In children with ADHD, but not the typically developing children, there was a significant relationship between interhemispheric SMN functional connectivity and mirror overflow . Our findings of stronger interhemispheric functional connectivity between homologous somatomotor regions in children with ADHD is consistent with previous transcranial magnetic stimulation and diffusion-tractography imaging studies suggesting that interhemispheric cortical inhibitory mechanisms may be compromised in children with ADHD. The observed brain-behavior correlation further suggests that abnormally strong interhemispheric SMN connectivity in children with ADHD may diminish their ability to suppress overflow movements.Mirror overflow is a developmental phenomenon defined as unintentional movements that mimic the execution of intentional movements in homologous muscles on the opposite side of the body. In children with attention-deficit/hyperactivity disorder (ADHD), mirror overflow is commonly excessive, abnormally persistent, and correlated with ADHD symptom severity. As such, it represents a promising clinical biomarker for disinhibited behavior associated with ADHD. Yet, the neural underpinnings of mirror overflow in ADHD remain unclear. Our objective was to test whether intrinsic interhemispheric functional connectivity between homologous regions of the somatomotor network (SMN) is associated with mirror overflow in school age children with and without ADHD using resting state functional magnetic resonance imaging. To this end, we quantified mirror overflow in 119 children  during a finger sequencing task using finger twitch transducers affixed to the index and ring fingers. Group ICA was used to identify right- and left-lateralized SMNs and subject-specific back reconstructed timecourses were correlated to obtain a measure of SMN interhemispheric connectivity. We found that children with ADHD showed increased mirror overflow ( ChildreTranscranial magnetic stimulation (TMS) and diffusion-tensor imaging (DTI) studies suggest interhemispheric cortical inhibitory mechanisms are compromised in children with ADHD . AdditioWe aimed to test whether intrinsic functional connectivity between homologous left and right somatomotor regions was associated with increased mirror overflow. Given that greater functional connectivity between brain regions reflects reduced functional segregation, we hypothesized that children with ADHD would display significantly greater interhemispheric functional connectivity between homologous somatomotor regions compared to TD children, and that, among children with ADHD, increased SMN interhemispheric functional connectivity would be associated with increased mirror overflow.22.1These studies were approved by the Johns Hopkins University Institutional Review Board. A parent or guardian of the participants signed informed consent and all participants provided verbal assent.2.2One hundred and nineteen 8- to 12-year-old children were included in this sample: 62 children with ADHD  and 57 TD children  . These pChildren with histories of neurologic illness or injury, genetic disorders, seizures, or intellectual disability were excluded from the study. Children with a full scale IQ (FSIQ) <80 on the Wechsler Intelligence Scale for Children, Fourth or Fifth Edition .Children taking non-stimulant medications, selective serotonin reuptake inhibitors, or other psychotropic medications were excluded. Those taking stimulant medications (34%) were asked to withhold their medications the day of and the day prior to participating in the study to avoid effects of stimulants on cognitive, behavioral, and motor measures.Children were included in the TD group only if they did not meet diagnostic criteria on either the diagnostic interview or the ADHD rating scale. Children in the TD group were deemed ineligible if they were taking any psychotropic medications, such as stimulants or mood stabilizers.2.4All participants completed a mock scanning session to habituate to the MRI environment. Resting state fMRI (rsfMRI) scans were acquired on a Philips 3 T scanner using an 8-channel  or a 32-channel  head coil using a single-shot, partially parallel, gradient-recalled echo planar sequence with sensitivity encoding . An ascending slice order was used, and the first 10 s were discarded at the time of acquisition to allow for magnetization stabilization.Functional MRI preprocessing procedures for group independent components analysis (group ICA) are described in detail elsewhere . For qua2.5http://icatb.source forge.net/; Medical Image Analysis Lab, Albuquerque, New Mexico). Prior to running group ICA, the dimension of each scan was estimated using an information theoretic approach  using principal component analysis to decrease computational demands, denoise the fMRI signal, and improve back-reconstruction of participant-specific spatial maps (SMs) and component timecourses (TCs) . ParticiICA was repeated on the 56 group-level PCs 100 times using the Infomax algorithm  with ran2.6t-tests confirmed that the spatial topography of subject-specific left and right SMN SMs were not significantly different between groups.We were specifically interested in extracting two resting-state somatomotor networks spatially corresponding to the left and right pre- and post-central gyri see . Spatialhttp://afni.nimh.nih.gov/afni) as an additional precaution against lingering noise artifact . The transducers were calibrated at 0° and 45° using AcqKnowledge software 4.2v (Biopac Systems Inc.) prior to beginning the task.Finger twitch transducers  were used to quantify mirror overflow  in degrees of displacement from a baseline position. Transducers were affixed to the index and ring fingers of the left and right hands over the metacorpophalangeal (MCP) joint to capture extension and flexion see A. The tr2.8B). Starting hand (left hand sequencing or right hand sequencing) for the first block was counterbalanced within diagnosis and sex. Ten blocks  of finger sequencing were collected. Correct positioning showed the tapping hand positioned upright with the palm of the hand facing the camera in front of the participant and the non-tapping hand resting over a pillow on the participant’s lap to avoid restriction of extension and flexion in their fingers, thus allowing for overflow movements. Ten seconds of baseline were collected before each block during which the tapping hand was held in tapping position, as described above, without tapping. Participants were instructed to tap the pads of their fingers to their thumb in sequence (one sequence: index-middle-ring-pinky) “as big and fast” as possible to ensure valid, independent taps. 11 sequences (45 taps) were collected per block. To calculate a total overflow (TOF) score, we summed overflow in the non-tapping hand across all left- hand finger sequencing and right-hand finger sequencing blocks  to assess mirror overflow in the non-tapping hand (in degrees of displacement) see B. Startig blocks . With th2.9Behavioral analyses did not include covariates. An analysis of variance (ANOVA) was used to assess effect of diagnosis on TOF. Linear regression was used to investigate the relationship between intrinsic interhemispheric SMN connectivity and TOF. We included an interaction term between SMN connectivity and diagnosis to test for group differences in this relationship while controlling for possible confounders, namely head motion as measured by mean framewise displacement (FD) and the head coil used during the scan. Due to the significant diagnostic difference in FSIQ score, imaging analyses were conducted with and without covarying for FSIQ and similar results were obtained. Therefore, the results reported do not include FSIQ as a covariate.2.10Deidentified data included in this study is available upon request by any author listed on this manuscript.33.13.1.1p < 0.001; d = 0.671), such that children with ADHD showed greater TOF compared to TD children . Children with ADHD who demonstrated stronger right-left interhemispheric SMN functional connectivity also demonstrated greater total overflow; a 0.1 increase in right-left SMN functional connectivity in the ADHD group resulted in a 93.2 increase in TOF compared with the TD group . Conversely, this effect was not observed in TD children .We found a significant interaction between diagnosis and interhemispheric SMN functional connectivity on TOF  metrics related to neural inhibition in children with ADHD . ShorterFurther support of deficient inhibition in children with ADHD is offered by Our findings are also consistent with existing structural MRI examinations of the corpus callosum (CC) in ADHD. McNally et al., found that girls with ADHD showed a relationship between measures of response control on a Go/No-Go task and the circumference of the isthmus . Given tIt is possible that both inhibitory and excitatory signaling would be affected by abnormalities in the CC. However, a study using TMS to examine inhibitory and excitatory signaling found that while children with ADHD show slightly higher intra-cortical facilitation compared to their TD peers, they show a more robust decrease in intra-cortical inhibition . AdditioTogether, these behavioral and neural findings give way to discussions about their practical and clinical significance. Atypical motor development, including the presence of excessive motor overflow, can negatively impact a child’s functioning in school and in social settings. For example, extraneous movements could hinder one’s performance in playing instruments or participating in athletics. Though motor overflow typically diminishes in adolescence as the motor system matures, findings from the current and previous literature can be used to create therapies for more quickly ameliorating excessive motor overflow .We should note a few limitations of the current study. First, our overall sample size was modest and included a small number of girls which limited our ability to examine effects of sex in our statistical models and generalize findings to girls with ADHD. Second, while strong correlations between resting state functional connectivity and mirror overflow suggests that interhemispheric somatomotor functional connectivity is important for suppression of this behavior, we can only speculate about the order of events that results in this brain-behavior correlation. Studying fMRI data alongside other imaging data, such as EEG or TMS, would help to understand the functional connectivity of networks directly underlying persistence of mirror overflow in children with ADHD.Therefore, future studies would benefit from the inclusion of electrophysiological measures of interhemispheric inhibition from TMS to examine interhemispheric inhibition of transcallosal networks that accompanies mirror overflow in children with ADHD. Furthermore, inclusion of diffusion tensor imaging (DTI) data would be advantageous for understanding whether structural abnormalities in interhemispheric connectivity are also associated with mirror overflow, in parallel to the current findings of increased interhemispheric SMN functional connectivity in ADHD. Lastly, an analysis of functional connectivity data during a finger sequencing task would allow for insight on the extent of interhemispheric functional connectivity between SMNs while motor overflow is occurring.5In summary, the present study investigated associations among diagnosis, interhemispheric SMN functional connectivity, and total overflow in children with and without ADHD. Children with ADHD demonstrated greater total overflow and interhemispheric SMN functional connectivity compared to their TD peers, as well as a positive relationship between SMN functional connectivity and TOF. The ADHD-associated increase in interhemispheric SMN connectivity we observed is consistent with the theory that interhemispheric cortical inhibitory mechanisms are disrupted in ADHD, leading to an impaired ability to suppress motor overflow movements. Future studies should continue to build on this work investigating the link between the neurobiology underlying unwanted movements and the mechanisms critical for impaired behavioral inhibitory capacity more generally in ADHD.C. Chen: Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Writing - review & editing. D. Lidstone: Data curation, Formal analysis, Software, Visualization, Writing - original draft, Writing - review & editing. D. Crocetti: Investigation, Resources, Project administration, Writing - review & editing. S.H. Mostofsky: Conceptualization, Funding acquisition, Supervision, Writing - review & editing. M.B. Nebel: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Writing - original draft, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The use of attention-deficit hyperactivity disorder (ADHD) medication is increasing worldwide, but its status in China is unknown. This research aimed to assess the trends of ADHD medication use in young Chinese patients between 2010 and 2019.Dispensing data related to ADHD medication use were extracted from the Hospital Prescription Analysis Cooperative Project of China. The trends in the yearly prescription number of ADHD drugs and corresponding cost were analyzed. We further stratified the data by age, sex, and specific drug.From 2010 to 2019, sampled prescriptions for ADHD medication increased from 902 to 4531, and the total expenditure increased rapidly from 276,580 to 2,412,308 Chinese Yuan. Prescriptions for males were almost fourfold more than that for females. Patients aged 6–11 years had the highest number of prescriptions for ADHD medication each year, accounting for more than 50% of the total number of prescriptions. The percentage of methylphenidate prescriptions decreased from 91.9% in 2010 to 76.9% in 2019, and the corresponding cost declined from 77.3% to 66.8%. In contrast, atomoxetine prescriptions increased progressively and accounted for about 24.5% of the total prescriptions at the end of the study.The use of ADHD drugs and the corresponding cost increased rapidly in China, and methylphenidate was the most frequently prescribed medicine. The increase in ADHD prescriptions requires attention to ensure that it reflects appropriate use, especially in patients aged 6–11 years. Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders in childhood and adolescence . It is aThis study was approved by the Ethics Committee of the Second Affiliated Hospital, Zhejiang University School of Medicine. The requirement for informed consent was waived owing to the retrospective nature of the study.Data were collated using the Hospital Prescription Analysis Cooperative Project database, which has been previously validated and extensively used for epidemiological studies in China –12. The P value of.05. All the statistical analyses were run in R .Hospitals located in six major regions of China  that participated in the program continuously from 2010 to 2019 were identified. All prescriptions for ADHD drugs for outpatients diagnosed with ADHD were identified and extracted for analysis. The currently available ADHD drugs in China are methylphenidate (N06BA04) and atomoxetine (N06BA09); this study only focuses on these two drugs. The study focused on prescriptions that were written for participants in the 2–24 year age group. The number of yearly prescriptions was calculated by adding up the prescriptions on the sampled days in each year. The yearly expenditure was calculated by adding up all the costs of the ADHD medications. It should be noted that yearly prescriptions and costs in this study corresponded to 40 sampled days in each year. The trends of yearly prescriptions and cost were assessed and further stratified by age and sex of patients on the prescriptions and the specific drug. Trends in proportions were tested by log-linear analysis. Other trends were analyzed by means of the Mann–Kendall trend test. The criterion for statistical significance was a two-sided P < 0.001), and the total expenditure increased rapidly from 276,580 in 2010 to 2,412,308 Chinese Yuan (CNY) in 2019 (P < 0.001).A total of 27,882 prescriptions written from 2010 to 2019 from 14 hospitals were included in the study. As shown in P = 0.013), and the corresponding cost declined from 77.3% to 66.8% (P = 0.005). Atomoxetine accounted for 33.2% of the cost and about 24.5% of the total prescriptions by 2019 . Prescriptions for males were about 80% of all prescriptions in each year, and no significant trend was noted (P = 0.957).Among all prescriptions for ADHD drugs, the major percentage comprised patients aged 6–11 years, and this percentage continued to increase during the study period . The numP = 0.011), especially in boys aged 12–18 years (P = 0.001). There was no change in the percentages of prescriptions over time for boys aged 2–5 years (P = 0.19) and 6–11 years (P = 0.483). Prescriptions for boys aged 19–24 years were associated with a minor portion and exhibited a decreasing trend (P = 0.011). Conversely, there was no significant trend in the percentages of prescriptions over time for female patients although the percentage in 2019 (14.9%) was lower than that in 2013 (18.8%) (P = 0.163). There was no significant trend in the prescriptions for females aged 2–5, 6–11, and 19–24 years . The percentage of prescriptions for females aged 12–18 years decreased (P = 0.003). For atomoxetine, an increase in the percentages of prescriptions for male patients (7.2% to 20.2%) during the study period was observed (P = 0.007). The increasing trend was also observed in prescriptions for boys aged 6–11 (P < 0.001) and 12–18 years (P = 0.021). There was no change in the percentage of prescription for males aged 2–5 years (P>0.05) and 19–24 years (P>0.05) over time. An increase in the percentage of prescriptions for females (1.0% to 4.4%) was observed (P = 0.017) similar to that in prescriptions for girls aged 6–11 years (P = 0.034) and 12–18 years (P = 0.005). There was no change in the percentages of prescriptions for girls aged 2–5 years (P>0.05) and 19–24 years (P>0.05).The specific ADHD drugs used in each age group and sex are shown in To the best of our knowledge, this is the first study to assess the trends of ADHD drug use in young outpatients in China. This study found that both prescriptions and the corresponding cost for ADHD drugs increased significantly from 2010 to 2019 and that the predominant drug for ADHD was methylphenidate.Similar to other countries, a dramatic increase in prescriptions and cost were found in this study , 14. ThePrescriptions for patients aged 6–18 years comprised the majority of ADHD medication use in China. This is similar in other countries , 18. HowIn terms of drug therapy, stimulants, such as methylphenidate, are considered the first-line pharmacological treatment for ADHD and have been used for the last 50 years. In this study, methylphenidate was also the most frequently prescribed ADHD medication. Whereas the mechanism by which it reduces symptoms in ADHD is not completely clear, it is believed that it enhances intrasynaptic concentrations of dopamine and noradrenaline in the frontal cortex as well as in the subcortical brain regions associated with motivation and reward. The common adverse effects of methylphenidate are appetite loss, abdominal pain, headaches, and sleep disturbance . FurtherAnother commonly used drug for ADHD in China is atomoxetine, which is used as a second-line treatment . PresentOne study shows that both methylphenidate and atomoxetine were effective in improving a wide range of emotional/behavioral problems in youths with ADHD. However, the methylphenidate group correlated with a greater improvement in aggressive behavior, somatic complaints, and conduct problems .Our study has certain limitations. The database only included the prescriptions of patients with ADHD, but the clinical profiles, including the severity or treatment outcome, were not measured and should be investigated in further studies. Sampling bias may exist because this study only involved hospitals located in six regions of China.This study provides the most recent national data on pediatric use of ADHD medications in China. The number of prescriptions for ADHD medications and the costs were found to increase rapidly. We, therefore, need to monitor trends of drug use with regard to ADHD medications and determine their safety in children with ADHD.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Ethics Committee of Second Affiliated Hospital, Zhejiang University School of Medicine. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.LY: conceptualization, visualization, resources, and writing of the review and editing. ZY and LY: methodology. ZW and LY: data curation. ZW, XW, ZY, and LY: formal analysis. ZW and XW: investigation. LY and XW: validation. ZW, XW, and ZY: writing of the original draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


After desiccation, an epiphytic resurrection fern recovers photosynthetic activity—whether or not it is connected to the rhizome—while xylem remains embolized. Pleopeltis polypodioides can survive extreme desiccation and recover physiological activity within hours of rehydration. Yet, how epiphytic DT ferns coordinate between deterioration and recovery of their hydraulic and photosynthetic systems remains poorly understood. We examined the functional status of the leaf vascular system, chlorophyll fluorescence, and photosynthetic rate during desiccation and rehydration of P. polypodioides. Xylem tracheids in the stipe embolized within 3–4 h during dehydration. When the leaf and rhizome received water, tracheids refilled after ∼24 h, which occurred along with dramatic structural changes in the stele. Photosynthetic rate and chlorophyll fluorescence recovered to predesiccation values within 12 h of rehydration, regardless of whether fronds were connected to their rhizome. Our data show that the epiphytic DT fern P. polypodioides can utilize foliar water uptake to rehydrate the leaf mesophyll and recover photosynthesis despite a broken hydraulic connection to the rhizome.The epiphytic resurrection—or desiccation-tolerant (DT)—fern  Desiccation-tolerant (DT) ferns live at the extreme margins of the environmental envelope capable of sustaining plant life. They can temporarily suspend most biological functions when fully dehydrated, reaching water potentials ∼−100 MPa after losing 90% of intercellular water and reaching equilibrium with the surrounding atmosphere . After pPleopeltis polypodioides (L.) Andrews and Windham, synonym Polypodium polypodioides; Polypodiaceae) is distributed through southeastern North America to South America  and the Coffee fern (Pellaea andromedifolia), xeric terrestrial resurrection ferns  surface of fronds, which are shield-like trichomes made of cellulose and lipids. In ferns within the Polypodiaceae and Anemiaceae, scales have been shown to protect leaf tissue against excess photodamage  determine whether losses of conductivity in the xylem due to embolism influenced photosynthetic capacity at different stages of dehydration and (2) observe the temporal dynamics of photosynthetic recovery and determine its dependence on the functional status of the xylem, or whether recovery can occur without a connection to the rhizome and root system. During the recovery phase, not only do the leaves need to restore turgor and photosynthetic capacity, but they also need a supply of water to sustain transpiration. If we observe similar timing of photosynthetic and hydraulic decline and recovery, then photosynthetic capacity is closely linked to the functional status of the xylem. On the other hand, if fronds without connection to the rhizome can recover photosynthetic capacity along a similar timeframe as fronds that are connected to the rhizome, then foliar water uptake could be the primary pathway of rehydration. Our study combines the use of multiple noninvasive techniques—including high-resolution X-ray computed tomography (microCT), chlorophyll fluorescence imaging, and gas exchange—to capture many previously unknown facets of desiccation and recovery dynamics. Given that ferns represent a critical evolutionary link between nonvascular plants and seed plants, studying resurrection dynamics in ferns—and accounting for any differences between terrestrial and epiphytic DT ferns—can inform our understanding of how resurrection dynamics differ across the phylogeny.Here, we examined the coordination between the functional status of hydraulic and photosynthetic systems that occurs during desiccation and recovery of the epiphytic resurrection fern The freehand stipe cross sections under both well hydrated Figure 1; and dehyTime-lapse imaging of the fronds during dehydration in the lab was consistent with previously documented changes in leaf shape . Leaves sd)) (n = 5). Fronds that were dehydrated for 24 h in our subsequent experiments were assumed to fall within this average range of RWC.During the first 24 h of bench dehydration, the RWC of frond and rhizome material declined steeply at first and then more slowly , with a sd) MPa under well-watered steady state conditions before the experimental dry down (sd) MPa over the 24- h desiccation period. All plants showed a steep initial decline in rhizome water potential over the first 4 h, followed by a slower decline. Given that the rhizome was tightly sealed in paraffin film during the dry-down, we note that the rate of decline and absolute maximum and minimum water potentials are likely conservative estimates. Frond water potentials measured over time under identical conditions with the pressure chamber showed similar declines over the same period  MPa (n = 6). These data, along with our time-lapse imaging and microCT imaging of the leaves, suggest that cell turgor is lost within the initial 3–4 h of the dry-down experiments as the leaves begin to curl.Pressure-volume curves revealed that the average frond water potential at the turgor loss point was −1.35 (±0.31 Embolism was visible in stipes attached to rhizome sections after just 3 h (Group B) of dehydration , and wassd) of the xylem area was embolized  = 200.525, P < 0.001). However, post-hoc analyses with a Bonferroni correction revealed that only the pairwise difference within Group D (i.e. between the 24 h dehydrated and rehydrated scans) was statistically significantly different (P = 0.007). The percent of xylem area embolized was negligible for the hydrated control during both measurements.Within 3 h of dehydration, 93.1% (±6.3 mbolized . Percentmbolized . Xylem rmbolized . Percentsd) (sd), and increased to 55.6% (±7.1 sd) after 24 h of dehydration, that is, over half of the stele separated from the surrounding cortex as the stipes dehydrated. Unlike in Selaginella (sd) and 11.8% (±6.6 sd) of the stele perimeter was in contact with a lacunae gap, respectively. The percentage of stele perimeter touching a gap was statistically different for the experimental groupings across time   = 4.2, P = 0.008). Post-hoc analyses with a Bonferroni adjustment revealed that only the pairwise difference between the 24-h dehydrated and 24-h rehydrated group was significantly different (P = 0.019).We then quantified the physical changes to the stele resulting from dehydration and rehydration by measuring the size of the lacunae that formed between the endodermis and the surrounding cortex. After 3 h of dehydration, large lacunae developed between the stele and the cortex, and the percent of the stele perimeter exposed to a lacuna rather than having direct contact with the cortex was 42.5% (±27.9 sd) . The extaginella , we did aginella  and 4. Tsd) , while the mean diameter of nonembolized tracheids was 5.6 µm , while the mean diameter of nonembolized tracheids was 7.5 µm  . We measv′/Fm′F measurements on leaves used for the microCT imaging revealed that mean abaxial v′/Fm′F was between 0.58 and 0.63 for all treatment groups at the start of the experiment  but remaining relatively constant after 12 h of dehydration and 12 h of rehydration in Group C  = 3.6, P = 0.06).Well-hydrated abaxial periment . The Fv′ Group C . Substan Group C . The repd values . The avev′/Fm′F imaged values was conducted  = 14.9, P < 0.0001; v′/Fm′F were significantly different when well-hydrated (P = 0.038) and after 24 h of dehydration (P = 0.002). We found that the peltate scales did not account for the difference in abaxial and adaxial v′/Fm′F, as experimental removal of the peltate scales did not lead to any difference in the abaxial values . Post-hoc analysis with a Bonferroni adjustment confirmed that the adaxial mean was significantly different from both abaxial means with and without peltate scales , while there was no significant difference between the abaxial means (P = 0.98).Since only abaxial frond tissue was imaged at the beamline, additional experimentation comparing adaxial and abaxial onducted . From thl values . The ANOTime-lapse images of detached fronds during benchtop dehydration and subsequent rehydration illustrated that detachment from the rhizome did not prevent fronds from rehydrating. Fronds began rehydrating immediately, and leaf tissue unfurled completely within 12 h. When in the presence of moisture and maintained in a humid environment, these detached fronds remained green for 3 months despite being disconnected from the rhizome .Time-lapse images of the rhizome-only rehydration show that half of the fronds unfurled completely within 18–23 h after rehydration . This isWell-hydrated photosynthetic rates were comparable across all groups before beginning the dehydration treatments . DiffereF = 3.654, P = 0.0003). However, post-hoc analyses with a Bonferroni adjustment revealed that the pairwise differences between groups at specific time points were only statistically significantly different at 39 h (16 h rehydrated) and at 43 h (20 h rehydrated) of the experiment (P= 0.028). The detached group at 16 h of rehydration also had a photosynthetic rate that was statistically different from that of the detached and n.r group (P = 0.029). Similarly, at 43 h into the experiment, the photosynthetic rate of the Attached group—which had been rehydrating for 20 h—was statistically different from the photosynthetic rate of the detached and n.r group (P = 0.037). Finally, the photosynthetic rate of the detached and n.r group at 43 h of dehydration was statistically different from the photosynthetic rate of the Hydrated control group (P = 0.039). There were no statistically significant differences between the photosynthetic rates of the attached and the detached groups during any stage of dehydration or rehydration.The photosynthetic rates were significantly different between treatment groups across the experimental time points  to the abaxial surface of the leaf, uptake of the dye was clearly visible within 15 min of application and uptake was isolated to the peltate scales . PeltateP. polypodioides experienced complete hydraulic failure of the stipe xylem during the dehydration period, but over the same period lost only ∼50% of its photosynthetic rate, and 10%–20% of its photosynthetic capacity, which we estimated using chlorophyll fluorescence as a proxy. The highly vulnerable stipe xylem and rapid decrease in water potential during the dehydration period was coordinated with rapid loss of water from the mesophyll, where the mean turgor loss point was −1.35 (±0.31 sd) MPa. This sequence of events hydraulically decouples the frond and rhizome tissues, thereby preventing excessively negative water potentials in the rhizome. This hydraulic connection then remained broken until both the rhizome and leaf were fully saturated for at least 24 h. Recovery of leaf shape, turgor, and photosynthesis was much more rapid, which strongly suggests that foliar rehydration can both provide sufficient water for rehydration and also sustain photosynthesis for some period of time. Unlike terrestrial DT ferns P. triangularis and P. andromedifolia  revealed that these ferns lose a large percentage of their water content over the course of the dry-downs, with a steep initial decline followed by slow, steady decline (sd), and the stipe tracheids were completely embolized. While it is important to note that the relative humidity varied between experiments depending on the facility , we still observed that the timing of the decline of the photosynthetic and hydraulic systems remains coupled during dehydration.The average RWC after 24 h of dehydration  were able to repair embolism on a faster timescale than 24 h. However, we scanned each fern only twice to minimize potential artifacts produced by the X-ray imaging system. Given that the resurrection process occurs within 3–4 h in the natural environment, and refilling here required leaf saturation for 12–24 h, our data suggest that it is unlikely that photosynthetic recovery and leaf rehydration is supported primarily by water conduction through the xylem. Instead, our data support the idea of foliar rehydration as the primary means of recovery that facilitates subsequent embolism repair.Nearly all of the tracheids in the stipes were embolized after 3 h of desiccation, and embolism persisted until rehydration occurred for 24 h . These rEmbolism repair is likely possible by at least two nonmutually exclusive mechanisms, which include both passive . Our mihe xylem .v′/Fm′F images from the beamline were not substantially different in Group B (3 h) and Group C (12 h) while they were dehydrated and rehydrated, but abaxial v′/Fm′F declined and recovered substantially during the 24-h dehydration and rehydration cycle of Group D  MPa—suggests that cavitation in the stipe may act as a “hydraulic fuse” . For RWC, fronds attached to their one-inch rhizome sections were weighed every 10 s during a 24-h dehydration to calculate the RWC of the plants. We used fronds attached to rhizome sections in order to maintain consistency between our experiments, instead of measuring RWC separately on frond and rhizome material. Prior to beginning the dehydration, each plant was allowed to reach a turgid state by floating frond and rhizome material in petri dishes of water for 2 h. The turgid weight of the plant material was then recorded on a Sartorius balance , and the balance was configured to record the weight of the plant material every 10 s using WinWedge software . After 24 h of dehydration, each plant was removed from the balance and oven-dried at 80°C for 24 h to record the oven-dried weight. This process was repeated for five plants. RWC for all plants was calculated as RWC % = [(Weight – Dry Weight)/(Turgid Weight – Dry Weight)]*100.Because all of our treatments simulated droughts through dehydration events, we characterized the severity of these dehydrations by measuring RWCs and water potentials over time, as well as by measuring gas exchange while fronds dehydrated  mIn addition to measuring RWC throughout a 24-h dehydration, we measured rhizome water potentials on a separate group of eight fronds during dehydration using stem psychrometers . To install the stem psychrometers on the rhizome, the outer layer of the rhizome was gently scraped away as close to the base of the frond as possible to expose the xylem and allow the thermocouple of the psychrometer to be installed. Psychrometers were then wrapped in parafilm around the rhizome to maintain connection to the plant; given that rhizomes were tightly sealed with parafilm to secure the psychrometers, the rate of rhizome water potential decline during dehydration is likely a conservative estimate. These psychrometers recorded water potentials every 10 min during a 24-h dehydration. After 24 h, the fronds were rehydrated by wrapping the fronds in damp paper towels and enclosing them in plastic bags.n = 6) using a Scholander pressure chamber  and following established protocols for bench drying methods from the literature and Prometheuswiki (Pressure-volume curves were conducted on fronds (heuswiki . For then = 3) to observe stomatal conductance and photosynthetic rate during a 6-h dehydration . For each frond, the chamber was set to maintain the following conditions: 400 ppm CO2, 50% RH, and 300 µmol (photons) m−2 s−1 PPFD . Fronds were kept well hydrated with damp paper towels wrapped around the leaf and rhizome tissue until the start of measurements. Next, the paper towels were removed, and we then recorded gas exchange every 30 s over the 6-h period as the fronds dehydrated.We measured gas exchange on fronds  was also placed inside the chamber to monitor air temperature and relative humidity every 30 min. A piece of thick black cardstock was positioned in the background ∼2.5 cm from the frond. We photographed the frond every minute for ∼5 d with a digital camera (Canon RP with a 100 mm f = 2.8 lens) and constant illumination from an LED light source . The frond began the experiment in the curled, dehydrated state. The leaf was then periodically sprayed with water to rehydrate the leaf while maintaining the relative humidity at ∼100% until the leaf had fully rehydrated. The leaf was then allowed to dry at ambient conditions as the relative humidity declined and reached equilibrium with the surrounding air. The image sequence was then assembled into a time-lapse movie using Adobe Premiere Pro . The temperature and relative humidity data were then added to the time-lapse video .−2 s−1) for 43 h (n = 5). After dehydration, we detached fronds from the rhizome and placed damp paper towels underneath the abaxial leaf surface in order to continually supply water to the leaf tissue. To avoid the damp paper towels from drying out, we also applied water to the leaf surface using a water dropper every hour during rehydration. These fronds were positioned under a time-lapse camera  and photographed hourly for a few weeks to document the physical changes of the frond during dehydration and track whether recovery of the fronds was possible without attachment to the rhizome but with the addition of foliar water. Twenty-four hours after beginning open-air rehydration, we placed the fronds inside 50-mL plastic centrifuge tubes with a wet paper towel to determine how long these detached fronds could survive in the continued presence of moisture without connection to the rhizome. Time-lapse imaging continued for a month to document the state of the detached fronds during rehydration  mydration .−2 s−1) for 24 h (n = 4). After 24 h of dehydration, the rhizomes were placed in petri dishes of water, carefully positioned so that the leaf material did not get wet. The time-lapse camera  photographed the fronds hourly for 36 h to track their rehydration via the rhizome  m rhizome .v′/Fm′F) on the abaxial side of 12 fronds with an IMAGING PAM M-Series MINI fluorometer . We then divided these 12 fronds into four experimental groups that would undergo different levels of dehydration (−2 s−1). Group C was dehydrated for 12 h, and Group D was dehydrated for 24 h, both under the same ambient bench conditions. We prepared the centrifuge tubes so they could be mounted in the microCT sample holder by drilling a hole through the bottom and attaching a screw that could be fixed and mounted in the X-ray beam. Fronds and their rhizome sections were then mounted in these tubes between two pieces of polystyrene foam to keep the stipe centered and immobile during the scanning period. Stipes were scanned at 15 keV using the 10× magnification and continuous tomography setting on the instrument, where the stipe was rotated in 0.125° increments over 180° following previous X-ray tomography methods  at the Lawrence Berkeley National Laboratory Advanced Light Source (ALS) in Berkeley, California. Prior to scanning, we captured an image of the spatial distribution of chlorophyll fluorescence (ydration . Group An = 4) to determine the hydration status of the leaf mesophyll.After we scanned each dehydrated group, we rehydrated the fronds by applying water with a water dropper to both the rhizome and fronds until the plants were covered in a film of water. Rhizome and frond sections were then enclosed in their centrifuge tubes to maintain a humid environment for rehydration. We allowed the ferns to recover for the same amount of time that they were dehydrated . We then repeated fluorescence imaging, and each frond in each group was scanned again in the rehydrated state. Finally, we scanned a few well-hydrated and dehydrated leaves  while they were dehydrated and then rehydrated.An IMAGING PAM M-Series MINI fluorometer  was used to capture images of the v′/Fm′F values of five fronds were compared in both well-hydrated and dehydrated states (24 h of dehydration). Since leaf curling occurs during dehydration, the pinnae were carefully flattened to expose the adaxial leaf surface during the 24-h dehydrated images. All fronds, whether well-hydrated or dehydrated, were imaged underneath clear plastic wrap that was clipped to a piece of cardboard in order to flatten the pinnae for imaging. Additional images were taken on five well-hydrated fronds from which we removed peltate scales from the abaxial leaf surface to determine if the presence of scales impacted the v′/Fm′F values of abaxial leaf tissue.Additional fluorescence imaging was conducted separately from the microCT experiments; in this separate imaging, the adaxial and the abaxial v′/Fm′F of the leaf tissue by creating 10 circular areas of interest (AOI), where the v′/Fm′F of each pixel was calculated and averaged for that AOI. The mean of these 10 AOIs was taken to produce a mean v′/Fm′F for that image. In some of the images both the adaxial and abaxial leaf surfaces were visible due to leaf curling during dehydration; separate AOIs and mean values were calculated for abaxial and adaxial leaf surfaces.The ImagingWinGigE software that accompanies the IMAGING PAM M-Series MINI fluorometer  was used to analyze the fluorescence images. We measured the n = 741 tracheids) to obtain an accurate tracheid diameter distribution. We also examined our image stacks in Aviso software  using 2D and 3D visualizations.The raw tomographic projection images from the microCT were reconstructed to stacks of 2,160 2D images using python programming. Once we obtained the reconstruction image stacks, we used ImageJ  for analysis of xylem and stipe anatomy. Specifically, we measured the cross sectional xylem area, embolized xylem area, stele area and perimeter, the lacunae gap area between the cortex and the stele, tracheid diameters, and whole stipe area on the center slice of each reconstruction stack (similar to parameters measured in ). To mea2, 50% RH, and 300 µmol (photons) m−2 s−1 PPFD . A hydrated control group was kept well hydrated for the duration of the experiment (n = 3 per group). Hydration was maintained for the hydrated control group by wrapping rhizomes or stipes ends in damp towels and gently placing them in 50-mL plastic centrifuge tubes. These tubes were placed in sealed plastic bags and the leaf material that remained outside of the tubes was wrapped in damp towels to create a humid microenvironment. This process was repeated for the attached and detached groups upon their rehydration.To determine the role of attachment to and detachment from the rhizome during dehydration and rehydration we measured photosynthesis using an LI-6400 equipped with the conifer chamber  on four groups of three fronds. The chamber conditions were set to 400 ppm COperiment . An attaPrior to measurements, all 12 fronds were photographed for leaf area calculation in ImageJ  to recalculate the photosynthetic measurements based on the leaf area inside the conifer chamber. If applicable, fronds were patted dry with a paper towel to remove any surface film that would inhibit transpiration before being enclosed in the chamber. Fronds were enclosed in the chamber just below the most basal pinnae, and allowed to equilibrate for 10–15 min before logging three measurements per frond. Fronds were measured at the beginning of the experiment prior to any dehydration or detachment from the rhizome to get a baseline photosynthetic rate under well-hydrated conditions. After baseline measurements were taken, we dehydrated fronds from the attached group and both detached groups, and detached fronds from the rhizome for both detached groups. Photosynthetic measurements were then repeated for each group at 3, 12, 16, 20, and 24 h. After 24 h, the attached group and one detached group were rehydrated, and measurements were repeated at 27, 35, 39, 43, and 47 h.We used the foliar water uptake tracer, LY, diluted to 1 mM v/v .All statistics were conducted within R version 3.6.2 . Repeated measures ANOVAs were calculated for percent xylem area embolized, The following materials are available in the online version of this article. Light micrographs of freehand stipe transverse sections of P. polypodioides when hydrated and dehydrated. X-ray microCT images of well-hydrated and dehydrated pinnae. Tracheid diameter distribution and diameters of embolized and nonembolized tracheids. Representative abaxial and adaxial Fv′/Fm′ images for well-hydrated and dehydrated states, and differences in abaxial and adaxial Fv′/Fm′ with or without peltate scales. Experimental groupings and the conditions for each group. Dehydration and rehydration of a P. polypodioides frond with concurrent relative humidity (blue line) and temperature (red line) measurements. Time-lapse movie of frond-only rehydration. Time-lapse movie of rhizome-only rehydration.kiab361_Supplementary_DataClick here for additional data file.


The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction. Attention deficit hyperactivity disorder (ADHD) remains one of the most prevalent neurobehavioral disorders of childhood and adolescence, affecting social, emotional, and academic functioning. The DSM-V  segregatNeuroimaging findings in ADHD using diffusion tensor imaging (DTI) or resting-state functional MRI (rsfMRI) support the concept that a dysfunction in neurocircuitry may underlie this disorder and that differences in neural connectivity could distinguish between subtypes see –16. BaseAlthough a number of studies have examined structural circuitry with DTI –39 and fAccording to the brain network theory, the structural network (white matter tracts) constrains the functional brain network , but functional connectivity also deviates from the observed structural architecture , 43. In The degree of coupling between the structural and functional networks (SC-FC) differs according to the brain state or pathology. SC-FC coupling can help characterize brain disorders such as schizophrenia , epilepsIn this study, we explored the SC-FC coupling in the DMN, ECN, and SAN, intrinsic networks in the brain, using a database  from the Healthy Brain Network (HBN) database . We prim−7). No significant differences in Wechsler Intelligence Scale for Children [WISC, We used subject data from the Healthy Brain Network (HBN) database  for thisen WISC,  was founWe analyzed 201 subjects’ diffusion MRI, resting-state fMRI, and T1-weighted MRI data selected from the available releases (1 and 2) of the HBN database  accordinb-values . Following the procedure for multiple b-value diffusion imaging data analysis [https://www.fmrib.ox.ac.uk/fsl) in the individual’s diffusion MRI space [We constructed a structural connectivity network (SC) for each participant with 90 network nodes from cortical and subcortical regions defined according to the automated labeling (AAL) map . The difanalysis , we usedRI space . First, RI space . We thenTo create the structural networks, we performed “BedpostX” (Bayesian estimation of diffusion parameters obtained using sampling techniques) that implements Markov Chain Monte Carlo sampling to test propagation directions according to probabilistic orientation distribution at each voxel . To estihttp://www.fil.ion.ucl.ac.uk/spm, Wellcome Trust Centre for Neuroimaging, London, UK) [http://neuroimage.yonsei.ac.kr/mnet/), which offers graph analytical functions for fMRI and DTI. All fMRI data underwent slice scan time correction, head motion correction, co-registration of T1-weighted images to the first EPI, and spatial normalization for converting the functional EPI into MNI template space using nonlinear transformation. The fMRI time-series for each of the 90 AAL regions used in the structural analysis was obtained from the normalized fMRI data by applying principal component analysis to all the voxel time series from each region to extract its representative temporal activity . We then regressed out six rigid motion parameters and their derivatives, three principal components from the white matter and the cerebrospinal fluid masks, linear and quadratic regressors. A high-pass filter with a cutoff frequency of 0.009Hz was applied. Functional connectivity matrices were constructed by calculating Fisher’s z-transformation of the correlation coefficients between every pair of the 90 brain regions.To construct a functional connectivity network (FC), we conducted standard preprocessing of the fMRI data using SPM12   and an iTo investigate the topological characteristics of ADHD-related brain subnetworks, we evaluated the structural and functional features of the DMN, ECN, and SAN. According to previous studies –70, we cG with the total number of nodes N, global efficiency Eglob is defined as the average of all node efficiencies:d is the length of the shortest path between nodes i and j.As the structure-function coupling does not provide detailed information on whether the functional network deviates from the structural network in a positive (compensatory) or negative (as a result of pathology) mode relative to the symptom, we used global efficiency as an interpretive clue in terms of information transfer efficiency . For eacTo evaluate global efficiency, we constructed the network by thresholding the connectivity matrices: structural networks were constructed with one or more fiber counts while functional networks were thresholded with a positive value corresponding to a FDR  < 0.05.For the metric of SC-FC coupling, we followed , 73 and https://github.com/Jfortin1/ComBatHarmonization).To reduce effects derived from the two different data acquisition sites, we harmonized the network property and SC-FC couplings using a ComBat model –78 that To evaluate group differences in the multi-site harmonized structural and functional connectivity features and the SC-FC coupling of the global network, we employed a one-way analysis of covariance (ANCOVA), with age and sex as covariates. For the functional connectivity analysis, we used FDR < 0.05 as the criteria for statistical significance.In order to add a behavioral dimension, we conducted correlation analyses of the individual subject scores on both the Strength and Weaknesses of ADHD-symptoms and Normal-behavior (SWAN) battery with SC-FC coupling. First, we conducted a correlation analysis between hyperactivity and inattention scores to test whether a relationship between these scores manifests in either of the two subtypes. These two scores differentiate the two subtypes and construct two different domains. Second, we conducted a correlation analysis between the hyperactivity scores and SC-FC couplings and between the inattention scores and SC-FC couplings in the DMN to assess subtype differences.The network analysis results of the whole brain network are presented in The analysis of covariance (ANCOVA) with age and sex as covariates revealed significant group differences in global efficiency of the structural connectivity within the DMN  = 7.28, p = 0.0009), ECN  = 5.24, p = 0.0061), and SAN  = 6.70, p = 0.0015). As shown in −8)  and ADHD-C  . A statiThe SC-FC coupling in the DMN of ADHD-I demonstrated a significant negative correlation with the hyperactivity score  ; whereasIn the brain subnetworks relevant to ADHD, an analysis of both structural efficiency and SC-FC coupling reveals differences between ADHD-I, ADHD-C, and TDC.The structural properties differ more than the functional properties of networks between children with ADHD and TDC. Only structural connectivity see  and the We found lower global structural efficiency in the whole-brain network of children with ADHD  comparedTo explore characteristics specific to ADHD subtypes, we focused on ADHD-related subnetworks, the DMN, ECN, and SAN, which other groups have used to identify impairments in various brain diseases , 55, 73.Similar to our whole-brain analysis, we found reduced global efficiency in the structural component of the DMN, ECN, and SAN in children with ADHD (more reduced in the ADHD-C group) relative to that in the TDC group . Again, The fundamental question of how flexible functions emerge from the structural network has prompted interest in the relationship between structural and functional connectivity , 43. TheIn these studies, high SC-FC coupling  has often been interpreted as a marker of rigidity or inflexibility at the cognitive or behavioral level. However, the biological mechanism of SC-FC coupling is not fully understood. Multifactorial explanations for a higher order relationship between structure and function include such possibilities as polysynaptic connections, anti-correlations via connections to an inhibitory input, and differences in neuromodulation . Jang, KThe strong coupling in the DMN in children with ADHD-C compared to children with ADHD-I suggests that the DMN in the ADHD-C group may operate with fewer indirect polysynaptic connections. Whereas, at the symptomatic level, the negative correlation between hyperactivity and SC-FC coupling indicates that this symptom domain may contribute to cognitive control in the ADHD-I group. This aspect of executive functioning includes both the ability to stop one type of behavior and the capacity to engage with a different interest or initiate an alternative behavior. Incorporating measures of both cognitive flexibility and cognitive control into future studies of ADHD subtypes and SC-FC coupling could help evaluate the significance of this finding. The SC-FC coupling measure (correlation coefficient) indicates how much the functional network converges to the “baseline” structural network. Thus, the rigidity or inflexibility (the degree of convergence) should be interpreted concerning the “inefficient” structural network in ADHD, particularly in the ADHD-C subtype.In the ADHD-C subtype, the interactions between brain regions in the DMN appear to be strongly constrained by an inefficient structural network topology. Considering that the DMN is involved in self-reflection, mind wandering, and internally oriented processes –16, 84, Currently, the DSM-V  segregatAlthough we have primarily explained the DMN of children with ADHD-C in terms of functional inflexibility over an inefficient structural topology, the ECN and SAN exhibit comparable degrees of structural network inefficiency. The ADHD-C group displayed higher SC-FC coupling in the ECN compared to controls, but no significant difference in SC-FC coupling between ADHD subtypes emerged. Of note, the SC-FC couplings in the SAN and ECN are relatively weak compared to the coupling observed in the DMN. A portion of the children measure in the zero-coupling zone (zero correlation coefficients). In addition, some children display anti-correlated SC-FC coupling in the SAN and ECN. A negative functional connectivity transmitted over a structural edge may explain the anti-correlated coupling between the SC and FC subnetworks of these children. Thus, the ECN and SAN may not submit to the same explanation as the DMN.We cannot generalize that the relationship between hyperactivity symptoms and the SC-FC coupling in the DMN extends to the other subnetworks. In addition, the inattentive symptoms in both ADHD-I and ADHD-C may not be attributable to dysfunction in a specific subnetwork but may relate to a combination of different subnetworks, for example, the ECN, SAN, and DMN, or to abnormal interactions between networks. These subnetworks may function differently in TDC, in children with ADHD-I, and in children with ADHD-C. This interpretation is consistent with the constellation of multiple dysfunctions witnessed in the DMN , 85, ECNThis study has several limitations. First, our control group of TDC does not reflect the gender imbalance found in the ADHD samples. A recent metanalysis of human connectome studies did not find significant gender differences , but thiSecond, the Healthy Brain Network (HBN) database incompletely documents the history of stimulant use and current medication status of the individual participants—Only 51 children over age 10 evaluated in the current study had a complete drug screening before the MRI scans. Prolonged stimulant use can enhance gray matter volume  and may Third, when the current study was conducted, the MRI and behavioral data of the release 1 and 2 were fully accessible. Thus, we consider the current results preliminary at this point and leave the analysis with more detailed parcellation maps and more data for a further study.Fourth, graph analysis of the brain requires the nodes, or anatomical parcellations to cover the entire cortex. We have followed this stipulation, but in doing so, we may have obscured more fine-grained details about the connectivity between brain regions. In particular, the extraction of time courses from relatively large brain regions that may support varied functions could effectively have washed out group differences in functional connectivity. Conversely, we cannot rule out that the heterogeneous composition of a node used in this study may increase the detection power between groups considering context or state-dependent changes in the functional atlas even within an individual . Since tFifth, the computations used to establish the functional connectivity of networks in this study do not incorporate deactivations. Because cognitive tasks and attention may rely on the DMN’s ability to effectively deactivate with respect to other networks, such as the ECN and the SAN, this analysis may overlook a crucial mechanism for the DMN’s role in ADHD. Further studies on the coupling between networks may be able to address this issue more successfully. Finally, the reliability of the functional connectivity of rsfMRI has been an issue . Thus, wIn summary, we investigated attention-related brain subnetworks in children with ADHD-I and ADHD-C using the relationship between structural and functional networks. We found SC-FC coupling differences between children with ADHD and TDC and between ADHD subtypes in the ADHD-related subnetworks . Although SC-FC coupling in the DMN significantly differed between ADHD subtypes, the subnetworks seem to work together to manifest a different degree of inattentiveness and hyperactivity in the ADHD-I and ADHD-C groups. Given that structural differences between individuals with ADHD and controls has been demonstrated in children but not adults, further work on the SC-FC coupling should include longitudinal data . These fS1 Table(DOCX)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.


In the pooled analyses, baseline WFIRS-P scores in all domains were significantly worse in participants with oppositional defiant disorder (ODD) than in those without ODD. Having combined type or hyperactive-impulsive type ADHD, being enrolled into a study in Europe, being male and being younger also had modest negative effects on baseline WFIRS-P scores. The present analysis of WFIRS-P response shows that previously reported group-level improvements in WFIRS-P functional impairment score translated into clinically relevant improvements in many individual participants. Functional impairment is a diverse and subjective construct that is influenced by multiple factors. Optimal management of individuals with ADHD should involve monitoring improvements in functioning and quality of life, as well as symptomatic improvement.Several recent phase 3 clinical trials of attention-deficit/hyperactivity disorder (ADHD) medications have used the Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P). Here, we assess WFIRS-P response in individual patients in two pivotal trials of lisdexamfetamine dimesylate (LDX) and guanfacine extended release (GXR). We also analysed pooled WFIRS-P data from seven phase 3 studies of ADHD medications to shed light on factors associated with baseline functional impairment. The proportion of patients with a change in WFIRS-P score that exceeded the minimal important difference (MID) criteria for response was greater for LDX than placebo in the Family, Learning and School, and Risky Activities domains, and was greater for GXR than placebo in the Social Activities, Learning and School, and Family domains. Responders had significantly worse baseline scores in all WFIRS-P domains (all  Attention-deficit/hyperactivity disorder (ADHD) is associated with functional impairment in multiple domains, such as daily life at home and at school , 2. TherThe Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P) is a psychometrically validated parent-rated instrument for assessing functional impairment in children with ADHD –8, and hp < 0.001). In the same study, treatment with the active reference arm osmotic-release oral system methylphenidate (OROS-MPH) showed a similar pattern of improvement [p < 0.01) [The WFIRS-P was used in two pivotal randomized, double-blind, placebo-controlled, efficacy and safety studies of the ADHD treatments lisdexamfetamine dimesylate (LDX) and guanfacine extended release (GXR) . Treatmerovement . In the  < 0.01) . Similar < 0.01) .The present analyses were conducted to enhance understanding of the clinical relevance of previously reported group-level WFIRS-P outcomes from clinical trials of ADHD medications. First, using the anchor-based WFIRS-P MID calculated by Hodgkins et al. , we asseStudies SPD489-325 and SPD503-316 were chosen for post hoc analysis of individual response because both have previously published group-level effect sizes in WFIRS-P domain and total scores for active and reference arms , 11.SPD489-325  was a 7-week, double-blind, efficacy and safety study of LDX in children and adolescents aged 6–17 years) with ADHD and an ADHD-RS-IV total score of at least 28, conducted in Europe (48 sites in 10 countries). Details of the study design, results and previous post hoc analyses have been published , 17–21. years wiSPD503-316  was a 10–13-week, double-blind, efficacy and safety study of GXR in children and adolescents (aged 6–17 years) with ADHD and an ADHD-RS-IV total score of at least 32 and a Clinical Global Impression-Severity (CGI-S) score of at least four, conducted in Europe (45 sites in 11 countries) and North America. Details of the study design, results and previous post hoc analyses have been published , 22.Both studies were placebo-controlled and included an active reference arm. In SPD489-325, participants were randomized (1:1:1) to receive dose-optimized LDX , placebo or OROS-MPH . In SPD503-316, participants were randomized (1:1:1) to receive dose-optimized GXR , placebo or ATX (10–100 mg/day). The studies were neither designed nor powered for comparisons between active treatments.p values were based on type III sum of squares from an analysis of covariance model for the change from baseline, including treatment group, age group, and country as a fixed effect, and baseline value as covariates [p values were not adjusted for multiplicity and are therefore non-inferential.Summaries of the pre-specified efficacy analyses from SPD489-325 and SPD503-316 are provided for context. In both studies, efficacy analyses were carried out using the full analysis set (FAS), defined as all participants who received at least one dose of investigational product. Least-squares means, effect sizes, and variates , 11. Excp values were not adjusted for multiplicity.WFIRS-P response in each of the six domains was defined as a change exceeding the published MID for that domain  . p valueTo investigate the impact of the degree of impairment at baseline on the magnitude of improvement during the study, post hoc analyses were undertaken to compare baseline WFIRS-P scores in responders and non-responders for each domain, and to assess correlation between baseline WFIRS-P score and score change from baseline in that domain. Participants in the FAS with data at both baseline and endpoint were assessed for studies SPD489-325 and SPD503-316. The FAS was chosen as the analysis population for consistency with previously reported studies, in which it was used as the primary population for efficacy analyses.To explore potential factors that might be negatively associated with WFIRS-P scores at baseline (before treatment), we pooled data from all LDX and GXR studies that included a baseline assessment using the WFIRS-P.As well as the two studies described above (SPD489-325 and SPD503-316), LDX studies SPD489-326 and SPD489-317, and GXR studies SPD503-315, SPD503-314 and SPD503-312 were included. All were randomized, double-blind, placebo-controlled studies, except for SPD489-317 which was a randomized, double-blind, head-to-head study with ATX. Key study information, and inclusion and exclusion criteria are summarized in Table The FAS from each study was pooled. Defined as all participants who received at least one dose of investigational product, the FAS was chosen as the analysis population for consistency with previously reported studies.g  [r). Only p values below 0.001 are quoted. Subgroup categories were selected based on a standard set of baseline clinical and demographic factors that were pre-defined in all seven studies.Participants were stratified into subgroups based on: presence of oppositional defiant disorder ; presence or absence of hyperactivity ; continent of enrolment (Europe or North America); and sex . Standardized mean differences (SMD) between subgroups were calculated as Hedges d small) . The effIn the SPD489-325 study, 336 participants were randomized and 317 were included in the FAS, of whom 80/104, 42/106 and 74/107 in the LDX, placebo and OROS-MPH groups, respectively, completed the study [In the SPD503-316 study, 338 participants were randomized and 337 were included in the FAS, of whom 91/114, 92/111 and 89/112 in the GXR, placebo and ATX groups, respectively, completed the double-blind period of the study [In both studies, the principal reason for discontinuation was lack of efficacy. Baseline demographics and disease characteristics were generally similar across treatment groups Table .The proportion of patients with a change in WFIRS-P score that exceeded the MID was significantly greater for LDX than placebo in the Family, Learning and School, and Risky Activities domains Fig. a. Mean iIn the OROS-MPH reference arm, the proportion of participants with a change in WFIRS-P score exceeding the MID was significantly greater than for placebo in the Family, Learning and School, Social Activities, and Risky Activities domains Fig. b. PlacebThe proportion of patients with a change in WFIRS-P score that exceeded the MID was greater for GXR than placebo in the Social Activities, Learning and School, and Family domains Fig. a. PlacebIn the ATX reference arm, the proportion of participants with a change in WFIRS-P score exceeding the MID was significantly greater than for placebo in the Social Activities, Learning and School, and Life Skills domains Fig. b. Placebp < 0.001) than non-responders (Table p < 0.001), with greater baseline impairment associated with larger score changes.In both studies, responders had significantly worse baseline scores in all WFIRS-P domains . Of these, 563 (26.8%) were girls, 863 (41.1%) were enrolled in Europe and 359 (17.1%) had comorbid ODD. In total, 1803 (85.9%) participants were diagnosed with combined or predominantly hyperactive-impulsive type ADHD and 295 (14.1%) with predominantly inattentive type ADHD .r   in participants with ODD and ADHD than in those without ODD Fig. c. SMDs bBaseline WFIRS-P scores were significantly worse in participants with combined or predominantly hyperactive-impulsive type ADHD than in those with predominantly inattentive type ADHD in all domains except for Child’s Self-Concept Fig. d. SMDs bThese post hoc analyses revealed that previously reported group-level improvements in WFIRS-P functional impairment did in fact translate into clinically relevant improvements in individual participants. Many participants receiving ADHD medications in two phase 3 clinical trials experienced meaningful and perceptible improvements in functioning, as measured using the WFIRS-P , 18. TheBy stratifying baseline WFIRS-P scores by patient characteristic subgroups in seven phase 3 clinical trials, the present analyses also showed that children and adolescents with comorbid ODD tend to have worse functional impairment than those without comorbid ODD. Having combined type or hyperactive-impulsive type ADHD, being enrolled into a study in Europe, being male and being younger also had modest negative effects on baseline WFIRS-P scores.In the responder analyses, the WFIRS-P Learning and School domain saw the greatest proportions of participants with above-MID improvements following treatment with LDX or OROS-MPH in study SPD489-325, or with GXR or ATX in study SPD503-316. Treatment with LDX, OROS-MPH and GXR (but not ATX) also led to improvements in the Family domain. The stimulants LDX and OROS-MPH were associated with improvements in the Risky Activities domain, whereas the non-stimulants GXR and ATX were associated with improvements in the Social Activities domain. This apparent difference between stimulants and non-stimulants is consistent with previous findings and may arise from factors including differing modes of action, as well as baseline variations in disease severity and characteristics in the study populations . Of noteIn an ADHD symptom-based responder analysis of SPD489-325, the differences in the proportion of responding patients between active medication and placebo were larger than those seen for any WFIRS-P domain. In the present analyses, the proportion of WFIRS-P responders was largest in the Learning and School domain with 60.0% for LDX and 18.4% for placebo. At endpoint in the symptom-based responder analysis, the percentages of patients categorized as responders were 74.2% for LDX, 55.9% for OROS-MPH and 10.7% for placebo. Although in these analyses, symptom response was not based on a MID, but was defined as a reduction of at least 30% in ADHD-RS-IV total score from baseline and a CGI-I score of 1 or 2 at endpoint . ConsistA recent study looked at the relationship between symptomatic improvement and functional improvement as measured by the WFIRS-P in an open-label study of OROS-MPH in children and adolescents with ADHD . SignifiIn the present analyses of baseline scores stratified by patient characteristic subgroups, differences between subgroups in WFIRS-P scores were generally similar in magnitude and direction to the differences in clinical symptom and severity scores. This relationship varied amongst WFIRS-P domains, however, with larger differences in the WFIRS-P Family and Risky Activities domains than in CGI-S score and ADHD-RS-IV total score differences between participants with and without ODD. In contrast, differences in the Life Skills, Child’s Self-Concept and Social Activities domains were smaller than for CGI-S and ADHD-RS-IV. These observed differences between WFIRS-P scores and ADHD-RS-IV and CGI-S scores further demonstrate the intersecting but distinct aspects of the response to treatment captured by the different instruments .Of the factors tested, ODD diagnosis had the largest influence on pooled baseline WFIRS-P scores and was associated with worse scores across all six domains. ADHD-RS-IV and CGI-I baseline scores were also worse in participants with ODD than in those without ODD, which is consistent with ODD and ADHD having some similar and overlapping features. The results of the present study likely reflect greater severity of both ADHD symptoms and related functional impairment amongst participants with ADHD and ODD than in those without ODD.ADHD subtype had a modest influence on functional impairment and individuals with combined/hyperactive-impulsive ADHD scored worse in most domains, particularly in the Family and Risky Activities domains, than those with predominantly inattentive ADHD. The effect of ADHD subtype was greatest on ADHD-RS-IV baseline score . The effects of age and sex on baseline WFIRS-P, ADHD-RS-IV and CGI-S scores were small. Respondents may not have scored impairments equally in patients of different ages. The requirement for CGI-S scores of four or more in many of the trials included in the pooled data set may have led to exclusion of girls with very mild functional impairment.The worse baseline WFIRS-P scores observed in Europe than in North America could be caused by the pooling of several studies with different enrolment criteria; for example, the minimum baseline ADHD-RS-IV total score varied between 28 and 32, but could also reflect a higher threshold for diagnosis in Europe than in North America. A telephone survey conducted in the USA in 2011 revealed that 11% of US school-aged children had received a diagnosis of ADHD , whereasA key strength of the responder analyses was the use of published MID values to define WFIRS-P response. Information about individual response is important to clinicians, in addition to what is known about group-level responses to treatment. Analysis of raw scores may not be the best way to tell if an individual has improved. To this end, MIDs provide an important way to interpret patient-reported outcomes, which are often the outcomes of most importance to patients and their families . A furthThe use of clinical trial populations was a key limitation of the present analysis. Pooling data from seven studies with different inclusion and exclusion criteria to increase statistical power may have confounded the analyses of baseline characteristics in subgroups. Notably, the response rates of active and placebo arms in SPD503-316 were higher than the response rates in SPD489-325, which is an argument against pooling outcome data. Another limitation of the present analyses is that clinical trial populations may not be representative of patients seen in general clinical practice. Further limitations include the short-term nature of the studies, which preclude evaluation of the long-term impact of ADHD treatments on functional outcomes. Improvements in functional impairment may develop over a longer period of treatment than relief of ADHD symptoms. Although using the parent-rated WFIRS-P overcomes possible unreliable self-rating by young people with ADHD, it also introduces the potential for misinterpretation of the affected individual’s true response. In addition, the studies described here were powered for the primary outcome of assessing efficacy using ADHD-RS-IV, whereas assessment of the WFIRS-P was a secondary outcome. Furthermore, studies SPD489-325 and SPD503-316 were not designed or powered for direct comparison of the test drugs  with the active controls . Finally, both the responder and baseline impairment analyses are limited by the risk of bias associated with post hoc analyses.The effect of ADHD medications in different subgroups could not be assessed in the present analyses because the numbers of participants in subgroups within each study were too small for reliable comparisons. Previous subgroup analyses have assessed the efficacy of GXR, LDX and OROS-MPH in participants with and without previous exposure to stimulant ADHD medication , 22, as In conclusion, the large placebo-adjusted effect sizes of ADHD medications in two short-term clinical studies did translate into clinically relevant improvements in WFIRS-P functional impairment scores for many patients receiving pharmacotherapy with LDX, OROS-MPH, GXR and ATX in the present responder analyses. Longer-term studies may be needed to reveal the treatment effects of ADHD medication on functional impairment beyond what has been shown here. Functional impairment is a diverse and subjective construct that is influenced by multiple factors including comorbidity, age, sex and geography. Not only relief of ADHD symptoms, but also a reduction of patients’ related but distinct functional impairments, is achievable with medication in many children and adolescents with ADHD. As such, optimal management should involve monitoring improvement in functioning and quality of life, as well as symptomatic improvement.


Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD).Using logistic regression models, we generated a risk tool in a representative population cohort  using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort , the 1993 Pelotas Birth Cohort  and the MTA clinical sample .https://ufrgs.br/prodah/adhd-calculator/.The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82  0.79–0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71–0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower –0.57 (95% CI 0.54–0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73–0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution. We selected the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort as the generating sample based on the following a priori defined criteria: population-based sample, largest sample.Our methods follow well-established probability models in medicine and recommendations of the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement . For the current study, we included 5113 subjects that were assessed for ADHD in childhood (age 7 or 10) and in the last available assessment (age 17).The ALSPAC is a prospective birth cohort study in the UK. Pregnant women with expected delivery dates between 1 April 1991 and 31 December 1992 were invited to participate. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Further details on assessments can be found elsewhere (Boyd N  =  2232) born from 1 January 1994 to 4 December 1995  Longitudinal Twin Study is a prospective birth cohort study designed to represent the UK population. In 1999–2000, investigators enrolled 1116 families with same-sex 5-year-old twins  is the largest clinical trial and observational follow-up conducted with children with ADHD. In the first phase of the study, investigators enrolled 579 children aged 7–10 years old with ADHD and assigned them to 14 months of one of four groups of management. Two years after baseline, 515 consented to enter an observational follow-up and a local normative comparison group of 289 classmates (258 without ADHD) was added. Assessments were conducted at 12, 14 and 16 years after baseline. Informed consent  was obtained for all participating families, using forms approved by both local institutional review boards and the NIH. Detailed design and methods have been presented in previous publications . We inclet al., et al., et al., et al., et al., et al., et al., In each sample, the outcome was a dichotomous ADHD definition in late adolescence or young adulthood. In ALSPAC, participants' parents completed the hyperactive subscale of the Strengths and Difficulties Questionnaire (SDQ-HS) at 17 years of age. The scale showed excellent discrimination against a DSM-IV diagnosis derived from the Development and Well-Being Assessment (DAWBA) conducted in a subsample of 1673 participants (area under the curve (AUC)  =  0.89, 95% CI 0.81–0.96). The best cut-off score to define diagnosis was at least six points on the SDQ-HS (sensitivity  =  83.3% and specificity  =  83.3%). In the E-Risk, ADHD was ascertained at age 18 years using structured interviews based on full DSM-5 criteria  in the ADHD symptom variable, and this model had better fit indices . Multiple imputation with chained equations (ten imputations) using the remaining predictors was used to deal with missing values in the predictor variables. We used a fixed number of ten iterations and assessed convergence with trace plots. In the ALSPAC cohort, for each of the 1000 bootstrap resamples, we have performed pooled regression coefficient estimates and variance across imputations with the command mi estimate in Stata  of the estimated probability against the actual outcome as an index of model performance. We have assessed optimism of internal validation with bootstrap inference using 1000 replications with the R package rms .We have developed the predictive model in the ALSPAC cohort. We ran a logistic regression including outcome (ADHD at last assessment) as the dependent variable and all eligible predictor variables as covariates. We inspected linearity assumptions of continuous variables by plotting the predictor and the logit of the outcome, and trough Box–Tidwell regressions. We derived the model using linear splines of equal sample sizes  of the model among individuals who endorsed a very low number of ADHD symptoms at baseline  in ALSPAC, E-Risk and Pelotas samples. We had also analysed the performance (measured by the c-statistic) of the model excluding one variable at each time. Finally, we present the variation of the predicted probability within fixed levels of ADHD symptoms to assess the contribution of the remaining variables to the model.We performed several sensitivity analyses to assess the robustness of our findings. We analysed the performance , which indicates very good discrimination , which indicates fair discrimination , which indicates poor discrimination   . The cal< 0.001) , althougWe tested the performance of the model for predicting late-onset ADHD in population samples, among only participants that endorsed few ADHD symptoms in childhood – the median or lower number of symptoms in their respective populations. The model had fair discrimination in these subgroups, except for the Pelotas sample in which the model already had poor discrimination .Table 3p < 0.001).We tested the model taking out one predictor at a time (eTable 2). The most relevant individual predictor was the level of ADHD symptoms in childhood. However, the model still had fair performance in the model without ADHD symptoms in childhood, with an AUC of 0.74 . The observed variance indicates that ADHD symptoms are not the only relevant predictive factor in the model. These findings analysed together clearly indicate that this is not a model based on just one variable.Considering that E-risk is the population cohort with the most comprehensive assessment of comorbid mental disorders, we tested the model's discrimination predicting adult anxiety and major depressive disorder. The performance was significantly lower than for ADHD, showing specificity for ADHD compared to other forms of adult psychopathology .http://www.ufrgs.br/prodah/adhd-calculator/.Predictive discrimination estimates using three different Machine Learning approaches were almost the same . In a secondary analysis, we also have developed one comprehensive predictive model with all samples at once, using site as one more predictor variable . A risk calculator can be found at et al., et al., et al., The widespread use of tools that predict clinical outcomes in medical practice has promoted the development and testing of preventive interventions, but this approach has been rarely attempted for mental health (Bitton and Gaziano, et al., et al., et al., Our next step was to validate the score in independent samples. First, we tested the score in another UK birth cohort, the E-Risk. Its performance in predicting adult ADHD was similar. This is an important finding because several risk models in mental health did not replicate well even in samples from similar settings (Kivipelto et al., We then tested the score in a third birth cohort from Brazil. We observed that the score was much less accurate with an AUC of 0.57. This finding is not surprising, since previous evidence suggests that the predictive discrimination of risk tools is lower in diverse sociocultural and ethnic populations (Chia et al., Models that predict a diagnosis of chronic disorders often include premorbid signs and symptoms of the disease as predictive factors. For example, the factor that increased discrimination the most in the recently published calculator for psychosis was the index diagnosis when presenting to secondary care, where psychotic disorders had the greatest weight compared to other disorders such as mood disorders (Fusar-Poli et al., We also conducted other secondary analyses to assess the robustness of our findings. We tested the impact of using other statistical methods on our results. We observed that the discrimination of the prediction models remained stable regardless of chosen statistical methods. Finally, we tested the hypothesis of whether the score was specific for the prediction of ADHD. This is an important proof-of-concept: personalised medicine has always been a challenge for the area of psychiatry, as it has been shown consistently that most identified biomarkers and risk factors associated with one mental disorder are also associated with several others (Cross-Disorder Group of the Psychiatric Genomics C et al., et al., et al., Previous cohort investigations included in the present study did not find a significant childhood DSM dichotomous ADHD diagnosis in the trajectory of late-onset ADHD (Agnew-Blais et al., et al., Our findings should be interpreted considering a set of limitations. First, the design and assessments of different samples were not uniform, limiting the discrimination of the score in the validating samples. Adult ADHD, for instance, was measured with a scale rather than with a structured interview in the generating sample, but not in the validating samples. It is possible, therefore, that the proposed estimated predictive discrimination in validating samples might actually be an underestimation. Further validating efforts with assessments that more closely resemble those of the generating sample might observe higher AUCs. However, this could also be seen as the strength of the study, since observed discrimination indices are considered good, even with different methodologies implemented in individual studies. Second, there was attrition in the generating sample's assessments. Nevertheless, potential selection bias does not appear to affect the prediction of outcomes in this cohort, as shown in previous publications (Boyd What is the clinical utility of this score, provided that previous literature already has shown that most variables included in our model that are non-specific risk factors for mental disorders and ADHD symptoms in childhood, as expected, are key predicted risk factors for adult ADHD? No the previous effort combined all these variables in a single risk calculator. Therefore, the only information that clinicians could offer was that some variables, like comorbidity with CD/ODD in childhood, increase the risk of persistence of ADHD. By using this calculator, attending clinicians can identify high-risk individuals to inform parents and guide decisions.Thus, we propose a multivariable risk model to predict ADHD in young adulthood based on childhood factors that have good discrimination in both population and clinical settings. Clinicians can use the model to guide long-term decisions based on the identification of children at high risk for future adult ADHD diagnosis. Also, it provides a framework for testing the effectiveness of preventive interventions focused on high-risk individuals. Furthermore, the score might be used to identify at-risk individuals for investigating neurobiological features including brain development. The lower discrimination observed in a middle-income country urges the discussion of how globally generalisable are the risk models that are currently being widely used in clinical practice. Indeed, even the well-established Framingham cardiovascular risk model is being subjected to criticism for its wide variation in performance across different populations. Therefore, future attempts to improve the current model should include setting-specific recalibration analyses that should then be translated to specific risk calculators to be used across different settings. Also, we suggest that cohorts use more standardised methods of collection of predictors and outcomes in psychiatry for the study of risk factors, so that we can disentangle whether failure to replicate is due to the heterogeneity of methods or population. Hence, our work adds to the need for validation of risk models in low- and middle-income countries.