Multi-granularity contrastive learning model for next POI recommendation.

Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suffer from the severe check-in data sparsity issue. However, taking into account region-level and category-level POI sequences can help address this issue. Moreover, collaborative information between different granularities of POI sequences is not well utilized, which can facilitate mutual enhancement and benefit to augment user preference learning. To address these challenges, we propose multi-granularity contrastive learning (MGCL) for next POI recommendation, which utilizes multi-granularity representation and contrastive learning to improve the next POI recommendation performance. Specifically, location-level POI graph, category-level, and region-level sequences are first constructed. Then, we use graph convolutional networks on POI graph to extract cross-user sequential transition patterns. Furthermore, self-attention networks are used to learn individual user sequential transition patterns for each granularity level. To capture the collaborative signals between multi-granularity, we apply the contrastive learning approach. Finally, we jointly train the recommendation and contrastive learning tasks. Extensive experiments demonstrate that MGCL is more effective than state-of-the-art methods.

The Role of Alpha-Synuclein in Neurodevelopmental Diseases.

Neurodevelopmental disorders are a group of diseases with cognitive, motor, and emotional development deficits. Alpha-synuclein (α-syn) is a synaptic protein involved in transmission and neurodevelopment. This protein was previously shown to be associated with several disorders, including Parkinson's disease. Furthermore, a close link between neurodevelopmental disorders and Parkinson's has also been found. Changes in synaptic function have been noticed in neurodevelopmental disorders, including autism spectrum disorder. Impaired neurogenesis and related cognitive problems have been associated with altered expression of α-syn. Various studies reported α-syn in different body fluids and tissues such as blood and serum. Alpha-synuclein can help in better understanding the pathogenesis of neurodevelopmental diseases and facilitating their early diagnosis. This review aims to go over the recent advances in the role of α-syn in the pathophysiology of neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, and motor and social impairment, and its value as a diagnostic biomarker.

The causal relationship between sleep and risk of psychiatric disorders: A two-sample mendelian randomization study.

Introduction: Sleep is associated with psychiatric disorders. However, their causality remains unknown. Methods: The study explored the causal relationship between seven sleep parameters (sleep duration, insomnia, sleep apnea, chronotype, daytime dozing, napping during the day, and snoring) and three psychiatric disorders including major depressive disorder (MDD), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS) summary data for sleep parameters were obtained from the United Kingdom biobank, FinnGen biobank, and EBI databases. MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW(fixed effects) were used to perform the MR analysis. The heterogeneity was detected by Cochran's Q statistic. The horizontal pleiotropy was detected by MR Egger. The sensitivity was investigated by the leave-one-out analysis. Results: Insomnia (OR = 2.02, 95%CI = 1.34-3.03, p = 0.001, False-discovery rate (FDR) corrected p-value = 0.011) and napping during the day (OR = 1.81, 95%CI = 1.34-2.44, FDR corrected p-value<0.001) were associated with an increased risk of MDD. Longer sleep duration (OR = 2.20, 95%CI = 1.24-3.90, FDR corrected p-value = 0.049) had an association with the increased risk of schizophrenia, while daytime dozing (OR = 4.44, 95%CI = 1.20-16.41, corrected p-value = 0.088)and napping during the day (OR = 2.11, 95%CI = 1.11-4.02, FDR corrected p-value = 0.088) had a suggestive association with an increased risk of schizophrenia. Longer sleep duration had a suggestive association with a decreased risk of ADHD (OR = 0.66, 95%CI = 0.42-0.93, FDR corrected p-value = 0.088). Conclusion: This study provides further evidence for a complex relationship between sleep and psychiatric disorders. Our findings highlight the potential benefits of addressing sleep problems in the prevention of psychiatric disorders.

Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model.

Although hypervalent iodine(III) reagents have become staples in organic chemistry, the exploration of their isoelectronic counterparts, namely hypervalent bromine(III) and chlorine(III) reagents, has been relatively limited, partly due to challenges in synthesizing and stabilizing these compounds. In this study, we conduct a thorough examination of both homolytic and heterolytic bond dissociation energies (BDEs) critical for assessing the chemical stability and functional group transfer capability of cyclic hypervalent halogen compounds using density functional theory (DFT) analysis. A moderate linear correlation was observed between the homolytic BDEs across different halogen centers, while a strong linear correlation was noted among the heterolytic BDEs across these centers. Furthermore, we developed a predictive model for both homolytic and heterolytic BDEs of cyclic hypervalent halogen compounds using machine learning algorithms. The results of this study could aid in estimating the chemical stability and functional group transfer capabilities of hypervalent bromine(III) and chlorine(III) reagents, thereby facilitating their development.

Neurocognitive performance in functional neurological disorder: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Cognitive complaints are common in functional neurological disorder (FND), but it is unclear whether objective neurocognitive deficits are present. This systematic review summarized validated/standardized cognitive test performance in FND samples across cognitive domains. METHODS: Embase, PsycInfo and MEDLINE were searched from inception to 15 May 2023, combining terms for FND and cognitive domains (e.g., attention, memory, executive functioning). Studies included a range of FND phenotypes (seizures, motor, cognitive disorder, mixed), compared to healthy or clinical controls. Risk of bias was assessed with the modified Newcastle-Ottawa Scale and a qualitative synthesis/narrative review of cognitive performance in FND was conducted. Test performance scores were extracted, and random effects meta-analyses were run where appropriate. This review was registered on PROSPERO, CRD42023423139. RESULTS: Fifty-six studies including 2260 individuals with FND were eligible. Although evidence for some impairments emerged across domains of executive functioning, attention, memory and psychomotor/processing speed, this was inconsistent across studies and FND phenotypes. Common confounds included group differences in demographics, medication and intellectual functioning. Only 24% of studies objectively assessed performance validity. Meta-analyses revealed higher scores on tests of naming (g = 0.67, 95% confidence interval [CI] 0.50, 0.84) and long-term memory (g = 0.43, 95% CI 0.13, 0.74) in functional seizures versus epilepsy, but no significant differences in working (g = -0.08, 95% CI -0.44, 0.29) or immediate (g = 0.25, 95% CI -0.02, 0.53) memory and cognitive flexibility (g = -0.01, 95% CI -0.29, 0.28). CONCLUSIONS: There is mixed evidence for objective cognitive deficits in FND. Future research should control for confounds, include tests of performance validity, and assess relationships between objective and subjective neurocognitive functioning.

MIST: Multi-instance selective transformer for histopathological subtype prediction.

Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance-level discrimination of histopathological images, (2) low inter-class and large intra-class variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi-instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective self-attention mechanism with multi-instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi-head self-attention (S-MSA) is well-designed to identify the representative instances by modeling the instance-to-instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance-to-bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.

Atypical local and global biological motion perception in children with attention deficit hyperactivity disorder.

Perceiving biological motion (BM) is crucial for human survival and social interaction. Many studies have reported impaired BM perception in autism spectrum disorder, which is characterised by deficits in social interaction. Children with attention deficit hyperactivity disorder (ADHD) often exhibit similar difficulties in social interaction. However, few studies have investigated BM perception in children with ADHD. Here, we compared differences in the ability to process local kinematic and global configurational cues, two fundamental abilities of BM perception, between typically developing and ADHD children. We further investigated the relationship between BM perception and social interaction skills measured using the Social Responsiveness Scale and examined the contributions of latent factors (e.g. sex, age, attention, and intelligence) to BM perception. The results revealed that children with ADHD exhibited atypical BM perception. Local and global BM processing showed distinct features. Local BM processing ability was related to social interaction skills, whereas global BM processing ability significantly improved with age. Critically, general BM perception (i.e. both local and global BM processing) may be affected by sustained attentional ability in children with ADHD. This relationship was primarily mediated by reasoning intelligence. These findings elucidate atypical BM perception in ADHD and the latent factors related to BM perception. Moreover, this study provides new evidence that BM perception is a hallmark of social cognition and advances our understanding of the potential roles of local and global processing in BM perception and social cognitive disorders.

Changes of attentional bias in patients with alcohol use disorder during abstinence: A longitudinal study.

BACKGROUND: Alcohol Use Disorder (AUD) is linked to an attentional bias towards alcohol-related cues (e.g. images, smells), which acquire incentive properties and promote continued consumption. METHOD: We investigated how the general and alcohol attentional bias evolved longitudinally in AUD patients along two periods of abstinence: t = 0 (baseline, 1-3 months of abstinence) and t = 1 (follow-up; 6 months of abstinence), as well as their relationship with alcohol-related variables. General and alcohol-specific attentional bias were evaluated by the Classic and the Alcohol Stroop tests (neutral and alcohol conditions) in abstinent AUD patients and controls. RESULTS: At t = 0, the AUD group exhibited both general and alcohol-specific attentional biases, with greater effect in the general bias. At t = 1, alcohol-specific attentional bias decreased specifically in the AUD group and reached control levels (with interference index levels increasing from 1-3 months to 6 months). However, general attentional bias showed a trend toward improvement but it did not significantly change through abstinence process (linear mixed models, controlling for age, BMI, sex and education). CONCLUSIONS: In AUD patients, general and alcohol attentional biases exhibit different trajectories during abstinence, with the attentional bias toward alcohol improving significantly throughout this process whereas general attentional bias is maintained.

Correcting for ERP latency jitter improves gaze-independent BCI decoding.

OBJECTIVE: Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze. METHODS: ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment. RESULTS & DISCUSSION: WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.

FreqSNet: a multiaxial integration of frequency and spatial domains for medical image segmentation.

Objective.In recent years, convolutional neural networks, which typically focus on extracting spatial domain features, have shown limitations in learning global contextual information. However, frequency domain can offer a global perspective that spatial domain methods often struggle to capture. To address this limitation, we propose FreqSNet, which leverages both frequency and spatial features for medical image segmentation.Approach.To begin, we propose a Frequency-Space Representation Aggregation Block (FSRAB) to replace conventional convolutions. FSRAB contains three frequency domain branches to capture global frequency information along different axial combinations, while a convolutional branch is designed to interact information across channels in local spatial features. Secondly, the Multiplex Expansion Attention Block (MEAB) extracts long-range dependency information using dilated convolutional blocks, while suppressing irrelevant information via attention mechanisms. Finally, the introduced Feature Integration Block enhances feature representation by integrating semantic features that fuse spatial and channel positional information.Main results.We validated our method on 5 public datasets, including BUSI, CVC-ClinicDB, CVC-ColonDB, ISIC-2018, and Luna16. On these datasets, our method achieved Intersection over Union (IoU) scores of 75.46%, 87.81%, 79.08%, 84.04%, and 96.99%, and Hausdorff Distance (HD) values of 22.22mm, 13.20mm, 13.08mm, 13.51mm, and 5.22mm, respectively. Compared to other state-of-the-art methods, our FreqSNet achieves better segmentation results.Significance.Our method can effectively combine frequency domain information with spatial domain features, enhancing the segmentation performance and generalization capability in medical image segmentation tasks.

Perceptual decoupling in the sustained attention to response task is unlikely.

Researchers dispute the cause of errors in high Go, low No Go target detection tasks, like the Sustained Attention to Response Task (SART). Some researchers propose errors in the SART are due to perceptual decoupling, where a participant is unaware of stimulus identity. This lack of external awareness causes an erroneous response. Other researchers suggest the majority of the errors in the SART are instead due to response leniency, not perceptual decoupling. Response delays may enable a participant who is initially unaware of stimulus identity, perceptually decoupled, to become aware of stimulus identity, or perceptually recoupled. If, however, the stimulus presentation time is shortened to the minimum necessary for stimulus recognition and the stimulus is disrupted with a structured mask, then there should be no time to enable perception to recouple even with a response delay. From the perceptual decoupling perspective, there should be no impact of a response delay on performance in this case. Alternatively if response bias is critical, then even in this case a response delay may impact performance. In this study, we shortened stimulus presentation time and added a structured mask. We examined whether a response delay impacted performance in the SART and tasks where the SART's response format was reversed. We expected a response delay would only impact signal detection theory bias, c, in the SART, where response leniency is an issue. In the reverse formatted SART, since bias was not expected to be lenient, we expected no impact or minimal impact of a response delay on response bias. These predictions were verified. Response bias is more critical in understanding SART performance, than perceptual decoupling, which is rare if it occurs at all in the SART.

Assessment of centanafadine in adults with ADHD: a matching adjusted indirect comparison versus methylphenidate hydrochloride extended release (Concerta).

Objective: To compare the safety and efficacy of centanafadine versus methylphenidate hydrochloride extended release (ER; Concerta) in adults with ADHD.Methods: In the absence of head-to-head trials, anchored matching-adjusted indirect comparisons (MAIC) were used to compare rates of adverse events reported across trials and mean change from baseline in Adult ADHD Investigator Symptom Rating Scale (AISRS) score between centanafadine and methylphenidate hydrochloride ER. Pooled patient-level data from two centanafadine trials (NCT03605680, NCT03605836) and aggregate data from one published methylphenidate hydrochloride ER trial (NCT00937040) were used. Characteristics of individual patients from the centanafadine trials were matched to aggregate baseline characteristics from the methylphenidate hydrochloride ER trial using propensity score weighting. A sensitivity analysis assessed the robustness of the results to the capping of extreme weights (i.e., above the 99th percentile).Results: Compared with methylphenidate hydrochloride ER, centanafadine was associated with significantly lower risk of dry mouth (risk difference [RD] in percentage points: -11.95), initial insomnia (-11.10), decreased appetite (-8.05), anxiety (-5.39), palpitations (-5.25), and feeling jittery (-4.73) though a significantly smaller reduction in AISRS score (4.16-point). In the sensitivity analysis, the safety results were consistent with the primary analysis but there was no significant difference in efficacy between centanafadine and methylphenidate hydrochloride ER.Conclusion: In this anchored MAIC, centanafadine had a better safety profile and possibly lower efficacy than methylphenidate hydrochloride ER. While the safety results were robust across analyses, there was no difference in efficacy between centanafadine and methylphenidate hydrochloride ER in the sensitivity analysis. Considering its favorable safety profile, centanafadine may be preferred among patients for whom treatment-related adverse events are a concern.

Reflections on appropriately liberalizing ART for groups requiring special attention in China.

PURPOSE: The continuous advancement of assisted reproductive technologies (ART) and the evolving attitudes towards marriage and fertility among the general public have led to an increasing number of groups requiring special attention (GRSA) desiring to fulfill their reproductive needs through these technologies. These groups include single women (including single mothers without children), same-sex couples, and women in high-risk occupations, among others. The purpose of this paper is to explore the feasibility of appropriately liberalizing ART for GRSA. METHODS: This paper discusses the advantages of a moderate liberalization of ART for GRSA from two perspectives: a theoretical basis and a practical significance level. It also analyzes the current constraints on liberalizing ART and presents suggestions for moderate liberalization. RESULTS: The moderate liberalization of ART can provide technical support for respecting and realizing the reproductive freedom of GRSA, which has certain theoretical and practical significance. However, it is also subject to constraints. CONCLUSION: We call for government to keep pace with the times, based on the current stage of political, economic, and social development, to further recognize and protect citizens' reproductive rights, prioritize the practical needs of the public, and explore policies and regulations for gradually loosening the restrictions on ART for GRSA.

The Relationship between SNAP25 and Some Common Human Neurological Syndromes.

Over the years, research on the pathogenesis of neurological diseases has progressed slowly worldwide. However, as the incidence rate continues to increase and the disease gradually develops, early diagnosis and treatment have become a top priority. SANP25, a protein present on the presynaptic membrane and involved in neurotransmitter release, is closely related to the loss or abnormal expression of synapses and neurons. SNAP25 deficiency can lead to synaptic disorders and inhibit neurotransmitter release. Therefore, a large amount of literature believes that SNAP25 gene mutation is a risk factor for many neurological diseases. This review used advanced search on PubMed to conduct extensive article searches for relevant literature. The search keywords included SNAP25 and Alzheimer's disease, SNAP25 and Parkinson's disease, and so on. After reading and summarizing the previous papers, the corresponding conclusions were obtained to achieve the purpose of the review. The deficiency or variation of SNAP25 might be related to the onset of schizophrenia, epilepsy, attention deficit/hypoactivity disorder, bipolar disorder effective disorder, and autism. SNAP25 has been found to be used as a neuropathological marker for neurological diseases, which could be the target of diagnosis or treatment of Alzheimer's disease and Parkinson's disease. Cerebrospinal Fluid (CSF) or blood has been found to enable more effective drug development.

A pilot longitudinal study of decrease in cognitive functions during the most painful day of the period among women with primary dysmenorrhea.

PURPOSE: Painful menstruation is a common problem associated with many limitations in day-to-day functioning. There is limited research on the temporary effects of menstrual pain on cognitive functioning. METHODS: A longitudinal pilot study was conducted. A group of 32 women was tested with the Brief Test of Adult Cognition by Telephone (BTACT), which consists of 7 tests measuring various cognitive functions. Participants were tested both on a regular, pain-free day and the most painful day of their period. RESULTS: The subjects displayed significantly lower results in several domains of cognitive functions during measurement on the most painful day of the period. We observed a decline in inhibitory and cognitive control, attention functioning and processing speed. CONCLUSIONS: The results provide tentative evidence for experiencing cognitive difficulties under the influence of menstrual pain and may contribute to raising awareness about related difficulties.

Efficacy and safety of monoamine reuptake inhibitors in attention deficit hyperactivity disorder: A Bayesian network meta-analysis.

The use of first-line drugs in clinical practice for attention deficit hyperactivity disorder (ADHD) is limited by their adverse effects. Many novel monoamine reuptake inhibitors (MRIs) with better safety profiles and comparable efficacy are also being tried for ADHD. This network meta-analysis (NMA) has evaluated the efficacy and safety of MRIs in ADHD. The data was extracted from 31 relevant clinical trials after a literature search on MEDLINE/PubMed, Embase, Scopus, Cochrane databases, and clinical trial registries. Quality assessment was performed using the risk of bias assessment tool (RoB2) by Cochrane Collaboration, and the random-effects model was used to estimate the effect size. Standardised mean difference (SMD) and 95% credible interval(95%CrI) were reported for the reduction in ADHD rating scale score. Network geometry was visualised, and node splitting was done for the closed triangles. Meta-regression was done for the duration of therapy. PRISMA-NMA guidelines were followed in selecting, analyzing, and reporting findings. The drugs showing significant reduction on the ADHD rating scale as compared to placebo are bupropion (SMD: 0.33; 95%CrI: 0.60,-0.059), dasotraline(SMD: 0.49; 95%CrI: 0.82,-0.16), venlafaxine(SMD: 0.71; 95%CrI: 1.3,-0.15), viloxazine(SMD: 0.45; 95%CrI: 0.77,-0.12). Other drugs (centanafadine, duloxetine, edivoxetine, reboxetine, tipepidine, vortioxetine) were no better than placebo in reducing symptom severity of ADHD. The efficacy of none of the drugs was found to be significantly different as compared to methylphenidate. Among all, duloxetine (OR:15; 95%CrI:1.8130) showed significantly more treatment-emergent adverse events than methylphenidate. In conclusion, venlafaxine, viloxazine, and bupropion are the most efficacious MRIs for ADHD symptom reduction as compared to placebo with high certainty of evidence.

scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: https://github.com/labiip/scGAAC.

Enhancing tunnel crack detection with linear seam using mixed stride convolution and attention mechanism.

Cracks in tunnel lining structures constitute a common and serious problem that jeopardizes the safety of traffic and the durability of the tunnel. The similarity between lining seams and cracks in terms of strength and morphological characteristics renders the detection of cracks in tunnel lining structures challenging. To address this issue, a new deep learning-based method for crack detection in tunnel lining structures is proposed. First, an improved attention mechanism is introduced for the morphological features of lining seams, which not only aggregates global spatial information but also features along two dimensions, height and width, to mine more long-distance feature information. Furthermore, a mixed strip convolution module leveraging four different directions of strip convolution is proposed. This module captures remote contextual information from various angles to avoid interference from background pixels. To evaluate the proposed approach, the two modules are integrated into a U-shaped network, and experiments are conducted on Tunnel200, a tunnel lining crack dataset, as well as the publicly available crack datasets Crack500 and DeepCrack. The results show that the approach outperforms existing methods and achieves superior performance on these datasets.

Cognitive Predictors of the Overlap of Reading and Math in Middle School.

Math and reading skills are known to be related, and predictors of each are well researched. What is less understood is the extent to which these predictors, uniquely and collectively, overlap with one another, are differentially important for different academic skills, and account for the overlap of math and reading. We examined 20 potential predictors from four domains (working memory, processing speed, attention, and language) using latent variables and both timed and untimed achievement skill, in a sample (N=212) of at-risk middle schoolers, half of whom were English learners. The predictors accounted for about half of the overlap among achievement skills, suggesting that other factors (e.g., domain specific skills) might also be relevant for the overlap. We also found some differential prediction (language for reading, working memory for math). The present results extend and refine our understanding of the contribution of these cognitive predictors for reading and math.