This narrative review examines two of the common comorbidities of attention-deficit/hyperactivity disorder, bipolar disorder (BD), and borderline personality disorder (BPD), which each share several common features with ADHD that can make assessment and diagnosis challenging. The review highlights some of the key symptomatic differences between adult ADHD and these disorders, allowing for more careful consideration before establishing a formal diagnosis. When the disorders are found to be comorbid, further complications may arise; thus, the review will also help to provide evidence-based treatment recommendations as well as suggestions on how to minimize adverse events. Incorporating evidence from systematic reviews, journal articles, randomized controlled trials, and case reports, this review highlights that the diagnosis of ADHD and some of its common comorbidities is challenging and requires full, in-depth assessment and management. The management strategies of these comorbidities will also be addressed, with emphasis on achieving mood stabilization for BD prior to initiating appropriate ADHD pharmacotherapy. Medications, specifically mood stabilizers, antipsychotics, and antidepressants, are fundamental in treating symptoms seen in BD and some cases of BPD, alongside psychotherapy and lifestyle modifications when appropriate. The review highlights the effectiveness of specific medications, including psychostimulants, atomoxetine, and bupropion, as add-on therapies to mood-stabilizing treatments for addressing ADHD symptoms in patients with these comorbidities. Despite limited research, the review will address various pharmacological and psychotherapeutic approaches for managing comorbid ADHD and BPD, emphasizing the need for further investigations to better understand the unique needs of this patient population. Throughout history and now into modern practice, ADHD has been regarded as a neurodevelopmental disorder in children—often slated to self-resolve during adolescence and young adulthood . In receWhile comorbidities vary across the psychodiagnostic spectrum, literature gaps present themselves when identifying the relationship of ADHD to personality disorders and bipolar disorder in particular—two very different but burdensome disorders that are often observed in patients with ADHD. Reports suggest that personality disorders are present in more than 50% of adults with ADHD—with particular attention focused on cluster B disorders like borderline personality disorder (BPD) . This isBoth bipolar I disorder and bipolar II disorder are found at higher rates among adult patients with ADHD . A recenThis narrative review employs a comprehensive approach to explore the management strategies for comorbid ADHD with BD and BPD in adults. Evidence from a range of peer-reviewed research articles, clinical guidelines, case reports, and expert opinions will provide a comprehensive overview of the topic in question.A search of academic databases, including PubMed, Embase, PsychINFO, and Google Scholar, was conducted to identify relevant articles published up to the knowledge cutoff date of July 2023. The search terms used included various combinations of “ADHD”, “bipolar disorder, “borderline personality disorder”, “comorbidity”, “management”, and “treatment strategies”.Articles published in peer-reviewed journals between 1999 and 2023 in the English language.Studies addressing the adult population  with ADHD and comorbid disorders and patients treated either with pharmacotherapy, psychotherapy, or both.Randomized control trials, systematic reviews, metanalysis, and observational studies were included.Articles that primarily focused on pediatric populations since borderline personality cannot be diagnosed prior to age 18.Articles written without relevance to ADHD, BD, or BPD. Secondary reports were excluded.Articles published prior to 1999.Qualitative studies of ADHD and comorbid disordersThe selected articles were reviewed independently and analyzed for relevance to the topic in question. Data extracted included information on treatment goals, medication regimens, psychotherapeutic approaches, and key points on diagnostic criteria and treatment in adults with ADHD and comorbid BD or BPD. The data collected were then synthesized to provide a coherent narrative review that addresses the research objectives. Emphasis was placed on the most relevant and logical treatment approaches for patients with these types of presentations.and patients with BD and, therefore, must be considered when identifying a diagnosis of either disorder in a presenting patient.Describing the diagnostic overlap of ADHD and bipolar disorder poses a challenge, specifically because of the cyclic nature of bipolar disorder symptoms. While there may be features commonly seen in manic or depressive episodes that are consistent with an ADHD presentation, it is important to take note of the transient nature of bipolar mania. The symptoms that tend to overlap between the two conditions are those that tend to present in a more subtle fashion—including persistent low self-esteem, issues with performance in school or work, and the presence of rapidly flowing thoughts that may be described as a ‘restless mind’ . These aPatients with BD who are experiencing a manic episode often present with patterns that involve excessive talkativeness, distractibility, and restlessness—all of these clinical features can also be observed in patients with ADHD . There aIt has been reported that the 12-month prevalence of bipolar disorder in the ADHD population is up to 7.4× higher than that of the general population . StudiesThere are also some features of BD mood episodes that are not seen in ADHD, which may help to distinguish between a bipolar manic episode and ADHD-related hyperactivity see . For insGrandiosity, or an inflated sense of self-esteem, is a prominent feature of bipolar mania or hypomania; these individuals frequently present with inflated or unrealistic perceptions about their own importance or abilities . These cSleep disturbances are a common feature of several psychiatric disorders, and both ADHD and BD are known to impact sleep—however, they do so in different ways, something that skilled diagnosticians are aware of. While ADHD is linked to sleep disturbances, it is not known to decrease the need for sleep, which is a feature seen in patients with bipolar mania . CliniciThe presence of psychotic features, which is sometimes observed in patients with BD experiencing a manic episode, is an example of a clear distinguishing characteristic that would not be seen in patients with even the most severe forms of ADHD .Bipolar disorder is known for its episodic or oscillatory disease course, with clear and distinct manic and depressive that have remarkable variations in cycling. This is different from ADHD, in which the patients with hyperactive features exhibit these characteristics as stable traits rather than variable states seen in mood episodes of bipolar disorder .Lastly, the onset of ADHD is earlier than typically observed for BD, with most symptoms first occurring before the age of 12. The age of onset of BD is typically later, with a peak in late adolescence and early adulthood [Much like bipolar disorder, the prevalence of ADHD in patients with BPD is high and has been described as an aggravating factor in patients who struggle with ADHD symptoms . It is eOne of the diagnostic criteria to consider when approaching the assessment of a patient who may have ADHD-BPD is the fact that the symptom of impulsivity can be associated with both diagnoses. Factors to consider, however, include the fact that these can be discernible based on the patterns of presentation. In ADHD, impulsivity is often represented via a pervasive pattern of impatience and interruption, characterized by talking over others and difficulty with waiting one’s turn. In BPD, the symptom domain of impulsivity is better observed in acts carried out while under the influence of intense emotion, including self-harm and disregard for safety . The DSMEmotional dysregulation is a feature of borderline personality disorder that is often represented by a series of thoughts, behaviors, and actions that are linked to an unstable sense of self—including frequent loss of temper, mood swings within the same day, and impaired stability in interpersonal relationships . While eOne of the main differences between ADHD and BPD is the state of inner tension that is described by experts in the field of BPD; this is best described as underlying feelings of stress and instability that produce behaviors that often manifest as self-destructive. The state of inner tension is also said to drive the intense and unstable nature of BPD patient relationships, the insufficient sense of self, and dissociative or paranoid symptoms that are not observed in ADHD. Patients with BPD may experience paranoid thoughts, such as being hated by others, and they are often distressed by these thoughts .There are important diagnostic considerations in patients with comorbid ADHD and BPD; both validated sources like the DSM-5, in addition to a surplus of anecdotal evidence from experienced clinicians, have identified a great deal of symptom overlap in each of these psychiatric conditions . DiscussThe primary similarities between ADHD and BPD are in the symptom domains of impulsivity and emotional dysregulation. There are, however, subtle differences in each diagnosis that psychiatrists and psychiatric care providers should be aware of.Impulsivity is defined differently by the DSM-5 in the context of each diagnosis. For BPD, the manual describes impulsive behaviors as those that are self-damaging, including impulsive sexual behaviors, spending, binge eating, or reckless driving. ADHD-related impulsivity is described as difficulties with waiting patiently, interrupting others, and blurting out answers before questions are completed . Again, Experts in the field of psychiatric comorbidities have identified ADHD-related impulsivity to be largely trait-based impulsivity, meaning that the impulsive characteristics of one’s personality are static, unchanging, and present in all social situations and circumstances. In contrast, impulsivity in BPD is clearly more state-based, indicating that impulsive behaviors are elucidated when the patient is experiencing high levels of stress . In one While emotional dysregulation is not a diagnostic criterion for ADHD like it is for BPD, researchers and clinicians alike have identified it as a finding in most ADHD patients. Emotional instability in BPD is a core psychopathological characteristic—the DSM-5 describes marked emotional reactivity, intense episodes of dysphoria and irritability, and uncontrollable anger . Up to 7As discussed previously, self-harm in the context of BPD-related impulsivity is one of the hallmark features of this personality disorder; one previous study solemnly reported that up to 75% of patients with BPD had attempted suicide at least once . The suiInterpersonal deficits are another key characteristic of BPD that, when seen in ADHD, would be considered more of an indirect effect rather than a core symptom . Adults Disturbances of identity are a unique and interesting feature of a few select psychiatric disorders, among which include dissociative disorders and BPD. These symptoms are described as markedly unstable senses of self, often represented by a fragmented identity and sudden and dramatic shifts in entities like values, opinions, friends, sexual identity, and career aspirations . IdentitIdentity disturbances may be connected to chronic feelings of emptiness, which is another DSM-5 criterion that is specific to borderline personality disorder .One may argue that the low self-esteem that is characteristic of ADHD may contribute to feelings of emptiness as well; however, there is a unique distinguishing factor between this and what is observed in BPD. One analysis of the disorder across the lifespan found that feelings of emptiness, as well as somatic and depressive symptoms, tend to worsen as patients get older . This isThe final feature of BPD that is not observed in ADHD is paranoid ideation, which is described as a transient experience in which patients may undergo frantic efforts to avoid abandonment in response to real or imagined stress . This isearlier age of onset of BDincreased frequency of mood episodes increased risk and number of suicide attemptsincreased likelihood of substance use disorderpoor response to mood-stabilizing medications .The management of patients with comorbid ADHD and bipolar disorder is an under-researched topic; however, there have been some findings established as effective and worthy of consideration. The clinical impact in patients with ADHD and bipolar disorder is arguably increased because of the impairment in functioning that they experience because of their ADHD, in between the cycling episodes of depression and mania that are characteristic of bipolar disorder. Many studies have identified additional risks that patients with BD are subject to in the presence of a comorbid ADHD diagnosis, including:The treatment goals in bipolar disorder typically revolve around achieving and maintaining mood stability, as well as preventing recurrent episodes of mania, hypomania, and depression . This wiWhen BD and ADHD co-occur, among the first steps in developing a treatment plan is determining which condition and which symptoms are most severe and disabling. Typically, mood stabilization is a priority because untreated mood episodes in BD can have more devastating consequences, including hospitalization . Once moOne 6-week prospective trial in 36 patients with diagnoses of ADHD and bipolar disorder showed that sustained-release bupropion was linked to a 55% reduction in ADHD symptoms and a 58% reduction in mood episodes associated with depressed mood in bipolar disorder patients, with little evidence of hypomania induction . In thisBipolar disorder, when not appropriately treated, may manifest itself in ways that may make it challenging for clinicians to discern between its symptoms and those of ADHD—thus, the first goal of treatment when approaching the management of these patients is to ensure that BD symptoms are adequately controlled, typically using mood-stabilizing treatments like lithium, lamotrigine, or divalproex sodium . MethylpOther medications, such as modafinil and armodafinil, were significantly superior to placebo in some studies. One small study of 85 patients with BD demonstrated that modafinil use was associated with improved depressive symptoms in the study population . SimilarAtomoxetine is another commonly prescribed medication for the treatment of ADHD, which is a selective norepinephrine reuptake inhibitor—the first of the non-stimulant pharmacotherapy options . While iEvidence has shown that there are noted interactions specifically between methylphenidate and anti-seizure medications, some of which may be used as mood stabilizers in the treatment of bipolar disorder . The antIt is best to think of the management of ADHD-BD using a hierarchical approach, with frequent assessment of treatment response, as seen in While bupropion has shown some benefit in this population, it is important to consider the therapeutic advantages of using various mood stabilizers as well. Lithium, for example, which is well-established as effective BD pharmacotherapy, has demonstrated efficacy in reducing addictive and impulsive behaviors associated with ADHD and, therefore, may help to reduce complications . One douThere is limited data for using other mood stabilizers such as valproate and lamotrigine in the ADHD population; however, valproate is known to help alleviate symptoms such as emotional dysregulation and impulsivity, thereby warranting consideration for ADHD + BD co-occurrence . SimilarThe Canadian Network for Mood and Anxiety Treatments (CANMAT) has guidelines specifically for the treatment of bipolar disorder when it co-occurs with ADHD. Again, the stabilization of mood symptoms in BD using pharmacotherapy is identified as the priority before addressing ADHD symptoms . ADHD phWhile atomoxetine, bupropion, and lisdexamfetamine are all supported by evidence of similar quality, because of limited data to support atomoxetine in the adult population as well as the risk of hypomanic induction, bupropion remains the preferred first-line agent for managing ADHD symptoms in the BD population. This is supported by recent literature that has demonstrated the clinically significant improvement in disease severity that bupropion has in patients with bipolar disorder, in addition to its dopaminergic properties that render it an effective choice for ADHD treatment . There wAdequate mood stabilization in patients with ADHD-BD should be achieved using mood stabilizers prior to initiation of ADHD pharmacotherapy.There are some case reports of atomoxetine-associated hypomanic induction, while methylphenidate and amphetamine salts are considered to be low-risk medications in patients receiving appropriate mood stabilization therapy, but amphetamine salts carry double the risk than methylphenidate.Monitor for drug–drug interactions, particularly between methylphenidate and anti-seizure medications that are being used for mood stabilization.Key Points:Much like bipolar disorder, the management of ADHD is challenging for providers of psychiatric care. The diagnostic features of ADHD and borderline personality disorder (BPD) share overlapping characteristics, including impulsivity, emotional dysregulation, and issues with attention and concentration. There is also evidence of greater treatment resistance in patients with comorbid ADHD and BPD when compared to those with either disorder in isolation .There have been case reports published detailing the successful use of methylphenidate for the treatment of comorbid ADHD and BPD, demonstrating that methylphenidate monotherapy is effective not only for ADHD symptom management but also for aggression and self-injurious behavior seen in BPD . There aSimilar investigations have identified that mood stabilization should be achieved before initiating ADHD treatment and that methylphenidate and amphetamine salts should both be avoided in patients with pervasive cluster B personality disorders that have malingering features . AccordiWhen available, psychotherapeutic modalities should be engaged with these patients to maximize treatment outcomes and allow patients to benefit from a multi-pronged approach to care. Dialectical behavior therapy (DBT) is a psychotherapeutic method adapted from the traditional cognitive-behavioral therapy (CBT) that focuses on skill building for patients with some function-impairing features of BPD, including an unstable sense of self, relationship instability, emotional lability, and impulsivity that may manifest as self-destruction. It is currently the first-line and most evidence-based treatment for BPD . It is aAside from DBT, there are other therapeutic options that may be available to patients if there are trained clinicians with the ability to provide these specialized services. Mentalization-based therapy and transference-focused therapy are examples of alternative psychotherapeutic modalities that have been proven to be helpful in the outpatient management of BPD, although they have not yet been studied in the ADHD-BPD population. Mentalization-based therapy can also be administered as either individual or group therapy, and it helps patients work through their impulses and develop strategies to think before reacting . BecauseMany off-label medications have been trialed for the management of ADHD when comorbid with BPD. Some of the symptoms of BPD, including feelings of emptiness, fear of abandonment, and identity disturbance, have been proven to be resistant to treatment with various agents, including olanzapine and quetiapine . ClonidiClinicians should also consider the risks and management difficulties that may accompany the treatment of a patient with ADHD-BPD. It is well-documented that patients with BPD have a propensity to use substances due in part to the symptoms of impulsivity that they experience and the experiences of inner tension and suicidality that often provoke the use of alcohol and drugs as a coping mechanism . It is eIt is evident that research into the management implications of ADHD-BPD is limited and that further investigations should be performed to better understand the unique needs of this patient population.Key Points:Methylphenidate treatment can be helpful not just in the management of ADHD symptoms but also in certain features of BPD, including impulsivity and executive dysfunction.Mood stabilization should be achieved before initiating stimulant therapy, and special caution should be taken in patients with suspected malingering behaviors.Dialectical behavior therapy remains the gold standard treatment for BPD, and it has shown to be effective in mitigating certain features of ADHD as well.This narrative review offers a thorough examination of ADHD comorbidity management in adults with BD and BPD while incorporating evidence from a variety of sources. The practical implications of this review are that it will help provide clinicians and researchers with guidance about recommendations for the diagnosis and management of a traditionally challenging patient population.One of the limitations that may be relevant in this review is the risk of selection bias, as the inclusion of studies depended on predefined criteria, potentially excluding some relevant research that was not incorporated. There was also a scarce amount of data available on certain topics, potentially impacting the quality of the recommendations. The quality of evidence was not assessed in this narrative review. The evidence also primarily stems from populations where Western medicine is practiced, so caution is needed when applying its findings in other cultural contexts. Selecting studies written in the English language only may have resulted in excluding other important studies.Future research into the management of BD and BPD in adults with comorbid ADHD should address several key areas. Primarily, more extensive and well-designed studies are needed to explore treatment options in further detail, like bupropion, atomoxetine, and other non-stimulant pharmacotherapies. Researchers may want to specifically focus on understanding the precise mechanisms underlying the interplay between ADHD and these comorbidities, allowing readers to better understand the neurobiological pathways at play. Larger-scale studies would also be beneficial to help diversify the patient population and allow for greater generalizability. Investigations into novel approaches, such as combination pharmacotherapies or other specific psychotherapeutic modalities, could be addressed. Because of the literature gap, the future of research in this field holds promise for better understanding the intricacies of these disorders and better serving the patient population.In conclusion, the management of ADHD comorbidities in the practice of psychiatry remains a complex process that requires a multifaceted approach. Adequate mood stabilization using mood stabilizers should take precedence before initiating ADHD pharmacotherapy in patients with both BD and BPD. While methylphenidate and mixed amphetamine salts are considered relatively low-risk medications when administered in conjunction with mood stabilization therapy, careful monitoring for drug–drug interactions, particularly in the case of methylphenidate and anti-seizure medications, is essential to ensure the safety of the patient. It is important to highlight the apparent efficacy that some medications used to treat ADHD, including methylphenidate, atomoxetine, and bupropion, have for treating certain features of BD as well, like impulsivity and executive dysfunction. Moreover, DBT remains the gold standard for the treatment of BPD, and it has also shown effectiveness in mitigating some aspects of ADHD. In summary, a tailored and holistic treatment approach that prioritizes mood destabilization in those with BD and BPD and considers individual symptom needs when managing the co-occurring ADHD. Bupropion, while ongoing research is required to obtain further evidence, can be a valuable add-on therapy to stabilize BD or BPD patients for therapeutic support for both ADHD and depressive mood symptoms.


Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine . This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness. Meanwhile, stimulant medications , amphetamine) and non-stimulant medications ) are two main categories of medications approved for treating ADHD. However, currently there is no clear medication treatment plan corresponding to each subtype, which is decided mainly relying on clinical experience and not always being effective. Reports have demonstrated that MPH can equally improve inattention symptoms in both ADHD-C and ADHD-I subtypes, while predominantly reducing hyperactivity in the former4. A rat study reported that both MPH and ATX improved overall attention, but the highest dose of MPH significantly increased impulsivity, while ATX induced decrease in impulsivity5. Therefore, for ADHD patient with pure inattention, the optimal medication selection is challenging in clinical practice. Thus, the identification of imaging-based ADHD biological subtypes (biotypes) that may exhibit greater sensitivity to specific drug therapies holds promise for guiding a more personalized treatment approach with enhanced effectiveness.Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neuropsychiatric diseases. As a highly complex and heterogeneous disorder with childhood onset9, though in a relatively highly piecemeal way, which most relied heavily on prior clinical subtype divisions based on pure symptoms3 and highlighted group-level differences via univariate statistic methods12. For example, ADHD-C were found to have hyper-connectivity in the pre-default mode network and lower network modularity compared to health controls (HC), whereas no such differences were found in the ADHD-I subtype13, whilst ADHD-C/HI to ADHD-I were often compared on the level of comorbidity, treatment response, and possible etiological factors14.Meanwhile, multiple functional magnetic resonance imaging (fMRI) analyses have been performed to study ADHD subtype18. For instance, Fair et al. adopted graph theory and community detection to clarify neuropsychological heterogeneity in typically developing children and children with ADHD, which yielded four unique subgroups each of which had unique patterns of factor scores19. Dias et al. performed community detection analysis and characterized heterogeneity in children with and without ADHD based on reward system connectivity20. These studies helped disentangle the disease heterogeneity, however, the replicability and utility of the identified ADHD biotypes require further research.In contrast, understanding ADHD heterogeneity how defined by multimodal neuroimaging and demographic features rather than symptoms can offer a novel perspective for unraveling the ADHD complexity and pathology, as it may provide critical information for developing a more targeted, effective treatment program based on objective measures. The existing studies on ADHD biotypes encompass investigations into brain function, structure and neuropathology using diverse methodologies24. Specifically, both convolutional neural network25 and generative adversarial network26 have been employed to identify biotypes, e.g., from mild cognitive impairment. However, one issue is that clustering methods often ignored the topological information of population networks24, i.e., complex associations between individuals were not fully explored, which may lead to sub-optimal performance in biotype identification. Considering graph convolutional network (GCN) is able to encode topological information of population networks to improve disease prediction27, and has performed well for community detections such as social networks28 and protein interaction networks29, we are motivated to integrate deep learning advances such as GCN and deep K-means to identify reliable, multi-factorial signatures for imaging-derived ADHD biotypes; so we can take an initial step towards developing biotype-guided treatment recommendations. Furthermore, public imaging data of children offers unprecedented opportunities for ADHD research. With the aid of publicly shared data from Adolescent Brain Cognitive Development (ABCD)30 study and local longitudinal medication treatment data, we have an excellent platform with sufficient statistical power and enriched multi-factors  for testing and validating the proposed method and the identified ADHD biotypes.Motivated by this, we aim to develop innovative MRI-compatible, deep learning approaches to identify reliable ADHD biotypes in addressing the afore-mentioned medical issues. Deep learning (DL) has gained wide attention in mental disorder analysis due to its capability in automatically learning useful feature expression from high dimensional neuroimaging data, though its application in ADHD is still rare. The time is ripe to capitalize on these sophisticated methodologic advances and foster the convergence between clinical psychiatry and imaging neuroscience. In biotype division, compared to traditional clustering methods, deep clustering is able to learn nonlinear properties, represent low-dimensional embeddings from high-dimensional neuroimaging input, and constrains these learned features to adapt to the clustering target, which maximizes the between-class distance and minimizes the within-class distance31, and evaluated the clustering performance of 4 popular algorithms with proposed method based on Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI)32  using both functional imaging and non-imaging phenotypic data. As illustrated in  (CHI)32 . As a rep < 0.05). Collectively, this is a novel attempt to identify and validate ADHD biotypes that might facilitate ADHD personalized treatment, pushing the boundaries of what is possible through innovative deep learning algorithms and assisting in clinical drug selection to enhanced cure rates.More importantly, we used a valuable ADHD dataset with longitudinal medication treatment to validate the reliability and utility of the identified ADHD biotypes from ABCD. This data was collected from Peking University Sixth Hospital  with fMRI, cognitive metrics, and symptom records. Interestingly, we found that ADHD biotypes identified in ABCD and PKU showed high similarity and replicability in FNC patterns. The most contributing FNCs and clinical records were compared dedicatedly between two biotypes . Specifi31, as shown in Two ADHD biotypes were identified, and the number of clusters was decided by the within-cluster sum of squared errors (SSE) using the elbow methodBiotype 1 and biotype 2 have different connectivity patterns from HC. The mean differences of FNC between 2 biotypes and HC are shown in The two biotypes and demographically matched healthy controls were compared in cognitive measures . OverallWe further tested whether the identified connectivity pattern was consistent in the independent data cohort. By projecting the identified ADHD biotypes from ABCD into the PKU dataset, subjects were divided into 2 subsets. We detected the top 100 discriminative connectivity from 1378 FNCs in two biotypes by using two-sample t-tests to estimate the group differences of FNC features. To explicitly characterize the contribution of each functional network, we grouped the 53 nodes into 7 networks according to the NeuroMark pipeline, as shown in 33. And we noticed that hyperactive/impulsive symptoms and total ADHD symptoms from RS-IV, and impulsivity–hyperactivity factor and hyperactivity index from CPRS showed significant differences between biotype 1 and biotype 2 In PKU, two-sample t-tests were used to estimate the group differences of the IQ scale and symptom scale, see Moreover, as shown in The consistent discriminative FNCs between biotype 1 and biotype 2 were highly correlated with several cognition scales. For example, the connectivity between the superior parietal lobule and precuneus was correlated with total intelligence  agglomerative clustering, (2) conventional K-Means, (3) DNN with deep K-Means, and (4) autoencoder GCN with K-Means with GCN-BSD based on Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI)32 32 . As a re (CHI)32 . The mos (CHI)32 .https://nda.nih.gov/abcd/) is an observation assessment of brain development in children in the US at 9–11 years of age to characterize psychological and neurobiological development from early adolescence to early adulthood30. Our data were analyzed from the ABCD Study curated annual release 3.0, which contains baseline data from 11,875 children. We determined ADHD same as previous studies71 by past and present mental disorder diagnoses using parent-reported responses to the self-administered computerized Schedule for Affective Disorders and Schizophrenia for School-Age Children for DSM-5 (K-SADS-5).The ABCD study . The severity of inattentive symptoms, hyperactive/impulsive symptoms, and total ADHD symptoms of all subjects were evaluated by the ADHD Rating Scale-IV (ADHD RS-IV). Conners’ Parent Rating Scale (CPRS)33 was used to assess the hyperactive/impulsive symptoms(see more details in the Supplementary file). The demographic information was listed in Table S2.Another in-house dataset was included in this study as a validation. This children’s cohort was approved by the Ethics Committee of Peking University Sixth Hospital. A total of 130 children with ADHD and 105 HCs (6–15 years old) were recruited. 44 patients received medical treatment, 17 of them were treated with atomoxetine (ATX), and 27 of them were treated with methylphenidate (MPH), and we recorded their symptom scales at baseline, week 1, week 2, week 3, week 4, and week 8. The diagnosis was made by a senior psychiatrist based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version (K-SADS-PL)http://trendscenter.org/software/gift and as a separate download at http://trendscenter.org/data) and pipeline used in74. For this template, fifty-three independent component networks (ICNs) were identified and arranged into 7 functional networks, including the subcortical (SC) (5 components), auditory (AU) (2 components), sensorimotor (SM) (9 components), visual (VI) (9 components), cognitive control (CC) (17 components), default mode (DM) (7 components), and cerebellar (CB) (4 components) networks. FNC is calculated using Pearson correlation, resulting in a 53×53 matrix for each subject. We extracted the upper triangle elements of the matrix as FNC features, namely, each subject has an FNC vector in the dimension of (53×52)/2 = 1378. Participants from the ABCD dataset were selected based on the availability of non-imaging measures and high-quality neuroimaging data. The quality assessment of the neuroimaging data was based on the “Quality Control and Recommended Inclusion Criteria for Structural Data” provided as a document in the “Release Notes Imaging Instruments” folder. Participants were excluded from the present analyses because of incomplete rsMRI data at baseline and/or because brain scans did not pass the ABCD Study’s quality control. After all quality control, we have 1069 ADHD participants included (see more details in the Supplementary file). And 1164 demographically matched HCs were included in our study. Their demographic and cognitive information were listed in Table S1.The resting-state fMRI data from two datasets were preprocessed with a combination of FMRIB Software Library v6.0 toolbox and Statistical Parametric Mapping 12 toolbox, including rigid body motion and distortion correction, spatial normalization into Montreal Neurological Institute space, and smoothing with a 6-mm full width at half maximum Gaussian kernel (please see more details in Supplementary file). We decomposed the preprocessed MRI by a fully automated spatially constrained ICA run on each individual subject separately based on the neuromark_fMRI_1.0 template ; Flanker Inhibitory Control and Attention Test Score (FICA); List Sorting Working Memory Score (LSWM); Dimensional Change Card Sort Test Score (DCCS); Pattern Comparison Processing Speed Test Score (PCPS); Picture Sequence Memory Test Score (PSM); Oral Reading Recognition Test Score (ORR); Cognition Fluid Composite Score; Crystallized Composite Score; Cognition Total Composite Score. Age-corrected T-scores were used in this study where higher values correspond to better performance.The ABCD baseline cognitive battery assessed by a well-validated National Institute of Health Toolbox was used as evaluation in this studyFor patients from PKU, the severity of inattentive symptoms, hyperactive/impulsive symptoms, and total ADHD symptoms of all subjects were evaluated by the ADHD Rating Scale-IV (ADHD RS-IV). Besides, the Conners’ Parent Rating Scale (CPRS) was used to assess the symptoms in children participants with ADHD. The CPRS is a widely used instrument for screening and evaluating ADHD-related symptoms as well as other behavioral problems frequently associated with ADHD in children. It contains 48 items and can be divided into six factors: conduct problems, learning problems, psychosomatic problems, impulsivity–hyperactivity, anxiety, and hyperactivity index. Verbal IQ, performance IQ, and full-scale IQ were measured by the Wechsler Child/Adult Intelligence Scale, Third Edition.GCN-BSD is an autoencoder-based GCN model. The proposed architecture consists of three main elements: an encoder, an embedding layer, and a decoder, as shown in 31. GCN-BSD was implemented with Pytorch and optimized using Adam optimizer77. Hyperparameters were set as: 32, and one performance indicator: ABCD cognitive battery. Statistical analyses were performed among ADHD biotypes and HC. A two-sample t-test was performed for all measures between biotypes and HC. The P-values were corrected for multiple comparisons using Bonferroni correction.Firstly, GCN-BSD was adopted in the ABCD dataset to identify ADHD biotypes. The number of clusters To validate the constancy of the identified ADHD biotype patterns, we used the mean FNC feature of two biotypes as templates and projected them into an independent dataset from PKU to extract similar biotype patterns. Specifically, for each subject in PKU, we calculated the Euclidean distance between individual FNC and two FNC templates, and then we divided the subject into a subgroup where the template showed more similarity than the other. Two-sample t-tests were used to calculate the group differences between two biotypes of the symptom scales, including RS-IV, CPRS, and Wechsler Child/Adult Intelligence Scale. Note that sites and scanners were regressed out before statistical analysis.The top 100 discriminative FNC in two biotypes were identified by two-sample t-tests in ABCD and PKU, respectively. We grouped the 100 FNCs into 7 brain networks according to the NeuroMark pipeline to analyze the functional connectivity on a large scale. Moreover, to discover the exact consistent brain patterns, we picked the overlapped top 100 FNCs in two separate datasets and obtained brain functional patterns for two biotypes. We further calculated the correlations between the identified overlapped FNC and ABCD cognitive battery, RS-IV, CPRS, Wechsler Child/Adult Intelligence Scale in two datasets, separately, in order to study the association between FNC and multiple symptoms.In order to explore whether the identified biotypes respond differently to medical treatment, we compared the treatment outcome under medication treatment of ATX and MPH after 1, 2, 3, 4 and 8 weeks. 44 out of 130 ADHD patients from PKU were recorded with longitudinal symptom and cognitive scores, who were divided into two biotypes according to the previous results. Particularly, 27 of 44 patients were classified as biotype 1 and the rest of them as biotype 2. To test whether there are differences in the benefits of the drug between 2 biotypes, we compared the symptom scores RS-IV and CPRS from baseline to week 8 by repetive two sample t-test.


Up to 80% of adults with attention deficit hyperactivity disorder (ADHD) may have a concomitant psychiatric disorder. ADHD and depression, combined with the demands of University life, can pose serious challenges for students. However, there are limited studies conducted on this topic in our country. Therefore, the current study assessed the magnitude, relationship, and determinants of ADHD and depressive symptoms among students at the University of Gondar.An institution-based cross-sectional study was employed among 1514 undergraduate students at the University of Gondar from June 1–20, 2022. A stratified, simple random sampling technique was applied. Structural equation modeling was employed. The degree of relationship was interpreted based on the adjusted regression coefficient with 95% confidence interval (CI) and p-value (<0.05).In our study, 37.6% [CI: 35.2–40.1%] and 20.01% [CI: 18.1–22.1%] of the participants had depressive and ADHD symptoms, respectively. Chronic illness , alcohol use , social support , problematic internet use (PIU) , and insomnia  had a direct relationship with depressive symptoms. Mother education level , birth order , chat chewing , and depressive symptoms  had a direct relationship with ADHD. Chronic illness , PIU , social support , and insomnia  had an indirect effect on ADHD.The prevalence of ADHD and depressive symptoms was high. Chronic disease, alcohol use, social support, PIU, and insomnia had a direct effect on depressive symptoms and an indirect effect on ADHD. Mother education, birth order, chat chewing, and depressive symptoms had a direct effect on ADHD. Our study provided useful clues for clinical treatment and school-based interventions that aim to improve college students’ mental well-being. It is better to design school-based intervention and prevention programs to achieve timely diagnosis and treatment of these disorders among university students. Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders explained by three main symptoms, such as inattention, hyperactivity, and impulsivity ,2. It isDespite the strong contribution of genetics, there is no single cause of ADHD. Its cause is thought to be a combination of genetic, biological, and environmental factors . EvidencADHD has an impact in all spheres of life ,14. UnivADHD is strongly associated with depression and mood disorders during adulthood . Even ifDepression is also a common mental disorder and the leading cause of disability . Among cDespite the fact that the diagnosis of ADHD among adults is growing four times faster compared to children , fewer tPrevious studies among adults or university students revealed that socio-demographic factors like being female , youngerAnd also socio-demographic factors like being female –50, age Although previous studies revealed a higher mental health problem among undergraduate students, the magnitude of ADHD and its relationship with other psychiatric disorders like depression have not previously been explored in our country. And also, ADHD is an emerging mental health problem and a neglected health issue in Africa . SymptomAlthough SEM has been applied in numerous fields, epidemiology and medical research have not yet made considerable use of it. It is a flexible framework for developing and evaluating complex relationships between numerous variables that enables researchers to use empirical models to assess the viability of an existing theory. The capacity to control measurement error is one of its biggest advantages. However, it is one of the greatest limitations of most studies. .Hence, exploring the magnitude of ADHD and depression symptoms as well as their relationship among undergraduate university students would shed light on the phenomenology of the problems among young adults seeking higher education and enable targeted early diagnosis and therapeutic interventions for vulnerable students. Given the coexisting mental illness of ADHD, evidence on the prevalence and relationship of adult ADHD with depression among young adult college students is vital to designing early diagnosis and intervention to inform appropriate management decisions in order to improve control measures and promote quality and safe mental health practices among students. In addition, this study provides updated and baseline information and methodological advancements for future researchers to further refine and prioritize specific areas of future research in the areas of ADHD, depression, and their relationship. Thus, this study examined the magnitude, a bidirectional relationship, and determinants of ADHD and depressive symptoms among students at the University of Gondar using non-recursive structural equation modeling.An institution-based cross-sectional study design was employed from June 1–20, 2022, at all five campuses  of the University of Gondar. A total of 6 colleges , 2 institutes, (Institute of Technology and Biotechnology), two faculties (Faculties of Informatics and Education) and one school (School of Law) are found in all campuses, offering 56 undergraduate and 64 postgraduate programs in regular, extension, distance, and summer programs . There wAll regular undergraduate students at the University of Gondar were the source population, and all regular undergraduate students registered for the 2022 academic year and available at the University of Gondar during the data collection period were the study population.In this study, all regular undergraduate students who registered at the University of Gondar for the academic year 2022 were included. However, students with medically confirmed thyroid disease and below the age of 18 were excluded from the study.Structural equation modeling (SEM) is a large-sample technique, and the sample size required is dependent on the complexity of the model. As a general rule of thumb, the minimum sample size should be no less than 5–20 times the number of parameters to be estimated . Thus, iAccordingly, there were 34 observed endogenous variables , insomnia, and social support respectively). Therefore, 29 path coefficients were needed since five of them were fixed to 1, in order to give the latent variable measurement scale. Two disturbance terms, two paths between ADHD and depression, and one covariance between disturbance terms and there are 34 error terms for those observed endogenous variables. There are also 26 observed exogenous variables  and three latent exogenous variables. Therefore, the total number of free parameters to be estimated is equal to34*2–5 = 63 path coefficients and error terms for latent variables3*2 = 6 path coefficients and variance for exogenous variables for ADHD only12*2 = 24 path coefficients and variance for exogenous variables for depression only14*3 = 42 path coefficients and variance for common factors for both outcomes.2 + 2+1 = 5 for the feedback loop, their disturbances, and covariance.seven significant covariances between error terms. The total number of parameters to be estimated was 147. Therefore, 147*10 = 1470. A non-response rate of 5% was considered. Then the final calculated sample size was 1544. Even though the sampling technique is stratified sampling and needs a design effect, available evidence declares that the design effect for the mean of a proportionate sample is no greater than 1. Thus, proportionate stratification cannot lead to a loss in precision but generally leads to some gain in precision , being worried about educational performance , history of stressful life events , ever alcohol use , PIU , and insomnia  implied a statistically significant indirect positive effect on ADHD. While social support , average number of sleeping hours , and average number of studying hours  had a negative indirect effect on ADHD , mother education level and birth order , and sleep and studying hours  were statistically significant and different from zero. This indicates that the variables are negatively correlated with each other.This study examined the magnitude relationship and determinants of depression and ADHD among UoG undergraduate students, Northwest Ethiopia using non-recursive SEM (simultaneous estimation of the feedback loop between depression and ADHD symptoms). And we found that the prevalence of depression and ADHD symptoms was considerable and estimated to be 37.6% (95% CI: 35.2–40.1%) and 20.01% (95% CI: 18.1–22.1%) respectively. Independent factors such as medically confirmed chronic disease, history of head injury and stressful life events, alcohol use, worrying about academic performance, number of sleeping and studying hours, social support, PIU, and insomnia were directly and indirectly related to depression and ADHD symptoms, respectively. While year of study, mother’s education level, birth order, chat chewing, and depressive symptoms had a direct effect on ADHD symptoms.The overall prevalence of depressive symptoms was found to be 37.6%. This figure is higher than previous reports from Ethiopian Universities ,52,96,97However, our finding is lower than previous findings from Arsi University  and the The presence of a medically confirmed chronic illness had a direct positive effect  on depressive symptoms. Which indicates students with medically confirmed chronic illness had higher levels of depression symptom scores as compared to their counterparts, keeping other variables in the model controlled. Similar findings were reported from a study conducted in Ethiopia  and otheAlcohol use had a significant positive direct effect  on depression symptoms when other variables in the model were controlled. This indicates that having a history of alcohol use increases the level of depression symptoms by 0.10 compared to their counterparts. This finding is comparable with a study conducted at Debre Birhan University . This caWorry about academic performance had a direct positive effect  on depressive symptoms. Being worried about academic performance increases depressive symptoms by 0.24. Which is consistent with studies conducted at UoG  and MakeSocial support has a direct negative effect  on depressive symptoms. When social support scores increase, scores for depressive symptoms decrease. This might be due to the disease nature of depression, since it has a feeling of inwardness and isolation. It is a huge energy-draining disorder. Thus, people with depression may fail to reach out and connect with others. Therefore, isolating themselves may deteriorate their symptoms. However, it has a positive role in mental health by helping individuals feel appreciated and connected with social networks . A similThe average number of sleeping hours per day had a direct negative effect  on depressive symptoms. This indicates that when the number of sleeping hours increases, the level of depression decreases, and students who have fewer sleep hours are more likely to experience depressive symptoms. This finding is similar to a study conducted in Iraq . Which cThe average number of studying hours per day had a direct negative effect  on depressive symptoms. This indicates that, as the number of study hours increases, scores for depressive symptoms decrease. This can be explained by the fact that when the number of study hours increases, students may have more confidence in their abilities and avoid worrying about taking tests. Which may improve their sleeping pattern, academic performance, and achievement . In contA history of stressful life events had a direct positive effect  on depressive symptoms. This indicates that having experienced stressful life events increases scores of depressive symptoms by 0.07, which is supported by a study conducted in Bangladesh . This caProblematic internet use had a direct positive effect  on depression. Thus, when the PIU score increases, the level of depression also increases. Which is supported by other studies conducted in Ethiopia , Kenya , Iran 1, Spain , India , and BanInsomnia had a direct positive effect  on depression. This can be explained as an increase in the score of insomnia increasing the level of depressive symptoms. Which is consistent with other studies conducted in Ethiopia  and SpaiOur study revealed that the prevalence of ADHD was found to be 20.01% 95% CI: 18.1–22.1%]. This finding is in line with a study conducted among University students in Kenya  on ADHD. This indicates that when the year of study goes up by 1, the severity of ADHD decreases by 0.10, keeping other variables controlled. This explains that when students become familiar with the University environment, the symptoms may depreciate due to an increase in reduced interpersonal skills and coping with lifestyle and academic difficulties .Our study also revealed that a mother’s education level had a direct negative effect  on ADHD. This indicates that when the mother’s educational level increases by one, the severity of ADHD symptoms decreases by 0.09. This can be explained by the relationship between educational level and risky health behaviors during pregnancy. When the level of parental education increases, they may keep themselves from engaging in risky health behaviors since complications during delivery can cause ADHD. Thus, another theory that can be raised is the relationship between education and pregnancy-related complications; hence, education has a direct effect on reducing pregnancy-related complications ,130. OurBirth order had a direct negative relationship  with ADHD. This indicates that when birth order increases by 1, the severity of ADHD symptoms decreases by 0.09. This can also be explained by the relationship of ADHD with perinatal risk factors and birth-related complications, which have a huge contribution to ADHD. Thus, first-born babies may have faced higher pregnancy and obstetric-related complications compared to high-order babies. As evidenced by the literature, primiparous women are at high risk of these aforementioned complications . As a reChat chewing had a direct positive influence on ADHD . This implied that having ever used chat increases ADHD by 0.18. The effect of chat on ADHD can be explained by its relationship with neurobehavioral functioning. A recently conducted systematic review revealed that long-term use of chat was associated with significant deficits in several cognitive domains, including learning, motor speed and coordination, set-shifting and response inhibition functions, cognitive flexibility, short-term memory, and conflict resolution . And a sMedically confirmed illness had a positive indirect effect  on ADHD. This finding is congruent with a population-based study conducted in Sweden . This miAnother significant positive indirect effect on ADHD came from being worried about educational performance . This might be due to the presence of continuous worry among persons with ADHD; hence, they may occasionally act impulsively; they are generally introspective in their daily lives, and these thoughts cause worry. And also because of bad past experiences, the participants did not trust themselves in the future. In addition to this lack of daily routine, which manifested as having an irregular lifestyle and consistently breaking promises to themselves and others .The average number of sleeping hours also had a protective indirect effect  on adult ADHD symptoms. This can be explained by the fact that adequate sleeping time can reduce behavioral symptoms . The curA history of stressful life events had a positive indirect effect  on ADHD. This finding is supported by a study conducted in Sweden . This caProblematic internet use had a statistically significant indirect positive effect  on ADHD through the mediational effect of depression, which is consistent with studies conducted in France  and TurkSocial support had an indirect protective effect  on ADHD. This might be due to the fact that the presence of social support from friends or family may reduce depression. This statement is also supported by another finding . AnotherInsomnia had a statistically significant indirect positive effect  on ADHD. This finding is consistent with a study conducted in Turkey . This miIn the non-recursive relationship, depression had a significant effect on ADHD . This indicates that an increased depression score increases ADHD. The path from ADHD to depression had a negative effect . However, the estimate was not statistically significant . This can be explained by the decline in total ADHD symptoms, especially hyperactivity . The effOur study applied non-recursive SEM to assess the bidirectional relationship between ADHD and depression. In addition to this, compared to previous studies conducted in Ethiopia, the current study used a relatively large sample size to determine the magnitude of depression.However, this study is not without limitations. We tested the causal relationship between outcome variables where the timelines were different, and our study included only university students, which may affect generalizability to all adults. In addition, we have utilized a self-administered questionnaire to study mental health problems; thus, participants may hide their past or current psychiatric history.The magnitude of ADHD and depression was found to be high. Factors like the presence of a medically confirmed chronic disease, a history of a head injury, a history of stressful life events, alcohol use, worrying about academic performance, the number of sleeping and studying hours, social support, PIU, and insomnia were significantly associated with depressive symptoms, while year of study, mother’s education level, birth order, khat chewing, and depressive symptoms were significantly related to ADHD. The presence of a medically confirmed chronic disease, worrying about academic performance, a history of stressful life events, alcohol use, a history of head injury, an average number of sleeping and studying hours, social support, PIU, and insomnia had a depression-mediated effect on ADHD.However, the path from ADHD to depression and other variables including age, sex, cannabis use, current alcohol use, prior residence, marital status, presence of a friend, history of parental psychiatric problems, father education level, average monthly allowance, history of smoking, physical exercise, history of academic failure, and self-reported family economic level did not have a statistically significant direct and/or indirect relationship with either outcome variable.Our results have important implications for clinical and school-based prevention and intervention. Therefore, it is better that UoG work more on early screening and recognition of ADHD symptoms among students. That could partly serve as a preventive instrument for other comorbidities, like depression. Health sector managers should pay attention to improving the knowledge of the general population and health care professionals about these mental disorders. Furthermore, considering the high prevalence of ADHD and depression among students, timely diagnosis and treatment of these disorders are important. Moreover, there is a need to design appropriate interventions for university students. More studies are needed to determine the definite prevalence and determinant factors in the whole country. Thus, future researchers better to explore the relationship and magnitude of ADHD and depression longitudinally through interview-based methods and a diagnostician such as a psychiatrist.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.


Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of cellular dynamics with minimal influence from batch effects. For inference, scTour simultaneously estimates the developmental pseudotime, delineates the vector field, and maps the transcriptomic latent space under a single, integrated framework. For prediction, scTour precisely reconstructs the underlying dynamics of unseen cellular states or a new independent dataset. scTour’s functionalities are demonstrated in a variety of biological processes from 19 datasets.The online version contains supplementary material available at 10.1186/s13059-023-02988-9. Among the challenges that decoding developmental processes at single-cell resolution using single-cell RNA sequencing (scRNA-seq) poses, a unique difficulty is that scRNA-seq can only capture static snapshots of cells. In addition, experimental assays such as lineage tracing and metabolic labelling are inaccessible to many biological systems particularly those involving human tissues –5. Many https://github.com/LiQian-XC/sctour.Here I introduce scTour, an innovative deep learning-based architecture that, in addition to overcoming the limitations detailed above, achieves multifaceted dissection of a variety of biological processes under a single model in an unsupervised manner. scTour simultaneously infers the developmental pseudotime, transcriptomic vector field, and latent space of cells, with all these inferences largely unaffected by batch effects inherent in the datasets. Another advantage is that the pseudotime estimation does not need input of a starting cell, and the vector field inference does not rely on the discrimination between spliced and unspliced mRNAs, rendering scTour applicable to other genomic data. Importantly, the inference of a low-dimensional latent space which combines the intrinsic transcriptome and extrinsic time information provides richer information for reconstructing a finer cell trajectory. Its insensitivity to batch effects also allows for unbiased integration of different datasets. Uniquely in scTour, the resulting model can be further employed to predict the transcriptomic properties and dynamics of unseen cellular states and even to predict the characteristics of a different dataset new to the model. These together make scTour a generative and powerful method for single-cell developmental data analysis. To demonstrate the superiority of scTour, I have applied it to a wide variety of dynamic biological processes including neurogenesis, pancreatic endocrinogenesis, skeletal muscle, thymic epithelial cell and embryonic development, hematopoiesis, and brain vasculature zonation (scRNA-seq), as well as reprogramming (single-nucleus RNA sequencing (snRNA-seq)) and human fetal retinal development (single-cell ATAC-sequencing (scATAC-seq)). In all of these systems, the accuracy and effectiveness of scTour in recapitulating the underlying cellular dynamics was validated. scTour is available as an open-source software at scTour is a new deep learning architecture that builds on the framework of variational autoencoder (VAE)  and neurmentclass2pt{minimork Fig. ”.Fig. 1sCompared to the latent ODE model proposed in the original neural ODE publication , scTour DE model . With thAs a result, scTour provides two main functionalities in deciphering cellular dynamics in a batch-insensitive manner: inference and prediction Fig. . For infI first evaluated scTour using a scRNA-seq dataset from the mouse dentate gyrus during postnatal development. The focus here was on the granule cell lineage which undergoes sequential transcriptomic changes from neuronal intermediate progenitor cells (nIPCs), neuroblasts, immature granule cells, to mature granule cells  4007 cea. FollowThe advantages of applying scTour to a linear and continuous developmental process are clear. To further test its capability in dealing with more complex processes, I next applied scTour to another scRNA-seq dataset from the developing mouse dentate gyrus which collected some extra immature pyramidal neurons from the hippocampus proper . I focusGiven scTour’s design of model-based prediction and implementation of mini-batch training, it was possible that a scTour model could be trained from a subset of data and the resulting model could be used to derive the characteristics of the entire dataset. To test this possibility, I trained scTour models on the same dataset but used a series of subsets ranging from 1 to 95% of all cells. The results highlighted the robustness of scTour, as both the granule and pyramidal cell lineages already manifested when the model was trained from as small as 1% of all cells , and finally commits to four major fates: α-cells, β-cells, δ-cells, and ε-cells. I started by training the scTour model using all the cellular states involved in this process. Here I compared the derived developmental pseudotime with scVelo’s latent time. This was because the latter was shown to delineate this process more accurately than diffusion pseudotime as it captured the earlier emergence of α-cells relative to β-cells [Fev + endocrine cells to terminal fates which was not revealed by scVelo’s latent time . I therefore applied scTour to a scRNA-seq dataset from the development of endocrine compartment of the mouse pancreas, as previously described in the scVelo publication , 23  whether scTour can infer the cellular dynamics of a discontinued process; and (2) whether the resulting model can be used to predict the properties of the held-out cellular state. This analysis demonstrated that scTour can recapitulate the discontinuous differentiation course, assigning near-identical pseudotime as compared to that from the analysis of the entire dataset  Drop-seq-based measuring of the developing human cortex  , fetal , juvenile , and adult ) [MECOM, CRABP2, CD24, and ID2 [H19, MEG3, and NEAT1) were all involved in muscle differentiation [H19 was mainly expressed from late embryonic to fetal stages while MEG3 was more enriched in late fetal and juvenile stages, whereas NEAT1 was exclusive to postnatal SCs  and genes encoding heat shock proteins , indicating the early activation of those SCs induced by the cell isolation procedure during the experimental collections [SPARCL1, CRYAB, and GPX3) [NUPR1, C10orf10/DEPP) [One of the major directions the original study sought to explore was the molecular changes of the skeletal muscle progenitor and stem cells along development. By using scTour to particularly analyze those cells including the prenatal myogenic progenitors (MPs) and postnatal satellite cells (SCs), the gradual transcriptomic changes for MPs during prenatal development and the evident separation between MPs and SCs were revealed Fig. d. There  and ID2 –38 were ntiation  and dispSCs Fig. e. For thlections  Fig. 6end GPX3) –43, as w10/DEPP) , 45 to mend GPX3 Another major question the original study aimed to answer was the developmental status of the in vitro skeletal muscle progenitor cells (SMPCs) that were derived from human pluripotent stem cells (hPSCs) through different directed differentiation protocols  . This waHere I present scTour, a novel deep learning architecture to perform multiple tasks in dissecting cellular dynamics. scTour starts from the raw gene expression matrix and ends with the full developmental dynamics revealed under a single framework, including the developmental pseudotime, vector field, and latent representations of cells. The resulting latent space, which is not available in many trajectory inference tools, offers information on trajectory reconstructions, cell stratifications, and data integrations. More importantly, all the inferences from scTour are largely unaffected by batch effects, and the ultimate estimates are driven by intrinsic biological signals. This presents a fascinating feature for exploring the cellular dynamics by integrating datasets from different studies, experimental platforms, and systems. scRNA-seq data integration has been a challenging task and scTour provides an easy way to approach this goal in the context of analysis of various dynamic processes.The uniqueness of scTour also lies in its prediction functionalities comprising predicting cell characteristics given the transcriptomes and predicting the transcriptomic latent space given the time interval. This prediction is robust across biological systems, species, and experimental platforms and provides a convenient way for cross-data comparisons by propagating the information from existing datasets to new ones.scTour also introduces an alternative way to calculate transcriptomic vector fields. Compared to the state-of-the-art RNA velocity , 8, scToDespite these advantages, scTour’s vector field cannot delineate the cycling processes in full compared to the RNA velocity  as input. Depending on the mode chosen for reconstructing the input, the abundance matrix is required to be log-transformed normalized expression (for MSE mode) or raw counts (for NB and ZINB modes). Selection of highly variable genes is suggested before model training to reduce runtime and improve the performance. Although the application of mini-batches in neural ODE is less straightforward , mini-bamentclass2pt{minimmentclass2pt{minimmentclass2pt{minimscTour provides the option to train the model with a subset of cells. Specifically, scTour first shuffles the entire dataset and then randomly samples a given proportion of cells from the shuffled data. The two rounds of randomness ensure the preservation of the cellular diversity. This step reduces the training time and has marginal influence on the model performance as shown in multiple datasets.After the model training, scTour assigns a developmental pseudotime to each cell based on the learned time neural net otential . SpecifiThe transcriptomic vector field is the learned differential equation The latent representations of cells in scTour are the weighted combination of Larger mentclass2pt{minimGiven the gene expression matrix of query cells from an unobserved cellular state or a new dataset, scTour predicts their developmental pseudotime by the time neural net k-nearest neighbors. Specifically, the developmental pseudotime k-nearest neighbors in the reference are obtained by comparing k-nearest neighbors:Regarding the prediction of the transcriptomic space given an unobserved time interval For each time point estimated, the resulting latent state The visualization of the transcriptomic vector field on a low-dimensional embedding such as UMAP is obtained using a similar approach as in velocyto  and scVeThe two datasets from the mouse dentate gyrus used in Fig. mentclass2pt{minimFor the second dataset downloaded from GEO (GSE104323) , the celFor the dataset shown in Additional file The dataset from the mouse pancreatic endocrine development , 23, 56 To test the ability of scTour to predict the dynamics of unseen cellular states, the model was trained by excluding one of the cell types and the resulting model from the remaining cell types was used for two purposes: (1) predicting the developmental pseudotime, transcriptomic vector field, and latent representation of the excluded cell type given its gene expression matrix. The accuracy of the prediction for the pseudotime was quantified through calculating the area under the receiver operating characteristic (ROC) curve (AUC) for the binary classifications between the held-out cell type and those preceding or after it. This calculation was based on the predicted pseudotime for the held-out cell type and the inferred pseudotime for the remaining ones using the functions “sklearn.metrics.roc_curve” and “sklearn.metrics.auc” provided in scikit-learn . (2) PreDatasets profiling the cortical excitatory neuron development used in Fig. For the first dataset, since the excitatory neuron subtypes in the original study were labelled with arbitrary numbers, I relabelled those cells according to the second dataset where the excitatory neuronal cells were named on the basis of their maturity along the differentiation path. Specifically, CellTypist  was usedThe scTour model was then trained based on the first dataset (training data) by using 60% of all cells, and 765 genes which were the intersection of the top 1000 highly variable genes from this data with the genes detected in all the other three datasets (test data). This model was used to infer the developmental pseudotime, transcriptomic vector field, and latent space  came from a study on embryonic gastruloids measured using 10x Genomics , 64. TheThis dataset 14,217 cells) profiled the human thymic epithelial development using 10x Genomics  cells pr. The celThis dataset (1195 cells) was from a gastrulating human embryo measured using Smart-seq2 , 66. TheThis dataset has 90 cells from human preimplantation embryos with single cells isolated by the mouth pipette , 67. ForThis dataset 251,203 cells) was from a time course of iPSC reprogramming measured using 10x Genomics 3 cells w. The oriThis snRNA-seq dataset  was from a study on human cell reprogramming , 69. SimThis scNT-seq dataset 1947 cells) was from in vitro culture of the CD34 + human hematopoietic stem and progenitor cells (HSPCs) 47 cells . The genThis dataset (3105 cells) was focused on the endothelial cells of the mouse brain , 71. To http://pklab.med.harvard.edu/peterk/review2020/ [This dataset 2726 cells) was from the E15.5 mouse retina 6 cells w and downiew2020/ . The scTiew2020/ . For obtThis scATAC-seq dataset (4883 cells) was from the fetal human retina , 76. PreTo benchmark scTour against existing methods including scVelo , PalantiFor each dataset, 20% of cells randomly sampled from the entire data were used to train the scTour model, which yielded the developmental pseudotime, transcriptomic vector field, and latent representations  or Dnmt3b (for D2) was designated as the starting cell when determining the pseudotime.The pseudotime estimation was conducted based on the tutorial at https://satijalab.org/seurat/articles/integration_introduction.html were followed [PAX6 (D1) when estimating the pseudotime.The Seurat Wrappers package was used to run Monocle 3 for D1 on the Seurat objects with or without batch effect correction, respectively. To obtain the batch-corrected Seurat object, the procedures from the tutorial at followed . The staThe pseudotime was estimated for D1 based on a two-dimensional UMAP embedding (derived from 30 PCs) and a vector of clustering labels (from Louvain clustering with a resolution of 0.2). The starting cluster was set to the one with the highest proportion of cycling progenitor cells. The final pseudotime was calculated as the average across the lineages. To rerun Slingshot by removing batch effects, the integration process from Seurat as described above was conducted to obtain batch-corrected UMAP and clustering labels for pseudotime estimation.The scVI model training was performed using the default parameters to generate a 10-dimensional latent space. This was run twice, with or without batch information provided, respectively. For D2, an additional factor “stage” was provided as a covariate along with the factor “sequencing.batch” provided as the batch key.GLI3, TFAP2C, PAX6, SOX2, EOMES, JUND, NFE2L2, SOX9, EMX2, and FOS) and excitatory neurons  [To examine the accuracy of the pseudotime estimated from different methods, for D1, the well-established marker genes along the excitatory neuron developmental trajectory were collected, including the markers for progenitors (d MYT1L) , 26. SpeTo check the consistency of the vector field across neighboring cells, consistency score, the same metric as defined in the scVelo publication, was computed here, which was the mean Pearson correlation coefficient calculated between the vector field of a given cell and its neighbors. To examine the correlation between scTour’s vector field and the RNA velocity, the cosine similarity was computed based on the projected velocities under the same UMAP embedding, that is, the weighted combination of unitary displacement vectors obtained from scTour and scVelo.To evaluate the degree of batch correction and biological signal conservation, three metrics from scArches  and scIBThe scRNA-seq data profiling the human limb muscle tissues across prenatal and postnatal development, as well as the hPSC-derived in vitro muscle cells from different protocols was downloaded at skeletal-muscle.cells.ucsc.edu , 82. To p-value adjusted by the Bonferroni correction. The top 100 most significant genes are shown in Fig. Next, another scTour model was trained focusing on the skeletal muscle progenitor and stem cells . The training was conducted on the basis of 90% of all cells, and 1791 genes which were the intersection of the top 2000 highly variable genes with genes expressed in the in vitro skeletal muscle progenitors. The resulting model was used for two purposes: (1) to infer the pseudotime and latent space (50%  Monocle . The sighttps://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k) and velocyto [Two datasets with only terminally differentiated cells were used to test the ability of scTour’s vector field to detect the absence of velocity: PBMC 3 k (from 10x Genomics)  and humavelocyto  was usedTo assess the impact of the parameter entclass1pt{minimaTo assess the contributions of mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimAdditional file 1: Fig. S1. Inferred vector field of cells during granule cell differentiation in the mouse dentate gyrus using scTour and scVelo. Fig. S2. Application of scTour to the developing mouse hippocampus dataset. Fig. S3. scTour’s inference is robust to cell subsampling. Fig. S4. scTour captures the developmental cellular dynamics in embryonic organoids. Fig. S5. scTour captures the developmental cellular dynamics in the human thymic epithelial cells. Fig. S6. scTour captures the developmental cellular dynamics in human gastrulation. Fig. S7. scTour captures the developmental cellular dynamics in human preimplantation. Fig. S8. scTour captures the cellular dynamics during iPSC reprogramming in mice. Fig. S9. scTour captures the cellular dynamics during iPSC reprogramming in humans. Fig. S10. scTour captures the developmental cellular dynamics during hematopoiesis. Fig. S11. scTour captures the anatomical topography of brain endothelial cells. Fig. S12. Superiority of scTour’s pseudotime over scVelo’s latent time in characterizing a discontinued process. Fig. S13. Comparisons of scTour’s vector field with velocities from other methods. Fig. S14. scTour predicts the cellular dynamics of unseen cellular states regardless of their positions along the developmental process. Fig. S15. scTour reconstructs the transcriptomic space at different developmental stages. Fig. S16. scTour predicts the latent space of unseen datasets. Fig. S17. Benchmarking scTour against existing methods using the dataset profiling mouse gastrulation and early organogenesis. Fig. S18. Application of scTour to scATAC-seq data. Fig. S19. Delineation of the cycling process by scTour’s vector field and RNA velocity. Fig. S20. Application of scTour to terminally differentiated cells. Fig. S21. Assessment of the parameter alpha in the objective function in a dataset of granule cell differentiation. Fig. S22. Assessment of the parameter alpha in the objective function in a dataset of pyramidal neuron and granule cell development. Fig. S23. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of granule cell development. Fig. S24. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of excitatory neuron development. Fig. S25. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of granule cell and pyramidal neuron development. Fig. S26. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of pancreatic endocrinogenesis.Additional file 2: Table S1. Neural networks used in scTour.Additional file 3: Table S2. Hyperparameters in scTour model training.Additional file 4: Table S3. Summary of the datasets used in this study.Additional file 5. Review history.