A voxel-based morphometric study of ageing in 465 normal adult human brains

Parkinson's Disease Risk Variant rs9638616 is Non-Specifically Associated with Altered Brain Structure and Function

Background

A genome-wide association study (GWAS) variant associated with Parkinson's disease (PD) risk in Asians, rs9638616, was recently reported, and maps to WBSCR17/GALNT17, which is involved in synaptic transmission and neurite development.

Objective

To test the association of the rs9638616 T allele with imaging-derived measures of brain microstructure and function.

Methods

We analyzed 3-Tesla MRI and genotyping data from 116 early PD patients (aged 66.8±9.0 years; 39% female; disease duration 1.25±0.71 years) and 57 controls (aged 68.7±7.4 years; 54% female), of Chinese ethnicity. We performed voxelwise analyses for imaging-genetic association of rs9638616 T allele with white matter tract fractional anisotropy (FA), grey matter volume and resting-state network functional connectivity.

Results

The rs9638616 T allele was associated with widespread lower white matter FA (t = -1.75, p = 0.042) and lower functional connectivity of the supplementary motor area (SMA) (t = -5.05, p = 0.001), in both PD and control groups. Interaction analysis comparing the association of rs9638616 and FA between PD and controls was non-significant. These imaging-derived phenotypes mediated the association of rs9638616 to digit span (indirect effect: β= -0.21 [-0.42,-0.05], p = 0.031) and motor severity (indirect effect: β= 0.15 [0.04,0.26], p = 0.045).

Conclusions

We have shown that a novel GWAS variant which is biologically linked to synaptic transmission is associated with white matter tract and functional connectivity dysfunction in the SMA, supported by changes in clinical motor scores. This provides pathophysiologic clues linking rs9638616 to PD risk and might contribute to future risk stratification models.

Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics

APOE allelic variation is critical in brain aging and Alzheimer's disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.

Interhemispheric asymmetrical change in gray matter volume in patients with unilateral hippocampal sclerosis

Objectives

To clarify the interhemispheric asymmetrical change in gray matter volume (GMV) in unilateral hippocampal sclerosis (HS), we compared changes in GMV relative to normal subjects between the HS and contralateral or non-HS sides.

Material and Methods

Forty-five patients with unilateral HS and 30 healthy subjects were enrolled. We quantified changes in GMV in the patients with HS as compared to GMV in the normal subjects by introducing the Z-score (Z-GMV) in each region or region of interest in unilateral HS. Then, we assessed the asymmetrically decreased regions, that is, regions with significantly higher Z-GMV on the HS side than the contralateral or non-HS side. Z-GMV was calculated according to the two templates of 58 regions per hemisphere covering the whole brain by anatomical automatic labeling (AAL) and 78 regions per cerebral hemisphere using the Anatomy Toolbox.

Results

Seven and four regions in AAL and 17 and 11 regions in Anatomy Toolbox were asymmetrically decreased in the Left Hand Side (LHS) and Right Hand Side (RHS), respectively. Hippocampus and Caudate in AAL, five subregions of the hippocampus (CA1-3, Dentate Gyrus and hippocampus-amygdala-transition-area and 4 extrahippocampal regions including two subregions in amygdala (CM: Centromedial, SF: Superficial), basal forebrain (BF) (Ch4), and thalamus (temporal) in anatomy toolbox were common among LHS and RHS concerning asymmetrically decreased regions.

Conclusion

By introducing Z-GMV, we demonstrated the regions with asymmetrically decreased GMV in LHS and RHS, and found that the hippocampus and extrahippocampal regions, including the BF, were the common asymmetrically decreased regions among LHS and RHS.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Brain asymmetry is globally different in males and females: exploring cortical volume, area, thickness, and mean curvature

Brain asymmetry is a cornerstone in the development of higher-level cognition, but it is unclear whether and how it differs in males and females. Asymmetry has been investigated using the laterality index, which compares homologous regions as pairwise weighted differences between the left and the right hemisphere. However, if asymmetry differences between males and females are global instead of pairwise, involving proportions between multiple brain areas, novel methodological tools are needed to evaluate them. Here, we used the Amsterdam Open MRI collection to investigate sexual dimorphism in brain asymmetry by comparing laterality index with the distance index, which is a global measure of differences within and across hemispheres, and with the subtraction index, which compares pairwise raw values in the left and right hemisphere. Machine learning models, robustness tests, and group analyses of cortical volume, area, thickness, and mean curvature revealed that, of the three indices, distance index was the most successful biomarker of sexual dimorphism. These findings suggest that left-right asymmetry in males and females involves global coherence rather than pairwise contrasts. Further studies are needed to investigate the biological basis of local and global asymmetry based on growth patterns under genetic, hormonal, and environmental factors.

Reduced Age-Related Gray Matter Loss in the Orbitofrontal Cortex in Long-Term Meditators

The orbitofrontal cortex (OFC) is a functionally heterogeneous brain region contributing to mental processes relating to meditation practices. The OFC has been reported to decline in volume with increasing age and differs in volume between meditation practitioners and non-practitioners. We hypothesized that the age-related decline of the OFC is diminished in meditation practitioners. We tested this hypothesis in a sample of 50 long-term meditators and 50 matched controls by correlating chronological age with regional gray matter volumes of the left and right OFC, as well as in seven left and right cytoarchitectonically defined subregions of the OFC (Fo1-Fo7). In both meditators and controls, we observed a negative relationship between age and OFC (sub)volumes, indicating that older participants have smaller OFC volumes. However, in meditators, the age-related decline was less steep compared to controls. These age-related differences reached significance for left and right Fo2, Fo3, Fo4, and Fo7, as well as left Fo5 and right Fo6. Since different subregions of the OFC are associated with distinct brain functions, further investigations are required to explore the functional implications of these findings in the context of meditation and the aging brain.

Association of Clinical and Neuroanatomic Factors With Response to Ventral Tegmental Area DBS in Chronic Cluster Headache

BACKGROUND AND OBJECTIVES

Deep brain stimulation (DBS) of the ventral tegmental area (VTA) is a surgical treatment option for selected patients with refractory chronic cluster headache (CCH). We aimed to identify clinical and structural neuroimaging factors associated with response to VTA DBS in CCH.

METHODS

This prospective observational cohort study examines consecutive patients with refractory CCH treated with VTA DBS by a multidisciplinary team in a single tertiary neuroscience center as part of usual care. Headache diaries and validated questionnaires were completed at baseline and regular follow-up intervals. All patients underwent T1-weighted structural MRI before surgery. We compared clinical features using multivariable logistic regression and neuroanatomic differences using voxel-based morphometry (VBM) between responders and nonresponders.

RESULTS

Over a 10-year period, 43 patients (mean age 53 years, SD 11.9), including 29 male patients, with a mean duration of CCH 12 years (SD 7.4), were treated and followed up for at least 1 year (mean follow-up duration 5.6 years). Overall, there was a statistically significant improvement in median attack frequency from 140 to 56 per month (Z = -4.95, p < 0.001), attack severity from 10/10 to 8/10 (Z = -4.83, p < 0.001), and duration from 110 to 60 minutes (Z = -3.48, p < 0.001). Twenty-nine (67.4%) patients experienced ≥50% improvement in attack frequency and were therefore classed as responders. There were no serious adverse events. The most common side effects were discomfort or pain around the battery site (7 patients) and transient diplopia and/or oscillopsia (6 patients). There were no differences in demographics, headache characteristics, or comorbidities between responders and nonresponders. VBM identified increased neural density in nonresponders in several brain regions, including the orbitofrontal cortex, anterior cingulate cortex, anterior insula, and amygdala, which were statistically significant (p < 0.001).

DISCUSSION

VTA DBS showed no serious adverse events, and, although there was no placebo control, was effective in approximately two-thirds of patients at long-term follow-up. This study did not reveal any reliable clinical predictors of response. However, nonresponders had increased neural density in brain regions linked to processing of pain and autonomic function, both of which are prominent in the pathophysiology of CCH.

Comparison of two corticosteroid regimens on brain volumetrics in patients with Duchenne muscular dystrophy

OBJECTIVE

Duchenne muscular dystrophy (DMD) is a neuromuscular disorder in which many patients also have neurobehavioral problems. Corticosteroids, the primary pharmacological treatment for DMD, have been shown to affect brain morphology in other conditions, but data in DMD are lacking. This study aimed to investigate the impact of two corticosteroid regimens on brain volumetrics in DMD using magnetic resonance imaging (MRI).

METHODS

In a cross-sectional, two-center study, T1-weighted MRI scans were obtained from three age-matched groups (9-18 years): DMD patients treated daily with deflazacort (DMDd, n = 20, scan site: Leuven), DMD patients treated intermittently with prednisone (DMDi, n = 20, scan site: Leiden), and healthy controls (n = 40, both scan sites). FSL was used to perform voxel-based morphometry analyses and to calculate intracranial, total brain, gray matter, white matter, and cerebrospinal fluid volumes. A MANCOVA was employed to compare global volumetrics between groups, with site as covariate.

RESULTS

Both patient groups displayed regional differences in gray matter volumes compared to the control group. The DMDd group showed a wider extent of brain regions affected and a greater difference overall. This was substantiated by the global volume quantification: the DMDd group, but not the DMDi group, showed significant differences in gray matter, white matter, and cerebrospinal fluid volumes compared to the control group, after correction for intracranial volume.

INTERPRETATION

Volumetric differences in the brain are considered part of the DMD phenotype. This study suggests an additional impact of corticosteroid treatment showing a contrast between pronounced alterations seen in patients receiving daily corticosteroid treatment and more subtle differences in those treated intermittently.

Progressive reduction in basal ganglia explains and predicts cerebral structural alteration in type 2 diabetes

Type 2 diabetes is consistently reported to be associated with reduced gray matter, mainly in the cortical-striatal-limbic networks. However, little is known about how the progression of diabetes affects cerebral gray matter. To investigate, we collected 543 age- and sex-matched participants of nondiabetes, prediabetes, and diabetes. Voxel-based morphometry using a linear trend model was performed to reveal brain regions associated with disease progression. The Granger causal network of structural covariance was used to assess the causal relationships of brain structural alterations according to disease progression. Multivariate pattern analysis was applied for the stage-specific predictions of hyperglycemia. We detected a linear trend of gray matter volume reduction in the basal ganglia with disease progression (P < 0.05, FWER corrected), which caused a reduction in bilateral temporal gyri, frontal pole, parahippocampus, and bilateral posterior cingulate/precuneus volumes. In addition, the gray matter pattern of the basal ganglia could predict patients with diabetes (accuracy 60.12%, p = 0.002). In conclusion, the basal ganglia is the brain area with progressive gray matter reduction as diabetes progress. The reduced volume in the basal ganglia causes widespread gray matter reductions throughout diabetes progression. These findings indicate that the basal ganglia play a key role in diabetes by affecting the cortical-striatal-limbic network.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

High resolution 0.5mm isotropic T1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas

High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.

Changes in brain perfusion with training-related visuomotor improvement in MS

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. A better understanding of the mechanisms supporting brain plasticity in MS would help to develop targeted interventions to promote recovery. A total of 29 MS patients and 19 healthy volunteers underwent clinical assessment and multi-modal MRI acquisition [fMRI during serial reaction time task (SRT), DWI, T1w structural scans and ASL of resting perfusion] at baseline and after 4-weeks of SRT training. Reduction of functional hyperactivation was observed in MS patients following the training, shown by the stronger reduction of the BOLD response during task execution compared to healthy volunteers. The functional reorganization was accompanied by a positive correlation between improvements in task accuracy and the change in resting perfusion after 4 weeks' training in right angular and supramarginal gyri in MS patients. No longitudinal changes in WM and GM measures and no correlation between task performance improvements and brain structure were observed in MS patients. Our results highlight a potential role for CBF as an early marker of plasticity, in terms of functional (cortical reorganization) and behavioral (performance improvement) changes in MS patients that may help to guide future interventions that exploit preserved plasticity mechanisms.

Anatomically compliant modes of variations: New tools for brain connectivity

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.

Predicting executive functioning from brain networks: modality specificity and age effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Brain structural changes in alternating hemiplegia of childhood using single-case voxel-based morphometry analysis

BACKGROUND AND PURPOSE

Alternating hemiplegia of childhood (AHC) is a rare neurodevelopmental disease caused by ATP1A3 mutations. Using voxel-based morphometry (VBM) analysis, we compared an AHC patient cohort with controls. Additionally, with single-case VBM analysis, we assessed the associations between clinical severity and brain volume in patients with AHC.

MATERIALS AND METHODS

To investigate structural brain changes in gray matter (GM) and white matter (WM) volumes between 9 patients with AHC and 20 age-matched controls, VBM analysis was performed using three-dimensional T1-weighted magnetic resonance imaging. Single-case VBM analysis was also performed on nine patients with AHC to investigate the associations between the respective volumes of GM/WM differences and the motor level, cognitive level, and status epilepticus severity in patients with AHC.

RESULTS

Compared with controls, patients with AHC showed significant GM volume reductions in both hippocampi and diffuse cerebellum, and there were WM reductions in both cerebral hemispheres. In patients with AHC, cases with more motor dysfunction, the less GM/WM volume of cerebellum was shown. Three of the six cases with cognitive dysfunction showed a clear GM volume reduction in the insulae. Five of the six cases with status epilepticus showed the GM volume reduction in hippocampi. One case had severe status epilepticus without motor dysfunction and showed no cerebellar atrophy.

CONCLUSION

With single-case VBM analysis, we could show the association between region-specific changes in brain volume and the severity of various clinical symptoms even in a small sample of subjects.

Single-subject cortical morphological brain networks across the adult lifespan

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond

BACKGROUND

Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age.

OBJECTIVE

The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations.

METHODS

We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology.

RESULTS

(1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear.

CONCLUSIONS

Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

The association between lifelong personality and clinical phenotype in the FTD-ALS spectrum

Introduction

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are two phenotypes of the same neurodegenerative disease, the FTD-ALS spectrum. What determines the development of one rather than the other phenotype is still unknown. Based on the clinical observation that patients' personality seems to differ between the two phenotypes, i.e., ALS patients tend to display kind, prosocial behaviors whereas FTD patients tend to present anti-social behaviors, and that these traits are often reported as pre-existing the disease onset by caregivers, we set up to study experimentally patients' personality in their premorbid life.

Methods

We first tested for differences between groups, then tested the association between premorbid personality and current functional organization of the brain. Premorbid personality of a cohort of forty patients, 27 FTD and 13 ALS, was explored through the NEO Personality Inventory 3 (NEO-PI-3), which analyses the five main personality factors, completed by the caregiver with reference to patient's personality 20 years before symptoms onset (premorbid). A subgroup of patients underwent a brain MRI including structural and resting-state functional MRI (rsfMRI).

Results

A significant difference between FTD and ALS in premorbid personality emerged in the Openness (133.92 FTD vs. 149.84 ALS, p = 0.01) and Extraversion (136.55 FTD vs. 150.53 ALS, p = 0.04) factors. This suggests that ALS patients had been, in their premorbid life, more open to new experiences, more sociable and optimistic than FTD patients. They also showed greater functional connectivity than both FTD and a control group in the Salience resting state network, over and above differences in gray matter atrophy. Finally, there was a positive correlation between premorbid Openness and functional connectivity in the Salience network across all patients, suggesting a possible association between premorbid personality and current functional organization of the brain, irrespective of the degree of atrophy.

Discussion

Our proof-of-concept results suggest that premorbid personality may eventually predispose to the development of one, rather than the other, phenotype in the FTD-ALS spectrum.

Magnetic Resonance T1w/T2w Ratio in the Putamen and Cerebellum as a Marker of Cognitive Impairment in MSA: a Longitudinal Study

The exact pathophysiology of cognitive impairment in multiple system atrophy (MSA) is unclear. In our longitudinal study, we aimed to analyze (I) the relationships between cognitive functions and some subcortical structures, such as putamen and cerebellum assessed by voxel-based morphometry (VBM) and T1-weighted/T2-weighted (T1w/T2w) ratio, and (II) the neuroimaging predictors of the progression of cognitive deficits. Twenty-six patients with MSA underwent a comprehensive neuropsychological battery, motor examination, and brain MRI at baseline (T0) and 1-year follow-up (T1). Patients were then divided according to cognitive status into MSA with normal cognition (MSA-NC) and MSA with mild cognitive impairment (MCI). At T1, we divided the sample according to worsening/non worsening of cognitive status compared to baseline evaluation. Logistic regression analysis showed that age (β =  - 9.45, p = .02) and T1w/T2w value in the left putamen (β = 230.64, p = .01) were significant predictors of global cognitive status at T0, explaining 65% of the variance. Logistic regression analysis showed that ∆-values of WM density in the cerebellum/brainstem (β = 2188.70, p = .02) significantly predicted cognitive worsening at T1, explaining 64% of the variance. Our results suggest a role for the putamen and cerebellum in the cognitive changes of MSA, probably due to their connections with the cortex. The putaminal T1w/T2w ratio may deserve further studies as a marker of cognitive impairment in MSA.

Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis

Brain gray matter (GM) reductions have been reported after breast cancer chemotherapy, typically in small and/or cross-sectional cohorts, most commonly using voxel-based morphometry (VBM). There has been little examination of approaches such as deformation-based morphometry (DBM), machine-learning-based brain aging metrics, or the relationship of clinical and demographic risk factors to GM reduction. This international data pooling study begins to address these questions. Participants included breast cancer patients treated with (CT+, n = 183) and without (CT-, n = 155) chemotherapy and noncancer controls (NC, n = 145), scanned pre- and post-chemotherapy or comparable intervals. VBM and DBM examined GM volume. Estimated brain aging was compared to chronological aging. Correlation analyses examined associations between VBM, DBM, and brain age, and between neuroimaging outcomes, baseline age, and time since chemotherapy completion. CT+ showed longitudinal GM volume reductions, primarily in frontal regions, with a broader spatial extent on DBM than VBM. CT- showed smaller clusters of GM reduction using both methods. Predicted brain aging was significantly greater in CT+ than NC, and older baseline age correlated with greater brain aging. Time since chemotherapy negatively correlated with brain aging and annual GM loss. This large-scale data pooling analysis confirmed findings of frontal lobe GM reduction after breast cancer chemotherapy. Milder changes were evident in patients not receiving chemotherapy. CT+ also demonstrated premature brain aging relative to NC, particularly at older age, but showed evidence for at least partial GM recovery over time. When validated in future studies, such knowledge could assist in weighing the risks and benefits of treatment strategies.

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

Estrogen receptor beta in astrocytes modulates cognitive function in mid-age female mice

Menopause is associated with cognitive deficits and brain atrophy, but the brain region and cell-specific mechanisms are not fully understood. Here, we identify a sex hormone by age interaction whereby loss of ovarian hormones in female mice at midlife, but not young age, induced hippocampal-dependent cognitive impairment, dorsal hippocampal atrophy, and astrocyte and microglia activation with synaptic loss. Selective deletion of estrogen receptor beta (ERβ) in astrocytes, but not neurons, in gonadally intact female mice induced the same brain effects. RNA sequencing and pathway analyses of gene expression in hippocampal astrocytes from midlife female astrocyte-ERβ conditional knock out (cKO) mice revealed Gluconeogenesis I and Glycolysis I as the most differentially expressed pathways. Enolase 1 gene expression was increased in hippocampi from both astrocyte-ERβ cKO female mice at midlife and from postmenopausal women. Gain of function studies showed that ERβ ligand treatment of midlife female mice reversed dorsal hippocampal neuropathology.

Lateralization of major white matter tracts during infancy is time-varying and tract-specific

Lateralization patterns are a major structural feature of brain white matter and have been investigated as a neural architecture that indicates and supports the specialization of cognitive processing and observed behaviors, e.g. language skills. Many neurodevelopmental disorders have been associated with atypical lateralization, reinforcing the need for careful measurement and study of this structural characteristic. Unfortunately, there is little consensus on the direction and magnitude of lateralization in major white matter tracts during the first months and years of life-the period of most rapid postnatal brain growth and cognitive maturation. In addition, no studies have examined white matter lateralization in a longitudinal pediatric sample-preventing confirmation of if and how white matter lateralization changes over time. Using a densely sampled longitudinal data set from neurotypical infants aged 0-6 months, we aim to (i) chart trajectories of white matter lateralization in 9 major tracts and (ii) link variable findings from cross-sectional studies of white matter lateralization in early infancy. We show that patterns of lateralization are time-varying and tract-specific and that differences in lateralization results during this period may reflect the dynamic nature of lateralization through development, which can be missed in cross-sectional studies.

oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.

Cytoarchitectonically Defined Volumes of Early Extrastriate Visual Cortex in Unmedicated Adults With Body Dysmorphic Disorder

BACKGROUND

Individuals with body dysmorphic disorder (BDD) misperceive that they have prominent defects in their appearance, resulting in preoccupations, time-consuming rituals, and distress. Previous neuroimaging studies have found abnormal activation patterns in the extrastriate visual cortex, which may underlie experiences of distorted perception of appearance. Correspondingly, we investigated gray matter volumes in individuals with BDD in the early extrastriate visual cortex using cytoarchitectonically defined maps that were previously derived from postmortem brains.

METHODS

We analyzed T1-weighted magnetic resonance imaging data from 133 unmedicated male and female participants (BDD: n = 65; healthy control subjects: n = 68). We used cytoarchitectonically defined probability maps for the early extrastriate cortex, consisting of areas corresponding to V2, V3d, V3v/VP, V3a, and V4v. Gray matter volumes were compared between groups, supplemented by testing associations with clinical symptoms.

RESULTS

The BDD group exhibited significantly larger gray matter volumes in the left and right early extrastriate cortex. Region-specific follow-up analyses revealed multiple subregions showing larger volumes in BDD, significant in the left V4v. There were no significant associations after corrections for multiple comparisons between gray matter volumes in early extrastriate cortex and BDD symptoms, comorbid symptoms, or duration of illness.

CONCLUSIONS

Greater volumes of the early extrastriate visual cortex were evident in those with BDD, which aligns with outcomes of prior studies revealing BDD-specific functional abnormalities in these regions. Enlarged volumes of the extrastriate cortex in BDD might manifest during neurodevelopment, which could predispose individuals to aberrant visual perception and contribute to the core phenotype of distortion of perception for appearance.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers

BACKGROUND

The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes.

METHODS

In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 "gene-negative" ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields.

RESULTS

Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses.

DISCUSSION

The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.

Accurate sex prediction of cisgender and transgender individuals without brain size bias

The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.

Investigating the effects of the aging brain on real tool use performance-an fMRI study

Introduction

Healthy aging affects several domains of cognitive and motor performance and is further associated with multiple structural and functional neural reorganization patterns. However, gap of knowledge exists, referring to the impact of these age-related alterations on the neural basis of tool use-an important, complex action involved in everyday life throughout the entire lifespan. The current fMRI study aims to investigate age-related changes of neural correlates involved in planning and executing a complex object manipulation task, further providing a better understanding of impaired tool use performance in apraxia patients.

Methods

A balanced number of sixteen older and younger healthy adults repeatedly manipulated everyday tools in an event-related Go-No-Go fMRI paradigm.

Results

Our data indicates that the left-lateralized network, including widely distributed frontal, temporal, parietal and occipital regions, involved in tool use performance is not subjected to age-related functional reorganization processes. However, age-related changes regarding the applied strategical procedure can be detected, indicating stronger investment into the planning, preparatory phase of such an action in older participants.

Radiological correlates of pseudobulbar affect: Corticobulbar and cerebellar components in primary lateral sclerosis

INTRODUCTION

Pseudobulbar affect (PBA) is a distressing symptom of a multitude of neurological conditions affecting patients with a rage of neuroinflammatory, neurovascular and neurodegenerative conditions. It manifests in disproportionate emotional responses to minimal or no contextual stimulus. It has considerable quality of life implications and treatment can be challenging.

METHODS

A prospective multimodal neuroimaging study was conducted to explore the neuroanatomical underpinnings of PBA in patients with primary lateral sclerosis (PLS). All participants underwent whole genome sequencing and screening for C9orf72 hexanucleotide repeat expansions, a comprehensive neurological assessment, neuropsychological screening (ECAS, HADS, FrSBe) and PBA was evaluated by the emotional lability questionnaire. Structural, diffusivity and functional MRI data were systematically evaluated in whole-brain (WB) data-driven and region of interest (ROI) hypothesis-driven analyses. In ROI analyses, functional and structural corticobulbar connectivity and cerebello-medullary connectivity alterations were evaluated separately.

RESULTS

Our data-driven whole-brain analyses revealed associations between PBA and white matter degeneration in descending corticobulbar as well as in commissural tracts. In our hypothesis-driven analyses, PBA was associated with increased right corticobulbar tract RD (p = 0.006) and decreased FA (p = 0.026). The left-hemispheric corticobulbar tract, as well as functional connectivity, showed similar tendencies. While uncorrected p-maps revealed both voxelwise and ROI trends for associations between PBA and cerebellar measures, these did not reach significance to unequivocally support the "cerebellar hypothesis".

CONCLUSIONS

Our data confirm associations between cortex-brainstem disconnection and the clinical severity of PBA. While our findings may be disease-specific, they are consistent with the classical cortico-medullary model of pseudobulbar affect.

Patterns of brain atrophy in recently-diagnosed relapsing-remitting multiple sclerosis

Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in recently-diagnosed RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N = 354) underwent 3T structural MRI <6 months after diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish regional patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the brain. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, cerebellar GM, brainstem, amygdala, basal ganglia, hippocampus, accumbens, thalamus and ventral diencephalon. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 26 regions and 16/26; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread GM and NAWM atrophy was observed in this large recently-diagnosed RRMS cohort, particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions; indicative of neurodegeneration across tissue types, and in accord with limited previous studies in early disease. Volumetric and VBM results emphasise different features of longitudinal lobar and loco-regional change, however identify consistent atrophy patterns across individuals. Atrophy measures targeted to specific brain regions may provide improved markers of neurodegeneration, and potential future imaging stratifiers and endpoints for clinical decision making and therapeutic trials.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study

BACKGROUND

Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease.

METHODS

We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC.

RESULTS

The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls.

CONCLUSION

The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

Brain structure and working memory adaptations associated with maturation and aging in mice

Introduction

As the population skews toward older age, elucidating mechanisms underlying human brain aging becomes imperative. Structural MRI has facilitated non-invasive investigation of lifespan brain morphology changes, yet this domain remains uncharacterized in rodents despite increasing use as models of disordered human brain aging.

Methods

Young (2m, n = 10), middle-age (10m, n = 10) and old (22m, n = 9) mice were utilized for maturational (young vs. middle-age) and aging-related (middle-age vs. old mice) comparisons. Regional brain volume was averaged across hemispheres and reduced to 32 brain regions. Pairwise group differences in regional volume were tested using general linear models, with total brain volume as a covariate. Sample-wide associations between regional brain volume and Y-maze performance were assessed using logistic regression, residualized for total brain volume. Both analyses corrected for multiple comparisons. Structural covariance networks were generated using the R package "igraph." Group differences in network centrality (degree), integration (mean distance), and segregation (transitivity, modularity) were tested across network densities (5-40%), using 5,000 (1,000 for degree) permutations with significance criteria of p < 0.05 at ≥5 consecutive density thresholds.

Results

Widespread significant maturational changes in volume occurred in 18 brain regions, including considerable loss in isocortex regions and increases in brainstem regions and white matter tracts. The aging-related comparison yielded 6 significant changes in brain volume, including further loss in isocortex regions and increases in white matter tracts. No significant volume changes were observed across either comparison for subcortical regions. Additionally, smaller volume of the anterior cingulate area (χ² = 2.325, p_BH = 0.044) and larger volume of the hippocampal formation (χ² = -2.180, p_BH = 0.044) were associated with poorer cognitive performance. Maturational network comparisons yielded significant degree changes in 9 regions, but no aging-related changes, aligning with network stabilization trends in humans. Maturational decline in modularity occurred (24-29% density), mirroring human trends of decreased segregation in young adulthood, while mean distance and transitivity remained stable.

Conclusion/Implications

These findings offer a foundational account of age effects on brain volume, structural brain networks, and working memory in mice, informing future work in facilitating translation between rodent models and human brain aging.

Unraveling age-related impairment of the neuromuscular system: exploring biomechanical and neurophysiological perspectives

With extended life expectancy, the quality of life of elders is a priority. Loss of mobility, increased morbidity and risks of falls have dramatic individual and societal impacts. Here we consider the age-related modifications of gait, from a biomechanical and neurophysiological perspective. Among the many factors of frailty involved (e.g., metabolic, hormonal, immunological), loss of muscle strength and neurodegenerative changes inducing slower muscle contraction may play a key role. We highlight that the impact of the multifactorial age-related changes in the neuromuscular systems results in common features of gait in the immature gait of infants and older adults. Besides, we also consider the reversibility of age-related neuromuscular deterioration by, on the one hand, exercise training, and the other hand, novel techniques such as direct spinal stimulation (tsDCS).

Brain structure and function link to variation in biobehavioral dimensions across the psychopathological continuum

In line with the Research Domain Criteria (RDoC) , we set out to investigate the brain basis of psychopathology within a transdiagnostic, dimensional framework. We performed an integrative structural-functional linked independent component analysis to study the relationship between brain measures and a broad set of biobehavioral measures in a sample (n = 295) with both mentally healthy participants and patients with diverse non-psychotic psychiatric disorders (i.e. mood, anxiety, addiction, and neurodevelopmental disorders). To get a more complete understanding of the underlying brain mechanisms, we used gray and white matter measures for brain structure and both resting-state and stress scans for brain function. The results emphasize the importance of the executive control network (ECN) during the functional scans for the understanding of transdiagnostic symptom dimensions. The connectivity between the ECN and the frontoparietal network in the aftermath of stress was correlated with symptom dimensions across both the cognitive and negative valence domains, and also with various other health-related biological and behavioral measures. Finally, we identified a multimodal component that was specifically associated with the diagnosis of autism spectrum disorder (ASD). The involvement of the default mode network, precentral gyrus, and thalamus across the different modalities of this component may reflect the broad functional domains that may be affected in ASD, like theory of mind, motor problems, and sensitivity to sensory stimuli, respectively. Taken together, the findings from our extensive, exploratory analyses emphasize the importance of a dimensional and more integrative approach for getting a better understanding of the brain basis of psychopathology.

Neurophysiological Markers of Premotor-Motor Network Plasticity Predict Motor Performance in Young and Older Adults

Aging is commonly associated with a decline in motor control and neural plasticity. Tuning cortico-cortical interactions between premotor and motor areas is essential for controlling fine manual movements. However, whether plasticity in premotor-motor circuits predicts hand motor abilities in young and elderly humans remains unclear. Here, we administered transcranial magnetic stimulation (TMS) over the ventral premotor cortex (PMv) and primary motor cortex (M1) using the cortico-cortical paired-associative stimulation (ccPAS) protocol to manipulate the strength of PMv-to-M1 connectivity in 14 young and 14 elderly healthy adults. We assessed changes in motor-evoked potentials (MEPs) during ccPAS as an index of PMv-M1 network plasticity. We tested whether the magnitude of MEP changes might predict interindividual differences in performance in two motor tasks that rely on premotor-motor circuits, i.e., the nine-hole pegboard test and a choice reaction task. Results show lower motor performance and decreased PMv-M1 network plasticity in elderly adults. Critically, the slope of MEP changes during ccPAS accurately predicted performance at the two tasks across age groups, with larger slopes (i.e., MEP increase) predicting better motor performance at baseline in both young and elderly participants. These findings suggest that physiological indices of PMv-M1 plasticity could provide a neurophysiological marker of fine motor control across age-groups.

Metabolic Asymmetry Relates to Clinical Characteristics and Brain Network Abnormalities in Alzheimer's Disease

BACKGROUND

Metabolic asymmetry has been observed in Alzheimer's disease (AD), but different studies have inconsistent viewpoints.

OBJECTIVE

To analyze the asymmetry of cerebral glucose metabolism in AD and investigate its clinical significance and potential metabolic network abnormalities.

METHODS

Standardized uptake value ratios (SUVRs) were obtained from 18F-FDG positron emission tomography (PET) images of all participants, and the asymmetry indices (AIs) were calculated according to the SUVRs. AD group was divided into left/right-dominant or bilateral symmetric hypometabolism (AD-L/AD-R or AD-BI) when more than half of the AIs of the 20 regions of interest (ROIs) were < -2SD, >2SD, or between±1SD. Differences in clinical features among the three AD groups were compared, and the abnormal network characteristics underlying metabolic asymmetry were explored.

RESULTS

In AD group, the proportions of AD-L, AD-R, and AD-BI were 28.4%, 17.9%, and 18.5%, respectively. AD-L/AD-R groups had younger age of onset and faster rate of cognitive decline than AD-BI group (p < 0.05). The absolute values of AIs in half of the 20 ROIs became higher at follow-up than at baseline (p < 0.05). Compared with those in AD-BI group, metabolic connection strength of network, global efficiency, cluster coefficient, degree centrality and local efficiency were lower, but shortest path length was longer in AD-L and AD-R groups (p < 0.05).

CONCLUSION

Asymmetric and symmetric hypometabolism may represent different clinical subtypes of AD, which may provide a clue for future studies on the heterogeneity of AD and help to optimize the design of clinical trials.

The waiting game: investigating the neurobiological transition from acute to persistent pain in adolescent rats

Persistent postsurgical pain affects 20% of youth undergoing a surgical procedure, with females exhibiting increased prevalence of chronic pain compared with males. This study sought to examine the sexually-dimorphic neurobiological changes underlying the transition from acute to persistent pain following surgery in adolescence. Male and female Sprague Dawley rats were randomly allocated to a sham or injury (plantar-incision surgery) condition and assessed for pain sensitivity while also undergoing magnetic resonance imaging at both an acute and chronic timepoint within adolescence. We found that injury resulted in persistent pain in both sexes, with females displaying most significant sensitivity. Injury resulted in significant gray matter density increases in brain areas including the cerebellum, caudate putamen/insula, and amygdala and decreases in the hippocampus, hypothalamus, nucleus accumbens, and lateral septal nucleus. Gray matter density changes in the hippocampus and lateral septal nucleus were driven by male rats whereas changes in the amygdala and caudate putamen/insula were driven by female rats. Overall, our results indicate persistent behavioral and neurobiological changes following surgery in adolescence, with sexually-dimorphic and age-specific outcomes, highlighting the importance of studying both sexes and adolescents, rather than extrapolating from male adult literature.

Brain volumetrics differ by Fiebig stage in acute HIV infection

OBJECTIVE

People with chronic HIV exhibit lower regional brain volumes compared to people without HIV (PWOH). Whether imaging alterations observed in chronic infection occur in acute HIV infection (AHI) remains unknown.

DESIGN

Cross-sectional study of Thai participants with AHI.

METHODS

One hundred and twelve Thai males with AHI (age 20-46) and 18 male Thai PWOH (age 18-40) were included. Individuals with AHI were stratified into early (Fiebig I-II; n  = 32) and late (Fiebig III-V; n  = 80) stages of acute infection using validated assays. T1-weighted scans were acquired using a 3 T MRI performed within five days of antiretroviral therapy (ART) initiation. Volumes for the amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen, and thalamus were compared across groups.

RESULTS

Participants in late Fiebig stages exhibited larger volumes in the nucleus accumbens (8% larger; P  = 0.049) and putamen (19%; P  < 0.001) when compared to participants in the early Fiebig. Compared to PWOH, participants in late Fiebig exhibited larger volumes of the amygdala (9% larger; P  = 0.002), caudate nucleus (11%; P  = 0.005), nucleus accumbens (15%; P  = 0.004), pallidum (19%; P  = 0.001), and putamen (31%; P  < 0.001). Brain volumes in the nucleus accumbens, pallidum, and putamen correlated modestly with stimulant use over the past four months among late Fiebig individuals ( P s < 0.05).

CONCLUSIONS

Findings indicate that brain volume alterations occur in acute infection, with the most prominent differences evident in the later stages of AHI. Additional studies are needed to evaluate mechanisms for possible brain disruption following ART, including viral factors and markers of neuroinflammation.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

The para-hippocampal-medial frontal gyrus functional connectivity mediates the relationship between dispositional optimism and procrastination

Procrastination is a prevalent phenomenon throughout the world, which can lead to worse consequences across life domains, such as academic performance, mental health, and even public policy. Despite the evidence for the association between dispositional optimism and procrastination, the neural mechanisms underlying this link remain unexplored. To address this issue, we employed voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the underlying links between dispositional optimism and procrastination in a large sample (N=408). The self-report results showed that dispositional optimism was negatively associated with procrastination (r= -.30, p<.001). The VBM analysis indicated that dispositional optimism was positively correlated with gray matter volumes (GMV) in the right para-hippocampal (rPHC), and negatively correlated with GMV in the left cerebellum. Moreover, the functional connectivity analysis with the rPHC as a seed region revealed that rPHC-rMFC (right medial frontal gyrus) functional connectivity was negatively associated with dispositional optimism. Furthermore, the mediation analysis showed that the rPHC-rMFC connectivity partially mediated the relationship between dispositional optimism and procrastination. These results suggested that the rPHC-rMFC connectivity engaged in less task aversiveness by episodic prospection may underlie the association between dispositional optimism and procrastination, which provides a new perspective to understand the relationship between dispositional optimism and procrastination. DATA AVAILABILITY STATEMENT: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Transdiagnostic brain correlates of self-reported trait impulsivity: A dimensional structure-symptom investigation

Impulsivity transcends psychiatric diagnoses and is often related to anhedonia. This ad hoc cross-sectional investigation explored 1) whether self-reported trait impulsivity mapped onto a common structural brain substrate across healthy controls (HCs) and psychiatric patients, and 2) in a more exploratory fashion, whether impulsivity and anhedonia were related to each other and shared overlapping brain correlates. Structural magnetic resonance imaging (sMRI) datasets from 234 participants including HCs (n = 109) and patients with opioid use disorder (OUD, n = 22), cocaine use disorder (CUD, n = 43), borderline personality disorder (BPD, n = 45) and schizophrenia (SZ, n = 15) were included. Trait impulsivity was measured with the Barratt Impulsiveness Scale (BIS-11) and anhedonia with a subscore of the Beck Depression Inventory (BDI). BIS-11 global score data were available for the entire sample, while data on the BIS-11 2nd order factors attentional, motor and non-planning were additionally in hand for a subsample consisting of HCs, OUD and BPD patients (n = 116). Voxel-based morphometry analyses were conducted for identifying dimensional associations between grey matter volume and impulsivity/anhedonia. Partial correlations were further performed to exploratory test the relationships between impulsivity and anhedonia and their corresponding volumetric brain substrates. Volume of the left opercular part of the inferior frontal gyrus (IFG) was negatively related to global impulsivity across the entire sample and specifically to motor impulsivity in the subsample of HCs, OUD and BPD patients. Across patients anhedonia expression was negatively correlated with left putamen volume. Although there was no relationship between global impulsivity and anhedonia across all patients, only across OUD and BPD patients anhedonia was positively associated with attentional impulsivity. Finally, also across OUD and BPD patients, motor impulsivity associated left IFG volume was positively linked with anhedonia-associated volume in the left putamen. Our findings suggest a critical role of left IFG volume in self-reported global impulsivity across healthy participants and patients with substance use disorder, BPD and SZ. Preliminary findings in OUD and BPD patients further suggests associations between impulsivity and anhedonia that are related to grey matter reductions in the left IFG and putamen.

Common and distinct fronto-striatal volumetric changes in heroin and cocaine use disorders

Different drugs of abuse impact the morphology of fronto-striatal dopaminergic targets in both common and unique ways. While dorsal striatal volume tracks with addiction severity across drug classes, opiates impact ventromedial prefrontal cortex (vmPFC) and nucleus accumbens (NAcc) neuroplasticity in preclinical models, and psychostimulants alter inhibitory control, rooted in cortical regions such as the inferior frontal gyrus (IFG). We hypothesized parallel grey matter volume changes associated with human heroin or cocaine use disorder: lower grey matter volume of vmPFC/NAcc in heroin use disorder and IFG in cocaine use disorder, and putamen grey matter volume to be associated with addiction severity measures (including craving) across both. In this cross-sectional study, we quantified grey matter volume (P < 0.05-corrected) in age/sex/IQ-matched individuals with heroin use disorder (n = 32, seven females), cocaine use disorder (n = 32, six females) and healthy controls (n = 32, six females) and compared fronto-striatal volume between groups using voxel-wise general linear models and non-parametric permutation-based tests. Overall, individuals with heroin use disorder had smaller vmPFC and NAcc/putamen volumes than healthy controls. Bilateral lower IFG grey matter volume patterns were specifically evident in cocaine versus heroin use disorders. Correlations between addiction severity measures and putamen grey matter volume did not reach nominal significance level in this sample. These results indicate alterations in dopamine-innervated regions (in the vmPFC and NAcc) in heroin addiction. For the first time we demonstrate lower IFG grey matter volume specifically in cocaine compared with heroin use disorder, suggesting a signature of reduced inhibitory control, which remains to be tested directly using select behavioural measures. Overall, results suggest substance-specific volumetric changes in human psychostimulant or opiate addiction, with implications for fine-tuning biomarker and treatment identification by primary drug of abuse.

Vimar/RAP1GDS1 promotes acceleration of brain aging after flies and mice reach middle age

Brain aging may accelerate after rodents reach middle age. However, the endogenous mediator that promotes this acceleration is unknown. We predict that the mediator may be expressed after an organism reaches middle age and dysregulates mitochondrial function. In the neurons of wild-type Drosophila (flies), we observed that mitochondria were fragmented in aged flies, and this fragmentation was associated with mitochondrial calcium overload. In a previous study, we found that mitochondrial fragmentation induced by calcium overload was reversed by the loss of Vimar, which forms a complex with Miro. Interestingly, Vimar expression was increased after the flies reached middle age. Overexpression of Vimar in neurons resulted in premature aging and mitochondrial calcium overload. In contrast, downregulation of Vimar in flies older than middle age promoted healthy aging. As the mouse homolog of Vimar, RAP1GDS1 expression was found to be increased after mice reached middle age; RAP1GDS1-transgenic and RAP1GDS1-knockdown mice displayed similar responses to flies with overexpressed and reduced Vimar expression, respectively. This research provides genetic evidence of a conserved endogenous mediator that promotes accelerated brain aging.

Brain-age prediction: A systematic comparison of machine learning workflows

The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-dataset accuracy, (2) cross-dataset generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-dataset mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-dataset MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-dataset and cross-dataset predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients compared to healthy controls. However, in the presence of age bias, the delta estimates in the patients varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.

Evaluation of transorbital sonography measures of optic nerve diameter in the context of global and regional brain volume in multiple sclerosis

Transorbital sonography (TOS) could be a swift and convenient method to detect the atrophy of the optic nerve, possibly providing a marker that might reflect other quantitative structural markers of multiple sclerosis (MS). Here we evaluate the utility of TOS as a complementary tool for assessing optic nerve atrophy, and investigate how TOS-derived measures correspond to volumetric brain markers in MS. We recruited 25 healthy controls (HC) and 45 patients with relapsing-remitting MS and performed B-mode ultrasonographic examination of the optic nerve. Patients additionally underwent MRI scans to obtain T1-weighted, FLAIR and STIR images. Optic nerve diameters (OND) were compared between HC, MS patients with and without history of optic neuritis (non-ON) using a mixed-effects ANOVA model. The relationship between within-subject-average OND and global and regional brain volumetric measures was investigated using FSL SIENAX, voxel-based morphometry and FSL FIRST. OND was significantly different between HC-MS (HC = 3.2 ± 0.4 mm, MS = 3 ± 0.4 mm; p < 0.019) and we found significant correlation between average OND and normalised whole brain (β = 0.42, p < 0.005), grey matter (β = 0.33, p < 0.035), white matter (β = 0.38, p < 0.012) and ventricular cerebrospinal fluid volume (β = - 0.36, p < 0.021) in the MS group. History of ON had no impact on the association between OND and volumetric data. In conclusion, OND is a promising surrogate marker in MS, that can be simply and reliably measured using TOS, and its derived measures correspond to brain volumetric measures. It should be further explored in larger and longitudinal studies.

Resistance Training Increases White Matter Density in Frail Elderly Women

We aimed to investigate the effects of maternal obesity on brain structure and metabolism in frail women, and their reversibility in response to exercise. We recruited 37 frail elderly women (20 offspring of lean/normal-weight mothers (OLM) and 17 offspring of obese/overweight mothers (OOM)) and nine non-frail controls to undergo magnetic resonance and diffusion tensor imaging (DTI), positron emission tomography with Fluorine-18-fluorodeoxyglucose (PET), and cognitive function tests (CERAD). Frail women were studied before and after a 4-month resistance training, and controls were studied once. White matter (WM) density (voxel-based morphometry) was higher in OLM than in OOM subjects. Exercise increased WM density in both OLM and OOM in the cerebellum in superior parietal regions in OLM and in cuneal and precuneal regions in OOM. OLM gained more WM density than OOM in response to intervention. No significant results were found from the Freesurfer analysis, nor from PET or DTI images. Exercise has an impact on brain morphology and cognition in elderly frail women.

Cognitive and Brain Gray Matter Changes in Children with Obstructive Sleep Apnea: A Voxel-Based Morphological Study

BACKGROUND

 To explore the neural difference between children with obstructive sleep apnea (OSA) and healthy controls, together with the relation between this difference and clinical severity indicator of children with OSA.

METHODS

 Twenty-seven children with OSA (7.6 ± 2.5 years, apnea hypopnea index [AHI]: 9.7 ± 5.3 events/h) and 30 healthy controls (7.8 ± 2.6 years, AHI: 1.7 ± 1.2 events/h) were recruited and matched with age, gender, and handedness. All children underwent 3.0 T magnetic resonance imaging of the brain and cognitive testing evaluating. Volumetric segmentation of cortical and subcortical structures and voxel-based morphometry were performed. Pearson's correlation analysis was performed between these features of gray matter volume (GMV) and obstructive apnea index (OAI) among children with OSA.

RESULTS

 In the comparison of children's Wechsler test scores of full-scale intelligence quotient and verbal intelligence quotient, the OSA group was significantly lower than the control group (p < 0.05). Compared with the control group, the GMV of many brain regions in the OSA group was significantly decreased (p < 0.05). In the correlation analysis of GMV and OAI in OSA group, right inferior frontal gyrus volume was significantly negatively correlated with OAI (r = - 0.49, p = 0.02).

CONCLUSION

 Children with OSA presented abnormal neural activities in some brain regions and impaired cognitive functions. This finding suggests an association between the OSA and decreased GMV in children.