Brain Imaging Data Research Articles

Alterations in surface-based amplitude of low-frequency fluctuations primary open-angle glaucoma link to neurotransmitter profiling and visual impairment severity.

The study aimed to examine alterations in surface-based amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) in primary open-angle glaucoma (POAG) patients using resting-state functional magnetic resonance imaging (rs-fMRI), and to investigate their relationships with visual function and molecular profiling. A total of 70 POAG patients and 45 age- and sex-matched healthy controls (HCs) underwent rs-fMRI scans. The differences between POAG and HCs groups were compared by two-sample t-test. Spearman's correlation analyses assessed the relationship between ALFF/fALFF values and ophthalmic parameters. Spatial correlation analysis of the patients-control difference map with brain imaging data further explores underlying neurobiological mechanisms. POAG patients displayed altered brain activity compared to HCs, including decreased ALFF/fALFF in the visual network and increased in the frontoparietal and default mode networks. They exhibited reduced fALFF in the somatomotor network and increased ALFF in the dorsal and ventral attention networks. These changes are linked to neurotransmitter systems, with fALFF particularly associated with the dopamine system. Moreover, the altered ALFF/fALFF in brain regions related to vision and attention - the occipital lobe, temporal lobe, parietal lobe, paracentral lobule, and frontal lobe correlated with ophthalmic examination parameters. Surface-based ALFF/fALFF in POAG decreased in visual processing regions and increased in brain regions related to cognitive control, working memory, and attention. These changes were linked to neurotransmitter distributions important for emotional stability and mental health, potentially informing treatment approaches for POAG patients.

Bridging Huntington’s disease research with big data science: harmonized neuroimaging datasets from multiple studies

Abstract Multiple neuroimaging datasets from Huntington’s disease (HD) studies are publicly available, but these datasets are in various formats, omit imaging metadata, and sometimes contain corrupt scans. We have created a platform to curate, harmonize, and distribute neuroimaging datasets from eight different studies: TRACK-HD, TRACKOn-HD, PREDICT-HD, IMAGE-HD, HD-YAS, SHIELD-HD, PEARL-HD, and LONGPDE10. The platform is organized into three conceptual levels to serve the research community with both raw and processed data. Raw data are converted into Brain Imaging Data Structure (BIDS) format, while processed data are obtained from pipelines such as Freesurfer and fmriprep. Studies that had followed the same participants were combined. After combining studies, the final six BIDS datasets include a total of 2,216 participants and 7,073 sessions. We outline tools, principles, and recommendations for future data management in HD research.

Exploring Music-Based Interventions for Executive Functioning and Emotional Well-Being in Stroke Rehabilitation: A Scoping Review

Purpose: Stroke is one of the leading causes of disability with life-long implications requiring assessment and treatment of several functional domains. This review identifies the results from research into music-based interventions (MBIs), including music therapy (MT), for executive functions (EFs) and emotional well-being (EWB) in adults with stroke and highlights opportunities for clinical practice and future research. Methods: APA PsycInfo (EBSCOhost), and CINAHL (EBSCOhost) were searched, in addition to grey literature. Results: A total of 49 studies were included and encompassed experimental, analytic, and descriptive observational studies, and case reports, involving a total of 1663 participants. In total, 32 studies included MT interventions, and 17 were MBIs. EFs were an outcome in 20.41%, and EWB in 61.22% of studies, for which active interventions were the most utilized. Overall, 73.47% of the studies reported positive results. Conclusions: This scoping review indicates that music interventions can be beneficial for the improvement of different aspects of EFs and EWB at different stages of stroke recovery. Further research may benefit clinical practice by including standardized protocols, outcome and self-reported measures, and brain imaging data to determine the effects of interventions and support evidence-based decisions for treatment policies for stroke survivors.

Effective Alzheimer’s disease detection using enhanced Xception blending with snapshot ensemble

Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer’s into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer’s with individualized care. Subsequent efforts will concentrate on enhancing the model’s efficacy via its generalization to a variety of datasets.

Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.

Atypical brain network topology of the triple network and cortico-subcortical network in autism spectrum disorder

The default mode network (DMN), salience network (SN), and central executive control network (CEN) form the well-known triple network, providing a framework for understanding various neurodevelopmental and psychiatric disorders. However, the topology of this network remains unclear in autism spectrum disorder (ASD). To gain a more profound understanding of ASD, we explored the topology of the triple network in ASD. Additionally, the striatum and thalamus are pivotal centres of information transmission within the brain, and the realization of various brain functions requires the coordination of cortical and subcortical structures. Therefore, we also investigated the topology of the cortico-subcortical network in ASD, which consists of the DMN, SN, CEN, striatum, and thalamus. Resting-state functional magnetic resonance imaging data on 208 ASD patients and 278 typically developing (TD) controls (8–18 years old) were obtained from the Autism Brain Imaging Data Exchange database. We performed graph theory analysis on the triple network and the cortico-subcortical network. The results showed that the triple network’s clustering coefficient, lambda, and network local efficiency values were significantly lower in ASD, and the nodal degree and efficiency of the medial prefrontal cortex also decreased. For the cortico-subcortical network, the sigma, clustering coefficient, gamma, and network local efficiency showed the same reduction, and the altered clustering coefficient negatively correlated with ASD manifestations. In addition, the interaction between the DMN and CEN was more robust in ASD patients. These findings enhance our understanding of ASD and suggest that subcortical structures should be more considered in future ASD related studies.

Genetic Influence of the Brain on Muscle Structure: A Mendelian Randomization Study of Sarcopenia.

The association between brain and sarcopenia has not been clarified. We aim to investigate the causal association between brain structure, function, gene expression and sarcopenia-related traits. All participants were Europeans. GWAS data of Brain Imaging Data Structure (BIDs) was from the UK Biobank. Gene expression in 13 brain regions was acquired from the GTEx Consortium. The sarcopenia-related traits, including appendicular lean mass (ALM), whole body lean mass (WBLM), grip strength and sarcopenia diagnosed by the European Working Group on Sarcopenia in Older People (EWGSOP) or Foundation for the National Institutes of Health (FNIH), were from the IEU website. The inverse variance weighted (IVW), MR Egger, weighted median, weighted mode and Wald ratio methods were used for a two-sample Mendelian randomization (MR) analysis between brain structures and sarcopenia-related traits. The summary-data-based MR (SMR) was used to investigate brain genes causally influencing sarcopenia. We calculated the F-value, odds ratio with 95% CI and p-value. For the sensitivity analysis, the heterogeneity I2 statistic, Cochrane's Q test, Egger's intercept test, MR-PRESSO and the leave-one-out sensitivity test were used. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI), transcription factor interaction prediction and miRNA interaction prediction analysis were used to reveal potential signalling pathways and mechanisms. We included 3144 imaging phenotypes from 8428 participants in BIDs data. One hundred forty-one BIDs were identified to causally influence ALM, 160 BIDs showed significant causal effect on the WBLM, and 86 BIDs showed significant causal effect on grip strength. There were 48 or 32 BIDs causally associated with sarcopenia diagnosed by the EWGSOP or FNIH criteria respectively. After FDR correction, there were 35 BIDs showing causal effect on the ALM, 28 BIDs showing causal effect on the WBLM and 7 BIDs showing causal effect on grip strength (p < 0.05). Twelve to forty-eight genes in different brain regions showed causal effect on all the five sarcopenia-related traits. MMP24-AS1, HLA-DRB6, HLA-DQA2, DDX42, BAG6, NUSAP1, LINC00940, NME1-NME2 and AS3MT in the amygdala region showed detrimental effect on all the five sarcopenia-related traits, whereas HLA-DRB1, HLA-DQB1-AS1 and C6ORF3 showed protective effect (p < 0.05). The gene enrichment analysis indicated these screened genes was mainly enriched in immune-related signalling. We discovered the causal effect of BIDs and brain gene expression on sarcopenia. The positive genes were mainly enriched in immune-related signalling, suggesting an immune-based cross-organ regulation mechanism of brain-muscle axis.

Right hemisphere stroke is linked to reduced social connectedness in the UK Biobank cohort

Social connectedness is fundamental to health and life satisfaction. Empathic capacities that support social connections are commonly impaired following damage to the brain’s right hemisphere, but how these acquired socio-emotional deficits correspond to real-world social outcomes remains unclear. Using anatomical brain imaging and behavioral data from a large sample of stroke survivors included in the UK Biobank (n = 209), we link damage to regions of the right hemisphere involved in emotion recognition to lower social relationship satisfaction and higher loneliness. The effect was driven by lesions to the right anterior insula and not explained by stroke extent and motor function; it was further corroborated by an exploratory analysis of social decline in a few participants for whom data were available from before and after a stroke to the right anterior insula (n = 3; comparison n = 13). These correlational findings provide new insight into the role of the right hemisphere in maintaining social connections and bear important implications for treatment and rehabilitation post-stroke.

Self-awareness from whole-body movements.

Humans can recognize their whole-body movements even when displayed as dynamic dot patterns. The sparse depiction of whole-body movements, coupled with a lack of visual experience watching ourselves in the world, has long implicated non-visual mechanisms to self-action recognition. We aimed to identify the neural systems for this ability. Using general linear modeling and multivariate analyses on human brain imaging data from male and female participants, we first found that cortical areas linked to motor processes, including frontoparietal and primary somatomotor cortices, exhibit greater engagement and functional connectivity when recognizing self-generated versus other-generated actions. Next, we show that these regions encode self-identity based on motor familiarity, even after regressing out idiosyncratic visual cues using multiple regression representational similarity analysis. Last, we found the reverse pattern for unfamiliar individuals: encoding localized to occipito-temporal visual regions. These findings suggest that self-awareness from actions emerges from the interplay of motor and visual processes.Significance Statement: We report for the first time that self-recognition from visual observation of our whole-body actions implicates brain regions associated with motor processes. On functional neuroimaging data, we found greater activity and unique representational patterns in brain areas and networks linked to motor processes when viewing our own actions relative to viewing the actions of others. These findings introduce an important role of motor mechanisms in distinguishing the self from others.

Toward understanding autism heterogeneity: Identifying clinical subgroups and neuroanatomical deviations.

Autism spectrum disorder ("autism") is a neurodevelopmental condition characterized by substantial behavioral and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the Autism Brain Imaging Data Exchange data sets (N autism = 861, N nonautistic individuals [N NAI] = 886, age range = 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism (N autism = 499). Next, we ran normative modeling on CT and subcortical parcellations (N autism = 690, N NAI = 744) and examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviors (RRBs). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in the cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the lack of improvement of the classifier's performance when distinguishing between the subgroups and NAI. Future research should aim to further reduce heterogeneity incorporating additional neuroanatomical clustering in even larger samples. This will eventually pave the way for more tailored behavioral interventions and improving clinical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Correspondence between Morphometric MRI and Metabolism Profile in Self-limited Epilepsy with Centrotemporal Spikes

Abstract Self-limited epilepsy with centrotemporal spikes (SeLECTS) is a common pediatric epilepsy, and this age-dependent subtype usually incidents in the golden period of brain development. The integration of multi-scale and cross-modal brain image data can help to analyze the neural mechanism of brain development. In this study, we aimed to test whether macroscale brain morphometric features were altered in children with SeLECTS, and how the differences were related to clinical phenotypes and brain metabolism profiles. We extracted five cortical morphometric features to construct morphometric similarity (MS) connectome. The study revealed significant case-control differences in the MS in children with SeLECTS, mainly involved in left prefrontal area, right precuneus, left supramarginal gyrus and medial temporal gyrus. The MS in supramarginal gyrus was negatively correlated with general intelligence measurements. And the global changes of the MS showed correlations between metabolism profiles of dopamine D2, glutamate and vesicular acetylcholine transporter distributions.

Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates.

The diversity of cognitive skills across primates remains both a fascinating and a controversial issue. Recent comparative studies provided conflicting results regarding the contribution of social vs ecological constraints to the evolution of cognition. Here, we used an interdisciplinary approach combining comparative cognitive neurosciences and behavioral ecology. Using brain imaging data from 16 primate species, we measured the size of two prefrontal brain regions, the frontal pole (FP) and the dorso-lateral prefrontal cortex (DLPFC), respectively, involved in metacognition and working memory, and examined their relation to a combination of socio-ecological variables. The size of these prefrontal regions, as well as the whole brain, was best explained by three variables: body mass, daily traveled distance (an index of ecological constraints), and population density (an index of social constraints). The strong influence of ecological constraints on FP and DLPFC volumes suggests that both metacognition and working memory are critical for foraging in primates. Interestingly, FP volume was much more sensitive to social constraints than DLPFC volume, in line with laboratory studies showing an implication of FP in complex social interactions. Thus, our data highlights the relative weight of social vs ecological constraints on the evolution of specific prefrontal brain regions and their associated cognitive operations in primates.

Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates

The diversity of cognitive skills across primates remains both a fascinating and a controversial issue. Recent comparative studies provided conflicting results regarding the contribution of social vs ecological constraints to the evolution of cognition. Here, we used an interdisciplinary approach combining comparative cognitive neurosciences and behavioral ecology. Using brain imaging data from 16 primate species, we measured the size of two prefrontal brain regions, the frontal pole (FP) and the dorso-lateral prefrontal cortex (DLPFC), respectively, involved in metacognition and working memory, and examined their relation to a combination of socio-ecological variables. The size of these prefrontal regions, as well as the whole brain, was best explained by three variables: body mass, daily traveled distance (an index of ecological constraints), and population density (an index of social constraints). The strong influence of ecological constraints on FP and DLPFC volumes suggests that both metacognition and working memory are critical for foraging in primates. Interestingly, FP volume was much more sensitive to social constraints than DLPFC volume, in line with laboratory studies showing an implication of FP in complex social interactions. Thus, our data highlights the relative weight of social vs ecological constraints on the evolution of specific prefrontal brain regions and their associated cognitive operations in primates.

Gray Matter Volume Correlates of Co-Occurring Depression in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) involves neurodevelopmental syndromes with significant deficits in communication, motor behaviors, emotional and social comprehension. Often, individuals with ASD exhibit co-occurring depression characterized by a change in mood and diminished interest in previously enjoyable activities. Due to communicative challenges and a lack of appropriate assessments in this cohort, co-occurring depression can often go undiagnosed during routine clinical examinations and, thus, its management neglected. The literature on co-occurring depression in adults with ASD is limited. Therefore, understanding the neural basis of the co-occurring psychopathology of depression in ASD is crucial for identifying brain-based markers for its timely and effective management.Using structural MRI and phenotypic data from the Autism Brain Imaging Data Exchange (ABIDE II) repository, we examined the pattern of relationship regional grey matter volume (rGMV) has with co-occurring depression and autism severity within regions of a priori interest in adults with ASD (n = 44; age = 17-28 years). Further, we performed an exploratory analysis of the rGMV differences between ASD and matched typically developed (TD, n = 39; age = 18-31 years) samples. The severity of co-occurring depression correlated negatively with the rGMV of the right thalamus. Additionally, a significant interaction was evident between the severity of co-occurring depression and core ASD symptoms towards explaining the rGMV in the left cerebellum crus II. The results further the understanding of the neurobiological underpinnings of co-occurring depression in adults with ASD towards exploring neuroimaging-based biomarkers in the same cohort.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Enhancing Autism Spectrum Disorder identification in multi-site MRI imaging: A multi-head cross-attention and multi-context approach for addressing variability in un-harmonized data

Multi-site MRI imaging poses a significant challenge due to the potential variations in images across different scanners at different sites. This variability can introduce ambiguity in further image analysis. Consequently, the image analysis techniques become site-dependent and scanner-dependent, implying that adjustments in the analysis methodologies are necessary for each scanner configuration. Further, implementing real-time modifications becomes intricate, particularly when incorporating a new type of scanner, as it requires adapting the analysis methods accordingly. Taking into account the aforementioned challenge, we have considered its implications for an Autism spectrum disorder (ASD) application. Our objective is to minimize the impact of site and scanner variability in the analysis, aiming to develop a model that remains effective across different scanners and sites. This entails devising a methodology that allows the same model to function seamlessly across multiple scanner configurations and sites. ASD, a behavioral disorder affecting child development, requires early detection. Clinical observation is time-consuming, prompting the use of fMRI with machine/deep learning for expedited diagnosis. Previous methods leverage fMRI’s functional connectivity but often rely on less generalized feature extractors and classifiers. Hence, there is significant room for improvement in the generalizability of detection methods across multi-site data, which is acquired from multiple scanners with different settings. In this study, we propose a Cross-Combination Multi-Scale Multi-Context Framework (CCMSMCF) capable of performing neuroimaging-based diagnostic classification of mental disorders for a multi-site dataset. Thus, this framework attains a degree of internal data harmonization, rendering it to some extent site and scanner-agnostic. Our proposed network, CCMSMCF, is constructed by integrating two sub-modules: the Multi-Head Attention Cross-Scale Module (MHACSM) and the Residual Multi-Context Module (RMCN). We also employ multiple loss functions in a novel manner for training the model, which includes Binary Cross Entropy, Dice loss, and Embedding Coupling loss. The model is validated on the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, which includes data from multiple scanners across different sites, and achieves promising results.

Mental rotation-related neural interactions between gender and cognitive strategy

Abstract A long-standing history of research has focused on the differences between men and women in cognitive tasks, including that men would be more accurate and faster than women in mental rotation (MR). This advantage suggests that men would use an object-based cognitive strategy (OBS) to perform MR, whereas women would rely more on an effector-based cognitive strategy (EBS). To test this hypothesis, participants in the present study performed MR using OBS and EBS (plus a control condition) while their brain activity was recorded using fMRI. As sex hormones have often been reported to influence spatial ability, we also assessed the relationship between MR and testosterone levels and digit ratio. Behavioral results showed that (1) men performed faster MR than women in the OBS and control conditions, (2) men were more accurate than women in the OBS condition, and (3) women performed better in OBS than the other two conditions. No relationship was found between MR and testosterone or digit ratio. fMRI data showed that women in the OBS condition had greater activation than men in the inferior frontal and somatosensory cortices. Salivary testosterone levels had no effect on whole-brain activity. Combining behavioral and brain imaging data, these findings suggest that the additional somatosensory activation found in women during OBS somehow affects their MR, preventing the use of a purely spatial strategy and promoting the use of body-based sensorimotor processing, which would result in lower accuracy. These results support that gender differences in MR would be better explained by considering their relationship with the cognitive strategies used to perform MR.

Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.MethodsIn this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).ResultsIn the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.ConclusionThis work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Autism Spectrum Disorder DetectionUsingMachine Learning

Convolutional Neural Networks (CNNs) have made significant strides in various domains such as image classifica- tion, speech recognition, automotive software engineering and neuroscience. In this study we leverage CNNs for the automated detection of Autism Spectrum Disorder using brain imaging data,specificallywe utilize resting-state functional Magnetic Resonance Imaging (f-MRI) data from the Autism Brain Imaging Exchange (ABIDE) dataset. To identify ASD patients, our approach relies on analyzing patterns of functional connectivity to classify ASD and control subjects. Experimental results demonstrate that our proposed model achieves an accuracy of 96.40% on the ABIDEI dataset using the CC400 functional parcellation atlas of thebrain. Notably our CNN model exhibits computational efficiency by employing fewer parameters comparedtostate-of-the-art techniques. Our findings suggest that the developed model shows promise for prescreeningasdpatients and warrants further evaluation with additional datasets.

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.