Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond.
Ontology highlight
ABSTRACT:
SUBMITTER: Sone D
PROVIDER: S-EPMC7910210 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects. Our study is a follow-up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs-fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.
| S-EPMC7267976 | biostudies-literature
Project description:Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.
| S-EPMC6362225 | biostudies-other
Project description:Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.
| S-EPMC9474919 | biostudies-literature
Project description:Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.
| S-EPMC9403648 | biostudies-literature
Project description:BackgroundRolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE.MethodsA multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity.DiscussionThe findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder.Trial registrationThe study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
| S-EPMC8039653 | biostudies-literature
Project description:We assessed the adherence to neuroimaging guidelines and the diagnostically relevant yield of neuroimaging in newly presenting early life epilepsy (ELE). There were 775 children with a new diagnosis of epilepsy (<3 years old at onset) who were recruited through the ELE study at 17 US pediatric epilepsy centers (2012-2015) and managed prospectively for 1 year. The data were analyzed to assess the proportion of children who underwent neuroimaging, the type of neuroimaging, and abnormalities. Of 725 children (93.5%) with neuroimaging, 714 had an MRI (87% with seizure protocols) and 11 had computed tomography or ultrasound only. Etiologically relevant abnormalities were present in 290 individuals (40%) and included: an acquired injury in 97 (13.4%), malformations of cortical development in 56 (7.7%), and other diffuse disorders of brain development in 51 (7.0%). Neuroimaging was abnormal in 160 of 262 (61%) children with abnormal development at diagnosis versus 113 of 463 (24%) children with typical development. Neuroimaging abnormalities were most common in association with focal seizure semiology (40%), spasms (47%), or unclear semiology (42%). In children without spasms or focal semiology with typical development, 29 of 185 (16%) had imaging abnormalities. Pathogenic genetic variants were identified in 53 of 121 (44%) children with abnormal neuroimaging in whom genetic testing was performed. Structural abnormalities occur commonly in ELE, and adherence to neuroimaging guidelines is high at US pediatric epilepsy centers. These data support the universal adoption of imaging guidelines because the yield is substantially high, even in the lowest risk group.
| S-EPMC6510984 | biostudies-literature
Project description:The blood-brain barrier (BBB) is a barrier protecting the brain and a milieu of continuous exchanges between blood and brain. There is emerging evidence that the BBB plays a major role in epileptogenesis and drug-resistant epilepsy, through several mechanisms, such as water homeostasis dysregulation, overexpression of drug transporters, and inflammation. Studies have shown abnormal water homeostasis in epileptic tissue and altered aquaporin-4 water channel expression in animal epilepsy models. This review focuses on abnormal water exchange in epilepsy and describes recent non-invasive MRI methods of quantifying water exchange. PLAIN LANGUAGE SUMMARY: Abnormal exchange between blood and brain contribute to seizures and epilepsy. The authors describe why correct water balance is necessary for healthy brain functioning and how it is impacted in epilepsy. This review also presents recent MRI methods to measure water exchange in human brain. These measures would improve our understanding of factors leading to seizures.
| S-EPMC11296120 | biostudies-literature
Project description:The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R2 of 0.81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2.66 years on 75 cognitively unimpaired adults as an independent set; whereas an R2 of 0.24 and a mean absolute error of 4.71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings.
| S-EPMC6861562 | biostudies-literature
Project description:Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.
| S-EPMC8423975 | biostudies-literature
Project description:The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.
| S-EPMC3355144 | biostudies-literature