Herein we report the evaluation of multiple ELP‐CLP conjugates, demonstrating that the inverse transition temperature (Tt) of the ELP‐CLPs can be manipulated by modifying the melting temperature (Tm) of the CLP domain, and that the overall hydrophilicity of the ELP‐CLP conjugate also may alter the Tt. Based on these design parameters, we demonstrate that the ELP‐CLP sequence (VPGFG)6‐(GPO)7GG can self‐assemble into stable vesicles at 25°C and dissociate at elevated temperatures by means of the unfolding of the CLP domain above its Tm. We also demonstrate here for the first time the ability of this ELP‐CLP vesicle to dissociate via a hypothermic temperature stimulus by means of exploiting the inverse transition temperature (Tt) phenomena found in ELPs. The development of design rules for manipulating the thermal properties of these bioconjugates will enable future modifications to either the ELP or CLP sequences to more finely tune the transitions of the conjugates for specific biomedical applications.Materials that respond to temporally defined exogenous cues continue to be an active pursuit of research toward on‐demand nanoparticle drug delivery applications, and using one or more exogenous temperature stimuli could significantly expand the application of nanoparticle‐based drug delivery formulations under both hyperthermal and hypothermal conditions. Previously we have reported the development of a biocompatible and thermoresponsive elastin‐  These dual stimuli‐responsive nanoparticles often employ thermal responsivity as one of the two stimuli, with designs employing pH/temperature‐, magnetic/temperature‐, light/redox‐, to pH/redox‐responsive particles among othersWhile the specificity offered by a mixed dual stimuli‐responsive  system is desired in certain cases, dual temperature‐responsive particles would have broad and diverse applications for drug delivery in multiple disease models. Specifically, a dual thermoresponsive particle able to dissociate upon application of either hyperthermal or hypothermal temperatures would be relevant for therapeutic regimens that include a co‐operative applied temperature stimulus. For instance, hyperthermal treatments have been used in conjunction with small molecule drugs for the treatment of cancer.Additionally, dual thermoresponsive particles may offer unique drug release kinetics from each of the two different temperature‐responsive domains,N‐isopropylacrylamide) (PNIPAAM),Interestingly, there are very few reports that focus on dual thermoresponsive polymeric nanoparticle systems.While many of these dual thermoresponsive polymer nanoparticle systems have been foundational in the field, there are a limited number of polymers with LCSTs in the physiologically‐relevant range, and the few reports that mention manipulation of the LCST require complex chemical modifications that range from the attachment/replacement of specific chemical handles or conjugation with peptides, proteins, or other polymers.AA‐YAA)n amino acid repeat sequence where XAA and YAA can be any L‐amino acid but are typically the imino acids proline and hydroxyproline . Similarly to native collagen protein, individual chains of CLPs with repeat lengths of n ≥ 6 form a left‐handed polyproline type II helix, and these strands in turn form a right‐handed triple helix upon alignment of the three individual chains with a one residue stagger between each chain.Tm) that is dependent on both the number of G‐P‐O repeats and the amino acid sequence; the effects of such parameters on Tm have been rigorously investigated by others.Nature‐inspired thermoresponsive biomaterials such as recombinantly engineered or chemically synthesized polypeptides serve as good alternatives to synthetic polymers, owing to the defined peptide sequence and chain length, which in turn provide precise control of structural, chemical, and thermodynamic properties.AA‐G)n, where XAA can be any amino acid with the exception of proline.Tt), above which they form a coacervate phase in which they are not soluble and aggregate; upon re‐cooling below the Tt they resolubilize.Tt for assembly and do not fully utilize this thermodynamic parameter for time‐activated release of encapsulated drug molecules.Similar to CLPs, ELPs are derived from a mammalian protein, in this case the elastin precursor protein tropoelastin. Most sequences that have been studied possess the characteristic repeat of 4GFOGER(GPO)4GG to the ELP enables the ELP to undergo an inverse phase transition and collapse, but only when the CLP domain is in triple helical form. The resulting folded ELP‐CLP trimers, upon coacervation of the ELP domain, were observed to spontaneously self‐assemble into vesicles with a bilayer comprising ELP‐CLP ternary subunits. These vesicles were shown to be stable over a significant temperature range but could dissociate at ~70°C due to the unfolding of the CLP triple helical domain.We previously reported that the short ELP peptide, (VPGFG)Tm values lower than that of the (GPO)4GFOGER(GPO)4GG CLP originally studied.Tm values of these four CLP sequences, as well as the Tm values of the corresponding ELP‐CLP conjugates (employing the ELP (VPGFG)6 used in our previous study), were determined by circular dichroism (CD) spectroscopy. The Tm values were then used to predict the self‐assembly (or lack thereof) and thermal responsiveness of the ELP‐CLP conjugates, and the resulting structures of the ELP‐CLP assemblies were characterized via dynamic light scattering (DLS), turbidity measurements, and transmission electron microscopy (TEM). Our studies highlight the dependence of ELP‐CLP conjugate Tt values on CLP Tm values as well as the hydrophobicity of the CLP. The work here also highlights the possibility of optimizing both the ELP and CLP sequences to create a dual thermoresponsive vesicle system that is biocompatibleHerein, we sought to not only lower this CLP‐mediated hyperthermal dissociation temperature of vesicles to a more clinically approachable value but also determine an underlying set of design rules for the ELP‐CLP vesicles and the role that CLPs have in their self‐assembly. To accomplish this, we synthesized a small library of CLP sequences that have previously been shown to possess 22.1l‐ascorbate, aminoguanidine hydrochloride, trifluoroacetic acid (TFA), triisopropylsilane (TIS), 4‐methylmorpholine, diisopropylethylamine (DIPEA), piperidine, Hellmanex® III cuvette cleaning solution, a Hellma® 45 × 12.5 × 4.5 mm Suprasil® quartz cuvette, and Nunc® MicroWell® MiniTrays were purchased from Sigma‐Aldrich, Inc. . Tris‐hydroxypropyltriazolylmethylamine (THPTA) was purchased from Click Chemistry Tools LLC . Activating reagent N‐[(1H‐benzotriazol‐1‐yl) (dimethylamino)methylene]‐N‐methyl‐methan‐aminium hexafluorophosphate N‐oxide (HBTU) and common Fmoc protected amino acids such as Fmoc‐l‐glycine‐OH, Fmoc‐l‐proline‐OH, Fmoc‐l‐hydroxyproline(t‐butyl)‐OH, Fmoc‐L‐valine‐OH, Fmoc‐L‐phenylalanine‐OH, Fmoc‐l‐glutamic acid(t‐butyl)‐OH, and Fmoc‐l‐arginine(Pbf)‐OH were purchased from AAPPTec, LLC . Specialty amino acids 4‐azidobutyric acid and Fmoc‐l‐propargylglycine‐OH were purchased from ChemPep Inc. . Low loading (LL) Rink Amide ProTide® Resin was purchased from CEM Corporation .Dimethylformamide (DMF), methylene chloride, HPLC‐grade water, HPLC‐grade acetonitrile, dimethylsulfoxide (DMSO), anhydrous diethylether (ethyl ether), 0.22 μm (13 mm OD) polyvinylidene difluoride (PVDF) syringe filters, sodium chloride, and Whatman grade 1 filter paper were purchased from Thermo Fisher Scientific . Phosphotungstic acid, copper (II) sulfate (Cu(II)sulfate), (+)‐sodium 2.23‐(GPO)6GG, N3‐(GPO)7GG, N3‐(GPP)10GG, N3‐(GPO)3GFOGER(GPO)3GG  and the ELP peptide NH2‐(VPGFG)6G′  were synthesized using the standard Fmoc/t‐butyl solid phase peptide synthesis strategy on a Tribute® automatic peptide synthesizer . Specific details of the peptide synthesis, cleavage, purification, and mass spectrometry protocols that were used can be found in Supporting Information The CLP peptides N2.36‐(GPO)6GG (denoted [F6‐GPO6]), (VPGFG)6‐(GPO)7GG, (denoted [F6‐GPO7]), (VPGFG)6‐(GPO)3GFOGER(GPO)3GG (denoted [F6‐GFOGER]), and (VPGFG)6‐(GPP)10 (denoted [F6‐GPP10]) were synthesized using an adaptation of the common copper (I)‐catalyzed azide‐alkyne cycloaddition (CuAAC) reaction protocol that has been summarized by Presolski et al.The ELP‐CLP conjugates (VPGFG)2.4In order to characterize triple helix formation, CD spectroscopy was performed on all peptides and conjugates. All CD experiments were performed using a Jasco 810 CD spectropolarimeter  with a 0.2 cm path length cuvette. Detailed experimental procedures of CD measurements and calculations are provided in Supporting Information, 2.52.5.1For vesicle formation, conjugates were first dissolved into a 1 mg/ml solution of either pure HPLC‐grade water or 100 mM NaCl aqueous solution. In all cases, the conjugate solution was capped and wrapped in parafilm and placed in an 80°C oven for ~2 hr. A prerinsed and precleaned 10 mm pathlength cuvette, 1 ml syringe, and 0.22 μm PVDF syringe filter were also incubated in the same oven during the final 30 min of conjugate solution incubation. After incubation, 1 mL of conjugate solution (80°C) was filtered at 80°C directly into the cuvette, which was subsequently capped and wrapped in parafilm before being placed in the DLS instrument , with the DLS cuvette well preset to a temperature of 80°C.Vesicle formation was induced by cooling the sample in a controlled manner in the cuvette well. The cooling steps proceeded in the following sequence: 80, 65, 50, 35, 30 (only for F6‐GFOGER), and 25°C, with a 5‐min equilibration at each temperature followed by three light scattering measurements. A non‐negative least squares analysis of the autocorrelation function was employed by the Malvern software to derive a set of translational diffusion coefficients for each measurement, which were then converted to the number average (n‐ave) diameters by utilizing the Stokes‐Einstein equation as has been previously reported.2.5.2In order to determine the diameter of vesicles through the inverse transition temperature process for a given conjugate, the solution of vesicles was slowly cooled in 5°C intervals from 25 to 5°C, with each temperature setting employing a 5‐min equilibration step followed by three light scattering measurements. As before, automatic attenuation and measurement position were employed. The scattering angle and refractive indices of the sample and water employed were the same as those noted in 2.6All of the vesicles were prepared for TEM in a similar fashion with either a single staining protocol or a triple staining protocol that was used to enable observation of the vesicle bilayer. Carbon‐coated copper grids were ionized with a PELCO easiGlow®  glow discharge unit to make the grids more hydrophilic prior to grid spotting. After overnight incubation of the sample at 25°C, 18 μl of 1 mg/ml vesicles were pipetted into a single well of a Nunc® MicroWell® Mini Tray. A freshly ionized grid was then gently rested on the surface of the vesicle droplet for 1 min, after which the grids were stained with phosphotungstic acid (1 wt% solution) and subsequently washed and blotted; detailed protocols are provided in Supporting Information Identical grid spotting procedures were employed for the F6‐GPO7 conjugate at higher temperatures. The analysis of F6‐GPO7 vesicle morphologies at the higher temperatures  required incubation and equilibration of all materials  in a Heratherm Oven  at the corresponding temperature for 20 min prior to grid spotting, which was subsequently performed inside the oven. Analysis of vesicle morphologies at the 4°C temperature required incubation and equilibration of all materials (listed above) within a 4°C cold room for 20 min, with the additional requirement that the vesicle and staining solutions were kept on ice until being distributed to the well plate for spotting.2.72.7.1Additional characterization of the vesicle inverse transition temperature was performed by UV‐Vis mediated detection of turbidity of vesicle solutions as a function of temperature. Directly after vesicle formation, a 1 mg/ml solution of vesicles was inserted into the cuvette well of a Cary 60 UV‐Vis spectrophotometer equipped with a temperature and magnetic stirring controller as well as a nitrogen gas line to constantly purge the cuvette well of condensing water vapor. To determine the inverse transition temperature, the turbidity of the F6‐GPO7 vesicle solution was continuously monitored with a 600 nm laser over the course of cooling from 25 to 0°C with a cooling rate of 1°C/min. The inverse transition temperature was defined as the temperature at which the turbidity was equal to that of 50% maximal turbidity, as in previous studies.To assess the reversibility of the dissolution and re‐assembly of vesicles, F6‐GPO7 vesicles were repeatedly thermally cycled from 25 to 0°C and back to 25°C, with continuous monitoring at 600 nm in a Cary 60 UV‐Vis spectrophotometer over the course of 125 min while the cuvette temperature controller was cycled (and incubated) between temperatures of 25 or 0°C. The dead‐time for cooling from 25 to 0°C was ~5 min, while the dead‐time for heating was ~1.5 min. In between heating and cooling steps, the 25 or 0°C temperature was held on average for 3 min while the turbidity or lack thereof was monitored.2.7.2The attempted particle formation procedure for F6‐GPP10 resulted in bulk aggregates. To assess the thermal responsiveness of these aggregates, turbidity measurements were performed with heating or cooling temperature gradients. To assess the hyperthermal mediated re‐dissolution of the F6‐GPP10 aggregates, a 1 mg/ml suspension of aggregates was heated in cuvette with a stir bar in a Cary 60 UV‐Vis instrument with a 400 rpm stirring rate and a 2.5°C/min heating rate while being simultaneously monitored for turbidity with a 600 nm laser. For determining the inverse transition temperature, the F6‐GPP10 aggregates were again suspended with stirring at 400 rpm and cooled from 25 to 0°C using a 1°C/min cooling rate. The bulk hyperthermal dissolution temperature and the hypothermal inverse transition temperatures for this conjugate were defined as the temperature where a 50% reduction in the maximal turbidity occurred.33.16‐(GPO)4GFOGER(GPO)4GG of our previous studies.3‐(GPO)4GFOGER(GPO)4GG) exhibited a melting temperature of 50°C. Given that the resulting vesicles formed from this conjugate underwent complete thermal dissociation only at relatively high temperatures (70°C),Tm values lower than that of the N3‐(GPO)4GFOGER(GPO)4GG sequence.AA position, as well as sequences that have relatively short repeat lengths (<8), have lower Tm values than CLP sequences that are high in O content and/or are relatively long.3) (N3‐(GPO)6GG, N3‐(GPO)7GG, N3‐(GPO)3GFOGER(GPO)3GG, and N3‐(GPP)10GG) were chosen for the present study. The (GPO)3GFOGER(GPO)3GG sequence, owing to its shorter length, exhibits a lower Tm relative to the sequence in our previous studies, and the (GPO)n sequences were chosen as they would be good comparators to this sequence without the inclusion of GFOGER residues.10 sequence was chosen to study the role of chain length and hydrophilicity on the self‐assembly of the vesicles with a CLP domain that exhibits a relatively low Tm.6G′) with a C‐terminal propargyl glycine (labeled G′) was employed as the ELP component for all of the ELP‐CLP conjugates that were made. All peptides were prepared via solid‐phase peptide synthesis procedures and purified via reverse phase high performance liquid chromatography (RP‐HPLC) Tt greater than 80°C.CD spectroscopy wavelength and temperature scans were performed on all pure ELP and CLP products. The ELP wavelength scans and temperature scan are provided in 3‐(GPO)7GG and N3‐(GPP)10GG exhibited the highest Tm values and (GPO)6GG the lowest Tm value, with N3‐(GPO)3GFOGER(GPO)3GG exhibiting an intermediate value owing to its longer repeat length but also to the destabilizing nonimino acid residues in the center of the sequence.Tm values to those reported in the literature are complicated by the fact that triple helical melting temperatures are dependent on both concentration and heating rate.Tm values in Table Tm 7 and (GPP)10)) facilitate comparisons between the self‐assembly of conjugates with different CLP chain lengths and/or hydrophobicities.The CLPs were shown to form triple helices that in turn could subsequently be unfolded upon heating . The exp3.2ELP‐CLP conjugates F6‐GPO6, F6‐GPO7, F6‐GFOGER, and F6‐GPP10 were synthesized by reacting the purified ELP and CLP components using the standard copper‐catalyzed CuAAC reaction. The ELP‐CLP conjugates were subsequently purified via RP‐HPLC and analyzed via UPLC‐MS methods that generated elution profiles and corresponding mass spectra that confirmed the purity of each conjugate . ConfirmAs we have previously reported, the formation of the CLP triple helix is a critical step that enables the self‐assembly of the ELP‐CLP vesicles.Tm of the conjugates showed the same stability trends as the corresponding CLP domains alone, with values of 31, 32, and 50°C for F6‐GPO6, F6‐GFOGER, and F6‐GPO7, respectively (Table 3‐(GPP)10GG melting temperature (Table The melting temperature of each of the ELP‐CLP conjugates (with the exception of F6‐GPP10) was determined by temperature scans that are shown in Figure re Table  below.Tm was ascribed to the ELP chains acting as a hydrophobic anchoring point that partially limited the unfolding of the triple helical domain.The melting temperatures of both F6‐GPO6 and F6‐GPO7 increased by approximately 4°C and 6°C, respectively, as compared with the values of the corresponding CLPs alone Figure  and 2, c3.3in situ. It is worth briefly noting that this vesicle formation process is significantly more facile than other vesicle formation techniques that require specific pH aqueous environments,In order to form vesicles, purified ELP‐CLP conjugates were dissolved in pure deionized water, heated to 80°C, and equilibrated for 2 hr so that the CLP domains of each conjugate would be sufficiently unfolded and the conjugate molecules dissolved in solution . VesicleDh) distributions at 80°C for all the conjugates were approximately within the expected range of the estimated length of a single ELP‐CLP subunit. This suggests that all of the conjugates adopted their monomeric form as nontriple helical sub‐units at 80°C, which is consistent with our previous reports.Dh values, and suggesting the formation of vesicles 4GFOGER(GPO)4GG conjugate occurred due to the reduction in the inverse transition temperature and collapse of the ELP domain that was facilitated by the increase in the local concentration of ELPs upon formation of the CLP triple helix.Tt is a function of ELP concentration .Tm, we suggest that changes in the local concentration alone are not sufficient to drive assembly. Indeed, when comparing the % of triple helical folding for F6‐GPO7, F6‐GPO6, and F6‐GFOGER  affect the magnitude of the shift in the ELP Tt values during the transition between the monomeric and the CLP‐conjugated and folded (trimeric) forms. This likely results from differences in the strength of the interactions of the CLP domains that consequently affect local ELP chain interactions and hydration state, and so modify the ELP transition and collapse.Tt values must be greater than their Tm values, and thus the ELP domains do not undergo coacervation at a temperature at which the CLP domain forms a triple helix. The ability of the ELP domain to undergo coacervation in the triple‐helical ELP‐CLP form was confirmed by studying the vesicle assembly of F6‐GFOGER in an ionic solution , as the addition of salt is a known method of lowering the Tt,Tm  and ~50°C . Furthermore, the fact that not all ELP‐CLP conjugates form vesicles at temperatures below their Tm implies that there may be some critical Tm at which the Tt of a given ELP domain shifts sufficiently such that self‐assembly can occur. In the context of this work, we speculate that this critical Tm is between 32 and 50°C.In contrast to the F6‐GPO6 and F6‐GFOGER conjugates, the F6‐GPO7 and F6‐GPP10 conjugates do self‐assemble, indicating 3.4The two conjugates (F6‐GPO7 and F6‐GPP10) which formed vesicles in water were monitored for an extended period of time at 25°C via DLS to ensure that the formed aggregates were colloidally stable over the timescales of the reported experiments. Continuous DLS analysis of the F6‐GPO7 vesicle solution was carried out over a 34‐hr period at 25°C, and throughout that time the hydrodynamic diameter, as well as the attenuation‐corrected photon count rate, remained relatively constant and the vesicles in solution appeared homogenous, well dispersed, and stable .Dh distributions throughout the heating process. Similar to the cooling distributions in Figure Dh at the CLP melting temperature of 50°C. With a further increase in temperature to 55°C, substantial reductions of the Dh took place; the Dh values did not change further at 65°C and 80°C, indicating that the vesicles had largely dissociated at temperatures just above the Tm. The similarity in the vesicle size distributions during cooling and heating suggested that vesicle formation and dissociation were reversible, as might be expected based on the reversibility of the inverse temperature transition of ELPs.Tt from its trimeric value to its monomeric value, ultimately resulting in vesicle dissociation and solubilization.To ensure that there was no significant hysteresis in assembly and disassembly, the F6‐GPO7 vesicles were heated over the same temperature range at which they were formed. Figure Tt was assessed via DLS experiments, by monitoring changes in vesicle size as a function of incremental cooling. As illustrated in Figure Dh distributions of the F6‐GPO7 conjugate upon cooling from 25 to 10°C indicated no significant change in vesicle size, although a significant drop in the average Dh was observed upon cooling from 10 to 5°C  for the F6‐GPO7 conjugate was 14.6°C  of the F6‐GPO6 and F6‐GFOGER that assembly would be expected to occur, particularly if the Tt were strictly a function of the localization of multiple chains of the F6 at the terminus of a CLP triple helix. Thus the Tt data here further highlight the role of the stability and hydrophilicity of the of the CLP domain in controlling the Tt of the ELP. In addition, although the Tt of the F6‐GPO7 is lower than what can be safely applied for cooling deep tissue,The apparent C Figure d.37, 77 T < Tt), stable vesicles (Tt < T < Tm), partially melted/dissociated vesicles (T ≈ Tm), and monomeric peptide conjugates resulting from dissociated vesicles (T > Tm), respectively. Representative TEM images of the vesicles at each of these temperatures are shown in Figure Tm of the F6‐GPO7 indicated via CD 10 CLP (Table Tm of the N3‐(GPP)10GG CLP , and also because of the more rapid heating rate used in this turbidity experiment relative to the slow heating rates used in the CD experiment. Although the turbidity data do not provide a precise Tm for the F6‐GPP10 conjugate aggregates, they do confirm that folding of the CLP is required for aggregation/assembly to occur.Despite the fact that the F6‐GPP10 conjugate precipitated with extended incubation, the temperature‐responsive properties of the F6‐GPP10 aggregates were assessed through turbidity measurements as a function of temperature. Starting with F6‐GPP10 conjugates in the aggregated state , the F6‐LP Table . The turLP Table a, until LP Table , in conty Tables . This liTt‐mediated dissolution for the F6‐GPP10 conjugate. The data for this experiment are presented in Figure Tt of the F6‐GPP10 conjugate was less than 0°C.At the end of the hyperthermal dissolution experiment, the F6‐GPP10 was then cooled and monitored for aggregate formation via turbidity measurements. Detailed analyses of the re‐aggregation of the F6‐GPP10 conjugate and the kinetics of aggregation as well as the morphological characterization of these aggregates is discussed in Supporting Information Tm values, between the F6‐GPO7 and F6‐GPP10 conjugates, suggests that contributions from the final hydration states of the trimerized CLPs can also play an important role in controlling the coacervation behavior of the ELP domain, consistent with the fact that (GPO)‐containing CLPs are moderately more hydrated relative to (GPP)‐containing CLPs.N‐methyl‐1,6‐OH 1,4,5,6‐tetrahydronicontinamide) will exhibit a higher Tt than the same ELP conjugated to a less hydrophilic moiety .Tm values), the difference in the hydration of the conjugates may have significant impacts on the Tt. Studies of ELP‐CLP conjugates with CLPs of varying hydrophobicity are necessary to further illuminate the molecular features of this aggregation phenomenon. Nevertheless, taken altogether, our data illustrate the rich range of association/assembly/aggregation behavior that can be accessed in ELP‐CLP conjugates via manipulation of the CLP domain.The highly different assembly/aggregation behavior, despite nearly identical estimated 4Tm) was found to be a critical parameter that can be used to tune the ELP domain's inverse transition temperature (Tt) from the single chain conjugate state to the trimeric state. The role of Tm in influencing Tt and controlling assembly  was indicated by the lack of vesicle assembly for the F6‐GPO6 and F6‐GFOGER conjugates near their Tm values, which is in contrast to the formation of well‐defined vesicles by the F6‐GPO7 conjugate near its Tm. Furthermore, of the conjugates studied, only the F6‐GPO7 conjugate exhibits a thermally accessible inverse transition temperature (Tt = 14.6°C) despite similarities in composition with other conjugates. The rapid and reversible low‐temperature assembly/disassembly of the F6‐GPO7 vesicles is consistent with the behavior of a true inverse transition temperature as reported for ELPs alone.In this work, we investigated the propensity of four different ELP‐CLP conjugates to self‐assemble into vesicles as a function of the sequence of the CLP domain. The CLP triple‐helix melting temperature (Tm such that assembly can occur. Although both F6‐GPO7 and F6‐GPP10 can form assembled/aggregated structures at temperatures below their nearly identical Tm values, the fact that F6‐GPP10 aggregates could not be solubilized at lower temperatures suggests the importance of the increased hydrophilicity of the GPO‐containing sequences in mediating solubilization of the ELP domains by means of the Tt phenomena.Our studies also suggest the important role of the hydration of the CLP domain as a parameter controlling ELP‐mediated aggregation/assembly for ELP‐CLP conjugates with a suitably high Tm but large differences in self‐assembly highlight the importance of the choice of ELP‐CLP pair in the design of dual temperature‐responsive delivery vehicles. The dual‐temperature responsiveness of the F6‐GPO7 conjugate further illustrates the feasibility of designing ELP‐CLP vesicles that can possess both hyperthermic and hypothermic drug release capabilities, with potential future applications in targeted drug release in the treatment of ECM‐related diseases.The small differences in Appendix S1: Supporting InformationClick here for additional data file.


The diagnosis-agnostic groups were significantly different than each other in phenotypic characteristics (SCQ: χ2(9) = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001) as well as cortical thickness in 75 regions of the brain. The analyses revealed disagreement between existing diagnostic labels and the diagnosis-agnostic homogeneous groups . Our results did not support the validity of existing diagnostic labels of ASD, ADHD, and OCD as distinct entities with respect to phenotype and cortical morphology.The validity of diagnostic labels of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) is an open question given the mounting evidence that these categories may not correspond to conditions with distinct etiologies, biologies, or phenotypes. The objective of this study was to determine the agreement between existing diagnostic labels and groups discovered based on a data-driven, diagnosis-agnostic approach integrating cortical neuroanatomy and core-domain phenotype features. A machine learning pipeline, called bagged-multiview clustering, was designed to discover homogeneous subgroups by integrating cortical thickness data and measures of core-domain phenotypic features of ASD, ADHD, and OCD. This study was conducted using data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multi-center study in Ontario, Canada. Participants ( In this context, several studies have revealed shared characteristics among ASD, ADHD, and OCD across various levels of analysis , as well as significant comorbidity among these disorders21. These studies commonly rely on case-control designs, which use diagnostic labels to define group-level statistics for comparisons. Although these approaches can identify group differences in means when distributions are close to normal, they cannot characterize group overlap in the presence of large within-group variability that may arise from existence of subgroups within each group. This is an important consideration when analyzing complex disorders, such as ASD, ADHD, and OCD, which present with strikingly large within-disorder heterogeneity in etiology29, neurobiology36, and phenotypic presentation37.Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) are complex neurodevelopmental disorders. There is emerging evidence that these diagnostic categories may not correspond to conditions with distinct etiology42 or on a single level of analysis43, however, to our knowledge cross-disorder, multi-level stratification has not been examined previously.The between-group overlap and the large within-group heterogeneity motivate a shift away from traditional case-control designs to trans-diagnostic analyses based on diagnosis-agnostic and continuous measures. This approach may provide insight into the structure of individual variability in biology and phenotype, including discovery of homogeneous subgroups and/or continua characterized by different biologies. To this end, we propose a data-driven, diagnosis-agnostic approach to derive sub-groups that share biological and phenotypic characteristics. Previous attempts have been made to discover homogeneous subgroups within each disorder31 given that genetic findings are rare and unknown for the majority of individuals with ASD, OCD, and ADHD. Cortical thickness is a heritable measure of cortical columnar structure, suggested to reflect cellular maturational changes in the cortex , as well as cognitive and behavioral differences46.We examined homogeneity in neuroanatomy, measured by cortical thickness, and core-domain phenotypic characteristics of each disorder. Neuroanatomical similarities can provide an intermediate phenotype that links multiple genetic variantsParticipants were recruited through the Province of Ontario (Canada) Neurodevelopmental Disorders Network (POND), a multi-center research network studying neurodevelopmental disorders. Participants who had capacity to consent provided informed consent. For others, consent was obtained from guardians and assent was obtained from the participants. Ethics approval was obtained from the research ethics boards at Holland Bloorview Kids Rehabilitation Hospital and the Hospital for Sick Children.47 and Autism Diagnostic Interview–Revised (ADI-R)48; ADHD: Parent Interview for Child Symptoms (PICS)49; OCD: K-SADS and the Children’s Yale–Brown Obsessive Compulsive Scale (CY-BOCS)50. The controls did not have a neurodevelopmental, psychiatric and/or neurological diagnosis and were born after 35 weeks gestation.The included participants were 6–18 years old, had sufficient English comprehension to complete the testing protocols, and did not have contraindications for MRI. For the clinical groups, a primary diagnosis of ASD, ADHD, or OCD was required. Diagnoses for the clinical groups were confirmed using in-depth assessments (ASD: Autism Diagnostic Observation Schedule–2 (ADOS)51), inattention  rating scale52), and obsessive-compulsive traits 53). Participants also completed the Child-Behaviour Checklist (CBCL)59. Full-scale IQ was estimated using the age-appropriate Wechsler or Stanford-Binet scales.Our analyses focused on primary domains affected in ASD, ADHD, and OCD, quantified using continuous measures of autism features 54. The pipeline applies a non-uniformity correction on the images54 followed by stereotaxic registration to the Montreal Neurologic Institute (MNI ICBM152) template (non-linear 6th generation target)56. Next, brains were masked, extracted, and classified into gray matter, white matter, and cerebrospinal fluid. Tissue classification images were used to generate gray and white matter surfaces61. A surface-diffusion kernel was applied62, and regions were registered to the automated anatomical labeling atlas65. Cortical thickness measurements were taken from the distance between the two smoothed surfaces66. Quality assurance was carried out at the time of the scan for motion artifact, and was analyzed through the CIVET quality control (QC) analysis pipeline. Scans that were flagged on the QC analysis were manually reviewed for quality and excluded if needed.Structural MRI data was collected on the 3-Tesla Siemens Trio TIM at the Hospital for Sick Children, in Toronto, Ontario for 184 participants. The remaining participants were scanned after a hardware updated to the Siemens Prisma scanner. Cortical thickness measures were extracted from T1-weighted images using the CIVET pipeline (version 2.1.0)Cortical thickness measurements from 76 regions of the brain were regressed against age, sex, and scanner type in a sequential manner and the z-scored residuals were used in subsequent analyses.67 and bagging68, a machine learning pipeline was designed to analyze the multi-dimensional brain-behavior data. This pipeline  dimensions of phenotypic data, cortical thickness measurements from seven (of 76) cortical regions, and the number of clusters randomly chosen between 2 and 15. The size of the random subsets was determined following seminal works in bagging71. The range for the number of clusters was determined based on visual inspection of affinity matrices. Each iteration generated a participant connectivity matrix, with entries of one if two participants were grouped in the same cluster and zero otherwise. To confirm that the clustering result was indicative of true connections between participants, the analyses were run on two sets created by (1) randomly sampling a uniform distribution across the range of the data, and (2) randomly permuting the cortical thickness and phenotypic data. The distribution of connectivity values due to random chance was computed. Two participants were deemed “similar” if they were grouped together more times than the 99th percentile of the connection values for the random data.The bagged-multiview clustering pipeline consisted of bagging and spectral clusteringth percentile of pairwise distances in each measure. Second, a subset of cortex features were chosen for fusion with the phenotype data. The selection maximized within-to-between cluster similarity using sequential-feature-forward selection72. Within-cluster similarity was defined as the median of the medians similarities for participants in the same cluster. Between-cluster similarity was the median of the 99th percentile similarity between participants in one clusters and those in all other clusters . The overall ratio was computed as the average of the within-to-between ratios, where the ratio for each cluster was weighted by the number of participants in that cluster. Participant similarities were obtained from an affinity matrix resulting from the fusion of cortical thickness and phenotypic matrices. The matrices corresponding to the same data type  were fused using element-wise arithmetic averaging. This type of fusion allows for clusters to match along any of the features combined. This procedure results in two affinity matrices: one for cortical thickness and one for phenotypic measurements. These two matrices were fused using a geometric mean. This requires the final clusters to match across cortical thickness and phenotypic dimensions. To further reduce variability and improve generalizability, the entire pipeline was run 10 times  and the median of the participant similarity matrices was used to generate the results reported in the following sections. The 10 iterations were performed further to reduce variability in clustering in a computationally efficient manner.For each iteration of clustering, the following steps were performed. First, using the Gaussian similarity function, affinity matrices were computed for the SCQ, SWAN, and TOCS scores as well as cortical thickness measurements for each of the 76 cortical regions . The parameter for the Gaussian kernel was set as the 7571. In particular, each feature’s prediction accuracy was calculated as the difference between the final labels and the labels generated in iterations where that feature was selected. The values of the feature were then permuted and the accuracy was again calculated. The feature weight was defined as the difference in accuracy before and after the feature is permuted.Feature weights were computed based on an adapted version of the permutation accuracy importance73: Roughly, this measure quantifies that amount of information shared between two clustering assignments. This measure takes on values between 0 (independent clusterings) and 1 .Normalized mutual information74: This measure is based on counting item pairs who fall in the same or different clusters based on two clusterings. The adjusted Rand score ranges between 0 and 1, with 1 indicating perfect agreement between to clusterings.Adjusted Rand score75: This measure quantifies the extent to which each data-driven cluster contains only participants from a single diagnostic group .Homogeneity75: This measures quantifies how well participants in the same diagnostic group are assigned to the same cluster .CompletenessTo evaluate the agreement between diagnostic labels and the data-driven cluster assignments, four measures were used:SCQ, SWAN, and TOCS scores, as well as cortical thickness values were compared across clusters using Kruskal–Wallis tests.2(3) = 10.1, p = 0.02), full-scale IQ (χ2(3) = 31.4, p < 0.0001), and measures of core-domain symptomatology namely, SCQ (χ2(3) = 134.8, p < 0.0001), SWAN (χ2(3) = 69.5, p < 0.0001), and TOCS (χ2(3) = 80.2, p < 0.0001). The age difference did not survive correction for multiple comparisons. Post-hoc analyses showed that the proportion of male to female participants was higher in the ASD and ADHD groups compared to the OCD and TD groups. Participants in the ASD group had lower median IQ scores compared to the OCD and TD groups, and the ADHD group had lower median IQ compared to the OCD group. Thirty-eight of the 226 participants were missing IQ data.Participant demographic information is shown in Table p < 0.0001). Interestingly, the ADHD group had significantly higher SCQ scores compared to the TD controls, and the ASD group had significantly elevated SWAN and TOCS scores compared to the TD groups.The ASD, ADHD, and OCD groups had significantly elevated scores compared to all groups on their respective core-domain measures . Homogeneity and completeness scores were less than 0.3, indicating that data-driven clusters do not represent a single diagnostic category .Based on the within-to-between similarity ratio, a 10-cluster solution was chosen for the remaining analyses  = 23.6, p = 0.005). Post-hoc testing showed that cluster 1 had significantly higher mean ranks than clusters 5 and 7 (p = 0.03).The Kruskal–Wallis test did not show significant cluster differences in age or sex proportions  = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001), but the cluster difference in TOCS scores was not significant  = 56.3, p < 0.0001) and Attention Problems (χ2(9) = 94.8, p < 0.0001), but not OCD Problems. Kruskal–Wallis tests showed a significant effect of cluster on cortical thickness in all regions (Bonferroni corrected p < 0.002), except for the left lingual gyrus.The Kruskal–Wallis test revealed a significant difference in SCQ and SWAN scores among clusters . Overall, our results suggest that homogeneity in the variables examined in our analyses does not align well with existing diagnostic categories. Instead, we observed that differences in the domains primarily affected in these disorders may exist along a continuum that includes typical development.We started with a grouping of participants categorized into four diagnostic groups, which differed significantly on scores on SCQ, SWAN, and TOCS. Our analyses resulted in a new grouping of these participants into more homogeneous subgroups, which differed significantly in SCQ and SWAN scores as well as cortical thickness, but did not align well with the original diagnostic labels.77. Several studies have also reported shared characteristics in brain structure, function, and connectivity82 in these disorders. Our results support the emerging recognition that the existing behaviorally-defined diagnostic labels may not capture etiologically, biologically, and phenomenologically homogeneous groups89.The majority of the data-driven clusters contained participants from multiple diagnostic categories, highlighting shared phenotypes and neurobiologies among the diagnostic groups. Social difficulties and inattention are commonly reported as shared features of ASD, ADHD, and OCD90 and inattention91. This is also consistent with the notion that multiple susceptibility genetic factors may interact with environmental conditions to lead to a continuous dimension of ASD-like and inattention traits, with neurodevelopmental disorders at the extremes of this continuum93. This motivates models of neurodevelopmental disorders which focus on continuous variations in traits instead of categorical diagnoses defined based on qualitative cut-offs. Future studies should consider examining other phenotypic characteristics and biological parameters  to comprehensively describe this continuum.Visually, our results are consistent with the notion that that the ASD-like features, and to some extent inattention traits, exist across a continuum that includes typical development. This model is supported by the substantial etiological overlap between these disorders and typical variation in social communication ability10 and white matter structure9 using the same cohort. In addition, a study of a community sample found that those with a sibling with ASD showed more ADHD, but not OCD traits compared to those without a sibling with ASD20. Replication on larger samples is needed to further explore shared characteristics and differences across these disorders.The data-driven clusters differed significantly in SCQ and SWAN scores, but not TOCS. This pattern was also replicated using the CBCL measures of social, attention, and OCD problems. Moreover, the majority of participants with an OCD diagnosis clustered together with the typical controls. This has been observed in two other studies which examined social perception abilitiesFinally, it is important to note that discovery of the exact clusters/subgroups that can be translated into clinical practice requires replication and integration of findings across a large number of studies and measures. This paper is a first step to accomplish this. Our results motivate a paradigm shift to challenge how ASD, ADHD, and OCD are currently defined, diagnosed, and treated. In particular, this paper adds to the evidence that these diagnoses may not exist as uniquely-defined diagnostic constructs, and highlights the need to discover other groupings that may be more closely aligned with biology and/or response to treatment.Our results also have implications for the research community. Most existing studies commonly rely on case-control designs, which use diagnostic labels to define group-level statistics for comparisons. These approaches are often not able to characterize group overlap in the presence of large within-group variability that is revealed in our study. In this context, our results highlight the need to move beyond traditional statistical approaches to more advanced computational approaches to examine variability and overlap in/across these disorders. To our knowledge, this is the first examination of cross-disorder, multi-level stratification across ASD, ADHD, and OCD.31.Our results add to the emerging evidence that the existing diagnostic categories may not be associated with unique patterns of difference in brain structure, paralleling a recent study showing significant heterogeneity in brain volume across 26 mouse models of ASD99, supramarginal gyrus99, angular gyrus100, middle frontal gyrus100, cingulate99, inferior frontal gyrus99, postcentral gyrus100, inferior temporal gyrus99), ADHD , and OCD .Broadly, the regions contributing most to the data-driven groupings were involved in social function, emotion processing, language, attention, and inhibitory control. Many of these regions have been previously implicated in studies of cortical morphology in ASD (e.g., middle temporal gyrus95, right supramarginal gyrus78, and the prefrontal cortex106 have been reported for ASD and ADHD. Looking at ADHD and OCD, differences have been reported in the cingulate cortex and dorsolateral prefrontal cortex13. Decreased volume in the anterior cingulate cortex has been suggested as a shared finding in ASD and OCD 12.Our results also overlap with those of the very few studies that have examined similarities and differences in brain structure across pairs of ASD, ADHD, and OCD. For example, disorder-specific differences in the left middle temporal gyrusOur analyses were conducted on a single measure of cortical structure and three phenotypic measures as well as a specific age group. These levels of analyses may not fully capture homogeneity across the disorders. Future work should consider running similar types of analyses using multiple measures that can comprehensively characterize the variability across neurodevelopmental disorders. These include brain structure and function and core and comorbid behavioral domains across the life-span, as well as genetic, epigenetic, metabolic, immune, and endocrine markers.The sample size used for the analyses reported in this paper was limited, with unequal distribution of participants across the diagnostic groups. Replication with larger sample sizes is needed.To our knowledge, this is the first study of diagnosis-agnostic homogeneity across ASD, ADHD, and OCD using data-driven discovery. Homogeneity in the variables examined in our analyses did not align well with existing diagnostic categories in the sample studied. These results add to the emerging body of literature questioning the validity of existing diagnostic constructs with respect to having distinct biological and phenotype presentation. The results of this study also highlight the need for a shift from case-control models to more complex analyses that can cope with the large between-disorder overlap and within-disorder variability.Supplementary Material


ADHD is considered a disorder of self-regulation. Recent research has shown that awareness of bodily states, referred to as interoceptive awareness, crucially contributes to self-regulatory processes. Impaired self-regulation in ADHD has been explained in terms of arousal regulation deficits in ADHD (the state regulation deficit (SRD) account). There is now ample support for the SRD account, however the exact reason for arousal regulation difficulties is not yet known. The SRD account explicitly refers to the ability to monitor one’s momentary bodily state as a prerequisite for effective state regulation. However, surprisingly, no study to date has tested the ability to become aware of bodily signals, i.e. interoceptive awareness, in ADHD. In the current study, we therefore compared interoceptive awareness between 24 adults with ADHD and 23 controls by means of both an objective (heartbeat perception task) and subjective measure (questionnaire) of interoceptive awareness. Results revealed a strikingly similar performance for both groups on both measures, suggesting preserved interoceptive awareness in adult ADHD. Attention-deficit/hyperactivity disorder (ADHD)  is a comSelf-regulation is the ongoing, dynamic, and adaptive modulation of internal state  or behavior, by oneself, mediated by central and peripheral physiology , and invThe fact that IA has so far been neglected in ADHD research is even more surprising if one realizes that since the early 70’s dysfunctional autonomic regulation has been proposed to contribute to disturbed self-regulation in ADHD. Zentall defined the optimal stimulation theory and argued that the hyperactive behavior is an attempt to increase stimulation by the child with ADHD experiencing insufficient sensory stimulation . SeveralAccording to the CEM, a prerequisite for effective state regulation is state monitoring: the evaluation system checks for discrepancies between the current and the required (target) arousal/activation state and in case of a discrepancy an individual can deliberately allocate extra effort in order to restore equilibrium. As can be seen in In the current study, we investigated IA in ADHD, by comparing adults with and without ADHD on objective and subjective indices of IA. IA was objectively measured by means of a well-validated heartbeat perception task ,15,16, tRecapitulating, ADHD is considered a self-regulation disorder. IA has been shown to be of crucial importance for self-regulation. Weakened self-regulation in ADHD has for a long time been linked to arousal regulation difficulties. It is not known whether these may be due to impaired IA in ADHD. We therefore studied for the first time IA in adults with ADHD, by means of both objective and subjective measures. Based on existing findings, indicating impaired self-regulation and state regulation, we hypothesized lower IA in adults with ADHD.M = 23.46 (SD = 4.48), 12 males, one left-handed). The control group consisted of 23 typically developed adults, matched on age and sex (mean age: M = 23.57 (SD = 3.17), 13 males, four left-handed). Groups did not differ for age  = 0.01, p = .926) or sex ratio (2χ(1) = 0.20, p = .654). The difference in IQ (controls: M = 111 (SD = 12); ADHD: M = 104 (SD = 13) between groups was marginally significant  = 3.99, p = .052).The study was approved by the local ethics committee of the Faculty of Psychological and Educational Sciences of Ghent University. Participants signed an informed consent. Twenty-five adults with ADHD between 19 and 37 years old  participated in this study. Data of one male participant were excluded because of the use of physical manipulations during the heartbeat perception task (see heartbeat perception task below). The results of the remaining 24 participants are reported . All adults with ADHD had a formal diagnosis established by a psychiatrist and completed a semi-structured clinical interview  . Adults M = 16.7 (SD = 7.6); ADHD: M = 50.4 (SD = 13.5). In the ADHD group, according to the ZVAH, presence of childhood ADHD was confirmed for 20 participants , while ADHD in adulthood was confirmed in 21 participants . The scores on the Adult Self-Report (ASR) [F = 42.19, p < .001) (controls: M = 55.7 (SD = 6.7); ADHD: M = 74.3 (SD = 12.0)). Data on substance abuse, depression and anxiety were also collected via the ASR. No difference in substance abuse, as measured with the DSM oriented scale of the ASR, was observed between groups  = 0.25, p = .620) (controls: M = 56.0 (SD = 6.9); ADHD: M = 56.9 (SD = 5.7)). The ADHD group scored significantly higher on the DSM oriented depression scale of the ASR compared to the control group  = 12.55, p = .001) (controls: M = 52.7 (SD = 3.7); ADHD: M = 59.8 (SD = 9.0)), while the group difference on the anxiety scale did not reach significance  = 2.89, p = 0.10) (controls: M = 53.1 (SD = 5.0); ADHD: M = 55.8 (SD = 6.0)).In the ADHD group, 15 individuals exceeded the cutoff (> 46) of the Wechsler Utah Rating Scale (WURS) , a measurt (ASR)  revealedF = 7.93, p = .007) (controls: M = 40.1 (SD = 7.0); ADHD: M = 48.6 (SD = 12.7)).Several factors that have previously been shown to affect IA and could confound the results were assessed. First, both anxiety and depression have been shown to be (differently) related to IA –54. As mhttp://www.pstnet.com/products/e-prime/) and presented on a 19-inch CRT monitor with 640x480 screen resolution and a 60 Hz refresh rate. All sessions took place during daytime for all participants.The Mental Tracking Method , a well-In accord with earlier studies using the Mental Tracking method ,22,41, pA heartbeat perception score was derived in keeping with previous studies ,22,41: pThe heartbeat perception task was administered twice, at the beginning and end of the total testing session (see procedure for details). A mean heartbeat perception score was calculated by summing both heartbeat perception scores (acquired at the beginning and end of the testing session), and dividing that sum by two .The electrocardiogram was recorded via two external electrodes from the Biosemi ActiveTwo system ; one electrode was placed on the left lower rib cage and one electrode was placed on the left upper rib cage. R-waves were counted offline by means of a custom-made R-top algorithm in Brain Vision Analyzer 2 software.stevenporges.com). This questionnaire consists of four different subscales with a total of 96 items. The subscale of interest for our study was the awareness subscale, which consists of 45 items (Cronbach’s α: .97 for both groups), questioning how aware participants are of their autonomic signals . Research has shown good reliability and validity measures for the awareness scale [Participants completed the Dutch translation of the BPQ , a subjeParticipants were seated in a sound-attenuated and dimly lit room, sitting approximately 60 cm in front of the computer screen. Each participant signed an informed consent prior to participation in the experiment and received monetary compensation for their participation. Then participants were explained about the tasks that would follow. This study was part of a larger experimental set-up. Two other behavioral tasks with a total duration of 50 min were administered in between both administrations of the heartbeat perception task; the results of these tasks will be reported elsewhere. Verbal as well as written instructions were given prior to the start of each task. This study was approved by the local ethics committee.ANOVAs with group (ADHD vs. control) as between-subjects factor were performed to compare performance between groups on the heartbeat perception task, and the subjective measure of IA as indexed by the mean score of the awareness subscale of the BPQ. To check for specificity, we also performed ANOVAs on the mean scores of the other subscales of the BPQ. Finally, links between the IA indices and symptoms of anxiety, depression, and alexithymia were explored by additional correlational analyses.r = .80, p < .001; control: r = .92, p < .001), indicating that the estimate of IA was reliable. Moreover, mean heartbeat perception scores obtained were comparable to previous studies. For the ADHD group, the mean heartbeat perception score was .81 (range: .45 - .97), while it was .83 (range: .45 - .97) in the control group. The between-group comparison in mean heartbeat perception score yielded no significant results  = 0.23, p = .634), with a striking similar standard deviation (.15 for both groups). The Data are reported in For both groups, the heartbeat perception score obtained at the beginning of the session correlated significantly with the heartbeat perception score acquired at the end  = 0.22, p = .641), nor the group by gender effect  = 0.02, p = .902) was significant. The main effect of gender was also not significant  = 0.03, p = .859).As previous research has indicated worse IA as indexed by the heartbeat perception task in females versus males , an addir = .05, p = .805; depression: r = -.04, p = .866; anxiety: r = -.10, p = .649), nor in the control group .No significant correlations between alexithymia, anxiety or depression scores and the mean heartbeat perception score were observed, neither in the ADHD group  = 1.41, p = .24). Although groups did not differ on the subscale measuring awareness, adults with ADHD scored higher on the other three subscales of the BPQ, namely stress response  = 10.84, p = .002), reactivity of the autonomic nervous system  = 6.81, p = .012), and stress style  = 5.85, p = .020, see The between-group comparison of the score obtained on the awareness subscale yielded no significant results , nor in the control group .No significant correlations between alexithymia, anxiety or depression scores and the score on the awareness subscale were found, neither in the ADHD group  to assess a self-report measure of IA. Performance on the heartbeat perception task was strikingly similar in adults with ADHD compared to healthy controls. Moreover, adults with ADHD and typically developed adults also did not differ on the self-report measure of IA. Findings therefore suggest preserved monitoring of bodily state in adult ADHD, which tentatively suggests that the state regulation deficit and related self-regulatory difficulties in ADHD may not be due to an inability to monitor the current bodily state.Several possible reasons for this null-result can be formulated. For instance, it could be related to the paradigm used in the current study. However, this suggestion is doubtful since the heartbeat perception task that we applied is a well validated and widely used paradigm to assess IA in different domains ,18, 22 aMoreover, and importantly, preserved IA in ADHD was confirmed by the self-report measure of IA, the awareness scale of the BPQ . The BPQOther factors previously shown to be related to IA could have potentially obscured our findings. Alexithymia, anxiety and depression have been (differently) related to IA –55. In tADHD is a heterogeneous neurodevelopmental disorder and we are aware that the findings may not generalize to all individuals with ADHD. Both adults with ADHD predominantly inattentive and combined subtypes were included in the study. It would be of interest to test for differences in IA between subgroups or subtypes of ADHD, however, in our study, this was not possible as separate groups were not large enough. Also, both men and women with ADHD were included. Groups were however carefully matched on gender and additional analyses showed no influence of gender. With regard to severity of ADHD symptoms, we feel confident that we tested a representative sample of adults with ADHD. All participants had a formal clinical diagnosis provided by a multidisciplinary team including a psychiatrist and this diagnosis was confirmed by a clinical interview. Also, although adults with ADHD did not differ on the awareness subscale of the BPQ, they were found to have more difficulties in autonomic reactivity, stress response and stress style , which is in line with previous research in adults with ADHD, in which elevated physiological stress responses and higher self-reported stress were observed –61. NoneThe findings suggest that the basic skill of IA is intact in ADHD during a simple heartbeat perception task, but this does not rule out the possibility that becoming aware of bodily signals might be disrupted in other situations in daily life, perhaps as a result of reduced attention or distraction. In other words, in the current situation, when distraction was very limited, adults with ADHD were equally well in monitoring their interoceptive signals, but it has to be tested whether this is still the case when other (distracting) events are happening and additional tasks have to performed, which often is the case in daily life. Similarly, our finding of intact basic ability to become aware of bodily signals, does not exclude the possibility that during situations that require state regulation, this ability is not disrupted. This could be addressed in future research. Alternatively, it could be that IA in ADHD is preserved also in other situations, but that interoceptive information is wrongly applied or interpreted. In other words, individuals with ADHD might be able to monitor the momentary body state but are not able to use the available information sufficiently. This can be further explored in future studies. The finding of preserved IA in ADHD as measured in the current study, instigates the debate on whether the self-regulatory difficulties in ADHD reflect a difficulty in allocating the required effort or are related to a general altered motivational style ,62. FurtSome limitations have to be mentioned. First, as this is the first study investigating IA in ADHD and ADHD is known to be a heterogeneous disorder, generalization to other samples with ADHD is difficult. Replication of these findings is thus warranted. Second, although the heartbeat perception task we used is a well-validated task that has been extensively applied in previous research, it would be informative if results hold for other heartbeat perception paradigms, such as a heartbeat discrimination task. Third, individuals with ADHD with and without use of medication (methylphenidate) were included in the study. Although we asked participants to stop their medication 48 hours prior to participation, in line with a lot of existing studies, it cannot be fully excluded that medication use may have influenced our findings. An exploratory data check did however not indicate a difference in IA scores between individuals with ADHD who were or were not taking methylphenidate in daily life.In summary, following recent research indicating IA to contribute to self-regulation and previous research inspired by the SRD model that unequivocally provided support for deficient regulation of energetic state in ADHD, the hypothesis was put forward that the ability to monitor the momentary bodily state may be impaired in ADHD, a hypothesis that had been overlooked in previous research. However, no support was rendered for altered IA in adults with ADHD. The finding of preserved monitoring of bodily state tentatively suggests that self-regulatory difficulties in ADHD may not be related to a lack of awareness about the current bodily state.


In the majority of indices showing ADHD persister–control differences, ADHD remitters differed from controls  but not from persisters. Similarly, connectivity measures were unrelated to continuous outcome measures of ADHD symptoms and impairment in participants with childhood ADHD. These findings indicate that adolescents and young adults with persistent and remitted ADHD share atypical over-connectivity profiles and reduced ability to modulate connectivity patterns with task demands, compared to controls. Task-based functional connectivity impairments may represent enduring deficits in individuals with childhood ADHD irrespective of diagnostic status in adolescence/young adulthood.We previously provided initial evidence for cognitive and event-related potential markers of persistence/remission of attention-deficit/hyperactivity disorder (ADHD) from childhood to adolescence and adulthood. Here, using a novel brain-network connectivity approach, we aimed to examine whether task-based functional connectivity reflects a marker of ADHD remission or an enduring deficit unrelated to ADHD outcome. High-density EEG was recorded in a follow-up of 110 adolescents and young adults with childhood ADHD  and 169 typically developing individuals during an arrow-flanker task, eliciting cognitive control. Functional connectivity was quantified with network-based graph-theory metrics before incongruent (high-conflict) target onset (pre-stimulus), during target processing (post-stimulus) and in the degree of change between pre-stimulus/post-stimulus. ADHD outcome was examined with parent-reported symptoms and impairment using both a categorical (DSM-IV) and a dimensional approach. Graph-theory measures converged in indicating that, compared to controls, ADHD persisters showed increased connectivity in pre-stimulus theta, alpha, and beta and in post-stimulus beta (all  Accumulating evidence suggests that atypical connectivity may be implicated in neurodevelopmental disorders7, such as attention-deficit/hyperactivity disorder (ADHD). In recent years, the study of functional connectivity in ADHD has contributed to the development of neurobiological models highlighting the role of multiple large-scale neural systems in the disorder13. These include the frontal–parietal network, the ventral–attentional network, and the default-mode network (DMN), involved in executive control, attentional processes, and introspective resting states, respectively14. In particular, it has been hypothesized that ADHD symptomatology may arise from a deviation from neurotypical synchronization and interaction within and between these large-scale networks during brain development16. Characterizing the atypical patterns of whole-brain functional connectivity across development may thus provide new insights into ADHD neurobiology.A coherent communication between brain regions organized in large-scale neural systems, or brain functional connectivity, is thought to have a key role in cognition and behavior19 and increased26 connectivity, for example, within and between the DMN and executive network. Task-based fMRI connectivity studies, which may allow for a more direct characterization of connectivity underlying impaired cognition and behavior28, have further shown not only hypo-connectivity in fronto-striato-cerebellar networks during sustained attention29 and inhibition32 but also hyper-connectivity within the DMN30 and between networks of reward-cognitive control integration33 in ADHD. The inconsistencies across previous findings may arise from relatively small samples in most studies and differences in methodology 34. In addition, fMRI connectivity measures synchronicity between precisely localized networks35 but may not fully capture synchronization between faster brain oscillations underlying fast-changing processes during cognitive tasks.Most studies to date have investigated functional connectivity in ADHD using resting-state functional magnetic-resonance imaging (fMRI) and reported both reduced37. Theta, alpha, and beta oscillations during cognitive tasks40, such as flanker tasks45, have been implicated in processes engaging top–down control networks that require coherent activity between cortically distributed regions. While most EEG connectivity studies on ADHD to date have focused on resting states48, available task-based connectivity studies in children and adolescents with ADHD indicate not only hyper-connectivity in alpha49 and beta37 oscillations during attentional tasks but also reduced fronto-parietal theta–alpha connectivity during a flanker task50. Yet, most EEG connectivity studies on ADHD have used connectivity metrics contaminated by volume-conduction artifacts . This methodological limitation can produce inflated connectivity estimates significantly affecting case–control differences52 and warrants further investigation with metrics uncontaminated by this issue. Furthermore, network approaches based on graph theory have been recently applied to characterize functional connectivity between large-scale brain networks and identify connectivity alterations53. Initial graph-theory evidence from one task-based EEG study showed atypical functional connectivity in children with ADHD54, but no study to date has been conducted on adolescents or adults45.Investigating functional connectivity using the sub-second temporal resolution of electroencephalography (EEG) instead allows for the measurement of a wider range of brain oscillatory phenomena, including transient changes in connectivity during cognition and behavior16, little is known on how functional connectivity alterations map onto ADHD developmental outcomes. Longitudinal studies show that ADHD persists, in full or in partial remission, in the majority of adolescents and adults clinically diagnosed in childhood, while a proportion of individuals remit across development56. Remission of ADHD may be explained in light of compatible neurodevelopmental models58, which posit that remission may underlie (1) a “normalization” of neural processes (markers of remission) that improve concurrently with clinical symptoms and impairment, whereby individuals with remitted ADHD (ADHD “remitters”) converge toward neurotypical individuals but diverge from individuals with persistent ADHD (ADHD “persisters”)61; and (2) enduring deficits that are unrelated to the clinical outcome, remaining impaired in both ADHD remitters and persisters compared to neurotypical controls62. The identification of such processes is important for elucidating the neurobiological mechanisms underlying remission/persistence and may point to candidate biomarkers for the development of new interventions. It has been hypothesized that improvements across development in higher-level executive functions would underlie ADHD remission, while persisting impairments in lower-level processes would be displayed regardless of later clinical outcome11. Most studies to date, however, found that cognitive performance indices of executive functioning do not distinguish between ADHD persisters and remitters and are thus insensitive to ADHD outcomes66. In line with these studies, our recent follow-up study of adolescents and young adults with childhood ADHD show, using a range of attentional, vigilance, and executive paradigms, that cognitive and event-related potential (ERP) markers of executive control  were insensitive to ADHD outcome67. Instead, cognitive-EEG measures of preparation–vigilance  and error detection  were markers of remission, distinguishing ADHD remitters from persisters67.Despite the hypothesis that a deviation from neurotypical large-scale connectivity profiles across development may be implicated in ADHD68. Despite the complementary benefit of EEG in investigating functional connectivity during cognitive processes36, no study to date has examined EEG connectivity in adult ADHD or in relation to remission/persistence.The investigation of task-based functional connectivity across distributed brain networks may provide new insights into the neural pathways of ADHD persistence/remission. The only available task-based connectivity study on persistence/remission reported lower fMRI fronto-thalamic connectivity during response preparation in ADHD persisters compared to remitters and controls during a cued reaction-time task61. In this new in-depth analysis of the data presented in our previous study61, we sought to take a whole-brain approach to test whether functional connectivity before and during the processing of incongruent stimuli, measured with graph-theory and connectivity metrics not contaminated by volume conduction, is atypical in persistent ADHD, and whether it represents a marker of ADHD remission or an enduring deficit. We hypothesized that both ADHD persisters and remitters would display functional connectivity alterations compared to neurotypical individuals during this task evoking high levels of cognitive control, consistent with most studies examining cognitive and EEG markers of executive processes and ADHD remission65.In the present EEG study, we aimed to investigate brain functional connectivity during a cognitive control task, the arrow flanker task, in adolescents and adults with childhood ADHD and neurotypical controls. We previously reported that, during incongruent (high-conflict) trials of this task, the N2 index of conflict monitoring and cognitive performance measures were insensitive to ADHD outcomes, while error-related ERPs were markers of remission69, including 110 adolescents and young adults who met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for combined-type ADHD in childhood (10 sibling pairs and 90 singletons) and 169 control participants (76 sibling pairs and 17 singletons)70. Participants with ADHD were initially recruited from specialized ADHD clinics and controls from schools in the UK69. Exclusion criteria at both assessments were: intelligence quotient (IQ) < 70, autism, epilepsy, brain disorders, and any genetic/medical disorder associated with externalizing behaviors that might mimic ADHD. Among those with childhood ADHD, at follow-up 87 (79%) continued to meet clinical (DSM-IV) levels of ADHD symptoms and impairment (ADHD persisters), while 23 (21%) were below the clinical cut-off (ADHD remitters)71 (see “ADHD diagnosis” below). Among ADHD remitters, 14 displayed ≥5 symptoms of inattention or hyperactivity–impulsivity but did not show functional impairment. Participants attended a single research session for clinical, IQ and cognitive-EEG assessments. An estimate of IQ was derived with the vocabulary and block design subtests of the Wechsler Abbreviated Scale of Intelligence72. ADHD persisters, remitters, and controls did not differ in age, but there were significantly more males in the remitted group than in the other two groups, with no females among ADHD remitters 60. A 48-h ADHD medication-free period was required before assessments. Parents of all participants gave informed consent following procedures approved by the London-Surrey Borders Research Ethics Committee (09/H0806/58).The sample consisted of 279 participants who were followed up on average 5.8 years SD = 1.1) after assessments in childhood.1 after 73 was conducted by trained researchers with parents of the ADHD probands to assess DSM-IV-defined ADHD presence and persistence of the 18 ADHD symptoms. Evidence of impairment commonly associated with ADHD was assessed with the Barkley’s functional impairment scale (BFIS)74. Parent-reported DIVA and impairments were used to determine ADHD status, as these were validated against objective markers (cognitive performance and EEG measures) in this sample, whereas the same objective markers showed limited agreement with self-reported ADHD75. Childhood ADHD participants were classified as “affected” at follow-up  if they showed ≥6 items in either the inattention or hyperactivity–impulsivity domains on the DIVA and ≥2 areas of impairments on the BFIS; they were classified as remitters otherwise. ADHD outcome was measured using a categorical definition of persistence based on diagnosis , as well as a dimensional approach based on continuous levels of symptoms of ADHD and impairments, to assess ADHD severity.The Diagnostic Interview for ADHD in Adults (DIVA)76. In each trial, a central fixation mark was replaced by a target arrow . Participants had to indicate whether this arrow pointed toward the left or right by pressing corresponding response buttons with their left or right index fingers. Two flanker arrows identical in shape and size to the target appeared 22 mm above and below the center of the target arrow 100 ms before each target. Both flankers either pointed in the same (congruent) or opposite (incongruent) direction to the target. Cognitive control and conflict monitoring are maximal during incongruent trials. When the target appeared, both target and flankers remained on the screen for 150 ms, with a new trial every 1650 ms. Two-hundred congruent and 200 incongruent trials were arranged in 10 blocks of 40 trials. Only incongruent trials were considered in the present in-depth analysis of the data included in our previous study61, as this high-conflict condition has proven more sensitive to ADHD–control differences in previous ERP analyses in this61 and other77 ADHD samples. For further details, see The task was an adaptation of the Eriksen Flanker paradigm designed to increase cognitive load78. Sections of data containing artifacts >±100 μV or with a voltage step ≥50 μV were automatically rejected. The artifact-free data were segmented in epochs between −650 and 1000 ms stimulus-locked to incongruent stimuli. Both trials with correct and incorrect responses were examined61. Only data containing ≥20 clean segments for condition were included in analyses, leaving 271 participants  for correctly-responded trials and 240  for incorrectly-responded trials.The EEG was recorded from a 62-channel extended 10–20 system , using a 500-Hz sampling rate, impedances under 10 kΩ, and recording reference at FCz. The electro-oculograms were recorded from electrodes above and below the left eye and at the outer canthi. Raw EEG recordings were down-sampled to 256 Hz, re-referenced to the average of all electrodes (turning FCz into an active channel), and filtered using Butterworth band-pass filters . All trials were visually inspected and sections containing electrical or movement artifacts were removed manually. Ocular artifacts were identified using Independent Component Analysis80. This measure was chosen to ignore spurious connections between brain signals caused by volume conduction, which can substantially limit the ability to measure functional associations using EEG channels. iCoh captures the non-instantaneous connectivity between brain activities from EEG channels that are phase-lagged 82. Since volume conduction affects multiple scalp channels with near-zero phase delays, connectivity measured with iCoh is not contaminated by near-instantaneous artifacts of volume conduction. iCoh was measured by isolating the imaginary part of the complex number phase coherence between two signals of same frequency51, estimated by calculating their cross-spectrum for each time point with Fast Fourier Transforms using the EEGLAB “newcrossf” function83 in Matlab . iCoh is measured on a scale between 0 and 1. When two signals at the same frequency have identical phase values, possibly due to volume conduction artifacts, iCoh = 0. Instead, if two signals are phase lagged, iCoh > 051. Values of iCoh for all possible electrode pairs (62 × 62) were computed in the theta (4–8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) bands 89. Graph theory is based on mathematical algorithms to quantify the relationships (“edges”) between brain signals from EEG channels, representing the “nodes” of a network. Unthresholded weighted iCoh matrices were used, in line with previous studies92, where each edge is equivalent to the measured iCoh of two electrodes to preserve essential information of a network structure94. Graph-theory metrics measure the degree of network segregation  and network integration and efficiency 95. The following commonly used graph measures were calculated96: average clustering coefficient ; global efficiency (how efficient the network is in transferring information); and characteristic path length and diameter . Graph-theory metrics were computed with the Brain Connectivity47 and BioNeCT  toolboxes. In order to examine the modulation of functional connectivity profiles with different conditions and correct vs incorrect performance in this task, we computed connectivity metrics before target  and during target processing , as well as separately for correctly- and incorrectly-responded trials.The high multi-dimensionality of the iCoh measures was disentangled with a graph-theory approach, which allows one to derive global network-based measures and describe functional associations in terms of network propertiesd effect sizes are presented along with test statistics, where d ≥ 0.20 is a small effect, d ≥ 0.50 a medium effect, and d ≥ 0.80 a large effect97. Given the large number of hypotheses tested, sensitivity analyses applied multiple-testing corrections with false discovery rate (FDR) on post hoc tests with the “multproc” package, using the Simes method, which identifies those tests that remain significant98.Connectivity metrics were examined with random-intercept linear models  in Stata 14 , testing for effects of group (ADHD persisters vs remitters vs controls), time window (pre-stimulus vs post-stimulus), response (correct vs incorrect), and their interaction (group-by-window-by-response). When the three-way interaction was not statistically significant, only statistically significant main effects and two-way interactions were included. For all measures, the within-group degree of change from pre-stimulus to post-stimulus was compared across groups using difference scores to examine how functional connectivity changes with task demands. All models controlled for age and took into account the degree of clustering due to family status. Cohen’s 60 and childhood IQ predicted ADHD outcome at follow-up71, all analyses were also re-run controlling for IQ to examine whether IQ contributes to the results. Finally, even though EEG functional connectivity does not provide a precise localization of functional networks, we examined brain connectivity within and between groups of electrodes from different cortical regions, following previous connectivity studies100: analyses were repeated using iCoh values within and between clusters of electrodes in different scalp regions  and between the two hemispheres (left/right)  condition (the same task condition in which connectivity was measured). We previously reported significantly increased mean reaction time (MRT), RTV, and number of errors in ADHD persisters compared to controls in the incongruent condition of this task and intermediate scores with non-significant differences in remittersIn trials where participants responded correctly, ADHD persisters showed greater clustering coefficient, global efficiency, and mean iCoh and lower path length and diameter compared to controls at all frequency bands in the pre-stimulus window (before target onset) and only in beta in the post-stimulus windows Table . SimilarAmong measures showing significant group-by-window interactions  on post hoc group comparisons  showed that all statistically significant differences between controls and ADHD remitters, and between controls and ADHD persisters remained significant. The only significant difference between ADHD persisters and remitters (in beta diameter) was no longer significant when correcting for multiple testing. All significant group differences on measures of pre-stimulus/post-stimulus change remained significant after multiple-testing corrections.p values of certain tests that were statistically significant in the full sample became trend-level effects (p = 0.05–0.10). All effect sizes were similar to those on the full sample, suggesting that these non-significant results may be due to lower power in this smaller sample.All results remained unchanged when rerunning analyses on the male-only sample , between antero-central, centro-posterior, and antero-posterior regions, and between the two hemispheres , network clustering (higher clustering coefficient), efficiency , and integration (lower path length and diameter) at all frequency bands prior to target onset in trials with correct behavioral responses, as well as during target processing in beta oscillations. This increased task-based functional connectivity is consistent with a previous EEG study reporting pre-target over-connectivity in children with ADHD61, reporting that executive-functioning measures are insensitive to ADHD outcomes in adolescence and adulthood65. They further extend our earlier findings of no differences between ADHD remitters and persisters on the N2 (reflecting conflict monitoring) and indices of cognitive performance in the same incongruent condition61. More broadly, our findings support the co-existence of separate neurobiological processes underpinning developmental pathways to remission and persistence of ADHD57: functional connectivity during high-conflict trials in this cognitive control task appears unrelated to clinical outcome, unlike measures of less-effortful, non-executive processes  identified as markers of remission in our previous cognitive-EEG studies67 and in fMRI studies104. A clinical implication is that connectivity impairments underlying executive-control processes may not be suitable targets for interventions or objective indicators of treatment monitoring, consistent with previous evidence of no effects of stimulants on EEG connectivity in ADHD105. Future studies should examine whether EEG functional connectivity during less effortful activities, such as attentional processes, represent markers of remission and candidate targets for new treatments, similar to measures of non-executive processes in our previous studies67.Our study represents the first investigation into EEG connectivity in adolescents and adults with remitted ADHD. In several functional connectivity measures sensitive to impairments in persisters, ADHD remitters were impaired compared to controls and indistinguishable from persisters, consistent with our hypotheses. ADHD remitters also showed the same reduction in all measures of pre-stimulus/post-stimulus change in theta connectivity displayed by persisters. As such, brain connectivity impairments during this cognitive control task were insensitive to ADHD remission/persistence in adolescence and early adulthood and may represent enduring deficits irrespective of current diagnostic status. Findings from dimensional analyses on ADHD severity supported these results, as most connectivity measures in participants with childhood ADHD were unrelated to continuous levels of ADHD symptoms and impairments. Of note, while results of categorical analyses were largely unchanged after correcting for multiple testing, the few significant associations between connectivity and functional impairment  did not survive multiple-testing corrections. These connectivity findings are consistent with previous cognitive-EEG studies, including our previous analyses on this sample61. In addition, in all groups functional connectivity was increased during incorrect responses compared to correct responses, both during the inactive pre-stimulus window and during processing of incongruent stimuli. This is in line with the interpretation that hyper-connectivity displayed in the ADHD groups was dysfunctional during this task. An additional analysis testing whether hyper-connectivity was related to impairments in cognitive performance during this task further confirmed this pattern, as pre-stimulus hyper-connectivity in theta and beta oscillations was associated with fewer correct responses in individuals with childhood ADHD and controls and with increased RTV in individuals with childhood ADHD. Increased functional connectivity in both ADHD persisters and remitters may thus contribute to the lack of differences in cognitive performance measures between ADHD remitters and persisters reported in our previous study61. Overall, a suboptimal pattern of hyper-connectivity underlying cognitive control processes may lead to dysfunctional behavioral responses, both in neurotypical individuals and in individuals with childhood ADHD.Of note, while widespread group differences emerged in correctly-responded trials, where participants successfully overcame the conflict generated by incongruent target and flanking stimuli, group differences in error trials emerged only in three measures of post-stimulus theta connectivity. An incorrect response likely represents a failure of cognitive control, required for selection of a correct response in the highly challenging incongruent condition. The limited group differences in connectivity during incorrect responses may suggest that a suboptimal pattern of functional connectivity may attenuate the differences in brain-network profiles between neurotypical individuals and individuals with ADHD, who are prone to making more incorrect responses in this taskd = 0.38–0.53) between ADHD remitters and controls, but negligible or small (d = 0.02–0.36) between remitters and persisters, in measures showing ADHD persister–control differences suggest that we had sufficient power to detect, with the current sample sizes, differences in connectivity with at least moderate effect sizes. In addition, our sample included young adults as well as adolescents who are still undergoing rapid cortical maturation. While analyses controlled for age, future follow-up assessments with participants having reached adulthood could provide further insight into developmental patterns. Finally, the relatively poor spatial resolution of scalp-EEG did not allow precise localization of the brain networks. Yet, the current EEG connectivity analyses allowed precise temporal resolution during two short time windows and both correct and incorrect behavioral responses, as well as connectivity estimates unaffected by volume-conduction artifacts and examination of whole-brain network properties. The results of local connectivity within and between cortical regions were further consistent with those of whole-brain analyses, indicating comparable effects in more localized networks.A limitation of this study is that, despite the large sample, the low ADHD remission rate at follow-up resulted in a relatively small group of remitters. Therefore, we could not exclude the possibility that some non-significant group differences could be due to low power. However, the moderate effect sizes (10, which do not follow the developmental pathways of clinical profiles. Future studies should explore the presence of potential compensatory mechanisms that may enable developmental improvements in clinical profiles and non-executive cognitive processes in individuals with remitted ADHD67, despite enduring functional connectivity alterations during cognitive control.In conclusion, we report new evidence of shared atypical task-based connectivity profiles in adolescents and young adults with persistent and remitted ADHD. These connectivity alterations may represent enduring deficits and neural signatures associated with having a history of childhood ADHD, but appear unrelated to follow-up diagnostic status. Connectivity impairments underlying executive processes may represent associated characteristics or risk factors in ADHDSupplementary Material.