Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder.

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Consistently lower volumes across thalamus nuclei in very premature-born adults.

Lasting thalamus volume reduction after preterm birth is a prominent finding. However, whether thalamic nuclei volumes are affected differentially by preterm birth and whether nuclei aberrations are relevant for cognitive functioning remains unknown. Using T1-weighted MR-images of 83 adults born very preterm (≤ 32 weeks' gestation; VP) and/or with very low body weight (≤ 1,500 g; VLBW) as well as of 92 full-term born (≥ 37 weeks' gestation) controls, we compared thalamic nuclei volumes of six subregions (anterior, lateral, ventral, intralaminar, medial, and pulvinar) across groups at the age of 26 years. To characterize the functional relevance of volume aberrations, cognitive performance was assessed by full-scale intelligence quotient using the Wechsler Adult Intelligence Scale and linked to volume reductions using multiple linear regression analyses. Thalamic volumes were significantly lower across all examined nuclei in VP/VLBW adults compared to controls, suggesting an overall rather than focal impairment. Lower nuclei volumes were linked to higher intensity of neonatal treatment, indicating vulnerability to stress exposure after birth. Furthermore, we found that single results for lateral, medial, and pulvinar nuclei volumes were associated with full-scale intelligence quotient in preterm adults, albeit not surviving correction for multiple hypotheses testing. These findings provide evidence that lower thalamic volume in preterm adults is observable across all subregions rather than focused on single nuclei. Data suggest the same mechanisms of aberrant thalamus development across all nuclei after premature birth.

Effects of healthy aging and mnemonic strategies on verbal memory performance across the adult lifespan: Mediating role of posterior hippocampus.

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.

Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves.

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes.

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Brains of endurance athletes differ in the association areas but not in the primary areas.

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.

Regional structural abnormalities in thalamus in idiopathic cervical dystonia.

BACKGROUND: The thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD. METHODS: Structural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD. RESULTS: Compared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group. CONCLUSION: This study highlights the neurobiological mechanisms of iCD related to thalamic volume changes.

Sex differences in structural covariance network based on MRI cortical morphometry: effects on episodic memory.

Sex differences in episodic memory (EM), remembering past events based on when and where they occurred, have been reported, but the neural mechanisms are unclear. T1-weighted images of 111 females and 61 males were acquired from the Dallas Lifespan Brain Study. Using surface-based morphometry and structural covariance (SC) analysis, we constructed structural covariance networks (SCN) based on cortical volume, and the global efficiency (Eglob) was computed to characterize network integration. The relationship between SCN and EM was examined by SC analysis among the top-n brain regions that were most relevant to EM performance. The number of SC connections (females: 3306; males: 437, P = 0.0212) and Eglob (females: 0.1845; males: 0.0417, P = 0.0408) of SCN in females were higher than those in males. The top-n brain regions with the strongest SC in females were located in auditory network, cingulo-opercular network (CON), and default mode network (DMN), and in males, they were located in frontoparietal network, CON, and DMN. These results confirmed that the Eglob of SCN in females was higher than males, sex differences in EM performance might be related to the differences in network-level integration. Our study highlights the importance of sex as a research variable in brain science.

Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network.

Autism spectrum disorder (ASD) is characterized by highly structural heterogeneity. However, most previous studies analyzed between-group differences through a structural covariance network constructed based on the ASD group level, ignoring the effect of between-individual differences. We constructed the gray matter volume-based individual differential structural covariance network (IDSCN) using T1-weighted images of 207 children (ASD/healthy controls: 105/102). We analyzed structural heterogeneity of ASD and differences among ASD subtypes obtained by a K-means clustering analysis based on evidently different covariance edges relative to healthy controls. The relationship between the distortion coefficients (DCs) calculated at the whole-brain, intra- and interhemispheric levels and the clinical symptoms of ASD subtypes was then examined. Compared with the control group, ASD showed significantly altered structural covariance edges mainly involved in the frontal and subcortical regions. Given the IDSCN of ASD, we obtained 2 subtypes, and the positive DCs of the 2 ASD subtypes were significantly different. Intra- and interhemispheric positive and negative DCs can predict the severity of repetitive stereotyped behaviors in ASD subtypes 1 and 2, respectively. These findings highlight the crucial role of frontal and subcortical regions in the heterogeneity of ASD and the necessity of studying ASD from the perspective of individual differences.

Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease.

Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.

Lower region-specific gray matter volume in females with atypical anorexia nervosa and anorexia nervosa.

OBJECTIVE: Few studies have focused on brain structure in atypical anorexia nervosa (atypical AN). This study investigates differences in gray matter volume (GMV) between females with anorexia nervosa (AN) and atypical AN, and healthy controls (HC). METHOD: Structural magnetic resonance imaging data were acquired for 37 AN, 23 atypical AN, and 41 HC female participants. Freesurfer was used to extract GMV, cortical thickness, and surface area for six brain lobes and associated cortical regions of interest (ROI). Primary analyses employed linear mixed-effects models to compare group differences in lobar GMV, followed by secondary analyses on ROIs within significant lobes. We also explored relationships between cortical gray matter and both body mass index (BMI) and symptom severity. RESULTS: Our primary analyses revealed significant lower GMV in frontal, temporal and parietal areas (FDR < .05) in AN and atypical AN when compared to HC. Lobar GMV comparisons were non-significant between atypical AN and AN. The parietal lobe exhibited the greatest proportion of affected cortical ROIs in both AN versus HC and atypical AN versus HC. BMI, but not symptom severity, was found to be associated with cortical GMV in the parietal, frontal, temporal, and cingulate lobes. No significant differences were observed in cortical thickness or surface area. DISCUSSION: We observed lower GMV in frontal, temporal, and parietal areas, when compared to HC, but no differences between AN and atypical AN. This indicates potentially overlapping structural phenotypes between these disorders and evidence of brain changes among those who are not below the clinical underweight threshold. PUBLIC SIGNIFICANCE: Despite individuals with atypical anorexia nervosa presenting above the clinical weight threshold, lower cortical gray matter volume was observed in partial, temporal, and frontal cortices, compared to healthy individuals. No significant differences were found in cortical gray matter volume between anorexia nervosa and atypical anorexia nervosa. This underscores the importance of continuing to assess and target weight gain in clinical care, even for those who are presenting above the low-weight clinical criteria.

Examining the interaction between prenatal stress and polygenic risk for attention-deficit/hyperactivity disorder on brain growth in childhood: Findings from the DREAM BIG consortium.

This study explored the interactions among prenatal stress, child sex, and polygenic risk scores (PGS) for attention-deficit/hyperactivity disorder (ADHD) on structural developmental changes of brain regions implicated in ADHD. We used data from two population-based birth cohorts: Growing Up in Singapore Towards healthy Outcomes (GUSTO) from Singapore (n = 113) and Generation R from Rotterdam, the Netherlands (n = 433). Prenatal stress was assessed using questionnaires. We obtained latent constructs of prenatal adversity and prenatal mood problems using confirmatory factor analyses. The participants were genotyped using genome-wide single nucleotide polymorphism arrays, and ADHD PGSs were computed. Magnetic resonance imaging scans were acquired at 4.5 and 6 years (GUSTO), and at 10 and 14 years (Generation R). We estimated the age-related rate of change for brain outcomes related to ADHD and performed (1) prenatal stress by sex interaction models, (2) prenatal stress by ADHD PGS interaction models, and (3) 3-way interaction models, including prenatal stress, sex, and ADHD PGS. We observed an interaction between prenatal stress and ADHD PGS on mean cortical thickness annual rate of change in Generation R (i.e., in individuals with higher ADHD PGS, higher prenatal stress was associated with a lower rate of cortical thinning, whereas in individuals with lower ADHD PGS, higher prenatal stress was associated with a higher rate of cortical thinning). None of the other tested interactions were statistically significant. Higher prenatal stress may promote a slower brain developmental rate during adolescence in individuals with higher ADHD genetic vulnerability, whereas it may promote a faster brain developmental rate in individuals with lower ADHD genetic vulnerability.

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction.

INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.

Amygdala atrophies in specific subnuclei in preclinical Alzheimer's disease.

INTRODUCTION: Magnetic resonance imaging (MRI) segmentation algorithms make it possible to study detailed medial temporal lobe (MTL) substructures as hippocampal subfields and amygdala subnuclei, offering opportunities to develop biomarkers for preclinical Alzheimer's disease (AD). METHODS: We identified the MTL substructures significantly associated with tau-positron emission tomography (PET) signal in 581 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI-3). We confirmed our results in our UCLouvain cohort including 110 non-demented individuals by comparing volumes between individuals with different visual Braak's stages and clinical diagnosis. RESULTS: Four amygdala subnuclei (cortical, central, medial, and accessory basal) were associated with tau in amyloid beta-positive (Aß+) clinically normal (CN) individuals, while the global amygdala and hippocampal volumes were not. Using UCLouvain data, we observed that both Braak I-II and Aß+ CN individuals had smaller volumes in these subnuclei, while no significant difference was observed in the global structure volumes or other subfields. CONCLUSION: Measuring specific amygdala subnuclei, early atrophy may serve as a marker of temporal tauopathy in preclinical AD, identifying individuals at risk of progression. HIGHLIGHTS: Amygdala atrophy is not homogeneous in preclinical Alzheimer's disease (AD). Tau pathology is associated with atrophy of specific amygdala subnuclei, specifically, the central, medial, cortical, and accessory basal subnuclei. Hippocampal and amygdala volume is not associated with tau in preclinical AD. Hippocampus and CA1-3 volume is reduced in preclinical AD, regardless of tau.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates.

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

Cerebello-cerebral resting-state functional connectivity in spinocerebellar ataxia type 3.

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by progressive motor and nonmotor deficits concomitant with degenerative pathophysiological changes within the cerebellum. The cerebellum is topographically organized into cerebello-cerebral circuits that create distinct functional networks regulating movement, cognition, and affect. SCA3-associated motor and nonmotor symptoms are possibly related not only to intracerebellar changes but also to disruption of the connectivity within these cerebello-cerebral circuits. However, to date, no comprehensive investigation of cerebello-cerebral connectivity in SCA3 has been conducted. The present study aimed to identify cerebello-cerebral functional connectivity alterations and associations with downstream clinical phenotypes and upstream topographic markers of cerebellar neurodegeneration in patients with SCA3. This study included 45 patients with SCA3 and 49 healthy controls. Voxel-based morphometry and resting-state functional magnetic resonance imaging (MRI) were performed to characterize the cerebellar atrophy and to examine the cerebello-cerebral functional connectivity patterns. Structural MRI confirmed widespread gray matter atrophy in the motor and cognitive cerebellum of patients with SCA3. We found reduced functional connectivity between the cerebellum and the cerebral cortical networks, including the somatomotor, frontoparietal, and default networks; however, increased connectivity was observed between the cerebellum and the dorsal attention network. These abnormal patterns correlated with the CAG repeat expansion and deficits in global cognition. Our results indicate the contribution of cerebello-cerebral networks to the motor and cognitive impairments in patients with SCA3 and reveal that such alterations occur in association with cerebellar atrophy. These findings add important insights into our understanding of the role of the cerebellum in SCA3.

Emotional recognition across the adult lifespan: Effects of age, sex, cognitive empathy, alexithymia traits, and amygdala subnuclei volumes.

The ability to recognize others' emotions is vital to everyday life. The goal of this study was to assess which emotions show age-related decline in recognition accuracy of facial emotional expressions across the entire adult lifespan and how this process is related to cognitive empathy (Theory of Mind [ToM]), alexithymia traits, and amygdala subnuclei volumes in a large cohort of healthy individuals. We recruited 140 healthy participants 18-85 years old. Facial affect processing was assessed with the Penn Emotion Recognition task (ER40) that contains images of the five basic emotions: Neutral, Happy, Sad, Angry, and Fearful. Structural magnetic resonance imaging (MRI) datasets were acquired on a 4.7T MRI system. Structural equation modeling was used to test the relationship between studied variables. We found that while both sexes demonstrated age-related reduction in recognition of happy emotions and preserved recognition of sadness, male participants showed age-related reduction in recognition of fear, while in female participants, age-related decline was linked to recognition of neutral and angry facial expressions. In both sexes, accurate recognition of sadness negatively correlated with alexithymia traits. On the other hand, better ToM capabilities in male participants were associated with improvement in recognition of positive and neutral emotions. Finally, none of the observed age-related reductions in emotional recognition were related to amygdala and its subnuclei volumes. In contrast, both global volume of amygdala and its cortical and centromedial subnuclei had significant direct effects on recognition of sad images.