effect of GCs. Mifepristone treatment yielded more significant results than metyrapone, reducing TNF-α expression in liver (only NSF mice) and cardiac tissue during ASF, indicating a pro-inflammatory role. Conversely, in the spleen of ASF-mice, mifepristone increased pro-inflammatory cytokines (TNF-α and IL-1ß), demonstrating an anti-inflammatory role. Furthermore, irrespective of sleep fragmentation, mifepristone increased pro-inflammatory cytokine gene expression in heart (IL-1ß), pre-frontal cortex (IL-1ß), and hypothalamus (IL-1ß). The results provide mixed evidence for pro- and anti-inflammatory functions of corticosterone to regulate inflammatory responses to acute sleep loss.

Dragon boat exercise reshapes the temporal-spatial dynamics of the brain.

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and ß (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

The gut microbiome in disorders of gut-brain interaction.

Functional gastrointestinal disorders (FGIDs), chronic disorders characterized by either abdominal pain, altered intestinal motility, or their combination, have a worldwide prevalence of more than 40% and impose a high socioeconomic burden with a significant decline in quality of life. Recently, FGIDs have been reclassified as disorders of gut-brain interaction (DGBI), reflecting the key role of the gut-brain bidirectional communication in these disorders and their impact on psychological comorbidities. Although, during the past decades, the field of DGBIs has advanced significantly, the molecular mechanisms underlying DGBIs pathogenesis and pathophysiology, and the role of the gut microbiome in these processes are not fully understood. This review aims to discuss the latest body of literature on the complex microbiota-gut-brain interactions and their implications in the pathogenesis of DGBIs. A better understanding of the existing communication pathways between the gut microbiome and the brain holds promise in developing effective therapeutic interventions for DGBIs.

Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity.

The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified "skeleton" of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain.

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome.

The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.