Structural MRI Research Articles

MRI-Derived AD Signature of Cortical Thinning and Plasma P-Tau217 for Predicting Alzheimer Dementia Among Community-Dwelling Older Adults.

Structural brain MRI and blood-based phosphorylated tau (p-tau) measures are among the least invasive and least expensive Alzheimer's disease (AD) biomarkers to date. The extent to which these biomarkers may outperform one another in predicting future Alzheimer dementia diagnosis is poorly understood, however. This study investigated 2 specific AD biomarkers, i.e., a cortical thickness signature of AD (AD-CT) and plasma p-tau217, for predicting Alzheimer dementia. Data came from community-dwelling older participants of the Religious Orders Study or the Rush Memory and Aging Project. AD-CT was obtained from 3T MRI scans using a magnetization-prepared rapid acquisition gradient echo sequence and by averaging thickness from previously identified cortical regions implicated in AD. Plasma p-tau217 was quantified using an immunoassay developed by Lilly Research Laboratories on the MSD platform. Both MRI scans and blood specimens were collected at the same visits, and subsequent diagnoses of Alzheimer dementia were determined through annual detailed clinical evaluations. Cox proportional hazards models examined the associations of the 2 biomarkers with incident Alzheimer dementia, and prediction accuracy was assessed using c-statistics. A total of 198 older adults, on average 84 years of age, were included. Over a mean follow-up of 4 years, 60 (30%) individuals developed Alzheimer dementia. AD-CT (hazard ratio: 1.71, 95% CI 1.26-2.31) and separately plasma p-tau217 (hazard ratio: 2.57, 95% CI 1.83-3.61) were associated with incident Alzheimer dementia. The c-statistic for prediction accuracy was consistently higher for plasma p-tau217 (between 0.74 and 0.81) than AD-CT (between 0.70 and 0.75) across a range of time horizons. Furthermore, with both biomarkers included in the same model, there was only modest improvement in the c-statistic due to AD-CT. Plasma p-tau217 outperforms an imaging-based cortical thickness signature of AD in predicting future Alzheimer dementia diagnosis. Furthermore, the AD cortical thickness signature adds little to the prediction accuracy above and beyond plasma p-tau217.

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Hoffmann's sign in cervical spondylotic myelopathy patients: pathological insights from neuroimaging.

Hoffmann's sign testing is a commonly used physical examination in clinical practice for patients with cervical spondylotic myelopathy (CSM). However, the pathophysiological mechanisms underlying its occurrence and development have not been thoroughly investigated. Therefore, the present study aimed to explore whether a positive Hoffmann's sign (PHS) in CSM patients is associated with spinal cord and brain remodeling and to identify potential neuroimaging biomarkers with diagnostic value. Seventy-six patients with CSM and 40 sex- and age-matched healthy controls (HCs) underwent multimodal MRI. Based on the results of the Hoffmann's sign examination, patients were divided into two groups: those with a PHS (n = 38) and those with a negative Hoffmann's sign (NHS; n = 38). Quantification of spinal cord and brain structural and functional parameters of the participants was performed using various methods, including functional connectivity analysis, voxel-based morphometry, and atlas-based analysis based on functional MRI and structural MRI data. Furthermore, this study conducted a correlation analysis between neuroimaging metrics and neurological function and utilized a support vector machine (SVM) algorithm for the classification of PHS and NHS. In comparison with the NHS and HC groups, PHS patients exhibited significant reductions in the cross-sectional area and fractional anisotropy (FA) of the lateral corticospinal tract (CST), reticulospinal tract (RST), and fasciculus cuneatus, concomitant with bilateral reductions in the volume of the lateral pallidum. The functional connectivity analysis indicated a reduction in functional connectivity between the left lateral pallidum and the right angular gyrus in the PHS group. The correlation analysis indicated a significant positive association between the CST and RST FA and the volume of the left lateral pallidum in PHS patients. Furthermore, all three variables exhibited a positive correlation with the patients' motor function. Finally, using multimodal neuroimaging metrics in conjunction with the SVM algorithm, PHS and NHS were classified with an accuracy rate of 85.53%. This research revealed a correlation between structural damage to the pallidum and RST and the presence of Hoffmann's sign as well as the motor function in patients with CSM. Features based on neuroimaging indicators have the potential to serve as biomarkers for assessing the extent of neuronal damage in CSM patients.

Volume loss in the left anterior-superior subunit of the hypothalamus in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) causes motor neuron loss and progressive paralysis. While traditionally viewed as motor neuron disease (MND), ALS also affects non-motor regions, such as the hypothalamus. This study aimed to quantify the hypothalamic subregion volumes in patients with ALS versus healthy controls (HCs) and examine their associations with demographic and clinical features. Forty-eight participants (24 ALS patients and 24 HCs) underwent structural MRI. A deep convolutional neural network was used for the automated segmentation of the hypothalamic subunits, including the anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tuberal (supTub), inferior tuberal (infTub), and posterior (posHyp). The neural network was validated using FreeSurfer v7.4.1, with individual head size variations normalized using total intracranial volume (TIV) normalization. Statistical analyses were performed for comparisons using independent sample t-tests. Correlations were calculated using Pearson's and Spearman's tests (p < 0.05). The standard mean difference (SMD) was used to compare the mean differences between parametric variables. The volume of the left a-sHyp hypothalamic subunit was significantly lower in ALS patients than in HCs (p = 0.023, SMD = -0.681). No significant correlation was found between the volume of the hypothalamic subunits, body mass index (BMI), and ALSFRS-R in patients with ALS. However, right a-sHyp (r = 0.420, p = 0.041) was correlated with disease duration, whereas right supTub (r = -0.471, p = 0.020) and left postHyp (r = -0.406, p = 0.049) were negatively correlated with age. There was no significant difference in the volume of hypothalamic subunits between males and females, and no significant difference was found between patients with revised ALS Functional Rating Scale (ALSFRS-R) scores ≤41 and >41 and those with a disease duration of 9 months or less. The main finding suggests atrophy of the left a-sHyp hypothalamic subunit in patients with ALS, which is supported by previous research as an extra-motor neuroimaging finding for ALS.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling.

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately detect early signs of reversible tissue damage, underlining the need for innovative diagnostic tools that can quantify changes in cerebral blood flow (CBF) with high specificity and sensitivity.This study integrates three-dimensional arterial spin labeling (3D-ASL) with structural MRI to develop comprehensive CBF atlases that cover all main functional regions of the brain. This innovative magnetic resonance imaging- arterial spin labeling (MRI-ASL) methodology provides a rapid and noninvasive means of quantifying region-specific CBF, offering a detailed view of CBF levels across different functional regions.The comparison between chronic cerebral ischemia (CCI) patients and healthy subjects revealed significantly diminished CBF across the cerebral functional regions in the constructed CBF atlases for the former. This approach not only allows for the efficient identification of CCI by analyzing concurrent decreases in CBF across critical areas relative to healthy distributions but also enables the tracking of treatment responses and rehabilitation progress through longitudinal CBF atlases.The CBF atlas developed using the MRI-ASL technique represents a novel advancement in the field of cerebral diagnostics and patient care. By comparing regional CBF levels against normative standards, this method enhances diagnostic capabilities, enabling clinicians to provide personalized care to patients with cerebral conditions.

Enhancing the Diagnostic Utility of ASL Imaging in Temporal Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework.

Positron Emission Tomography (PET) using fluorodeoxyglucose (FDG-PET) is a standard imaging modality for detecting areas of hypometabolism associated with the seizure onset zone (SOZ) in temporal lobe epilepsy (TLE). However, FDG-PET is costly and involves the use of a radioactive tracer. Arterial Spin Labeling (ASL) offers an MRI-based quantification of cerebral blood flow (CBF) that could also help localize the SOZ, but its performance in doing so, relative to FDG-PET, is limited. In this study, we seek to improve ASL's diagnostic performance by developing a deep learning framework for synthesizing FDG-PET-like images from ASL and structural MRI inputs. We included 68 epilepsy patients, out of which 36 had well lateralized TLE. We compared the coupling between FDG-PET and ASL CBF values in different brain regions, as well as the asymmetry of these values across the brain. We additionally assessed each modality's ability to lateralize the SOZ across brain regions. Using our paired PET-ASL data, we developed FlowGAN, a generative adversarial neural network (GAN) that synthesizes PET-like images from ASL and T1-weighted MRI inputs. We tested our synthetic PET images against the actual PET images of subjects to assess their ability to reproduce clinically meaningful hypometabolism and asymmetries in TLE. We found variable coupling between PET and ASL CBF values across brain regions. PET and ASL had high coupling in neocortical temporal and frontal brain regions (Spearman's r > 0.30, p < 0.05) but low coupling in mesial temporal structures (Spearman's r < 0.30, p > 0.05). Both whole brain PET and ASL CBF asymmetry values provided good separability between left and right TLE subjects, but PET (AUC = 0.96, 95% CI: [0.88, 1.00]) outperformed ASL (AUC = 0.81; 95% CI: [0.65, 0.96]). FlowGAN-generated images demonstrated high structural similarity to actual PET images (SSIM = 0.85). Globally, asymmetry values were better correlated between synthetic PET and original PET than between ASL CBF and original PET, with a mean correlation increase of 0.15 (95% CI: [0.07, 0.24], p<0.001, Cohen's d = 0.91). Furthermore, regions that had poor ASL-PET correlation (e.g. mesial temporal structures) showed the greatest improvement with synthetic PET images. FlowGAN improves ASL's diagnostic performance, generating synthetic PET images that closely mimic actual FDG-PET in depicting hypometabolism associated with TLE. This approach could improve non-invasive SOZ localization, offering a promising tool for epilepsy presurgical assessment. It potentially broadens the applicability of ASL in clinical practice and could reduce reliance on FDG-PET for epilepsy and other neurological disorders.

Brain signaling becomes less integrated and more segregated with age

Abstract The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

Decreased gray matter volume in the anterior cerebellar of attention deficit/hyperactivity disorder comorbid oppositional defiant disorder children with associated cerebellar-cerebral hyperconnectivity: insights from a combined structural MRI and resting-state fMRI study.

Attention deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are highly comorbid. Many prior investigations have found that ADHD relates to anatomical abnormalities in gray matter. The abnormal gray matter of ADHD comorbid ODD is still poorly understood. This study aimed to explore the effect of comorbid ODD on gray matter volume (GMV) and functional alterations in ADHD. All data were provided by the ADHD-200 Preprocessed Repository, including 27 ADHD-only children, 27 ADHD + ODD children, and 27 healthy controls aged 9-14 years. Voxel-based morphometry (VBM) and functional connectivity (FC) of resting-state functional magnetic resonance imaging (fMRI) were used to compare the difference in GMV and FC between ADHD + ODD, ADHD-only, and healthy children. The results showed that ADHD children with comorbid ODD had a more significant reduction in cerebellar volume, mainly in the anterior regions of the cerebellum (Cerebellum_4_5). The Cerebellum_4_5 showed increased functional connectivity with multiple cortical regions. These brain regions include numerous executive functioning (EF) and brain default mode network (DMN) nodes. The GMV abnormalities and excessive connectivity between brain regions may further exacerbate the emotional and cognitive deficits associated with ADHD.

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor

IntroductionDistinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.MethodsWe enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.ResultsBoth structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.ConclusionMachine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

Neurophysiological and brain structural insights into cyclin-dependent kinase-like 5 deficiency disorder: Visual and auditory evoked potentials and MRI analysis

ObjectiveCDKL5 deficiency disorder (CDD), an epileptic encephalopathy for which novel therapeutics are under development, lacks valid and reliable measures of therapeutic efficacy. We aimed to elucidate the neurophysiological and brain structural features of CDD patients and identify objective indicators reflecting the clinical severity. MethodsTwelve CDD patients and 12 healthy controls (HCs) participated. The clinical severity of CDD was scored using the CDD severity assessment (CDD-SA). The participants underwent visual evoked potential (VEP), auditory brainstem response (ABR), structural MRI, and diffusion tensor imaging (DTI) analyses. Measurements from each modality were compared with normal values of age-matched cohorts (VEP and ABR) or statistically compared between CDD patients and HCs (MRI). ResultsVEP showed a significant correlation between P100 latency and CDD-SA in CDD patients. ABR showed abnormalities in six patients (50%), including prolonged V-wave latency (n = 2), prolonged inter-peak latency between waves I and V (n = 3), and mild hearing loss (n = 4). Structural MRI showed a significant reduction in cortical volume in the left pars triangularis and right cerebellum compared with HCs. DTI showed a widespread decrease in fractional anisotropy and an increase in mean and radial diffusivity compared with HCs. ConclusionCDD patients had reduced cortical volume in the left pars triangularis, a brain region crucial for speech, and one-third of patients had mild hearing loss. These changes may be involved in language impairments in CDD patients. Additionally, P100 latency significantly correlated with the clinical severity. These features can be used to assess the clinical severity of CDD.

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children.

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

Understanding the impact of congenital infections and perinatal viral exposures on the developing brain using white matter magnetic resonance imaging: a scoping review

BackgroundMagnetic Resonance Imaging (MRI)-based imaging techniques are useful for assessing white matter (WM) structural and microstructural integrity in the context of infection and inflammation. The purpose of this scoping review was to assess the range of work on the use of WM neuroimaging approaches to understand the impact of congenital and perinatal viral infections or exposures on the developing brain.MethodsThis scoping review was conducted according to the Arksey and O’ Malley framework. A literature search was performed in Web of Science, Scopus and PubMed for primary research articles published from database conception up to January 2022. Studies evaluating the use of MRI-based WM imaging techniques in congenital and perinatal viral infections or exposures were included. Results were grouped by age and infection.ResultsA total of 826 articles were identified for screening and 28 final articles were included. Congenital and perinatal infections represented in the included studies were cytomegalovirus (CMV) infection (n = 12), human immunodeficiency virus (HIV) infection (n = 11) or exposure (n = 2) or combined (n = 2), and herpes simplex virus (HSV) infection (n = 1). The represented MRI-based WM imaging methods included structural MRI and diffusion-weighted and diffusion tensor MRI (DWI/ DTI). Regions with the most frequently reported diffusion metric group differences included the cerebellar region, corticospinal tract and association fibre WM tracts in both children with HIV infection and children who are HIV-exposed uninfected. In qualitative imaging studies, WM hyperintensities were the most frequently reported brain abnormality in children with CMV infection and children with HSV infection.ConclusionThere was evidence that WM imaging techniques can play a role as diagnostic and evaluation tools assessing the impact of congenital infections and perinatal viral exposures on the developing brain. The high sensitivity for identifying WM hyperintensities suggests structural brain MRI is a useful neurodiagnostic modality in assessing children with congenital CMV infection, while the DTI changes associated with HIV suggest metrics such as fractional anisotropy have the potential to be specific markers of subtle impairment or WM damage in neuroHIV.

A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation

BackgroundTranscranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed. New methodThe pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers. ResultsModels of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ. Comparison with existing methodsCurrent methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers. ConclusionThe pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

Structural and Functional Characteristics of the Dorsolateral Prefrontal Cortex in Schizophrenia Spectrum Disorders and Their Associations with Onset Age

Background: the dorsolateral prefrontal cortex (DLPFC) is one of the latest brain structures to mature during the ontogeny, and its structural and functional abnormalities play an important role in the pathogenesis of schizophrenia. As schizophrenia spectrum disorders usually start before the complete brain maturation and their earlier onset is coupled with worse prognosis, we suggested that earlier illness onset is related to more pronounced aberrations of the DLPFC. The aim of study was to analyze the associations of the onset age of schizophrenia spectrum disorders with structural and functional characteristics of the DLPFC that differentiated patients with schizophrenia spectrum disorders from healthy controls. Patients and methods: male patients with a diagnosis of schizophrenia spectrum disorders (n = 82) and healthy controls (n = 86) underwent structural MRI and functional resting-state fMRI. Cortical thickness and whole-brain functional connectivity of the DLPFC as well as local coherence and amplitude of low-frequency fluctuations of haemodynamic signal in the DLPFC were analyzed. Results: patients demonstrated a decreased gray matter thickness in the DLPFC bilaterally along with aberrant (predominantly decreased) functional connectivity of the DLPFC with other brain structures in each hemisphere. These measures were not associated with the age of illness onset. Conclusions: structural and functional abnormalities revealed in this study coincide with conventional view on the DLPFC as one of the key regions in schizophrenia spectrum disorders pathogenesis, however, these aberrations were not related to the age of psychosis onset. Possible interpretations of our results and limitations of the study are discussed in the article.

Association between hearing ability and cortical morphology in the elderly: multiparametric mapping, cognitive relevance, and neurobiological underpinnings

Hearing impairment is a common condition in the elderly. However, a comprehensive understanding of its neural correlates is still lacking. We recruited 284 elderly adults who underwent structural MRI, magnetic resonance spectroscopy, audiometry, and cognitive assessments. Individual hearing abilities indexed by pure tone average (PTA) were correlated with multiple structural MRI-derived cortical morphological indices. For regions showing significant correlations, mediation analyses were performed to examine their role in the relationship between hearing ability and cognitive function. Finally, the correlation maps between hearing ability and cortical morphology were linked with publicly available connectomic gradient, transcriptomic, and neurotransmitter maps. Poorer hearing was related to cortical thickness (CT) reductions in widespread regions and gyrification index (GI) reductions in the right Area 52 and Insular Granular Complex. The GI in the right Area 52 mediated the relationship between hearing ability and executive function. This mediating effect was further modulated by glutamate and N-acetylaspartate levels in the right auditory region. The PTA-CT correlation map followed microstructural connectomic hierarchy, were related to genes involved in certain biological processes (e.g., glutamate metabolic process), cell types (e.g., excitatory neurons and astrocytes), and developmental stages (i.e., childhood to young adulthood), and covaried with dopamine receptor 1, dopamine transporter, and fluorodopa. The PTA-GI correlation map was related to 5-hydroxytryptamine receptor 2a. Poorer hearing is associated with cortical thinning and folding reductions, which may be engaged in the relationship between hearing impairment and cognitive decline in the elderly and have different neurobiological substrates. See the Acknowledgements section.

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality.

Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

Volumetric and diffusion MRI abnormalities associated with dysarthria in multiple sclerosis

Abstract Up to half of all people with multiple sclerosis experience communication difficulties due to dysarthria, a disorder that impacts the motor aspects of speech production. Dysarthria in multiple sclerosis is linked to cerebellar dysfunction, disease severity and lesion load, but the neuroanatomical substrates of these symptoms remain unclear. In this study, 52 participants with multiple sclerosis and 14 age- and sex-matched healthy controls underwent structural and diffusion MRI, clinical assessment of disease severity and cerebellar dysfunction, and a battery of motor speech tasks. Assessments of regional brain volume and white matter integrity, and their relationships with clinical and speech measures, were undertaken. White matter tracts of interest included the interhemispheric sensorimotor tract, cerebello-thalamo-cortical tract, and arcuate fasciculus, based on their roles in motor and speech behaviours. Volumetric analyses were targeted to Broca’s area, Wernicke’s area, the corpus callosum, thalamus, and cerebellum. Our results indicated that multiple sclerosis participants scored worse on all motor speech tasks. Fixel-based diffusion MRI analyses showed significant evidence of white matter tract atrophy in each tract of interest. Correlational analyses further indicated that higher speech naturalness - a perceptual measure of dysarthria – and lower reading rate were associated with axonal damage in the interhemispheric sensorimotor tract and left arcuate fasciculus in people with multiple sclerosis. Axonal damage in all tracts of interest also correlated with clinical scales sensitive to cerebellar dysfunction. Participants with multiple sclerosis had lower volumes of the thalamus and corpus callosum compared to controls, though no brain volumetrics correlated with measures of dysarthria. These findings indicate that axonal damage, particularly when measured using diffusion metrics, underpin dysarthria in multiple sclerosis.

The neuroanatomical correlates of daily habitual tendencies and mediating effect on the association between daily habitual tendencies and symptoms of behavioral addictions

Habitual behaviors significantly shape our daily actions. Furthermore, habit formation is proposed as a key mechanism contributing to the development and maintenance of addiction. However, the neural substrates underlying daily habitual tendencies and their contribution to behavioral addiction symptoms in everyday life remain poorly understood. To explore these questions, we conducted a comprehensive analysis of data from 219 individuals who underwent neuroimaging (structural MRI) assessments alongside evaluations of their daily habitual tendencies and symptoms of Internet Gaming Disorder (IGD) and Problematic Smartphone Use (PSU). Using voxel-based morphometry, meta-analytic decoding, and mediation analysis, we found that daily habitual tendencies were positively correlated with larger gray matter volumes in the ventromedial prefrontal cortex (vmPFC), precuneus, superior frontal gyrus (SFG), inferior temporal gyrus (ITG), and supplementary motor area (SMA). Notably, the midline regions, including the vmPFC and precuneus, play a crucial role in value-based computation, emotional regulation, social cognition, and self-referential thinking. Individual variations in gray matter volumes within these regions served as mediators, influencing the bidirectional relationship between daily habitual tendencies and IGD symptoms. However, vmPFC variations were specifically found to mediate the pathway from PSU to daily habitual tendencies. Our findings suggest that the morphological architecture of the vmPFC and precuneus is associated with habitual tendencies in daily life and may mediate the development of addictive behaviors. This study contributes to a more nuanced understanding of the neuroanatomical basis of daily habitual tendencies and their role in addictive behaviors.

Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia

BackgroundThe gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine – have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain.MethodsIn the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function.ResultsOverall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses.ConclusionsPlasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.