From the Cover: What the fly’s nose tells the fly’s brain

The fly olfactory system has a three-layer architecture: The fly’s olfactory receptor neurons send odor information to the first layer (the encoder) where this information is formatted as combinatorial odor code, one which is maximally informative, with the most informative neurons firing fastest. This first layer then sends the encoded odor information to the second layer (decoder), which consists of about 2,000 neurons that receive the odor information and “break” the code. For each odor, the amplitude of the synaptic odor input to the 2,000 second-layer neurons is approximately normally distributed across the population, which means that only a very small fraction of neurons receive a large input. Each odor, however, activates its own population of large-input neurons and so a small subset of the 2,000 neurons serves as a unique tag for the odor. Strong inhibition prevents most of the second-stage neurons from firing spikes, and therefore spikes from only the small population of large-input neurons is relayed to the third stage. This selected population provides the third stage (the user) with an odor label that can be used to direct behavior based on what odor is present.A hallmark of at least three major brain structures found in essentially all vertebrates—cerebellum, hippocampus, and olfactory system—is an architecture with three stages of information processing (Fig. 1). In the first stage (the encoder), information arriving from other brain areas is assembled into a combinatorial code and relayed, with a massive expansion of neuron number, to the second stage. This code is “broken” by the second stage (the decoder), and passed to the third stage, where the desired pieces of decoded information are selected for use in other brain regions.Open in a separate windowFig. 1.Schematic representation of the Marr motif. Four sensory neurons at the left (circles represent their cell bodies) send their axons to two regions of neuropil (dotted circles) in the first (encoder) stage of the three-stage circuit. Additional circuitry (not illustrated) produces interactions between the two neuropil regions. Dendrites of the two stage 1 projection neurons (cell bodies of precerebellar neurons are the circles) collect and format the sensory information as a combinatorial code. This coded information is then sent over the precerebellar neuron axons to stage 2 (decoder). Synaptic connections (dark dots) are made on the dendrites of four stage 2 neurons (granule cell bodies represented by four circles), and the output, the broken code, is sent at the right of the diagram to stage 3 (not represented). Additional circuitry responsible for breaking the combinatorial code in the second stage is not shown.The first proposal for the operation of this three-stage processing architecture was made by Marr (1), over four decades ago, to explain the function of the cerebellum, and I shall refer to the first two (encoder/decoder) stages of the architecture as the Marr motif. According to Marr, the encoder stage provides the pattern (neuronal activity compiled in precerebellar nuclei) that is relayed to cerebellar granule cells (the decoder stage). In granule cells, the pattern provided by the precerebellar neurons is separated by spreading the information over many more neurons (there are many more granule cells than precerebellar neurons), and by quieting most of the granule cells with strong inhibition from Golgi inhibitory neurons. These inhibitory neurons collect the output of many granule cells and feed it back to them. Finally, in the third stage (Purkinje cells), some parts of the separated pattern relayed by the granule cells are selected for labeling by concurrent climbing fiber activity that adjusts the strength of synapses conveying the chosen signals to Purkinje cells. Whenever labeled signals happened to recur, the modified synapses cause selected Purkinje cells change their firing rates and provide an output based on the tagged signals.Although Marr’s proposal has been enormously influential, we still do not understand the combinatorial code (Marr’s pattern) generated by the precerebellar neurons, nor do we know how it is decoded by the granule cells (Marr’s pattern separation). What appears to be this same three-stage architecture is used by Drosophila for the first three levels of its olfactory system (2), but the fly system is much smaller, simpler, and more completely understood than any vertebrate version. Here I exploit the simplicity and extensive knowledge about the fly olfactory system to learn the properties of the combinatorial odor code, and how it is decoded. My hope is that what is learned from the fly will help us understand the similar three-level architecture in vertebrates.     

Lag threads organize the brain’s intrinsic activity

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.Spontaneous (intrinsic) neural activity is ubiquitously present in the mammalian brain, as first noted by Hans Berger (1). Spontaneous activity persists in all physiological states, although the statistical properties of this activity are modified by level of arousal and ongoing behavior (2–8). Invasive studies in animals using diverse techniques—for example, local field potentials (9–11), voltage-sensitive dyes (12–15), and calcium imaging (4, 16, 17)—have demonstrated richly organized intrinsic activity at multiple temporal and spatial scales. The most used technique for studying whole-brain intrinsic activity in humans is resting-state functional magnetic resonance imaging (rs-fMRI). Biswal et al. first reported that slow (<0.1 Hz) spontaneous fluctuations of the blood oxygen level-dependent (BOLD) signal are temporally synchronous within the somatomotor system (18). This basic result has since been extended to multiple functional systems spanning the entire brain (19–22). Synchrony of intrinsic activity is widely referred to as functional connectivity; the associated topographies are known as resting-state networks (RSNs) (23) and, equivalently, intrinsic connectivity networks (24).Almost all prior rs-fMRI studies have used either seed-based correlation mapping (25) or spatial independent components analysis (sICA) (26). Critically, neither or these techniques provide for the possibility that activity within RSNs may exhibit temporal lags on a time scale finer than the temporal sampling density. However, we recently demonstrated highly reproducible lags on the order of ∼1 s by application of parabolic interpolation to rs-fMRI data acquired at a rate of one volume every 3 s (SI Appendix, Fig. S1) (27). Moreover, this lag structure can be modified, with appropriate focality, by a variety of task paradigms (27).Investigations of rs-fMRI lag structure previously have been limited by the concern that observed lags may reflect regional differences in the kinetics of neurovascular coupling rather than primary neural processes (28, 29). However, our previous dimensionality analysis demonstrated that there are at least two independent lag processes within the brain (27). The neurovascular model can account for only one of these. Hence, there must be at least one lag process that is genuinely of neural origin. We have since made significant methodological improvements (Theory and Fig. 1) that enable a more detailed characterization of lag structure in BOLD rs-fMRI data. We report our results in two parts.Fig. 1.Illustration of lag threads. A shows three patterns of propagation (lag threads) through six nodes. The objective is to demonstrate the mapping between lag structure and PCA. The illustration is not intended as a model of propagation in neural tissue. ...In part I, we present an expanded view of the lag structure within the normal adult human brain derived from BOLD rs-fMRI data in 1,376 individuals. Specifically, we show that at least eight orthogonal lag processes can be reproducibly demonstrated. We refer to these processes as “threads” by way of analogy with modern computer programming practice in which single applications contain multiple, independent thread sequences.In part II, we investigate the relation between lag threads and zero-lag temporal correlations—that is, conventional, resting-state functional connectivity. We find that, although there is no simple relation between lag and zero-lag temporal correlation over all pairs of voxels, apparent propagation is largely unidirectional within RSNs. We also show that the zero-lag temporal correlation structure of rs-fMRI arises as a consequence of lags, whereas the reverse is not true. These results suggest that lag threads account for observed patterns of zero-lag temporal synchrony and that RSNs are an emergent property of lag structure.     

Breakdown of the brain’s functional network modularity with awareness

Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain’s functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain’s functional connectivity.Three broad classes of models have been proposed to explain the neural basis of awareness, with these classes primarily differing on the predicted extent of neural information changes associated with conscious perception. According to focal theories, awareness results from local changes in neural activity in either the perceptual substrates (1–3) or in higher-level nodes of information processing pathways (4). By contrast, network-level theories posit that awareness is tightly associated with activation of parietofrontal attention networks of the brain (5–11). Finally, global models propose that awareness results from widespread changes in the activation state (12–15) and functional connectivity (16–19) of the brain. Though there is strong experimental support for network-level theories, there is scant experimental evidence in favor of truly sweeping, widespread changes in brain activity with conscious perception despite the fact that global scale models have recently come to prominence in the theoretical landscape of this field.Using a graph theoretical approach applied to ultra-high-field fMRI data, here we experimentally tested a key tenet of global theories: the widespread emergence of large-scale functional connectivity with awareness. Graph theory analyses are ideal tools to test global models of awareness because they can provide concise measures of the integration and segregation of interconnected nodes of a system (20). Applied to functional imaging data, we treat individual brain regions of interest (ROIs) as nodes, functional connectivity between ROIs as edges, and functional brain networks as interconnected modules of nodes. When examining a large set of ROIs that encompass the different networks of the human cerebral cortex (21, 22), we can apply graph theory analyses to estimate the extent to which key measures of global information processing are altered by the state of awareness. This approach has been previously applied to study differences in cognitive states (23–31). Although recent studies have taken advantage of graph theory analysis to examine the connectivity patterns that precede a conscious event (32) or following pharmacologically induced loss of consciousness (33), this approach had yet to be used for characterizing the topology associated with conscious target perception per se, a necessary test for global theories of awareness.If the changes with awareness are truly global, one should see such changes even if the task does not require complex discrimination, identification, and semantic processes that may recruit vast extents of cortical tissue that are not necessarily associated with conscious perception; in other words, these global changes should appear even for the simple conscious detection of a flashed disk. For this reason, we had participants perform an elementary masked target detection task (Fig. 1) while being scanned at ultra-high field (7 T). The task included three trial types: forward-masked, backward-masked, and no-target conditions. In the forward-masked (paracontrast) condition, a 133-ms-duration annular mask offset 33 ms before the target (a disk whose exterior border coincided with the interior border of the annulus) presented for 33 ms. In the backward-masked (metacontrast) condition, the order of mask/target presentation was reversed while keeping all timing parameters the same. Under such conditions, forward masking of targets has been shown to impair target detection more than backward masking (34, 35). Consequently, the mask/target orderings provided a manipulation of target awareness while maintaining the same mask and target presentation times across both forward- and backward-masked conditions. Because on each trial, participants made a detection response about the presence or absence of the target followed by a confidence rating on their response, subjects’ performance could be assessed on both an objective (discriminability index d′) and subjective (confidence rating) measure of awareness (36). In turn, only trials in which the target was either seen (aware) or unseen (unaware) at high confidence levels were used for analysis of brain imaging data. Finally, because the report of the percept was 12 s removed from the stimulus presentations (Fig. 1), the task design precluded initiation of the motor response itself from influencing estimates of awareness. Although response selection and motor preparation processes likely occur during this period, similar preparation would occur across all conditions.Open in a separate windowFig. 1.Schematic of behavioral paradigm with forward-masked and backward-masked trial types (no-target trials not shown). On each trial, participants responded whether they detected the target stimulus and indicated a confidence rating for their answer (Methods).     

Newborn’s brain activity signals the origin of word memories

Recent research has shown that specific areas of the human brain are activated by speech from the time of birth. However, it is currently unknown whether newborns'' brains also encode and remember the sounds of words when processing speech. The present study investigates the type of information that newborns retain when they hear words and the brain structures that support word-sound recognition. Forty-four healthy newborns were tested with the functional near-infrared spectroscopy method to establish their ability to memorize the sound of a word and distinguish it from a phonetically similar one, 2 min after encoding. Right frontal regions—comparable to those activated in adults during retrieval of verbal material—showed a characteristic neural signature of recognition when newborns listened to a test word that had the same vowel of a previously heard word. In contrast, a characteristic novelty response was found when a test word had different vowels than the familiar word, despite having the same consonants. These results indicate that the information carried by vowels is better recognized by newborns than the information carried by consonants. Moreover, these data suggest that right frontal areas may support the recognition of speech sequences from the very first stages of language acquisition.     

Testing a biosynthetic theory of the genetic code: fact or artifact?

It has long been conjectured that the canonical genetic code evolved from a simpler primordial form that encoded fewer amino acids [e.g., Crick, F. H. C. (1968) J. Mol. Biol. 38, 367-379]. The most influential form of this idea, "code coevolution" [Wong, J. T.-F. (1975) Proc. Natl. Acad. Sci. USA 72, 1909-1912], proposes that the genetic code coevolved with the invention of biosynthetic pathways for new amino acids. It further proposes that a comparison of modern codon assignments with the conserved metabolic pathways of amino acid biosynthesis can inform us about this history of code expansion. Here we re-examine the biochemical basis of this theory to test the validity of its statistical support. We show that the theory's definition of "precursor-product" amino acid pairs is unjustified biochemically because it requires the energetically unfavorable reversal of steps in extant metabolic pathways to achieve desired relationships. In addition, the theory neglects important biochemical constraints when calculating the probability that chance could assign precursor-product amino acids to contiguous codons. A conservative correction for these errors reveals a surprisingly high 23% probability that apparent patterns within the code are caused purely by chance. Finally, even this figure rests on post hoc assumptions about primordial codon assignments, without which the probability rises to 62% that chance alone could explain the precursor-product pairings found within the code. Thus we conclude that coevolution theory cannot adequately explain the structure of the genetic code.     

Interleukin-18 genetic polymorphisms contribute differentially to the susceptibility to Crohn’s disease

AIM: To investigate the correlation between interleukin-18 (IL-18) gene polymorphisms and the risk of developing Crohn’s disease (CD).METHODS: The PubMed, CISCOM, CINAHL, Web of Science, EBSCO, Cochrane Library, MEDLINE, EMBASE and CBM databases were searched without any language restrictions using combinations of keywords relating to CD and IL-18 for relevant articles published before November 1^st, 2013. Screening of the published studies retrieved from searches was based on our stringent inclusion and exclusion criteria and resulted in seven eligible studies for meta-analysis. A meta-analysis was conducted using a random-effects model with STATA 12.0 software. Crude odds ratios (ORs) with 95% confidence intervals (95%CI) were calculated.RESULTS: Seven case-control studies, with a total of 1930 CD cases and 1930 healthy subjects, met our inclusion criteria. The results of our meta-analysis indicated that the IL-18 rs1946518 A>C and rs187238 G>C polymorphisms may correlate with an increased risk of CD under five genetic models (all P < 0.05). Furthermore, we observed positive associations between the IL-18 rs360718 A>C polymorphism and CD risk under three genetic models (C allele vs A allele: OR = 2.03, 95%CI: 1.20-3.43, P = 0.008; CC vs AA+AC: OR = 2.39, 95%CI: 1.2-4.43, P = 0.006; CC vs AC: OR = 2.31, 95%CI: 1.22-4.38, P = 0.010). However, such associations were not found for the IL-18 rs917997 C>T, codon 35 A>C and rs1946519 G>T polymorphisms (all P > 0.05). A subgroup analysis was conducted to investigate the effect of ethnicity on an individual’s susceptibility to CD. Our results revealed positive correlations between IL-18 genetic polymorphisms and an increased risk of CD among Asians and Africans (all P < 0.05), but not among Caucasians (all P > 0.05).CONCLUSION: This meta-analysis indicated that the IL-18 rs1946518 A>C, rs187238 G>C and rs360718 A>C polymorphisms may contribute to susceptibility to CD, especially among Asians and Africans. These polymorphisms are known to reduce IL-18 mRNA and protein levels.     

Association of VEGF genetic polymorphisms with the clinical characteristics of non-Hodgkin’s lymphoma

Purpose  

Vascular endothelial growth factor (VEGF) plays an important role in tumor angiogenesis and cancer progression. The VEGF genetic polymorphisms were shown to be independently associated with an adverse outcome in various malignancies. We investigated the possible associations of two polymorphisms (−2578C/A and +936C/T) in the VEGF gene with the clinicopathologic parameters for patients with non-Hodgkin’s lymphoma (NHL).     

Self-affirmation alters the brain’s response to health messages and subsequent behavior change

Health communications can be an effective way to increase positive health behaviors and decrease negative health behaviors; however, those at highest risk are often most defensive and least open to such messages. For example, increasing physical activity among sedentary individuals affects a wide range of important mental and physical health outcomes, but has proven a challenging task. Affirming core values (i.e., self-affirmation) before message exposure is a psychological technique that can increase the effectiveness of a wide range of interventions in health and other domains; however, the neural mechanisms of affirmation’s effects have not been studied. We used functional magnetic resonance imaging (fMRI) to examine neural processes associated with affirmation effects during exposure to potentially threatening health messages. We focused on an a priori defined region of interest (ROI) in ventromedial prefrontal cortex (VMPFC), a brain region selected for its association with self-related processing and positive valuation. Consistent with our hypotheses, those in the self-affirmation condition produced more activity in VMPFC during exposure to health messages and went on to increase their objectively measured activity levels more. These findings suggest that affirmation of core values may exert its effects by allowing at-risk individuals to see the self-relevance and value in otherwise-threatening messages.Promoting physical activity and decreasing sedentary behavior are major strategies to manage and prevent chronic diseases (1–16). In particular, sedentary behavior increases risk, independent of other types of activity, and exchanging sedentary for even light activity has physiological and psychological benefits (17–23). However, sedentary lifestyle is still prevalent despite worldwide efforts to increase activity; according to the World Health Organization, “60% to 85% of people in the world—from both developed and developing countries—lead sedentary lifestyles” (24). Thus, effective, theory-driven behavior change interventions are critical (25, 26).One major difficulty in decreasing sedentary and other health risk behaviors through health communication tools is that self-relevant health messages can be perceived to be threatening to self-worth and are often met with resistance. This phenomenon speaks to a classic and problematic paradox: those at highest risk are likely to be defensive, reducing openness to altering risk behaviors (27).     

Heterozygous carriers for Wilson’s disease—magnetic spectroscopy changes in the brain

Wilson’s disease (WD) is an autosomal recessive disorder and the WD heterozygote carriers (Hzc) should not exhibit symptoms of the disease. The aim of this study was to assess 12 WD Hzc by brain Proton MR Spectroscopy. In three cases, the levels of caeruloplasmin, and in one case, serum copper, were below our normal range. In two Hzc the aspartate and alanine aminotransferase levels in the blood were slightly increased, however, no ultrasonographic liver changes were detected. The brain metabolite analysis showed a statistically significant higher mean ratio of Glx/Cr and Lip/Cr in MRS in Hzc in both the pallidum and thalami compared to control subjects. Our results suggest that WD Hzc may accumulate free copper in the basal ganglia.     

GTP energy dependence of endocytosis and autophagy in the aging brain and Alzheimer’s disease

Increased interest in the aging and Alzheimer’s disease (AD)-related impairments in autophagy in the brain raise important questions about regulation and treatment. Since many steps in endocytosis and autophagy depend on GTPases, new measures of cellular GTP levels are needed to evaluate energy regulation in aging and AD. The recent development of ratiometric GTP sensors (GEVALS) and findings that GTP levels are not homogenous inside cells raise new issues of regulation of GTPases by the local availability of GTP. In this review, we highlight the metabolism of GTP in relation to the Rab GTPases involved in formation of early endosomes, late endosomes, and lysosomal transport to execute the autophagic degradation of damaged cargo. Specific GTPases control macroautophagy (mitophagy), microautophagy, and chaperone-mediated autophagy (CMA). By inference, local GTP levels would control autophagy, if not in excess. Additional levels of control are imposed by the redox state of the cell, including thioredoxin involvement. Throughout this review, we emphasize the age-related changes that could contribute to deficits in GTP and AD. We conclude with prospects for boosting GTP levels and reversing age-related oxidative redox shift to restore autophagy. Therefore, GTP levels could regulate the numerous GTPases involved in endocytosis, autophagy, and vesicular trafficking. In aging, metabolic adaptation to a sedentary lifestyle could impair mitochondrial function generating less GTP and redox energy for healthy management of amyloid and tau proteostasis, synaptic function, and inflammation.

     

Neonatal screening: from the ‘Guthrie age’ to the ‘genetic age’

Summary Newborn screening has ‘traditionally’ been performed to detect metabolic or endocrine diseases that are severe, frequent and treatable, according to criteria established in the late 1960s. Technological advances in laboratory testing over the past ten years open new possibilities. However, many new problems have to be explored before the establishment or expansion of a newborn screening programme. The purpose of this paper is to present some of the major problems that screening programmes will face in the near future. Competing interests: None declared     

Lag in maturation of the brain’s intrinsic functional architecture in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood, and there is great interest in understanding its neurobiological basis. A prominent neurodevelopmental hypothesis proposes that ADHD involves a lag in brain maturation. Previous work has found support for this hypothesis, but examinations have been limited to structural features of the brain (e.g., gray matter volume or cortical thickness). More recently, a growing body of work demonstrates that the brain is functionally organized into a number of large-scale networks, and the connections within and between these networks exhibit characteristic patterns of maturation. In this study, we investigated whether individuals with ADHD (age 7.2–21.8 y) exhibit a lag in maturation of the brain’s developing functional architecture. Using connectomic methods applied to a large, multisite dataset of resting state scans, we quantified the effect of maturation and the effect of ADHD at more than 400,000 connections throughout the cortex. We found significant and specific maturational lag in connections within default mode network (DMN) and in DMN interconnections with two task positive networks (TPNs): frontoparietal network and ventral attention network. In particular, lag was observed within the midline core of the DMN, as well as in DMN connections with right lateralized prefrontal regions (in frontoparietal network) and anterior insula (in ventral attention network). Current models of the pathophysiology of attention dysfunction in ADHD emphasize altered DMN–TPN interactions. Our finding of maturational lag specifically in connections within and between these networks suggests a developmental etiology for the deficits proposed in these models.Attention-deficit/hyperactivity disorder (ADHD) is a serious neuropsychiatric disorder characterized by inattention, hyperactivity, and impulsivity. One influential neurodevelopmental model of the disorder posits a lag in the maturational trajectories of key features of the brain (1–4). This model has mostly been investigated by examining developmental pathways of structural features of the brain (3, 5–8). In recent years, however, theorists have increasingly used resting state functional MRI (fMRI)—scanning participants in a task-free resting state—to explore the brain’s functional architecture. This work has led to the recognition that the human brain is organized into several large-scale intrinsic connectivity networks (ICNs), each associated with specific neurocognitive functions (9, 10). ICNs have been shown to undergo significant maturation from childhood to early adulthood, with individual ICNs exhibiting spatially specific reliable patterns of integration (increased connectivity with age) and segregation (decreased connectivity with age) with other ICNs (11–17). These advances raise possibilities for investigating maturational lag in ADHD in the developing ICN architecture of the brain (18).Independent lines of research suggest that attention dysfunction in ADHD is linked to altered ICN interrelationships. According to current theoretical models (19, 20), inattention in ADHD involves altered competitive balance between (i) default network, an ICN implicated in internally directed mentation (21, 22); and (ii) several task-positive ICNs (TPNs), including dorsal attention network (DAN), ventral attention network (VAN), and frontoparietal network (FPN), which are involved in cognitively demanding externally focused processing. Consistent with these models, previous resting state fMRI studies in ADHD have reliably found abnormalities in functional connections within DMN (23, 24) and in its interconnections with TPNs (25–27). Importantly, however, it is not currently known whether these abnormalities reliably observed in ADHD are linked to maturational lag. The current study sought to investigate this question. Based on current network models of ADHD, we hypothesized that maturational lag in ADHD in the brain’s intrinsic functional architecture would be focused within DMN and in its interconnections with three TPNs: DAN, VAN, and FPN.To test this hypothesis, we used recently developed whole-brain connectomic methods (28–30). Traditional seed-based strategies examine connectivity using a single or a handful of regions of interest (ROIs) or average connectivity values across entire ICNs. However, recent work demonstrates that ICN interrelationships are not unitary; rather, connectivity alterations during maturation in DMN and TPNs are highly variable across individual connections within ICNs (13, 14, 31, 32). Thus, conventional seed-based strategies are likely to produce summary measures that do not capture underlying fine-grained patterns of variation or can miss trends that are detectable only when looking across large populations of connections. Connectomic methods remedy this problem by examining connectivity patterns among hundreds of seeds. To produce comprehensive connectomic maps, we placed 907 ROIs at regular intervals throughout the cortex and calculated connectivity between each pair of ROIs (410,871 unique connections). Using a multiple regression approach, we then calculated the effect of age and the effect of ADHD at each connection of the connectome, controlling for nuisance effects (sex, IQ, handedness, motion, and scanner site). This regression modeling yielded two comprehensive maps of effect of age and effect of ADHD, respectively. Examining patterns of spatial correspondence across populations of connections in these whole-cortex connectomic maps provides a powerful way to investigate the maturational lag hypothesis.In our connectomic framework, we operationalized maturational lag as spatial co-occurrence of effects of age and effects of ADHD at the same connections. In particular, lag exists at an individual connection when the effect of ADHD at that connection opposes the effect of maturation. The presence of lag in a population of connections can be tested statistically by using a count-based framework that compares the number of observed lagged connections to a suitable chance distribution. Alternatively, a correlation-based framework can be used to investigate whether the strength of the effect of maturation is proportionately opposed by the effect of ADHD. That is, to the extent that a connection tends to more strongly integrate with age (i.e., effect of age is more positive), then its connectivity should be correspondingly reduced in ADHD relative to typically developing controls (TDCs). Conversely, to the extent that a connection tends to more strongly segregate with age (i.e., effect of age is more negative), then its connectivity should be correspondingly increased in ADHD relative to TDCs.We used both correlation- and count-based tests to investigate the spatial co-occurrence of effects of age and opposed effects of ADHD that is predicted by the maturational lag hypothesis. ICNs exhibit substantial intra- and internetwork dependency across connections. Thus, we assessed the significance of all statistical tests with nonparametric permutation tests, which are robust to deviations from independence and normality assumptions of conventional tests (33). We calculated estimates of effect of age and effect of ADHD used in these tests from participants in the ADHD-200 sample (34). This sample consisted of 481 TDCs and 275 children with ADHD and, after demographic and quality control exclusions, encompassed 421 participants (288 TDC and 135 ADHD). In addition, we also performed a partially independent second analysis. In particular, the preceding connectomic analyses were done a second time using effect of age estimates derived from a different sample, 155 TDC participants from the multisite Autism Brain Image Date Exchange (ABIDE) sample (35). The effect of ADHD estimates were derived from the ADHD-200 sample, and thus it bears emphasis that this second analysis is not completely independent from the first. Results were remarkably similar in this second analysis, and (with the exception of a single statistical test) all significant statistical tests reported below from the first analysis were also statistically significant in the second analysis (SI Results). Although not a fully independent replication, this second analysis nonetheless provides additional support for the reliability of our findings.