Structural brain correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study.

Sustained attention is important for maintaining cognitive function and autonomy during ageing, yet older people often show reductions in this domain. The role of the underlying neurobiology is not yet well understood, with most neuroimaging studies primarily focused on fMRI. Here, we utilise sMRI to investigate the relationships between age, structural brain volumes and sustained attention performance. Eighty-nine healthy older adults (50-84 years, Mage 65.5 (SD=8.4) years, 74 f) underwent MRI brain scanning and completed two sustained attention tasks: a rapid visual information processing (RVP) task and sustained attention to response task (SART). Independent hierarchical linear regressions demonstrated that greater volumes of white matter hyperintensities (WMH) were associated with worse RVP_A' performance, whereas greater grey matter volumes were associated with better RVP_A' performance. Further, greater cerebral white matter volumes were associated with better SART_d' performance. Importantly, mediation analyses revealed that both grey and white matter volumes completely mediated the relationship between ageing and sustained attention. These results explain disparate attentional findings in older adults, highlighting the intervening role of brain structure.

Automated registration-based skull stripping procedure for feline neuroimaging.

Skull stripping is a fundamental preprocessing step in modern neuroimaging analyses that consists of removing non-brain voxels from structural images. When performed entirely manually, this laborious step can be rate-limiting for analyses, with the potential to influence the population size chosen. This emphasizes the need for a fully- or semi-automated masking procedure to decrease man-hours without an associated decline in accuracy. These algorithms are plentiful in human neuroimaging but are relatively lacking for the plethora of animal species used in research. Unfortunately, software designed for humans cannot be easily transformed for animal use due to the high amount of tailoring required to accurately account for the considerable degree of variation within the highly folded human cortex. As most animals have a relatively less complex cerebral morphology, intersubject variability is consequently decreased, presenting the possibility to simply warp the brain mask of a template image into subject space for the purpose of skull stripping. This study presents the use of the Cat Automated Registration-based Skull Stripper (CARSS) tool on feline structural images. Validation metrics revealed that this method was able to perform on par with manual raters on >90 % of scans tested, and that its consistency across multiple runs was superior to that of masking performed by two independent raters. Additionally, CARSS outperformed three well-known skull stripping programs on the validation dataset. Despite a handful of manual interventions required, the presented tool reduced the man-hours required to skull strip 60 feline images over tenfold when compared to a fully manual approach, proving to be invaluable for feline neuroimaging studies, particularly those with large population sizes.

Harnessing technology to measure individual differences in spatial thinking in early childhood from a relational developmental systems perspective.

According to the Relational Developmental Systems perspective, the development of individual differences in spatial thinking (e.g., mental rotation, spatial reorientation, and spatial language) are attributed to various psychological (e.g., children's cognitive strategies), biological (e.g., structure and function of hippocampus), and cultural systems (e.g., caregiver spatial language input). Yet, measuring the development of individual differences in spatial thinking in young children, as well as the psychological, biological, and cultural systems that influence the development of these abilities, presents unique challenges. The current paper outlines ways to harness available technology including eye-tracking, eye-blink conditioning, MRI, Zoom, and LENA technology, to study the development of individual differences in young children's spatial thinking. The technologies discussed offer ways to examine children's spatial thinking development from different levels of analyses (i.e., psychological, biological, cultural), thereby allowing us to advance the study of developmental theory. We conclude with a discussion of the use of artificial intelligence.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

Starting the pill during adolescence: Age of onset and duration of use influence morphology of the hippocampus and ventromedial prefrontal cortex.

From adolescence, women become more likely to experience fear dysregulation. Oral contraceptives (OCs) can modulate the brain regions involved in fear processes. OCs are generally used for years and often initiated during adolescence, a sensitive period where certain brain regions involved in the fear circuitry are still undergoing important reorganization. It remains unknown whether OC use during adolescence may induce long-lasting changes in the fear circuitry. This study aimed to examine whether age of onset moderated the relationship between duration of use and fear-related brain structures. We collected structural MRI data in 98 healthy adult women (61 current users, 37 past users) and extracted grey matter volumes (GMV) and cortical thickness (CT) of key regions of the fear circuitry. Non-linear multiple regressions revealed interaction effects between age of onset and quadratic duration of use on GMV of the right hippocampus and right ventromedial prefrontal cortex (vmPFC). Among women who initiated OCs earlier in adolescence, a short duration of use was associated with smaller hippocampal GMV and thicker vmPFC compared to a longer duration of use. For both GMV and CT of the right vmPFC, women with an early OC onset had more grey matter at a short duration of use than those with a later onset. Our results suggest that OC use earlier in adolescence may induce lasting effects on structural correlates of fear learning and its regulation. These findings support further investigation into the timing of OC use to better comprehend how OCs could disrupt normal brain development processes.

Neural correlates of anxiety in adult-onset isolated dystonia.

Psychiatric disturbances are commonly associated with adult-onset isolated dystonia (AOID); however, the mechanisms underlying psychiatric abnormalities in AOID remain unknown. We aimed to investigate the structural and functional brain changes in AOID patients with anxiety, and identify imaging biomarkers for diagnosing anxiety. Structural and functional magnetic resonance was performed on 69 AOID patients and 35 healthy controls (HCs). The Hamilton Anxiety Scale (HAMA) was used to assess anxiety symptoms in AOID patients and assign patients to AOID with and without anxiety groups. Group differences in grey matter volume, amplitude of low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity (ReHo) were evaluated. Area under the receiver operating characteristic curve (ROC AUC) was used as a metric to identify imaging biomarkers for diagnosing anxiety. AOID patients with anxiety exhibited an increased ALFF and ReHo in the left angular gyrus (ANG.L) compared with those without and HCs (voxel P<0.001 and cluster P<0.05, corrected using GRF). A significant positive correlation was observed between ALFF (r = 0.627, P<0.001) and ReHo (r = 0.515, P<0.001) in the ANG.L and HAMA scores in AOID patients. ALFF and ReHo in the ANG.L exhibited an ROC AUC of 0.904 and 0.851, respectively, in distinguishing AOID patients with anxiety from those without and an ROC AUC of 0.887 and 0.853, respectively, in distinguishing AOID patients with anxiety from HCs. These findings provide new insights into the pathophysiology of psychiatric disturbances and highlight potential candidate biomarkers for identifying anxiety in AOID patients.

Baseline brain volume predicts home-based transcranial direct current stimulation effects on inattention in adults with attention-deficit/hyperactivity disorder.

BACKGROUND: Home-based transcranial direct current stimulation (Hb-tDCS) is a non-invasive brain stimulation technique that utilizes low-intensity electric currents delivered via scalp electrodes to modulate brain activity. It holds significant promise for addressing inattention in adults with attention-deficit/hyperactivity disorder (ADHD). However, its effectiveness varies among individuals, and predicting outcomes remains uncertain, partially due to the influence of individual differences in ADHD-related brain anatomy. METHODS: We analyzed data from a subsample, composed by twenty-nine adult patients with ADHD, of the Treatment of Inattention Symptoms in Adult Patients with ADHD (TUNED) trial. Fourteen patients underwent active anodal right cathodal left dorsolateral prefrontal cortex (DLPFC) Hb-tDCS for 4 weeks and fifteen received sham-related tDCS intervention. Inattention outcome was evaluated at both baseline and endpoint (4th week). Baseline structural measures of the DLPFC, anterior cingulate cortex (ACC) and subcortical structures, previously associated with ADHD, were quantified. Several linear mixed models, with a three-way interaction between the fixed predictors brain volume or thickness, time, and treatment were calculated. Multiple comparison corrections were applied using the Benjamini-Hochberg method. RESULTS: Baseline volume of the left DLPFC regions middle frontal gyrus (t (25) = 3.33, p-adjusted = 0.045, Cohen's d = 1.33, 95% CI = [0.45, 2.19]), inferior frontal gyrus (orbital part) (t (25) = 3.10, p-adjusted = 0.045, Cohen's d = 1.24, 95% CI = [0.37, 2.08]), and of the left ACC supragenual (t (25) = 3.15, p-adjusted = 0.045, Cohen's d = 1.26, 95% CI = [0.39, 2.11]) presented significant association with the inattentive score improvement only in the active tDCS group. More specifically, the smaller these regions were, the more the symptoms improved following anodal right cathodal left DLPFC Hb-tDCS. CONCLUSION: Hb-tDCS was associated with greater improvement in brain areas related to attention regulation. Brain MRI can be potentially used to predict clinical response to tDCS in ADHD adults.

Structural brain differences in patients with schizophrenia spectrum disorders with and without auditory verbal hallucinations.

Schizophrenia spectrum disorders (SSD) are debilitating, with auditory verbal hallucinations (AVHs) being a core characteristic. While gray matter volume (GMV) reductions are commonly replicated in SSD populations, the neural basis of AVHs remains unclear. Using previously published data, this study comprises two main analyses, one of GMV dissimilarities between SSD and healthy controls (HC), and one of GMV differences specifically associated with AVHs. Structural brain images from 71 adults with (n = 46) and without (n = 25) SSD were employed. Group differences in GMVs of the cortex, anterior cingulate (ACC), superior temporal gyrus (STG), hippocampi, and thalami were assessed. Additionally, volumes of left Heschl's gyrus (HG) in a subgroup experiencing AVHs (AVH+, n = 23) were compared with those of patients who did not (AVH-, n = 23). SSD patients displayed reduced GMVs of the cortex, ACC, STG, hippocampi, and thalami compared to HC. AVH+ had significantly reduced left HG volume when compared to AVH-. Finally, a right-lateralized ventral prefrontal cluster was found to be uniquely associated with AVH severity. This study corroborates previous findings of GMV reductions in SSD cohorts. Chiefly, our secondary analysis suggests that AVHs are associated with language areas and their contralateral homologues.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology.

BACKGROUND: Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Population modelling, often referred to as normative modelling, provides a unified framework for studying age-specific and sex-specific divergences in brain development. METHODS: Here we used population modelling and a large, multi-site neuroimaging dataset (N = 4255 after quality control) to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD). RESULTS: We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume, that was localised to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness, but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only. CONCLUSIONS: These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.

Decoding MRI-informed brain age using mutual information.

OBJECTIVE: We aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability. METHODS: We utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT). RESULTS: Among four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80). CONCLUSION: This is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age. CRITICAL RELEVANCE STATEMENT: Mutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals' brain age. KEY POINTS: Mutual information (MI) interprets estimated brain age with morphometric features. Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age. Cerebrospinal fluid volume in the cingulate has the highest MI value. Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value. The value of mutual information underscores the key brain regions related to brain age.

Altered individual-level morphological similarity network in children with growth hormone deficiency.

BACKGROUND: Accumulating evidences indicate regional grey matter (GM) morphology alterations in pediatric growth hormone deficiency (GHD); however, large-scale morphological brain networks (MBNs) undergo these patients remains unclear. OBJECTIVE: To investigate the topological organization of individual-level MBNs in pediatric GHD. METHODS: Sixty-one GHD and 42 typically developing controls (TDs) were enrolled. Inter-regional morphological similarity of GM was taken to construct individual-level MBNs. Between-group differences of topological parameters and network-based statistics analysis were compared. Finally, association relationship between network properties and clinical variables was analyzed. RESULTS: Compared to TDs, GHD indicated a disturbance in the normal small-world organization, reflected by increased Lp, γ, λ, σ and decreased Cp, Eglob (all PFDR < 0.017). Regarding nodal properties, GHD exhibited increased nodal profiles at cerebellum 4-5, central executive network-related left inferior frontal gyrus, limbic regions-related right posterior cingulate gyrus, left hippocampus, and bilateral pallidum, thalamus (all PFDR < 0.05). Meanwhile, GHD exhibited decreased nodal profiles at sensorimotor network -related bilateral paracentral lobule, default-mode network-related left superior frontal gyrus, visual network -related right lingual gyrus, auditory network-related right superior temporal gyrus and bilateral amygdala, right cerebellum 3, bilateral cerebellum 10, vermis 1-2, 3, 4-5, 6 (all PFDR < 0.05). Furthermore, serum markers and behavior scores in GHD group were correlated with altered nodal profiles (P ≤ 0.046, uncorrected). CONCLUSION: GHD undergo an extensive reorganization in large-scale individual-level MBNs, probably due to abnormal cortico-striatal-thalamo-cerebellum loops, cortico-limbic-cerebellum, dorsal visual-sensorimotor-striatal, and auditory-cerebellum circuitry. This study highlights the crucial role of abnormal morphological connectivity underlying GHD, which might result in their relatively slower development in motor, cognitive, and linguistic functional within behavior problem performance.

Deep learning for the harmonization of structural MRI scans: a survey.

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.

Subcortical volumes in offspring with a multigenerational family history of depression - A study across two cohorts.

BACKGROUND: Having multiple previous generations with depression in the family increases offspring risk for psychopathology. Parental depression has been associated with smaller subcortical brain volumes in their children, but whether two prior generations with depression is associated with further decreases is unclear. METHODS: Using two independent cohorts, 1) a Three-Generation Study (TGS, N = 65) with direct clinical interviews of adults and children across all three generations, and 2) the Adolescent Brain Cognitive Development Study (ABCD, N = 10,626) of 9-10 year-old children with family history assessed by a caregiver, we tested whether having more generations of depression in the family was associated with smaller subcortical volumes (using structural MRI). RESULTS: In TGS, caudate, pallidum and putamen showed decreasing volumes with higher familial risk for depression. Having a parent and a grandparent with depression was associated with decreased volume compared to having no familial depression in these regions. Putamen volume was associated with depression at eight-year follow-up. In ABCD, smaller pallidum and putamen were associated with family history, which was driven by parental depression, regardless of grandparental depression. LIMITATIONS: Discrepancies between cohorts could be due to interview type (clinical or self-report) and informant (individual or common informant), sample size or age. Future analyses of follow-up ABCD waves will be able to assess whether effects of grandparental depression on brain markers become more apparent as the children enter young adulthood. CONCLUSIONS: Basal ganglia regional volumes are significantly smaller in offspring with a family history of depression in two independent cohorts.

Non-linear development of brain morphometry in child and adolescent offspring of individuals with bipolar disorder or schizophrenia.

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at increased risk of developing psychopathology. Structural brain alterations have been found in child and adolescent offspring of patients with bipolar disorder and schizophrenia, but the developmental trajectories of brain anatomy in this high-familial-risk population are still unclear. 300 T1-weighted scans were obtained of 187 offspring of at least one parent diagnosed with bipolar disorder (n=80) or schizophrenia (n=53) and offspring of parents without severe mental illness (n=54). The age range was 8 to 23 years old; 113 offspring underwent two scans. Global brain measures and regional cortical thickness and surface area were computed. A generalized additive mixed model was used to capture non-linear age trajectories. Offspring of parents with schizophrenia had smaller total brain volume than offspring of parents with bipolar disorder (d=-0.20, p=0.004) and control offspring (d=-0.22, p=0.005) and lower mean cortical thickness than control offspring (d=-0.23, p<0.001). Offspring of parents with schizophrenia showed differential age trajectories of mean cortical thickness and cerebral white matter volume compared with control offspring (both p's=0.003). Regionally, offspring of parents with schizophrenia had a significantly different trajectory of cortical thickness in the middle temporal gyrus versus control offspring (p<0.001) and bipolar disorder offspring (p=0.001), which was no longer significant after correcting for mean cortical thickness. These findings suggest that particularly familial high risk of schizophrenia is related to reductions and deviating developmental trajectories of global brain structure measures, which were not driven by specific regions.

Amygdala and hippocampal substructure volumes and their association with improvement in mood symptoms in patients with mood disorders undergoing electroconvulsive therapy.

Electroconvulsive therapy (ECT) demonstrates favorable outcomes in the management of severe depressive disorders. ECT has been consistently associated with volumetric increases in the amygdala and hippocampus. However, the underlying mechanisms of these structural changes and their association to clinical improvement remains unclear. In this cross-sectional structural MRI study, we assessed the difference in amygdala subnuclei and hippocampus subfields in n = 37 patients with either unipolar or bipolar disorder immediately after eighth ECT sessions compared to (n = 40) demographically matched patients in partial remission who did not receive ECT (NoECT group). Relative to NoECT, the ECT group showed significantly larger bilateral amygdala volumes post-treatment, with the effect originating from the lateral, basal, and paralaminar nuclei and the left corticoamydaloid transition area. No significant group differences were observed for the hippocampal or cortical volumes. ECT was associated with a significant decrease in depressive symptoms. However, there were no significant correlations between amygdala subnuclei volumes and symptom improvement. Our study corroborates previous reports on increased amygdalae volumes following ECT and further identifies the subnuclei driving this effect. However, the therapeutic effect of ECT does not seem to be directly related to structural changes in the amygdala.

Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on ß-amyloid (Aß) positive patients with amnestic mild cognitive impairment (aMCI). METHODS: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aß-negative = 220; SCD, Aß positive and negative = 139; aMCI, Aß-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aß positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Gender and age related brain structural and functional alterations in children with autism spectrum disorder.

To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.

Evaluation of Cognitive Functions in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention: A Prospective Pilot Study.

PURPOSE: It is not clear whether cognitive functions are impaired in young patients with acute coronary syndrome (ACS). This study aims to detect whether or not there is cognitive impairment and cerebral changes in young patients with ACS undergoing percutaneous coronary intervention (PCI). PATIENTS AND METHODS: All 50 patients with ACS who were treated with primary PCI were eligible for this prospective study. All participants had normal cognitive function before ACS. Brain magnetic resonance imaging (MRI) was performed to quantify changes in brain white and gray matter. Cognitive functions (CFs) were evaluated by seven cognitive tests. Patients were categorized by MRI findings and test scores were compared from the first day to after the first month. RESULTS: We determined 25 patients with impaired CFs on the first day. After the first month, we identified 18 patients with transient impaired CFs. No structural difference was observed between impaired CF and normal CF. While 25 patients had a score of 1 according to Fazekas, 10 patients had a score of 1 according to MTLA. While the mean Stroop test completion time and Stroop test error rate scores were significantly higher on the first day than after the first month in the Fazekas+ group (p = 0.003, p < 0.001, respectively), other cognitive test scores-except clock drawing test, digital span forwards, and backwards-were significantly lower on the first day compared to after the first month in the Fazekas+ group (p < 0.05). CONCLUSIONS: Patients with ACS have transient impairment in cognitive functions. Acute coronary syndrome is not associated with structural changes in the brain.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.