X constitutes a scientific improvement (to a greater or lesser extent) with respect to X. The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to increase their understanding of X. This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both.What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it (thus interpreted). Roughly, the question is here understood to concern what type of cognitive change with respect to a topic  What is scientific progress?The progress of science is astounding. Just two centuries ago, people suffering from infectious diseases would have been told that their illnesses were caused by ‘miasma’, i.e. impure air arising from decomposing organic matter. Progress was made in the late 19th century, when the miasma theory was replaced by the theory that infectious diseases are caused by unobservably small entities passing between organisms, i.e. ‘germs’. The progress that has since been made builds on this theory, e.g. in the discovery that some infectious diseases (such as tuberculosis and the plague) are caused by bacteria, while others  are caused by viruses. So at least on the topic of infectious diseases, scientists have made significant progress over the years. But why? In virtue of what do these developments count as progressive? unclear, misguided, and even pointless: (i) What would it even be to advance a philosophical account of scientific progress? (ii) Doesn’t science progress in a variety of quite different ways, depending on the scientific field, its methodology, or even the particular research project in question? (iii) And even supposing that some general account of scientific progress could be provided, what would be the point of such an exercise?It is natural to worry that this question is too ‘philosophical’, in the pejorative sense of the term, to admit of a definitive answer. For example, Chang describes it as “one of the most significant issues in the philosophy of science today”, but then immediately notes its “immense difficulty” Chang , p. 1. PIn this paper, my first aim is to show that these worries can be convincingly allayed. In response to (i), I will argue that the question, ‘What is scientific progress?’, has at least one interpretation on which the question itself is perfectly clear and intelligible. In response to (ii), I will argue that, on this interpretation, there is no particular reason to think that a general account of scientific progress cannot be provided. Finally, in response to (iii), I will argue that this interpretation of the question makes evident why answering the question thus interpreted is important—viz., not just because of its intrinsic intellectual importance, but also due to the practical implications of different answers.understand that phenomenon, a proposal closely akin to what I have previously called the noetic account of scientific progress , then we should surely modify, or re-engineer, the concept of scientific progress so as to fit with our considered judgments about what sorts of pursuits are in fact most valuable.1improvements in a general sense, the debate about scientific progress concerns a narrower class of changes that Niiniluoto labels ‘cognitive’. Dellsén , by that cognitive change. Both constituting and promoting progress thus count as improvements, but the latter counts as an improvement only in virtue of leading to or probabilifying an occurrence of the former at some later time.3Third, some recent discussions of scientific progress have introduced a useful distinction between what constitutes scientific progress and what merely promotes it . As this makes clear, there is no reason to think there is a unified general story to tell about what promotes progress across all scientific disciplines. Indeed, what presently promotes progress within some discipline might cease to do so, or do so to a lesser extent, in the future, given technological or methodological changes. So there is a strong case to be made for a kind of ‘pluralism’ about what promotes progress. Note, however, that it does not follow that we should be pluralists about what constitutes progress, since these different ways of promoting progress might well be instrumental for achieving the same type of cognitive improvement.Fourth, scientific progress is gradable—a matter of degree—in the sense that a given episode can be more or less progressive, perhaps in addition to being progressive outright (in a binary sense).progress-on-X), and a more general, across-topic concept of scientific progress . Consider an episode that exhibits cognitive improvement with respect to one topic progress-on-X, such an episode can simply be described as simultaneously exhibiting progress on overall progress, then the question of whether there is progress in that sense during the episode presumably turns on whether the there was enough progress made on overall progress can be defined as the aggregation or sum of degrees of progress (and regress) on the various different topics X therefore seems to be the more fundamental notion of the two, we will primarily be concerned with it in what follows.A fifth and final point is that we can distinguish between a topic-specific concept of scientific progress X constitutes a (greater or lesser degree of) scientific improvement with respect to X?What type of cognitive change with respect to a given topic In the introduction, I mentioned three types of worries about our original question, ‘What is scientific progress?’. The first was that the question was unclear. That, I submit, need no longer worry us, since the original question can now be replaced by the painstakingly precise (Q). The second worry was that science clearly progresses in different ways, depending on the scientific field, methodology, or research project. That worry is assuaged by pointing out that different disciplines may promote progress in different ways even if the same type of cognitive change constitutes progress across those disciplines. Furthermore, by relativizing progress to a particular topic X, we have opened up the possibility that what constitutes progress with regard to one topic To summarize, then: scientific progress is a type of understanding. To a first approximation, the account holds that scientific progress with respect to X consists in a change in the publicly available information about X that helps us increase our understanding of X, where ‘increased understanding’ is defined as gaining a more accurate or comprehensive model of X’s dependence relations, such as its causal relations. In so far as such dependence relations ground explanation and prediction, e.g. through causal explanation, this account implies a strong link between scientific progress, on the one hand, and explanation and prediction, on the other. In this respect, the current account resembles the original noetic account , that centers around the concept of  Dellsén . Indeed,Let me first make a methodological point. In what follows, I offer a definition of the the relevant notion of understanding, before defining progress in terms of that notion. Although I believe that this definition of understanding is at least as good as any alternative definition on offer,dependency model. This is a model of the dependence relations that aspects of a given phenomenon stand in, or fail to stand in, to other aspects of the phenomenon, or of other phenomena. Such a model thus contains information about relations between (aspects of) phenomena—both ‘positive’ information about how they are related, and ‘negative’ information about how they fail to be related. The relations in question are dependence relations, the paradigmatic instance of which is causation, but which may include other dependence relations such as grounding. The relata of these relations are variables, rather than specific or actual values of such variables; they may be either continuous  or discreet . Thus dependence relations encode information not just about the actual state of some phenomenon, but also how the phenomenon would have been different if other things had been different in some specific way.The definition of understanding to which I appeal in what follows makes use of the notion of a x from a relaxed position, is given by k is a positive constant specifying the ‘stiffness’ of the spring (the minus sign indicates that that the force x). So if the object is pulled a distance x and then released, then assuming as an idealization that no other forces act on the object, the force not depend on, e.g. its volume. This is a paradigmatic example of a (very simple) dependency model, in this case of a composite phenomenon consisting of an elastic spring and an object attached to it.So a dependency model of a phenomenon, in so far as it is accurate and comprehensive, encodes information about dependencies. Most dependency models that are even just somewhat accurate and comprehensive will be enormously complex, but let me illustrate with a simple, toy example. According to Hooke’s law, the force exerted by a spring on an object fastened to it, displaced at a distance k is determined. Now, k can clearly be calculated from Hooke’s law by plugging in actual values for the force x. But what kdepends on has to do with various facts about the spring itself, e.g. its length at relaxed position, the number of coils, the diameters of those coils, and the material from which the spring is constructed. The relation between k and these facts about the spring is arguably not causation; rather, it is something closer to grounding.In the above example, all the dependence relations involved are causal—at least arguably so. As I have intimated, however, this is not always the case. Suppose we supplement this model with information about how the spring’s stiffness accurate dependency model, by contrast, would correct some of the inaccuracies contained in the above model. For example, it is of course not true of any real system of this sort that the only force that acts on the object is due to the spring. Hence we cannot really identify the F in A more SunderstandsX if and only if S grasps a sufficiently accurate and comprehensive dependency model of X; and S’s degree of understanding of X is proportional to the accuracy and comprehensiveness of their dependency model of X. I note immediately that the target of this type of understanding, X, is some part of our world; not a mere representation thereof, such as a theory, concept, or explanation. In the literature on understanding, this type of understanding is generally referred to as ‘objectual understanding’, and often contrasted with ‘understanding why’ or ‘explanatory understanding’  increase their understanding of X.Now, how do we get from this definition of understanding to an account of scientific progress, i.e. to an answer to (Q)? We might say that scientific progress, i.e. the type of cognitive change with respect to a given phenomenon their cognitive attitudes? In light of this problem, I suggest we move to a conception of scientific progress according to which it is not the cognitive attitudes of those who make scientific progress that determine whether an episode is progressive; rather, progress is determined by the publicly available information, such as that contained in peer-reviewed journal articles, on the basis of which any relevant member of society (including scientists but not excluding non-scientists) can form or sustain the relevant type of cognitive attitudes. In the case of the noetic account, then, I suggest that what matters to progress on X is whether changes in the publicly available scientific information makes it possible for relevant members of society to increase their understanding of X.10On further reflection, however, this exclusive focus on the cognitive attitudes of scientists themselves seems unmotivated. If all that really improved through scientifically progressive episodes were the scientists’ own attitudes, e.g. in increasing their understanding, then why should non-scientists care about scientific progress at all? In particular, how could the extensive funding of ‘pure’ scientific research, with no clear practical benefits for non-scientists, be justified if scientific progress merely consisted in some scientists improving The noetic account (restated): The type of cognitive change with respect to a given phenomenon X that constitutes (a greater or lesser degree of) scientific improvement relative to X is a change due to scientific research in the publicly available information that enables relevant members of society to increase their understanding of X.This somewhat Procrustean formulation of the account is meant to explicitly mirror the question to which it is an answer, (Q). More colloquially, the noetic account thus reformulated holds that scientific progress consists in making available scientific information that helps us as a society to better understand relevant phenomena. Given the identification of understanding with dependency modelling, scientific progress enables us to model dependencies in these and related phenomena—which, in turn, helps us explain, manipulate and predict them on a regular basis.We are now—finally!—in a position to formulate a revised noetic account of scientific progress:promoting scientific progress even if there is a single type of cognitive change that constitutes progress  is meant to measure the extent to which a theory captures the complete truth about the world in a given conceptual framework. Thus, one way in which Briefly, the truthlikeness account holds that scientific progress occurs when accepted scientific theories get closer to the truth, i.e. become more truthlike. In the special case of one theory T is defined relative to a language L. Roughly, T’s truthlikeness is then a measure of the similarity between a maximally specific claim C* in L, which fully captures everything that is true, and a disjunction of other such maximally specific claims L, which captures the content of T by effectively listing all the maximally specific possible states of affairs allowed by T  corresponds quite closely to the noetic account’s two notions of accuracy and comprehensiveness (of dependency models). Thus, were it not for certain connotations of the term ‘truthlikeness’, such as the language-relativity therein and its focus on theories rather than dependency models, it would not be too misleading to state the noetic account in terms of increasing truthlikeness of dependency models.theories, the noetic account focuses on (increasingly accurate and comprehensive) dependency models. The main difference between these is that dependency models target specific phenomena in the world, whereas theories are more general and abstract claims with no particular target.use (or apply) theories to gain understanding of phenomena, i.e. to construct dependency models thereof. Earlier we saw how Hooke’s law, a depends on its mass m, the displacement distance x, and the spring’s stiffness k. Since true or truthlike theories undergird understanding in this way, they are profoundly important for scientific progress from the noetic account’s point of view.Regarding the differences between the noetic and truthlikeness accounts, note that where the truthlikeness account focuses on (increasingly truthlike) With that said, increasing understanding and increasing truthlikeness can come apart; when they do, progress follows the former rather than the latter. Consider first cases in which already existing theories are used to construct new dependency models that are either more accurate or more comprehensive than previous models. In such cases, theory stands still while understanding marches on. Our simple example of the spring provides a case in point. In constructing the dependency model of the system, with which we see (among other things) what and how the object’s acceleration depends on, we did not increase the truthlikeness of our theories. Admittedly, there is a sense in which a new ‘theory’ was added when we derived Another way in which increasing understanding can come apart from increasing truthlikeness concerns the use of idealizations to gain understanding. For our purposes, idealizations can be understood as falsehoods that are deliberately included in some representation. Now, in some cases, accepting theories with idealizations increases the truthlikeness of accepted theories in a straightforward way, since the idealized theory may capture part of the complete truth about the world in a way that previous theories failed to do—even when the new theory contains an idealization and is thus false , and thus potentially provide us with great deal of understanding.entirely spurious correlations: statistical correlations between two or more phenomena that aren’t due to any dependence  relation between those phenomena, or between these phenomena and some other phenomenon. For example, it is presumably entirely spurious that the average margarine consumption in the U.S. was highly correlated  guide to the latter. Thus, correlations often accumulate knowledge. The key term ‘knowledge’ is notoriously difficult to define, and Bird agrees with Williamson reflective judgments rather than our untutored intuitions. Of course, the outcome of such reflections might be that we we may end up agreeing with what our previous selves were already inclined to say, i.e. with our original intuitions—but that, too, would be a reflective judgment.Many commentators disagree with Bird’s intuitions about such cases  to defeat the justification that would otherwise be conferred on D were so far off track?Thus consider cases where, in a particular scientific domain or discipline pangenesis theory, proposed in 1868; Francis Galton’s stirp theory, proposed in 1879; and August Weismann’s germ-plasm theory, proposed in 1892. Shortly thereafter, in 1902–1904, Walter Sutton and Theodor Boveri independently developed versions of the currently accepted chromosome theory, according to which chromosomes located in all dividing cells carry genetic information from parent to offspring. Assuming that the chromosome theory is indeed correct, the three earlier theories were all fundamentally mistaken, in that each posited some non-existent carrier of genetic material—‘gemmules’ for Darwin, ‘stirps’ for Galton, and ‘germ-plasm’ for Weismann. Now consider a point in time shortly after Sutton and Boveri’s theory was proposed, e.g. 1905. Did their theory contribute to scientific progress at that time?A historical case, familiar from debates about the pessimistic meta-induction . Thus, as soon as the chromosome theory was made publicly available, primarily via Sutton’s publication of the theory in the recently established n Sutton , 1903, tTo reiterate an earlier point, this does not imply that scientific evidence or confirmation is of no relevance to scientific progress on the noetic account. If the chromosome theory had not been supported by (first-order) scientific evidence, such as Boveri’s experiments with sea urchins and Sutton’s work on grasshoppers, the theory would probably not have been published at all, and it would certainly not have achieved the status that it later did. In this way, ordinary first-order scientific evidence is crucial to scientific progress on noetic account. What is not crucial—indeed, irrelevant—is whether there is some historical higher-order evidence available which would prevent this first-order scientific evidence from providing the type of epistemic justification for scientists’ beliefs in the chromosome theory that would make them constitute knowledge.In this section, I consider several challenges to the noetic account that aim to show that it is too narrow to accommodate the full range of cases that plausibly fall under scientific progress. The general worry here is that by identifying scientific progress with enabling increased understanding, rather than with some more general developments such as increased truthlikeness of accepted theories, we have excluded a variety of developments that ought to count as progressive. I will consider three specific versions of this worry, viz. that the noetic account is too narrow in virtue of (i) making metaphysical assumptions regarding dependence relations, (ii) excluding scientifically important classification schemes, and (iii) excluding discoveries of previously unknown phenomena. My contention will be that, ultimately, none of these charges hit home because the noetic account is not as narrow as one might have thought.or increased truthlikeness.Let me first acknowledge, however, that there are two alternative strategies for responding to these challenges. The first is to concede that the noetic account is too narrow as it stands, and subsequently modify the account so as to incorporate other developments than those that enable increased understanding. There are many ways to do this. Most straightforwardly, one might combine elements of the noetic account with elements of alternative accounts, such as the truthlikeness account—and say, for example, that progress consists in enabling increased understanding A second alternative strategy is a revisionist one. Faced with the charge that the noetic account is too narrow, e.g. in virtue of counting classification schemes as non-progressive, one could argue that its narrowness is a virtue rather than a vice. The narrowness of an account of scientific progress is what gives it its critical bite—its potential for serving as the basis of philosophically informed decisions about which research projects to pursue . Note, for example, that an account of scientific progress that accommodates all scientific developments as progressive can’t ever deliver the verdict that some projects are not worth pursuing at all, which is one of the purposes to which such an account would be put.The first challenge that I will consider is that the noetic account appears to assume, in a way that the truthlikeness and epistemic accounts do not, that there are certain metaphysical relations in the world, e.g. causation and grounding, which our dependency models come to accurately represent to some degree in cases of scientific progress. But what if the world is metaphysically sparse, devoid of necessary connections between distinct existences, as per Hume’s dictum Wilson ? What ifThe short response to this challenge is that, appearances perhaps to the contrary, the noetic account is compatible with metaphysical outlooks that entirely reject necessary connections in nature. All that’s required for understanding is that there be some facts of the matter about how one thing depends on (e.g. is caused by) another. It does not matter whether these facts of the matter are ultimately facts about the fundamental fabric of reality, or whether they are instead reducible to or explained by other features of reality, such as regularities in our experiences, our human psychology, or our social practices. Thus, for example, one can easily pair the noetic account with a regularity theory of causation, such as Mackie’s , on whicwater by reducing it to As far as grounding is concerned, the situation is essentially similar although slightly more delicate. A complication comes from the fact that some authors use the term ‘grounding’ in a way that prejudges metaphysical questions, e.g. about the independent existence and fundamentality of the grounding relation itself, or about the grounding entity being more fundamental than what it grounds . Any discussion of this system is complicated by the fact that there is not agreement among biologists about which species should be grouped together at each rank . Generally, then, whether a given classification scheme counts as constituting or promoting progress, or as doing neither, depends on the classification scheme in question, and the use to which it is put. So, on the noetic account, the relationship between progress and classification will have to be evaluated on a case-by-case basis. I hope it’s clear, however, that the noetic account does plausibly count as progressive two of the most prominent classification schemes in current science, viz. the periodic table and Linnaean taxonomy.existential discoveries, viz. empirical or theoretical uncoverings of previously unknown entities.28I turn now to a final challenge to the noetic account. Roughly, the challenge is to accommodate discoveries of new phenomena, such as previously unknown biological species, new physical effects, and archeological findings. The worry is that such discoveries might not enable anyone to increase their understanding since they don’t necessarily contain information about dependence relations. A closely related worry is that the noetic account might not count theoretical postulations of  entities as progressive, again because the mere posit that an entity exists doesn’t necessarily contain information about dependence relations. What unifies these worries is the concern that the noetic account does not account for progress through what we may call a fortiori, had the researchers ‘discovered’ only a particular grain of sand, this adds even less—if indeed anything at all—to scientific progress. To take an even more extreme example, consider Charles Dawson’s discovery in 1921 of the skull fragments that became known as the ‘Piltdown man’. The composition of these fragments, with canine teeth but a human-like skull, suggested that they came from an early humanoid that might serve as the ‘missing link’ in the evolution of humans from other primates. However, this discovery was not progressive  since the skull fragments turned out to be have been fraudulently put together in an effort to deceive archeologists—probably by Dawson himself  stand in a dependence relation to already known entities (neutrons and protons) that we are hoping to understand better. There are also cases in which the discovery of an entity indirectly reveals something about dependence relations between other entities. For example, the discovery of the platypus, the first egg-laying mammal to be discovered by Europeans, revealed (to Europeans) that the distinctively mammalian properties of having mammary glands and fur/hair, for example, are not caused by the same speciation event as those that cause most mammals to give birth to live offspring. Put differently, the discovery of the platypus conveys information about the evolutionary lineage of mammals, which of course is a type of information about dependence relations between mammalian species and their ancestral species.evidence for claims about dependence relations which in turn increase our understanding. For example, consider Brownian motion, the random fluctuation of particles suspended in liquids or gases, which was discovered already in 1827 by the botanist Robert Brown. Since Brown merely observed the phenomenon, and did not explain it in any way, his discovery conveyed no understanding at the time, and thus didn’t constitute progress on the noetic account. However, Brown’s discovery promoted progress in so far as it caused Albert Einstein .30Finally, even when existential discoveries do not constitute progress by conveying information about dependence relations, and even when they don’t promote progress through being evidence for claims about dependence relations, there is still a third way in which existential discoveries may facilitate progress on the noetic account. Obviously, one cannot understand something that hasn’t been discovered. So when we discover an entity or phenomenon I thus conclude that existential discoveries may count as progressive in three distinct ways. Many such discoveries, e.g. of the up and down quarks, constitute progress, since they reveal information about what other more familiar phenomena, e.g. neutrons and protons, depend on. Other existential discoveries primarily serve to promote progress through constituting evidence for claims about dependence relations, e.g. in the way that Brownian motion led to our current understanding of heat and matter. Finally, all existential discoveries enable progress on the discovered phenomenon itself; thus, in so far as we care to make progress on that phenomenon, such discoveries automatically promote progress.What is scientific progress? In this paper, I have sought to address this question in two ways. On the one hand, I have precisified the question itself by introducing various distinctions, such as that between constituting and promoting progress, and between progress-on-X and overall progress. Thus precisified, I have suggested that the most fundamental question of scientific progress concerns what type of cognitive change with respect to a topic X constitutes a scientific improvement (to a greater or lesser extent) with respect to X. On the other hand, I have advanced and defended a revised version of the noetic account of scientific progress. A cognitive change constitutes a scientific improvement on X just in case it makes scientific results publicly available so as to enable relevant members of society, including scientists themselves, to increase their understanding of X. I have sought to show how this account can explain various features of scientific practice that are puzzling or inexplicable on alternative accounts, such as why idealized theories are not always abandoned when more accurate alternative become available, why discovering entirely spurious correlations plays a minimal role in scientific practice, and why higher-order evidence (e.g. from pessimistic meta-inductions) is not an obstacle to scientific progress. Finally, I have defended the noetic account against several challenges that accuse the noetic account of being too narrow to accommodate the full range of cases of scientific progress.


Several peaks in the spectral region between 1600 and 1000 cm−1 showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.The ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm The potential application of different body fluids typically ranging from their identification to the successful extraction of DNA and its profiling2. All of these fluids experience instant biochemical change as they come out from the body. This change can be referred as aging4. Some of these changes are rapid and the remaining are gradual3, but both the changes are significant to reveal one of the most important aspects of forensic examination; ‘time since the deposition’ of a crime4. The estimation of ‘time since deposition (TSD) of a body fluid at the crime scene potentially solves the problem of situating the time of an offense3. The TSD of body fluids additionally counter a wide range of issues regarding the crime scene investigation4. Most of the TSD studies are based on bloodstain aging7 except for a solitary publication on semen very recently8. All these studies except one9 investigated the TSD for an extended period, escaping the initial changes in the fluid. With the inception of the TSD estimation study in the early twentieth century, various researchers have investigated this phenomenon on bloodstains4. Since then the techniques used for TSD estimation went through a drastic evolutionary transformation. The analytical technology gradually transformed into nondestructive from destructive methods10. A variety of techniques including Gas chromatography11, Liquid chromatography14, Oxygen electrode15, microRNA based assay19, Color transformation chart20, Electron paramagnetic resonance23, Reflectance spectroscopy in Ultraviolet, Visible and Infrared (IR) region26, smartphone imaging27, IR absorption29, Raman scattering30 and fluorescence spectroscopy is explored around the world for the TSD estimation of blood 31 and semen8. As an age prediction tool, most of these methods realistically depict promising results. Yet, few methodological limitations hinder the universal acceptability of one or more of these techniques as the stand-alone tool for TSD studies except IR absorption and Raman scattering collectively known as vibrational spectroscopy11. Leading researchers in the forensic sciences community around the world are significantly attracted towards the vibrational spectroscopic technique due to its several advantages over others. It reliably provides a rapid non-destructive analysis of evidence in forensic investigation11. The last decade showed significant growth in the TSD estimation studies by using vibrational spectroscopic techniques4. Despite the equal importance of semen, saliva, and urine as evidence in crime scene investigations, blood always received special attention in the research community33. All the three aforesaid fluids including blood are consistently found in the broad range of homicides, suicides, rapes, and other sexual assaults35. Although, it is not very frequent that an investigating team can reach the crime scene as early as the fluid is still under drying process, but in several instances, investigators may come across a situation where the fluid is still drying. The earliest time after an offense has enormous importance as this is the time when most of the information in a crime scene remains unchanged and also during this timeframe, the most significant information can be obtained from postmortem. Additionally, the empirical relation between the initial spectral change and TSD can help an investigator to differentiate between two or more overlapped older and fresh stains9. Zhang et al.9 explored this specific and significant dimension of body fluid aging in 2016 and established a potential age estimation framework from the initial changes in the ATR-FTIR spectra of bloodstain collected from Sprague Dawley rats up to its drying under different conditions  as the drying of fluid is affected by several factors like the component of the fluid, environmental conditions, and the supporting substrate36. The present study is carefully focused to analyze the initial infrared spectral changes of semen, saliva, and urine as they dry out and estimate the TSD based on the absorption changes in specific peaks. Since, Zha et al.8 experimented with the change of semen stains up to 1 week, in which the first spectra were acquired 12 hours after the deposition of semen ignoring the initial changes. Hence, semen is also included in the present study. In Attenuated total reflectance (ATR) Fourier transforms infrared (FTIR) spectroscopy is combined with the chemometric approach to investigate the spectral changes and estimate the TSD respectively.Body fluids are recurrently confronted as major evidence in violent crimes9. In this study, the major change is observed in two regions of the spectra i.e., 3300–2800 cm−1 (lipids are the major contributors) and 1700–950 cm−1 39. The list of peaks identified in the spectra of all three fluids is summarized in Table 42. The spectra in other reported articles were collected at least 4–6 h from the time of deposition40. Although several peaks were identified in the spectra of each fluid during the study, only a few (age-linked peaks) showed linear changes in their absorption intensity with time. At the initial stage, a similar phenomenon has been observed in the drying of every body fluid. For the first several minutes, only two strong absorption peaks were visible  and 1637 cm−1 (approx.) (scissoring of two H atoms bonded with O molecule) appeared due to the high amount of water in all the fresh fluid samples45. Similar results were obtained in the study by Zhang et al., on blood9. After a certain amount of time multiple significant peaks corresponding to the biochemical profile of the fluids are revealed throughout the fingerprint region of the IR spectra  phase I due to the excess amount of water in the diluted sample. The longest phase I observed in the Semen samples and the shortest in the saliva samples. Phase II was relatively similar in the spectra of both raw and diluted samples. The duration of phase II of three body fluids was relatively the same (10–12 min). Table Except for the concentrated samples of fluids, diluted samples of 2:3 ratio (40%) were also prepared to investigate the changes in the drying of the fluids. The dilution was kept constant for all the fluids and a similar extended drying time had been recorded. Multiple dilutions can also alter the drying time as these factors can be studied in future studies on these fluids separately with other factors as experimented by Zhang et al., on blood49. Except for similar height, the peak corresponding to O–H stretching was broader than the peak due to H–O–H scissoring. Except for urine, the absorption intensity of these two peaks showed insignificant change throughout phase I in the spectra of semen and saliva , showed analogous results with the study by Zhang et al.9 as the peak at 3273 cm−1 showed a significant decline in the mean absorbance with time during phase I  and 1637 cm−1 (Amide I) showed no shift during the whole drying (phases I and II) process of semen stain but the former one sharpens with time and rapidly declined during phase II , two medium  and three weak  significant age-linked peaks were observed , 1448 cm−1 (Methylene: CH2 and CH3), 1392 cm−1 (Fatty acids and polysaccharides), 1059 cm−1 (Prostate-specific antigen). Few more researchers reported the IR spectra of semen in several body fluid identification research articles40. Phase II spectra of saliva showed the shift of strong peaks at 3273 cm−1 (O–H stretching) to 3286 cm−1 (amide A) and 1637 cm−1 (H–O–H scissoring) to 1645 cm−1 (amide I) that indicated the initiation of this phase , four weak  significant age-linked peaks were found , one medium (3346 cm−1: H–O-H stretching), and 2 weak (1156 cm−1: urea and 1081 cm−1: Glycosylated proteins) peaks were observed in the phase II spectra of urine samples  values in both the models for all the three fluids except diluted urine, showed low values  and RMSEP  , 5 , and 4 (urine) peaks were selected to calculate the TSD of the body fluids. Irrespective of the variation in the numbers of age-linked peaks for the three body fluids, it was evident that the regression models successfully estimated the TSD with very high accuracy. Both Principal Component Regression (PCR) Analysis and Partial Least Square Regression (PLSR) are strong chemometric tools for the estimation of TSD of body fluids as reported in previous studiesR2 values. Hence, the results  of this study potentially be used as a reference for the further TSD studies on these three fluids with a longer timeframe including different factors.In the present study three forensically significant body fluids other than blood i.e., semen, saliva, and urine were considered to explore the instantaneous changes in their ATR-FTIR spectra up to their drying. This study revealed that all the body fluids undergo significant water loss at the initial stage of their ex-vivo degradation. This rapid loss of water significantly divided the drying process into two phases as the first phase consists of slow evaporation with minimal spectral change with a major contribution of water and the second phase depicts relatively faster evaporation. These two phases can be distinguishable from the ATR-FTIR spectra of each fluid which is a significant marker for estimating the time since deposition of the fluid(s). This study also revealed the spectral regions of interest for the TSD estimation of these fluids as saliva and urine are not explored previously for this purpose. Additionally, if a body fluid is accidentally diluted during the deposition on a wet nonporous substrate during the earliest phase post-deposition, the accuracy of the estimation of TSD can be significantly altered as the water evaporation potentially take more time. In the practical scenario, fresh body fluid samples in liquid and semi-liquid (fluid samples with loss of a certain quantity of water since deposition) conditions from any non-porous surface  can be easily collected through a pipette-like apparatus. We can acquire the spectra of the freshly collected fluid with a portable IR instrument containing an ATR-FTIR crystal face exclusively dedicated to forensic crime scene investigation purposes. Although the drying process of body fluids is different on porous substrates like, cloth fabrics, carpets, etc. Hence, further experiments on this subject can be framed based on several factors like, different concentrations, quantities, and interference of porous substrates. Despite, relatively low changes in the IR absorption, chemometric tools like; PCR and PLSR successfully estimate the TSD for each fluid during the initial spectral changes with a very low RMSECV and high Samples of saliva, semen, and urine were collected from randomly selected eight healthy volunteers (28–40 years). All the methods of this study were carried out in accordance with the World Medical Association Declaration of Helsinki. The experimental protocols were approved by the Ethical Committee of Maharani Laxmi Bai Medical College (4647/IEC/2020/SC-1), Bundelkhand University, Jhansi, Uttar Pradesh, India. All the donors were informed about the nature and procedure of the work and written informed consent was taken from each donor. Each body fluid from a donor was collected separately in a glass test tube(s) just before the spectral analysis to avoid any loss of time after its release from the human body. All the fluids were collected by voluntary secretion, ejaculation, and excretion process without using any invasive technique. Spectra of fluids were obtained in two different concentrations i.e., 100% and 40% (2:3) to investigate the effect of water content on the drying time. The dilution concentration was randomly selected for this study to investigate the difference in drying time between concentrated and dilute fluid samples. 40% solutions of all the fluids were prepared by mixing 2 parts of the fluid and 3 parts of distilled water in a test tube instantly after the collection of the fluid. For experimental purposes, saliva and urine samples were repeatedly taken for three consecutive days, and semen was collected three times with an interval of 3 days to observe any intra-variations of their composition. All the repeated samples were collected from the same individuals.−1 to 500 cm−1 with 12 scans and a resolution of 4/cm. In this study, the term ‘drying out’ is the time point at which there was a negligible change between the three consecutive scans taken at a 2-min interval26. At this point, the droplet transformed into a dried stain. Before analyzing the obtained data, all the spectra of body fluids were preprocessed by using Unscrambler X software with several spectral corrections i.e., baseline offset, spectral smoothing with Savitzky-Golay algorithm including 13 smoothing points and 3 polynomial orders in a symmetric kernel and range normalization53.IR spectra of all the body fluid samples were collected by a ‘Spectrum Two’ FTIR spectrophotometer manufactured by Perkin Elmer corporation, equipped with a 2 mm diameter diamond crystal ATR accessory and spectrum software (Version 10.0). The spectrum software is used for the collection of spectra. The crystal face was cleaned with a 70% methanol solution before drop the fluid sample for each spectral measurement. One drop (approximately 50 µL) of each body fluid was separately added onto the crystal from a constant height of 4 cm. The crystal was used as the drying surface to reduce any loss of water from the fluids during the transfer of the samples from the substrates. Throughout the whole study, the volumes of fluids were kept relatively constant. The experiment was performed in the month of January. The approximate temperature and relative humidity during the complete spectral collection varied between 13 and 17 °C and 56–75%, respectively. As the ambient conditions during the study did not vary significantly, the effect of variable temperature and humidity were not considered in this study. The spectra of each fluid were collected with an interval of two minutes immediately after placing the droplet on the crystal within the range of 4000 cmR) and coefficient of determination (R2) between the TSD and changes in the absorbance values for each body fluid was established with a fitting correlation equation. All the equations showed a ‘P’ value of < 0.05 which is statistically significant. PCR and PLSR are the two most frequently used tools for prediction model creation and estimation studies. Both of these tools decompose the multiple X variables with respect to the values of Y variables and generate single values for each sample and establish a correlation between X and Y.1 In the present study, the time has been considered as Y and the different age-linked peaks with absorbance values are considered as X. All the chemometric analyses were performed by using ‘Unscrambler X’  software. RMSECV and RMSEP were calculated to check the consistency and predictive ability of the regression model. Higher R2 values and lower RMSE values are indicative of a good prediction model. Full cross-validation was performed for each fluid. Eight samples randomly for each fluid were selected for the model creation and two randomly left out samples were applied for external validation purposes.The mean values of the absorbance of all the significant peaks and the drying time were calculated. Peak identification, fitting, and statistical analysis were carried out by using Origin Pro 2016 and Microsoft excel 2019 software. The correlation coefficient (


Emotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD. Emotion dysregulation, although not a core symptom, is a frequently co-occurring clinical problem2. Emotion dysregulation refers to the inability to adequately modulate and control emotions3. Its prevalence within the ADHD population changes with age, from 25 to 45% in children to 30–70% in adults, and its co-occurrence is associated with reduced well-being, risky behavior, and social impairments2. Especially individuals with ADHD and impulsivity-hyperactivity symptoms often suffer from emotion dysregulation4. However, the neural roots of ADHD associated emotion dysregulation remain unclear.Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by core symptoms of inattention and/or hyperactivity-impulsivity that may persist well into adulthood5, it is precisely these structures in which deviant—often reduced—activity is frequently found in childhood and adult ADHD studies1. Implicit emotion regulation, on the other hand, is an unconscious stimulus-driven process based on experience-based reward estimations. The ventromedial prefrontal cortex and the anterior cingulate cortex have been linked to implicit emotion regulation5. With regard to ADHD, it appears that not only structures related to explicit emotion regulation, i.e. structures for cognitive control and reappraisal, are affected, but also those associated with implicit emotion regulation and rather fundamental emotion reactivity processes6. Functional connectivity studies consistently showed deviations in structures of the limbic system including the orbitofrontal, ventromedial prefrontal and anterior cingulate cortex in patients with ADHD13. Also, task-based fMRI studies using emotion perception and processing tasks in individuals with ADHD found evidence for functional abnormalities in the amygdala and insula, possibly indicating increased bottom-up emotional reactivity15. Task-based fMRI studies related to implicit emotion regulation, i.e., using fear extinction via habituation or emotional Stroop paradigms, which controlled for differences in cognitive control, found ADHD-specific differences in the ventral anterior cingulate and ventromedial prefrontal cortex18. Given the heterogeneity of ADHD, existing neuroimaging studies, and postulated neurocognitive models, e.g., the dual-pathway model19, one might expect ADHD-associated emotion dysregulation to be similarly complex, with both explicit and implicit regulatory processes accounting for it.In emotion dysregulation, a distinction between explicit and implicit emotion regulation has been made. Explicit emotion regulation requires conscious effort and is commonly achieved by applying cognitive control and reappraisal strategies. While cognitive control and reappraisal are particularly associated with activity in structures of the ventral attention and frontoparietal network21. Connectivity changes beyond the amygdala have, however, not been investigated, and it remains uncertain whether the reported associations are indeed ADHD- or possibly even ADHD presentation-specific.While several studies, using task-based as well as resting state fMRI, reported brain activity deviations in structures commonly associated with emotion processing and emotion regulation, few have attempted to directly correlate corresponding activation patterns with emotion dysregulation and none has investigated changes specific to ADHS presentations. Two childhood ADHD studies used seed-based connectivity approaches focusing on the amygdala. They reported associations between high emotion dysregulation scores and reduced negative connectivity with the insula and frontoparietal structures as well as increased positive connectivity with the anterior cingulate cortex13 to describe changes in network topology of functional connectivity. These studies show increased local connectivity and efficiency and decreased global integration. It is however unclear how such changes in information processing properties of brain networks relate to emotional deficits. We gauged emotional dysregulation through structural equation modelling (SEM), using a combination of several self and informant scales assessing emotional problems, emotional lability, and conduct problems as well as one experimental task of emotion recognition. SEM is particularly well suited for testing the significance of certain assumed (group-specific) relations while simultaneously estimating latent variables embedded within the relational model. We focused our analysis on nodal topology measures and aimed to identify those brain regions whose local, functional brain network integration specifically contributes to emotion dysregulation in predominately inattentive ADHD (ADHD-I) and ADHD with symptoms of hyperactivity-impulsivity (ADHD-C/H).To investigate the relationship between emotional dysregulation and functional brain network organization, we used graph theory to analyze fMRI resting-state data from healthy individuals and individuals with ADHD with and without hyperactivity-impulsivity symptoms. We captured the centrality of each brain network node and analyzed whether nodes were highly integrated and connected, either locally towards their direct neighboring nodes or globally with the entire network. Graph theory-based methods have previously been used in ADHD researchWe hypothesized that an ADHD-specific association exists between the functional brain network measures for local network integration and emotion regulation. Thereby, we focused on measures of network topology within frontoparietal and limbic brain regions that have previously been associated with emotion regulation and processing. We assumed that the strongest relation would be found in individuals with predominately hyperactive-impulsive ADHD.Age and sex did not significantly differ between the ADHD and control group. Participants with ADHD showed higher scores on all four measures of emotional problems and dysregulation. Further, compared to controls, participants with ADHD had a significantly lower IQ, partially showed oppositional defiant disorder (ODD) or conduct disorder (CD) comorbid diagnoses and, in general, more often used stimulant medication. ADHD presentations groups did not significantly differ with regard to age, sex, IQ, the four measures of emotion dysregulation, or stimulant use. The demographic details of the sample are given in Table To identify brain regions in which emotion dysregulation is specifically linked with functional brain network topology as a function of ADHD presentation we combined graph analysis with SEM.p < 0.001, β = 0.909), SDQ’s emotional symptoms subscale , SDQ’s conduct symptoms subscale , and CPRS-R:L’s emotional lability subscale . These variables were considered for the subsequent SEM, while MINDS-GERT for emotion recognition  and ICU for callousness-unemotional traits  were discarded. Compared to healthy controls , and parameter estimates of the completely standardized solution. Further, Table 2, degrees of freedom, p-values of the models, goodness-of-fit measures , p-values that are the result of the χ2 difference tests between the main models and the corresponding models with fixed regression parameters, and significant pairs of the post-hoc two-group SEM.Second, to identify group dependent differences between network topology and the latent variable gauging emotion dysregulation, we used three-group SEM and estimated regression parameters for the group-specific relationship between the latent emotion dysregulation variable and measures of nodal network topology. Pooling over different density levels of connectivity, the SEM revealed group differences in the relation between clustering coefficient as well as local efficiency measures, respectively, and the latent variable for emotion dysregulation in the right insula see Fig. a. For paOnly in the ADHD-C/H group were the topology measures of the insula significantly related to emotion dysregulation. The respective models showed satisfactory goodness-of-fit measures. Results were highly significant for the density-integrated models and significant group-specific differences were found across multiple density thresholds below 25% connectivity see Fig. c, d. Resp-values < 0.01). For none of the other regions examined did we find significant group-specific differences in the relationship between the latent emotion dysregulation variable and measures of nodal network topology.While not surviving FDR-controlling procedures on the integrated level, SEM with density thresholds between 35 and 40% connectivity revealed group differences in the relationship between eigenvector centrality and the latent variable for emotion dysregulation in the right DLPFC. For participants with ADHD-C/H the regression parameter estimates of the SEM were greatest, while they were smallest for healthy controls. Significant group-specific differences in the relation between topology measures and emotion dysregulation on the uncorrected level occurred for measures of nodes in the right ventromedial prefrontal cortex, frontal pole, right insula, right anterior mid-cingulate cortex, and left insula .Robustness of the results was controlled by evaluating the group-specific differences in overall connectivity strength of the individual networks. The analysis of overall connectivity did not reveal significant group differences. Finally, the multi-group SEM analysis was repeated using an alternative parcellation scheme to investigate the robustness of the results with respect to the chosen parcellationEmotion dysregulation is a key component of many psychiatric conditions, including young adult ADHD. We identified properties of functional brain network topology related to a latent measure of emotion dysregulation by using a combination of graph theoretical methods with structural equation modelling. Our results provide evidence that only in individuals with ADHD-C/H emotion dysregulation is accompanied by increases in local efficiency and clustering of the right insula.22. It plays an essential role in implicit emotion regulation via reciprocal connections with the ventromedial prefrontal cortex and other regions particularly associated with experience-based reward estimations23. Throughout brain maturation, functional network formation is characterized by specific patterns of integrative and segregating processes24. These processes are also reflected in measures such as the clustering coefficient and local efficiency. Local efficiency in particular has previously been used as a measure of local brain segregation and it was suggested that increased local efficiency goes along with a loss of global brain network integration. For example, it was shown that higher local efficiency is associated with lower global efficiency and reduced performance during cognitive tasks25. Prior research consistently provides evidence of delays in the neural maturation of individuals with ADHD27. Increases in the insula’s clustering and local efficiency, i.e. functional connectivity increases between neighboring nodes, may indeed, originate from reduced or delayed network-forming processes. This may negatively affect the efficiency with which the insula performs its integrative tasks and helps to appropriately evaluate emotional stimuli or to facilitate emotion regulation. Our results may reflect ADHD presentation-specific deficiencies in functional network forming processes. At the behavioral level, this could lead to inappropriate behavior and some of the commonly observed emotional problems in ADHD.Local efficiency and clustering measure how interconnected a node's neighbors are to each other. Increases imply stronger functional connections between structures directly connected to the respective brain region. Here the insula was identified as showing increased connections to its nearest neighbors contributing to emotion dysregulation in ADHD-C/H. The insula integrates interoceptive states with emotional information via connections to other limbic regions such as the amygdala4. Indeed, in the comparison of the different ADHD presentations, the combined type had shown local functional hyperconnectivity in regions associated with emotion processing and implicit emotion regulation, namely the vmPFC28. Previously, it was observed that deviations of functional connectivity in ADHD-I occur most clearly in areas of the frontoparietal network, especially the dorsolateral prefrontal cortex, whereas for ADHD-C deviations are most pronounced in areas of the default mode network, i.e. in areas associated with motivation and emotion processing. Since ADHD-I is predominantly characterized by inattention symptoms, it has been suggested that problems in top-down control systems are more likely to underlie this presentation, whereas predominately combined ADHD appears to be more clearly associated with motivation and emotion processing networks29. This could not be confirmed with the analysis carried out here. We found no evidence that nodal topology in top-down control regions would be associated with emotion dysregulation in ADHD-I. On the contrary, the right dorsolateral prefrontal cortex showed an ADHD-C/H dependent association between eigenvector centrality and emotion dysregulation. However, these results should be regarded with caution, as they were only seen at two density thresholds. Taken together, the clinical distinction between ADHD presentations with and without hyperactivity-impulsivity symptoms and the associated prevalence differences commonly observed in co-occurring problems such as emotion dysregulation may be reflected in differential deviations of the underlying functional organization of the brain.The positive association between the functional connectivity of structures directly connected to the insula, i.e. local efficiency and clustering, and emotional functioning in ADHD-C/H was neither found in healthy controls nor participants with ADHD-I. Our results are consistent with expectations, given that this subgroup was shown to be most severely affected by co-occurring emotion dysregulationright insula only. While ADHD is not understood as a generally right-lateralized disorder, some functions are more strongly associated with right hemisphere processing30. Indeed, the right hemisphere is assumed to take a dominant role in emotion processing. Regarding the regulation of emotions, findings are not as conclusive. However, evidence concerning the insula suggests that processes for integrating interoceptive and emotional/motivational information are rather right lateralized31. Note that the left insula yielded—like the dorsolateral prefrontal cortex, right ventromedial prefrontal cortex, frontal pole and cingulate cortex—similar but weaker results, which did, however, not survive correction for multiple comparisons on the integrated level.Significant results were found for the 32. While emotion recognition is an important prerequisite for the regulation of emotions, adolescents with and without emotion dysregulation have shown similar abilities in recognizing emotions, suggesting again a conceptual difference33. Nevertheless, deficits in both are frequent in ADHD, a common occurrence is, however, not imperative34.The latent variable for emotion dysregulation encompassed self-reported emotional distress, emotional and conduct symptoms and informant-reported emotional lability. Callous-unemotional traits and emotion recognition did not contribute significantly. Their relation to the other emotional measures  appeared to be rather negligible. This is not surprising as callous-unemotional traits are characterized by deficient affect and lack of empathy, which is a conceptually different aspect of emotion21 and using only unidimensional emotional measures. Compared to prior neuroimaging studies, we used a relatively large and well characterized sample of young adult ADHD participants and healthy controls. Also, cases with missing values were excluded from the data set before performing the analyses. Nevertheless, even larger sample sizes may be required in future studies to obtain appropriate power and avoid parameter biases in SEM  were IQ  < 70, diagnoses of autism or schizophrenia, and neurological disorders. For controls, it was required that neither they nor any of their first-degree relatives had a prior ADHD diagnosis.Data were taken from NeuroIMAGE II, the third wave of an integrated genetics-cognition-MRI-phenotype project on ADHD39 and parent, teacher, and self-report versions of the Conners’ rating scale 42. Both the K-SADS and the Conners’ rating scales provide operational definitions of the 18 behavioral ADHD symptoms defined in the DSM-IV. In both, symptoms are subdivided by symptom type, i.e., inattentive symptoms and hyperactive-impulsive symptoms. All diagnostic and phenotypic data was acquired on the same day as the fMRI data. A detailed description of the diagnostic procedures is given by von Rhein et al.38.Reassessment of the ADHD diagnosis was established by combining information from the Kiddie Schedule for Affective Disorders (K-SADS)Participants with a subthreshold ADHD diagnosis (2–4 ADHD-specific symptoms), left-handedness, excessive movement during the scanning, or insufficient quality of rs-fMRI or questionnaire data were excluded. 147 participants were used for the final analysis, including 31 participants with ADHD but without hyperactivity-impulsivity symptoms, i.e., predominantly inattentive ADHD (ADHD-I), 25 participants with ADHD and hyperactivity-impulsivity symptoms (ADHD-C/H), i.e., 21 with combined type ADHD and 4 with predominantly hyperactive-impulsive ADHD, and 91 healthy controls. Demographic information are given in Table 43.Forty-eight hours prior to testing, stimulant medication use was discontinued. Data acquisition took place at the Donders Institute for Cognitive Neuroimaging, Radboud University Nijmegen, Netherlands. Participants (and their parents when < 18 years old) gave written informed consent for participation. In accordance with relevant guidelines and regulations, ethical approval was granted by the regional ethics board . Data analysis was pre-registered using the open science framework (osf.io/rdyp6) 2, voxel-size = 3.5 × 3.5 × 3.0 mm3, inter-slice gap = 0.5 mm). T1-weighted high-resolution structural volumes were acquired with an MPRAGE sequence .Whole-brain imaging was performed on a 1.5 T Magnetom Avanto . BOLD-sensitive resting-state functional volumes were acquired using a T2*-weighted EPI sequence . Each of the 266 volumes consisted of 37 axial slices of size 64 × 64  44. The resting-state time series data were skull stripped, realigned to the middle volume of the series, co-registered to the structural T1, and spatially smoothed using a 6 mm full width at half maximum Gaussian kernel (FWHM). ICA-AROMA45 was used to account for secondary movement artefacts. Residual noise was further reduced by nuisance regression including a linear trend and average times series measured within the white matter and cerebrospinal fluid. High-pass filtering was conducted at 0.008 Hz. Prior to network analysis, time series were warped to MNI152 space . Root mean squared framewise displacement was calculated. A threshold of 0.25 was applied to exclude 25 participants with extreme movement from further analysis. Root mean squared framewise displacement did not significantly differ between the groups .Preprocessing mostly relied on FMRIB algorithms  with NetworkX  46. Parcellation of preprocessed time series data was realized using a hemisphere-specific functional brain template with 268 parcels47. It was created using graph-theory based parcellation that ensures functional homogeneity within the parcels of the atlas, even across different individuals. The atlas thus minimizes the likelihood that different functional areas lie within a single parcel47. For 221 relevant parcels, covered by the MR measurement, subject-specific average intensity time series were calculated. Correlation matrices were created by computing pairwise Pearson’s correlations between the extracted time series. Matrices were Fisher’s z-transformed and absolute values were taken. Absolute value transformation was performed as preprocessing of the present data did not involve global signal regression and as anti-correlations are thought to be functionally relevant48. Due to the naturally low density of negative correlations, we refrained from performing specific analysis for positive and negative correlations. To distinguish differences in network density from those of network topology49, matrices were binarized based on seven equally spaced density thresholds with a minimum density of 0.10 and maximum density of 0.4050. In this range of low to medium network densities, previous studies found significant associations between network topology and ADHD symptoms13. Thus, seven threshold-specific graphs for each subject were investigated in the following graph analysis. Nodal topology measures were calculated for 70 of the 221 nodes. These 70 nodes were chosen based on their association with parcels overlapping with the orbitofrontal cortex, dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior cingulate cortex, posterior parietal cortex, insula, ventral striatum, amygdala, and hippocampus as defined by the Harvard–Oxford Brain Atlas by more than 30%. These brain regions have been previously documented to be involved in emotion processing, its regulation, and brain dysfunctions in ADHD6.For graph analysis we used Python 3.5  51. Lavaan  was used for SEM52. Prior to SEM, multi-group confirmatory factor analysis was used to identify data suitable for calculation of the latent emotion dysregulation variable. NeuroIMAGE II includes 6 tasks, questionnaires, or questionnaire subscales, respectively, that gauge emotional problems, emotional lability, and associated features: the Kessler Psychological distress scale, NESDA version of K-1053 with 10 items for the assessment of emotional problems including anxiety and depression, the strength and difficulties questionnaire subscales 54, the emotional lability subscale of the Conners’ parent rating scale 42, the Inventory of Callous-Unemotional traits (ICU)55 , and the MINDS Testmanager’s gradual emotion recognition task (GERT)56 (accuracy of correct emotion classification).Statistical analyses were conducted with R software  without taking into account the different ADHD presentations. However, those models did not produce good model-fit (as measured by the standard goodness-of-fit measures described below) and had no significant results. One reason may be that the ADHD scores were derived from a parents’ questionnaire for children’s ADHD symptoms (CPRS-R:L) which may be less valid than a clinical diagnosis.2-values were compared between the two models (χ2-difference-test). Following the approach of Ginestet et al.49, a model was considered significant if it revealed a significant effect on the density-integrated level (averaged over densities). Subsequently, significance was tested on each density threshold to provide detailed information on whether effects were found with stronger or weaker network connections.Alternatively, we chose a multi-group SEM approach using the clinical diagnoses. The diagnoses reflect all available diagnostic information and may thus provide more accurate models. We investigated the group dependent differences  in the association between topology measures and the latent variable gauging emotion dysregulation. Note, that by investigating group-related differences we aimed to identify associations between inter-individual differences in local brain network topology and emotional dysregulation that are specific for individuals with different ADHD presentation. This is different from a mediation model approach (see above) that aims to identify brain regions whose significant correlation with emotional dysregulation only reflects an indirect link via ADHD severity and thus might not directly involve emotion regulation. For each node of interest and each density-specific topology measure, one multi-group model was built. Models consisted of the latent variable, questionnaire scores, associated parameter estimates and variances as well as the nodal topology measure variable with its regression parameter for the latent variable see Fig. . Factor p-values to investigate pairwise between-group differences. These two-group models resembled the three-group models described above, except that each only considered participants from two of the three groups. Alpha inflation due to multiple comparisons was controlled by the Benjamini–Hochberg false discovery rate (FDR) procedure57. The comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) were calculated to evaluate the models’ goodness-of-fit. Acceptable goodness-of-fit measures should at least be above 0.95 for the CFI, below 0.08 for the SRMR, and below 0.10 for the RMSEA58.We used post-hoc two-group SEMs with Bonferroni-corrected 59. Furthermore, an alternative parcellation scheme was used to investigate the robustness of the results with respect to the chosen parcellation approach. For this, we used the AAL atlas60, further subdivided with K-mean clustering to adapt the size and number of parcels to that of Finn et al.47. Procedures for calculating topology measures and SEM analyses were conducted as described above.To further control the robustness of the results, we evaluated whether group-specific differences in overall connectivity strength of the underlying adjacency matrices existSupplementary Information 1.


Face memory impairments are common but heterogeneous in autism spectrum disorder (ASD), which may be influenced by co-occurrence with attention-deficit/hyperactivity disorder (ADHD). Here, we aimed to investigate the phenotype change of face memory in children with ASD comorbid ADHD symptoms, and discuss the potential role of executive function (EF). Ninety-eight children were analyzed in the present study, including ASD− , ADHD (n = 23), ASD+  and neurotypical controls . All participants completed two tests: face encoding and retrieving task and Wisconsin Card Sorting Test (WCST) for measuring face memory and EF, respectively. Results revealed that: compared with the NTC group, children with ASD− exhibited lower accuracy in both face encoding and retrieving, and participants with ASD+ showed lower accuracy only in the retrieving, whereas no differences were found among participants with ADHD. Moreover, in the ASD+ group, face encoding performance was correlated with response perseverative errors (RPE) and failure to maintain sets (FMS) of WCST; significantly, there were no group differences between ASD+ and NTC in these two indices. The transdiagnostic profiles indicated that comorbid ADHD symptoms could modulate the face encoding deficiency of ASD, which may be partially compensated by EF. Shared and distinct intervention strategies to improve social cognition are recommended for children undergoing treatment for each condition. Face memory impairment, being associated with social and cognition development, has been a candidate neurocognitive endophenotype of autism spectrum disorder (ASD) ,2,3,4. FASD in children often co-occurs with symptoms of attention-deficit/hyperactivity disorder (ADHD), reaching approximately 1/2~2/3 ,11, thatThe deficiency of face memory has been widely reported in the ASD population ,21,22, hStudies have revealed that children having ASD with comorbid ADHD symptoms often present more severer social difficulties ,33. The To understand the underlying mechanisms of cognitive impairment in ASD and ADHD, executive function (EF) is one among the most robust tools ,37,38. IWith a growing awareness of the complexity and significance of comorbidity in ADHD and ASD, an increasing number of studies have encouraged transdiagnostic approaches to explain their similarities and differences in etiology, associated impairments, and the interventions . Thus, tThe study procedure was approved by the medical ethics committee of the affiliated institute of the authors (consistent with the Declaration of Helsinki) prior to recruitment of participants. Written informed consent was obtained from the guardians. All participants did not take any central nervous system active medications in recent half years.A convenience sample of 106 right-handed children, aged 6–12 years, was recruited through advertisement. The inclusion criteria were as follows: (1) having a full intellectual quotient (FIQ) score ≥70, with normal naked vision or corrected-vision without color blindness; (2) having the ability to speak and comprehend Mandarin; (3) having no history or presence of neurological or severe medical illness, such as learning disorder, schizophrenia, bipolar disorder, major depressive disorder, anxiety disorder, etc.; and (4) completing all the administered measures. In addition, children with NTC were required to have normal  scores on both rating scales of ASD and ADHD symptoms.Children with ASD or ADHD were previously diagnosed by professional pediatricians in licensed hospitals in Guangzhou City or Shenzhen City, and the diagnosis certificates were obtained from the guardians. Their diagnosis were then re-confirmed by two pediatricians and psychiatrists experienced in ASD and ADHD assessment, according to the DSM-5 . Based oThe Chinese version of the Wechsler Intelligence Scale for Children, fourth edition (C-WISC-IV), was used to assess the cognitive profile of all participants, which included full intellectual quotient (FIQ) and four sub-indices: verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI).The Child Autism Rating Scale (CARS)  and SociThe paradigm consisted of a face encoding task (FET) and a face retrieving task (FRT) to assess face memory performance see . Both taFace encoding task (FET): Each trial was preceded by a fixation (the character ‘+’) presented at the center of the screen for 1000 ms. Next, single pictures of neutral faces were presented for 4000 ms in a pseudo-randomized order, and participants were required to distinguish the gender of the facial images. They were instructed to press the ‘F’ button if the person was female and press the ‘J’ button if the person was male. Following this, the stimulus was replaced by a blank screen for another 1000 ms to complete the trial.Face retrieving task (FRT): Similar to FET, each trial of FRT began with a fixation for 1000 ms. This was followed by a target stimulus which was presented at the center of the screen for 1000 ms, and participants were asked to actively memorize each face. Next, two pictures—one previously presented and another new image—of faces were presented side by side for 4000 ms. Participants were requested to select the previously presented face by pressing the ‘F’ button if it appeared on the left, while pressing the ‘J’ button if it appeared on the right. During the interval between the target and probe stimulus (1000 ms), the participants were asked to read out the two numbers appearing on the screen. Similar to FET, the trial ended with a blank screen for another 1000 ms.In addition, the mask blocks were used as the baseline condition. The stimulus were gray scrambled images which required participants to press the ‘J’ button within 4000 ms. Each task consisted of five blocks, including six trials of target stimulus and six mask stimuli, such that the FET was completed within 6 min and FRT was done within 7 min. There were two practice blocks before the actual task, to enable the participants to learn the rules. The mean response time (RT) and accuracy (ACC) were recorded.The face images were selected from the Chinese Facial Affective Picture System. The height of the picture was 300 pixels and the width was 260 pixels. The paradigm was designed using E-prime 2.0 and displayed on a 20-inch LCD screen (1920 × 1080 pixels resolution). The visual stimuli were subtended at a visual angle of 0.5–1 degree in the horizontal plane at eye-level.The Wisconsin Card Sorting Test (WCST) is a well-established measurement of EF . Here, EData analysis was performed using the SPSS 25.0 software. First, multiple Shapiro–Wilk tests were conducted to inspect the distribution of quantitative variables, including age, score of CARS, score of SRS, score of SNAP-IV, score of WCST indices and the accuracy (ACC) and response time (RT) of face encoding and retrieving. Based on the results, parametric tests were employed for normally distributed variables and non-parametric tests were used for non-normally distributed variables .Second, a one-way analysis of variance (ANOVA) or Kruskal–Wallis test was performed to determine the group differences in demographic characteristics, related symptoms, and face memory performance, and a Bonferroni correction was used for multiple comparisons. In addition, analysis of covariance (ANCOVA) was applied to compare face memory performance among the four groups after controlling for age and PSI/FIQ.α = 0.05.Third, a Pearson/Spearman correlation analysis was carried out to test the relationship between face encoding and retrieving ability and the symptoms of ASD and ADHD. Furthermore, multi-level linear regression analysis was employed to explore the relationship between EF and face memory performance. The size of test was set at Z score beyond ± 3 of the sample as a whole). Thus, a total of 98 participants  were included in the final analysis.Six participants including children with ASD− (n = 2), ADHD (n = 1), and ASD+ (n = 3) were excluded as they could not accomplish the behavioral tasks. Furthermore, an initial screening based on the omission rate of the mask-task was conducted to yield two outlier participants (F(3.94) = 2.602, p = 0.745) or sex distribution . Instead, group differences were observed in the FIQ (F(3.94) = 5.395, p = 0.002, η2 = 0.147) and PSI (H(3.94) = 21.954, p < 0.001) scores. Furthermore, the post hoc test showed that the ASD+ group presented lower FIQ scores compared to the NTC group , and all clinical groups had lower PSI scores than NTC .The sociodemographic and clinical information of the participants are shown in H(3.94) = 80.265, p < 0.001), SRS (F(3.94) = 30.821, p < 0.001, η2 = 0.496), and SNAP-IV (F(3.94) = 35.030, p < 0.001, η2 = 0.531) among the groups. In the scores on CARS, children with ASD− and ASD+ scored higher than those with ADHD and NTC , and children with ADHD scored higher than NTC . In SRS, the ASD+ group scored higher than the other three groups , and both ASD− and ADHD groups scored higher than NTC . In SNAP-IV, the post hoc test demonstrated that the ADHD and ASD+ groups scored higher than the ASD− and NTC groups . This reflected that the sample of the four groups was well-characterized in the clinical profile.There were significant differences in the total score of CARS  was further conducted after controlling for age and PSI, and age and FIQ, respectively. Considering our former study which indicated that face memory performance would be affected by processing speed index of WISC-IV , and thaF(3.94) = 1.131, p = 0.341), nor between group and PSI (F(3.94) = 1.215, p = 0.309). Notably, there were significant group differences in ACC of encoding (F(3.94) = 5.145, p = 0.002, η2 = 0.144) and ACC of retrieving (F(3.94) = 9.641, p < 0.001, η2 = 0.239). Multiple comparisons (using Bonferroni correction) indicated that children with ASD− exhibited a lower ACC than children with ADHD  and NTC  in FET; and in FRT, both children with ASD− and those with ASD+ displayed a lower ACC than those with ADHD and NTC .The ANCOVA results revealed that there were no significant interactions between group and age , social awareness , social cognition  and autistic behaviors , while ACC-retrieving  and RT-retrieving  were associated with social cognition. In contrast, in the ADHD group, only ACC-retrieving was found to be negatively correlated with social motivation . As for children with ASD+, ACC-encoding was associated with SRS scores, including total score , and social motivation , and RT-retrieving was associated with social awareness . Moreover, it was found that RT-encoding was correlated with SNAP total score  and the sub-index of hyperactivity . Additionally, a multiple linear regression controlling age and FIQ was conducted, and the results are demonstrated in H(3.94) = 18.781, p < 0.001; all pairwise comparison, p < 0.05); and participants with ADHD and those with ASD+ scored higher than NTC in the index of RE (H(3.94) = 16.886, p = 0.001; ADHD vs. NTC, p = 0.001; ASD+ vs. NTC, p = 0.006). There were no group differences in RPE and FMS  = 7.660, p = 0.011, R2 = 0.258), and RPE predicted 26.8% individual difference of RT-retrieving in the ADHD group (F(1.21) = 7.672, p = 0.011, R2 = 0.268). In the ASD+ group, RPE and FMS predicted 33.8% performance of RT-encoding (F(1.20) = 5.104, p = 0.016, R2 = 0.338), and RPE predicted 35.3% performance of RT-retrieving (F(1.21) = 11.438, p = 0.003, R2 = 0.353). None of the of the EF indices predicted the performance of face encoding and retrieving in the NTC group. A Spearman correlation analysis between face memory and EF was also conducted, and the results are demonstrated in To explore the relationship between EF and performance of face encoding and retrieving performance, a multi-linear regression analysis (stepwise) adjusting for age and FIQ was employed. The results see  illustraOn the basis of disclosing the transdiagnostic commonalities and differences between ASD and ADHD, this study has elucidated an interesting issue about the phenotype change of face memory in children with ASD comorbid ADHD symptoms. The findings revealed that, (1) children with ASD− exhibited lower accuracy than NTC in both face encoding and retrieving, and participants with ADHD behaved similar to NTC, while those with ASD+ performed poorer than NTC only in face retrieving; (2) the face memory performance of all clinical groups were correlated with social behaviors reflected by SRS scores, but children with ASD+ continued to show an association between face encoding and ADHD symptoms measured by SNAP scores; (3) diverse indices of EF assessed by WCST could partially explain the manifestation of face memory in children with ASD−, ADHD and ASD+, respectively.As we hypothesized, the current study replicated the well-documented findings that children with ASD have general face memory deficits ,61,62, aConsistent with previous studies ,49,65, oThe above findings indicated that the features of face memory could differentiate ASD and ADHD to be the transdiagnostic cognitive phenotype. Several causes could be responsible for their diverse behavioral presentation, and the distinct anomalous social visual attention pattern is a relevant issue yet to be explored in depth . PatientWhile there were prone to more severe social dysfunction, participants with ASD comorbid ADHD did not demonstrate poorer performance in face memory. The behavioral data suggested that children with the comorbid condition may present an additive effect: they performed as well as NTC in the face encoding task (like participants with ADHD), and made more errors in the face retrieving (similar to participants with ASD). This indicated that co-occurrence with ADHD symptoms would make the cognitive profile of ASD unpredictable, rather than having a simple effect of aggravation or alleviation of preexisting deficits. Regarding face memory phenotype change in children with ASD comorbid ADHD symptoms, some speculations could be made, which are presented below.First, being affected by the ADHD trait, children with ASD comorbid ADHD symptoms may adopt an ADHD-like processing pattern during face encoding. Correlation analysis showed that the face encoding competence of the ASD+ group was associated with ADHD symptoms except for the established relationship between face memory and social function. As previous studies have emphasized the predominant role of attention condition, such as with attention allocation and distractibility in memory processing of both ASD and ADHD ,73,74, aSecond, the phenomenon of mismatch between social dysfunction and face memory deficiency in children with the comorbid condition could be associated with the cognitive compensation mechanism. Recently, some scholars have pointed out that some patients with ASD may “compensate” for their underlying difficulties: in order to navigate the challenges associated with their core symptoms, they may mobilize more other cognitive resources such as EF and IQ ,77,78. LImportantly, recent perspectives have proposed that the special relationship between EF and social cognition deficits could explain the co-occurrence of ADHD symptoms in ASD . MoreoveOf further interest, some scholars suggested that the ability of EF may have an important compensation function in patients with ASD ,78. In tThe present study has some limitations. First, the age span across our participants (6.02~12.64 years old) was considered too wide, and the intelligence level of ASD+ and NTC groups were mismatched. Thus, age and PSI/FIQ were included as covariance. Further studies are warranted to test the effects of age. Second, although the data dissociated the different phases of face memory, studies combined with ERP may better isolate the neural responses of encoding and retrieval. Accordingly, these findings should not be overinterpreted, and more precise experimental conditions and large sample sizes would increase the strength of evidence. Third, the measurement of WCST could not precisely discriminate the diverse components of EF, and WCST only measures the cool EF. Considering the significance of EF on the underlying mechanisms of neurocognitive changes in ASD, the relationship between EF and face memory should be thoroughly explored.The present study confirmed that face memory in children with ASD was impaired in both the encoding and retrieving phases. Importantly, the study provides a new perspective and empirical evidence to show that co-occurring ADHD symptoms could modulate the face encoding deficits of ASD, which may be associated with EF. These findings, delineating the transdiagnostic profiles among participants with ASD, ADHD, and their comorbidity, propose that shared and distinct intervention approaches may be needed for children with each of the disorders, to improve social cognition. This study sheds light on the transdiagnostic neurocognitive basis of the conditions and emphasizes the importance of considering ADHD comorbidity in ASD when recommending treatment plans.